Merge pull request #3119 from freqtrade/new_release

New release 2020.3

.coveragerc

@@ -1,6 +1,7 @@
 [run]
 omit =
     scripts/*
+    freqtrade/templates/*
     freqtrade/vendor/*
     freqtrade/__main__.py
     tests/*

.github/workflows/ci.yml

@@ -0,0 +1,259 @@
+name: Freqtrade CI
+
+on:
+  push:
+    branches:
+      - master
+      - develop
+      - github_actions_tests
+    tags:
+    release:
+      types: [published]
+  pull_request:
+  schedule:
+    - cron:  '0 5 * * 4'
+
+jobs:
+  build:
+
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ ubuntu-18.04, macos-latest ]
+        python-version: [3.7, 3.8]
+
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Cache_dependencies
+      uses: actions/cache@v1
+      id: cache
+      with:
+        path: ~/dependencies/
+        key: ${{ runner.os }}-dependencies
+
+    - name: pip cache (linux)
+      uses: actions/cache@preview
+      if: startsWith(matrix.os, 'ubuntu')
+      with:
+        path: ~/.cache/pip
+        key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
+
+    - name: pip cache (macOS)
+      uses: actions/cache@preview
+      if: startsWith(matrix.os, 'macOS')
+      with:
+        path: ~/Library/Caches/pip
+        key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
+
+    - name: TA binary *nix
+      if: steps.cache.outputs.cache-hit != 'true'
+      run: |
+        cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
+
+    - name: Installation - *nix
+      run: |
+        python -m pip install --upgrade pip
+        export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
+        export TA_LIBRARY_PATH=${HOME}/dependencies/lib
+        export TA_INCLUDE_PATH=${HOME}/dependencies/include
+        pip install -r requirements-dev.txt
+        pip install -e .
+
+    - name: Tests
+      run: |
+        pytest --random-order --cov=freqtrade --cov-config=.coveragerc
+
+    - name: Coveralls
+      if: (startsWith(matrix.os, 'ubuntu') && matrix.python-version == '3.8')
+      env:
+        # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
+        COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
+      run: |
+        # Allow failure for coveralls
+        coveralls -v  || true
+
+    - name: Backtesting
+      run: |
+        cp config.json.example config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
+
+    - name: Hyperopt
+      run: |
+        cp config.json.example config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
+
+    - name: Flake8
+      run: |
+        flake8
+
+    - name: Mypy
+      run: |
+        mypy freqtrade scripts
+
+    - name: Slack Notification
+      uses: homoluctus/slatify@v1.8.0
+      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+        type: ${{ job.status }}
+        job_name: '*Freqtrade CI ${{ matrix.os }}*'
+        mention: 'here'
+        mention_if: 'failure'
+        channel: '#notifications'
+        url: ${{ secrets.SLACK_WEBHOOK }}
+
+  build_windows:
+
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ windows-latest ]
+        python-version: [3.7, 3.8]
+
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Pip cache (Windows)
+      uses: actions/cache@preview
+      if: startsWith(runner.os, 'Windows')
+      with:
+        path: ~\AppData\Local\pip\Cache
+        key: ${{ matrix.os }}-${{ matrix.python-version }}-pip
+
+    - name: Installation
+      run: |
+        ./build_helpers/install_windows.ps1
+
+    - name: Tests
+      run: |
+        pytest --random-order --cov=freqtrade --cov-config=.coveragerc
+
+    - name: Backtesting
+      run: |
+        cp config.json.example config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
+
+    - name: Hyperopt
+      run: |
+        cp config.json.example config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
+
+    - name: Flake8
+      run: |
+        flake8
+
+    - name: Mypy
+      run: |
+        mypy freqtrade scripts
+
+    - name: Slack Notification
+      uses: homoluctus/slatify@v1.8.0
+      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+        type: ${{ job.status }}
+        job_name: '*Freqtrade CI windows*'
+        mention: 'here'
+        mention_if: 'failure'
+        channel: '#notifications'
+        url: ${{ secrets.SLACK_WEBHOOK }}
+
+  docs_check:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Documentation syntax
+      run: |
+        ./tests/test_docs.sh
+
+    - name: Slack Notification
+      uses: homoluctus/slatify@v1.8.0
+      if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+        type: ${{ job.status }}
+        job_name: '*Freqtrade Docs*'
+        channel: '#notifications'
+        url: ${{ secrets.SLACK_WEBHOOK }}
+
+  deploy:
+    needs: [ build, build_windows, docs_check ]
+    runs-on: ubuntu-18.04
+    if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.8
+
+    - name: Extract branch name
+      shell: bash
+      run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})"
+      id: extract_branch
+
+    - name: Build distribution
+      run: |
+        pip install -U setuptools wheel
+        python setup.py sdist bdist_wheel
+
+    - name: Publish to PyPI (Test)
+      uses: pypa/gh-action-pypi-publish@master
+      if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
+      with:
+        user: __token__
+        password: ${{ secrets.pypi_test_password }}
+        repository_url: https://test.pypi.org/legacy/
+
+    - name: Publish to PyPI
+      uses: pypa/gh-action-pypi-publish@master
+      if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
+      with:
+        user: __token__
+        password: ${{ secrets.pypi_password }}
+
+    - name: Build and test and push docker image
+      env:
+        IMAGE_NAME: freqtradeorg/freqtrade
+        DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
+        DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
+        BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
+      run: |
+        build_helpers/publish_docker.sh
+
+    - name: Build raspberry image for ${{ steps.extract_branch.outputs.branch }}_pi
+      uses: elgohr/Publish-Docker-Github-Action@2.7
+      with:
+        name: freqtradeorg/freqtrade:${{ steps.extract_branch.outputs.branch }}_pi
+        username: ${{ secrets.DOCKER_USERNAME }}
+        password: ${{ secrets.DOCKER_PASSWORD }}
+        dockerfile: Dockerfile.pi
+        # cache: true
+        cache: ${{ github.event_name != 'schedule' }}
+        tag_names: true
+
+    - name: Slack Notification
+      uses: homoluctus/slatify@v1.8.0
+      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+        type: ${{ job.status }}
+        job_name: '*Freqtrade CI Deploy*'
+        mention: 'here'
+        mention_if: 'failure'
+        channel: '#notifications'
+        url: ${{ secrets.SLACK_WEBHOOK }}
+

.github/workflows/docker_update_readme.yml

@@ -0,0 +1,18 @@
+name: Update Docker Hub Description
+on:
+  push:
+    branches:
+    - master
+
+jobs:
+  dockerHubDescription:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v1
+    - name: Docker Hub Description
+      uses: peter-evans/dockerhub-description@v2.1.0
+      env:
+        DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }}
+        DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
+        DOCKERHUB_REPOSITORY: freqtradeorg/freqtrade
+

.gitignore

@@ -6,7 +6,6 @@ user_data/*
 !user_data/strategy/sample_strategy.py
 !user_data/notebooks
 user_data/notebooks/*
-!user_data/notebooks/*example.ipynb
 freqtrade-plot.html
 freqtrade-profit-plot.html
 

.travis.yml

@@ -1,4 +1,3 @@
-sudo: true
 os:
 - linux
 dist: xenial
@@ -11,10 +10,10 @@ env:
   global:
     - IMAGE_NAME=freqtradeorg/freqtrade
 install:
-- cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
+- cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies; cd ..
 - export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
 - export TA_LIBRARY_PATH=${HOME}/dependencies/lib
-- export TA_INCLUDE_PATH=${HOME}/dependencies/lib/include
+- export TA_INCLUDE_PATH=${HOME}/dependencies/include
 - pip install -r requirements-dev.txt
 - pip install -e .
 jobs:
@@ -24,31 +23,34 @@ jobs:
       script:
       - pytest --random-order --cov=freqtrade --cov-config=.coveragerc
       # Allow failure for coveralls
-      - coveralls || true
+      # - coveralls || true
       name: pytest
     - script:
       - cp config.json.example config.json
-      - freqtrade --datadir tests/testdata backtesting
+      - freqtrade create-userdir --userdir user_data
+      - freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
       name: backtest
     - script:
       - cp config.json.example config.json
-      - freqtrade --datadir tests/testdata hyperopt -e 5
+      - freqtrade create-userdir --userdir user_data
+      - freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
       name: hyperopt
     - script: flake8
       name: flake8
     - script:
       # Test Documentation boxes -
       # !!! <TYPE>: is not allowed!
-      - grep -Er '^!{3}\s\S+:' docs/*; test $? -ne 0
+      # !!! <TYPE> "title" - Title needs to be quoted!
+      - grep -Er '^!{3}\s\S+:|^!{3}\s\S+\s[^"]' docs/*; test $? -ne 0
       name: doc syntax
     - script: mypy freqtrade scripts
       name: mypy
 
-    - stage: docker
+    # - stage: docker
-      if: branch in (master, develop, feat/improve_travis) AND (type in (push, cron))
+    #   if: branch in (master, develop, feat/improve_travis) AND (type in (push, cron))
-      script:
+    #   script:
-        - build_helpers/publish_docker.sh
+    #     - build_helpers/publish_docker.sh
-      name: "Build and test and push docker image"
+    #   name: "Build and test and push docker image"
 
 notifications:
   slack:

CONTRIBUTING.md

@@ -48,7 +48,7 @@ pytest tests/test_<file_name>.py::test_<method_name>
 #### Run Flake8
 
 ```bash
-flake8 freqtrade
+flake8 freqtrade tests scripts
 ```
 
 We receive a lot of code that fails the `flake8` checks.
@@ -109,11 +109,11 @@ Exceptions:
 
 Contributors may be given commit privileges. Preference will be given to those with:
 
-1. Past contributions to FreqTrade and other related open-source projects. Contributions to FreqTrade include both code (both accepted and pending) and friendly participation in the issue tracker and Pull request reviews. Quantity and quality are considered.
+1. Past contributions to Freqtrade and other related open-source projects. Contributions to Freqtrade include both code (both accepted and pending) and friendly participation in the issue tracker and Pull request reviews. Quantity and quality are considered.
 1. A coding style that the other core committers find simple, minimal, and clean.
 1. Access to resources for cross-platform development and testing.
 1. Time to devote to the project regularly.
 
-Being a Committer does not grant write permission on `develop` or `master` for security reasons (Users trust FreqTrade with their Exchange API keys).
+Being a Committer does not grant write permission on `develop` or `master` for security reasons (Users trust Freqtrade with their Exchange API keys).
 
 After being Committer for some time, a Committer may be named Core Committer and given full repository access.

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.7.5-slim-stretch
+FROM python:3.8.2-slim-buster
 
 RUN apt-get update \
     && apt-get -y install curl build-essential libssl-dev \
@@ -24,3 +24,5 @@ RUN pip install numpy --no-cache-dir \
 COPY . /freqtrade/
 RUN pip install -e . --no-cache-dir
 ENTRYPOINT ["freqtrade"]
+# Default to trade mode
+CMD [ "trade" ]

Dockerfile.pi

@@ -38,3 +38,4 @@ RUN ~/berryconda3/bin/pip install -e . --no-cache-dir
 RUN [ "cross-build-end" ]
 
 ENTRYPOINT ["/root/berryconda3/bin/python","./freqtrade/main.py"]
+CMD [ "trade" ]

MANIFEST.in

@@ -2,3 +2,4 @@ include LICENSE
 include README.md
 include config.json.example
 recursive-include freqtrade *.py
+recursive-include freqtrade/templates/ *.j2 *.ipynb

README.md

@@ -1,6 +1,6 @@
 # Freqtrade
 
-[![Build Status](https://travis-ci.org/freqtrade/freqtrade.svg?branch=develop)](https://travis-ci.org/freqtrade/freqtrade)
+[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
 [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
 [![Documentation](https://readthedocs.org/projects/freqtrade/badge/)](https://www.freqtrade.io)
 [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
@@ -25,7 +25,8 @@ hesitate to read the source code and understand the mechanism of this bot.
 ## Exchange marketplaces supported
 
 - [X] [Bittrex](https://bittrex.com/)
-- [X] [Binance](https://www.binance.com/) ([*Note for binance users](#a-note-on-binance))
+- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#blacklists))
+- [X] [Kraken](https://kraken.com/)
 - [ ] [113 others to tests](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
 
 ## Documentation
@@ -62,7 +63,6 @@ git checkout develop
 
 For any other type of installation please refer to [Installation doc](https://www.freqtrade.io/en/latest/installation/).
 
-
 ## Basic Usage
 
 ### Bot commands
@@ -106,7 +106,7 @@ optional arguments:
 
 ### Telegram RPC commands
 
-Telegram is not mandatory. However, this is a great way to control your bot. More details on our [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
+Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on our [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
 
 - `/start`: Starts the trader
 - `/stop`: Stops the trader
@@ -129,11 +129,6 @@ The project is currently setup in two main branches:
 - `master` - This branch contains the latest stable release. The bot 'should' be stable on this branch, and is generally well tested.
 - `feat/*` - These are feature branches, which are being worked on heavily. Please don't use these unless you want to test a specific feature.
 
-## A note on Binance
-
-For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
-Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB order unsellable as the expected amount is not there anymore.
-
 ## Support
 
 ### Help / Slack

bin/freqtrade

@@ -1,11 +1,11 @@
 #!/usr/bin/env python3
 
 import sys
-import warnings
+import logging
 
-from freqtrade.main import main
+logger = logging.getLogger(__name__)
 
-warnings.warn(
+
-    "Deprecated - To continue to run the bot like this, please run `pip install -e .` again.",
+logger.error("DEPRECATED installation detected, please run `pip install -e .` again.")
-    DeprecationWarning)
+
-main(sys.argv[1:])
+sys.exit(2)

build_helpers/install_windows.ps1

@@ -0,0 +1,17 @@
+# Downloads don't work automatically, since the URL is regenerated via javascript.
+# Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
+# Invoke-WebRequest -Uri "https://download.lfd.uci.edu/pythonlibs/xxxxxxx/TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl" -OutFile "TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl"
+
+python -m pip install --upgrade pip
+
+$pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
+
+if ($pyv -eq '3.7') {
+    pip install build_helpers\TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl
+}
+if ($pyv -eq '3.8') {
+    pip install build_helpers\TA_Lib-0.4.17-cp38-cp38-win_amd64.whl
+}
+
+pip install -r requirements-dev.txt
+pip install -e .

build_helpers/publish_docker.sh

@@ -1,17 +1,17 @@
 #!/bin/sh
-# - export TAG=`if [ "$TRAVIS_BRANCH" == "develop" ]; then echo "latest"; else echo $TRAVIS_BRANCH ; fi`
-# Replace / with _ to create a valid tag
-TAG=$(echo "${TRAVIS_BRANCH}" | sed -e "s/\//_/")
 
+# Replace / with _ to create a valid tag
+TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
+echo "Running for ${TAG}"
 
 # Add commit and commit_message to docker container
-echo "${TRAVIS_COMMIT} ${TRAVIS_COMMIT_MESSAGE}" > freqtrade_commit
+echo "${GITHUB_SHA}" > freqtrade_commit
 
-if [ "${TRAVIS_EVENT_TYPE}" = "cron" ]; then
+if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
-    echo "event ${TRAVIS_EVENT_TYPE}: full rebuild - skipping cache"
+    echo "event ${GITHUB_EVENT_NAME}: full rebuild - skipping cache"
     docker build -t freqtrade:${TAG} .
 else
-    echo "event ${TRAVIS_EVENT_TYPE}: building with cache"
+    echo "event ${GITHUB_EVENT_NAME}: building with cache"
     # Pull last build to avoid rebuilding the whole image
     docker pull ${IMAGE_NAME}:${TAG}
     docker build --cache-from ${IMAGE_NAME}:${TAG} -t freqtrade:${TAG} .
@@ -23,7 +23,7 @@ if [ $? -ne 0 ]; then
 fi
 
 # Run backtest
-docker run --rm -it -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} --datadir /tests/testdata backtesting
+docker run --rm -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy DefaultStrategy
 
 if [ $? -ne 0 ]; then
     echo "failed running backtest"
@@ -38,12 +38,12 @@ if [ $? -ne 0 ]; then
 fi
 
 # Tag as latest for develop builds
-if [ "${TRAVIS_BRANCH}" = "develop" ]; then
+if [ "${TAG}" = "develop" ]; then
     docker tag freqtrade:$TAG ${IMAGE_NAME}:latest
 fi
 
 # Login
-echo "$DOCKER_PASS" | docker login -u $DOCKER_USER --password-stdin
+docker login -u $DOCKER_USERNAME -p $DOCKER_PASSWORD
 
 if [ $? -ne 0 ]; then
     echo "failed login"

config.json.example

@@ -2,6 +2,7 @@
     "max_open_trades": 3,
     "stake_currency": "BTC",
     "stake_amount": 0.05,
+    "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "USD",
     "ticker_interval": "5m",
     "dry_run": false,
@@ -22,7 +23,7 @@
     "ask_strategy":{
         "use_order_book": false,
         "order_book_min": 1,
-        "order_book_max": 9,
+        "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
         "ignore_roi_if_buy_signal": false
@@ -43,7 +44,7 @@
             "DASH/BTC",
             "ZEC/BTC",
             "XLM/BTC",
-            "NXT/BTC",
+            "XRP/BTC",
             "TRX/BTC",
             "ADA/BTC",
             "XMR/BTC"
@@ -52,11 +53,13 @@
             "DOGE/BTC"
         ]
     },
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
     "edge": {
         "enabled": false,
         "process_throttle_secs": 3600,
         "calculate_since_number_of_days": 7,
-        "capital_available_percentage": 0.5,
         "allowed_risk": 0.01,
         "stoploss_range_min": -0.01,
         "stoploss_range_max": -0.1,
@@ -68,7 +71,7 @@
         "remove_pumps": false
     },
     "telegram": {
-        "enabled": true,
+        "enabled": false,
         "token": "your_telegram_token",
         "chat_id": "your_telegram_chat_id"
     },

config_binance.json.example

@@ -2,6 +2,7 @@
     "max_open_trades": 3,
     "stake_currency": "BTC",
     "stake_amount": 0.05,
+    "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "USD",
     "ticker_interval": "5m",
     "dry_run": true,
@@ -22,7 +23,7 @@
     "ask_strategy":{
         "use_order_book": false,
         "order_book_min": 1,
-        "order_book_max": 9,
+        "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
         "ignore_roi_if_buy_signal": false
@@ -37,28 +38,33 @@
             "rateLimit": 200
         },
         "pair_whitelist": [
-            "AST/BTC",
+            "ALGO/BTC",
-            "ETC/BTC",
+            "ATOM/BTC",
-            "ETH/BTC",
+            "BAT/BTC",
+            "BCH/BTC",
+            "BRD/BTC",
             "EOS/BTC",
+            "ETH/BTC",
             "IOTA/BTC",
+            "LINK/BTC",
             "LTC/BTC",
-            "MTH/BTC",
+            "NEO/BTC",
-            "NCASH/BTC",
+            "NXS/BTC",
-            "TNT/BTC",
             "XMR/BTC",
-            "XLM/BTC",
+            "XRP/BTC",
-            "XRP/BTC"
+            "XTZ/BTC"
         ],
         "pair_blacklist": [
             "BNB/BTC"
         ]
     },
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
     "edge": {
         "enabled": false,
         "process_throttle_secs": 3600,
         "calculate_since_number_of_days": 7,
-        "capital_available_percentage": 0.5,
         "allowed_risk": 0.01,
         "stoploss_range_min": -0.01,
         "stoploss_range_max": -0.1,

config_full.json.example

@@ -2,8 +2,11 @@
     "max_open_trades": 3,
     "stake_currency": "BTC",
     "stake_amount": 0.05,
+    "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "USD",
     "amount_reserve_percent": 0.05,
+    "amend_last_stake_amount": false,
+    "last_stake_amount_min_ratio": 0.5,
     "dry_run": false,
     "ticker_interval": "5m",
     "trailing_stop": false,
@@ -22,6 +25,7 @@
         "sell": 30
     },
     "bid_strategy": {
+        "price_side": "bid",
         "use_order_book": false,
         "ask_last_balance": 0.0,
         "order_book_top": 1,
@@ -31,9 +35,10 @@
         }
     },
     "ask_strategy":{
+        "price_side": "ask",
         "use_order_book": false,
         "order_book_min": 1,
-        "order_book_max": 9,
+        "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
         "ignore_roi_if_buy_signal": false
@@ -50,14 +55,18 @@
         "buy": "gtc",
         "sell": "gtc"
     },
-    "pairlist": {
+    "pairlists": [
+        {"method": "StaticPairList"},
+        {
             "method": "VolumePairList",
-        "config": {
             "number_assets": 20,
             "sort_key": "quoteVolume",
-            "precision_filter": false
+            "refresh_period": 1800
-        }
         },
+        {"method": "PrecisionFilter"},
+        {"method": "PriceFilter", "low_price_ratio": 0.01},
+        {"method": "SpreadFilter", "max_spread_ratio": 0.005}
+    ],
     "exchange": {
         "name": "bittrex",
         "sandbox": false,
@@ -92,7 +101,6 @@
         "enabled": false,
         "process_throttle_secs": 3600,
         "calculate_since_number_of_days": 7,
-        "capital_available_percentage": 0.5,
         "allowed_risk": 0.01,
         "stoploss_range_min": -0.01,
         "stoploss_range_max": -0.1,
@@ -123,5 +131,7 @@
         "heartbeat_interval": 60
     },
     "strategy": "DefaultStrategy",
-    "strategy_path": "user_data/strategies/"
+    "strategy_path": "user_data/strategies/",
+    "dataformat_ohlcv": "json",
+    "dataformat_trades": "jsongz"
 }

config_kraken.json.example

@@ -2,6 +2,7 @@
     "max_open_trades": 5,
     "stake_currency": "EUR",
     "stake_amount": 10,
+    "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "EUR",
     "ticker_interval": "5m",
     "dry_run": true,
@@ -22,7 +23,7 @@
     "ask_strategy":{
         "use_order_book": false,
         "order_book_min": 1,
-        "order_book_max": 9,
+        "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
         "ignore_roi_if_buy_signal": false
@@ -38,19 +39,38 @@
             "rateLimit": 1000
         },
         "pair_whitelist": [
-            "ETH/EUR",
+            "ADA/EUR",
+            "ATOM/EUR",
+            "BAT/EUR",
+            "BCH/EUR",
             "BTC/EUR",
-            "BCH/EUR"
+            "DAI/EUR",
+            "DASH/EUR",
+            "EOS/EUR",
+            "ETC/EUR",
+            "ETH/EUR",
+            "LINK/EUR",
+            "LTC/EUR",
+            "QTUM/EUR",
+            "REP/EUR",
+            "WAVES/EUR",
+            "XLM/EUR",
+            "XMR/EUR",
+            "XRP/EUR",
+            "XTZ/EUR",
+            "ZEC/EUR"
         ],
         "pair_blacklist": [
 
         ]
     },
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
     "edge": {
         "enabled": false,
         "process_throttle_secs": 3600,
         "calculate_since_number_of_days": 7,
-        "capital_available_percentage": 0.5,
         "allowed_risk": 0.01,
         "stoploss_range_min": -0.01,
         "stoploss_range_max": -0.1,

docker-compose.yml

@@ -3,6 +3,18 @@ version: '3'
 services:
   freqtrade:
     image: freqtradeorg/freqtrade:master
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./Dockerfile.technical"
+    restart: unless-stopped
+    container_name: freqtrade
     volumes:
       - "./user_data:/freqtrade/user_data"
-      - "./config.json:/freqtrade/config.json"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/freqtrade.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
+      --config /freqtrade/user_data/config.json
+      --strategy SampleStrategy

docs/advanced-hyperopt.md

@@ -0,0 +1,91 @@
+# Advanced Hyperopt
+
+This page explains some advanced Hyperopt topics that may require higher
+coding skills and Python knowledge than creation of an ordinal hyperoptimization
+class.
+
+## Derived hyperopt classes
+
+Custom hyperop classes can be derived in the same way [it can be done for strategies](strategy-customization.md#derived-strategies).
+
+Applying to hyperoptimization, as an example, you may override how dimensions are defined in your optimization hyperspace:
+
+```python
+class MyAwesomeHyperOpt(IHyperOpt):
+    ...
+    # Uses default stoploss dimension
+
+class MyAwesomeHyperOpt2(MyAwesomeHyperOpt):
+    @staticmethod
+    def stoploss_space() -> List[Dimension]:
+        # Override boundaries for stoploss
+        return [
+            Real(-0.33, -0.01, name='stoploss'),
+        ]
+```
+
+and then quickly switch between hyperopt classes, running optimization process with hyperopt class you need in each particular case:
+
+```
+$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt ...
+or
+$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 ...
+```
+
+## Creating and using a custom loss function
+
+To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
+For the sample below, you then need to add the command line parameter `--hyperopt-loss SuperDuperHyperOptLoss` to your hyperopt call so this function is being used.
+
+A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in [userdata/hyperopts](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py).
+
+``` python
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+TARGET_TRADES = 600
+EXPECTED_MAX_PROFIT = 3.0
+MAX_ACCEPTED_TRADE_DURATION = 300
+
+class SuperDuperHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the default loss function for hyperopt
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for better results
+        This is the legacy algorithm (used until now in freqtrade).
+        Weights are distributed as follows:
+        * 0.4 to trade duration
+        * 0.25: Avoiding trade loss
+        * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
+        """
+        total_profit = results.profit_percent.sum()
+        trade_duration = results.trade_duration.mean()
+
+        trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
+        profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
+        duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
+        result = trade_loss + profit_loss + duration_loss
+        return result
+```
+
+Currently, the arguments are:
+
+* `results`: DataFrame containing the result  
+    The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):  
+    `pair, profit_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reason`
+* `trade_count`: Amount of trades (identical to `len(results)`)
+* `min_date`: Start date of the hyperopting TimeFrame
+* `min_date`: End date of the hyperopting TimeFrame
+
+This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
+
+!!! Note
+    This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
+
+!!! Note
+    Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.

docs/advanced-setup.md

@@ -0,0 +1,92 @@
+# Advanced Post-installation Tasks
+
+This page explains some advanced tasks and configuration options that can be performed after the bot installation and may be uselful in some environments.
+
+If you do not know what things mentioned here mean, you probably do not need it.
+
+## Configure the bot running as a systemd service
+
+Copy the `freqtrade.service` file to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
+
+!!! Note
+    Certain systems (like Raspbian) don't load service unit files from the user directory. In this case, copy `freqtrade.service` into `/etc/systemd/user/` (requires superuser permissions).
+
+After that you can start the daemon with:
+
+```bash
+systemctl --user start freqtrade
+```
+
+For this to be persistent (run when user is logged out) you'll need to enable `linger` for your freqtrade user.
+
+```bash
+sudo loginctl enable-linger "$USER"
+```
+
+If you run the bot as a service, you can use systemd service manager as a software watchdog monitoring freqtrade bot 
+state and restarting it in the case of failures. If the `internals.sd_notify` parameter is set to true in the 
+configuration or the `--sd-notify` command line option is used, the bot will send keep-alive ping messages to systemd 
+using the sd_notify (systemd notifications) protocol and will also tell systemd its current state (Running or Stopped) 
+when it changes. 
+
+The `freqtrade.service.watchdog` file contains an example of the service unit configuration file which uses systemd 
+as the watchdog.
+
+!!! Note
+    The sd_notify communication between the bot and the systemd service manager will not work if the bot runs in a Docker container.
+
+## Advanced Logging
+
+On many Linux systems the bot can be configured to send its log messages to `syslog` or `journald` system services. Logging to a remote `syslog` server is also available on Windows. The special values for the `--logfilename` command line option can be used for this.
+
+### Logging to syslog
+
+To send Freqtrade log messages to a local or remote `syslog` service use the `--logfilename` command line option with the value in the following format:
+
+* `--logfilename syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
+
+The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
+
+So, the following are the examples of possible usages:
+
+* `--logfilename syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
+* `--logfilename syslog` -- same as above, the shortcut for `/dev/log`.
+* `--logfilename syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
+* `--logfilename syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
+* `--logfilename syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
+
+Log messages are send to `syslog` with the `user` facility. So you can see them with the following commands:
+
+* `tail -f /var/log/user`, or 
+* install a comprehensive graphical viewer (for instance, 'Log File Viewer' for Ubuntu).
+
+On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfilename syslog` or `--logfilename journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
+
+For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
+```
+if $programname startswith "freqtrade" then -/var/log/freqtrade.log
+```
+to one of the rsyslog configuration files, for example at the end of the `/etc/rsyslog.d/50-default.conf`.
+
+For `syslog` (`rsyslog`), the reduction mode can be switched on. This will reduce the number of repeating messages. For instance, multiple bot Heartbeat messages will be reduced to a single message when nothing else happens with the bot. To achieve this, set in `/etc/rsyslog.conf`:
+```
+# Filter duplicated messages
+$RepeatedMsgReduction on
+```
+
+### Logging to journald
+
+This needs the `systemd` python package installed as the dependency, which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
+
+To send Freqtrade log messages to `journald` system service use the `--logfilename` command line option with the value in the following format:
+
+* `--logfilename journald` -- send log messages to `journald`.
+
+Log messages are send to `journald` with the `user` facility. So you can see them with the following commands:
+
+* `journalctl -f` -- shows Freqtrade log messages sent to `journald` along with other log messages fetched by `journald`.
+* `journalctl -f -u freqtrade.service` -- this command can be used when the bot is run as a `systemd` service.
+
+There are many other options in the `journalctl` utility to filter the messages, see manual pages for this utility.
+
+On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfilename syslog` or `--logfilename journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.

docs/backtesting.md

@@ -11,29 +11,30 @@ Now you have good Buy and Sell strategies and some historic data, you want to te
 real data. This is what we call
 [backtesting](https://en.wikipedia.org/wiki/Backtesting).
 
-Backtesting will use the crypto-currencies (pairs) from your config file
+Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
-and load ticker data from `user_data/data/<exchange>` by default.
+If no data is available for the exchange / pair / timeframe (ticker interval) combination, backtesting will ask you to download them first using `freqtrade download-data`.
-If no data is available for the exchange / pair / ticker interval combination, backtesting will
-ask you to download them first using `freqtrade download-data`.
 For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
 
 The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
 
+!!! Tip "Using dynamic pairlists for backtesting"
+    While using dynamic pairlists during backtesting is not possible, a dynamic pairlist using current data can be generated via the [`test-pairlist`](utils.md#test-pairlist) command, and needs to be specified as `"pair_whitelist"` attribute in the configuration.
+
 ### Run a backtesting against the currencies listed in your config file
 
-#### With 5 min tickers (Per default)
+#### With 5 min candle (OHLCV) data (per default)
 
 ```bash
 freqtrade backtesting
 ```
 
-#### With 1 min tickers
+#### With 1 min candle (OHLCV) data
 
 ```bash
 freqtrade backtesting --ticker-interval 1m
 ```
 
-#### Using a different on-disk ticker-data source
+#### Using a different on-disk historical candle (OHLCV) data source
 
 Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory. 
 You can then use this data for backtesting as follows:
@@ -45,7 +46,7 @@ freqtrade --datadir user_data/data/bittrex-20180101 backtesting
 #### With a (custom) strategy file
 
 ```bash
-freqtrade -s SampleStrategy backtesting
+freqtrade backtesting -s SampleStrategy
 ```
 
 Where `-s SampleStrategy` refers to the class name within the strategy file `sample_strategy.py` found in the `freqtrade/user_data/strategies` directory.
@@ -72,16 +73,22 @@ The exported trades can be used for [further analysis](#further-backtest-result-
 freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json
 ```
 
+Please also read about the [strategy startup period](strategy-customization.md#strategy-startup-period).
+
 #### Supplying custom fee value
 
 Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.
-To account for this in backtesting, you can use `--fee 0.001` to supply this value to backtesting.
+To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
-This fee must be a percentage, and will be applied twice (once for trade entry, and once for trade exit).
+This fee must be a ratio, and will be applied twice (once for trade entry, and once for trade exit).
+
+For example, if the buying and selling commission fee is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
 
 ```bash
 freqtrade backtesting --fee 0.001
 ```
 
+!!! Note
+    Only supply this option (or the corresponding configuration parameter) if you want to experiment with different fee values. By default, Backtesting fetches the default fee from the exchange pair/market info.
 
 #### Running backtest with smaller testset by using timerange
 
@@ -112,45 +119,46 @@ A backtesting result will look like that:
 
 ```
 ========================================================= BACKTESTING REPORT ========================================================
-| pair     |   buy count |   avg profit % |   cum profit % |   tot profit BTC |   tot profit % | avg duration   |   profit |   loss |
+| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws |  Losses |
-|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
+|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
-| ADA/BTC  |          35 |          -0.11 |          -3.88 |      -0.00019428 |          -1.94 | 4:35:00        |       14 |     21 |
+| ADA/BTC  |     35 |          -0.11 |          -3.88 |      -0.00019428 |          -1.94 | 4:35:00        |    14 |      0 |      21 |
-| ARK/BTC  |          11 |          -0.41 |          -4.52 |      -0.00022647 |          -2.26 | 2:03:00        |        3 |      8 |
+| ARK/BTC  |     11 |          -0.41 |          -4.52 |      -0.00022647 |          -2.26 | 2:03:00        |     3 |      0 |       8 |
-| BTS/BTC  |          32 |           0.31 |           9.78 |       0.00048938 |           4.89 | 5:05:00        |       18 |     14 |
+| BTS/BTC  |     32 |           0.31 |           9.78 |       0.00048938 |           4.89 | 5:05:00        |    18 |      0 |      14 |
-| DASH/BTC |          13 |          -0.08 |          -1.07 |      -0.00005343 |          -0.53 | 4:39:00        |        6 |      7 |
+| DASH/BTC |     13 |          -0.08 |          -1.07 |      -0.00005343 |          -0.53 | 4:39:00        |     6 |      0 |       7 |
-| ENG/BTC  |          18 |           1.36 |          24.54 |       0.00122807 |          12.27 | 2:50:00        |        8 |     10 |
+| ENG/BTC  |     18 |           1.36 |          24.54 |       0.00122807 |          12.27 | 2:50:00        |     8 |      0 |      10 |
-| EOS/BTC  |          36 |           0.08 |           3.06 |       0.00015304 |           1.53 | 3:34:00        |       16 |     20 |
+| EOS/BTC  |     36 |           0.08 |           3.06 |       0.00015304 |           1.53 | 3:34:00        |    16 |      0 |      20 |
-| ETC/BTC  |          26 |           0.37 |           9.51 |       0.00047576 |           4.75 | 6:14:00        |       11 |     15 |
+| ETC/BTC  |     26 |           0.37 |           9.51 |       0.00047576 |           4.75 | 6:14:00        |    11 |      0 |      15 |
-| ETH/BTC  |          33 |           0.30 |           9.96 |       0.00049856 |           4.98 | 7:31:00        |       16 |     17 |
+| ETH/BTC  |     33 |           0.30 |           9.96 |       0.00049856 |           4.98 | 7:31:00        |    16 |      0 |      17 |
-| IOTA/BTC |          32 |           0.03 |           1.09 |       0.00005444 |           0.54 | 3:12:00        |       14 |     18 |
+| IOTA/BTC |     32 |           0.03 |           1.09 |       0.00005444 |           0.54 | 3:12:00        |    14 |      0 |      18 |
-| LSK/BTC  |          15 |           1.75 |          26.26 |       0.00131413 |          13.13 | 2:58:00        |        6 |      9 |
+| LSK/BTC  |     15 |           1.75 |          26.26 |       0.00131413 |          13.13 | 2:58:00        |     6 |      0 |       9 |
-| LTC/BTC  |          32 |          -0.04 |          -1.38 |      -0.00006886 |          -0.69 | 4:49:00        |       11 |     21 |
+| LTC/BTC  |     32 |          -0.04 |          -1.38 |      -0.00006886 |          -0.69 | 4:49:00        |    11 |      0 |      21 |
-| NANO/BTC |          17 |           1.26 |          21.39 |       0.00107058 |          10.70 | 1:55:00        |       10 |      7 |
+| NANO/BTC |     17 |           1.26 |          21.39 |       0.00107058 |          10.70 | 1:55:00        |    10 |      0 |       7 |
-| NEO/BTC  |          23 |           0.82 |          18.97 |       0.00094936 |           9.48 | 2:59:00        |       10 |     13 |
+| NEO/BTC  |     23 |           0.82 |          18.97 |       0.00094936 |           9.48 | 2:59:00        |    10 |      0 |      13 |
-| REQ/BTC  |           9 |           1.17 |          10.54 |       0.00052734 |           5.27 | 3:47:00        |        4 |      5 |
+| REQ/BTC  |      9 |           1.17 |          10.54 |       0.00052734 |           5.27 | 3:47:00        |     4 |      0 |       5 |
-| XLM/BTC  |          16 |           1.22 |          19.54 |       0.00097800 |           9.77 | 3:15:00        |        7 |      9 |
+| XLM/BTC  |     16 |           1.22 |          19.54 |       0.00097800 |           9.77 | 3:15:00        |     7 |      0 |       9 |
-| XMR/BTC  |          23 |          -0.18 |          -4.13 |      -0.00020696 |          -2.07 | 5:30:00        |       12 |     11 |
+| XMR/BTC  |     23 |          -0.18 |          -4.13 |      -0.00020696 |          -2.07 | 5:30:00        |    12 |      0 |      11 |
-| XRP/BTC  |          35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00        |       12 |     23 |
+| XRP/BTC  |     35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00        |    12 |      0 |      23 |
-| ZEC/BTC  |          22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00        |        7 |     15 |
+| ZEC/BTC  |     22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00        |     7 |      0 |      15 |
-| TOTAL    |         429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |      186 |    243 |
+| TOTAL    |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |     243 |
 ========================================================= SELL REASON STATS =========================================================
-| Sell Reason        |   Count |
+| Sell Reason        |   Sells |  Wins |  Draws |  Losses |
-|:-------------------|--------:|
+|:-------------------|--------:|------:|-------:|--------:|
-| trailing_stop_loss |     205 |
+| trailing_stop_loss |     205 |   150 |      0 |      55 |
-| stop_loss          |     166 |
+| stop_loss          |     166 |     0 |      0 |     166 |
-| sell_signal        |      56 |
+| sell_signal        |      56 |    36 |      0 |      20 |
-| force_sell         |       2 |
+| force_sell         |       2 |     0 |      0 |       2 |
 ====================================================== LEFT OPEN TRADES REPORT ======================================================
-| pair     |   buy count |   avg profit % |   cum profit % |   tot profit BTC |   tot profit % | avg duration   |   profit |   loss |
+| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws |  Losses |
-|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
+|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
-| ADA/BTC  |           1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |        1 |      0 |
+| ADA/BTC  |      1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |     1 |      0 |       0 |
-| LTC/BTC  |           1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |        1 |      0 |
+| LTC/BTC  |      1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |     1 |      0 |       0 |
-| TOTAL    |           2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |        2 |      0 |
+| TOTAL    |      2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |     2 |      0 |       0 |
 ```
 
 The 1st table contains all trades the bot made, including "left open trades".
 
 The 2nd table contains a recap of sell reasons.
+This table can tell you which area needs some additional work (i.e. all `sell_signal` trades are losses, so we should disable the sell-signal or work on improving that).
 
 The 3rd table contains all trades the bot had to `forcesell` at the end of the backtest period to present a full picture.
 This is necessary to simulate realistic behaviour, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
@@ -191,7 +199,10 @@ Since backtesting lacks some detailed information about what happens within a ca
 - Buys happen at open-price
 - Sell signal sells happen at open-price of the following candle
 - Low happens before high for stoploss, protecting capital first.
-- ROI sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
+- ROI
+  - sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
+  - sells are never "below the candle", so a ROI of 2% may result in a sell at 2.4% if low was at 2.4% profit
+  - Forcesells caused by `<N>=-1` ROI entries use low as sell value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
 - Stoploss sells happen exactly at stoploss price, even if low was lower
 - Trailing stoploss
   - High happens first - adjusting stoploss
@@ -212,7 +223,7 @@ You can then load the trades to perform further analysis as shown in our [data a
 
 To compare multiple strategies, a list of Strategies can be provided to backtesting.
 
-This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple
+This is limited to 1 timeframe (ticker interval) value per run. However, data is only loaded once from disk so if you have multiple
 strategies you'd like to compare, this will give a nice runtime boost.
 
 All listed Strategies need to be in the same directory.
@@ -226,11 +237,11 @@ There will be an additional table comparing win/losses of the different strategi
 Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
 
 ```
-=========================================================== Strategy Summary ===========================================================
+=========================================================== STRATEGY SUMMARY ===========================================================
-| Strategy    |   buy count |   avg profit % |   cum profit % |   tot profit BTC |   tot profit % | avg duration   |   profit |   loss |
+| Strategy    |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws | Losses |
-|:------------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
+|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|
-| Strategy1   |         429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |      186 |    243 |
+| Strategy1   |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |    243 |
-| Strategy2   |        1487 |          -0.13 |        -197.58 |      -0.00988917 |         -98.79 | 4:43:00        |      662 |    825 |
+| Strategy2   |   1487 |          -0.13 |        -197.58 |      -0.00988917 |         -98.79 | 4:43:00        |   662 |      0 |    825 |
 ```
 
 ## Next step

docs/bot-usage.md

@@ -5,20 +5,18 @@ This page explains the different parameters of the bot and how to run it.
 !!! Note
     If you've used `setup.sh`, don't forget to activate your virtual environment (`source .env/bin/activate`) before running freqtrade commands.
 
-
 ## Bot commands
 
 ```
-usage: freqtrade [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
+usage: freqtrade [-h] [-V]
-                 [--userdir PATH] [-s NAME] [--strategy-path PATH]
+                 {trade,backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
-                 [--db-url PATH] [--sd-notify]
-                 {backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
                  ...
 
 Free, open source crypto trading bot
 
 positional arguments:
-  {backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
+  {trade,backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
+    trade               Trade module.
     backtesting         Backtesting module.
     edge                Edge module.
     hyperopt            Hyperopt module.
@@ -32,25 +30,49 @@ positional arguments:
 
 optional arguments:
   -h, --help            show this help message and exit
+  -V, --version         show program's version number and exit
+
+```
+
+### Bot trading commands
+
+```
+usage: freqtrade trade [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
+                       [--userdir PATH] [-s NAME] [--strategy-path PATH]
+                       [--db-url PATH] [--sd-notify] [--dry-run]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --db-url PATH         Override trades database URL, this is useful in custom
+                        deployments (default: `sqlite:///tradesv3.sqlite` for
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
+  --sd-notify           Notify systemd service manager.
+  --dry-run             Enforce dry-run for trading (removes Exchange secrets
+                        and simulates trades).
+
+Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified.
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
+                        Specify configuration file (default:
-                        Multiple --config options may be used. Can be set to
+                        `userdir/config.json` or `config.json` whichever
-                        `-` to read config from stdin.
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
+
+Strategy arguments:
   -s NAME, --strategy NAME
-                        Specify strategy class name (default:
+                        Specify strategy class name which will be used by the
-                        `DefaultStrategy`).
+                        bot.
   --strategy-path PATH  Specify additional strategy lookup path.
-  --db-url PATH         Override trades database URL, this is useful in custom
+.
-                        deployments (default: `sqlite:///tradesv3.sqlite` for
-                        Live Run mode, `sqlite://` for Dry Run).
-  --sd-notify           Notify systemd service manager.
 
 ```
 
@@ -60,7 +82,7 @@ The bot allows you to select which configuration file you want to use by means o
 the `-c/--config` command line option:
 
 ```bash
-freqtrade -c path/far/far/away/config.json
+freqtrade trade -c path/far/far/away/config.json
 ```
 
 Per default, the bot loads the `config.json` configuration file from the current
@@ -73,22 +95,22 @@ The bot allows you to use multiple configuration files by specifying multiple
 defined in the latter configuration files override parameters with the same name
 defined in the previous configuration files specified in the command line earlier.
 
-For example, you can make a separate configuration file with your key and secrete
+For example, you can make a separate configuration file with your key and secret
 for the Exchange you use for trading, specify default configuration file with
-empty key and secrete values while running in the Dry Mode (which does not actually
+empty key and secret values while running in the Dry Mode (which does not actually
 require them):
 
 ```bash
-freqtrade -c ./config.json
+freqtrade trade -c ./config.json
 ```
 
 and specify both configuration files when running in the normal Live Trade Mode:
 
 ```bash
-freqtrade -c ./config.json -c path/to/secrets/keys.config.json
+freqtrade trade -c ./config.json -c path/to/secrets/keys.config.json
 ```
 
-This could help you hide your private Exchange key and Exchange secrete on you local machine
+This could help you hide your private Exchange key and Exchange secret on you local machine
 by setting appropriate file permissions for the file which contains actual secrets and, additionally,
 prevent unintended disclosure of sensitive private data when you publish examples
 of your configuration in the project issues or in the Internet.
@@ -134,7 +156,7 @@ In `user_data/strategies` you have a file `my_awesome_strategy.py` which has
 a strategy class called `AwesomeStrategy` to load it:
 
 ```bash
-freqtrade --strategy AwesomeStrategy
+freqtrade trade --strategy AwesomeStrategy
 ```
 
 If the bot does not find your strategy file, it will display in an error
@@ -149,7 +171,7 @@ This parameter allows you to add an additional strategy lookup path, which gets
 checked before the default locations (The passed path must be a directory!):
 
 ```bash
-freqtrade --strategy AwesomeStrategy --strategy-path /some/directory
+freqtrade trade --strategy AwesomeStrategy --strategy-path /some/directory
 ```
 
 #### How to install a strategy?
@@ -165,7 +187,7 @@ using `--db-url`. This can also be used to specify a custom database
 in production mode. Example command:
 
 ```bash
-freqtrade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
+freqtrade trade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
 ```
 
 ## Backtesting commands
@@ -173,9 +195,11 @@ freqtrade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
 Backtesting also uses the config specified via `-c/--config`.
 
 ```
-usage: freqtrade backtesting [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
+usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                             [--max_open_trades INT]
+                             [-d PATH] [--userdir PATH] [-s NAME]
-                             [--stake_amount STAKE_AMOUNT] [--fee FLOAT]
+                             [--strategy-path PATH] [-i TICKER_INTERVAL]
+                             [--timerange TIMERANGE] [--max-open-trades INT]
+                             [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
                              [--eps] [--dmmp]
                              [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
                              [--export EXPORT] [--export-filename PATH]
@@ -187,10 +211,12 @@ optional arguments:
                         `1d`).
   --timerange TIMERANGE
                         Specify what timerange of data to use.
-  --max_open_trades INT
+  --max-open-trades INT
-                        Specify max_open_trades to use.
+                        Override the value of the `max_open_trades`
-  --stake_amount STAKE_AMOUNT
+                        configuration setting.
-                        Specify stake_amount.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
   --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
                         entry and exit).
   --eps, --enable-position-stacking
@@ -211,11 +237,32 @@ optional arguments:
   --export EXPORT       Export backtest results, argument are: trades.
                         Example: `--export=trades`
   --export-filename PATH
-                        Save backtest results to the file with this filename
+                        Save backtest results to the file with this filename.
-                        (default: `user_data/backtest_results/backtest-
+                        Requires `--export` to be set as well. Example:
-                        result.json`). Requires `--export` to be set as well.
+                        `--export-filename=user_data/backtest_results/backtest
-                        Example: `--export-filename=user_data/backtest_results
+                        _today.json`
-                        /backtest_today.json`
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
 
 ```
 
@@ -223,20 +270,22 @@ optional arguments:
 
 The first time your run Backtesting, you will need to download some historic data first.
 This can be accomplished by using `freqtrade download-data`.  
-Check the corresponding [help page section](backtesting.md#Getting-data-for-backtesting-and-hyperopt) for more details
+Check the corresponding [Data Downloading](data-download.md) section for more details
 
 ## Hyperopt commands
 
 To optimize your strategy, you can use hyperopt parameter hyperoptimization
-to find optimal parameter values for your stategy.
+to find optimal parameter values for your strategy.
 
 ```
-usage: freqtrade hyperopt [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
+usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
-                          [--max_open_trades INT]
+                          [--userdir PATH] [-s NAME] [--strategy-path PATH]
-                          [--stake_amount STAKE_AMOUNT] [--fee FLOAT]
+                          [-i TICKER_INTERVAL] [--timerange TIMERANGE]
-                          [--customhyperopt NAME] [--hyperopt-path PATH]
+                          [--max-open-trades INT]
-                          [--eps] [-e INT]
+                          [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
-                          [-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
+                          [--hyperopt NAME] [--hyperopt-path PATH] [--eps]
+                          [-e INT]
+                          [--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]]
                           [--dmmp] [--print-all] [--no-color] [--print-json]
                           [-j JOBS] [--random-state INT] [--min-trades INT]
                           [--continue] [--hyperopt-loss NAME]
@@ -248,24 +297,25 @@ optional arguments:
                         `1d`).
   --timerange TIMERANGE
                         Specify what timerange of data to use.
-  --max_open_trades INT
+  --max-open-trades INT
-                        Specify max_open_trades to use.
+                        Override the value of the `max_open_trades`
-  --stake_amount STAKE_AMOUNT
+                        configuration setting.
-                        Specify stake_amount.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
   --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
                         entry and exit).
-  --customhyperopt NAME
+  --hyperopt NAME       Specify hyperopt class name which will be used by the
-                        Specify hyperopt class name (default:
+                        bot.
-                        `DefaultHyperOpt`).
+  --hyperopt-path PATH  Specify additional lookup path for Hyperopt and
-  --hyperopt-path PATH  Specify additional lookup path for Hyperopts and
                         Hyperopt Loss functions.
   --eps, --enable-position-stacking
                         Allow buying the same pair multiple times (position
                         stacking).
   -e INT, --epochs INT  Specify number of epochs (default: 100).
-  -s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...], --spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]
+  --spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]
                         Specify which parameters to hyperopt. Space-separated
-                        list. Default: `all`.
+                        list.
   --dmmp, --disable-max-market-positions
                         Disable applying `max_open_trades` during backtest
                         (same as setting `max_open_trades` to a very high
@@ -273,7 +323,7 @@ optional arguments:
   --print-all           Print all results, not only the best ones.
   --no-color            Disable colorization of hyperopt results. May be
                         useful if you are redirecting output to a file.
-  --print-json          Print best result detailization in JSON format.
+  --print-json          Print best results in JSON format.
   -j JOBS, --job-workers JOBS
                         The number of concurrently running jobs for
                         hyperoptimization (hyperopt worker processes). If -1
@@ -291,9 +341,34 @@ optional arguments:
                         class (IHyperOptLoss). Different functions can
                         generate completely different results, since the
                         target for optimization is different. Built-in
-                        Hyperopt-loss-functions are: DefaultHyperOptLoss,
+                        Hyperopt-loss-functions are:
-                        OnlyProfitHyperOptLoss, SharpeHyperOptLoss.(default:
+                        DefaultHyperOptLoss, OnlyProfitHyperOptLoss,
-                        `DefaultHyperOptLoss`).
+                        SharpeHyperOptLoss, SharpeHyperOptLossDaily,
+                        SortinoHyperOptLoss, SortinoHyperOptLossDaily.
+                        (default: `DefaultHyperOptLoss`).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+
 ```
 
 ## Edge commands
@@ -301,8 +376,10 @@ optional arguments:
 To know your trade expectancy and winrate against historical data, you can use Edge.
 
 ```
-usage: freqtrade edge [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
+usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
-                      [--max_open_trades INT] [--stake_amount STAKE_AMOUNT]
+                      [--userdir PATH] [-s NAME] [--strategy-path PATH]
+                      [-i TICKER_INTERVAL] [--timerange TIMERANGE]
+                      [--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
                       [--fee FLOAT] [--stoplosses STOPLOSS_RANGE]
 
 optional arguments:
@@ -312,10 +389,12 @@ optional arguments:
                         `1d`).
   --timerange TIMERANGE
                         Specify what timerange of data to use.
-  --max_open_trades INT
+  --max-open-trades INT
-                        Specify max_open_trades to use.
+                        Override the value of the `max_open_trades`
-  --stake_amount STAKE_AMOUNT
+                        configuration setting.
-                        Specify stake_amount.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
   --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
                         entry and exit).
   --stoplosses STOPLOSS_RANGE
@@ -324,6 +403,28 @@ optional arguments:
                         (without any space). Example:
                         `--stoplosses=-0.01,-0.1,-0.001`
 
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+
 ```
 
 To understand edge and how to read the results, please read the [edge documentation](edge.md).

docs/configuration.md

@@ -38,105 +38,177 @@ The prevelance for all Options is as follows:
 
 Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
 
-|  Command | Default | Description |
+|  Parameter | Description |
-|----------|---------|-------------|
+|------------|-------------|
-| `max_open_trades` | 3 | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades)
+| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation which can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
-| `stake_currency` | BTC | **Required.** Crypto-currency used for trading.
+| `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
-| `stake_amount` | 0.05 | **Required.** Amount of crypto-currency your bot will use for each trade. Per default, the bot will use (0.05 BTC x 3) = 0.15 BTC in total will be always engaged. Set it to `"unlimited"` to allow the bot to use all available balance.
+| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Positive float or `"unlimited"`.
-| `amount_reserve_percent` | 0.05 | Reserve some amount in min pair stake amount. Default is 5%. The bot will reserve `amount_reserve_percent` + stop-loss value when calculating min pair stake amount in order to avoid possible trade refusals.
+| `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> **Datatype:** Positive float between `0.1` and `1.0`.
-| `ticker_interval` | [1m, 5m, 15m, 30m, 1h, 1d, ...] | The ticker interval to use (1min, 5 min, 15 min, 30 min, 1 hour or 1 day). Default is 5 minutes. [Strategy Override](#parameters-in-the-strategy).
+| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
-| `fiat_display_currency` | USD | **Required.** Fiat currency used to show your profits. More information below.
+| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
-| `dry_run` | true | **Required.** Define if the bot must be in Dry-run or production mode.
+| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> **Datatype:** Positive Float as ratio.
-| `dry_run_wallet` | 999.9 | Overrides the default amount of 999.9 stake currency units in the wallet used by the bot running in the Dry Run mode if you need it for any reason.
+| `ticker_interval` | The timeframe (ticker interval) to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
-| `process_only_new_candles` | false | If set to true indicators are processed only once a new candle arrives. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy).
+| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
-| `minimal_roi` | See below | Set the threshold in percent the bot will use to sell a trade. More information below. [Strategy Override](#parameters-in-the-strategy).
+| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
-| `stoploss` | -0.10 | Value of the stoploss in percent used by the bot. More information below. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
+| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in the Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
-| `trailing_stop` | false | Enables trailing stop-loss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
+| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> **Datatype:** Boolean
-| `trailing_stop_positive` | 0 | Changes stop-loss once profit has been reached. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
+| `minimal_roi` | **Required.** Set the threshold in percent the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
-| `trailing_stop_positive_offset` | 0 | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
+| `stoploss` |  **Required.** Value of the stoploss in percent used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).  <br> **Datatype:** Float (as ratio)
-| `trailing_only_offset_is_reached` | false | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
+| `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Boolean
-| `unfilledtimeout.buy` | 10 | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled.
+| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
-| `unfilledtimeout.sell` | 10 | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled.
+| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
-| `bid_strategy.ask_last_balance` | 0.0 | **Required.** Set the bidding price. More information [below](#understand-ask_last_balance).
+| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> **Datatype:** Boolean
-| `bid_strategy.use_order_book` | false | Allows buying of pair using the rates in Order Book Bids.
+| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
-| `bid_strategy.order_book_top` | 0 | Bot will use the top N rate in Order Book Bids. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in Order Book Bids.
+| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
-| `bid_strategy. check_depth_of_market.enabled` | false | Does not buy if the % difference of buy orders and sell orders is met in Order Book.
+| `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
-| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | 0 | The % difference of buy orders and sell orders found in Order Book. A value lesser than 1 means sell orders is greater, while value greater than 1 means buy orders is higher.
+| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook-enabled).
-| `ask_strategy.use_order_book` | false | Allows selling of open traded pair using the rates in Order Book Asks.
+| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
-| `ask_strategy.order_book_min` | 0 | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
+| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.*  <br> **Datatype:** Positive Integer
-| `ask_strategy.order_book_max` | 0 | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
+| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
-| `ask_strategy.use_sell_signal` | true | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy).
+| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.*  <br> **Datatype:** Float (as ratio)
-| `ask_strategy.sell_profit_only` | false | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy).
+| `ask_strategy.price_side` | Select the side of the spread the bot should look at to get the sell rate. [More information below](#sell-price-side).<br> *Defaults to `ask`.* <br> **Datatype:** String (either `ask` or `bid`).
-| `ask_strategy.ignore_roi_if_buy_signal` | false | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy).
+| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> **Datatype:** Boolean
-| `order_types` | None | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).
+| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
-| `order_time_in_force` | None | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy).
+| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
-| `exchange.name` |  | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename).
+| `ask_strategy.use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
-| `exchange.sandbox` | false | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.
+| `ask_strategy.sell_profit_only` | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
-| `exchange.key` | '' | API key to use for the exchange. Only required when you are in production mode. ***Keep it in secrete, do not disclose publicly.***
+| `ask_strategy.ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
-| `exchange.secret` | '' | API secret to use for the exchange. Only required when you are in production mode. ***Keep it in secrete, do not disclose publicly.***
+| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
-| `exchange.password` | '' | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests. ***Keep it in secrete, do not disclose publicly.***
+| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
-| `exchange.pair_whitelist` | [] | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Can be overriden by dynamic pairlists (see [below](#dynamic-pairlists)).
+| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
-| `exchange.pair_blacklist` | [] | List of pairs the bot must absolutely avoid for trading and backtesting. Can be overriden by dynamic pairlists (see [below](#dynamic-pairlists)).
+| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> **Datatype:** Boolean
-| `exchange.ccxt_config` | None | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation)
+| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
-| `exchange.ccxt_async_config` | None | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation)
+| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
-| `exchange.markets_refresh_interval` | 60 | The interval in minutes in which markets are reloaded.
+| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
-| `edge` | false | Please refer to [edge configuration document](edge.md) for detailed explanation.
+| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#dynamic-pairlists)). <br> **Datatype:** List
-| `experimental.block_bad_exchanges` | true | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now.
+| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#dynamic-pairlists)). <br> **Datatype:** List
-| `pairlist.method` | StaticPairList | Use static or dynamic volume-based pairlist. [More information below](#dynamic-pairlists).
+| `exchange.ccxt_config` | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
-| `pairlist.config` | None | Additional configuration for dynamic pairlists. [More information below](#dynamic-pairlists).
+| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
-| `telegram.enabled` | true | **Required.** Enable or not the usage of Telegram.
+| `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
-| `telegram.token` | token | Your Telegram bot token. Only required if `telegram.enabled` is `true`. ***Keep it in secrete, do not disclose publicly.***
+| `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
-| `telegram.chat_id` | chat_id | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. ***Keep it in secrete, do not disclose publicly.***
+| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
-| `webhook.enabled` | false | Enable usage of Webhook notifications
+| `pairlists` | Define one or more pairlists to be used. [More information below](#dynamic-pairlists). <br>*Defaults to `StaticPairList`.*  <br> **Datatype:** List of Dicts
-| `webhook.url` | false | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details.
+| `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
-| `webhook.webhookbuy` | false | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
+| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
-| `webhook.webhooksell` | false | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
+| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
-| `webhook.webhookstatus` | false | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
+| `webhook.enabled` | Enable usage of Webhook notifications <br> **Datatype:** Boolean
-| `db_url` | `sqlite:///tradesv3.sqlite`| Declares database URL to use. NOTE: This defaults to `sqlite://` if `dry_run` is `True`.
+| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `initial_state` | running | Defines the initial application state. More information below.
+| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `forcebuy_enable` | false | Enables the RPC Commands to force a buy. More information below.
+| `webhook.webhookbuycancel` | Payload to send on buy order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `strategy` | DefaultStrategy | Defines Strategy class to use.
+| `webhook.webhooksell` | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `strategy_path` | null | Adds an additional strategy lookup path (must be a directory).
+| `webhook.webhooksellcancel` | Payload to send on sell order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `internals.process_throttle_secs` | 5 | **Required.** Set the process throttle. Value in second.
+| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `internals.heartbeat_interval` | 60 | Print heartbeat message every X seconds. Set to 0 to disable heartbeat messages.
+| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
-| `internals.sd_notify` | false | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details.
+| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4
-| `logfile` | | Specify Logfile. Uses a rolling strategy of 10 files, with 1Mb per file.
+| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>**Datatype:** Integer between 1024 and 65535
-| `user_data_dir` | cwd()/user_data | Directory containing user data. Defaults to `./user_data/`.
+| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
+| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
+| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
+| `initial_state` | Defines the initial application state. More information below. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
+| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> **Datatype:** Boolean
+| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
+| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
+| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Intege
+| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
+| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
+| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
+| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
+| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
+| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
 
 ### Parameters in the strategy
 
 The following parameters can be set in either configuration file or strategy.
 Values set in the configuration file always overwrite values set in the strategy.
 
-* `ticker_interval`
 * `minimal_roi`
+* `ticker_interval`
 * `stoploss`
 * `trailing_stop`
 * `trailing_stop_positive`
 * `trailing_stop_positive_offset`
+* `trailing_only_offset_is_reached`
 * `process_only_new_candles`
 * `order_types`
 * `order_time_in_force`
+* `stake_currency`
+* `stake_amount`
+* `unfilledtimeout`
 * `use_sell_signal` (ask_strategy)
 * `sell_profit_only` (ask_strategy)
 * `ignore_roi_if_buy_signal` (ask_strategy)
 
-### Understand stake_amount
+### Configuring amount per trade
 
-The `stake_amount` configuration parameter is an amount of crypto-currency your bot will use for each trade.
+There are several methods to configure how much of the stake currency the bot will use to enter a trade. All methods respect the [available balance configuration](#available-balance) as explained below.
-The minimal value is 0.0005. If there is not enough crypto-currency in
+
-the account an exception is generated.
+#### Available balance
-To allow the bot to trade all the available `stake_currency` in your account set
+
+By default, the bot assumes that the `complete amount - 1%` is at it's disposal, and when using [dynamic stake amount](#dynamic-stake-amount), it will split the complete balance into `max_open_trades` buckets per trade.
+Freqtrade will reserve 1% for eventual fees when entering a trade and will therefore not touch that by default.
+
+You can configure the "untouched" amount by using the `tradable_balance_ratio` setting.
+
+For example, if you have 10 ETH available in your wallet on the exchange and `tradable_balance_ratio=0.5` (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers this as available balance. The rest of the wallet is untouched by the trades.
+
+!!! Warning
+    The `tradable_balance_ratio` setting applies to the current balance (free balance + tied up in trades). Therefore, assuming the starting balance of 1000, a configuration with `tradable_balance_ratio=0.99` will not guarantee that 10 currency units will always remain available on the exchange. For example, the free amount may reduce to 5 units if the total balance is reduced to 500 (either by a losing streak, or by withdrawing balance).
+
+#### Amend last stake amount
+
+Assuming we have the tradable balance of 1000 USDT, `stake_amount=400`, and `max_open_trades=3`.
+The bot would open 2 trades, and will be unable to fill the last trading slot, since the requested 400 USDT are no longer available, since 800 USDT are already tied in other trades.
+
+To overcome this, the option `amend_last_stake_amount` can be set to `True`, which will enable the bot to reduce stake_amount to the available balance in order to fill the last trade slot.
+
+In the example above this would mean:
+
+- Trade1: 400 USDT
+- Trade2: 400 USDT
+- Trade3: 200 USDT
+
+!!! Note
+    This option only applies with [Static stake amount](#static-stake-amount) - since [Dynamic stake amount](#dynamic-stake-amount) divides the balances evenly.
+
+!!! Note
+    The minimum last stake amount can be configured using `amend_last_stake_amount` - which defaults to 0.5 (50%). This means that the minimum stake amount that's ever used is `stake_amount * 0.5`. This avoids very low stake amounts, that are close to the minimum tradable amount for the pair and can be refused by the exchange.
+
+#### Static stake amount
+
+The `stake_amount` configuration statically configures the amount of stake-currency your bot will use for each trade.
+
+The minimal configuration value is 0.0001, however, please check your exchange's trading minimums for the stake currency you're using to avoid problems.
+
+This setting works in combination with `max_open_trades`. The maximum capital engaged in trades is `stake_amount * max_open_trades`.
+For example, the bot will at most use (0.05 BTC x 3) = 0.15 BTC, assuming a configuration of `max_open_trades=3` and `stake_amount=0.05`.
+
+!!! Note
+    This setting respects the [available balance configuration](#available-balance).
+
+#### Dynamic stake amount
+
+Alternatively, you can use a dynamic stake amount, which will use the available balance on the exchange, and divide that equally by the amount of allowed trades (`max_open_trades`).
+
+To configure this, set `stake_amount="unlimited"`. We also recommend to set `tradable_balance_ratio=0.99` (99%) - to keep a minimum balance for eventual fees.
+
+In this case a trade amount is calculated as:
+
+```python
+currency_balance / (max_open_trades - current_open_trades)
+```
+
+To allow the bot to trade all the available `stake_currency` in your account (minus `tradable_balance_ratio`) set
 
 ```json
 "stake_amount" : "unlimited",
+"tradable_balance_ratio": 0.99,
 ```
 
-In this case a trade amount is calclulated as:
+!!! Note
+    This configuration will allow increasing / decreasing stakes depending on the performance of the bot (lower stake if bot is loosing, higher stakes if the bot has a winning record, since higher balances are available).
 
-```python
+!!! Note "When using Dry-Run Mode"
-currency_balanse / (max_open_trades - current_open_trades)
+    When using `"stake_amount" : "unlimited",` in combination with Dry-Run, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve over time. It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
-```
 
 ### Understand minimal_roi
 
@@ -158,6 +230,9 @@ This parameter can be set in either Strategy or Configuration file. If you use i
 `minimal_roi` value from the strategy file.
 If it is not set in either Strategy or Configuration, a default of 1000% `{"0": 10}` is used, and minimal roi is disabled unless your trade generates 1000% profit.
 
+!!! Note "Special case to forcesell after a specific time"
+    A special case presents using `"<N>": -1` as ROI. This forces the bot to sell a trade after N Minutes, no matter if it's positive or negative, so represents a time-limited force-sell.
+
 ### Understand stoploss
 
 Go to the [stoploss documentation](stoploss.md) for more details.
@@ -190,13 +265,6 @@ before asking the strategy if we should buy or a sell an asset. After each wait
 every opened trade wether or not we should sell, and for all the remaining pairs (either the dynamic list of pairs or
 the static list of pairs) if we should buy.
 
-### Understand ask_last_balance
-
-The `ask_last_balance` configuration parameter sets the bidding price. Value `0.0` will use `ask` price, `1.0` will
-use the `last` price and values between those interpolate between ask and last
-price. Using `ask` price will guarantee quick success in bid, but bot will also
-end up paying more then would probably have been necessary.
-
 ### Understand order_types
 
 The `order_types` configuration parameter maps actions (`buy`, `sell`, `stoploss`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
@@ -215,6 +283,11 @@ If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
 `emergencysell` is an optional value, which defaults to `market` and is used when creating stoploss on exchange orders fails.
 The below is the default which is used if this is not configured in either strategy or configuration file.
 
+Since `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.
+`stoploss` defines the stop-price - and limit should be slightly below this. This defaults to 0.99 / 1% (configurable via `stoploss_on_exchange_limit_ratio`).
+Calculation example: we bought the asset at 100$.
+Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the stoploss will happen between 95$ and 94.05$.
+
 Syntax for Strategy:
 
 ```python
@@ -224,7 +297,8 @@ order_types = {
     "emergencysell": "market",
     "stoploss": "market",
     "stoploss_on_exchange": False,
-    "stoploss_on_exchange_interval": 60
+    "stoploss_on_exchange_interval": 60,
+    "stoploss_on_exchange_limit_ratio": 0.99,
 }
 ```
 
@@ -254,7 +328,7 @@ Configuration:
 !!! Note
     If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new order.
 
-!!! Warning stoploss_on_exchange failures
+!!! Warning "Warning: stoploss_on_exchange failures"
     If stoploss on exchange creation fails for some reason, then an "emergency sell" is initiated. By default, this will sell the asset using a market order. The order-type for the emergency-sell can be changed by setting the `emergencysell` value in the `order_types` dictionary - however this is not advised.
 
 ### Understand order_time_in_force
@@ -268,7 +342,7 @@ This is most of the time the default time in force. It means the order will rema
 on exchange till it is canceled by user. It can be fully or partially fulfilled.
 If partially fulfilled, the remaining will stay on the exchange till cancelled.
 
-**FOK (Full Or Kill):**
+**FOK (Fill Or Kill):**
 
 It means if the order is not executed immediately AND fully then it is canceled by the exchange.
 
@@ -298,16 +372,18 @@ The possible values are: `gtc` (default), `fok` or `ioc`.
 
 Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports over 100 cryptocurrency
 exchange markets and trading APIs. The complete up-to-date list can be found in the
-[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python). However, the bot was tested
+[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
-with only Bittrex and Binance.
+ However, the bot was tested by the development team with only Bittrex, Binance and Kraken,
-
+ so the these are the only officially supported exhanges:
-The bot was tested with the following exchanges:
 
 - [Bittrex](https://bittrex.com/): "bittrex"
 - [Binance](https://www.binance.com/): "binance"
+- [Kraken](https://kraken.com/): "kraken"
 
 Feel free to test other exchanges and submit your PR to improve the bot.
 
+Some exchanges require special configuration, which can be found on the [Exchange-specific Notes](exchanges.md) documentation page.
+
 #### Sample exchange configuration
 
 A exchange configuration for "binance" would look as follows:
@@ -331,13 +407,13 @@ This configuration enables binance, as well as rate limiting to avoid bans from
     Optimal settings for rate limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
     We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that `"enableRateLimit"` is enabled and increase the `"rateLimit"` parameter step by step.
 
-#### Advanced FreqTrade Exchange configuration
+#### Advanced Freqtrade Exchange configuration
 
 Advanced options can be configured using the `_ft_has_params` setting, which will override Defaults and exchange-specific behaviours.
 
 Available options are listed in the exchange-class as `_ft_has_default`.
 
-For example, to test the order type `FOK` with Kraken, and modify candle_limit to 200 (so you only get 200 candles per call):
+For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
 
 ```json
 "exchange": {
@@ -370,6 +446,206 @@ The valid values are:
 "BTC", "ETH", "XRP", "LTC", "BCH", "USDT"
 ```
 
+## Prices used for orders
+
+Prices for regular orders can be controlled via the parameter structures `bid_strategy` for buying and `ask_strategy` for selling.
+Prices are always retrieved right before an order is placed, either by querying the exchange tickers or by using the orderbook data.
+
+!!! Note
+    Orderbook data used by Freqtrade are the data retrieved from exchange by the ccxt's function `fetch_order_book()`, i.e. are usually data from the L2-aggregated orderbook, while the ticker data are the structures returned by the ccxt's `fetch_ticker()`/`fetch_tickers()` functions. Refer to the ccxt library [documentation](https://github.com/ccxt/ccxt/wiki/Manual#market-data) for more details.
+
+### Buy price
+
+#### Check depth of market
+
+When check depth of market is enabled (`bid_strategy.check_depth_of_market.enabled=True`), the buy signals are filtered based on the orderbook depth (sum of all amounts) for each orderbook side.
+
+Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) side depth and the resulting delta is compared to the value of the `bid_strategy.check_depth_of_market.bids_to_ask_delta` parameter. The buy order is only executed if the orderbook delta is greater than or equal to the configured delta value.
+
+!!! Note
+    A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
+
+#### Buy price side
+
+The configuration setting `bid_strategy.price_side` defines the side of the spread the bot looks for when buying.
+
+The following displays an orderbook.
+
+``` explanation
+...
+103
+102
+101  # ask
+-------------Current spread
+99   # bid
+98
+97
+...
+```
+
+If `bid_strategy.price_side` is set to `"bid"`, then the bot will use 99 as buying price.  
+In line with that, if `bid_strategy.price_side` is set to `"ask"`, then the bot will use 101 as buying price.
+
+Using `ask` price often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
+Taker fees instead of maker fees will most likely apply even when using limit buy orders.
+Also, prices at the "ask" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
+
+#### Buy price with Orderbook enabled
+
+When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the configured side (`bid_strategy.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
+
+#### Buy price without Orderbook enabled
+
+The following section uses `side` as the configured `bid_strategy.price_side`.
+
+When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
+
+The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
+
+### Sell price
+
+#### Sell price side
+
+The configuration setting `ask_strategy.price_side` defines the side of the spread the bot looks for when selling.
+
+The following displays an orderbook:
+
+``` explanation
+...
+103
+102
+101  # ask
+-------------Current spread
+99   # bid
+98
+97
+...
+```
+
+If `ask_strategy.price_side` is set to `"ask"`, then the bot will use 101 as selling price.  
+In line with that, if `ask_strategy.price_side` is set to `"bid"`, then the bot will use 99 as selling price.
+
+#### Sell price with Orderbook enabled
+
+When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the configured orderbook side are validated for a profitable sell-possibility based on the strategy configuration (`minimal_roi` conditions) and the sell order is placed at the first profitable spot.
+
+!!! Note
+    Using `order_book_max` higher than `order_book_min` only makes sense when ask_strategy.price_side is set to `"ask"`.
+
+The idea here is to place the sell order early, to be ahead in the queue.
+
+A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting `ask_strategy.order_book_min` and `ask_strategy.order_book_max` to the same number.
+
+!!! Warning "Order_book_max > 1 - increased risks for stoplosses!"
+    Using `ask_strategy.order_book_max` higher than 1 will increase the risk the stoploss on exchange is cancelled too early, since an eventual [stoploss on exchange](#understand-order_types) will be cancelled as soon as the order is placed.
+    Also, the sell order will remain on the exchange for `unfilledtimeout.sell` (or until it's filled) - which can lead to missed stoplosses (with or without using stoploss on exchange).
+
+!!! Warning "Order_book_max > 1 in dry-run"
+    Using `ask_strategy.order_book_max` higher than 1 will result in improper dry-run results (significantly better than real orders executed on exchange), since dry-run assumes orders to be filled almost instantly.
+    It is therefore advised to not use this setting for dry-runs.
+
+
+#### Sell price without Orderbook enabled
+
+When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
+
+## Pairlists
+
+Pairlists define the list of pairs that the bot should trade.
+There are [`StaticPairList`](#static-pair-list) and dynamic Whitelists available.
+
+[`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter) act as filters, removing low-value pairs.
+
+All pairlists can be chained, and a combination of all pairlists will become your new whitelist. Pairlists are executed in the sequence they are configured. You should always configure either `StaticPairList` or `DynamicPairList` as starting pairlists.
+
+Inactive markets and blacklisted pairs are always removed from the resulting `pair_whitelist`.
+
+### Available Pairlists
+
+* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
+* [`VolumePairList`](#volume-pair-list)
+* [`PrecisionFilter`](#precision-filter)
+* [`PriceFilter`](#price-pair-filter)
+* [`SpreadFilter`](#spread-filter)
+
+!!! Tip "Testing pairlists"
+    Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) subcommand to test your configuration quickly.
+
+#### Static Pair List
+
+By default, the `StaticPairList` method is used, which uses a statically defined pair whitelist from the configuration. 
+
+It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.
+
+```json
+"pairlists": [
+    {"method": "StaticPairList"}
+    ],
+```
+
+#### Volume Pair List
+
+`VolumePairList` selects `number_assets` top pairs based on `sort_key`, which can be one of `askVolume`, `bidVolume` and `quoteVolume` and defaults to `quoteVolume`.
+
+`VolumePairList` considers outputs of previous pairlists unless  it's the first configured pairlist, it does not consider `pair_whitelist`, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
+
+`refresh_period` allows setting the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
+
+`VolumePairList` is based on the ticker data, as reported by the ccxt library:
+
+* The `bidVolume` is the volume (amount) of current best bid in the orderbook.
+* The `askVolume` is the volume (amount) of current best ask in the orderbook.
+* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
+
+```json
+"pairlists": [{
+        "method": "VolumePairList",
+        "number_assets": 20,
+        "sort_key": "quoteVolume",
+        "refresh_period": 1800,
+],
+```
+
+#### Precision Filter
+
+Filters low-value coins which would not allow setting a stoploss.
+
+#### Price Pair Filter
+
+The `PriceFilter` allows filtering of pairs by price.
+Currently, only `low_price_ratio` is implemented, where a raise of 1 price unit (pip) is below the `low_price_ratio` ratio.
+This option is disabled by default, and will only apply if set to <> 0.
+
+Calculation example:  
+Min price precision is 8 decimals. If price is 0.00000011 - one step would be 0.00000012 - which is almost 10% higher than the previous value. 
+
+These pairs are dangerous since it may be impossible to place the desired stoploss - and often result in high losses.
+
+#### Spread Filter
+Removes pairs that have a difference between asks and bids above the specified ratio (default `0.005`).
+Example:
+If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027 the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005` 
+
+### Full Pairlist example
+
+The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting by `quoteVolume` and applies both [`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter), filtering all assets where 1 priceunit is > 1%.
+
+```json
+"exchange": {
+    "pair_whitelist": [],
+    "pair_blacklist": ["BNB/BTC"]
+},
+"pairlists": [
+    {
+        "method": "VolumePairList",
+        "number_assets": 20,
+        "sort_key": "quoteVolume",
+    },
+    {"method": "PrecisionFilter"},
+    {"method": "PriceFilter", "low_price_ratio": 0.01}
+    ],
+```
+
 ## Switch to Dry-run mode
 
 We recommend starting the bot in the Dry-run mode to see how your bot will
@@ -385,7 +661,7 @@ creating trades on the exchange.
 "db_url": "sqlite:///tradesv3.dryrun.sqlite",
 ```
 
-3. Remove your Exchange API key and secrete (change them by empty values or fake credentials):
+3. Remove your Exchange API key and secret (change them by empty values or fake credentials):
 
 ```json
 "exchange": {
@@ -396,41 +672,18 @@ creating trades on the exchange.
 }
 ```
 
-Once you will be happy with your bot performance running in the Dry-run mode,
+Once you will be happy with your bot performance running in the Dry-run mode, you can switch it to production mode.
-you can switch it to production mode.
 
-### Dynamic Pairlists
+!!! Note
+    A simulated wallet is available during dry-run mode, and will assume a starting capital of `dry_run_wallet` (defaults to 1000).
 
-Dynamic pairlists select pairs for you based on the logic configured.
+### Considerations for dry-run
-The bot runs against all pairs (with that stake) on the exchange, and a number of assets
-(`number_assets`) is selected based on the selected criteria.
 
-By default, the `StaticPairList` method is used.
+* API-keys may or may not be provided. Only Read-Only operations (i.e. operations that do not alter account state) on the exchange are performed in the dry-run mode.
-The Pairlist method is configured as `pair_whitelist` parameter under the `exchange`
+* Wallets (`/balance`) are simulated.
-section of the configuration.
+* Orders are simulated, and will not be posted to the exchange.
-
+* In combination with `stoploss_on_exchange`, the stop_loss price is assumed to be filled.
-**Available Pairlist methods:**
+* Open orders (not trades, which are stored in the database) are reset on bot restart.
-
-* `StaticPairList`
-  * It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.
-* `VolumePairList`
-  * It selects `number_assets` top pairs based on `sort_key`, which can be one of
-`askVolume`, `bidVolume` and `quoteVolume`, defaults to `quoteVolume`.
-  * There is a possibility to filter low-value coins that would not allow setting a stop loss
-(set `precision_filter` parameter to `true` for this).
-
-Example:
-
-```json
-"pairlist": {
-        "method": "VolumePairList",
-        "config": {
-            "number_assets": 20,
-            "sort_key": "quoteVolume",
-            "precision_filter": false
-        }
-    },
-```
 
 ## Switch to production mode
 
@@ -438,6 +691,11 @@ In production mode, the bot will engage your money. Be careful, since a wrong
 strategy can lose all your money. Be aware of what you are doing when
 you run it in production mode.
 
+### Setup your exchange account
+
+You will need to create API Keys (usually you get `key` and `secret`, some exchanges require an additional `password`) from the Exchange website and you'll need to insert this into the appropriate fields in the configuration or when asked by the `freqtrade new-config` command.
+API Keys are usually only required for live trading (trading for real money, bot running in "production mode", executing real orders on the exchange) and are not required for the bot running in dry-run (trade simulation) mode. When you setup the bot in dry-run mode, you may fill these fields with empty values.
+
 ### To switch your bot in production mode
 
 **Edit your `config.json`  file.**
@@ -457,12 +715,11 @@ you run it in production mode.
         "secret": "08a9dc6db3d7b53e1acebd9275677f4b0a04f1a5",
         ...
 }
-
 ```
-!!! Note
-    If you have an exchange API key yet, [see our tutorial](/pre-requisite).
 
-### Using proxy with FreqTrade
+You should also make sure to read the [Exchanges](exchanges.md) section of the documentation to be aware of potential configuration details specific to your exchange.
+
+### Using proxy with Freqtrade
 
 To use a proxy with freqtrade, add the kwarg `"aiohttp_trust_env"=true` to the `"ccxt_async_kwargs"` dict in the exchange section of the configuration.
 
@@ -482,14 +739,13 @@ export HTTPS_PROXY="http://addr:port"
 freqtrade
 ```
 
+## Embedding Strategies
 
-### Embedding Strategies
+Freqtrade provides you with with an easy way to embed the strategy into your configuration file.
-
-FreqTrade provides you with with an easy way to embed the strategy into your configuration file.
 This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
 in your chosen config file.
 
-#### Encoding a string as BASE64
+### Encoding a string as BASE64
 
 This is a quick example, how to generate the BASE64 string in python
 

docs/data-analysis.md

@@ -8,6 +8,27 @@ You can analyze the results of backtests and trading history easily using Jupyte
 * Don't forget to start a Jupyter notebook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
 * Copy the example notebook before use so your changes don't get clobbered with the next freqtrade update.
 
+### Using virtual environment with system-wide Jupyter installation
+
+Sometimes it can be desired to use a system-wide installation of Jupyter notebook, and use a jupyter kernel from the virtual environment.
+This prevents you from installing the full jupyter suite multiple times per system, and provides an easy way to switch between tasks (freqtrade / other analytics tasks).
+
+For this to work, first activate your virtual environment and run the following commands:
+
+``` bash
+# Activate virtual environment
+source .env/bin/activate
+
+pip install ipykernel
+ipython kernel install --user --name=freqtrade
+# Restart jupyter (lab / notebook)
+# select kernel "freqtrade" in the notebook
+```
+
+!!! Note
+    This section is provided for completeness, the Freqtrade Team won't provide full support for problems with this setup and will recommend to install Jupyter in the virtual environment directly, as that is the easiest way to get jupyter notebooks up and running. For help with this setup please refer to the [Project Jupyter](https://jupyter.org/) [documentation](https://jupyter.org/documentation) or [help channels](https://jupyter.org/community).
+
+    
 ## Fine print  
 
 Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.

docs/data-download.md

@@ -8,10 +8,156 @@ If no additional parameter is specified, freqtrade will download data for `"1m"`
 Exchange and pairs will come from `config.json` (if specified using `-c/--config`).
 Otherwise `--exchange` becomes mandatory.
 
-!!! Tip Updating existing data
+!!! Tip "Tip: Updating existing data"
     If you already have backtesting data available in your data-directory and would like to refresh this data up to today, use `--days xx` with a number slightly higher than the missing number of days. Freqtrade will keep the available data and only download the missing data.
     Be carefull though: If the number is too small (which would result in a few missing days), the whole dataset will be removed and only xx days will be downloaded.
 
+### Usage
+
+```
+usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
+                               [--pairs-file FILE] [--days INT] [--dl-trades] [--exchange EXCHANGE]
+                               [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
+                               [--erase] [--data-format-ohlcv {json,jsongz}] [--data-format-trades {json,jsongz}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Show profits for only these pairs. Pairs are space-separated.
+  --pairs-file FILE     File containing a list of pairs to download.
+  --days INT            Download data for given number of days.
+  --dl-trades           Download trades instead of OHLCV data. The bot will resample trades to the desired timeframe as specified as
+                        --timeframes/-t.
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no config is provided.
+  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
+                        Specify which tickers to download. Space-separated list. Default: `1m 5m`.
+  --erase               Clean all existing data for the selected exchange/pairs/timeframes.
+  --data-format-ohlcv {json,jsongz}
+                        Storage format for downloaded candle (OHLCV) data. (default: `json`).
+  --data-format-trades {json,jsongz}
+                        Storage format for downloaded trades data. (default: `jsongz`).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
+                        to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+
+### Data format
+
+Freqtrade currently supports 2 dataformats, `json` (plain "text" json files) and `jsongz` (a gzipped version of json files).
+By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
+
+This can be changed via the `--data-format-ohlcv` and `--data-format-trades` parameters respectivly.
+
+If the default dataformat has been changed during download, then the keys `dataformat_ohlcv` and `dataformat_trades` in the configuration file need to be adjusted to the selected dataformat as well.
+
+!!! Note
+    You can convert between data-formats using the [convert-data](#subcommand-convert-data) and [convert-trade-data](#subcommand-convert-trade-data) methods.
+
+#### Subcommand convert data
+
+```
+usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                              [-d PATH] [--userdir PATH]
+                              [-p PAIRS [PAIRS ...]] --format-from
+                              {json,jsongz} --format-to {json,jsongz}
+                              [--erase]
+                              [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Show profits for only these pairs. Pairs are space-
+                        separated.
+  --format-from {json,jsongz}
+                        Source format for data conversion.
+  --format-to {json,jsongz}
+                        Destination format for data conversion.
+  --erase               Clean all existing data for the selected
+                        exchange/pairs/timeframes.
+  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
+                        Specify which tickers to download. Space-separated
+                        list. Default: `1m 5m`.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+
+##### Example converting data
+
+The following command will convert all candle (OHLCV) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
+It'll also remove original json data files (`--erase` parameter).
+
+``` bash
+freqtrade convert-data --format-from json --format-to jsongz --data-dir ~/.freqtrade/data/binance -t 5m 15m --erase
+```
+
+#### Subcommand convert-trade data
+
+```
+usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                    [-d PATH] [--userdir PATH]
+                                    [-p PAIRS [PAIRS ...]] --format-from
+                                    {json,jsongz} --format-to {json,jsongz}
+                                    [--erase]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Show profits for only these pairs. Pairs are space-
+                        separated.
+  --format-from {json,jsongz}
+                        Source format for data conversion.
+  --format-to {json,jsongz}
+                        Destination format for data conversion.
+  --erase               Clean all existing data for the selected
+                        exchange/pairs/timeframes.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+
+##### Example converting trades
+
+The following command will convert all available trade-data in `~/.freqtrade/data/kraken` from jsongz to json.
+It'll also remove original jsongz data files (`--erase` parameter).
+
+``` bash
+freqtrade convert-trade-data --format-from jsongz --format-to json --data-dir ~/.freqtrade/data/kraken --erase
+```
+
 ### Pairs file
 
 In alternative to the whitelist from `config.json`, a `pairs.json` file can be used.
@@ -46,15 +192,15 @@ Then run:
 freqtrade download-data --exchange binance
 ```
 
-This will download ticker data for all the currency pairs you defined in `pairs.json`.
+This will download historical candle (OHLCV) data for all the currency pairs you defined in `pairs.json`.
 
 ### Other Notes
 
 - To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
-- To change the exchange used to download the tickers, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
+- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
 - To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
-- To download ticker data for only 10 days, use `--days 10` (defaults to 30 days).
+- To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
-- Use `--timeframes` to specify which tickers to download. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute tickers.
+- Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
 - To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
 
 ### Trades (tick) data
@@ -78,10 +224,8 @@ freqtrade download-data --exchange binance --pairs XRP/ETH ETH/BTC --days 20 --d
 !!! Warning
     The historic trades are not available during Freqtrade dry-run and live trade modes because all exchanges tested provide this data with a delay of few 100 candles, so it's not suitable for real-time trading.
 
-### Historic Kraken data
+!!! Note "Kraken user"
-
+    Kraken users should read [this](exchanges.md#historic-kraken-data) before starting to download data.
-The Kraken API does only provide 720 historic candles, which is sufficient for FreqTrade dry-run and live trade modes, but is a problem for backtesting.
-To download data for the Kraken exchange, using `--dl-trades` is mandatory, otherwise the bot will download the same 720 candles over and over, and you'll not have enough backtest data.
 
 ## Next step
 

docs/developer.md

@@ -1,6 +1,6 @@
 # Development Help
 
-This page is intended for developers of FreqTrade, people who want to contribute to the FreqTrade codebase or documentation, or people who want to understand the source code of the application they're running.
+This page is intended for developers of Freqtrade, people who want to contribute to the Freqtrade codebase or documentation, or people who want to understand the source code of the application they're running.
 
 All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel in [slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) where you can ask questions.
 
@@ -46,15 +46,18 @@ def test_method_to_test(caplog):
 The fastest and easiest way to start up is to use docker-compose.develop which gives developers the ability to start the bot up with all the required dependencies, *without* needing to install any freqtrade specific dependencies on your local machine.
 
 #### Install
+
 * [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
 * [docker](https://docs.docker.com/install/)
 * [docker-compose](https://docs.docker.com/compose/install/)
 
 #### Starting the bot
 ##### Use the develop dockerfile
+
 ``` bash
 rm docker-compose.yml && mv docker-compose.develop.yml docker-compose.yml
 ```
+
 #### Docker Compose
 
 ##### Starting
@@ -62,9 +65,11 @@ rm docker-compose.yml && mv docker-compose.develop.yml docker-compose.yml
 ``` bash
 docker-compose up
 ```
+
 ![Docker compose up](https://user-images.githubusercontent.com/419355/65456322-47f63a80-de06-11e9-90c6-3c74d1bad0b8.png)
 
 ##### Rebuilding
+
 ``` bash
 docker-compose build
 ```
@@ -77,8 +82,8 @@ that can be effected by `docker-compose up` or `docker-compose run freqtrade_dev
 ``` bash
 docker-compose exec freqtrade_develop /bin/bash
 ```
-![image](https://user-images.githubusercontent.com/419355/65456522-ba671a80-de06-11e9-9598-df9ca0d8dcac.png)
 
+![image](https://user-images.githubusercontent.com/419355/65456522-ba671a80-de06-11e9-9598-df9ca0d8dcac.png)
 
 ## Modules
 
@@ -95,22 +100,22 @@ This is a simple provider, which however serves as a good example on how to star
 
 Next, modify the classname of the provider (ideally align this with the Filename).
 
-The base-class provides the an instance of the bot (`self._freqtrade`), as well as the configuration (`self._config`), and initiates both `_blacklist` and `_whitelist`.
+The base-class provides an instance of the exchange (`self._exchange`) the pairlist manager (`self._pairlistmanager`), as well as the main configuration (`self._config`), the pairlist dedicated configuration (`self._pairlistconfig`) and the absolute position within the list of pairlists.
 
 ```python
-        self._freqtrade = freqtrade
+        self._exchange = exchange
+        self._pairlistmanager = pairlistmanager
         self._config = config
-        self._whitelist = self._config['exchange']['pair_whitelist']
+        self._pairlistconfig = pairlistconfig
-        self._blacklist = self._config['exchange'].get('pair_blacklist', [])
+        self._pairlist_pos = pairlist_pos
 ```
 
-
 Now, let's step through the methods which require actions:
 
-#### configuration
+#### Pairlist configuration
 
 Configuration for PairListProvider is done in the bot configuration file in the element `"pairlist"`.
-This Pairlist-object may contain a `"config"` dict with additional configurations for the configured pairlist.
+This Pairlist-object may contain configurations with additional configurations for the configured pairlist.
 By convention, `"number_assets"` is used to specify the maximum number of pairs to keep in the whitelist. Please follow this to ensure a consistent user experience.
 
 Additional elements can be configured as needed. `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successfull and dynamic.
@@ -120,34 +125,35 @@ Additional elements can be configured as needed. `VolumePairList` uses `"sort_ke
 Returns a description used for Telegram messages.
 This should contain the name of the Provider, as well as a short description containing the number of assets. Please follow the format `"PairlistName - top/bottom X pairs"`.
 
-#### refresh_pairlist
+#### filter_pairlist
 
 Override this method and run all calculations needed in this method.
 This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
 
-Assign the resulting whiteslist to `self._whitelist` and `self._blacklist` respectively. These will then be used to run the bot in this iteration. Pairs with open trades will be added to the whitelist to have the sell-methods run correctly.
+It get's passed a pairlist (which can be the result of previous pairlists) as well as `tickers`, a pre-fetched version of `get_tickers()`.
 
-Please also run `self._validate_whitelist(pairs)` and to check and remove pairs with inactive markets. This function is available in the Parent class (`StaticPairList`) and should ideally not be overwritten.
+It must return the resulting pairlist (which may then be passed into the next pairlist filter).
+
+Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filters. Use this if you limit your result to a certain number of pairs - so the endresult is not shorter than expected.
 
 ##### sample
 
 ``` python
-    def refresh_pairlist(self) -> None:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         # Generate dynamic whitelist
-        pairs = self._gen_pair_whitelist(self._config['stake_currency'], self._sort_key)
+        pairs = self._calculate_pairlist(pairlist, tickers)
-        # Validate whitelist to only have active market pairs
+        return pairs
-        self._whitelist = self._validate_whitelist(pairs)[:self._number_pairs]
 ```
 
 #### _gen_pair_whitelist
 
 This is a simple method used by `VolumePairList` - however serves as a good example.
-It implements caching (`@cached(TTLCache(maxsize=1, ttl=1800))`) as well as a configuration option to allow different (but similar) strategies to work with the same PairListProvider.
+In VolumePairList, this implements different methods of sorting, does early validation so only the expected number of pairs is returned.
 
 ## Implement a new Exchange (WIP)
 
 !!! Note
-    This section is a Work in Progress and is not a complete guide on how to test a new exchange with FreqTrade.
+    This section is a Work in Progress and is not a complete guide on how to test a new exchange with Freqtrade.
 
 Most exchanges supported by CCXT should work out of the box.
 
@@ -159,7 +165,7 @@ Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need
 
 ### Incomplete candles
 
-While fetching OHLCV data, we're may end up getting incomplete candles (Depending on the exchange).
+While fetching candle (OHLCV) data, we may end up getting incomplete candles (depending on the exchange).
 To demonstrate this, we'll use daily candles (`"1d"`) to keep things simple.
 We query the api (`ct.fetch_ohlcv()`) for the timeframe and look at the date of the last entry. If this entry changes or shows the date of a "incomplete" candle, then we should drop this since having incomplete candles is problematic because indicators assume that only complete candles are passed to them, and will generate a lot of false buy signals. By default, we're therefore removing the last candle assuming it's incomplete.
 
@@ -168,74 +174,116 @@ To check how the new exchange behaves, you can use the following snippet:
 ``` python
 import ccxt
 from datetime import datetime
-from freqtrade.data.converter import parse_ticker_dataframe
+from freqtrade.data.converter import ohlcv_to_dataframe
 ct = ccxt.binance()
 timeframe = "1d"
 pair = "XLM/BTC"  # Make sure to use a pair that exists on that exchange!
 raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
 
 # convert to dataframe
-df1 = parse_ticker_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
+df1 = ohlcv_to_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
 
-print(df1["date"].tail(1))
+print(df1.tail(1))
 print(datetime.utcnow())
 ```
 
 ``` output
-19   2019-06-08 00:00:00+00:00
+                         date      open      high       low     close  volume  
+499 2019-06-08 00:00:00+00:00  0.000007  0.000007  0.000007  0.000007   26264344.0  
 2019-06-09 12:30:27.873327
 ```
 
 The output will show the last entry from the Exchange as well as the current UTC date.
 If the day shows the same day, then the last candle can be assumed as incomplete and should be dropped (leave the setting `"ohlcv_partial_candle"` from the exchange-class untouched / True). Otherwise, set `"ohlcv_partial_candle"` to `False` to not drop Candles (shown in the example above).
+Another way is to run this command multiple times in a row and observe if the volume is changing (while the date remains the same).
 
 ## Updating example notebooks
 
 To keep the jupyter notebooks aligned with the documentation, the following should be ran after updating a example notebook.
 
 ``` bash
-jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace user_data/notebooks/strategy_analysis_example.ipynb
+jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace freqtrade/templates/strategy_analysis_example.ipynb
-jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown user_data/notebooks/strategy_analysis_example.ipynb --stdout > docs/strategy_analysis_example.md
+jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown freqtrade/templates/strategy_analysis_example.ipynb --stdout > docs/strategy_analysis_example.md
 ```
 
+## Continuous integration
+
+This documents some decisions taken for the CI Pipeline.
+
+* CI runs on all OS variants, Linux (ubuntu), macOS and Windows.
+* Docker images are build for the branches `master` and `develop`.
+* Raspberry PI Docker images are postfixed with `_pi` - so tags will be `:master_pi` and `develop_pi`.
+* Docker images contain a file, `/freqtrade/freqtrade_commit` containing the commit this image is based of.
+* Full docker image rebuilds are run once a week via schedule.
+* Deployments run on ubuntu.
+* ta-lib binaries are contained in the build_helpers directory to avoid fails related to external unavailability.
+* All tests must pass for a PR to be merged to `master` or `develop`.
+
 ## Creating a release
 
 This part of the documentation is aimed at maintainers, and shows how to create a release.
 
 ### Create release branch
 
-``` bash
+First, pick a commit that's about one week old (to not include latest additions to releases).
-# make sure you're in develop branch
-git checkout develop
 
+``` bash
 # create new branch
-git checkout -b new_release
+git checkout -b new_release <commitid>
 ```
 
-* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7-1` should we need to do a second release that month.
+Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
+
+* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
 * Commit this part
 * push that branch to the remote and create a PR against the master branch
 
 ### Create changelog from git commits
 
 !!! Note
-    Make sure that both master and develop are up-todate!.
+    Make sure that the master branch is uptodate!
 
 ``` bash
 # Needs to be done before merging / pulling that branch.
-git log --oneline --no-decorate --no-merges master..develop
+git log --oneline --no-decorate --no-merges master..new_release
+```
+
+To keep the release-log short, best wrap the full git changelog into a collapsible details secction.
+
+```markdown
+<details>
+<summary>Expand full changelog</summary>
+
+... Full git changelog
+
+</details>
 ```
 
 ### Create github release / tag
 
 Once the PR against master is merged (best right after merging):
 
-* Use the button "Draft a new release" in the Github UI (subsection releases)
+* Use the button "Draft a new release" in the Github UI (subsection releases).
 * Use the version-number specified as tag.
 * Use "master" as reference (this step comes after the above PR is merged).
 * Use the above changelog as release comment (as codeblock)
 
-### After-release
+## Releases
 
-* Update version in develop by postfixing that with `-dev` (`2019.6 -> 2019.6-dev`).
+### pypi
-* Create a PR against develop to update that branch.
+
+To create a pypi release, please run the following commands:
+
+Additional requirement: `wheel`, `twine` (for uploading), account on pypi with proper permissions.
+
+``` bash
+python setup.py sdist bdist_wheel
+
+# For pypi test (to check if some change to the installation did work)
+twine upload --repository-url https://test.pypi.org/legacy/ dist/*
+
+# For production:
+twine upload dist/*
+```
+
+Please don't push non-releases to the productive / real pypi instance.

docs/docker.md

@@ -1,4 +1,4 @@
-# Using FreqTrade with Docker
+# Using Freqtrade with Docker
 
 ## Install Docker
 
@@ -8,13 +8,141 @@ Start by downloading and installing Docker CE for your platform:
 * [Windows](https://docs.docker.com/docker-for-windows/install/)
 * [Linux](https://docs.docker.com/install/)
 
+Optionally, [docker-compose](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
+
 Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
 
-## Download the official FreqTrade docker image
+## Freqtrade with docker-compose
+
+Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
+
+!!! Note
+    The following section assumes that docker and docker-compose is installed and available to the logged in user.
+
+!!! Note
+    All below comands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
+
+### Docker quick start
+
+Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
+
+``` bash
+mkdir ft_userdata
+cd ft_userdata/
+# Download the docker-compose file from the repository
+curl https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docker-compose.yml -o docker-compose.yml
+
+# Pull the freqtrade image
+docker-compose pull
+
+# Create user directory structure
+docker-compose run --rm freqtrade create-userdir --userdir user_data
+
+# Create configuration - Requires answering interactive questions
+docker-compose run --rm freqtrade new-config --config user_data/config.json
+```
+
+The above snippet creates a new directory called "ft_userdata", downloads the latest compose file and pulls the freqtrade image.
+The last 2 steps in the snippet create the directory with user-data, as well as (interactively) the default configuration based on your selections.
+
+!!! Note
+    You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
+
+#### Adding your strategy
+
+The configuration is now available as `user_data/config.json`.
+You should now copy your strategy to `user_data/strategies/` - and add the Strategy class name to the `docker-compose.yml` file, replacing `SampleStrategy`. If you wish to run the bot with the SampleStrategy, just leave it as it is.
+
+!!! Warning
+    The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
+    Please always backtest the strategy and use dry-run for some time before risking real money!
+
+Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
+
+``` bash
+docker-compose up -d
+```
+
+#### Docker-compose logs
+
+Logs will be written to `user_data/freqtrade.log`. 
+Alternatively, you can check the latest logs using `docker-compose logs -f`.
+
+#### Database
+
+The database will be in the user_data directory as well, and will be called `user_data/tradesv3.sqlite`.
+
+#### Updating freqtrade with docker-compose
+
+To update freqtrade when using docker-compose is as simple as running the following 2 commands:
+
+``` bash
+# Download the latest image
+docker-compose pull
+# Restart the image
+docker-compose up -d
+```
+
+This will first pull the latest image, and will then restart the container with the just pulled version.
+
+!!! Note
+    You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
+
+#### Going from here
+
+Advanced users may edit the docker-compose file further to include all possible options or arguments.
+
+All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
+
+!!! Note "`docker-compose run --rm`"
+    Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
+
+##### Example: Download data with docker-compose
+
+Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
+
+``` bash
+docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
+```
+
+Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
+
+##### Example: Backtest with docker-compose
+
+Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
+
+``` bash
+docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
+```
+
+Head over to the [Backtesting Documentation](backtesting.md) to learn more.
+
+#### Additional dependencies with docker-compose
+
+If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
+For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) for an example).
+
+You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
+
+``` yaml
+    image: freqtrade_custom
+    build:
+      context: .
+      dockerfile: "./Dockerfile.<yourextension>"
+```
+
+You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
+
+## Freqtrade with docker without docker-compose
+
+!!! Warning
+    The below documentation is provided for completeness and assumes that you are somewhat familiar with running docker containers. If you're just starting out with docker, we recommend to follow the [Freqtrade with docker-compose](#freqtrade-with-docker-compose) instructions.
+
+### Download the official Freqtrade docker image
 
 Pull the image from docker hub.
 
-Branches / tags available can be checked out on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
+Branches / tags available can be checked out on [Dockerhub tags page](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
 
 ```bash
 docker pull freqtradeorg/freqtrade:develop
@@ -26,7 +154,7 @@ To update the image, simply run the above commands again and restart your runnin
 
 Should you require additional libraries, please [build the image yourself](#build-your-own-docker-image).
 
-!!! Note Docker image update frequency
+!!! Note "Docker image update frequency"
     The official docker images with tags `master`, `develop` and `latest` are automatically rebuild once a week to keep the base image uptodate.
     In addition to that, every merge to `develop` will trigger a rebuild for `develop` and `latest`.
 
@@ -160,16 +288,18 @@ docker run -d \
   -v ~/.freqtrade/config.json:/freqtrade/config.json \
   -v ~/.freqtrade/user_data/:/freqtrade/user_data \
   -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-  freqtrade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
+  freqtrade trade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
 ```
 
 !!! Note
-    db-url defaults to `sqlite:///tradesv3.sqlite` but it defaults to `sqlite://` if `dry_run=True` is being used.
+    When using docker, it's best to specify `--db-url` explicitly to ensure that the database URL and the mounted database file match.
-    To override this behaviour use a custom db-url value: i.e.: `--db-url sqlite:///tradesv3.dryrun.sqlite`
 
 !!! Note
     All available bot command line parameters can be added to the end of the `docker run` command.
 
+!!! Note
+    You can define a [restart policy](https://docs.docker.com/config/containers/start-containers-automatically/) in docker. It can be useful in some cases to use the `--restart unless-stopped` flag (crash of freqtrade or reboot of your system).
+
 ### Monitor your Docker instance
 
 You can use the following commands to monitor and manage your container:
@@ -199,7 +329,7 @@ docker run -d \
   -v ~/.freqtrade/config.json:/freqtrade/config.json \
   -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
   -v ~/.freqtrade/user_data/:/freqtrade/user_data/ \
-  freqtrade --strategy AwsomelyProfitableStrategy backtesting
+  freqtrade backtesting --strategy AwsomelyProfitableStrategy
 ```
 
 Head over to the [Backtesting Documentation](backtesting.md) for more details.

docs/edge.md

@@ -9,6 +9,7 @@ This page explains how to use Edge Positioning module in your bot in order to en
     Edge does not consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else are ignored in its calculation.
 
 ## Introduction
+
 Trading is all about probability. No one can claim that he has a strategy working all the time. You have to assume that sometimes you lose.
 
 But it doesn't mean there is no rule, it only means rules should work "most of the time". Let's play a game: we toss a coin, heads: I give you 10$, tails: you give me 10$. Is it an interesting game? No, it's quite boring, isn't it?
@@ -22,43 +23,61 @@ Let's complicate it more: you win 80% of the time but only 2$, I win 20% of the
 The question is: How do you calculate that? How do you know if you wanna play?
 
 The answer comes to two factors:
+
 - Win Rate
 - Risk Reward Ratio
 
 ### Win Rate
+
 Win Rate (*W*) is is the mean over some amount of trades (*N*) what is the percentage of winning trades to total number of trades (note that we don't consider how much you gained but only if you won or not).
 
+```
 W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N
+```
 
 Complementary Loss Rate (*L*) is defined as
 
+```
 L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N
+```
 
 or, which is the same, as
 
+```
 L = 1 – W
+```
 
 ### Risk Reward Ratio
+
 Risk Reward Ratio (*R*) is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose:
 
+```
 R = Profit / Loss
+```
 
 Over time, on many trades, you can calculate your risk reward by dividing your average profit on winning trades by your average loss on losing trades:
 
+```
 Average profit = (Sum of profits) / (Number of winning trades)
 
 Average loss = (Sum of losses) / (Number of losing trades)
 
 R = (Average profit) / (Average loss)
+```
 
 ### Expectancy
+
 At this point we can combine *W* and *R* to create an expectancy ratio. This is a simple process of multiplying the risk reward ratio by the percentage of winning trades and subtracting the percentage of losing trades, which is calculated as follows:
 
+```
 Expectancy Ratio = (Risk Reward Ratio X Win Rate) – Loss Rate = (R X W) – L
+```
 
 So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
 
+```
 Expectancy = (5 X 0.28) – 0.72 = 0.68
+```
 
 Superficially, this means that on average you expect this strategy’s trades to return .68 times the size of your loses. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
 
@@ -69,6 +88,7 @@ You can also use this value to evaluate the effectiveness of modifications to th
 **NOTICE:** It's important to keep in mind that Edge is testing your expectancy using historical data, there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology, but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
 
 ## How does it work?
+
 If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over *N* trades for each stoploss. Here is an example:
 
 | Pair   |      Stoploss      |  Win Rate | Risk Reward Ratio | Expectancy |
@@ -83,6 +103,7 @@ The goal here is to find the best stoploss for the strategy in order to have the
 Edge module then forces stoploss value it evaluated to your strategy dynamically.
 
 ### Position size
+
 Edge also dictates the stake amount for each trade to the bot according to the following factors:
 
 - Allowed capital at risk
@@ -90,13 +111,17 @@ Edge also dictates the stake amount for each trade to the bot according to the f
 
 Allowed capital at risk is calculated as follows:
 
+```
 Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade)
+```
 
 Stoploss is calculated as described above against historical data.
 
 Your position size then will be:
 
+```
 Position size = (Allowed capital at risk) / Stoploss
+```
 
 Example:
 
@@ -115,94 +140,24 @@ Available capital doesn’t change before a position is sold. Let’s assume tha
 So the Bot receives another buy signal for trade 4 with a stoploss at 2% then your position size would be **0.055 / 0.02 = 2.75 ETH**.
 
 ## Configurations
+
 Edge module has following configuration options:
 
-#### enabled
+|  Parameter | Description |
-If true, then Edge will run periodically.
+|------------|-------------|
-
+| `enabled` | If true, then Edge will run periodically. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
-(defaults to false)
+| `process_throttle_secs` | How often should Edge run in seconds. <br>*Defaults to `3600` (once per hour).* <br> **Datatype:** Integer
-
+| `calculate_since_number_of_days` | Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy. <br> **Note** that it downloads historical data so increasing this number would lead to slowing down the bot. <br>*Defaults to `7`.* <br> **Datatype:** Integer
-#### process_throttle_secs
+| `capital_available_percentage` | **DEPRECATED - [replaced with `tradable_balance_ratio`](configuration.md#Available balance)** This is the percentage of the total capital on exchange in stake currency. <br>As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital. <br>*Defaults to `0.5`.* <br> **Datatype:** Float
-How often should Edge run in seconds?
+| `allowed_risk` | Ratio of allowed risk per trade. <br>*Defaults to `0.01` (1%)).* <br> **Datatype:** Float
-
+| `stoploss_range_min` | Minimum stoploss. <br>*Defaults to `-0.01`.* <br> **Datatype:** Float
-(defaults to 3600 so one hour)
+| `stoploss_range_max` | Maximum stoploss. <br>*Defaults to `-0.10`.* <br> **Datatype:** Float
-
+| `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br> **Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br> **Datatype:** Float
-#### calculate_since_number_of_days
+| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
-Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy
+| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
-Note that it downloads historical data so increasing this number would lead to slowing down the bot.
+| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
-
+| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe (ticker interval). As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
-(defaults to 7)
+| `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> **Datatype:** Boolean
-
-#### capital_available_percentage
-This is the percentage of the total capital on exchange in stake currency.
-
-As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital.
-
-(defaults to 0.5)
-
-#### allowed_risk
-Percentage of allowed risk per trade.
-
-(defaults to 0.01 so 1%)
-
-#### stoploss_range_min
-
-Minimum stoploss.
-
-(defaults to -0.01)
-
-#### stoploss_range_max
-
-Maximum stoploss.
-
-(defaults to -0.10)
-
-#### stoploss_range_step
-
-As an example if this is set to -0.01 then Edge will test the strategy for \[-0.01, -0,02, -0,03 ..., -0.09, -0.10\] ranges.
-Note than having a smaller step means having a bigger range which could lead to slow calculation.
-
-If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10.
-
-(defaults to -0.01)
-
-#### minimum_winrate
-
-It filters out pairs which don't have at least minimum_winrate.
-
-This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio.
-
-(defaults to 0.60)
-
-#### minimum_expectancy
-
-It filters out pairs which have the expectancy lower than this number.
-
-Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return.
-
-(defaults to 0.20)
-
-#### min_trade_number
-
-When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable.
-
-Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something.
-
-(defaults to 10, it is highly recommended not to decrease this number)
-
-#### max_trade_duration_minute
-
-Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.
-
-**NOTICE:** While configuring this value, you should take into consideration your ticker interval. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).
-
-(defaults to 1 day, i.e. to 60 * 24 = 1440 minutes)
-
-#### remove_pumps
-
-Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.
-
-(defaults to false)
 
 ## Running Edge independently
 
@@ -235,7 +190,7 @@ An example of its output:
 ### Update cached pairs with the latest data
 
 Edge requires historic data the same way as backtesting does.
-Please refer to the [download section](backtesting.md#Getting-data-for-backtesting-and-hyperopt) of the documentation for details.
+Please refer to the [Data Downloading](data-download.md) section of the documentation for details.
 
 ### Precising stoploss range
 

docs/exchanges.md

@@ -0,0 +1,86 @@
+# Exchange-specific Notes
+
+This page combines common gotchas and informations which are exchange-specific and most likely don't apply to other exchanges.
+
+## Binance
+
+!!! Tip "Stoploss on Exchange"
+    Binance supports `stoploss_on_exchange` and uses stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
+
+### Blacklists
+
+For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
+Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB order unsellable as the expected amount is not there anymore.
+
+### Binance sites
+
+Binance has been split into 3, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
+
+* [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
+* [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
+* [binance.je](https://www.binance.je/) - Binance Jersey, trading fiat currencies. Use exchange id: `binanceje`.
+
+## Kraken
+
+!!! Tip "Stoploss on Exchange"
+    Kraken supports `stoploss_on_exchange` and uses stop-loss-market orders. It provides great advantages, so we recommend to benefit from it, however since the resulting order is a stoploss-market order, sell-rates are not guaranteed, which makes this feature less secure than on other exchanges. This limitation is based on kraken's policy [source](https://blog.kraken.com/post/1234/announcement-delisting-pairs-and-temporary-suspension-of-advanced-order-types/) and [source2](https://blog.kraken.com/post/1494/kraken-enables-advanced-orders-and-adds-10-currency-pairs/) - which has stoploss-limit orders disabled.
+
+### Historic Kraken data
+
+The Kraken API does only provide 720 historic candles, which is sufficient for Freqtrade dry-run and live trade modes, but is a problem for backtesting.
+To download data for the Kraken exchange, using `--dl-trades` is mandatory, otherwise the bot will download the same 720 candles over and over, and you'll not have enough backtest data.
+
+## Bittrex
+
+### Order types
+
+Bittrex does not support market orders. If you have a message at the bot startup about this, you should change order type values set in your configuration and/or in the strategy from `"market"` to `"limit"`. See some more details on this [here in the FAQ](faq.md#im-getting-the-exchange-bittrex-does-not-support-market-orders-message-and-cannot-run-my-strategy).
+
+### Restricted markets
+
+Bittrex split its exchange into US and International versions.
+The International version has more pairs available, however the API always returns all pairs, so there is currently no automated way to detect if you're affected by the restriction.
+
+If you have restricted pairs in your whitelist, you'll get a warning message in the log on Freqtrade startup for each restricted pair.
+
+The warning message will look similar to the following:
+
+``` output
+[...] Message: bittrex {"success":false,"message":"RESTRICTED_MARKET","result":null,"explanation":null}"
+```
+
+If you're an "International" customer on the Bittrex exchange, then this warning will probably not impact you.
+If you're a US customer, the bot will fail to create orders for these pairs, and you should remove them from your whitelist.
+
+You can get a list of restricted markets by using the following snippet:
+
+``` python
+import ccxt
+ct = ccxt.bittrex()
+_ = ct.load_markets()
+res = [ f"{x['MarketCurrency']}/{x['BaseCurrency']}" for x in ct.publicGetMarkets()['result'] if x['IsRestricted']]
+print(res)
+```
+
+## All exchanges
+
+Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
+
+
+## Random notes for other exchanges
+
+* The Ocean (exchange id: `theocean`) exchange uses Web3 functionality and requires `web3` python package to be installed:
+```shell
+$ pip3 install web3
+```
+
+### Getting latest price / Incomplete candles
+
+Most exchanges return current incomplete candle via their OHLCV/klines API interface.
+By default, Freqtrade assumes that incomplete candle is fetched from the exchange and removes the last candle assuming it's the incomplete candle.
+
+Whether your exchange returns incomplete candles or not can be checked using [the helper script](developer.md#Incomplete-candles) from the Contributor documentation.
+
+Due to the danger of repainting, Freqtrade does not allow you to use this incomplete candle.
+
+However, if it is based on the need for the latest price for your strategy - then this requirement can be acquired using the [data provider](strategy-customization.md#possible-options-for-dataprovider) from within the strategy.

docs/faq.md

@@ -4,7 +4,7 @@
 
 ### The bot does not start
 
-Running the bot with `freqtrade --config config.json` does show the output `freqtrade: command not found`.
+Running the bot with `freqtrade trade --config config.json` does show the output `freqtrade: command not found`.
 
 This could have the following reasons:
 
@@ -45,15 +45,65 @@ the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-c
 
 You can use the `/forcesell all` command from Telegram.
 
-### I get the message "RESTRICTED_MARKET"
+### I'm getting the "RESTRICTED_MARKET" message in the log
 
 Currently known to happen for US Bittrex users.  
-Bittrex split its exchange into US and International versions.
-The International version has more pairs available, however the API always returns all pairs, so there is currently no automated way to detect if you're affected by the restriction.
 
-If you have restricted pairs in your whitelist, you'll get a warning message in the log on FreqTrade startup for each restricted pair.
+Read [the Bittrex section about restricted markets](exchanges.md#restricted-markets) for more information.
-If you're an "International" Customer on the Bittrex exchange, then this warning will probably not impact you.
+
-If you're a US customer, the bot will fail to create orders for these pairs, and you should remove them from your Whitelist.
+### I'm getting the "Exchange Bittrex does not support market orders." message and cannot run my strategy
+
+As the message says, Bittrex does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Probably your strategy was written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Bittrex).
+
+To fix it for Bittrex, redefine order types in the strategy to use "limit" instead of "market":
+
+```
+    order_types = {
+        ...
+        'stoploss': 'limit',
+        ...
+    }
+```
+
+Same fix should be done in the configuration file, if order types are defined in your custom config rather than in the strategy.
+
+### How do I search the bot logs for something?
+
+By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility subcommands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.
+
+* In unix shells, this normally can be done as simple as:
+```shell
+$ freqtrade --some-options 2>&1 >/dev/null | grep 'something'
+```
+(note, `2>&1` and `>/dev/null` should be written in this order)
+
+* Bash interpreter also supports so called process substitution syntax, you can grep the log for a string with it as:
+```shell
+$ freqtrade --some-options 2> >(grep 'something') >/dev/null
+```
+or
+```shell
+$ freqtrade --some-options 2> >(grep -v 'something' 1>&2)
+```
+
+* You can also write the copy of Freqtrade log messages to a file with the `--logfile` option:
+```shell
+$ freqtrade --logfile /path/to/mylogfile.log --some-options
+```
+and then grep it as:
+```shell
+$ cat /path/to/mylogfile.log | grep 'something'
+```
+or even on the fly, as the bot works and the logfile grows:
+```shell
+$ tail -f /path/to/mylogfile.log | grep 'something'
+```
+from a separate terminal window.
+
+On Windows, the `--logfilename` option is also supported by Freqtrade and you can use the `findstr` command to search the log for the string of interest:
+```
+> type \path\to\mylogfile.log | findstr "something"
+```
 
 ## Hyperopt module
 

docs/hyperopt.md

@@ -6,8 +6,12 @@ algorithms included in the `scikit-optimize` package to accomplish this. The
 search will burn all your CPU cores, make your laptop sound like a fighter jet
 and still take a long time.
 
+In general, the search for best parameters starts with a few random combinations and then uses Bayesian search with a
+ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace
+that minimizes the value of the [loss function](#loss-functions).
+
 Hyperopt requires historic data to be available, just as backtesting does.
-To learn how to get data for the pairs and exchange you're interrested in, head over to the [Data Downloading](data-download.md) section of the documentation.
+To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
 
 !!! Bug
     Hyperopt can crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
@@ -15,30 +19,50 @@ To learn how to get data for the pairs and exchange you're interrested in, head
 ## Prepare Hyperopting
 
 Before we start digging into Hyperopt, we recommend you to take a look at
-the sample hyperopt file located in [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt.py).
+the sample hyperopt file located in [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt.py).
 
 Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar and a lot of code can be copied across from the strategy.
 
+The simplest way to get started is to use `freqtrade new-hyperopt --hyperopt AwesomeHyperopt`.
+This will create a new hyperopt file from a template, which will be located under `user_data/hyperopts/AwesomeHyperopt.py`.
+
 ### Checklist on all tasks / possibilities in hyperopt
 
 Depending on the space you want to optimize, only some of the below are required:
 
-* fill `populate_indicators` - probably a copy from your strategy
 * fill `buy_strategy_generator` - for buy signal optimization
-* fill `indicator_space` - for buy signal optimzation
+* fill `indicator_space` - for buy signal optimization
 * fill `sell_strategy_generator` - for sell signal optimization
-* fill `sell_indicator_space` - for sell signal optimzation
+* fill `sell_indicator_space` - for sell signal optimization
 
-Optional, but recommended:
+!!! Note
+    `populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
 
+Optional - can also be loaded from a strategy:
+
+* copy `populate_indicators` from your strategy - otherwise default-strategy will be used
 * copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
 * copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
 
+!!! Note
+    Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
+
 Rarely you may also need to override:
 
 * `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
-* `generate_roi_table` - for custom ROI optimization (if you need more than 4 entries in the ROI table)
+* `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
 * `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
+* `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
+
+!!! Tip "Quickly optimize ROI, stoploss and trailing stoploss"
+    You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything (i.e. without creation of a "complete" Hyperopt class with dimensions, parameters, triggers and guards, as described in this document) from the default hyperopt template by relying on your strategy to do most of the calculations.
+
+    ```python
+    # Have a working strategy at hand.
+    freqtrade new-hyperopt --hyperopt EmptyHyperopt
+
+    freqtrade hyperopt --hyperopt EmptyHyperopt --spaces roi stoploss trailing --strategy MyWorkingStrategy --config config.json -e 100
+    ```
 
 ### 1. Install a Custom Hyperopt File
 
@@ -51,17 +75,17 @@ Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts
 
 There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing:
 
-- Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
+* Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
-- Inside `populate_buy_trend()` - applying the parameters.
+* Inside `populate_buy_trend()` - applying the parameters.
 
 There you have two different types of indicators: 1. `guards` and 2. `triggers`.
 
 1. Guards are conditions like "never buy if ADX < 10", or never buy if current price is over EMA10.
-2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower bollinger band".
+2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower Bollinger band".
 
 Hyperoptimization will, for each eval round, pick one trigger and possibly
 multiple guards. The constructed strategy will be something like
-"*buy exactly when close price touches lower bollinger band, BUT only if
+"*buy exactly when close price touches lower Bollinger band, BUT only if
 ADX > 10*".
 
 If you have updated the buy strategy, i.e. changed the contents of
@@ -79,9 +103,10 @@ Place the corresponding settings into the following methods
 The configuration and rules are the same than for buy signals.
 To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
 
-#### Using ticker-interval as part of the Strategy
+#### Using timeframe as a part of the Strategy
 
-The Strategy exposes the ticker-interval as `self.ticker_interval`. The same value is available as class-attribute `HyperoptName.ticker_interval`.
+The Strategy class exposes the timeframe (ticker interval) value as the `self.ticker_interval` attribute.
+The same value is available as class-attribute `HyperoptName.ticker_interval`.
 In the case of the linked sample-value this would be `SampleHyperOpt.ticker_interval`.
 
 ## Solving a Mystery
@@ -135,6 +160,9 @@ So let's write the buy strategy using these values:
                         dataframe['macd'], dataframe['macdsignal']
                     ))
 
+            # Check that volume is not 0
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),
@@ -148,21 +176,17 @@ So let's write the buy strategy using these values:
 Hyperopting will now call this `populate_buy_trend` as many times you ask it (`epochs`)
 with different value combinations. It will then use the given historical data and make
 buys based on the buy signals generated with the above function and based on the results
-it will end with telling you which paramter combination produced the best profits.
+it will end with telling you which parameter combination produced the best profits.
-
-The search for best parameters starts with a few random combinations and then uses a
-regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination
-that minimizes the value of the [loss function](#loss-functions).
 
 The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
 When you want to test an indicator that isn't used by the bot currently, remember to
-add it to the `populate_indicators()` method in `hyperopt.py`.
+add it to the `populate_indicators()` method in your custom hyperopt file.
 
 ## Loss-functions
 
 Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
 
-By default, FreqTrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
+By default, Freqtrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
 
 A different loss function can be specified by using the `--hyperopt-loss <Class-name>` argument.
 This class should be in its own file within the `user_data/hyperopts/` directory.
@@ -171,65 +195,12 @@ Currently, the following loss functions are builtin:
 
 * `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function)
 * `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
-* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on the trade returns)
+* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on trade returns relative to standard deviation)
+* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation)
+* `SortinoHyperOptLoss` (optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation)
+* `SortinoHyperOptLossDaily` (optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation)
 
-### Creating and using a custom loss function
+Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
-
-To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
-For the sample below, you then need to add the command line parameter `--hyperopt-loss SuperDuperHyperOptLoss` to your hyperopt call so this fuction is being used.
-
-A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_loss.py)
-
-``` python
-from freqtrade.optimize.hyperopt import IHyperOptLoss
-
-TARGET_TRADES = 600
-EXPECTED_MAX_PROFIT = 3.0
-MAX_ACCEPTED_TRADE_DURATION = 300
-
-class SuperDuperHyperOptLoss(IHyperOptLoss):
-    """
-    Defines the default loss function for hyperopt
-    """
-
-    @staticmethod
-    def hyperopt_loss_function(results: DataFrame, trade_count: int,
-                               min_date: datetime, max_date: datetime,
-                               *args, **kwargs) -> float:
-        """
-        Objective function, returns smaller number for better results
-        This is the legacy algorithm (used until now in freqtrade).
-        Weights are distributed as follows:
-        * 0.4 to trade duration
-        * 0.25: Avoiding trade loss
-        * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
-        """
-        total_profit = results.profit_percent.sum()
-        trade_duration = results.trade_duration.mean()
-
-        trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
-        profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
-        duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
-        result = trade_loss + profit_loss + duration_loss
-        return result
-```
-
-Currently, the arguments are:
-
-* `results`: DataFrame containing the result  
-    The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):  
-    `pair, profit_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reason`
-* `trade_count`: Amount of trades (identical to `len(results)`)
-* `min_date`: Start date of the hyperopting TimeFrame
-* `min_date`: End date of the hyperopting TimeFrame
-
-This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
-
-!!! Note
-    This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
-
-!!! Note
-    Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
 
 ## Execute Hyperopt
 
@@ -239,15 +210,15 @@ Because hyperopt tries a lot of combinations to find the best parameters it will
 We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
 
 ```bash
-freqtrade -c config.json hyperopt --customhyperopt <hyperoptname> -e 5000 --spaces all
+freqtrade hyperopt --config config.json --hyperopt <hyperoptname> -e 5000 --spaces all
 ```
 
 Use `<hyperoptname>` as the name of the custom hyperopt used.
 
-The `-e` flag will set how many evaluations hyperopt will do. We recommend
+The `-e` option will set how many evaluations hyperopt will do. We recommend
 running at least several thousand evaluations.
 
-The `--spaces all` flag determines that all possible parameters should be optimized. Possibilities are listed below.
+The `--spaces all` option determines that all possible parameters should be optimized. Possibilities are listed below.
 
 !!! Note
     By default, hyperopt will erase previous results and start from scratch. Continuation can be archived by using `--continue`.
@@ -255,11 +226,11 @@ The `--spaces all` flag determines that all possible parameters should be optimi
 !!! Warning
     When switching parameters or changing configuration options, make sure to not use the argument `--continue` so temporary results can be removed.
 
-### Execute Hyperopt with Different Ticker-Data Source
+### Execute Hyperopt with different historical data source
 
-If you would like to hyperopt parameters using an alternate ticker data that
+If you would like to hyperopt parameters using an alternate historical data set that
-you have on-disk, use the `--datadir PATH` option. Default hyperopt will
+you have on-disk, use the `--datadir PATH` option. By default, hyperopt
-use data from directory `user_data/data`.
+uses data from directory `user_data/data`.
 
 ### Running Hyperopt with Smaller Testset
 
@@ -270,9 +241,17 @@ For example, to use one month of data, pass the following parameter to the hyper
 freqtrade hyperopt --timerange 20180401-20180501
 ```
 
+### Running Hyperopt using methods from a strategy
+
+Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
+
+```bash
+freqtrade hyperopt --strategy SampleStrategy --customhyperopt SampleHyperopt
+```
+
 ### Running Hyperopt with Smaller Search Space
 
-Use the `--spaces` argument to limit the search space used by hyperopt.
+Use the `--spaces` option to limit the search space used by hyperopt.
 Letting Hyperopt optimize everything is a huuuuge search space. Often it
 might make more sense to start by just searching for initial buy algorithm.
 Or maybe you just want to optimize your stoploss or roi table for that awesome
@@ -285,8 +264,12 @@ Legal values are:
 * `sell`: just search for a new sell strategy
 * `roi`: just optimize the minimal profit table for your strategy
 * `stoploss`: search for the best stoploss value
+* `trailing`: search for the best trailing stop values
+* `default`: `all` except `trailing`
 * space-separated list of any of the above values for example `--spaces roi stoploss`
 
+The default Hyperopt Search Space, used when no `--space` command line option is specified, does not include the `trailing` hyperspace. We recommend you to run optimization for the `trailing` hyperspace separately, when the best parameters for other hyperspaces were found, validated and pasted into your custom strategy.
+
 ### Position stacking and disabling max market positions
 
 In some situations, you may need to run Hyperopt (and Backtesting) with the
@@ -295,7 +278,7 @@ In some situations, you may need to run Hyperopt (and Backtesting) with the
 By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
 open trade is allowed for every traded pair. The total number of trades open for all pairs
 is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
-some potential trades to be hidden (or masked) by previosly open trades.
+some potential trades to be hidden (or masked) by previously open trades.
 
 The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times,
 while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades`
@@ -308,6 +291,16 @@ number).
 You can also enable position stacking in the configuration file by explicitly setting
 `"position_stacking"=true`.
 
+### Reproducible results
+
+The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with a leading asterisk sign at the Hyperopt output.
+
+The initial state for generation of these random values (random state) is controlled by the value of the `--random-state` command line option. You can set it to some arbitrary value of your choice to obtain reproducible results.
+
+If you have not set this value explicitly in the command line options, Hyperopt seeds the random state with some random value for you. The random state value for each Hyperopt run is shown in the log, so you can copy and paste it into the `--random-state` command line option to repeat the set of the initial random epochs used.
+
+If you have not changed anything in the command line options, configuration, timerange, Strategy and Hyperopt classes, historical data and the Loss Function -- you should obtain same hyperoptimization results with same random state value used.
+
 ## Understand the Hyperopt Result
 
 Once Hyperopt is completed you can use the result to create a new strategy.
@@ -341,8 +334,7 @@ So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that t
 (dataframe['rsi'] < 29.0)
 ```
 
-Translating your whole hyperopt result as the new buy-signal
+Translating your whole hyperopt result as the new buy-signal would then look like:
-would then look like:
 
 ```python
 def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
@@ -361,19 +353,13 @@ You can use the `--print-all` command line option if you would like to see all r
 
 ### Understand Hyperopt ROI results
 
-If you are optimizing ROI (i.e. if optimization search-space contains 'all' or 'roi'), your result will look as follows and include a ROI table:
+If you are optimizing ROI (i.e. if optimization search-space contains 'all', 'default' or 'roi'), your result will look as follows and include a ROI table:
 
 ```
 Best result:
 
     44/100:    135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
 
-Buy hyperspace params:
-{   'adx-value': 44,
-    'rsi-value': 29,
-    'adx-enabled': False,
-    'rsi-enabled': True,
-    'trigger': 'bb_lower'}
 ROI table:
 {   0: 0.10674,
     21: 0.09158,
@@ -393,28 +379,29 @@ In order to use this best ROI table found by Hyperopt in backtesting and for liv
         118: 0
     }
 ```
+
 As stated in the comment, you can also use it as the value of the `minimal_roi` setting in the configuration file.
 
 #### Default ROI Search Space
 
-If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values can vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
+If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used timeframes, values are rounded to 5 digits after the decimal point):
 
 | # step | 1m     |                   | 5m       |             | 1h         |                   | 1d           |                   |
-|---|---|---|---|---|---|---|---|---|
+| ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
 | 1      | 0      | 0.01161...0.11992 | 0        | 0.03...0.31 | 0          | 0.06883...0.71124 | 0            | 0.12178...1.25835 |
 | 2      | 2...8  | 0.00774...0.04255 | 10...40  | 0.02...0.11 | 120...480  | 0.04589...0.25238 | 2880...11520 | 0.08118...0.44651 |
 | 3      | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 |
 | 4      | 6...44 | 0.0               | 30...220 | 0.0         | 360...2640 | 0.0               | 8640...63360 | 0.0               |
 
-These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the ticker interval used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the ticker interval used.
+These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe (ticker interval) used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
 
 If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
 
-Override the `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps). A sample for these methods can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py).
+Override the `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps). A sample for these methods can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
 
 ### Understand Hyperopt Stoploss results
 
-If you are optimizing stoploss values (i.e. if optimization search-space contains 'all' or 'stoploss'), your result will look as follows and include stoploss:
+If you are optimizing stoploss values (i.e. if optimization search-space contains 'all', 'default' or 'stoploss'), your result will look as follows and include stoploss:
 
 ```
 Best result:
@@ -437,17 +424,57 @@ In order to use this best stoploss value found by Hyperopt in backtesting and fo
     # This attribute will be overridden if the config file contains "stoploss"
     stoploss = -0.27996
 ```
+
 As stated in the comment, you can also use it as the value of the `stoploss` setting in the configuration file.
 
 #### Default Stoploss Search Space
 
-If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace can vary in the range -0.35...-0.02, which is sufficient in most cases.
+If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace vary in the range -0.35...-0.02, which is sufficient in most cases.
 
 If you have the `stoploss_space()` method in your custom hyperopt file, remove it in order to utilize Stoploss hyperoptimization space generated by Freqtrade by default.
 
-Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py).
+Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
 
-### Validate backtesting results
+### Understand Hyperopt Trailing Stop results
+
+If you are optimizing trailing stop values (i.e. if optimization search-space contains 'all' or 'trailing'), your result will look as follows and include trailing stop parameters:
+
+```
+Best result:
+
+    45/100:    606 trades. Avg profit  1.04%. Total profit  0.31555614 BTC ( 630.48Σ%). Avg duration 150.3 mins. Objective: -1.10161
+
+Trailing stop:
+{   'trailing_only_offset_is_reached': True,
+    'trailing_stop': True,
+    'trailing_stop_positive': 0.02001,
+    'trailing_stop_positive_offset': 0.06038}
+```
+
+In order to use these best trailing stop parameters found by Hyperopt in backtesting and for live trades/dry-run, copy-paste them as the values of the corresponding attributes of your custom strategy:
+
+```
+    # Trailing stop
+    # These attributes will be overridden if the config file contains corresponding values.
+    trailing_stop = True
+    trailing_stop_positive = 0.02001
+    trailing_stop_positive_offset = 0.06038
+    trailing_only_offset_is_reached = True
+```
+
+As stated in the comment, you can also use it as the values of the corresponding settings in the configuration file.
+
+#### Default Trailing Stop Search Space
+
+If you are optimizing trailing stop values, Freqtrade creates the 'trailing' optimization hyperspace for you. By default, the `trailing_stop` parameter is always set to True in that hyperspace, the value of the `trailing_only_offset_is_reached` vary between True and False, the values of the `trailing_stop_positive` and `trailing_stop_positive_offset` parameters vary in the ranges 0.02...0.35 and 0.01...0.1 correspondingly, which is sufficient in most cases.
+
+Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py).
+
+## Show details of Hyperopt results
+
+After you run Hyperopt for the desired amount of epochs, you can later list all results for analysis, select only best or profitable once, and show the details for any of the epochs previously evaluated. This can be done with the `hyperopt-list` and `hyperopt-show` subcommands. The usage of these subcommands is described in the [Utils](utils.md#list-hyperopt-results) chapter.
+
+## Validate backtesting results
 
 Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
 

docs/index.md

@@ -1,5 +1,5 @@
 # Freqtrade
-[![Build Status](https://travis-ci.org/freqtrade/freqtrade.svg?branch=develop)](https://travis-ci.org/freqtrade/freqtrade)
+[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
 [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
 [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
 
@@ -11,8 +11,10 @@
 <a class="github-button" href="https://github.com/freqtrade/freqtrade/archive/master.zip" data-icon="octicon-cloud-download" data-size="large" aria-label="Download freqtrade/freqtrade on GitHub">Download</a>
 <!-- Place this tag where you want the button to render. -->
 <a class="github-button" href="https://github.com/freqtrade" data-size="large" aria-label="Follow @freqtrade on GitHub">Follow @freqtrade</a>
+
 ## Introduction
-Freqtrade is a cryptocurrency trading bot written in Python.
+
+Freqtrade is a crypto-currency algorithmic trading software developed in python (3.6+) and supported on Windows, macOS and Linux.
 
 !!! Danger "DISCLAIMER"
     This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
@@ -23,18 +25,15 @@ Freqtrade is a cryptocurrency trading bot written in Python.
 
 ## Features
 
- - Based on Python 3.6+: For botting on any operating system — Windows, macOS and Linux.
+- Develop your Strategy: Write your strategy in python, using [pandas](https://pandas.pydata.org/). Example strategies to inspire you are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
- - Persistence: Persistence is achieved through sqlite database.
+- Download market data: Download historical data of the exchange and the markets your may want to trade with.
- - Dry-run mode: Run the bot without playing money.
+- Backtest: Test your strategy on downloaded historical data.
- - Backtesting: Run a simulation of your buy/sell strategy with historical data.
+- Optimize: Find the best parameters for your strategy using hyperoptimization which employs machining learning methods. You can optimize buy, sell, take profit (ROI), stop-loss and trailing stop-loss parameters for your strategy.
- - Strategy Optimization by machine learning: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
+- Select markets: Create your static list or use an automatic one based on top traded volumes and/or prices (not available during backtesting). You can also explicitly blacklist markets you don't want to trade.
- - Edge position sizing: Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market.
+- Run: Test your strategy with simulated money (Dry-Run mode) or deploy it with real money (Live-Trade mode).
- - Whitelist crypto-currencies: Select which crypto-currency you want to trade or use dynamic whitelists based on market (pair) trade volume.
+- Run using Edge (optional module): The concept is to find the best historical [trade expectancy](edge.md#expectancy) by markets based on variation of the stop-loss and then allow/reject markets to trade. The sizing of the trade is based on a risk of a percentage of your capital.
- - Blacklist crypto-currencies: Select which crypto-currency you want to avoid.
+- Control/Monitor: Use Telegram or a REST API (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
- - Manageable via Telegram or REST APi: Manage the bot with Telegram or via the builtin REST API.
+- Analyse: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
- - Display profit/loss in fiat: Display your profit/loss in any of 33 fiat currencies supported.
- - Daily summary of profit/loss: Receive the daily summary of your profit/loss.
- - Performance status report: Receive the performance status of your current trades.
 
 ## Requirements
 
@@ -52,20 +51,23 @@ To run this bot we recommend you a cloud instance with a minimum of:
 
 ### Software requirements
 
+- Docker (Recommended)
+
+Alternatively
+
 - Python 3.6.x
 - pip (pip3)
 - git
 - TA-Lib
 - virtualenv (Recommended)
-- Docker (Recommended)
 
 ## Support
 
-Help / Slack
+### Help / Slack
-For any questions not covered by the documentation or for further information about the bot, we encourage you to join our Slack channel.
+For any questions not covered by the documentation or for further information about the bot, we encourage you to join our passionate  Slack community.
 
-Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) to join Slack channel.
+Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) to join the Freqtrade Slack channel.
 
 ## Ready to try?
 
-Begin by reading our installation guide [here](installation).
+Begin by reading our installation guide [for docker](docker.md), or for [installation without docker](installation.md).

docs/installation.md

@@ -2,6 +2,8 @@
 
 This page explains how to prepare your environment for running the bot.
 
+Please consider using the prebuilt [docker images](docker.md) to get started quickly while trying out freqtrade evaluating how it operates.
+
 ## Prerequisite
 
 ### Requirements
@@ -14,36 +16,37 @@ Click each one for install guide:
 * [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
 * [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
 
-### API keys
+ We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
-
-Before running your bot in production you will need to setup few
-external API. In production mode, the bot will require valid Exchange API
-credentials. We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot) (optional but recommended).
-
-### Setup your exchange account
-
-You will need to create API Keys (Usually you get `key` and `secret`) from the Exchange website and insert this into the appropriate fields in the configuration or when asked by the installation script.
 
 ## Quick start
 
-Freqtrade provides a Linux/MacOS script to install all dependencies and help you to configure the bot.
+Freqtrade provides the Linux/MacOS Easy Installation script to install all dependencies and help you configure the bot.
-
-!!! Note
-    Python3.6 or higher and the corresponding pip are assumed to be available. The install-script will warn and stop if that's not the case.
-
-```bash
-git clone git@github.com:freqtrade/freqtrade.git
-cd freqtrade
-git checkout develop
-./setup.sh --install
-```
 
 !!! Note
     Windows installation is explained [here](#windows).
 
-## Easy Installation - Linux Script
+The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the Easy Installation script, if it's available for your platform.
 
-If you are on Debian, Ubuntu or MacOS freqtrade provides a script to Install, Update, Configure, and Reset your bot.
+!!! Note "Version considerations"
+    When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `master` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
+
+!!! Note
+    Python3.6 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.
+
+This can be achieved with the following commands:
+
+```bash
+git clone https://github.com/freqtrade/freqtrade.git
+cd freqtrade
+git checkout master  # Optional, see (1)
+./setup.sh --install
+```
+
+(1) This command switches the cloned repository to the use of the `master` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout master`/`git checkout develop` commands.
+
+## Easy Installation Script (Linux/MacOS)
+
+If you are on Debian, Ubuntu or MacOS Freqtrade provides the script to install, update, configure and reset the codebase of your bot.
 
 ```bash
 $ ./setup.sh
@@ -56,25 +59,25 @@ usage:
 
 ** --install **
 
-This script will install everything you need to run the bot:
+With this option, the script will install the bot and most dependencies:
+You will need to have git and python3.6+ installed beforehand for this to work.
 
 * Mandatory software as: `ta-lib`
-* Setup your virtualenv
+* Setup your virtualenv under `.env/`
-* Configure your `config.json` file
 
-This script is a combination of `install script` `--reset`, `--config`
+This option is a combination of installation tasks, `--reset` and `--config`.
 
 ** --update **
 
-Update parameter will pull the last version of your current branch and update your virtualenv.
+This option will pull the last version of your current branch and update your virtualenv. Run the script with this option periodically to update your bot.
 
 ** --reset **
 
-Reset parameter will hard reset your branch (only if you are on `master` or `develop`) and recreate your virtualenv.
+This option will hard reset your branch (only if you are on either `master` or `develop`) and recreate your virtualenv.
 
 ** --config **
 
-Config parameter is a `config.json` configurator. This script will ask you questions to setup your bot and create your `config.json`.
+DEPRECATED - use `freqtrade new-config -c config.json` instead.
 
 ------
 
@@ -95,35 +98,43 @@ sudo apt-get update
 sudo apt-get install build-essential git
 ```
 
-#### Raspberry Pi / Raspbian
+### Raspberry Pi / Raspbian
 
-Before installing FreqTrade on a Raspberry Pi running the official Raspbian Image, make sure you have at least Python 3.6 installed. The default image only provides Python 3.5. Probably the easiest way to get a recent version of python is [miniconda](https://repo.continuum.io/miniconda/).
+The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/) from at least September 2019.
+This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
 
-The following assumes that miniconda3 is installed and available in your environment. Since the last miniconda3 installation file uses python 3.4, we will update to python 3.6 on this installation.
+Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
-It's recommended to use (mini)conda for this as installation/compilation of `numpy` and `pandas` takes a long time.
-
-Additional package to install on your Raspbian, `libffi-dev` required by cryptography (from python-telegram-bot).
 
 ``` bash
-conda config --add channels rpi
+sudo apt-get install python3-venv libatlas-base-dev
-conda install python=3.6
+git clone https://github.com/freqtrade/freqtrade.git
-conda create -n freqtrade python=3.6
+cd freqtrade
-conda activate freqtrade
-conda install pandas numpy
 
-sudo apt install libffi-dev
+bash setup.sh -i
-python3 -m pip install -r requirements-common.txt
-python3 -m pip install -e .
 ```
 
+!!! Note "Installation duration"
+    Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
+
 !!! Note
-    This does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
+    The above does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
     We do not advise to run hyperopt on a Raspberry Pi, since this is a very resource-heavy operation, which should be done on powerful machine.
 
 ### Common
 
 #### 1. Install TA-Lib
 
+Use the provided ta-lib installation script
+
+```bash
+sudo ./build_helpers/install_ta-lib.sh
+```
+
+!!! Note
+    This will use the ta-lib tar.gz included in this repository.
+
+##### TA-Lib manual installation
+
 Official webpage: https://mrjbq7.github.io/ta-lib/install.html
 
 ```bash
@@ -151,13 +162,13 @@ python3 -m venv .env
 source .env/bin/activate
 ```
 
-#### 3. Install FreqTrade
+#### 3. Install Freqtrade
 
 Clone the git repository:
 
 ```bash
 git clone https://github.com/freqtrade/freqtrade.git
-
+cd freqtrade
 ```
 
 Optionally checkout the master branch to get the latest stable release:
@@ -166,59 +177,38 @@ Optionally checkout the master branch to get the latest stable release:
 git checkout master
 ```
 
-#### 4. Initialize the configuration
+#### 4. Install python dependencies
-
-```bash
-cd freqtrade
-cp config.json.example config.json
-```
-
-> *To edit the config please refer to [Bot Configuration](configuration.md).*
-
-#### 5. Install python dependencies
 
 ``` bash
 python3 -m pip install --upgrade pip
 python3 -m pip install -e .
 ```
 
+#### 5. Initialize the configuration
+
+```bash
+# Initialize the user_directory
+freqtrade create-userdir --userdir user_data/
+
+# Create a new configuration file
+freqtrade new-config --config config.json
+```
+
+> *To edit the config please refer to [Bot Configuration](configuration.md).*
+
 #### 6. Run the Bot
 
 If this is the first time you run the bot, ensure you are running it in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
 
 ```bash
-freqtrade -c config.json
+freqtrade trade -c config.json
 ```
 
 *Note*: If you run the bot on a server, you should consider using [Docker](docker.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
 
-#### 7. [Optional] Configure `freqtrade` as a `systemd` service
+#### 7. (Optional) Post-installation Tasks
 
-From the freqtrade repo... copy `freqtrade.service` to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
+On Linux, as an optional post-installation task, you may wish to setup the bot to run as a `systemd` service or configure it to send the log messages to the `syslog`/`rsyslog` or `journald` daemons. See [Advanced Logging](advanced-setup.md#advanced-logging) for details.
-
-After that you can start the daemon with:
-
-```bash
-systemctl --user start freqtrade
-```
-
-For this to be persistent (run when user is logged out) you'll need to enable `linger` for your freqtrade user.
-
-```bash
-sudo loginctl enable-linger "$USER"
-```
-
-If you run the bot as a service, you can use systemd service manager as a software watchdog monitoring freqtrade bot 
-state and restarting it in the case of failures. If the `internals.sd_notify` parameter is set to true in the 
-configuration or the `--sd-notify` command line option is used, the bot will send keep-alive ping messages to systemd 
-using the sd_notify (systemd notifications) protocol and will also tell systemd its current state (Running or Stopped) 
-when it changes. 
-
-The `freqtrade.service.watchdog` file contains an example of the service unit configuration file which uses systemd 
-as the watchdog.
-
-!!! Note
-    The sd_notify communication between the bot and the systemd service manager will not work if the bot runs in a Docker container.
 
 ------
 
@@ -242,6 +232,12 @@ If that is not available on your system, feel free to try the instructions below
 
 ### Install freqtrade manually
 
+!!! Note
+    Make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
+
+!!! Hint
+    Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Conda section](#using-conda) in this document for more information.
+
 #### Clone the git repository
 
 ```bash
@@ -281,3 +277,18 @@ The easiest way is to download install Microsoft Visual Studio Community [here](
 
 Now you have an environment ready, the next step is
 [Bot Configuration](configuration.md).
+
+## Troubleshooting
+
+### MacOS installation error
+
+Newer versions of MacOS may have installation failed with errors like `error: command 'g++' failed with exit status 1`.
+
+This error will require explicit installation of the SDK Headers, which are not installed by default in this version of MacOS.
+For MacOS 10.14, this can be accomplished with the below command.
+
+``` bash
+open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg
+```
+
+If this file is inexistant, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.

docs/plotting.md

@@ -23,13 +23,16 @@ The `freqtrade plot-dataframe` subcommand shows an interactive graph with three
 Possible arguments:
 
 ```
-usage: freqtrade plot-dataframe [-h] [-p PAIRS [PAIRS ...]]
+usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                [-d PATH] [--userdir PATH] [-s NAME]
+                                [--strategy-path PATH] [-p PAIRS [PAIRS ...]]
                                 [--indicators1 INDICATORS1 [INDICATORS1 ...]]
                                 [--indicators2 INDICATORS2 [INDICATORS2 ...]]
                                 [--plot-limit INT] [--db-url PATH]
                                 [--trade-source {DB,file}] [--export EXPORT]
                                 [--export-filename PATH]
-                                [--timerange TIMERANGE]
+                                [--timerange TIMERANGE] [-i TICKER_INTERVAL]
+                                [--no-trades]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -48,21 +51,46 @@ optional arguments:
                         values cause huge files. Default: 750.
   --db-url PATH         Override trades database URL, this is useful in custom
                         deployments (default: `sqlite:///tradesv3.sqlite` for
-                        Live Run mode, `sqlite://` for Dry Run).
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
   --trade-source {DB,file}
                         Specify the source for trades (Can be DB or file
                         (backtest file)) Default: file
   --export EXPORT       Export backtest results, argument are: trades.
                         Example: `--export=trades`
   --export-filename PATH
-                        Save backtest results to the file with this filename
+                        Save backtest results to the file with this filename.
-                        (default: `user_data/backtest_results/backtest-
+                        Requires `--export` to be set as well. Example:
-                        result.json`). Requires `--export` to be set as well.
+                        `--export-filename=user_data/backtest_results/backtest
-                        Example: `--export-filename=user_data/backtest_results
+                        _today.json`
-                        /backtest_today.json`
   --timerange TIMERANGE
                         Specify what timerange of data to use.
+  -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
+                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
+                        `1d`).
+  --no-trades           Skip using trades from backtesting file and DB.
 
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
 ```
 
 Example:
@@ -79,11 +107,11 @@ The `-p/--pairs` argument can be used to specify pairs you would like to plot.
 Specify custom indicators.
 Use `--indicators1` for the main plot and `--indicators2` for the subplot below (if values are in a different range than prices).
 
-!!! tip
+!!! Tip
     You will almost certainly want to specify a custom strategy! This can be done by adding `-s Classname` / `--strategy ClassName` to the command.
 
 ``` bash
-freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH --indicators1 sma ema --indicators2 macd
+freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --indicators1 sma ema --indicators2 macd
 ```
 
 ### Further usage examples
@@ -91,52 +119,115 @@ freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH --indicators1 sma
 To plot multiple pairs, separate them with a space:
 
 ``` bash
-freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH XRP/ETH
+freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH XRP/ETH
 ```
 
 To plot a timerange (to zoom in)
 
 ``` bash
-freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH --timerange=20180801-20180805
+freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --timerange=20180801-20180805
 ```
 
 To plot trades stored in a database use `--db-url` in combination with `--trade-source DB`:
 
 ``` bash
-freqtrade --strategy AwesomeStrategy plot-dataframe --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH --trade-source DB
+freqtrade plot-dataframe --strategy AwesomeStrategy --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH --trade-source DB
 ```
 
 To plot trades from a backtesting result, use `--export-filename <filename>`
 
 ``` bash
-freqtrade --strategy AwesomeStrategy plot-dataframe --export-filename user_data/backtest_results/backtest-result.json -p BTC/ETH
+freqtrade plot-dataframe --strategy AwesomeStrategy --export-filename user_data/backtest_results/backtest-result.json -p BTC/ETH
 ```
 
+### Plot dataframe basics
+
+![plot-dataframe2](assets/plot-dataframe2.png)
+
+The `plot-dataframe` subcommand requires backtesting data, a strategy and either a backtesting-results file or a database, containing trades corresponding to the strategy.
+
+The resulting plot will have the following elements:
+
+* Green triangles: Buy signals from the strategy. (Note: not every buy signal generates a trade, compare to cyan circles.)
+* Red triangles: Sell signals from the strategy. (Also, not every sell signal terminates a trade, compare to red and green squares.)
+* Cyan circles: Trade entry points.
+* Red squares: Trade exit points for trades with loss or 0% profit.
+* Green squares: Trade exit points for profitable trades.
+* Indicators with values corresponding to the candle scale (e.g. SMA/EMA), as specified with `--indicators1`.
+* Volume (bar chart at the bottom of the main chart).
+* Indicators with values in different scales (e.g. MACD, RSI) below the volume bars, as specified with `--indicators2`.
+
+!!! Note "Bollinger Bands"
+    Bollinger bands are automatically added to the plot if the columns `bb_lowerband` and `bb_upperband` exist, and are painted as a light blue area spanning from the lower band to the upper band.
+
+#### Advanced plot configuration
+
+An advanced plot configuration can be specified in the strategy in the `plot_config` parameter.
+
+Additional features when using plot_config include:
+
+* Specify colors per indicator
+* Specify additional subplots
+
+The sample plot configuration below specifies fixed colors for the indicators. Otherwise consecutive plots may produce different colorschemes each time, making comparisons difficult.
+It also allows multiple subplots to display both MACD and RSI at the same time.
+
+Sample configuration with inline comments explaining the process:
+
+``` python
+    plot_config = {
+        'main_plot': {
+            # Configuration for main plot indicators.
+            # Specifies `ema10` to be red, and `ema50` to be a shade of gray
+            'ema10': {'color': 'red'},
+            'ema50': {'color': '#CCCCCC'},
+            # By omitting color, a random color is selected.
+            'sar': {},
+        },
+        'subplots': {
+            # Create subplot MACD
+            "MACD": {
+                'macd': {'color': 'blue'},
+                'macdsignal': {'color': 'orange'},
+            },
+            # Additional subplot RSI
+            "RSI": {
+                'rsi': {'color': 'red'},
+            }
+        }
+    }
+```
+
+!!! Note
+    The above configuration assumes that `ema10`, `ema50`, `macd`, `macdsignal` and `rsi` are columns in the DataFrame created by the strategy.
+
 ## Plot profit
 
 ![plot-profit](assets/plot-profit.png)
 
-The `freqtrade plot-profit` subcommand shows an interactive graph with three plots:
+The `plot-profit` subcommand shows an interactive graph with three plots:
 
-1) Average closing price for all pairs
+* Average closing price for all pairs.
-2) The summarized profit made by backtesting.
+* The summarized profit made by backtesting.
 Note that this is not the real-world profit, but more of an estimate.
-3) Profit for each individual pair
+* Profit for each individual pair.
 
 The first graph is good to get a grip of how the overall market progresses.
 
 The second graph will show if your algorithm works or doesn't.
 Perhaps you want an algorithm that steadily makes small profits, or one that acts less often, but makes big swings.
+This graph will also highlight the start (and end) of the Max drawdown period.
 
 The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
 
 Possible options for the `freqtrade plot-profit` subcommand:
 
 ```
-usage: freqtrade plot-profit [-h] [-p PAIRS [PAIRS ...]]
+usage: freqtrade plot-profit [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                             [-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
                              [--timerange TIMERANGE] [--export EXPORT]
                              [--export-filename PATH] [--db-url PATH]
-                             [--trade-source {DB,file}]
+                             [--trade-source {DB,file}] [-i TICKER_INTERVAL]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -148,17 +239,35 @@ optional arguments:
   --export EXPORT       Export backtest results, argument are: trades.
                         Example: `--export=trades`
   --export-filename PATH
-                        Save backtest results to the file with this filename
+                        Save backtest results to the file with this filename.
-                        (default: `user_data/backtest_results/backtest-
+                        Requires `--export` to be set as well. Example:
-                        result.json`). Requires `--export` to be set as well.
+                        `--export-filename=user_data/backtest_results/backtest
-                        Example: `--export-filename=user_data/backtest_results
+                        _today.json`
-                        /backtest_today.json`
   --db-url PATH         Override trades database URL, this is useful in custom
                         deployments (default: `sqlite:///tradesv3.sqlite` for
-                        Live Run mode, `sqlite://` for Dry Run).
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
   --trade-source {DB,file}
                         Specify the source for trades (Can be DB or file
                         (backtest file)) Default: file
+  -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
+                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
+                        `1d`).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
 
 ```
 

docs/requirements-docs.txt

@@ -1,2 +1,2 @@
-mkdocs-material==4.4.3
+mkdocs-material==4.6.3
 mdx_truly_sane_lists==1.2

docs/rest-api.md

@@ -16,13 +16,20 @@ Sample configuration:
     },
 ```
 
-!!! Danger Security warning
+!!! Danger "Security warning"
     By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
 
-!!! Danger Password selection
+!!! Danger "Password selection"
     Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
 
-You can then access the API by going to `http://127.0.0.1:8080/api/v1/version` to check if the API is running correctly.
+You can then access the API by going to `http://127.0.0.1:8080/api/v1/ping` in a browser to check if the API is running correctly.
+This should return the response:
+
+``` output
+{"status":"pong"}
+```
+
+All other endpoints return sensitive info and require authentication, so are not available through a web browser.
 
 To generate a secure password, either use a password manager, or use the below code snipped.
 
@@ -58,7 +65,7 @@ docker run -d \
   -v ~/.freqtrade/user_data/:/freqtrade/user_data \
   -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
   -p 127.0.0.1:8080:8080 \
-  freqtrade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
+  freqtrade trade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
 ```
 
 !!! Danger "Security warning"
@@ -67,7 +74,7 @@ docker run -d \
 ## Consuming the API
 
 You can consume the API by using the script `scripts/rest_client.py`.
-The client script only requires the `requests` module, so FreqTrade does not need to be installed on the system.
+The client script only requires the `requests` module, so Freqtrade does not need to be installed on the system.
 
 ``` bash
 python3 scripts/rest_client.py <command> [optional parameters]
@@ -99,6 +106,7 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 | `stop` | | Stops the trader
 | `stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
 | `reload_conf` | | Reloads the configuration file
+| `show_config` | | Shows part of the current configuration with relevant settings to operation
 | `status` | | Lists all open trades
 | `count` | | Displays number of trades used and available
 | `profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
@@ -165,6 +173,10 @@ reload_conf
         Reload configuration
         :returns: json object
 
+show_config
+        Returns part of the configuration, relevant for trading operations.
+        :return: json object containing the version
+
 start
         Start the bot if it's in stopped state.
         :returns: json object

docs/stoploss.md

@@ -3,74 +3,101 @@
 The `stoploss` configuration parameter is loss in percentage that should trigger a sale.
 For example, value `-0.10` will cause immediate sell if the profit dips below -10% for a given trade. This parameter is optional.
 
-Most of the strategy files already include the optimal `stoploss`
+Most of the strategy files already include the optimal `stoploss` value.
-value. This parameter is optional. If you use it in the configuration file, it will take over the
-`stoploss` value from the strategy file.
 
-## Stop Loss support
+!!! Info
+    All stoploss properties mentioned in this file can be set in the Strategy, or in the configuration. Configuration values will override the strategy values.
+
+## Stop Loss Types
 
 At this stage the bot contains the following stoploss support modes:
 
-1. static stop loss, defined in either the strategy or configuration.
+1. Static stop loss.
-2. trailing stop loss, defined in the configuration.
+2. Trailing stop loss.
-3. trailing stop loss, custom positive loss, defined in configuration.
+3. Trailing stop loss, custom positive loss.
+4. Trailing stop loss only once the trade has reached a certain offset.
+
+Those stoploss modes can be *on exchange* or *off exchange*. If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfully. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
+
+In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.
+
+For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
+The bot cannot do this every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
+So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
+This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
 
 !!! Note
-    All stoploss properties can be configured in either Strategy or configuration. Configuration values override strategy values.
+    Stoploss on exchange is only supported for Binance (stop-loss-limit) and Kraken (stop-loss-market) as of now.
-
-Those stoploss modes can be *on exchange* or *off exchange*. If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfuly. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
-
-In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary. As an example in case of trailing stoploss if the order is on the exchange and the market is going up then the bot automatically cancels the previous stoploss order and put a new one with a stop value higher than previous one. It is clear that the bot cannot do it every 5 seconds otherwise it gets banned. So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
-
-!!! Note
-    Stoploss on exchange is only supported for Binance as of now.
 
 ## Static Stop Loss
 
-This is very simple, basically you define a stop loss of x in your strategy file or alternative in the configuration, which
+This is very simple, you define a stop loss of x (as a ratio of price, i.e. x * 100% of price). This will try to sell the asset once the loss exceeds the defined loss.
-will overwrite the strategy definition. This will basically try to sell your asset, the second the loss exceeds the defined loss.
 
 ## Trailing Stop Loss
 
-The initial value for this stop loss, is defined in your strategy or configuration. Just as you would define your Stop Loss normally.
+The initial value for this is `stoploss`, just as you would define your static Stop loss.
-To enable this Feauture all you have to do is to define the configuration element:
+To enable trailing stoploss:
 
-``` json
+``` python
-"trailing_stop" : True
+trailing_stop = True
 ```
 
-This will now activate an algorithm, which automatically moves your stop loss up every time the price of your asset increases.
+This will now activate an algorithm, which automatically moves the stop loss up every time the price of your asset increases.
 
-For example, simplified math,
+For example, simplified math:
 
-* you buy an asset at a price of 100$
+* the bot buys an asset at a price of 100$
-* your stop loss is defined at 2%
+* the stop loss is defined at 2%
-* which means your stop loss, gets triggered once your asset dropped below 98$
+* the stop loss would get triggered once the asset dropps below 98$
-* assuming your asset now increases to 102$
+* assuming the asset now increases to 102$
-* your stop loss, will now be 2% of 102$ or 99.96$
+* the stop loss will now be 2% of 102$ or 99.96$
-* now your asset drops in value to 101$, your stop loss, will still be 99.96$
+* now the asset drops in value to 101$, the stop loss will still be 99.96$ and would trigger at 99.96$.
 
-basically what this means is that your stop loss will be adjusted to be always be 2% of the highest observed price
+In summary: The stoploss will be adjusted to be always be 2% of the highest observed price.
 
-### Custom positive loss
+### Custom positive stoploss
 
-Due to demand, it is possible to have a default stop loss, when you are in the red with your buy, but once your profit surpasses a certain percentage,
+It is also possible to have a default stop loss, when you are in the red with your buy, but once your profit surpasses a certain percentage, the system will utilize a new stop loss, which can have a different value.
-the system will utilize a new stop loss, which can be a different value. For example your default stop loss is 5%, but once you have 1.1% profit,
+For example your default stop loss is 5%, but once you have 1.1% profit, it will be changed to be only a 1% stop loss, which trails the green candles until it goes below them.
-it will be changed to be only a 1% stop loss, which trails the green candles until it goes below them.
 
-Both values can be configured in the main configuration file and requires `"trailing_stop": true` to be set to true.
+Both values require `trailing_stop` to be set to true.
 
-``` json
+``` python
-    "trailing_stop_positive":  0.01,
+    trailing_stop_positive = 0.01
-    "trailing_stop_positive_offset":  0.011,
+    trailing_stop_positive_offset = 0.011
-    "trailing_only_offset_is_reached": false
 ```
 
 The 0.01 would translate to a 1% stop loss, once you hit 1.1% profit.
+Before this, `stoploss` is used for the trailing stoploss.
 
-You should also make sure to have this value (`trailing_stop_positive_offset`) lower than your minimal ROI, otherwise minimal ROI will apply first and sell your trade.
+Read the [next section](#trailing-only-once-offset-is-reached) to keep stoploss at 5% of the entry point.
+
+!!! Tip
+    Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.
+
+### Trailing only once offset is reached
+
+It is also possible to use a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
 
 If `"trailing_only_offset_is_reached": true` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`.
+This option can be used with or without `trailing_stop_positive`, but uses `trailing_stop_positive_offset` as offset.
+
+``` python
+    trailing_stop_positive_offset = 0.011
+    trailing_only_offset_is_reached = true
+```
+
+Simplified example:
+
+``` python
+    stoploss = 0.05
+    trailing_stop_positive_offset = 0.03
+    trailing_only_offset_is_reached = True
+```
+
+* the bot buys an asset at a price of 100$
+* the stop loss is defined at 5%
+* the stop loss will remain at 95% until profit reaches +3%
 
 ## Changing stoploss on open trades
 

docs/strategy-customization.md

@@ -1,4 +1,4 @@
-# Optimization
+# Strategy Customization
 
 This page explains where to customize your strategies, and add new
 indicators.
@@ -7,24 +7,28 @@ indicators.
 
 This is very simple. Copy paste your strategy file into the directory `user_data/strategies`.
 
-Let assume you have a class called `AwesomeStrategy` in the file `awesome-strategy.py`:
+Let assume you have a class called `AwesomeStrategy` in the file `AwesomeStrategy.py`:
 
-1. Move your file into `user_data/strategies` (you should have `user_data/strategies/awesome-strategy.py`
+1. Move your file into `user_data/strategies` (you should have `user_data/strategies/AwesomeStrategy.py`
 2. Start the bot with the param `--strategy AwesomeStrategy` (the parameter is the class name)
 
 ```bash
-freqtrade --strategy AwesomeStrategy
+freqtrade trade --strategy AwesomeStrategy
 ```
 
-## Change your strategy
+## Develop your own strategy
 
-The bot includes a default strategy file. However, we recommend you to
+The bot includes a default strategy file.
-use your own file to not have to lose your parameters every time the default
+Also, several other strategies are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
-strategy file will be updated on Github. Put your custom strategy file
-into the directory `user_data/strategies`.
 
-Best copy the test-strategy and modify this copy to avoid having bot-updates override your changes.
+You will however most likely have your own idea for a strategy.
-`cp  user_data/strategies/sample_strategy.py user_data/strategies/awesome-strategy.py`
+This document intends to help you develop one for yourself.
+
+To get started, use `freqtrade new-strategy --strategy AwesomeStrategy`.
+This will create a new strategy file from a template, which will be located under `user_data/strategies/AwesomeStrategy.py`.
+
+!!! Note
+    This is just a template file, which will most likely not be profitable out of the box.
 
 ### Anatomy of a strategy
 
@@ -45,19 +49,19 @@ The current version is 2 - which is also the default when it's not set explicitl
 Future versions will require this to be set.
 
 ```bash
-freqtrade --strategy AwesomeStrategy
+freqtrade trade --strategy AwesomeStrategy
 ```
 
-**For the following section we will use the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/sample_strategy.py)
+**For the following section we will use the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py)
 file as reference.**
 
-!!! Note Strategies and Backtesting
+!!! Note "Strategies and Backtesting"
     To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware
     that during backtesting the full time-interval is passed to the `populate_*()` methods at once.
     It is therefore best to use vectorized operations (across the whole dataframe, not loops) and
     avoid index referencing (`df.iloc[-1]`), but instead use `df.shift()` to get to the previous candle.
 
-!!! Warning Using future data
+!!! Warning "Warning: Using future data"
     Since backtesting passes the full time interval to the `populate_*()` methods, the strategy author
     needs to take care to avoid having the strategy utilize data from the future.
     Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
@@ -80,7 +84,7 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
     Performance Note: For the best performance be frugal on the number of indicators
     you are using. Let uncomment only the indicator you are using in your strategies
     or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
-    :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
+    :param dataframe: Dataframe with data from the exchange
     :param metadata: Additional information, like the currently traded pair
     :return: a Dataframe with all mandatory indicators for the strategies
     """
@@ -114,9 +118,40 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
 ```
 
 !!! Note "Want more indicator examples?"
-    Look into the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/sample_strategy.py).
+    Look into the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py).
     Then uncomment indicators you need.
 
+### Strategy startup period
+
+Most indicators have an instable startup period, in which they are either not available, or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
+To account for this, the strategy can be assigned the `startup_candle_count` attribute.
+This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
+
+In this example strategy, this should be set to 100 (`startup_candle_count = 100`), since the longest needed history is 100 candles.
+
+``` python
+    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+```
+
+By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.
+
+!!! Warning
+    `startup_candle_count` should be below `ohlcv_candle_limit` (which is 500 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
+
+#### Example
+
+Let's try to backtest 1 month (January 2019) of 5m candles using the an example strategy with EMA100, as above.
+
+``` bash
+freqtrade backtesting --timerange 20190101-20190201 --ticker-interval 5m
+```
+
+Assuming `startup_candle_count` is set to 100, backtesting knows it needs 100 candles to generate valid buy signals. It will load data from `20190101 - (100 * 5m)` - which is ~2019-12-31 15:30:00.
+If this data is available, indicators will be calculated with this extended timerange. The instable startup period (up to 2019-01-01 00:00:00) will then be removed before starting backtesting.
+
+!!! Note
+    If data for the startup period is not available, then the timerange will be adjusted to account for this startup period - so Backtesting would start at 2019-01-01 08:30:00.
+
 ### Buy signal rules
 
 Edit the method `populate_buy_trend()` in your strategy file to update your buy strategy.
@@ -214,6 +249,23 @@ minimal_roi = {
 
 While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
 
+To use times based on candle duration (ticker_interval or timeframe), the following snippet can be handy.
+This will allow you to change the ticket_interval for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
+
+``` python
+from freqtrade.exchange import timeframe_to_minutes
+
+class AwesomeStrategy(IStrategy):
+
+    ticker_interval = "1d"
+    ticker_interval_mins = timeframe_to_minutes(ticker_interval)
+    minimal_roi = {
+        "0": 0.05,                             # 5% for the first 3 candles
+        str(ticker_interval_mins * 3)): 0.02,  # 2% after 3 candles
+        str(ticker_interval_mins * 6)): 0.01,  # 1% After 6 candles
+    }
+```
+
 ### Stoploss
 
 Setting a stoploss is highly recommended to protect your capital from strong moves against you.
@@ -232,13 +284,14 @@ If your exchange supports it, it's recommended to also set `"stoploss_on_exchang
 
 For more information on order_types please look [here](configuration.md#understand-order_types).
 
-### Ticker interval
+### Timeframe (ticker interval)
 
 This is the set of candles the bot should download and use for the analysis.
 Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
 
-Please note that the same buy/sell signals may work with one interval, but not the other.
+Please note that the same buy/sell signals may work well with one timeframe, but not with the others.
-This setting is accessible within the strategy by using `self.ticker_interval`.
+
+This setting is accessible within the strategy methods as the `self.ticker_interval` attribute.
 
 ### Metadata dict
 
@@ -283,35 +336,35 @@ Please always check the mode of operation to select the correct method to get da
 #### Possible options for DataProvider
 
 - `available_pairs` - Property with tuples listing cached pairs with their intervals (pair, interval).
-- `ohlcv(pair, ticker_interval)` - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.
+- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
-- `historic_ohlcv(pair, ticker_interval)` - Returns historical data stored on disk.
+- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
-- `get_pair_dataframe(pair, ticker_interval)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
+- `get_pair_dataframe(pair, timeframe)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
 - `orderbook(pair, maximum)` - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
 - `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on Market data structure.
 - `runmode` - Property containing the current runmode.
 
-#### Example: fetch live ohlcv / historic data for the first informative pair
+#### Example: fetch live / historical candle (OHLCV) data for the first informative pair
 
 ``` python
 if self.dp:
     inf_pair, inf_timeframe = self.informative_pairs()[0]
     informative = self.dp.get_pair_dataframe(pair=inf_pair,
-                                             ticker_interval=inf_timeframe)
+                                             timeframe=inf_timeframe)
 ```
 
-!!! Warning Warning about backtesting
+!!! Warning "Warning about backtesting"
     Be carefull when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
     for the backtesting runmode) provides the full time-range in one go,
     so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).
 
-!!! Warning Warning in hyperopt
+!!! Warning "Warning in hyperopt"
     This option cannot currently be used during hyperopt.
 
 #### Orderbook
 
 ``` python
 if self.dp:
-    if self.dp.runmode in ('live', 'dry_run'):
+    if self.dp.runmode.value in ('live', 'dry_run'):
         ob = self.dp.orderbook(metadata['pair'], 1)
         dataframe['best_bid'] = ob['bids'][0][0]
         dataframe['best_ask'] = ob['asks'][0][0]
@@ -325,8 +378,8 @@ if self.dp:
 
 ``` python
 if self.dp:
-    for pair, ticker in self.dp.available_pairs:
+    for pair, timeframe in self.dp.available_pairs:
-        print(f"available {pair}, {ticker}")
+        print(f"available {pair}, {timeframe}")
 ```
 
 #### Get data for non-tradeable pairs
@@ -374,6 +427,97 @@ if self.wallets:
 - `get_used(asset)` - currently tied up balance (open orders)
 - `get_total(asset)` - total available balance - sum of the 2 above
 
+### Additional data (Trades)
+
+A history of Trades can be retrieved in the strategy by querying the database.
+
+At the top of the file, import Trade.
+
+```python
+from freqtrade.persistence import Trade
+```
+
+The following example queries for the current pair and trades from today, however other filters can easily be added.
+
+``` python
+if self.config['runmode'].value in ('live', 'dry_run'):
+    trades = Trade.get_trades([Trade.pair == metadata['pair'],
+                               Trade.open_date > datetime.utcnow() - timedelta(days=1),
+                               Trade.is_open == False,
+                ]).order_by(Trade.close_date).all()
+    # Summarize profit for this pair.
+    curdayprofit = sum(trade.close_profit for trade in trades)
+```
+
+Get amount of stake_currency currently invested in Trades:
+
+``` python
+if self.config['runmode'].value in ('live', 'dry_run'):
+    total_stakes = Trade.total_open_trades_stakes()
+```
+
+Retrieve performance per pair.
+Returns a List of dicts per pair.
+
+``` python
+if self.config['runmode'].value in ('live', 'dry_run'):
+    performance = Trade.get_overall_performance()
+```
+
+Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015).
+
+``` json
+{'pair': "ETH/BTC", 'profit': 0.015, 'count': 5}
+```
+
+!!! Warning
+    Trade history is not available during backtesting or hyperopt.
+
+### Prevent trades from happening for a specific pair
+
+Freqtrade locks pairs automatically for the current candle (until that candle is over) when a pair is sold, preventing an immediate re-buy of that pair.
+
+Locked pairs will show the message `Pair <pair> is currently locked.`.
+
+#### Locking pairs from within the strategy
+
+Sometimes it may be desired to lock a pair after certain events happen (e.g. multiple losing trades in a row).
+
+Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until)`.
+`until` must be a datetime object in the future, after which trading will be reenabled for that pair.
+
+Locks can also be lifted manually, by calling `self.unlock_pair(pair)`.
+
+To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
+
+!!! Note
+    Locked pairs are not persisted, so a restart of the bot, or calling `/reload_conf` will reset locked pairs.
+
+!!! Warning
+    Locking pairs is not functioning during backtesting.
+
+##### Pair locking example
+
+``` python
+from freqtrade.persistence import Trade
+from datetime import timedelta, datetime, timezone
+# Put the above lines a the top of the strategy file, next to all the other imports
+# --------
+
+# Within populate indicators (or populate_buy):
+if self.config['runmode'].value in ('live', 'dry_run'):
+   # fetch closed trades for the last 2 days
+    trades = Trade.get_trades([Trade.pair == metadata['pair'],
+                               Trade.open_date > datetime.utcnow() - timedelta(days=2),
+                               Trade.is_open == False,
+                ]).all()
+    # Analyze the conditions you'd like to lock the pair .... will probably be different for every strategy
+    sumprofit = sum(trade.close_profit for trade in trades)
+    if sumprofit < 0:
+        # Lock pair for 12 hours
+        self.lock_pair(metadata['pair'], until=datetime.now(timezone.utc) + timedelta(hours=12))
+```
+
 ### Print created dataframe
 
 To inspect the created dataframe, you can issue a print-statement in either `populate_buy_trend()` or `populate_sell_trend()`.
@@ -398,19 +542,35 @@ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
 Printing more than a few rows is also possible (simply use  `print(dataframe)` instead of `print(dataframe.tail())`), however not recommended, as that will be very verbose (~500 lines per pair every 5 seconds).
 
-### Where can i find a strategy template?
-
-The strategy template is located in the file
-[user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/sample_strategy.py).
-
 ### Specify custom strategy location
 
 If you want to use a strategy from a different directory you can pass `--strategy-path`
 
 ```bash
-freqtrade --strategy AwesomeStrategy --strategy-path /some/directory
+freqtrade trade --strategy AwesomeStrategy --strategy-path /some/directory
 ```
 
+### Derived strategies
+
+The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
+
+``` python
+class MyAwesomeStrategy(IStrategy):
+    ...
+    stoploss = 0.13
+    trailing_stop = False
+    # All other attributes and methods are here as they
+    # should be in any custom strategy...
+    ...
+
+class MyAwesomeStrategy2(MyAwesomeStrategy):
+    # Override something
+    stoploss = 0.08
+    trailing_stop = True
+```
+
+Both attributes and methods may be overriden, altering behavior of the original strategy in a way you need.
+
 ### Common mistakes when developing strategies
 
 Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.

docs/strategy_analysis_example.md

@@ -1,24 +1,28 @@
 # Strategy analysis example
 
-Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
+Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.
+The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.
 
 ## Setup
 
 
 ```python
 from pathlib import Path
+from freqtrade.configuration import Configuration
+
 # Customize these according to your needs.
 
+# Initialize empty configuration object
+config = Configuration.from_files([])
+# Optionally, use existing configuration file
+# config = Configuration.from_files(["config.json"])
+
 # Define some constants
-ticker_interval = "5m"
+config["ticker_interval"] = "5m"
 # Name of the strategy class
-strategy_name = 'SampleStrategy'
+config["strategy"] = "SampleStrategy"
-# Path to user data
-user_data_dir = Path('user_data')
-# Location of the strategy
-strategy_location = user_data_dir / 'strategies'
 # Location of the data
-data_location = Path(user_data_dir, 'data', 'binance')
+data_location = Path(config['user_data_dir'], 'data', 'binance')
 # Pair to analyze - Only use one pair here
 pair = "BTC_USDT"
 ```
@@ -29,7 +33,7 @@ pair = "BTC_USDT"
 from freqtrade.data.history import load_pair_history
 
 candles = load_pair_history(datadir=data_location,
-                            ticker_interval=ticker_interval,
+                            timeframe=config["ticker_interval"],
                             pair=pair)
 
 # Confirm success
@@ -44,9 +48,7 @@ candles.head()
 ```python
 # Load strategy using values set above
 from freqtrade.resolvers import StrategyResolver
-strategy = StrategyResolver({'strategy': strategy_name,
+strategy = StrategyResolver.load_strategy(config)
-                            'user_data_dir': user_data_dir,
-                            'strategy_path': strategy_location}).strategy
 
 # Generate buy/sell signals using strategy
 df = strategy.analyze_ticker(candles, {'pair': pair})
@@ -86,7 +88,7 @@ Analyze a trades dataframe (also used below for plotting)
 from freqtrade.data.btanalysis import load_backtest_data
 
 # Load backtest results
-trades = load_backtest_data(user_data_dir / "backtest_results/backtest-result.json")
+trades = load_backtest_data(config["user_data_dir"] / "backtest_results/backtest-result.json")
 
 # Show value-counts per pair
 trades.groupby("pair")["sell_reason"].value_counts()
@@ -107,6 +109,21 @@ trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
 trades.groupby("pair")["sell_reason"].value_counts()
 ```
 
+## Analyze the loaded trades for trade parallelism
+This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with `--disable-max-market-positions`.
+
+`analyze_trade_parallelism()` returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.
+
+
+```python
+from freqtrade.data.btanalysis import analyze_trade_parallelism
+
+# Analyze the above
+parallel_trades = analyze_trade_parallelism(trades, '5m')
+
+parallel_trades.plot()
+```
+
 ## Plot results
 
 Freqtrade offers interactive plotting capabilities based on plotly.
@@ -116,11 +133,14 @@ Freqtrade offers interactive plotting capabilities based on plotly.
 from freqtrade.plot.plotting import  generate_candlestick_graph
 # Limit graph period to keep plotly quick and reactive
 
+# Filter trades to one pair
+trades_red = trades.loc[trades['pair'] == pair]
+
 data_red = data['2019-06-01':'2019-06-10']
 # Generate candlestick graph
 graph = generate_candlestick_graph(pair=pair,
                                    data=data_red,
-                                   trades=trades,
+                                   trades=trades_red,
                                    indicators1=['sma20', 'ema50', 'ema55'],
                                    indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
                                   )

docs/telegram-usage.md

@@ -53,8 +53,9 @@ official commands. You can ask at any moment for help with `/help`.
 | `/stop` | | Stops the trader
 | `/stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
 | `/reload_conf` | | Reloads the configuration file
+| `/show_config` | | Shows part of the current configuration with relevant settings to operation
 | `/status` | | Lists all open trades
-| `/status table` | | List all open trades in a table format
+| `/status table` | | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
 | `/count` | | Displays number of trades used and available
 | `/profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
 | `/forcesell <trade_id>` | | Instantly sells the given trade  (Ignoring `minimum_roi`).
@@ -93,7 +94,7 @@ Once all positions are sold, run `/stop` to completely stop the bot.
 
 `/reload_conf` resets "max_open_trades" to the value set in the configuration and resets this command. 
 
-!!! warning
+!!! Warning
    The stop-buy signal is ONLY active while the bot is running, and is not persisted anyway, so restarting the bot will cause this to reset.
 
 ### /status

docs/utils.md

@@ -2,6 +2,235 @@
 
 Besides the Live-Trade and Dry-Run run modes, the `backtesting`, `edge` and `hyperopt` optimization subcommands, and the `download-data` subcommand which prepares historical data, the bot contains a number of utility subcommands. They are described in this section.
 
+## Create userdir
+
+Creates the directory structure to hold your files for freqtrade.
+Will also create strategy and hyperopt examples for you to get started.
+Can be used multiple times - using `--reset` will reset the sample strategy and hyperopt files to their default state. 
+
+```
+usage: freqtrade create-userdir [-h] [--userdir PATH] [--reset]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+  --reset               Reset sample files to their original state.
+```
+
+!!! Warning
+    Using `--reset` may result in loss of data, since this will overwrite all sample files without asking again.
+
+```
+├── backtest_results
+├── data
+├── hyperopt_results
+├── hyperopts
+│   ├── sample_hyperopt_advanced.py
+│   ├── sample_hyperopt_loss.py
+│   └── sample_hyperopt.py
+├── notebooks
+│   └── strategy_analysis_example.ipynb
+├── plot
+└── strategies
+    └── sample_strategy.py
+```
+
+## Create new config
+
+Creates a new configuration file, asking some questions which are important selections for a configuration.
+
+```
+usage: freqtrade new-config [-h] [-c PATH]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
+                        to read config from stdin.
+```
+
+!!! Warning
+    Only vital questions are asked. Freqtrade offers a lot more configuration possibilities, which are listed in the [Configuration documentation](configuration.md#configuration-parameters)
+
+### Create config examples
+
+```
+$ freqtrade new-config --config config_binance.json
+
+? Do you want to enable Dry-run (simulated trades)?  Yes
+? Please insert your stake currency: BTC
+? Please insert your stake amount: 0.05
+? Please insert max_open_trades (Integer or 'unlimited'): 3
+? Please insert your timeframe (ticker interval): 5m
+? Please insert your display Currency (for reporting): USD
+? Select exchange  binance
+? Do you want to enable Telegram?  No
+```
+
+## Create new strategy
+
+Creates a new strategy from a template similar to SampleStrategy.
+The file will be named inline with your class name, and will not overwrite existing files.
+
+Results will be located in `user_data/strategies/<strategyclassname>.py`.
+
+``` output
+usage: freqtrade new-strategy [-h] [--userdir PATH] [-s NAME]
+                              [--template {full,minimal}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --template {full,minimal}
+                        Use a template which is either `minimal` or `full`
+                        (containing multiple sample indicators). Default:
+                        `full`.
+
+```
+
+### Sample usage of new-strategy
+
+```bash
+freqtrade new-strategy --strategy AwesomeStrategy
+```
+
+With custom user directory
+
+```bash
+freqtrade new-strategy --userdir ~/.freqtrade/ --strategy AwesomeStrategy
+```
+
+## Create new hyperopt
+
+Creates a new hyperopt from a template similar to SampleHyperopt.
+The file will be named inline with your class name, and will not overwrite existing files.
+
+Results will be located in `user_data/hyperopts/<classname>.py`.
+
+``` output
+usage: freqtrade new-hyperopt [-h] [--userdir PATH] [--hyperopt NAME]
+                              [--template {full,minimal}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+  --hyperopt NAME       Specify hyperopt class name which will be used by the
+                        bot.
+  --template {full,minimal}
+                        Use a template which is either `minimal` or `full`
+                        (containing multiple sample indicators). Default:
+                        `full`.
+```
+
+### Sample usage of new-hyperopt
+
+```bash
+freqtrade new-hyperopt --hyperopt AwesomeHyperopt
+```
+
+With custom user directory
+
+```bash
+freqtrade new-hyperopt --userdir ~/.freqtrade/ --hyperopt AwesomeHyperopt
+```
+
+## List Strategies and List Hyperopts
+
+Use the `list-strategies` subcommand to see all strategies in one particular directory and the `list-hyperopts` subcommand to list custom Hyperopts.
+
+These subcommands are useful for finding problems in your environment with loading strategies or hyperopt classes: modules with strategies or hyperopt classes that contain errors and failed to load are printed in red (LOAD FAILED), while strategies or hyperopt classes with duplicate names are printed in yellow (DUPLICATE NAME).
+
+```
+usage: freqtrade list-strategies [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                 [-d PATH] [--userdir PATH]
+                                 [--strategy-path PATH] [-1] [--no-color]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --strategy-path PATH  Specify additional strategy lookup path.
+  -1, --one-column      Print output in one column.
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+```
+usage: freqtrade list-hyperopts [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                [-d PATH] [--userdir PATH]
+                                [--hyperopt-path PATH] [-1] [--no-color]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --hyperopt-path PATH  Specify additional lookup path for Hyperopt and
+                        Hyperopt Loss functions.
+  -1, --one-column      Print output in one column.
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+
+!!! Warning
+    Using these commands will try to load all python files from a directory. This can be a security risk if untrusted files reside in this directory, since all module-level code is executed.
+
+Example: Search default strategies and hyperopts directories (within the default userdir).
+
+``` bash
+freqtrade list-strategies
+freqtrade list-hyperopts
+```
+
+Example: Search strategies and hyperopts directory within the userdir.
+
+``` bash
+freqtrade list-strategies --userdir ~/.freqtrade/
+freqtrade list-hyperopts --userdir ~/.freqtrade/
+```
+
+Example: Search dedicated strategy path.
+
+``` bash
+freqtrade list-strategies --strategy-path ~/.freqtrade/strategies/
+```
+
+Example: Search dedicated hyperopt path.
+
+``` bash
+freqtrade list-hyperopt --hyperopt-path ~/.freqtrade/hyperopts/
+```
+
 ## List Exchanges
 
 Use the `list-exchanges` subcommand to see the exchanges available for the bot.
@@ -29,23 +258,34 @@ All exchanges supported by the ccxt library: _1btcxe, acx, adara, allcoin, anxpr
 
 ## List Timeframes
 
-Use the `list-timeframes` subcommand to see the list of ticker intervals (timeframes) available for the exchange.
+Use the `list-timeframes` subcommand to see the list of timeframes (ticker intervals) available for the exchange.
 
 ```
-usage: freqtrade list-timeframes [-h] [--exchange EXCHANGE] [-1]
+usage: freqtrade list-timeframes [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--exchange EXCHANGE] [-1]
 
 optional arguments:
   -h, --help            show this help message and exit
-  --exchange EXCHANGE  Exchange name (default: `bittrex`). Only valid if no
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no config is provided.
-                       config is provided.
   -1, --one-column      Print output in one column.
 
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
+                        to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
 ```
 
 * Example: see the timeframes for the 'binance' exchange, set in the configuration file:
 
 ```
-$ freqtrade -c config_binance.json list-timeframes
+$ freqtrade list-timeframes -c config_binance.json
 ...
 Timeframes available for the exchange `binance`: 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d, 3d, 1w, 1M
 ```
@@ -69,14 +309,16 @@ You can print info about any pair/market with these subcommands - and you can fi
 These subcommands have same usage and same set of available options:
 
 ```
-usage: freqtrade list-markets [-h] [--exchange EXCHANGE] [--print-list]
+usage: freqtrade list-markets [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                              [--print-json] [-1] [--print-csv]
+                              [-d PATH] [--userdir PATH] [--exchange EXCHANGE]
+                              [--print-list] [--print-json] [-1] [--print-csv]
                               [--base BASE_CURRENCY [BASE_CURRENCY ...]]
                               [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
                               [-a]
 
-usage: freqtrade list-pairs [-h] [--exchange EXCHANGE] [--print-list]
+usage: freqtrade list-pairs [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                            [--print-json] [-1] [--print-csv]
+                            [-d PATH] [--userdir PATH] [--exchange EXCHANGE]
+                            [--print-list] [--print-json] [-1] [--print-csv]
                             [--base BASE_CURRENCY [BASE_CURRENCY ...]]
                             [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]] [-a]
 
@@ -95,6 +337,22 @@ optional arguments:
                         Specify quote currency(-ies). Space-separated list.
   -a, --all             Print all pairs or market symbols. By default only
                         active ones are shown.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
 ```
 
 By default, only active pairs/markets are shown. Active pairs/markets are those that can currently be traded
@@ -116,7 +374,7 @@ $ freqtrade list-pairs --quote USD --print-json
 human-readable list with summary:
 
 ```
-$ freqtrade -c config_binance.json list-pairs --all --base BTC ETH --quote USDT USD --print-list
+$ freqtrade list-pairs -c config_binance.json --all --base BTC ETH --quote USDT USD --print-list
 ```
 
 * Print all markets on exchange "Kraken", in the tabular format:
@@ -124,3 +382,136 @@ $ freqtrade -c config_binance.json list-pairs --all --base BTC ETH --quote USDT
 ```
 $ freqtrade list-markets --exchange kraken --all
 ```
+
+## Test pairlist
+
+Use the `test-pairlist` subcommand to test the configuration of [dynamic pairlists](configuration.md#pairlists).
+
+Requires a configuration with specified `pairlists` attribute.
+Can be used to generate static pairlists to be used during backtesting / hyperopt.
+
+```
+usage: freqtrade test-pairlist [-h] [-c PATH]
+                               [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
+                               [-1] [--print-json]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  --quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]
+                        Specify quote currency(-ies). Space-separated list.
+  -1, --one-column      Print output in one column.
+  --print-json          Print list of pairs or market symbols in JSON format.
+```
+
+### Examples
+
+Show whitelist when using a [dynamic pairlist](configuration.md#pairlists).
+
+```
+freqtrade test-pairlist --config config.json --quote USDT BTC
+```
+
+## List Hyperopt results
+
+You can list the hyperoptimization epochs the Hyperopt module evaluated previously with the `hyperopt-list` subcommand.
+
+```
+usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                               [-d PATH] [--userdir PATH] [--best]
+                               [--profitable] [--min-trades INT]
+                               [--max-trades INT] [--min-avg-time FLOAT]
+                               [--max-avg-time FLOAT] [--min-avg-profit FLOAT]
+                               [--max-avg-profit FLOAT]
+                               [--min-total-profit FLOAT]
+                               [--max-total-profit FLOAT] [--no-color]
+                               [--print-json] [--no-details]
+                               [--export-csv FILE]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --best                Select only best epochs.
+  --profitable          Select only profitable epochs.
+  --min-trades INT      Select epochs with more than INT trades.
+  --max-trades INT      Select epochs with less than INT trades.
+  --min-avg-time FLOAT  Select epochs on above average time.
+  --max-avg-time FLOAT  Select epochs on under average time.
+  --min-avg-profit FLOAT
+                        Select epochs on above average profit.
+  --max-avg-profit FLOAT
+                        Select epochs on below average profit.
+  --min-total-profit FLOAT
+                        Select epochs on above total profit.
+  --max-total-profit FLOAT
+                        Select epochs on below total profit.
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --print-json          Print best result detailization in JSON format.
+  --no-details          Do not print best epoch details.
+  --export-csv FILE     Export to CSV-File. This will disable table print.
+                        Example: --export-csv hyperopt.csv
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+```
+ 
+### Examples
+
+List all results, print details of the best result at the end:
+```
+freqtrade hyperopt-list
+```
+
+List only epochs with positive profit. Do not print the details of the best epoch, so that the list can be iterated in a script:
+```
+freqtrade hyperopt-list --profitable --no-details
+```
+
+## Show details of Hyperopt results
+
+You can show the details of any hyperoptimization epoch previously evaluated by the Hyperopt module with the `hyperopt-show` subcommand.
+
+```
+usage: freqtrade hyperopt-show [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                               [-d PATH] [--userdir PATH] [--best]
+                               [--profitable] [-n INT] [--print-json]
+                               [--no-header]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --best                Select only best epochs.
+  --profitable          Select only profitable epochs.
+  -n INT, --index INT   Specify the index of the epoch to print details for.
+  --print-json          Print best result detailization in JSON format.
+  --no-header           Do not print epoch details header.
+```
+
+### Examples
+
+Print details for the epoch 168 (the number of the epoch is shown by the `hyperopt-list` subcommand or by Hyperopt itself during hyperoptimization run):
+
+```
+freqtrade hyperopt-show -n 168
+```
+
+Prints JSON data with details for the last best epoch (i.e., the best of all epochs):
+
+```
+freqtrade hyperopt-show --best -n -1 --print-json --no-header
+```

docs/webhook-config.md

@@ -15,10 +15,20 @@ Sample configuration (tested using IFTTT).
             "value2": "limit {limit:8f}",
             "value3": "{stake_amount:8f} {stake_currency}"
         },
+        "webhookbuycancel": {
+            "value1": "Cancelling Open Buy Order for {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "{stake_amount:8f} {stake_currency}"
+        },
         "webhooksell": {
             "value1": "Selling {pair}",
             "value2": "limit {limit:8f}",
-            "value3": "profit: {profit_amount:8f} {stake_currency}"
+            "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
+        },
+        "webhooksellcancel": {
+            "value1": "Cancelling Open Sell Order for {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
         },
         "webhookstatus": {
             "value1": "Status: {status}",
@@ -40,10 +50,29 @@ Possible parameters are:
 * `exchange`
 * `pair`
 * `limit`
+* `amount`
+* `open_date`
 * `stake_amount`
 * `stake_currency`
 * `fiat_currency`
 * `order_type`
+* `current_rate`
+
+### Webhookbuycancel
+
+The fields in `webhook.webhookbuycancel` are filled when the bot cancels a buy order. Parameters are filled using string.format.
+Possible parameters are:
+
+* `exchange`
+* `pair`
+* `limit`
+* `amount`
+* `open_date`
+* `stake_amount`
+* `stake_currency`
+* `fiat_currency`
+* `order_type`
+* `current_rate`
 
 ### Webhooksell
 
@@ -58,11 +87,34 @@ Possible parameters are:
 * `open_rate`
 * `current_rate`
 * `profit_amount`
-* `profit_percent`
+* `profit_ratio`
 * `stake_currency`
 * `fiat_currency`
 * `sell_reason`
 * `order_type`
+* `open_date`
+* `close_date`
+
+### Webhooksellcancel
+
+The fields in `webhook.webhooksellcancel` are filled when the bot cancels a sell order. Parameters are filled using string.format.
+Possible parameters are:
+
+* `exchange`
+* `pair`
+* `gain`
+* `limit`
+* `amount`
+* `open_rate`
+* `current_rate`
+* `profit_amount`
+* `profit_ratio`
+* `stake_currency`
+* `fiat_currency`
+* `sell_reason`
+* `order_type`
+* `open_date`
+* `close_date`
 
 ### Webhookstatus
 

environment.yml

@@ -45,7 +45,7 @@ dependencies:
   - pip:
     # Required for app
     - cython
-    - coinmarketcap
+    - pycoingecko
     - ccxt
     - TA-Lib
     - py_find_1st

freqtrade.service

@@ -6,7 +6,7 @@ After=network.target
 # Set WorkingDirectory and ExecStart to your file paths accordingly
 # NOTE: %h will be resolved to /home/<username>
 WorkingDirectory=%h/freqtrade
-ExecStart=/usr/bin/freqtrade
+ExecStart=/usr/bin/freqtrade trade
 Restart=on-failure
 
 [Install]

freqtrade.service.watchdog

@@ -6,7 +6,7 @@ After=network.target
 # Set WorkingDirectory and ExecStart to your file paths accordingly
 # NOTE: %h will be resolved to /home/<username>
 WorkingDirectory=%h/freqtrade
-ExecStart=/usr/bin/freqtrade --sd-notify
+ExecStart=/usr/bin/freqtrade trade --sd-notify
 
 Restart=always
 #Restart=on-failure

freqtrade/__init__.py

@@ -1,44 +1,27 @@
-""" FreqTrade bot """
+""" Freqtrade bot """
-__version__ = '2019.10'
+__version__ = '2020.3'
 
 if __version__ == 'develop':
 
     try:
         import subprocess
+
         __version__ = 'develop-' + subprocess.check_output(
             ['git', 'log', '--format="%h"', '-n 1'],
             stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
+
+        # from datetime import datetime
+        # last_release = subprocess.check_output(
+        #     ['git', 'tag']
+        # ).decode('utf-8').split()[-1].split(".")
+        # # Releases are in the format "2020.1" - we increment the latest version for dev.
+        # prefix = f"{last_release[0]}.{int(last_release[1]) + 1}"
+        # dev_version = int(datetime.now().timestamp() // 1000)
+        # __version__ = f"{prefix}.dev{dev_version}"
+
+        #  subprocess.check_output(
+        #     ['git', 'log', '--format="%h"', '-n 1'],
+        #     stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
     except Exception:
         # git not available, ignore
         pass
-
-
-class DependencyException(Exception):
-    """
-    Indicates that an assumed dependency is not met.
-    This could happen when there is currently not enough money on the account.
-    """
-
-
-class OperationalException(Exception):
-    """
-    Requires manual intervention and will usually stop the bot.
-    This happens when an exchange returns an unexpected error during runtime
-    or given configuration is invalid.
-    """
-
-
-class InvalidOrderException(Exception):
-    """
-    This is returned when the order is not valid. Example:
-    If stoploss on exchange order is hit, then trying to cancel the order
-    should return this exception.
-    """
-
-
-class TemporaryError(Exception):
-    """
-    Temporary network or exchange related error.
-    This could happen when an exchange is congested, unavailable, or the user
-    has networking problems. Usually resolves itself after a time.
-    """

freqtrade/commands/__init__.py

@@ -0,0 +1,28 @@
+# flake8: noqa: F401
+"""
+Commands module.
+Contains all start-commands, subcommands and CLI Interface creation.
+
+Note: Be careful with file-scoped imports in these subfiles.
+    as they are parsed on startup, nothing containing optional modules should be loaded.
+"""
+from freqtrade.commands.arguments import Arguments
+from freqtrade.commands.build_config_commands import start_new_config
+from freqtrade.commands.data_commands import (start_convert_data,
+                                              start_download_data)
+from freqtrade.commands.deploy_commands import (start_create_userdir,
+                                                start_new_hyperopt,
+                                                start_new_strategy)
+from freqtrade.commands.hyperopt_commands import (start_hyperopt_list,
+                                                  start_hyperopt_show)
+from freqtrade.commands.list_commands import (start_list_exchanges,
+                                              start_list_hyperopts,
+                                              start_list_markets,
+                                              start_list_strategies,
+                                              start_list_timeframes)
+from freqtrade.commands.optimize_commands import (start_backtesting,
+                                                  start_edge, start_hyperopt)
+from freqtrade.commands.pairlist_commands import start_test_pairlist
+from freqtrade.commands.plot_commands import (start_plot_dataframe,
+                                              start_plot_profit)
+from freqtrade.commands.trade_commands import start_trading

freqtrade/commands/arguments.py

@@ -0,0 +1,349 @@
+"""
+This module contains the argument manager class
+"""
+import argparse
+from functools import partial
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from freqtrade.commands.cli_options import AVAILABLE_CLI_OPTIONS
+from freqtrade.constants import DEFAULT_CONFIG
+
+ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"]
+
+ARGS_STRATEGY = ["strategy", "strategy_path"]
+
+ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
+
+ARGS_COMMON_OPTIMIZE = ["ticker_interval", "timerange",
+                        "max_open_trades", "stake_amount", "fee"]
+
+ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
+                                        "strategy_list", "export", "exportfilename"]
+
+ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
+                                        "position_stacking", "epochs", "spaces",
+                                        "use_max_market_positions", "print_all",
+                                        "print_colorized", "print_json", "hyperopt_jobs",
+                                        "hyperopt_random_state", "hyperopt_min_trades",
+                                        "hyperopt_continue", "hyperopt_loss"]
+
+ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
+
+ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column", "print_colorized"]
+
+ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column", "print_colorized"]
+
+ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"]
+
+ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
+
+ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one_column",
+                   "print_csv", "base_currencies", "quote_currencies", "list_pairs_all"]
+
+ARGS_TEST_PAIRLIST = ["config", "quote_currencies", "print_one_column", "list_pairs_print_json"]
+
+ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
+
+ARGS_BUILD_CONFIG = ["config"]
+
+ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
+
+ARGS_BUILD_HYPEROPT = ["user_data_dir", "hyperopt", "template"]
+
+ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase"]
+ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
+
+ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
+                      "timeframes", "erase", "dataformat_ohlcv", "dataformat_trades"]
+
+ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
+                       "db_url", "trade_source", "export", "exportfilename",
+                       "timerange", "ticker_interval", "no_trades"]
+
+ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
+                    "trade_source", "ticker_interval"]
+
+ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
+                      "hyperopt_list_min_trades", "hyperopt_list_max_trades",
+                      "hyperopt_list_min_avg_time", "hyperopt_list_max_avg_time",
+                      "hyperopt_list_min_avg_profit", "hyperopt_list_max_avg_profit",
+                      "hyperopt_list_min_total_profit", "hyperopt_list_max_total_profit",
+                      "print_colorized", "print_json", "hyperopt_list_no_details",
+                      "export_csv"]
+
+ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
+                      "print_json", "hyperopt_show_no_header"]
+
+NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
+                    "list-markets", "list-pairs", "list-strategies",
+                    "list-hyperopts", "hyperopt-list", "hyperopt-show",
+                    "plot-dataframe", "plot-profit"]
+
+NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
+
+
+class Arguments:
+    """
+    Arguments Class. Manage the arguments received by the cli
+    """
+
+    def __init__(self, args: Optional[List[str]]) -> None:
+        self.args = args
+        self._parsed_arg: Optional[argparse.Namespace] = None
+
+    def get_parsed_arg(self) -> Dict[str, Any]:
+        """
+        Return the list of arguments
+        :return: List[str] List of arguments
+        """
+        if self._parsed_arg is None:
+            self._build_subcommands()
+            self._parsed_arg = self._parse_args()
+
+        return vars(self._parsed_arg)
+
+    def _parse_args(self) -> argparse.Namespace:
+        """
+        Parses given arguments and returns an argparse Namespace instance.
+        """
+        parsed_arg = self.parser.parse_args(self.args)
+
+        # Workaround issue in argparse with action='append' and default value
+        # (see https://bugs.python.org/issue16399)
+        # Allow no-config for certain commands (like downloading / plotting)
+        if ('config' in parsed_arg and parsed_arg.config is None):
+            conf_required = ('command' in parsed_arg and parsed_arg.command in NO_CONF_REQURIED)
+
+            if 'user_data_dir' in parsed_arg and parsed_arg.user_data_dir is not None:
+                user_dir = parsed_arg.user_data_dir
+            else:
+                # Default case
+                user_dir = 'user_data'
+                # Try loading from "user_data/config.json"
+            cfgfile = Path(user_dir) / DEFAULT_CONFIG
+            if cfgfile.is_file():
+                parsed_arg.config = [str(cfgfile)]
+            else:
+                # Else use "config.json".
+                cfgfile = Path.cwd() / DEFAULT_CONFIG
+                if cfgfile.is_file() or not conf_required:
+                    parsed_arg.config = [DEFAULT_CONFIG]
+
+        return parsed_arg
+
+    def _build_args(self, optionlist, parser):
+
+        for val in optionlist:
+            opt = AVAILABLE_CLI_OPTIONS[val]
+            parser.add_argument(*opt.cli, dest=val, **opt.kwargs)
+
+    def _build_subcommands(self) -> None:
+        """
+        Builds and attaches all subcommands.
+        :return: None
+        """
+        # Build shared arguments (as group Common Options)
+        _common_parser = argparse.ArgumentParser(add_help=False)
+        group = _common_parser.add_argument_group("Common arguments")
+        self._build_args(optionlist=ARGS_COMMON, parser=group)
+
+        _strategy_parser = argparse.ArgumentParser(add_help=False)
+        strategy_group = _strategy_parser.add_argument_group("Strategy arguments")
+        self._build_args(optionlist=ARGS_STRATEGY, parser=strategy_group)
+
+        # Build main command
+        self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
+        self._build_args(optionlist=['version'], parser=self.parser)
+
+        from freqtrade.commands import (start_create_userdir, start_convert_data,
+                                        start_download_data,
+                                        start_hyperopt_list, start_hyperopt_show,
+                                        start_list_exchanges, start_list_hyperopts,
+                                        start_list_markets, start_list_strategies,
+                                        start_list_timeframes, start_new_config,
+                                        start_new_hyperopt, start_new_strategy,
+                                        start_plot_dataframe, start_plot_profit,
+                                        start_backtesting, start_hyperopt, start_edge,
+                                        start_test_pairlist, start_trading)
+
+        subparsers = self.parser.add_subparsers(dest='command',
+                                                # Use custom message when no subhandler is added
+                                                # shown from `main.py`
+                                                # required=True
+                                                )
+
+        # Add trade subcommand
+        trade_cmd = subparsers.add_parser('trade', help='Trade module.',
+                                          parents=[_common_parser, _strategy_parser])
+        trade_cmd.set_defaults(func=start_trading)
+        self._build_args(optionlist=ARGS_TRADE, parser=trade_cmd)
+
+        # Add backtesting subcommand
+        backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.',
+                                                parents=[_common_parser, _strategy_parser])
+        backtesting_cmd.set_defaults(func=start_backtesting)
+        self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
+
+        # Add edge subcommand
+        edge_cmd = subparsers.add_parser('edge', help='Edge module.',
+                                         parents=[_common_parser, _strategy_parser])
+        edge_cmd.set_defaults(func=start_edge)
+        self._build_args(optionlist=ARGS_EDGE, parser=edge_cmd)
+
+        # Add hyperopt subcommand
+        hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.',
+                                             parents=[_common_parser, _strategy_parser],
+                                             )
+        hyperopt_cmd.set_defaults(func=start_hyperopt)
+        self._build_args(optionlist=ARGS_HYPEROPT, parser=hyperopt_cmd)
+
+        # add create-userdir subcommand
+        create_userdir_cmd = subparsers.add_parser('create-userdir',
+                                                   help="Create user-data directory.",
+                                                   )
+        create_userdir_cmd.set_defaults(func=start_create_userdir)
+        self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
+
+        # add new-config subcommand
+        build_config_cmd = subparsers.add_parser('new-config',
+                                                 help="Create new config")
+        build_config_cmd.set_defaults(func=start_new_config)
+        self._build_args(optionlist=ARGS_BUILD_CONFIG, parser=build_config_cmd)
+
+        # add new-strategy subcommand
+        build_strategy_cmd = subparsers.add_parser('new-strategy',
+                                                   help="Create new strategy")
+        build_strategy_cmd.set_defaults(func=start_new_strategy)
+        self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
+
+        # add new-hyperopt subcommand
+        build_hyperopt_cmd = subparsers.add_parser('new-hyperopt',
+                                                   help="Create new hyperopt")
+        build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
+        self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
+
+        # Add list-strategies subcommand
+        list_strategies_cmd = subparsers.add_parser(
+            'list-strategies',
+            help='Print available strategies.',
+            parents=[_common_parser],
+        )
+        list_strategies_cmd.set_defaults(func=start_list_strategies)
+        self._build_args(optionlist=ARGS_LIST_STRATEGIES, parser=list_strategies_cmd)
+
+        # Add list-hyperopts subcommand
+        list_hyperopts_cmd = subparsers.add_parser(
+            'list-hyperopts',
+            help='Print available hyperopt classes.',
+            parents=[_common_parser],
+        )
+        list_hyperopts_cmd.set_defaults(func=start_list_hyperopts)
+        self._build_args(optionlist=ARGS_LIST_HYPEROPTS, parser=list_hyperopts_cmd)
+
+        # Add list-exchanges subcommand
+        list_exchanges_cmd = subparsers.add_parser(
+            'list-exchanges',
+            help='Print available exchanges.',
+            parents=[_common_parser],
+        )
+        list_exchanges_cmd.set_defaults(func=start_list_exchanges)
+        self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
+
+        # Add list-timeframes subcommand
+        list_timeframes_cmd = subparsers.add_parser(
+            'list-timeframes',
+            help='Print available ticker intervals (timeframes) for the exchange.',
+            parents=[_common_parser],
+        )
+        list_timeframes_cmd.set_defaults(func=start_list_timeframes)
+        self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
+
+        # Add list-markets subcommand
+        list_markets_cmd = subparsers.add_parser(
+            'list-markets',
+            help='Print markets on exchange.',
+            parents=[_common_parser],
+        )
+        list_markets_cmd.set_defaults(func=partial(start_list_markets, pairs_only=False))
+        self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_markets_cmd)
+
+        # Add list-pairs subcommand
+        list_pairs_cmd = subparsers.add_parser(
+            'list-pairs',
+            help='Print pairs on exchange.',
+            parents=[_common_parser],
+        )
+        list_pairs_cmd.set_defaults(func=partial(start_list_markets, pairs_only=True))
+        self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_pairs_cmd)
+
+        # Add test-pairlist subcommand
+        test_pairlist_cmd = subparsers.add_parser(
+            'test-pairlist',
+            help='Test your pairlist configuration.',
+        )
+        test_pairlist_cmd.set_defaults(func=start_test_pairlist)
+        self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_cmd)
+
+        # Add download-data subcommand
+        download_data_cmd = subparsers.add_parser(
+            'download-data',
+            help='Download backtesting data.',
+            parents=[_common_parser],
+        )
+        download_data_cmd.set_defaults(func=start_download_data)
+        self._build_args(optionlist=ARGS_DOWNLOAD_DATA, parser=download_data_cmd)
+
+        # Add convert-data subcommand
+        convert_data_cmd = subparsers.add_parser(
+            'convert-data',
+            help='Convert candle (OHLCV) data from one format to another.',
+            parents=[_common_parser],
+        )
+        convert_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=True))
+        self._build_args(optionlist=ARGS_CONVERT_DATA_OHLCV, parser=convert_data_cmd)
+
+        # Add convert-trade-data subcommand
+        convert_trade_data_cmd = subparsers.add_parser(
+            'convert-trade-data',
+            help='Convert trade data from one format to another.',
+            parents=[_common_parser],
+        )
+        convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))
+        self._build_args(optionlist=ARGS_CONVERT_DATA, parser=convert_trade_data_cmd)
+
+        # Add Plotting subcommand
+        plot_dataframe_cmd = subparsers.add_parser(
+            'plot-dataframe',
+            help='Plot candles with indicators.',
+            parents=[_common_parser, _strategy_parser],
+        )
+        plot_dataframe_cmd.set_defaults(func=start_plot_dataframe)
+        self._build_args(optionlist=ARGS_PLOT_DATAFRAME, parser=plot_dataframe_cmd)
+
+        # Plot profit
+        plot_profit_cmd = subparsers.add_parser(
+            'plot-profit',
+            help='Generate plot showing profits.',
+            parents=[_common_parser],
+        )
+        plot_profit_cmd.set_defaults(func=start_plot_profit)
+        self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)
+
+        # Add hyperopt-list subcommand
+        hyperopt_list_cmd = subparsers.add_parser(
+            'hyperopt-list',
+            help='List Hyperopt results',
+            parents=[_common_parser],
+        )
+        hyperopt_list_cmd.set_defaults(func=start_hyperopt_list)
+        self._build_args(optionlist=ARGS_HYPEROPT_LIST, parser=hyperopt_list_cmd)
+
+        # Add hyperopt-show subcommand
+        hyperopt_show_cmd = subparsers.add_parser(
+            'hyperopt-show',
+            help='Show details of Hyperopt results',
+            parents=[_common_parser],
+        )
+        hyperopt_show_cmd.set_defaults(func=start_hyperopt_show)
+        self._build_args(optionlist=ARGS_HYPEROPT_SHOW, parser=hyperopt_show_cmd)

freqtrade/commands/build_config_commands.py

@@ -0,0 +1,193 @@
+import logging
+from pathlib import Path
+from typing import Any, Dict
+
+from questionary import Separator, prompt
+
+from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
+from freqtrade.exchange import available_exchanges, MAP_EXCHANGE_CHILDCLASS
+from freqtrade.misc import render_template
+from freqtrade.exceptions import OperationalException
+logger = logging.getLogger(__name__)
+
+
+def validate_is_int(val):
+    try:
+        _ = int(val)
+        return True
+    except Exception:
+        return False
+
+
+def validate_is_float(val):
+    try:
+        _ = float(val)
+        return True
+    except Exception:
+        return False
+
+
+def ask_user_overwrite(config_path: Path) -> bool:
+    questions = [
+        {
+            "type": "confirm",
+            "name": "overwrite",
+            "message": f"File {config_path} already exists. Overwrite?",
+            "default": False,
+        },
+    ]
+    answers = prompt(questions)
+    return answers['overwrite']
+
+
+def ask_user_config() -> Dict[str, Any]:
+    """
+    Ask user a few questions to build the configuration.
+    Interactive questions built using https://github.com/tmbo/questionary
+    :returns: Dict with keys to put into template
+    """
+    questions = [
+        {
+            "type": "confirm",
+            "name": "dry_run",
+            "message": "Do you want to enable Dry-run (simulated trades)?",
+            "default": True,
+        },
+        {
+            "type": "text",
+            "name": "stake_currency",
+            "message": "Please insert your stake currency:",
+            "default": 'BTC',
+        },
+        {
+            "type": "text",
+            "name": "stake_amount",
+            "message": "Please insert your stake amount:",
+            "default": "0.01",
+            "validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_float(val),
+        },
+        {
+            "type": "text",
+            "name": "max_open_trades",
+            "message": f"Please insert max_open_trades (Integer or '{UNLIMITED_STAKE_AMOUNT}'):",
+            "default": "3",
+            "validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_int(val)
+        },
+        {
+            "type": "text",
+            "name": "ticker_interval",
+            "message": "Please insert your timeframe (ticker interval):",
+            "default": "5m",
+        },
+        {
+            "type": "text",
+            "name": "fiat_display_currency",
+            "message": "Please insert your display Currency (for reporting):",
+            "default": 'USD',
+        },
+        {
+            "type": "select",
+            "name": "exchange_name",
+            "message": "Select exchange",
+            "choices": [
+                "binance",
+                "binanceje",
+                "binanceus",
+                "bittrex",
+                "kraken",
+                Separator(),
+                "other",
+            ],
+        },
+        {
+            "type": "autocomplete",
+            "name": "exchange_name",
+            "message": "Type your exchange name (Must be supported by ccxt)",
+            "choices": available_exchanges(),
+            "when": lambda x: x["exchange_name"] == 'other'
+        },
+        {
+            "type": "password",
+            "name": "exchange_key",
+            "message": "Insert Exchange Key",
+            "when": lambda x: not x['dry_run']
+        },
+        {
+            "type": "password",
+            "name": "exchange_secret",
+            "message": "Insert Exchange Secret",
+            "when": lambda x: not x['dry_run']
+        },
+        {
+            "type": "confirm",
+            "name": "telegram",
+            "message": "Do you want to enable Telegram?",
+            "default": False,
+        },
+        {
+            "type": "password",
+            "name": "telegram_token",
+            "message": "Insert Telegram token",
+            "when": lambda x: x['telegram']
+        },
+        {
+            "type": "text",
+            "name": "telegram_chat_id",
+            "message": "Insert Telegram chat id",
+            "when": lambda x: x['telegram']
+        },
+    ]
+    answers = prompt(questions)
+
+    if not answers:
+        # Interrupted questionary sessions return an empty dict.
+        raise OperationalException("User interrupted interactive questions.")
+
+    return answers
+
+
+def deploy_new_config(config_path: Path, selections: Dict[str, Any]) -> None:
+    """
+    Applies selections to the template and writes the result to config_path
+    :param config_path: Path object for new config file. Should not exist yet
+    :param selecions: Dict containing selections taken by the user.
+    """
+    from jinja2.exceptions import TemplateNotFound
+    try:
+        exchange_template = MAP_EXCHANGE_CHILDCLASS.get(
+            selections['exchange_name'], selections['exchange_name'])
+
+        selections['exchange'] = render_template(
+            templatefile=f"subtemplates/exchange_{exchange_template}.j2",
+            arguments=selections
+            )
+    except TemplateNotFound:
+        selections['exchange'] = render_template(
+            templatefile=f"subtemplates/exchange_generic.j2",
+            arguments=selections
+        )
+
+    config_text = render_template(templatefile='base_config.json.j2',
+                                  arguments=selections)
+
+    logger.info(f"Writing config to `{config_path}`.")
+    config_path.write_text(config_text)
+
+
+def start_new_config(args: Dict[str, Any]) -> None:
+    """
+    Create a new strategy from a template
+    Asking the user questions to fill out the templateaccordingly.
+    """
+
+    config_path = Path(args['config'][0])
+    if config_path.exists():
+        overwrite = ask_user_overwrite(config_path)
+        if overwrite:
+            config_path.unlink()
+        else:
+            raise OperationalException(
+                f"Configuration file `{config_path}` already exists. "
+                "Please delete it or use a different configuration file name.")
+    selections = ask_user_config()
+    deploy_new_config(config_path, selections)

freqtrade/commands/cli_options.py

@@ -1,7 +1,7 @@
 """
 Definition of cli arguments used in arguments.py
 """
-import argparse
+from argparse import ArgumentTypeError
 
 from freqtrade import __version__, constants
 
@@ -12,12 +12,24 @@ def check_int_positive(value: str) -> int:
         if uint <= 0:
             raise ValueError
     except ValueError:
-        raise argparse.ArgumentTypeError(
+        raise ArgumentTypeError(
             f"{value} is invalid for this parameter, should be a positive integer value"
         )
     return uint
 
 
+def check_int_nonzero(value: str) -> int:
+    try:
+        uint = int(value)
+        if uint == 0:
+            raise ValueError
+    except ValueError:
+        raise ArgumentTypeError(
+            f"{value} is invalid for this parameter, should be a non-zero integer value"
+        )
+    return uint
+
+
 class Arg:
     # Optional CLI arguments
     def __init__(self, *args, **kwargs):
@@ -36,7 +48,8 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "logfile": Arg(
         '--logfile',
-        help='Log to the file specified.',
+        help="Log to the file specified. Special values are: 'syslog', 'journald'. "
+             "See the documentation for more details.",
         metavar='FILE',
     ),
     "version": Arg(
@@ -46,7 +59,8 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "config": Arg(
         '-c', '--config',
-        help=f'Specify configuration file (default: `{constants.DEFAULT_CONFIG}`). '
+        help=f'Specify configuration file (default: `userdir/{constants.DEFAULT_CONFIG}` '
+        f'or `config.json` whichever exists). '
         f'Multiple --config options may be used. '
         f'Can be set to `-` to read config from stdin.',
         action='append',
@@ -62,12 +76,16 @@ AVAILABLE_CLI_OPTIONS = {
         help='Path to userdata directory.',
         metavar='PATH',
     ),
+    "reset": Arg(
+        '--reset',
+        help='Reset sample files to their original state.',
+        action='store_true',
+    ),
     # Main options
     "strategy": Arg(
         '-s', '--strategy',
-        help='Specify strategy class name (default: `%(default)s`).',
+        help='Specify strategy class name which will be used by the bot.',
         metavar='NAME',
-        default='DefaultStrategy',
     ),
     "strategy_path": Arg(
         '--strategy-path',
@@ -86,6 +104,11 @@ AVAILABLE_CLI_OPTIONS = {
         help='Notify systemd service manager.',
         action='store_true',
     ),
+    "dry_run": Arg(
+        '--dry-run',
+        help='Enforce dry-run for trading (removes Exchange secrets and simulates trades).',
+        action='store_true',
+    ),
     # Optimize common
     "ticker_interval": Arg(
         '-i', '--ticker-interval',
@@ -96,14 +119,14 @@ AVAILABLE_CLI_OPTIONS = {
         help='Specify what timerange of data to use.',
     ),
     "max_open_trades": Arg(
-        '--max_open_trades',
+        '--max-open-trades',
-        help='Specify max_open_trades to use.',
+        help='Override the value of the `max_open_trades` configuration setting.',
         type=int,
         metavar='INT',
     ),
     "stake_amount": Arg(
-        '--stake_amount',
+        '--stake-amount',
-        help='Specify stake_amount.',
+        help='Override the value of the `stake_amount` configuration setting.',
         type=float,
     ),
     # Backtesting
@@ -136,7 +159,7 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "exportfilename": Arg(
         '--export-filename',
-        help='Save backtest results to the file with this filename (default: `%(default)s`). '
+        help='Save backtest results to the file with this filename. '
         'Requires `--export` to be set as well. '
         'Example: `--export-filename=user_data/backtest_results/backtest_today.json`',
         metavar='PATH',
@@ -156,14 +179,13 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     # Hyperopt
     "hyperopt": Arg(
-        '--customhyperopt',
+        '--hyperopt',
-        help='Specify hyperopt class name (default: `%(default)s`).',
+        help='Specify hyperopt class name which will be used by the bot.',
         metavar='NAME',
-        default=constants.DEFAULT_HYPEROPT,
     ),
     "hyperopt_path": Arg(
         '--hyperopt-path',
-        help='Specify additional lookup path for Hyperopts and Hyperopt Loss functions.',
+        help='Specify additional lookup path for Hyperopt and Hyperopt Loss functions.',
         metavar='PATH',
     ),
     "epochs": Arg(
@@ -174,12 +196,11 @@ AVAILABLE_CLI_OPTIONS = {
         default=constants.HYPEROPT_EPOCH,
     ),
     "spaces": Arg(
-        '-s', '--spaces',
+        '--spaces',
-        help='Specify which parameters to hyperopt. Space-separated list. '
+        help='Specify which parameters to hyperopt. Space-separated list.',
-        'Default: `%(default)s`.',
+        choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'default'],
-        choices=['all', 'buy', 'sell', 'roi', 'stoploss'],
         nargs='+',
-        default='all',
+        default='default',
     ),
     "print_all": Arg(
         '--print-all',
@@ -200,6 +221,13 @@ AVAILABLE_CLI_OPTIONS = {
         action='store_true',
         default=False,
     ),
+    "export_csv": Arg(
+        '--export-csv',
+        help='Export to CSV-File.'
+        ' This will disable table print.'
+        ' Example: --export-csv hyperopt.csv',
+        metavar='FILE',
+    ),
     "hyperopt_jobs": Arg(
         '-j', '--job-workers',
         help='The number of concurrently running jobs for hyperoptimization '
@@ -236,7 +264,8 @@ AVAILABLE_CLI_OPTIONS = {
         help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
         'Different functions can generate completely different results, '
         'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
-        'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss.'
+        'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily, '
+        'SortinoHyperOptLoss, SortinoHyperOptLossDaily.'
         '(default: `%(default)s`).',
         metavar='NAME',
         default=constants.DEFAULT_HYPEROPT_LOSS,
@@ -312,6 +341,30 @@ AVAILABLE_CLI_OPTIONS = {
              'desired timeframe as specified as --timeframes/-t.',
         action='store_true',
     ),
+    "format_from": Arg(
+        '--format-from',
+        help='Source format for data conversion.',
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        required=True,
+    ),
+    "format_to": Arg(
+        '--format-to',
+        help='Destination format for data conversion.',
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        required=True,
+    ),
+    "dataformat_ohlcv": Arg(
+        '--data-format-ohlcv',
+        help='Storage format for downloaded candle (OHLCV) data. (default: `%(default)s`).',
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        default='json'
+    ),
+    "dataformat_trades": Arg(
+        '--data-format-trades',
+        help='Storage format for downloaded trades data. (default: `%(default)s`).',
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        default='jsongz'
+    ),
     "exchange": Arg(
         '--exchange',
         help=f'Exchange name (default: `{constants.DEFAULT_EXCHANGE}`). '
@@ -331,19 +384,25 @@ AVAILABLE_CLI_OPTIONS = {
         help='Clean all existing data for the selected exchange/pairs/timeframes.',
         action='store_true',
     ),
+    # Templating options
+    "template": Arg(
+        '--template',
+        help='Use a template which is either `minimal` or '
+        '`full` (containing multiple sample indicators). Default: `%(default)s`.',
+        choices=['full', 'minimal'],
+        default='full',
+    ),
     # Plot dataframe
     "indicators1": Arg(
         '--indicators1',
         help='Set indicators from your strategy you want in the first row of the graph. '
-        'Space-separated list. Example: `ema3 ema5`. Default: `%(default)s`.',
+        "Space-separated list. Example: `ema3 ema5`. Default: `['sma', 'ema3', 'ema5']`.",
-        default=['sma', 'ema3', 'ema5'],
         nargs='+',
     ),
     "indicators2": Arg(
         '--indicators2',
         help='Set indicators from your strategy you want in the third row of the graph. '
-        'Space-separated list. Example: `fastd fastk`. Default: `%(default)s`.',
+        "Space-separated list. Example: `fastd fastk`. Default: `['macd', 'macdsignal']`.",
-        default=['macd', 'macdsignal'],
         nargs='+',
     ),
     "plot_limit": Arg(
@@ -354,6 +413,11 @@ AVAILABLE_CLI_OPTIONS = {
         metavar='INT',
         default=750,
     ),
+    "no_trades": Arg(
+        '--no-trades',
+        help='Skip using trades from backtesting file and DB.',
+        action='store_true',
+    ),
     "trade_source": Arg(
         '--trade-source',
         help='Specify the source for trades (Can be DB or file (backtest file)) '
@@ -361,4 +425,79 @@ AVAILABLE_CLI_OPTIONS = {
         choices=["DB", "file"],
         default="file",
     ),
+    # hyperopt-list, hyperopt-show
+    "hyperopt_list_profitable": Arg(
+        '--profitable',
+        help='Select only profitable epochs.',
+        action='store_true',
+    ),
+    "hyperopt_list_best": Arg(
+        '--best',
+        help='Select only best epochs.',
+        action='store_true',
+    ),
+    "hyperopt_list_min_trades": Arg(
+        '--min-trades',
+        help='Select epochs with more than INT trades.',
+        type=check_int_positive,
+        metavar='INT',
+    ),
+    "hyperopt_list_max_trades": Arg(
+        '--max-trades',
+        help='Select epochs with less than INT trades.',
+        type=check_int_positive,
+        metavar='INT',
+    ),
+    "hyperopt_list_min_avg_time": Arg(
+        '--min-avg-time',
+        help='Select epochs on above average time.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_max_avg_time": Arg(
+        '--max-avg-time',
+        help='Select epochs on under average time.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_min_avg_profit": Arg(
+        '--min-avg-profit',
+        help='Select epochs on above average profit.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_max_avg_profit": Arg(
+        '--max-avg-profit',
+        help='Select epochs on below average profit.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_min_total_profit": Arg(
+        '--min-total-profit',
+        help='Select epochs on above total profit.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_max_total_profit": Arg(
+        '--max-total-profit',
+        help='Select epochs on below total profit.',
+        type=float,
+        metavar='FLOAT',
+    ),
+    "hyperopt_list_no_details": Arg(
+        '--no-details',
+        help='Do not print best epoch details.',
+        action='store_true',
+    ),
+    "hyperopt_show_index": Arg(
+        '-n', '--index',
+        help='Specify the index of the epoch to print details for.',
+        type=check_int_nonzero,
+        metavar='INT',
+    ),
+    "hyperopt_show_no_header": Arg(
+        '--no-header',
+        help='Do not print epoch details header.',
+        action='store_true',
+    ),
 }

freqtrade/commands/data_commands.py

@@ -0,0 +1,90 @@
+import logging
+import sys
+from typing import Any, Dict, List
+
+import arrow
+
+from freqtrade.configuration import TimeRange, setup_utils_configuration
+from freqtrade.data.converter import (convert_ohlcv_format,
+                                      convert_trades_format)
+from freqtrade.data.history import (convert_trades_to_ohlcv,
+                                    refresh_backtest_ohlcv_data,
+                                    refresh_backtest_trades_data)
+from freqtrade.exceptions import OperationalException
+from freqtrade.resolvers import ExchangeResolver
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def start_download_data(args: Dict[str, Any]) -> None:
+    """
+    Download data (former download_backtest_data.py script)
+    """
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
+
+    timerange = TimeRange()
+    if 'days' in config:
+        time_since = arrow.utcnow().shift(days=-config['days']).strftime("%Y%m%d")
+        timerange = TimeRange.parse_timerange(f'{time_since}-')
+
+    if 'pairs' not in config:
+        raise OperationalException(
+            "Downloading data requires a list of pairs. "
+            "Please check the documentation on how to configure this.")
+
+    logger.info(f'About to download pairs: {config["pairs"]}, '
+                f'intervals: {config["timeframes"]} to {config["datadir"]}')
+
+    pairs_not_available: List[str] = []
+
+    # Init exchange
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+    # Manual validations of relevant settings
+    exchange.validate_pairs(config['pairs'])
+    for timeframe in config['timeframes']:
+        exchange.validate_timeframes(timeframe)
+
+    try:
+
+        if config.get('download_trades'):
+            pairs_not_available = refresh_backtest_trades_data(
+                exchange, pairs=config["pairs"], datadir=config['datadir'],
+                timerange=timerange, erase=bool(config.get("erase")),
+                data_format=config['dataformat_trades'])
+
+            # Convert downloaded trade data to different timeframes
+            convert_trades_to_ohlcv(
+                pairs=config["pairs"], timeframes=config["timeframes"],
+                datadir=config['datadir'], timerange=timerange, erase=bool(config.get("erase")),
+                data_format_ohlcv=config['dataformat_ohlcv'],
+                data_format_trades=config['dataformat_trades'],
+                )
+        else:
+            pairs_not_available = refresh_backtest_ohlcv_data(
+                exchange, pairs=config["pairs"], timeframes=config["timeframes"],
+                datadir=config['datadir'], timerange=timerange, erase=bool(config.get("erase")),
+                data_format=config['dataformat_ohlcv'])
+
+    except KeyboardInterrupt:
+        sys.exit("SIGINT received, aborting ...")
+
+    finally:
+        if pairs_not_available:
+            logger.info(f"Pairs [{','.join(pairs_not_available)}] not available "
+                        f"on exchange {exchange.name}.")
+
+
+def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
+    """
+    Convert data from one format to another
+    """
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+    if ohlcv:
+        convert_ohlcv_format(config,
+                             convert_from=args['format_from'], convert_to=args['format_to'],
+                             erase=args['erase'])
+    else:
+        convert_trades_format(config,
+                              convert_from=args['format_from'], convert_to=args['format_to'],
+                              erase=args['erase'])

freqtrade/commands/deploy_commands.py

@@ -0,0 +1,112 @@
+import logging
+import sys
+from pathlib import Path
+from typing import Any, Dict
+
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.configuration.directory_operations import (copy_sample_files,
+                                                          create_userdata_dir)
+from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
+from freqtrade.exceptions import OperationalException
+from freqtrade.misc import render_template
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def start_create_userdir(args: Dict[str, Any]) -> None:
+    """
+    Create "user_data" directory to contain user data strategies, hyperopt, ...)
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    if "user_data_dir" in args and args["user_data_dir"]:
+        userdir = create_userdata_dir(args["user_data_dir"], create_dir=True)
+        copy_sample_files(userdir, overwrite=args["reset"])
+    else:
+        logger.warning("`create-userdir` requires --userdir to be set.")
+        sys.exit(1)
+
+
+def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: str) -> None:
+    """
+    Deploy new strategy from template to strategy_path
+    """
+    indicators = render_template(templatefile=f"subtemplates/indicators_{subtemplate}.j2",)
+    buy_trend = render_template(templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",)
+    sell_trend = render_template(templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",)
+    plot_config = render_template(templatefile=f"subtemplates/plot_config_{subtemplate}.j2",)
+
+    strategy_text = render_template(templatefile='base_strategy.py.j2',
+                                    arguments={"strategy": strategy_name,
+                                               "indicators": indicators,
+                                               "buy_trend": buy_trend,
+                                               "sell_trend": sell_trend,
+                                               "plot_config": plot_config,
+                                               })
+
+    logger.info(f"Writing strategy to `{strategy_path}`.")
+    strategy_path.write_text(strategy_text)
+
+
+def start_new_strategy(args: Dict[str, Any]) -> None:
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    if "strategy" in args and args["strategy"]:
+        if args["strategy"] == "DefaultStrategy":
+            raise OperationalException("DefaultStrategy is not allowed as name.")
+
+        new_path = config['user_data_dir'] / USERPATH_STRATEGIES / (args["strategy"] + ".py")
+
+        if new_path.exists():
+            raise OperationalException(f"`{new_path}` already exists. "
+                                       "Please choose another Strategy Name.")
+
+        deploy_new_strategy(args['strategy'], new_path, args['template'])
+
+    else:
+        raise OperationalException("`new-strategy` requires --strategy to be set.")
+
+
+def deploy_new_hyperopt(hyperopt_name: str, hyperopt_path: Path, subtemplate: str) -> None:
+    """
+    Deploys a new hyperopt template to hyperopt_path
+    """
+    buy_guards = render_template(
+        templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",)
+    sell_guards = render_template(
+        templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",)
+    buy_space = render_template(
+        templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",)
+    sell_space = render_template(
+        templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",)
+
+    strategy_text = render_template(templatefile='base_hyperopt.py.j2',
+                                    arguments={"hyperopt": hyperopt_name,
+                                               "buy_guards": buy_guards,
+                                               "sell_guards": sell_guards,
+                                               "buy_space": buy_space,
+                                               "sell_space": sell_space,
+                                               })
+
+    logger.info(f"Writing hyperopt to `{hyperopt_path}`.")
+    hyperopt_path.write_text(strategy_text)
+
+
+def start_new_hyperopt(args: Dict[str, Any]) -> None:
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    if "hyperopt" in args and args["hyperopt"]:
+        if args["hyperopt"] == "DefaultHyperopt":
+            raise OperationalException("DefaultHyperopt is not allowed as name.")
+
+        new_path = config['user_data_dir'] / USERPATH_HYPEROPTS / (args["hyperopt"] + ".py")
+
+        if new_path.exists():
+            raise OperationalException(f"`{new_path}` already exists. "
+                                       "Please choose another Strategy Name.")
+        deploy_new_hyperopt(args['hyperopt'], new_path, args['template'])
+    else:
+        raise OperationalException("`new-hyperopt` requires --hyperopt to be set.")

freqtrade/commands/hyperopt_commands.py

@@ -0,0 +1,184 @@
+import logging
+from operator import itemgetter
+from typing import Any, Dict, List
+
+from colorama import init as colorama_init
+
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.exceptions import OperationalException
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def start_hyperopt_list(args: Dict[str, Any]) -> None:
+    """
+    List hyperopt epochs previously evaluated
+    """
+    from freqtrade.optimize.hyperopt import Hyperopt
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    print_colorized = config.get('print_colorized', False)
+    print_json = config.get('print_json', False)
+    export_csv = config.get('export_csv', None)
+    no_details = config.get('hyperopt_list_no_details', False)
+    no_header = False
+
+    filteroptions = {
+        'only_best': config.get('hyperopt_list_best', False),
+        'only_profitable': config.get('hyperopt_list_profitable', False),
+        'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
+        'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
+        'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
+        'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
+        'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
+        'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
+        'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
+        'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
+    }
+
+    trials_file = (config['user_data_dir'] /
+                   'hyperopt_results' / 'hyperopt_results.pickle')
+
+    # Previous evaluations
+    trials = Hyperopt.load_previous_results(trials_file)
+    total_epochs = len(trials)
+
+    trials = _hyperopt_filter_trials(trials, filteroptions)
+
+    if print_colorized:
+        colorama_init(autoreset=True)
+
+    if not export_csv:
+        try:
+            Hyperopt.print_result_table(config, trials, total_epochs,
+                                        not filteroptions['only_best'], print_colorized, 0)
+        except KeyboardInterrupt:
+            print('User interrupted..')
+
+    if trials and not no_details:
+        sorted_trials = sorted(trials, key=itemgetter('loss'))
+        results = sorted_trials[0]
+        Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
+
+    if trials and export_csv:
+        Hyperopt.export_csv_file(
+            config, trials, total_epochs, not filteroptions['only_best'], export_csv
+        )
+
+
+def start_hyperopt_show(args: Dict[str, Any]) -> None:
+    """
+    Show details of a hyperopt epoch previously evaluated
+    """
+    from freqtrade.optimize.hyperopt import Hyperopt
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    print_json = config.get('print_json', False)
+    no_header = config.get('hyperopt_show_no_header', False)
+    trials_file = (config['user_data_dir'] /
+                   'hyperopt_results' / 'hyperopt_results.pickle')
+    n = config.get('hyperopt_show_index', -1)
+
+    filteroptions = {
+        'only_best': config.get('hyperopt_list_best', False),
+        'only_profitable': config.get('hyperopt_list_profitable', False),
+        'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
+        'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
+        'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
+        'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
+        'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
+        'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
+        'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
+        'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
+    }
+
+    # Previous evaluations
+    trials = Hyperopt.load_previous_results(trials_file)
+    total_epochs = len(trials)
+
+    trials = _hyperopt_filter_trials(trials, filteroptions)
+    trials_epochs = len(trials)
+
+    if n > trials_epochs:
+        raise OperationalException(
+            f"The index of the epoch to show should be less than {trials_epochs + 1}.")
+    if n < -trials_epochs:
+        raise OperationalException(
+            f"The index of the epoch to show should be greater than {-trials_epochs - 1}.")
+
+    # Translate epoch index from human-readable format to pythonic
+    if n > 0:
+        n -= 1
+
+    if trials:
+        val = trials[n]
+        Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,
+                                     header_str="Epoch details")
+
+
+def _hyperopt_filter_trials(trials: List, filteroptions: dict) -> List:
+    """
+    Filter our items from the list of hyperopt results
+    """
+    if filteroptions['only_best']:
+        trials = [x for x in trials if x['is_best']]
+    if filteroptions['only_profitable']:
+        trials = [x for x in trials if x['results_metrics']['profit'] > 0]
+    if filteroptions['filter_min_trades'] > 0:
+        trials = [
+            x for x in trials
+            if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
+        ]
+    if filteroptions['filter_max_trades'] > 0:
+        trials = [
+            x for x in trials
+            if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
+        ]
+    if filteroptions['filter_min_avg_time'] is not None:
+        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        trials = [
+            x for x in trials
+            if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
+        ]
+    if filteroptions['filter_max_avg_time'] is not None:
+        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        trials = [
+            x for x in trials
+            if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
+        ]
+    if filteroptions['filter_min_avg_profit'] is not None:
+        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        trials = [
+            x for x in trials
+            if x['results_metrics']['avg_profit']
+            > filteroptions['filter_min_avg_profit']
+        ]
+    if filteroptions['filter_max_avg_profit'] is not None:
+        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        trials = [
+            x for x in trials
+            if x['results_metrics']['avg_profit']
+            < filteroptions['filter_max_avg_profit']
+        ]
+    if filteroptions['filter_min_total_profit'] is not None:
+        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        trials = [
+            x for x in trials
+            if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
+        ]
+    if filteroptions['filter_max_total_profit'] is not None:
+        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        trials = [
+            x for x in trials
+            if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
+        ]
+
+    logger.info(f"{len(trials)} " +
+                ("best " if filteroptions['only_best'] else "") +
+                ("profitable " if filteroptions['only_profitable'] else "") +
+                "epochs found.")
+
+    return trials

freqtrade/commands/list_commands.py

@@ -1,46 +1,27 @@
+import csv
 import logging
 import sys
 from collections import OrderedDict
 from pathlib import Path
 from typing import Any, Dict, List
 
-import arrow
+from colorama import init as colorama_init
-import csv
+from colorama import Fore, Style
 import rapidjson
 from tabulate import tabulate
 
-from freqtrade import OperationalException
+from freqtrade.configuration import setup_utils_configuration
-from freqtrade.configuration import Configuration, TimeRange
+from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
-from freqtrade.configuration.directory_operations import create_userdata_dir
+from freqtrade.exceptions import OperationalException
-from freqtrade.data.history import (convert_trades_to_ohlcv,
+from freqtrade.exchange import (available_exchanges, ccxt_exchanges,
-                                    refresh_backtest_ohlcv_data,
+                                market_is_active, symbol_is_pair)
-                                    refresh_backtest_trades_data)
-from freqtrade.exchange import (available_exchanges, ccxt_exchanges, market_is_active,
-                                symbol_is_pair)
 from freqtrade.misc import plural
-from freqtrade.resolvers import ExchangeResolver
+from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.state import RunMode
 
 logger = logging.getLogger(__name__)
 
 
-def setup_utils_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
-    """
-    Prepare the configuration for utils subcommands
-    :param args: Cli args from Arguments()
-    :return: Configuration
-    """
-    configuration = Configuration(args, method)
-    config = configuration.get_config()
-
-    config['exchange']['dry_run'] = True
-    # Ensure we do not use Exchange credentials
-    config['exchange']['key'] = ''
-    config['exchange']['secret'] = ''
-
-    return config
-
-
 def start_list_exchanges(args: Dict[str, Any]) -> None:
     """
     Print available exchanges
@@ -57,78 +38,75 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
             print(f"Exchanges available for Freqtrade: {', '.join(exchanges)}")
 
 
-def start_create_userdir(args: Dict[str, Any]) -> None:
+def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
-    """
+    if print_colorized:
-    Create "user_data" directory to contain user data strategies, hyperopts, ...)
+        colorama_init(autoreset=True)
-    :param args: Cli args from Arguments()
+        red = Fore.RED
-    :return: None
+        yellow = Fore.YELLOW
-    """
+        reset = Style.RESET_ALL
-    if "user_data_dir" in args and args["user_data_dir"]:
-        create_userdata_dir(args["user_data_dir"], create_dir=True)
     else:
-        logger.warning("`create-userdir` requires --userdir to be set.")
+        red = ''
-        sys.exit(1)
+        yellow = ''
+        reset = ''
+
+    names = [s['name'] for s in objs]
+    objss_to_print = [{
+        'name': s['name'] if s['name'] else "--",
+        'location': s['location'].name,
+        'status': (red + "LOAD FAILED" + reset if s['class'] is None
+                   else "OK" if names.count(s['name']) == 1
+                   else yellow + "DUPLICATE NAME" + reset)
+    } for s in objs]
+
+    print(tabulate(objss_to_print, headers='keys', tablefmt='psql', stralign='right'))
 
 
-def start_download_data(args: Dict[str, Any]) -> None:
+def start_list_strategies(args: Dict[str, Any]) -> None:
     """
-    Download data (former download_backtest_data.py script)
+    Print files with Strategy custom classes available in the directory
     """
-    config = setup_utils_configuration(args, RunMode.OTHER)
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
-    timerange = TimeRange()
+    directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
-    if 'days' in config:
+    strategy_objs = StrategyResolver.search_all_objects(directory, not args['print_one_column'])
-        time_since = arrow.utcnow().shift(days=-config['days']).strftime("%Y%m%d")
+    # Sort alphabetically
-        timerange = TimeRange.parse_timerange(f'{time_since}-')
+    strategy_objs = sorted(strategy_objs, key=lambda x: x['name'])
 
-    if 'pairs' not in config:
+    if args['print_one_column']:
-        raise OperationalException(
+        print('\n'.join([s['name'] for s in strategy_objs]))
-            "Downloading data requires a list of pairs. "
-            "Please check the documentation on how to configure this.")
-
-    dl_path = Path(config['datadir'])
-    logger.info(f'About to download pairs: {config["pairs"]}, '
-                f'intervals: {config["timeframes"]} to {dl_path}')
-
-    pairs_not_available: List[str] = []
-
-    # Init exchange
-    exchange = ExchangeResolver(config['exchange']['name'], config).exchange
-    try:
-
-        if config.get('download_trades'):
-            pairs_not_available = refresh_backtest_trades_data(
-                exchange, pairs=config["pairs"], datadir=Path(config['datadir']),
-                timerange=timerange, erase=config.get("erase"))
-
-            # Convert downloaded trade data to different timeframes
-            convert_trades_to_ohlcv(
-                pairs=config["pairs"], timeframes=config["timeframes"],
-                datadir=Path(config['datadir']), timerange=timerange, erase=config.get("erase"))
     else:
-            pairs_not_available = refresh_backtest_ohlcv_data(
+        _print_objs_tabular(strategy_objs, config.get('print_colorized', False))
-                exchange, pairs=config["pairs"], timeframes=config["timeframes"],
-                dl_path=Path(config['datadir']), timerange=timerange, erase=config.get("erase"))
 
-    except KeyboardInterrupt:
-        sys.exit("SIGINT received, aborting ...")
 
-    finally:
+def start_list_hyperopts(args: Dict[str, Any]) -> None:
-        if pairs_not_available:
+    """
-            logger.info(f"Pairs [{','.join(pairs_not_available)}] not available "
+    Print files with HyperOpt custom classes available in the directory
-                        f"on exchange {exchange.name}.")
+    """
+    from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    directory = Path(config.get('hyperopt_path', config['user_data_dir'] / USERPATH_HYPEROPTS))
+    hyperopt_objs = HyperOptResolver.search_all_objects(directory, not args['print_one_column'])
+    # Sort alphabetically
+    hyperopt_objs = sorted(hyperopt_objs, key=lambda x: x['name'])
+
+    if args['print_one_column']:
+        print('\n'.join([s['name'] for s in hyperopt_objs]))
+    else:
+        _print_objs_tabular(hyperopt_objs, config.get('print_colorized', False))
 
 
 def start_list_timeframes(args: Dict[str, Any]) -> None:
     """
     Print ticker intervals (timeframes) available on Exchange
     """
-    config = setup_utils_configuration(args, RunMode.OTHER)
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
     # Do not use ticker_interval set in the config
     config['ticker_interval'] = None
 
     # Init exchange
-    exchange = ExchangeResolver(config['exchange']['name'], config, validate=False).exchange
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
 
     if args['print_one_column']:
         print('\n'.join(exchange.timeframes))
@@ -144,10 +122,10 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
     :param pairs_only: if True print only pairs, otherwise print all instruments (markets)
     :return: None
     """
-    config = setup_utils_configuration(args, RunMode.OTHER)
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
 
     # Init exchange
-    exchange = ExchangeResolver(config['exchange']['name'], config, validate=False).exchange
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
 
     # By default only active pairs/markets are to be shown
     active_only = not args.get('list_pairs_all', False)
@@ -214,7 +192,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
             else:
                 # print data as a table, with the human-readable summary
                 print(f"{summary_str}:")
-                print(tabulate(tabular_data, headers='keys', tablefmt='pipe'))
+                print(tabulate(tabular_data, headers='keys', tablefmt='psql', stralign='right'))
         elif not (args.get('print_one_column', False) or
                   args.get('list_pairs_print_json', False) or
                   args.get('print_csv', False)):

freqtrade/commands/optimize_commands.py

@@ -0,0 +1,107 @@
+import logging
+from typing import Any, Dict
+
+from freqtrade import constants
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.exceptions import DependencyException, OperationalException
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
+    """
+    Prepare the configuration for the Hyperopt module
+    :param args: Cli args from Arguments()
+    :return: Configuration
+    """
+    config = setup_utils_configuration(args, method)
+
+    no_unlimited_runmodes = {
+        RunMode.BACKTEST: 'backtesting',
+        RunMode.HYPEROPT: 'hyperoptimization',
+    }
+    if (method in no_unlimited_runmodes.keys() and
+            config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT):
+        raise DependencyException(
+            f'The value of `stake_amount` cannot be set as "{constants.UNLIMITED_STAKE_AMOUNT}" '
+            f'for {no_unlimited_runmodes[method]}')
+
+    return config
+
+
+def start_backtesting(args: Dict[str, Any]) -> None:
+    """
+    Start Backtesting script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    # Import here to avoid loading backtesting module when it's not used
+    from freqtrade.optimize.backtesting import Backtesting
+
+    # Initialize configuration
+    config = setup_optimize_configuration(args, RunMode.BACKTEST)
+
+    logger.info('Starting freqtrade in Backtesting mode')
+
+    # Initialize backtesting object
+    backtesting = Backtesting(config)
+    backtesting.start()
+
+
+def start_hyperopt(args: Dict[str, Any]) -> None:
+    """
+    Start hyperopt script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    # Import here to avoid loading hyperopt module when it's not used
+    try:
+        from filelock import FileLock, Timeout
+        from freqtrade.optimize.hyperopt import Hyperopt
+    except ImportError as e:
+        raise OperationalException(
+            f"{e}. Please ensure that the hyperopt dependencies are installed.") from e
+    # Initialize configuration
+    config = setup_optimize_configuration(args, RunMode.HYPEROPT)
+
+    logger.info('Starting freqtrade in Hyperopt mode')
+
+    lock = FileLock(Hyperopt.get_lock_filename(config))
+
+    try:
+        with lock.acquire(timeout=1):
+
+            # Remove noisy log messages
+            logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
+            logging.getLogger('filelock').setLevel(logging.WARNING)
+
+            # Initialize backtesting object
+            hyperopt = Hyperopt(config)
+            hyperopt.start()
+
+    except Timeout:
+        logger.info("Another running instance of freqtrade Hyperopt detected.")
+        logger.info("Simultaneous execution of multiple Hyperopt commands is not supported. "
+                    "Hyperopt module is resource hungry. Please run your Hyperopt sequentially "
+                    "or on separate machines.")
+        logger.info("Quitting now.")
+        # TODO: return False here in order to help freqtrade to exit
+        # with non-zero exit code...
+        # Same in Edge and Backtesting start() functions.
+
+
+def start_edge(args: Dict[str, Any]) -> None:
+    """
+    Start Edge script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    from freqtrade.optimize.edge_cli import EdgeCli
+    # Initialize configuration
+    config = setup_optimize_configuration(args, RunMode.EDGE)
+    logger.info('Starting freqtrade in Edge mode')
+
+    # Initialize Edge object
+    edge_cli = EdgeCli(config)
+    edge_cli.start()

freqtrade/commands/pairlist_commands.py

@@ -0,0 +1,42 @@
+import logging
+from typing import Any, Dict
+
+import rapidjson
+
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.resolvers import ExchangeResolver
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def start_test_pairlist(args: Dict[str, Any]) -> None:
+    """
+    Test Pairlist configuration
+    """
+    from freqtrade.pairlist.pairlistmanager import PairListManager
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
+
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+
+    quote_currencies = args.get('quote_currencies')
+    if not quote_currencies:
+        quote_currencies = [config.get('stake_currency')]
+    results = {}
+    for curr in quote_currencies:
+        config['stake_currency'] = curr
+        # Do not use ticker_interval set in the config
+        pairlists = PairListManager(exchange, config)
+        pairlists.refresh_pairlist()
+        results[curr] = pairlists.whitelist
+
+    for curr, pairlist in results.items():
+        if not args.get('print_one_column', False):
+            print(f"Pairs for {curr}: ")
+
+        if args.get('print_one_column', False):
+            print('\n'.join(pairlist))
+        elif args.get('list_pairs_print_json', False):
+            print(rapidjson.dumps(list(pairlist), default=str))
+        else:
+            print(pairlist)

freqtrade/commands/plot_commands.py

@@ -1,11 +1,11 @@
 from typing import Any, Dict
 
-from freqtrade import OperationalException
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.exceptions import OperationalException
 from freqtrade.state import RunMode
-from freqtrade.utils import setup_utils_configuration
 
 
-def validate_plot_args(args: Dict[str, Any]):
+def validate_plot_args(args: Dict[str, Any]) -> None:
     if not args.get('datadir') and not args.get('config'):
         raise OperationalException(
             "You need to specify either `--datadir` or `--config` "

freqtrade/commands/trade_commands.py

@@ -0,0 +1,27 @@
+import logging
+
+from typing import Any, Dict
+
+
+logger = logging.getLogger(__name__)
+
+
+def start_trading(args: Dict[str, Any]) -> int:
+    """
+    Main entry point for trading mode
+    """
+    # Import here to avoid loading worker module when it's not used
+    from freqtrade.worker import Worker
+
+    # Create and run worker
+    worker = None
+    try:
+        worker = Worker(args)
+        worker.run()
+    except KeyboardInterrupt:
+        logger.info('SIGINT received, aborting ...')
+    finally:
+        if worker:
+            logger.info("worker found ... calling exit")
+            worker.exit()
+    return 0

freqtrade/configuration/__init__.py

@@ -1,4 +1,7 @@
-from freqtrade.configuration.arguments import Arguments  # noqa: F401
+# flake8: noqa: F401
-from freqtrade.configuration.timerange import TimeRange  # noqa: F401
+
-from freqtrade.configuration.configuration import Configuration  # noqa: F401
+from freqtrade.configuration.config_setup import setup_utils_configuration
-from freqtrade.configuration.config_validation import validate_config_consistency  # noqa: F401
+from freqtrade.configuration.check_exchange import check_exchange, remove_credentials
+from freqtrade.configuration.timerange import TimeRange
+from freqtrade.configuration.configuration import Configuration
+from freqtrade.configuration.config_validation import validate_config_consistency

freqtrade/configuration/arguments.py

@@ -1,196 +0,0 @@
-"""
-This module contains the argument manager class
-"""
-import argparse
-from functools import partial
-from pathlib import Path
-from typing import Any, Dict, List, Optional
-
-from freqtrade import constants
-from freqtrade.configuration.cli_options import AVAILABLE_CLI_OPTIONS
-
-ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"]
-
-ARGS_STRATEGY = ["strategy", "strategy_path"]
-
-ARGS_MAIN = ARGS_COMMON + ARGS_STRATEGY + ["db_url", "sd_notify"]
-
-ARGS_COMMON_OPTIMIZE = ["ticker_interval", "timerange",
-                        "max_open_trades", "stake_amount", "fee"]
-
-ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
-                                        "strategy_list", "export", "exportfilename"]
-
-ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
-                                        "position_stacking", "epochs", "spaces",
-                                        "use_max_market_positions", "print_all",
-                                        "print_colorized", "print_json", "hyperopt_jobs",
-                                        "hyperopt_random_state", "hyperopt_min_trades",
-                                        "hyperopt_continue", "hyperopt_loss"]
-
-ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
-
-ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"]
-
-ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
-
-ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one_column",
-                   "print_csv", "base_currencies", "quote_currencies", "list_pairs_all"]
-
-ARGS_CREATE_USERDIR = ["user_data_dir"]
-
-ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
-                      "timeframes", "erase"]
-
-ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit", "db_url",
-                       "trade_source", "export", "exportfilename", "timerange", "ticker_interval"]
-
-ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
-                    "trade_source", "ticker_interval"]
-
-NO_CONF_REQURIED = ["download-data", "list-timeframes", "list-markets", "list-pairs",
-                    "plot-dataframe", "plot-profit"]
-
-NO_CONF_ALLOWED = ["create-userdir", "list-exchanges"]
-
-
-class Arguments:
-    """
-    Arguments Class. Manage the arguments received by the cli
-    """
-    def __init__(self, args: Optional[List[str]]) -> None:
-        self.args = args
-        self._parsed_arg: Optional[argparse.Namespace] = None
-        self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
-
-    def _load_args(self) -> None:
-        self._build_args(optionlist=ARGS_MAIN)
-        self._build_subcommands()
-
-    def get_parsed_arg(self) -> Dict[str, Any]:
-        """
-        Return the list of arguments
-        :return: List[str] List of arguments
-        """
-        if self._parsed_arg is None:
-            self._load_args()
-            self._parsed_arg = self._parse_args()
-
-        return vars(self._parsed_arg)
-
-    def _parse_args(self) -> argparse.Namespace:
-        """
-        Parses given arguments and returns an argparse Namespace instance.
-        """
-        parsed_arg = self.parser.parse_args(self.args)
-
-        # When no config is provided, but a config exists, use that configuration!
-        subparser = parsed_arg.subparser if 'subparser' in parsed_arg else None
-
-        # Workaround issue in argparse with action='append' and default value
-        # (see https://bugs.python.org/issue16399)
-        # Allow no-config for certain commands (like downloading / plotting)
-        if (parsed_arg.config is None
-                and subparser not in NO_CONF_ALLOWED
-                and ((Path.cwd() / constants.DEFAULT_CONFIG).is_file()
-                     or (subparser not in NO_CONF_REQURIED))):
-            parsed_arg.config = [constants.DEFAULT_CONFIG]
-
-        return parsed_arg
-
-    def _build_args(self, optionlist, parser=None):
-        parser = parser or self.parser
-
-        for val in optionlist:
-            opt = AVAILABLE_CLI_OPTIONS[val]
-            parser.add_argument(*opt.cli, dest=val, **opt.kwargs)
-
-    def _build_subcommands(self) -> None:
-        """
-        Builds and attaches all subcommands.
-        :return: None
-        """
-        from freqtrade.optimize import start_backtesting, start_hyperopt, start_edge
-        from freqtrade.utils import (start_create_userdir, start_download_data,
-                                     start_list_exchanges, start_list_timeframes,
-                                     start_list_markets)
-
-        subparsers = self.parser.add_subparsers(dest='subparser')
-
-        # Add backtesting subcommand
-        backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.')
-        backtesting_cmd.set_defaults(func=start_backtesting)
-        self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
-
-        # Add edge subcommand
-        edge_cmd = subparsers.add_parser('edge', help='Edge module.')
-        edge_cmd.set_defaults(func=start_edge)
-        self._build_args(optionlist=ARGS_EDGE, parser=edge_cmd)
-
-        # Add hyperopt subcommand
-        hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.')
-        hyperopt_cmd.set_defaults(func=start_hyperopt)
-        self._build_args(optionlist=ARGS_HYPEROPT, parser=hyperopt_cmd)
-
-        # add create-userdir subcommand
-        create_userdir_cmd = subparsers.add_parser('create-userdir',
-                                                   help="Create user-data directory.")
-        create_userdir_cmd.set_defaults(func=start_create_userdir)
-        self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
-
-        # Add list-exchanges subcommand
-        list_exchanges_cmd = subparsers.add_parser(
-            'list-exchanges',
-            help='Print available exchanges.'
-        )
-        list_exchanges_cmd.set_defaults(func=start_list_exchanges)
-        self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
-
-        # Add list-timeframes subcommand
-        list_timeframes_cmd = subparsers.add_parser(
-            'list-timeframes',
-            help='Print available ticker intervals (timeframes) for the exchange.'
-        )
-        list_timeframes_cmd.set_defaults(func=start_list_timeframes)
-        self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
-
-        # Add list-markets subcommand
-        list_markets_cmd = subparsers.add_parser(
-            'list-markets',
-            help='Print markets on exchange.'
-        )
-        list_markets_cmd.set_defaults(func=partial(start_list_markets, pairs_only=False))
-        self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_markets_cmd)
-
-        # Add list-pairs subcommand
-        list_pairs_cmd = subparsers.add_parser(
-            'list-pairs',
-            help='Print pairs on exchange.'
-        )
-        list_pairs_cmd.set_defaults(func=partial(start_list_markets, pairs_only=True))
-        self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_pairs_cmd)
-
-        # Add download-data subcommand
-        download_data_cmd = subparsers.add_parser(
-            'download-data',
-            help='Download backtesting data.'
-        )
-        download_data_cmd.set_defaults(func=start_download_data)
-        self._build_args(optionlist=ARGS_DOWNLOAD_DATA, parser=download_data_cmd)
-
-        # Add Plotting subcommand
-        from freqtrade.plot.plot_utils import start_plot_dataframe, start_plot_profit
-        plot_dataframe_cmd = subparsers.add_parser(
-            'plot-dataframe',
-            help='Plot candles with indicators.'
-        )
-        plot_dataframe_cmd.set_defaults(func=start_plot_dataframe)
-        self._build_args(optionlist=ARGS_PLOT_DATAFRAME, parser=plot_dataframe_cmd)
-
-        # Plot profit
-        plot_profit_cmd = subparsers.add_parser(
-            'plot-profit',
-            help='Generate plot showing profits.'
-        )
-        plot_profit_cmd.set_defaults(func=start_plot_profit)
-        self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)

freqtrade/configuration/check_exchange.py

@@ -1,15 +1,28 @@
 import logging
 from typing import Any, Dict
 
-from freqtrade import OperationalException
+from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import (available_exchanges, get_exchange_bad_reason,
-                                is_exchange_known_ccxt, is_exchange_bad,
+                                is_exchange_bad, is_exchange_known_ccxt,
                                 is_exchange_officially_supported)
 from freqtrade.state import RunMode
 
 logger = logging.getLogger(__name__)
 
 
+def remove_credentials(config: Dict[str, Any]) -> None:
+    """
+    Removes exchange keys from the configuration and specifies dry-run
+    Used for backtesting / hyperopt / edge and utils.
+    Modifies the input dict!
+    """
+    config['exchange']['key'] = ''
+    config['exchange']['secret'] = ''
+    config['exchange']['password'] = ''
+    config['exchange']['uid'] = ''
+    config['dry_run'] = True
+
+
 def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
     """
     Check if the exchange name in the config file is supported by Freqtrade
@@ -21,7 +34,8 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
              and thus is not known for the Freqtrade at all.
     """
 
-    if config['runmode'] in [RunMode.PLOT] and not config.get('exchange', {}).get('name'):
+    if (config['runmode'] in [RunMode.PLOT, RunMode.UTIL_NO_EXCHANGE, RunMode.OTHER]
+       and not config.get('exchange', {}).get('name')):
         # Skip checking exchange in plot mode, since it requires no exchange
         return True
     logger.info("Checking exchange...")

freqtrade/configuration/config_setup.py

@@ -0,0 +1,25 @@
+import logging
+from typing import Any, Dict
+
+from .config_validation import validate_config_consistency
+from .configuration import Configuration
+from .check_exchange import remove_credentials
+from freqtrade.state import RunMode
+
+logger = logging.getLogger(__name__)
+
+
+def setup_utils_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
+    """
+    Prepare the configuration for utils subcommands
+    :param args: Cli args from Arguments()
+    :return: Configuration
+    """
+    configuration = Configuration(args, method)
+    config = configuration.get_config()
+
+    # Ensure we do not use Exchange credentials
+    remove_credentials(config)
+    validate_config_consistency(config)
+
+    return config

freqtrade/configuration/config_validation.py

@@ -1,11 +1,13 @@
 import logging
+from copy import deepcopy
 from typing import Any, Dict
 
 from jsonschema import Draft4Validator, validators
 from jsonschema.exceptions import ValidationError, best_match
 
-from freqtrade import constants, OperationalException
+from freqtrade import constants
-
+from freqtrade.exceptions import OperationalException
+from freqtrade.state import RunMode
 
 logger = logging.getLogger(__name__)
 
@@ -41,15 +43,20 @@ def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
     :param conf: Config in JSON format
     :return: Returns the config if valid, otherwise throw an exception
     """
+    conf_schema = deepcopy(constants.CONF_SCHEMA)
+    if conf.get('runmode', RunMode.OTHER) in (RunMode.DRY_RUN, RunMode.LIVE):
+        conf_schema['required'] = constants.SCHEMA_TRADE_REQUIRED
+    else:
+        conf_schema['required'] = constants.SCHEMA_MINIMAL_REQUIRED
     try:
-        FreqtradeValidator(constants.CONF_SCHEMA).validate(conf)
+        FreqtradeValidator(conf_schema).validate(conf)
         return conf
     except ValidationError as e:
         logger.critical(
             f"Invalid configuration. See config.json.example. Reason: {e}"
         )
         raise ValidationError(
-            best_match(Draft4Validator(constants.CONF_SCHEMA).iter_errors(conf)).message
+            best_match(Draft4Validator(conf_schema).iter_errors(conf)).message
         )
 
 
@@ -61,9 +68,27 @@ def validate_config_consistency(conf: Dict[str, Any]) -> None:
     :param conf: Config in JSON format
     :return: Returns None if everything is ok, otherwise throw an OperationalException
     """
+
     # validating trailing stoploss
     _validate_trailing_stoploss(conf)
     _validate_edge(conf)
+    _validate_whitelist(conf)
+    _validate_unlimited_amount(conf)
+
+    # validate configuration before returning
+    logger.info('Validating configuration ...')
+    validate_config_schema(conf)
+
+
+def _validate_unlimited_amount(conf: Dict[str, Any]) -> None:
+    """
+    If edge is disabled, either max_open_trades or stake_amount need to be set.
+    :raise: OperationalException if config validation failed
+    """
+    if (not conf.get('edge', {}).get('enabled')
+       and conf.get('max_open_trades') == float('inf')
+       and conf.get('stake_amount') == constants.UNLIMITED_STAKE_AMOUNT):
+        raise OperationalException("`max_open_trades` and `stake_amount` cannot both be unlimited.")
 
 
 def _validate_trailing_stoploss(conf: Dict[str, Any]) -> None:
@@ -111,3 +136,17 @@ def _validate_edge(conf: Dict[str, Any]) -> None:
             "Edge and VolumePairList are incompatible, "
             "Edge will override whatever pairs VolumePairlist selects."
         )
+
+
+def _validate_whitelist(conf: Dict[str, Any]) -> None:
+    """
+    Dynamic whitelist does not require pair_whitelist to be set - however StaticWhitelist does.
+    """
+    if conf.get('runmode', RunMode.OTHER) in [RunMode.OTHER, RunMode.PLOT,
+                                              RunMode.UTIL_NO_EXCHANGE, RunMode.UTIL_EXCHANGE]:
+        return
+
+    for pl in conf.get('pairlists', [{'method': 'StaticPairList'}]):
+        if (pl.get('method') == 'StaticPairList'
+                and not conf.get('exchange', {}).get('pair_whitelist')):
+            raise OperationalException("StaticPairList requires pair_whitelist to be set.")

freqtrade/configuration/configuration.py

@@ -7,17 +7,16 @@ from copy import deepcopy
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional
 
-from freqtrade import OperationalException, constants
+from freqtrade import constants
 from freqtrade.configuration.check_exchange import check_exchange
-from freqtrade.configuration.config_validation import (validate_config_consistency,
-                                                       validate_config_schema)
 from freqtrade.configuration.deprecated_settings import process_temporary_deprecated_settings
 from freqtrade.configuration.directory_operations import (create_datadir,
                                                           create_userdata_dir)
 from freqtrade.configuration.load_config import load_config_file
+from freqtrade.exceptions import OperationalException
 from freqtrade.loggers import setup_logging
 from freqtrade.misc import deep_merge_dicts, json_load
-from freqtrade.state import RunMode
+from freqtrade.state import NON_UTIL_MODES, TRADING_MODES, RunMode
 
 logger = logging.getLogger(__name__)
 
@@ -81,9 +80,8 @@ class Configuration:
         if 'ask_strategy' not in config:
             config['ask_strategy'] = {}
 
-        # validate configuration before returning
+        if 'pairlists' not in config:
-        logger.info('Validating configuration ...')
+            config['pairlists'] = []
-        validate_config_schema(config)
 
         return config
 
@@ -93,19 +91,23 @@ class Configuration:
         :return: Configuration dictionary
         """
         # Load all configs
-        config: Dict[str, Any] = self.load_from_files(self.args["config"])
+        config: Dict[str, Any] = self.load_from_files(self.args.get("config", []))
 
         # Keep a copy of the original configuration file
         config['original_config'] = deepcopy(config)
 
+        self._process_logging_options(config)
+
+        self._process_runmode(config)
+
         self._process_common_options(config)
 
+        self._process_trading_options(config)
+
         self._process_optimize_options(config)
 
         self._process_plot_options(config)
 
-        self._process_runmode(config)
-
         # Check if the exchange set by the user is supported
         check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
 
@@ -113,8 +115,6 @@ class Configuration:
 
         process_temporary_deprecated_settings(config)
 
-        validate_config_consistency(config)
-
         return config
 
     def _process_logging_options(self, config: Dict[str, Any]) -> None:
@@ -130,21 +130,9 @@ class Configuration:
 
         setup_logging(config)
 
-    def _process_common_options(self, config: Dict[str, Any]) -> None:
+    def _process_trading_options(self, config: Dict[str, Any]) -> None:
-
+        if config['runmode'] not in TRADING_MODES:
-        self._process_logging_options(config)
+            return
-
-        # Set strategy if not specified in config and or if it's non default
-        if self.args.get("strategy") != constants.DEFAULT_STRATEGY or not config.get('strategy'):
-            config.update({'strategy': self.args.get("strategy")})
-
-        self._args_to_config(config, argname='strategy_path',
-                             logstring='Using additional Strategy lookup path: {}')
-
-        if ('db_url' in self.args and self.args["db_url"] and
-                self.args["db_url"] != constants.DEFAULT_DB_PROD_URL):
-            config.update({'db_url': self.args["db_url"]})
-            logger.info('Parameter --db-url detected ...')
 
         if config.get('dry_run', False):
             logger.info('Dry run is enabled')
@@ -158,13 +146,23 @@ class Configuration:
 
         logger.info(f'Using DB: "{config["db_url"]}"')
 
+    def _process_common_options(self, config: Dict[str, Any]) -> None:
+
+        # Set strategy if not specified in config and or if it's non default
+        if self.args.get("strategy") or not config.get('strategy'):
+            config.update({'strategy': self.args.get("strategy")})
+
+        self._args_to_config(config, argname='strategy_path',
+                             logstring='Using additional Strategy lookup path: {}')
+
+        if ('db_url' in self.args and self.args["db_url"] and
+                self.args["db_url"] != constants.DEFAULT_DB_PROD_URL):
+            config.update({'db_url': self.args["db_url"]})
+            logger.info('Parameter --db-url detected ...')
+
         if config.get('forcebuy_enable', False):
             logger.warning('`forcebuy` RPC message enabled.')
 
-        # Setting max_open_trades to infinite if -1
-        if config.get('max_open_trades') == -1:
-            config['max_open_trades'] = float('inf')
-
         # Support for sd_notify
         if 'sd_notify' in self.args and self.args["sd_notify"]:
             config['internals'].update({'sd_notify': True})
@@ -179,6 +177,9 @@ class Configuration:
             config['exchange']['name'] = self.args["exchange"]
             logger.info(f"Using exchange {config['exchange']['name']}")
 
+        if 'pair_whitelist' not in config['exchange']:
+            config['exchange']['pair_whitelist'] = []
+
         if 'user_data_dir' in self.args and self.args["user_data_dir"]:
             config.update({'user_data_dir': self.args["user_data_dir"]})
         elif 'user_data_dir' not in config:
@@ -195,6 +196,7 @@ class Configuration:
         if self.args.get('exportfilename'):
             self._args_to_config(config, argname='exportfilename',
                                  logstring='Storing backtest results to {} ...')
+            config['exportfilename'] = Path(config['exportfilename'])
         else:
             config['exportfilename'] = (config['user_data_dir']
                                         / 'backtest_results/backtest-result.json')
@@ -209,19 +211,23 @@ class Configuration:
         self._args_to_config(config, argname='position_stacking',
                              logstring='Parameter --enable-position-stacking detected ...')
 
+        # Setting max_open_trades to infinite if -1
+        if config.get('max_open_trades') == -1:
+            config['max_open_trades'] = float('inf')
+
         if 'use_max_market_positions' in self.args and not self.args["use_max_market_positions"]:
             config.update({'use_max_market_positions': False})
             logger.info('Parameter --disable-max-market-positions detected ...')
             logger.info('max_open_trades set to unlimited ...')
         elif 'max_open_trades' in self.args and self.args["max_open_trades"]:
             config.update({'max_open_trades': self.args["max_open_trades"]})
-            logger.info('Parameter --max_open_trades detected, '
+            logger.info('Parameter --max-open-trades detected, '
                         'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
-        else:
+        elif config['runmode'] in NON_UTIL_MODES:
             logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
 
         self._args_to_config(config, argname='stake_amount',
-                             logstring='Parameter --stake_amount detected, '
+                             logstring='Parameter --stake-amount detected, '
                              'overriding stake_amount to: {} ...')
 
         self._args_to_config(config, argname='fee',
@@ -277,6 +283,9 @@ class Configuration:
         self._args_to_config(config, argname='print_json',
                              logstring='Parameter --print-json detected ...')
 
+        self._args_to_config(config, argname='export_csv',
+                             logstring='Parameter --export-csv detected: {}')
+
         self._args_to_config(config, argname='hyperopt_jobs',
                              logstring='Parameter -j/--job-workers detected: {}')
 
@@ -292,6 +301,45 @@ class Configuration:
         self._args_to_config(config, argname='hyperopt_loss',
                              logstring='Using Hyperopt loss class name: {}')
 
+        self._args_to_config(config, argname='hyperopt_show_index',
+                             logstring='Parameter -n/--index detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_best',
+                             logstring='Parameter --best detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_profitable',
+                             logstring='Parameter --profitable detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_min_trades',
+                             logstring='Parameter --min-trades detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_trades',
+                             logstring='Parameter --max-trades detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_min_avg_time',
+                             logstring='Parameter --min-avg-time detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_avg_time',
+                             logstring='Parameter --max-avg-time detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_min_avg_profit',
+                             logstring='Parameter --min-avg-profit detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_avg_profit',
+                             logstring='Parameter --max-avg-profit detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_min_total_profit',
+                             logstring='Parameter --min-total-profit detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_max_total_profit',
+                             logstring='Parameter --max-total-profit detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_no_details',
+                             logstring='Parameter --no-details detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_show_no_header',
+                             logstring='Parameter --no-header detected: {}')
+
     def _process_plot_options(self, config: Dict[str, Any]) -> None:
 
         self._args_to_config(config, argname='pairs',
@@ -311,20 +359,34 @@ class Configuration:
         self._args_to_config(config, argname='erase',
                              logstring='Erase detected. Deleting existing data.')
 
+        self._args_to_config(config, argname='no_trades',
+                             logstring='Parameter --no-trades detected.')
+
         self._args_to_config(config, argname='timeframes',
                              logstring='timeframes --timeframes: {}')
 
         self._args_to_config(config, argname='days',
                              logstring='Detected --days: {}')
+
         self._args_to_config(config, argname='download_trades',
                              logstring='Detected --dl-trades: {}')
 
+        self._args_to_config(config, argname='dataformat_ohlcv',
+                             logstring='Using "{}" to store OHLCV data.')
+
+        self._args_to_config(config, argname='dataformat_trades',
+                             logstring='Using "{}" to store trades data.')
+
     def _process_runmode(self, config: Dict[str, Any]) -> None:
 
+        self._args_to_config(config, argname='dry_run',
+                             logstring='Parameter --dry-run detected, '
+                             'overriding dry_run to: {} ...')
+
         if not self.runmode:
             # Handle real mode, infer dry/live from config
             self.runmode = RunMode.DRY_RUN if config.get('dry_run', True) else RunMode.LIVE
-            logger.info(f"Runmode set to {self.runmode}.")
+            logger.info(f"Runmode set to {self.runmode.value}.")
 
         config.update({'runmode': self.runmode})
 
@@ -380,7 +442,7 @@ class Configuration:
             config['pairs'] = config.get('exchange', {}).get('pair_whitelist')
         else:
             # Fall back to /dl_path/pairs.json
-            pairs_file = Path(config['datadir']) / "pairs.json"
+            pairs_file = config['datadir'] / "pairs.json"
             if pairs_file.exists():
                 with pairs_file.open('r') as f:
                     config['pairs'] = json_load(f)

freqtrade/configuration/deprecated_settings.py

@@ -5,7 +5,7 @@ Functions to handle deprecated settings
 import logging
 from typing import Any, Dict
 
-from freqtrade import OperationalException
+from freqtrade.exceptions import OperationalException
 
 
 logger = logging.getLogger(__name__)
@@ -13,7 +13,7 @@ logger = logging.getLogger(__name__)
 
 def check_conflicting_settings(config: Dict[str, Any],
                                section1: str, name1: str,
-                               section2: str, name2: str):
+                               section2: str, name2: str) -> None:
     section1_config = config.get(section1, {})
     section2_config = config.get(section2, {})
     if name1 in section1_config and name2 in section2_config:
@@ -28,7 +28,7 @@ def check_conflicting_settings(config: Dict[str, Any],
 
 def process_deprecated_setting(config: Dict[str, Any],
                                section1: str, name1: str,
-                               section2: str, name2: str):
+                               section2: str, name2: str) -> None:
     section2_config = config.get(section2, {})
 
     if name2 in section2_config:
@@ -57,3 +57,36 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
                                'experimental', 'sell_profit_only')
     process_deprecated_setting(config, 'ask_strategy', 'ignore_roi_if_buy_signal',
                                'experimental', 'ignore_roi_if_buy_signal')
+
+    if not config.get('pairlists') and not config.get('pairlists'):
+        config['pairlists'] = [{'method': 'StaticPairList'}]
+        logger.warning(
+            "DEPRECATED: "
+            "Pairlists must be defined explicitly in the future."
+            "Defaulting to StaticPairList for now.")
+
+    if config.get('pairlist', {}).get("method") == 'VolumePairList':
+        logger.warning(
+            "DEPRECATED: "
+            f"Using VolumePairList in pairlist is deprecated and must be moved to pairlists. "
+            "Please refer to the docs on configuration details")
+        pl = {'method': 'VolumePairList'}
+        pl.update(config.get('pairlist', {}).get('config'))
+        config['pairlists'].append(pl)
+
+    if config.get('pairlist', {}).get('config', {}).get('precision_filter'):
+        logger.warning(
+            "DEPRECATED: "
+            f"Using precision_filter setting is deprecated and has been replaced by"
+            "PrecisionFilter. Please refer to the docs on configuration details")
+        config['pairlists'].append({'method': 'PrecisionFilter'})
+
+    if (config.get('edge', {}).get('enabled', False)
+       and 'capital_available_percentage' in config.get('edge', {})):
+        logger.warning(
+            "DEPRECATED: "
+            "Using 'edge.capital_available_percentage' has been deprecated in favor of "
+            "'tradable_balance_ratio'. Please migrate your configuration to "
+            "'tradable_balance_ratio' and remove 'capital_available_percentage' "
+            "from the edge configuration."
+        )

freqtrade/configuration/directory_operations.py

@@ -1,13 +1,15 @@
 import logging
-from typing import Any, Dict, Optional
+import shutil
 from pathlib import Path
+from typing import Any, Dict, Optional
 
-from freqtrade import OperationalException
+from freqtrade.exceptions import OperationalException
+from freqtrade.constants import USER_DATA_FILES
 
 logger = logging.getLogger(__name__)
 
 
-def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> str:
+def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Path:
 
     folder = Path(datadir) if datadir else Path(f"{config['user_data_dir']}/data")
     if not datadir:
@@ -18,10 +20,10 @@ def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> str
     if not folder.is_dir():
         folder.mkdir(parents=True)
         logger.info(f'Created data directory: {datadir}')
-    return str(folder)
+    return folder
 
 
-def create_userdata_dir(directory: str, create_dir=False) -> Path:
+def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
     """
     Create userdata directory structure.
     if create_dir is True, then the parent-directory will be created if it does not exist.
@@ -31,7 +33,8 @@ def create_userdata_dir(directory: str, create_dir=False) -> Path:
     :param create_dir: Create directory if it does not exist.
     :return: Path object containing the directory
     """
-    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "plot", "strategies", ]
+    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "notebooks",
+                "plot", "strategies", ]
     folder = Path(directory)
     if not folder.is_dir():
         if create_dir:
@@ -48,3 +51,26 @@ def create_userdata_dir(directory: str, create_dir=False) -> Path:
         if not subfolder.is_dir():
             subfolder.mkdir(parents=False)
     return folder
+
+
+def copy_sample_files(directory: Path, overwrite: bool = False) -> None:
+    """
+    Copy files from templates to User data directory.
+    :param directory: Directory to copy data to
+    :param overwrite: Overwrite existing sample files
+    """
+    if not directory.is_dir():
+        raise OperationalException(f"Directory `{directory}` does not exist.")
+    sourcedir = Path(__file__).parents[1] / "templates"
+    for source, target in USER_DATA_FILES.items():
+        targetdir = directory / target
+        if not targetdir.is_dir():
+            raise OperationalException(f"Directory `{targetdir}` does not exist.")
+        targetfile = targetdir / source
+        if targetfile.exists():
+            if not overwrite:
+                logger.warning(f"File `{targetfile}` exists already, not deploying sample file.")
+                continue
+            else:
+                logger.warning(f"File `{targetfile}` exists already, overwriting.")
+        shutil.copy(str(sourcedir / source), str(targetfile))

freqtrade/configuration/load_config.py

@@ -1,13 +1,15 @@
 """
 This module contain functions to load the configuration file
 """
-import rapidjson
 import logging
+import re
 import sys
+from pathlib import Path
 from typing import Any, Dict
 
-from freqtrade import OperationalException
+import rapidjson
 
+from freqtrade.exceptions import OperationalException
 
 logger = logging.getLogger(__name__)
 
@@ -15,6 +17,26 @@ logger = logging.getLogger(__name__)
 CONFIG_PARSE_MODE = rapidjson.PM_COMMENTS | rapidjson.PM_TRAILING_COMMAS
 
 
+def log_config_error_range(path: str, errmsg: str) -> str:
+    """
+    Parses configuration file and prints range around error
+    """
+    if path != '-':
+        offsetlist = re.findall(r'(?<=Parse\serror\sat\soffset\s)\d+', errmsg)
+        if offsetlist:
+            offset = int(offsetlist[0])
+            text = Path(path).read_text()
+            # Fetch an offset of 80 characters around the error line
+            subtext = text[offset-min(80, offset):offset+80]
+            segments = subtext.split('\n')
+            if len(segments) > 3:
+                # Remove first and last lines, to avoid odd truncations
+                return '\n'.join(segments[1:-1])
+            else:
+                return subtext
+    return ''
+
+
 def load_config_file(path: str) -> Dict[str, Any]:
     """
     Loads a config file from the given path
@@ -29,5 +51,12 @@ def load_config_file(path: str) -> Dict[str, Any]:
         raise OperationalException(
             f'Config file "{path}" not found!'
             ' Please create a config file or check whether it exists.')
+    except rapidjson.JSONDecodeError as e:
+        err_range = log_config_error_range(path, str(e))
+        raise OperationalException(
+            f'{e}\n'
+            f'Please verify the following segment of your configuration:\n{err_range}'
+            if err_range else 'Please verify your configuration file for syntax errors.'
+        )
 
     return config

freqtrade/configuration/timerange.py

@@ -1,12 +1,16 @@
 """
 This module contains the argument manager class
 """
+import logging
 import re
 from typing import Optional
 
 import arrow
 
 
+logger = logging.getLogger(__name__)
+
+
 class TimeRange:
     """
     object defining timerange inputs.
@@ -27,8 +31,36 @@ class TimeRange:
         return (self.starttype == other.starttype and self.stoptype == other.stoptype
                 and self.startts == other.startts and self.stopts == other.stopts)
 
+    def subtract_start(self, seconds: int) -> None:
+        """
+        Subtracts <seconds> from startts if startts is set.
+        :param seconds: Seconds to subtract from starttime
+        :return: None (Modifies the object in place)
+        """
+        if self.startts:
+            self.startts = self.startts - seconds
+
+    def adjust_start_if_necessary(self, timeframe_secs: int, startup_candles: int,
+                                  min_date: arrow.Arrow) -> None:
+        """
+        Adjust startts by <startup_candles> candles.
+        Applies only if no startup-candles have been available.
+        :param timeframe_secs: Timeframe in seconds e.g. `timeframe_to_seconds('5m')`
+        :param startup_candles: Number of candles to move start-date forward
+        :param min_date: Minimum data date loaded. Key kriterium to decide if start-time
+                         has to be moved
+        :return: None (Modifies the object in place)
+        """
+        if (not self.starttype or (startup_candles
+                                   and min_date.timestamp >= self.startts)):
+            # If no startts was defined, or backtest-data starts at the defined backtest-date
+            logger.warning("Moving start-date by %s candles to account for startup time.",
+                           startup_candles)
+            self.startts = (min_date.timestamp + timeframe_secs * startup_candles)
+            self.starttype = 'date'
+
     @staticmethod
-    def parse_timerange(text: Optional[str]):
+    def parse_timerange(text: Optional[str]) -> 'TimeRange':
         """
         Parse the value of the argument --timerange to determine what is the range desired
         :param text: value from --timerange

freqtrade/constants.py

@@ -6,36 +6,44 @@ bot constants
 DEFAULT_CONFIG = 'config.json'
 DEFAULT_EXCHANGE = 'bittrex'
 PROCESS_THROTTLE_SECS = 5  # sec
-DEFAULT_TICKER_INTERVAL = 5  # min
 HYPEROPT_EPOCH = 100  # epochs
 RETRY_TIMEOUT = 30  # sec
-DEFAULT_STRATEGY = 'DefaultStrategy'
-DEFAULT_HYPEROPT = 'DefaultHyperOpt'
 DEFAULT_HYPEROPT_LOSS = 'DefaultHyperOptLoss'
 DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
-DEFAULT_DB_DRYRUN_URL = 'sqlite://'
+DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
 UNLIMITED_STAKE_AMOUNT = 'unlimited'
 DEFAULT_AMOUNT_RESERVE_PERCENT = 0.05
 REQUIRED_ORDERTIF = ['buy', 'sell']
 REQUIRED_ORDERTYPES = ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
+ORDERBOOK_SIDES = ['ask', 'bid']
 ORDERTYPE_POSSIBILITIES = ['limit', 'market']
 ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
-AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList']
+AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
-DRY_RUN_WALLET = 999.9
+                       'PrecisionFilter', 'PriceFilter', 'SpreadFilter']
+AVAILABLE_DATAHANDLERS = ['json', 'jsongz']
+DRY_RUN_WALLET = 1000
 MATH_CLOSE_PREC = 1e-14  # Precision used for float comparisons
+DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
 
-TICKER_INTERVALS = [
+USERPATH_HYPEROPTS = 'hyperopts'
-    '1m', '3m', '5m', '15m', '30m',
+USERPATH_STRATEGIES = 'strategies'
-    '1h', '2h', '4h', '6h', '8h', '12h',
+USERPATH_NOTEBOOKS = 'notebooks'
-    '1d', '3d', '1w',
+
-]
+# Soure files with destination directories within user-directory
+USER_DATA_FILES = {
+    'sample_strategy.py': USERPATH_STRATEGIES,
+    'sample_hyperopt_advanced.py': USERPATH_HYPEROPTS,
+    'sample_hyperopt_loss.py': USERPATH_HYPEROPTS,
+    'sample_hyperopt.py': USERPATH_HYPEROPTS,
+    'strategy_analysis_example.ipynb': USERPATH_NOTEBOOKS,
+}
 
 SUPPORTED_FIAT = [
     "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK",
     "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY",
     "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN",
     "RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD",
-    "BTC", "XBT", "ETH", "XRP", "LTC", "BCH", "USDT"
+    "BTC", "ETH", "XRP", "LTC", "BCH"
 ]
 
 MINIMAL_CONFIG = {
@@ -56,17 +64,27 @@ MINIMAL_CONFIG = {
 CONF_SCHEMA = {
     'type': 'object',
     'properties': {
-        'max_open_trades': {'type': 'integer', 'minimum': -1},
+        'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
-        'ticker_interval': {'type': 'string', 'enum': TICKER_INTERVALS},
+        'ticker_interval': {'type': 'string'},
-        'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
+        'stake_currency': {'type': 'string'},
         'stake_amount': {
-            "type": ["number", "string"],
+            'type': ['number', 'string'],
-            "minimum": 0.0005,
+            'minimum': 0.0001,
-            "pattern": UNLIMITED_STAKE_AMOUNT
+            'pattern': UNLIMITED_STAKE_AMOUNT
+        },
+        'tradable_balance_ratio': {
+            'type': 'number',
+            'minimum': 0.1,
+            'maximum': 1,
+            'default': 0.99
+        },
+        'amend_last_stake_amount': {'type': 'boolean', 'default': False},
+        'last_stake_amount_min_ratio': {
+            'type': 'number', 'minimum': 0.0, 'maximum': 1.0, 'default': 0.5
         },
         'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
         'dry_run': {'type': 'boolean'},
-        'dry_run_wallet': {'type': 'number'},
+        'dry_run_wallet': {'type': 'number', 'default': DRY_RUN_WALLET},
         'process_only_new_candles': {'type': 'boolean'},
         'minimal_roi': {
             'type': 'object',
@@ -84,8 +102,8 @@ CONF_SCHEMA = {
         'unfilledtimeout': {
             'type': 'object',
             'properties': {
-                'buy': {'type': 'number', 'minimum': 3},
+                'buy': {'type': 'number', 'minimum': 1},
-                'sell': {'type': 'number', 'minimum': 10}
+                'sell': {'type': 'number', 'minimum': 1}
             }
         },
         'bid_strategy': {
@@ -96,8 +114,10 @@ CONF_SCHEMA = {
                     'minimum': 0,
                     'maximum': 1,
                     'exclusiveMaximum': False,
+                },
+                'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'bid'},
                 'use_order_book': {'type': 'boolean'},
-                    'order_book_top': {'type': 'number', 'maximum': 20, 'minimum': 1},
+                'order_book_top': {'type': 'integer', 'maximum': 20, 'minimum': 1},
                 'check_depth_of_market': {
                     'type': 'object',
                     'properties': {
@@ -106,15 +126,15 @@ CONF_SCHEMA = {
                     }
                 },
             },
-            },
             'required': ['ask_last_balance']
         },
         'ask_strategy': {
             'type': 'object',
             'properties': {
+                'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'ask'},
                 'use_order_book': {'type': 'boolean'},
-                'order_book_min': {'type': 'number', 'minimum': 1},
+                'order_book_min': {'type': 'integer', 'minimum': 1},
-                'order_book_max': {'type': 'number', 'minimum': 1, 'maximum': 50},
+                'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
                 'use_sell_signal': {'type': 'boolean'},
                 'sell_profit_only': {'type': 'boolean'},
                 'ignore_roi_if_buy_signal': {'type': 'boolean'}
@@ -151,13 +171,16 @@ CONF_SCHEMA = {
                 'block_bad_exchanges': {'type': 'boolean'}
             }
         },
-        'pairlist': {
+        'pairlists': {
+            'type': 'array',
+            'items': {
                 'type': 'object',
                 'properties': {
                     'method': {'type': 'string', 'enum': AVAILABLE_PAIRLISTS},
                     'config': {'type': 'object'}
                 },
-            'required': ['method']
+                'required': ['method'],
+            }
         },
         'telegram': {
             'type': 'object',
@@ -173,7 +196,9 @@ CONF_SCHEMA = {
             'properties': {
                 'enabled': {'type': 'boolean'},
                 'webhookbuy': {'type': 'object'},
+                'webhookbuycancel': {'type': 'object'},
                 'webhooksell': {'type': 'object'},
+                'webhooksellcancel': {'type': 'object'},
                 'webhookstatus': {'type': 'object'},
             },
         },
@@ -184,8 +209,8 @@ CONF_SCHEMA = {
                 'listen_ip_address': {'format': 'ipv4'},
                 'listen_port': {
                     'type': 'integer',
-                    "minimum": 1024,
+                    'minimum': 1024,
-                    "maximum": 65535
+                    'maximum': 65535
                 },
                 'username': {'type': 'string'},
                 'password': {'type': 'string'},
@@ -197,11 +222,22 @@ CONF_SCHEMA = {
         'forcebuy_enable': {'type': 'boolean'},
         'internals': {
             'type': 'object',
+            'default': {},
             'properties': {
-                'process_throttle_secs': {'type': 'number'},
+                'process_throttle_secs': {'type': 'integer'},
                 'interval': {'type': 'integer'},
                 'sd_notify': {'type': 'boolean'},
             }
+        },
+        'dataformat_ohlcv': {
+            'type': 'string',
+                    'enum': AVAILABLE_DATAHANDLERS,
+                    'default': 'json'
+        },
+        'dataformat_trades': {
+            'type': 'string',
+                    'enum': AVAILABLE_DATAHANDLERS,
+                    'default': 'jsongz'
         }
     },
     'definitions': {
@@ -218,7 +254,6 @@ CONF_SCHEMA = {
                     'type': 'array',
                     'items': {
                         'type': 'string',
-                        'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
                     },
                     'uniqueItems': True
                 },
@@ -226,7 +261,6 @@ CONF_SCHEMA = {
                     'type': 'array',
                     'items': {
                         'type': 'string',
-                        'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
                     },
                     'uniqueItems': True
                 },
@@ -235,37 +269,52 @@ CONF_SCHEMA = {
                 'ccxt_config': {'type': 'object'},
                 'ccxt_async_config': {'type': 'object'}
             },
-            'required': ['name', 'pair_whitelist']
+            'required': ['name']
         },
         'edge': {
             'type': 'object',
             'properties': {
-                "enabled": {'type': 'boolean'},
+                'enabled': {'type': 'boolean'},
-                "process_throttle_secs": {'type': 'integer', 'minimum': 600},
+                'process_throttle_secs': {'type': 'integer', 'minimum': 600},
-                "calculate_since_number_of_days": {'type': 'integer'},
+                'calculate_since_number_of_days': {'type': 'integer'},
-                "allowed_risk": {'type': 'number'},
+                'allowed_risk': {'type': 'number'},
-                "capital_available_percentage": {'type': 'number'},
+                'capital_available_percentage': {'type': 'number'},
-                "stoploss_range_min": {'type': 'number'},
+                'stoploss_range_min': {'type': 'number'},
-                "stoploss_range_max": {'type': 'number'},
+                'stoploss_range_max': {'type': 'number'},
-                "stoploss_range_step": {'type': 'number'},
+                'stoploss_range_step': {'type': 'number'},
-                "minimum_winrate": {'type': 'number'},
+                'minimum_winrate': {'type': 'number'},
-                "minimum_expectancy": {'type': 'number'},
+                'minimum_expectancy': {'type': 'number'},
-                "min_trade_number": {'type': 'number'},
+                'min_trade_number': {'type': 'number'},
-                "max_trade_duration_minute": {'type': 'integer'},
+                'max_trade_duration_minute': {'type': 'integer'},
-                "remove_pumps": {'type': 'boolean'}
+                'remove_pumps': {'type': 'boolean'}
             },
-            'required': ['process_throttle_secs', 'allowed_risk', 'capital_available_percentage']
+            'required': ['process_throttle_secs', 'allowed_risk']
         }
     },
-    'anyOf': [
+}
-        {'required': ['exchange']}
+
-    ],
+SCHEMA_TRADE_REQUIRED = [
-    'required': [
+    'exchange',
     'max_open_trades',
     'stake_currency',
     'stake_amount',
+    'tradable_balance_ratio',
+    'last_stake_amount_min_ratio',
     'dry_run',
+    'dry_run_wallet',
+    'ask_strategy',
     'bid_strategy',
     'unfilledtimeout',
+    'stoploss',
+    'minimal_roi',
+    'internals',
+    'dataformat_ohlcv',
+    'dataformat_trades',
+]
+
+SCHEMA_MINIMAL_REQUIRED = [
+    'exchange',
+    'dry_run',
+    'dataformat_ohlcv',
+    'dataformat_trades',
 ]
-}

freqtrade/data/btanalysis.py

@@ -3,11 +3,11 @@ Helpers when analyzing backtest data
 """
 import logging
 from pathlib import Path
-from typing import Dict
+from typing import Dict, Union, Tuple
 
 import numpy as np
 import pandas as pd
-import pytz
+from datetime import timezone
 
 from freqtrade import persistence
 from freqtrade.misc import json_load
@@ -20,7 +20,7 @@ BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "du
                    "open_rate", "close_rate", "open_at_end", "sell_reason"]
 
 
-def load_backtest_data(filename) -> pd.DataFrame:
+def load_backtest_data(filename: Union[Path, str]) -> pd.DataFrame:
     """
     Load backtest data file.
     :param filename: pathlib.Path object, or string pointing to the file.
@@ -47,21 +47,23 @@ def load_backtest_data(filename) -> pd.DataFrame:
                                       utc=True,
                                       infer_datetime_format=True
                                       )
-    df['profitabs'] = df['close_rate'] - df['open_rate']
+    df['profit'] = df['close_rate'] - df['open_rate']
     df = df.sort_values("open_time").reset_index(drop=True)
     return df
 
 
-def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int) -> pd.DataFrame:
+def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataFrame:
     """
     Find overlapping trades by expanding each trade once per period it was open
-    and then counting overlaps
+    and then counting overlaps.
     :param results: Results Dataframe - can be loaded
-    :param freq: Frequency used for the backtest
+    :param timeframe: Timeframe used for backtest
-    :param max_open_trades: parameter max_open_trades used during backtest run
+    :return: dataframe with open-counts per time-period in timeframe
-    :return: dataframe with open-counts per time-period in freq
     """
-    dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
+    from freqtrade.exchange import timeframe_to_minutes
+    timeframe_min = timeframe_to_minutes(timeframe)
+    dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time,
+                                     freq=f"{timeframe_min}min"))
              for row in results[['open_time', 'close_time']].iterrows()]
     deltas = [len(x) for x in dates]
     dates = pd.Series(pd.concat(dates).values, name='date')
@@ -69,8 +71,23 @@ def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int
 
     df2 = pd.concat([dates, df2], axis=1)
     df2 = df2.set_index('date')
-    df_final = df2.resample(freq)[['pair']].count()
+    df_final = df2.resample(f"{timeframe_min}min")[['pair']].count()
-    return df_final[df_final['pair'] > max_open_trades]
+    df_final = df_final.rename({'pair': 'open_trades'}, axis=1)
+    return df_final
+
+
+def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
+                          max_open_trades: int) -> pd.DataFrame:
+    """
+    Find overlapping trades by expanding each trade once per period it was open
+    and then counting overlaps
+    :param results: Results Dataframe - can be loaded
+    :param timeframe: Frequency used for the backtest
+    :param max_open_trades: parameter max_open_trades used during backtest run
+    :return: dataframe with open-counts per time-period in freq
+    """
+    df_final = analyze_trade_parallelism(results, timeframe)
+    return df_final[df_final['open_trades'] > max_open_trades]
 
 
 def load_trades_from_db(db_url: str) -> pd.DataFrame:
@@ -89,9 +106,9 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
                "stop_loss", "initial_stop_loss", "strategy", "ticker_interval"]
 
     trades = pd.DataFrame([(t.pair,
-                            t.open_date.replace(tzinfo=pytz.UTC),
+                            t.open_date.replace(tzinfo=timezone.utc),
-                            t.close_date.replace(tzinfo=pytz.UTC) if t.close_date else None,
+                            t.close_date.replace(tzinfo=timezone.utc) if t.close_date else None,
-                            t.calc_profit(), t.calc_profit_percent(),
+                            t.calc_profit(), t.calc_profit_ratio(),
                             t.open_rate, t.close_rate, t.amount,
                             (round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
                              if t.close_date else None),
@@ -106,22 +123,32 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
                             t.stop_loss, t.initial_stop_loss,
                             t.strategy, t.ticker_interval
                             )
-                           for t in Trade.query.all()],
+                           for t in Trade.get_trades().all()],
                           columns=columns)
 
     return trades
 
 
-def load_trades(source: str, db_url: str, exportfilename: str) -> pd.DataFrame:
+def load_trades(source: str, db_url: str, exportfilename: Path,
+                no_trades: bool = False) -> pd.DataFrame:
     """
     Based on configuration option "trade_source":
     * loads data from DB (using `db_url`)
     * loads data from backtestfile (using `exportfilename`)
+    :param source: "DB" or "file" - specify source to load from
+    :param db_url: sqlalchemy formatted url to a database
+    :param exportfilename: Json file generated by backtesting
+    :param no_trades: Skip using trades, only return backtesting data columns
+    :return: DataFrame containing trades
     """
+    if no_trades:
+        df = pd.DataFrame(columns=BT_DATA_COLUMNS)
+        return df
+
     if source == "DB":
         return load_trades_from_db(db_url)
     elif source == "file":
-        return load_backtest_data(Path(exportfilename))
+        return load_backtest_data(exportfilename)
 
 
 def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> pd.DataFrame:
@@ -134,16 +161,17 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> p
     return trades
 
 
-def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame], column: str = "close"):
+def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
+                                 column: str = "close") -> pd.DataFrame:
     """
     Combine multiple dataframes "column"
-    :param tickers: Dict of Dataframes, dict key should be pair.
+    :param data: Dict of Dataframes, dict key should be pair.
     :param column: Column in the original dataframes to use
     :return: DataFrame with the column renamed to the dict key, and a column
         named mean, containing the mean of all pairs.
     """
-    df_comb = pd.concat([tickers[pair].set_index('date').rename(
+    df_comb = pd.concat([data[pair].set_index('date').rename(
-        {column: pair}, axis=1)[pair] for pair in tickers], axis=1)
+        {column: pair}, axis=1)[pair] for pair in data], axis=1)
 
     df_comb['mean'] = df_comb.mean(axis=1)
 
@@ -161,12 +189,37 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     :return: Returns df with one additional column, col_name, containing the cumulative profit.
     """
     from freqtrade.exchange import timeframe_to_minutes
-    ticker_minutes = timeframe_to_minutes(timeframe)
+    timeframe_minutes = timeframe_to_minutes(timeframe)
-    # Resample to ticker_interval to make sure trades match candles
+    # Resample to timeframe to make sure trades match candles
-    _trades_sum = trades.resample(f'{ticker_minutes}min', on='close_time')[['profitperc']].sum()
+    _trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time')[['profitperc']].sum()
     df.loc[:, col_name] = _trades_sum.cumsum()
     # Set first value to 0
     df.loc[df.iloc[0].name, col_name] = 0
     # FFill to get continuous
     df[col_name] = df[col_name].ffill()
     return df
+
+
+def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time',
+                           value_col: str = 'profitperc'
+                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
+    """
+    Calculate max drawdown and the corresponding close dates
+    :param trades: DataFrame containing trades (requires columns close_time and profitperc)
+    :param date_col: Column in DataFrame to use for dates (defaults to 'close_time')
+    :param value_col: Column in DataFrame to use for values (defaults to 'profitperc')
+    :return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+    profit_results = trades.sort_values(date_col)
+    max_drawdown_df = pd.DataFrame()
+    max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
+    max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
+    max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
+
+    high_date = profit_results.loc[max_drawdown_df['high_value'].idxmax(), date_col]
+    low_date = profit_results.loc[max_drawdown_df['drawdown'].idxmin(), date_col]
+
+    return abs(min(max_drawdown_df['drawdown'])), high_date, low_date

freqtrade/data/converter.py

@@ -2,44 +2,64 @@
 Functions to convert data from one format to another
 """
 import logging
+from datetime import datetime, timezone
+from typing import Any, Dict
 
 import pandas as pd
 from pandas import DataFrame, to_datetime
 
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
 
 logger = logging.getLogger(__name__)
 
 
-def parse_ticker_dataframe(ticker: list, ticker_interval: str, pair: str, *,
+def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
-                           fill_missing: bool = True,
+                       fill_missing: bool = True, drop_incomplete: bool = True) -> DataFrame:
-                           drop_incomplete: bool = True) -> DataFrame:
     """
-    Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
+    Converts a list with candle (OHLCV) data (in format returned by ccxt.fetch_ohlcv)
-    :param ticker: ticker list, as returned by exchange.async_get_candle_history
+    to a Dataframe
-    :param ticker_interval: ticker_interval (e.g. 5m). Used to fill up eventual missing data
+    :param ohlcv: list with candle (OHLCV) data, as returned by exchange.async_get_candle_history
+    :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
     :param pair: Pair this data is for (used to warn if fillup was necessary)
     :param fill_missing: fill up missing candles with 0 candles
                          (see ohlcv_fill_up_missing_data for details)
     :param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
     :return: DataFrame
     """
-    logger.debug("Parsing tickerlist to dataframe")
+    logger.debug(f"Converting candle (OHLCV) data to dataframe for pair {pair}.")
-    cols = ['date', 'open', 'high', 'low', 'close', 'volume']
+    cols = DEFAULT_DATAFRAME_COLUMNS
-    frame = DataFrame(ticker, columns=cols)
+    df = DataFrame(ohlcv, columns=cols)
 
-    frame['date'] = to_datetime(frame['date'],
+    df['date'] = to_datetime(df['date'], unit='ms', utc=True, infer_datetime_format=True)
-                                unit='ms',
-                                utc=True,
-                                infer_datetime_format=True)
 
-    # Some exchanges return int values for volume and even for ohlc.
+    # Some exchanges return int values for Volume and even for OHLC.
     # Convert them since TA-LIB indicators used in the strategy assume floats
     # and fail with exception...
-    frame = frame.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
+    df = df.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
                           'volume': 'float'})
+    return clean_ohlcv_dataframe(df, timeframe, pair,
+                                 fill_missing=fill_missing,
+                                 drop_incomplete=drop_incomplete)
 
+
+def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
+                          fill_missing: bool = True,
+                          drop_incomplete: bool = True) -> DataFrame:
+    """
+    Clense a OHLCV dataframe by
+      * Grouping it by date (removes duplicate tics)
+      * dropping last candles if requested
+      * Filling up missing data (if requested)
+    :param data: DataFrame containing candle (OHLCV) data.
+    :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
+    :param pair: Pair this data is for (used to warn if fillup was necessary)
+    :param fill_missing: fill up missing candles with 0 candles
+                         (see ohlcv_fill_up_missing_data for details)
+    :param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
+    :return: DataFrame
+    """
     # group by index and aggregate results to eliminate duplicate ticks
-    frame = frame.groupby(by='date', as_index=False, sort=True).agg({
+    data = data.groupby(by='date', as_index=False, sort=True).agg({
         'open': 'first',
         'high': 'max',
         'low': 'min',
@@ -48,16 +68,16 @@ def parse_ticker_dataframe(ticker: list, ticker_interval: str, pair: str, *,
     })
     # eliminate partial candle
     if drop_incomplete:
-        frame.drop(frame.tail(1).index, inplace=True)
+        data.drop(data.tail(1).index, inplace=True)
         logger.debug('Dropping last candle')
 
     if fill_missing:
-        return ohlcv_fill_up_missing_data(frame, ticker_interval, pair)
+        return ohlcv_fill_up_missing_data(data, timeframe, pair)
     else:
-        return frame
+        return data
 
 
-def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair: str) -> DataFrame:
+def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame:
     """
     Fills up missing data with 0 volume rows,
     using the previous close as price for "open", "high" "low" and "close", volume is set to 0
@@ -65,16 +85,16 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair:
     """
     from freqtrade.exchange import timeframe_to_minutes
 
-    ohlc_dict = {
+    ohlcv_dict = {
         'open': 'first',
         'high': 'max',
         'low': 'min',
         'close': 'last',
         'volume': 'sum'
     }
-    ticker_minutes = timeframe_to_minutes(ticker_interval)
+    timeframe_minutes = timeframe_to_minutes(timeframe)
     # Resample to create "NAN" values
-    df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)
+    df = dataframe.resample(f'{timeframe_minutes}min', on='date').agg(ohlcv_dict)
 
     # Forwardfill close for missing columns
     df['close'] = df['close'].fillna(method='ffill')
@@ -92,8 +112,26 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair:
     return df
 
 
+def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date') -> DataFrame:
+    """
+    Trim dataframe based on given timerange
+    :param df: Dataframe to trim
+    :param timerange: timerange (use start and end date if available)
+    :param: df_date_col: Column in the dataframe to use as Date column
+    :return: trimmed dataframe
+    """
+    if timerange.starttype == 'date':
+        start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
+        df = df.loc[df[df_date_col] >= start, :]
+    if timerange.stoptype == 'date':
+        stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
+        df = df.loc[df[df_date_col] <= stop, :]
+    return df
+
+
 def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
     """
+    TODO: This should get a dedicated test
     Gets order book list, returns dataframe with below format per suggested by creslin
     -------------------------------------------------------------------
      b_sum       b_size       bids       asks       a_size       a_sum
@@ -116,23 +154,84 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
     return frame
 
 
-def trades_to_ohlcv(trades: list, timeframe: str) -> list:
+def trades_to_ohlcv(trades: list, timeframe: str) -> DataFrame:
     """
-    Converts trades list to ohlcv list
+    Converts trades list to OHLCV list
+    TODO: This should get a dedicated test
     :param trades: List of trades, as returned by ccxt.fetch_trades.
-    :param timeframe: Ticker timeframe to resample data to
+    :param timeframe: Timeframe to resample data to
-    :return: ohlcv timeframe as list (as returned by ccxt.fetch_ohlcv)
+    :return: OHLCV Dataframe.
     """
     from freqtrade.exchange import timeframe_to_minutes
-    ticker_minutes = timeframe_to_minutes(timeframe)
+    timeframe_minutes = timeframe_to_minutes(timeframe)
     df = pd.DataFrame(trades)
     df['datetime'] = pd.to_datetime(df['datetime'])
     df = df.set_index('datetime')
 
-    df_new = df['price'].resample(f'{ticker_minutes}min').ohlc()
+    df_new = df['price'].resample(f'{timeframe_minutes}min').ohlc()
-    df_new['volume'] = df['amount'].resample(f'{ticker_minutes}min').sum()
+    df_new['volume'] = df['amount'].resample(f'{timeframe_minutes}min').sum()
-    df_new['date'] = df_new.index.astype("int64") // 10 ** 6
+    df_new['date'] = df_new.index
     # Drop 0 volume rows
     df_new = df_new.dropna()
-    columns = ["date", "open", "high", "low", "close", "volume"]
+    return df_new[DEFAULT_DATAFRAME_COLUMNS]
-    return list(zip(*[df_new[x].values.tolist() for x in columns]))
+
+
+def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
+    """
+    Convert trades from one format to another format.
+    :param config: Config dictionary
+    :param convert_from: Source format
+    :param convert_to: Target format
+    :param erase: Erase souce data (does not apply if source and target format are identical)
+    """
+    from freqtrade.data.history.idatahandler import get_datahandler
+    src = get_datahandler(config['datadir'], convert_from)
+    trg = get_datahandler(config['datadir'], convert_to)
+
+    if 'pairs' not in config:
+        config['pairs'] = src.trades_get_pairs(config['datadir'])
+    logger.info(f"Converting trades for {config['pairs']}")
+
+    for pair in config['pairs']:
+        data = src.trades_load(pair=pair)
+        logger.info(f"Converting {len(data)} trades for {pair}")
+        trg.trades_store(pair, data)
+        if erase and convert_from != convert_to:
+            logger.info(f"Deleting source Trade data for {pair}.")
+            src.trades_purge(pair=pair)
+
+
+def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
+    """
+    Convert OHLCV from one format to another
+    :param config: Config dictionary
+    :param convert_from: Source format
+    :param convert_to: Target format
+    :param erase: Erase souce data (does not apply if source and target format are identical)
+    """
+    from freqtrade.data.history.idatahandler import get_datahandler
+    src = get_datahandler(config['datadir'], convert_from)
+    trg = get_datahandler(config['datadir'], convert_to)
+    timeframes = config.get('timeframes', [config.get('ticker_interval')])
+    logger.info(f"Converting candle (OHLCV) for timeframe {timeframes}")
+
+    if 'pairs' not in config:
+        config['pairs'] = []
+        # Check timeframes or fall back to ticker_interval.
+        for timeframe in timeframes:
+            config['pairs'].extend(src.ohlcv_get_pairs(config['datadir'],
+                                                       timeframe))
+    logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")
+
+    for timeframe in timeframes:
+        for pair in config['pairs']:
+            data = src.ohlcv_load(pair=pair, timeframe=timeframe,
+                                  timerange=None,
+                                  fill_missing=False,
+                                  drop_incomplete=False,
+                                  startup_candles=0)
+            logger.info(f"Converting {len(data)} candles for {pair}")
+            trg.ohlcv_store(pair=pair, timeframe=timeframe, data=data)
+            if erase and convert_from != convert_to:
+                logger.info(f"Deleting source data for {pair} / {timeframe}")
+                src.ohlcv_purge(pair=pair, timeframe=timeframe)

freqtrade/data/dataprovider.py

@@ -1,11 +1,10 @@
 """
 Dataprovider
 Responsible to provide data to the bot
-including Klines, tickers, historic data
+including ticker and orderbook data, live and historical candle (OHLCV) data
 Common Interface for bot and strategy to access data.
 """
 import logging
-from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple
 
 from pandas import DataFrame
@@ -37,52 +36,53 @@ class DataProvider:
     @property
     def available_pairs(self) -> List[Tuple[str, str]]:
         """
-        Return a list of tuples containing pair, ticker_interval for which data is currently cached.
+        Return a list of tuples containing (pair, timeframe) for which data is currently cached.
         Should be whitelist + open trades.
         """
         return list(self._exchange._klines.keys())
 
-    def ohlcv(self, pair: str, ticker_interval: str = None, copy: bool = True) -> DataFrame:
+    def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
         """
-        Get ohlcv data for the given pair as DataFrame
+        Get candle (OHLCV) data for the given pair as DataFrame
         Please use the `available_pairs` method to verify which pairs are currently cached.
         :param pair: pair to get the data for
-        :param ticker_interval: ticker interval to get data for
+        :param timeframe: Timeframe to get data for
         :param copy: copy dataframe before returning if True.
                      Use False only for read-only operations (where the dataframe is not modified)
         """
         if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
-            return self._exchange.klines((pair, ticker_interval or self._config['ticker_interval']),
+            return self._exchange.klines((pair, timeframe or self._config['ticker_interval']),
                                          copy=copy)
         else:
             return DataFrame()
 
-    def historic_ohlcv(self, pair: str, ticker_interval: str = None) -> DataFrame:
+    def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
         """
-        Get stored historic ohlcv data
+        Get stored historical candle (OHLCV) data
         :param pair: pair to get the data for
-        :param ticker_interval: ticker interval to get data for
+        :param timeframe: timeframe to get data for
         """
         return load_pair_history(pair=pair,
-                                 ticker_interval=ticker_interval or self._config['ticker_interval'],
+                                 timeframe=timeframe or self._config['ticker_interval'],
-                                 datadir=Path(self._config['datadir'])
+                                 datadir=self._config['datadir']
                                  )
 
-    def get_pair_dataframe(self, pair: str, ticker_interval: str = None) -> DataFrame:
+    def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:
         """
-        Return pair ohlcv data, either live or cached historical -- depending
+        Return pair candle (OHLCV) data, either live or cached historical -- depending
         on the runmode.
         :param pair: pair to get the data for
-        :param ticker_interval: ticker interval to get data for
+        :param timeframe: timeframe to get data for
+        :return: Dataframe for this pair
         """
         if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
-            # Get live ohlcv data.
+            # Get live OHLCV data.
-            data = self.ohlcv(pair=pair, ticker_interval=ticker_interval)
+            data = self.ohlcv(pair=pair, timeframe=timeframe)
         else:
-            # Get historic ohlcv data (cached on disk).
+            # Get historical OHLCV data (cached on disk).
-            data = self.historic_ohlcv(pair=pair, ticker_interval=ticker_interval)
+            data = self.historic_ohlcv(pair=pair, timeframe=timeframe)
         if len(data) == 0:
-            logger.warning(f"No data found for ({pair}, {ticker_interval}).")
+            logger.warning(f"No data found for ({pair}, {timeframe}).")
         return data
 
     def market(self, pair: str) -> Optional[Dict[str, Any]]:

freqtrade/data/history.py

@@ -1,442 +0,0 @@
-"""
-Handle historic data (ohlcv).
-
-Includes:
-* load data for a pair (or a list of pairs) from disk
-* download data from exchange and store to disk
-"""
-
-import logging
-import operator
-from datetime import datetime
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
-
-import arrow
-from pandas import DataFrame
-
-from freqtrade import OperationalException, misc
-from freqtrade.configuration import TimeRange
-from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
-from freqtrade.exchange import Exchange, timeframe_to_minutes
-
-logger = logging.getLogger(__name__)
-
-
-def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]:
-    """
-    Trim tickerlist based on given timerange
-    """
-    if not tickerlist:
-        return tickerlist
-
-    start_index = 0
-    stop_index = len(tickerlist)
-
-    if timerange.starttype == 'date':
-        while (start_index < len(tickerlist) and
-               tickerlist[start_index][0] < timerange.startts * 1000):
-            start_index += 1
-
-    if timerange.stoptype == 'date':
-        while (stop_index > 0 and
-               tickerlist[stop_index-1][0] > timerange.stopts * 1000):
-            stop_index -= 1
-
-    if start_index > stop_index:
-        raise ValueError(f'The timerange [{timerange.startts},{timerange.stopts}] is incorrect')
-
-    return tickerlist[start_index:stop_index]
-
-
-def load_tickerdata_file(datadir: Path, pair: str, ticker_interval: str,
-                         timerange: Optional[TimeRange] = None) -> Optional[list]:
-    """
-    Load a pair from file, either .json.gz or .json
-    :return: tickerlist or None if unsuccessful
-    """
-    filename = pair_data_filename(datadir, pair, ticker_interval)
-    pairdata = misc.file_load_json(filename)
-    if not pairdata:
-        return []
-
-    if timerange:
-        pairdata = trim_tickerlist(pairdata, timerange)
-    return pairdata
-
-
-def store_tickerdata_file(datadir: Path, pair: str,
-                          ticker_interval: str, data: list, is_zip: bool = False):
-    """
-    Stores tickerdata to file
-    """
-    filename = pair_data_filename(datadir, pair, ticker_interval)
-    misc.file_dump_json(filename, data, is_zip=is_zip)
-
-
-def load_trades_file(datadir: Path, pair: str,
-                     timerange: Optional[TimeRange] = None) -> List[Dict]:
-    """
-    Load a pair from file, either .json.gz or .json
-    :return: tradelist or empty list if unsuccesful
-    """
-    filename = pair_trades_filename(datadir, pair)
-    tradesdata = misc.file_load_json(filename)
-    if not tradesdata:
-        return []
-
-    return tradesdata
-
-
-def store_trades_file(datadir: Path, pair: str,
-                      data: list, is_zip: bool = True):
-    """
-    Stores tickerdata to file
-    """
-    filename = pair_trades_filename(datadir, pair)
-    misc.file_dump_json(filename, data, is_zip=is_zip)
-
-
-def _validate_pairdata(pair, pairdata, timerange: TimeRange):
-    if timerange.starttype == 'date' and pairdata[0][0] > timerange.startts * 1000:
-        logger.warning('Missing data at start for pair %s, data starts at %s',
-                       pair, arrow.get(pairdata[0][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
-    if timerange.stoptype == 'date' and pairdata[-1][0] < timerange.stopts * 1000:
-        logger.warning('Missing data at end for pair %s, data ends at %s',
-                       pair, arrow.get(pairdata[-1][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
-
-
-def load_pair_history(pair: str,
-                      ticker_interval: str,
-                      datadir: Path,
-                      timerange: Optional[TimeRange] = None,
-                      refresh_pairs: bool = False,
-                      exchange: Optional[Exchange] = None,
-                      fill_up_missing: bool = True,
-                      drop_incomplete: bool = True
-                      ) -> DataFrame:
-    """
-    Loads cached ticker history for the given pair.
-    :param pair: Pair to load data for
-    :param ticker_interval: Ticker-interval (e.g. "5m")
-    :param datadir: Path to the data storage location.
-    :param timerange: Limit data to be loaded to this timerange
-    :param refresh_pairs: Refresh pairs from exchange.
-        (Note: Requires exchange to be passed as well.)
-    :param exchange: Exchange object (needed when using "refresh_pairs")
-    :param fill_up_missing: Fill missing values with "No action"-candles
-    :param drop_incomplete: Drop last candle assuming it may be incomplete.
-    :return: DataFrame with ohlcv data
-    """
-
-    # The user forced the refresh of pairs
-    if refresh_pairs:
-        download_pair_history(datadir=datadir,
-                              exchange=exchange,
-                              pair=pair,
-                              ticker_interval=ticker_interval,
-                              timerange=timerange)
-
-    pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
-
-    if pairdata:
-        if timerange:
-            _validate_pairdata(pair, pairdata, timerange)
-        return parse_ticker_dataframe(pairdata, ticker_interval, pair=pair,
-                                      fill_missing=fill_up_missing,
-                                      drop_incomplete=drop_incomplete)
-    else:
-        logger.warning(
-            f'No history data for pair: "{pair}", interval: {ticker_interval}. '
-            'Use `freqtrade download-data` to download the data'
-        )
-        return None
-
-
-def load_data(datadir: Path,
-              ticker_interval: str,
-              pairs: List[str],
-              refresh_pairs: bool = False,
-              exchange: Optional[Exchange] = None,
-              timerange: Optional[TimeRange] = None,
-              fill_up_missing: bool = True,
-              ) -> Dict[str, DataFrame]:
-    """
-    Loads ticker history data for a list of pairs
-    :return: dict(<pair>:<tickerlist>)
-    TODO: refresh_pairs is still used by edge to keep the data uptodate.
-        This should be replaced in the future. Instead, writing the current candles to disk
-        from dataprovider should be implemented, as this would avoid loading ohlcv data twice.
-        exchange and refresh_pairs are then not needed here nor in load_pair_history.
-    """
-    result: Dict[str, DataFrame] = {}
-
-    for pair in pairs:
-        hist = load_pair_history(pair=pair, ticker_interval=ticker_interval,
-                                 datadir=datadir, timerange=timerange,
-                                 refresh_pairs=refresh_pairs,
-                                 exchange=exchange,
-                                 fill_up_missing=fill_up_missing)
-        if hist is not None:
-            result[pair] = hist
-    return result
-
-
-def pair_data_filename(datadir: Path, pair: str, ticker_interval: str) -> Path:
-    pair_s = pair.replace("/", "_")
-    filename = datadir.joinpath(f'{pair_s}-{ticker_interval}.json')
-    return filename
-
-
-def pair_trades_filename(datadir: Path, pair: str) -> Path:
-    pair_s = pair.replace("/", "_")
-    filename = datadir.joinpath(f'{pair_s}-trades.json.gz')
-    return filename
-
-
-def _load_cached_data_for_updating(datadir: Path, pair: str, ticker_interval: str,
-                                   timerange: Optional[TimeRange]) -> Tuple[List[Any],
-                                                                            Optional[int]]:
-    """
-    Load cached data to download more data.
-    If timerange is passed in, checks whether data from an before the stored data will be
-    downloaded.
-    If that's the case then what's available should be completely overwritten.
-    Only used by download_pair_history().
-    """
-
-    since_ms = None
-
-    # user sets timerange, so find the start time
-    if timerange:
-        if timerange.starttype == 'date':
-            since_ms = timerange.startts * 1000
-        elif timerange.stoptype == 'line':
-            num_minutes = timerange.stopts * timeframe_to_minutes(ticker_interval)
-            since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
-
-    # read the cached file
-    # Intentionally don't pass timerange in - since we need to load the full dataset.
-    data = load_tickerdata_file(datadir, pair, ticker_interval)
-    # remove the last item, could be incomplete candle
-    if data:
-        data.pop()
-    else:
-        data = []
-
-    if data:
-        if since_ms and since_ms < data[0][0]:
-            # Earlier data than existing data requested, redownload all
-            data = []
-        else:
-            # a part of the data was already downloaded, so download unexist data only
-            since_ms = data[-1][0] + 1
-
-    return (data, since_ms)
-
-
-def download_pair_history(datadir: Path,
-                          exchange: Optional[Exchange],
-                          pair: str,
-                          ticker_interval: str = '5m',
-                          timerange: Optional[TimeRange] = None) -> bool:
-    """
-    Download the latest ticker intervals from the exchange for the pair passed in parameters
-    The data is downloaded starting from the last correct ticker interval data that
-    exists in a cache. If timerange starts earlier than the data in the cache,
-    the full data will be redownloaded
-
-    Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
-
-    :param pair: pair to download
-    :param ticker_interval: ticker interval
-    :param timerange: range of time to download
-    :return: bool with success state
-    """
-    if not exchange:
-        raise OperationalException(
-            "Exchange needs to be initialized when downloading pair history data"
-        )
-
-    try:
-        logger.info(
-            f'Download history data for pair: "{pair}", interval: {ticker_interval} '
-            f'and store in {datadir}.'
-        )
-
-        data, since_ms = _load_cached_data_for_updating(datadir, pair, ticker_interval, timerange)
-
-        logger.debug("Current Start: %s", misc.format_ms_time(data[1][0]) if data else 'None')
-        logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
-
-        # Default since_ms to 30 days if nothing is given
-        new_data = exchange.get_historic_ohlcv(pair=pair, ticker_interval=ticker_interval,
-                                               since_ms=since_ms if since_ms
-                                               else
-                                               int(arrow.utcnow().shift(
-                                                   days=-30).float_timestamp) * 1000)
-        data.extend(new_data)
-
-        logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
-        logger.debug("New End: %s", misc.format_ms_time(data[-1][0]))
-
-        store_tickerdata_file(datadir, pair, ticker_interval, data=data)
-        return True
-
-    except Exception as e:
-        logger.error(
-            f'Failed to download history data for pair: "{pair}", interval: {ticker_interval}. '
-            f'Error: {e}'
-        )
-        return False
-
-
-def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes: List[str],
-                                dl_path: Path, timerange: Optional[TimeRange] = None,
-                                erase=False) -> List[str]:
-    """
-    Refresh stored ohlcv data for backtesting and hyperopt operations.
-    Used by freqtrade download-data
-    :return: Pairs not available
-    """
-    pairs_not_available = []
-    for pair in pairs:
-        if pair not in exchange.markets:
-            pairs_not_available.append(pair)
-            logger.info(f"Skipping pair {pair}...")
-            continue
-        for ticker_interval in timeframes:
-
-            dl_file = pair_data_filename(dl_path, pair, ticker_interval)
-            if erase and dl_file.exists():
-                logger.info(
-                    f'Deleting existing data for pair {pair}, interval {ticker_interval}.')
-                dl_file.unlink()
-
-            logger.info(f'Downloading pair {pair}, interval {ticker_interval}.')
-            download_pair_history(datadir=dl_path, exchange=exchange,
-                                  pair=pair, ticker_interval=str(ticker_interval),
-                                  timerange=timerange)
-    return pairs_not_available
-
-
-def download_trades_history(datadir: Path,
-                            exchange: Exchange,
-                            pair: str,
-                            timerange: Optional[TimeRange] = None) -> bool:
-    """
-    Download trade history from the exchange.
-    Appends to previously downloaded trades data.
-    """
-    try:
-
-        since = timerange.startts * 1000 if timerange and timerange.starttype == 'date' else None
-
-        trades = load_trades_file(datadir, pair)
-
-        from_id = trades[-1]['id'] if trades else None
-
-        logger.debug("Current Start: %s", trades[0]['datetime'] if trades else 'None')
-        logger.debug("Current End: %s", trades[-1]['datetime'] if trades else 'None')
-
-        new_trades = exchange.get_historic_trades(pair=pair,
-                                                  since=since if since else
-                                                  int(arrow.utcnow().shift(
-                                                      days=-30).float_timestamp) * 1000,
-                                                  #  until=xxx,
-                                                  from_id=from_id,
-                                                  )
-        trades.extend(new_trades[1])
-        store_trades_file(datadir, pair, trades)
-
-        logger.debug("New Start: %s", trades[0]['datetime'])
-        logger.debug("New End: %s", trades[-1]['datetime'])
-        logger.info(f"New Amount of trades: {len(trades)}")
-        return True
-
-    except Exception as e:
-        logger.error(
-            f'Failed to download historic trades for pair: "{pair}". '
-            f'Error: {e}'
-        )
-        return False
-
-
-def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir: Path,
-                                 timerange: TimeRange, erase=False) -> List[str]:
-    """
-    Refresh stored trades data.
-    Used by freqtrade download-data
-    :return: Pairs not available
-    """
-    pairs_not_available = []
-    for pair in pairs:
-        if pair not in exchange.markets:
-            pairs_not_available.append(pair)
-            logger.info(f"Skipping pair {pair}...")
-            continue
-
-        dl_file = pair_trades_filename(datadir, pair)
-        if erase and dl_file.exists():
-            logger.info(
-                f'Deleting existing data for pair {pair}.')
-            dl_file.unlink()
-
-        logger.info(f'Downloading trades for pair {pair}.')
-        download_trades_history(datadir=datadir, exchange=exchange,
-                                pair=pair,
-                                timerange=timerange)
-    return pairs_not_available
-
-
-def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
-                            datadir: Path, timerange: TimeRange, erase=False) -> None:
-    """
-    Convert stored trades data to ohlcv data
-    """
-    for pair in pairs:
-        trades = load_trades_file(datadir, pair)
-        for timeframe in timeframes:
-            ohlcv_file = pair_data_filename(datadir, pair, timeframe)
-            if erase and ohlcv_file.exists():
-                logger.info(f'Deleting existing data for pair {pair}, interval {timeframe}.')
-                ohlcv_file.unlink()
-            ohlcv = trades_to_ohlcv(trades, timeframe)
-            # Store ohlcv
-            store_tickerdata_file(datadir, pair, timeframe, data=ohlcv)
-
-
-def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
-    """
-    Get the maximum timeframe for the given backtest data
-    :param data: dictionary with preprocessed backtesting data
-    :return: tuple containing min_date, max_date
-    """
-    timeframe = [
-        (arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
-        for frame in data.values()
-    ]
-    return min(timeframe, key=operator.itemgetter(0))[0], \
-        max(timeframe, key=operator.itemgetter(1))[1]
-
-
-def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
-                           max_date: datetime, ticker_interval_mins: int) -> bool:
-    """
-    Validates preprocessed backtesting data for missing values and shows warnings about it that.
-
-    :param data: preprocessed backtesting data (as DataFrame)
-    :param pair: pair used for log output.
-    :param min_date: start-date of the data
-    :param max_date: end-date of the data
-    :param ticker_interval_mins: ticker interval in minutes
-    """
-    # total difference in minutes / interval-minutes
-    expected_frames = int((max_date - min_date).total_seconds() // 60 // ticker_interval_mins)
-    found_missing = False
-    dflen = len(data)
-    if dflen < expected_frames:
-        found_missing = True
-        logger.warning("%s has missing frames: expected %s, got %s, that's %s missing values",
-                       pair, expected_frames, dflen, expected_frames - dflen)
-    return found_missing

freqtrade/data/history/__init__.py

@@ -0,0 +1,14 @@
+"""
+Handle historic data (ohlcv).
+
+Includes:
+* load data for a pair (or a list of pairs) from disk
+* download data from exchange and store to disk
+"""
+
+from .history_utils import (convert_trades_to_ohlcv,  # noqa: F401
+                            get_timerange, load_data, load_pair_history,
+                            refresh_backtest_ohlcv_data,
+                            refresh_backtest_trades_data, refresh_data,
+                            validate_backtest_data)
+from .idatahandler import get_datahandler  # noqa: F401

freqtrade/data/history/history_utils.py

@@ -0,0 +1,375 @@
+import logging
+import operator
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import arrow
+from pandas import DataFrame
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
+from freqtrade.data.converter import ohlcv_to_dataframe, trades_to_ohlcv
+from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import Exchange
+
+logger = logging.getLogger(__name__)
+
+
+def load_pair_history(pair: str,
+                      timeframe: str,
+                      datadir: Path, *,
+                      timerange: Optional[TimeRange] = None,
+                      fill_up_missing: bool = True,
+                      drop_incomplete: bool = True,
+                      startup_candles: int = 0,
+                      data_format: str = None,
+                      data_handler: IDataHandler = None,
+                      ) -> DataFrame:
+    """
+    Load cached ohlcv history for the given pair.
+
+    :param pair: Pair to load data for
+    :param timeframe: Timeframe (e.g. "5m")
+    :param datadir: Path to the data storage location.
+    :param data_format: Format of the data. Ignored if data_handler is set.
+    :param timerange: Limit data to be loaded to this timerange
+    :param fill_up_missing: Fill missing values with "No action"-candles
+    :param drop_incomplete: Drop last candle assuming it may be incomplete.
+    :param startup_candles: Additional candles to load at the start of the period
+    :param data_handler: Initialized data-handler to use.
+                         Will be initialized from data_format if not set
+    :return: DataFrame with ohlcv data, or empty DataFrame
+    """
+    data_handler = get_datahandler(datadir, data_format, data_handler)
+
+    return data_handler.ohlcv_load(pair=pair,
+                                   timeframe=timeframe,
+                                   timerange=timerange,
+                                   fill_missing=fill_up_missing,
+                                   drop_incomplete=drop_incomplete,
+                                   startup_candles=startup_candles,
+                                   )
+
+
+def load_data(datadir: Path,
+              timeframe: str,
+              pairs: List[str], *,
+              timerange: Optional[TimeRange] = None,
+              fill_up_missing: bool = True,
+              startup_candles: int = 0,
+              fail_without_data: bool = False,
+              data_format: str = 'json',
+              ) -> Dict[str, DataFrame]:
+    """
+    Load ohlcv history data for a list of pairs.
+
+    :param datadir: Path to the data storage location.
+    :param timeframe: Timeframe (e.g. "5m")
+    :param pairs: List of pairs to load
+    :param timerange: Limit data to be loaded to this timerange
+    :param fill_up_missing: Fill missing values with "No action"-candles
+    :param startup_candles: Additional candles to load at the start of the period
+    :param fail_without_data: Raise OperationalException if no data is found.
+    :param data_format: Data format which should be used. Defaults to json
+    :return: dict(<pair>:<Dataframe>)
+    """
+    result: Dict[str, DataFrame] = {}
+    if startup_candles > 0 and timerange:
+        logger.info(f'Using indicator startup period: {startup_candles} ...')
+
+    data_handler = get_datahandler(datadir, data_format)
+
+    for pair in pairs:
+        hist = load_pair_history(pair=pair, timeframe=timeframe,
+                                 datadir=datadir, timerange=timerange,
+                                 fill_up_missing=fill_up_missing,
+                                 startup_candles=startup_candles,
+                                 data_handler=data_handler
+                                 )
+        if not hist.empty:
+            result[pair] = hist
+
+    if fail_without_data and not result:
+        raise OperationalException("No data found. Terminating.")
+    return result
+
+
+def refresh_data(datadir: Path,
+                 timeframe: str,
+                 pairs: List[str],
+                 exchange: Exchange,
+                 data_format: str = None,
+                 timerange: Optional[TimeRange] = None,
+                 ) -> None:
+    """
+    Refresh ohlcv history data for a list of pairs.
+
+    :param datadir: Path to the data storage location.
+    :param timeframe: Timeframe (e.g. "5m")
+    :param pairs: List of pairs to load
+    :param exchange: Exchange object
+    :param timerange: Limit data to be loaded to this timerange
+    """
+    data_handler = get_datahandler(datadir, data_format)
+    for pair in pairs:
+        _download_pair_history(pair=pair, timeframe=timeframe,
+                               datadir=datadir, timerange=timerange,
+                               exchange=exchange, data_handler=data_handler)
+
+
+def _load_cached_data_for_updating(pair: str, timeframe: str, timerange: Optional[TimeRange],
+                                   data_handler: IDataHandler) -> Tuple[DataFrame, Optional[int]]:
+    """
+    Load cached data to download more data.
+    If timerange is passed in, checks whether data from an before the stored data will be
+    downloaded.
+    If that's the case then what's available should be completely overwritten.
+    Otherwise downloads always start at the end of the available data to avoid data gaps.
+    Note: Only used by download_pair_history().
+    """
+    start = None
+    if timerange:
+        if timerange.starttype == 'date':
+            # TODO: convert to date for conversion
+            start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
+
+    # Intentionally don't pass timerange in - since we need to load the full dataset.
+    data = data_handler.ohlcv_load(pair, timeframe=timeframe,
+                                   timerange=None, fill_missing=False,
+                                   drop_incomplete=True, warn_no_data=False)
+    if not data.empty:
+        if start and start < data.iloc[0]['date']:
+            # Earlier data than existing data requested, redownload all
+            data = DataFrame(columns=DEFAULT_DATAFRAME_COLUMNS)
+        else:
+            start = data.iloc[-1]['date']
+
+    start_ms = int(start.timestamp() * 1000) if start else None
+    return data, start_ms
+
+
+def _download_pair_history(datadir: Path,
+                           exchange: Exchange,
+                           pair: str, *,
+                           timeframe: str = '5m',
+                           timerange: Optional[TimeRange] = None,
+                           data_handler: IDataHandler = None) -> bool:
+    """
+    Download latest candles from the exchange for the pair and timeframe passed in parameters
+    The data is downloaded starting from the last correct data that
+    exists in a cache. If timerange starts earlier than the data in the cache,
+    the full data will be redownloaded
+
+    Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
+
+    :param pair: pair to download
+    :param timeframe: Timeframe (e.g "5m")
+    :param timerange: range of time to download
+    :return: bool with success state
+    """
+    data_handler = get_datahandler(datadir, data_handler=data_handler)
+
+    try:
+        logger.info(
+            f'Download history data for pair: "{pair}", timeframe: {timeframe} '
+            f'and store in {datadir}.'
+        )
+
+        # data, since_ms = _load_cached_data_for_updating_old(datadir, pair, timeframe, timerange)
+        data, since_ms = _load_cached_data_for_updating(pair, timeframe, timerange,
+                                                        data_handler=data_handler)
+
+        logger.debug("Current Start: %s",
+                     f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
+        logger.debug("Current End: %s",
+                     f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
+
+        # Default since_ms to 30 days if nothing is given
+        new_data = exchange.get_historic_ohlcv(pair=pair,
+                                               timeframe=timeframe,
+                                               since_ms=since_ms if since_ms else
+                                               int(arrow.utcnow().shift(
+                                                   days=-30).float_timestamp) * 1000
+                                               )
+        # TODO: Maybe move parsing to exchange class (?)
+        new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,
+                                           fill_missing=False, drop_incomplete=True)
+        if data.empty:
+            data = new_dataframe
+        else:
+            data = data.append(new_dataframe)
+
+        logger.debug("New  Start: %s",
+                     f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
+        logger.debug("New End: %s",
+                     f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
+
+        data_handler.ohlcv_store(pair, timeframe, data=data)
+        return True
+
+    except Exception as e:
+        logger.error(
+            f'Failed to download history data for pair: "{pair}", timeframe: {timeframe}. '
+            f'Error: {e}'
+        )
+        return False
+
+
+def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes: List[str],
+                                datadir: Path, timerange: Optional[TimeRange] = None,
+                                erase: bool = False, data_format: str = None) -> List[str]:
+    """
+    Refresh stored ohlcv data for backtesting and hyperopt operations.
+    Used by freqtrade download-data subcommand.
+    :return: List of pairs that are not available.
+    """
+    pairs_not_available = []
+    data_handler = get_datahandler(datadir, data_format)
+    for pair in pairs:
+        if pair not in exchange.markets:
+            pairs_not_available.append(pair)
+            logger.info(f"Skipping pair {pair}...")
+            continue
+        for timeframe in timeframes:
+
+            if erase:
+                if data_handler.ohlcv_purge(pair, timeframe):
+                    logger.info(
+                        f'Deleting existing data for pair {pair}, interval {timeframe}.')
+
+            logger.info(f'Downloading pair {pair}, interval {timeframe}.')
+            _download_pair_history(datadir=datadir, exchange=exchange,
+                                   pair=pair, timeframe=str(timeframe),
+                                   timerange=timerange, data_handler=data_handler)
+    return pairs_not_available
+
+
+def _download_trades_history(exchange: Exchange,
+                             pair: str, *,
+                             timerange: Optional[TimeRange] = None,
+                             data_handler: IDataHandler
+                             ) -> bool:
+    """
+    Download trade history from the exchange.
+    Appends to previously downloaded trades data.
+    """
+    try:
+
+        since = timerange.startts * 1000 if timerange and timerange.starttype == 'date' else None
+
+        trades = data_handler.trades_load(pair)
+
+        from_id = trades[-1]['id'] if trades else None
+
+        logger.debug("Current Start: %s", trades[0]['datetime'] if trades else 'None')
+        logger.debug("Current End: %s", trades[-1]['datetime'] if trades else 'None')
+
+        # Default since_ms to 30 days if nothing is given
+        new_trades = exchange.get_historic_trades(pair=pair,
+                                                  since=since if since else
+                                                  int(arrow.utcnow().shift(
+                                                      days=-30).float_timestamp) * 1000,
+                                                  from_id=from_id,
+                                                  )
+        trades.extend(new_trades[1])
+        data_handler.trades_store(pair, data=trades)
+
+        logger.debug("New Start: %s", trades[0]['datetime'])
+        logger.debug("New End: %s", trades[-1]['datetime'])
+        logger.info(f"New Amount of trades: {len(trades)}")
+        return True
+
+    except Exception as e:
+        logger.error(
+            f'Failed to download historic trades for pair: "{pair}". '
+            f'Error: {e}'
+        )
+        return False
+
+
+def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir: Path,
+                                 timerange: TimeRange, erase: bool = False,
+                                 data_format: str = 'jsongz') -> List[str]:
+    """
+    Refresh stored trades data for backtesting and hyperopt operations.
+    Used by freqtrade download-data subcommand.
+    :return: List of pairs that are not available.
+    """
+    pairs_not_available = []
+    data_handler = get_datahandler(datadir, data_format=data_format)
+    for pair in pairs:
+        if pair not in exchange.markets:
+            pairs_not_available.append(pair)
+            logger.info(f"Skipping pair {pair}...")
+            continue
+
+        if erase:
+            if data_handler.trades_purge(pair):
+                logger.info(f'Deleting existing data for pair {pair}.')
+
+        logger.info(f'Downloading trades for pair {pair}.')
+        _download_trades_history(exchange=exchange,
+                                 pair=pair,
+                                 timerange=timerange,
+                                 data_handler=data_handler)
+    return pairs_not_available
+
+
+def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
+                            datadir: Path, timerange: TimeRange, erase: bool = False,
+                            data_format_ohlcv: str = 'json',
+                            data_format_trades: str = 'jsongz') -> None:
+    """
+    Convert stored trades data to ohlcv data
+    """
+    data_handler_trades = get_datahandler(datadir, data_format=data_format_trades)
+    data_handler_ohlcv = get_datahandler(datadir, data_format=data_format_ohlcv)
+
+    for pair in pairs:
+        trades = data_handler_trades.trades_load(pair)
+        for timeframe in timeframes:
+            if erase:
+                if data_handler_ohlcv.ohlcv_purge(pair, timeframe):
+                    logger.info(f'Deleting existing data for pair {pair}, interval {timeframe}.')
+            ohlcv = trades_to_ohlcv(trades, timeframe)
+            # Store ohlcv
+            data_handler_ohlcv.ohlcv_store(pair, timeframe, data=ohlcv)
+
+
+def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
+    """
+    Get the maximum common timerange for the given backtest data.
+
+    :param data: dictionary with preprocessed backtesting data
+    :return: tuple containing min_date, max_date
+    """
+    timeranges = [
+        (arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
+        for frame in data.values()
+    ]
+    return (min(timeranges, key=operator.itemgetter(0))[0],
+            max(timeranges, key=operator.itemgetter(1))[1])
+
+
+def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
+                           max_date: datetime, timeframe_min: int) -> bool:
+    """
+    Validates preprocessed backtesting data for missing values and shows warnings about it that.
+
+    :param data: preprocessed backtesting data (as DataFrame)
+    :param pair: pair used for log output.
+    :param min_date: start-date of the data
+    :param max_date: end-date of the data
+    :param timeframe_min: Timeframe in minutes
+    """
+    # total difference in minutes / timeframe-minutes
+    expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_min)
+    found_missing = False
+    dflen = len(data)
+    if dflen < expected_frames:
+        found_missing = True
+        logger.warning("%s has missing frames: expected %s, got %s, that's %s missing values",
+                       pair, expected_frames, dflen, expected_frames - dflen)
+    return found_missing

freqtrade/data/history/idatahandler.py

@@ -0,0 +1,232 @@
+"""
+Abstract datahandler interface.
+It's subclasses handle and storing data from disk.
+
+"""
+import logging
+from abc import ABC, abstractclassmethod, abstractmethod
+from copy import deepcopy
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict, List, Optional, Type
+
+from pandas import DataFrame
+
+from freqtrade.configuration import TimeRange
+from freqtrade.data.converter import clean_ohlcv_dataframe, trim_dataframe
+from freqtrade.exchange import timeframe_to_seconds
+
+logger = logging.getLogger(__name__)
+
+
+class IDataHandler(ABC):
+
+    def __init__(self, datadir: Path) -> None:
+        self._datadir = datadir
+
+    @abstractclassmethod
+    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
+        """
+        Returns a list of all pairs with ohlcv data available in this datadir
+        for the specified timeframe
+        :param datadir: Directory to search for ohlcv files
+        :param timeframe: Timeframe to search pairs for
+        :return: List of Pairs
+        """
+
+    @abstractmethod
+    def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :timeframe: Timeframe - used to generate filename
+        :data: Dataframe containing OHLCV data
+        :return: None
+        """
+
+    @abstractmethod
+    def _ohlcv_load(self, pair: str, timeframe: str,
+                    timerange: Optional[TimeRange] = None,
+                    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+
+    @abstractmethod
+    def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :param timeframe: Timeframe (e.g. "5m")
+        :return: True when deleted, false if file did not exist.
+        """
+
+    @abstractmethod
+    def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        """
+
+    @abstractclassmethod
+    def trades_get_pairs(cls, datadir: Path) -> List[str]:
+        """
+        Returns a list of all pairs for which trade data is available in this
+        :param datadir: Directory to search for ohlcv files
+        :return: List of Pairs
+        """
+
+    @abstractmethod
+    def trades_store(self, pair: str, data: List[Dict]) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: List of Dicts containing trade data
+        """
+
+    @abstractmethod
+    def trades_append(self, pair: str, data: List[Dict]):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: List of Dicts containing trade data
+        """
+
+    @abstractmethod
+    def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> List[Dict]:
+        """
+        Load a pair from file, either .json.gz or .json
+        :param pair: Load trades for this pair
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+
+    @abstractmethod
+    def trades_purge(self, pair: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :return: True when deleted, false if file did not exist.
+        """
+
+    def ohlcv_load(self, pair, timeframe: str,
+                   timerange: Optional[TimeRange] = None,
+                   fill_missing: bool = True,
+                   drop_incomplete: bool = True,
+                   startup_candles: int = 0,
+                   warn_no_data: bool = True
+                   ) -> DataFrame:
+        """
+        Load cached candle (OHLCV) data for the given pair.
+
+        :param pair: Pair to load data for
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange
+        :param fill_missing: Fill missing values with "No action"-candles
+        :param drop_incomplete: Drop last candle assuming it may be incomplete.
+        :param startup_candles: Additional candles to load at the start of the period
+        :param warn_no_data: Log a warning message when no data is found
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        # Fix startup period
+        timerange_startup = deepcopy(timerange)
+        if startup_candles > 0 and timerange_startup:
+            timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
+
+        pairdf = self._ohlcv_load(pair, timeframe,
+                                  timerange=timerange_startup)
+        if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
+            return pairdf
+        else:
+            enddate = pairdf.iloc[-1]['date']
+
+            if timerange_startup:
+                self._validate_pairdata(pair, pairdf, timerange_startup)
+                pairdf = trim_dataframe(pairdf, timerange_startup)
+                if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
+                    return pairdf
+
+            # incomplete candles should only be dropped if we didn't trim the end beforehand.
+            pairdf = clean_ohlcv_dataframe(pairdf, timeframe,
+                                           pair=pair,
+                                           fill_missing=fill_missing,
+                                           drop_incomplete=(drop_incomplete and
+                                                            enddate == pairdf.iloc[-1]['date']))
+            self._check_empty_df(pairdf, pair, timeframe, warn_no_data)
+            return pairdf
+
+    def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str, warn_no_data: bool):
+        """
+        Warn on empty dataframe
+        """
+        if pairdf.empty:
+            if warn_no_data:
+                logger.warning(
+                    f'No history data for pair: "{pair}", timeframe: {timeframe}. '
+                    'Use `freqtrade download-data` to download the data'
+                )
+            return True
+        return False
+
+    def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
+        """
+        Validates pairdata for missing data at start end end and logs warnings.
+        :param pairdata: Dataframe to validate
+        :param timerange: Timerange specified for start and end dates
+        """
+
+        if timerange.starttype == 'date':
+            start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
+            if pairdata.iloc[0]['date'] > start:
+                logger.warning(f"Missing data at start for pair {pair}, "
+                               f"data starts at {pairdata.iloc[0]['date']:%Y-%m-%d %H:%M:%S}")
+        if timerange.stoptype == 'date':
+            stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
+            if pairdata.iloc[-1]['date'] < stop:
+                logger.warning(f"Missing data at end for pair {pair}, "
+                               f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
+
+
+def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
+    """
+    Get datahandler class.
+    Could be done using Resolvers, but since this may be called often and resolvers
+    are rather expensive, doing this directly should improve performance.
+    :param datatype: datatype to use.
+    :return: Datahandler class
+    """
+
+    if datatype == 'json':
+        from .jsondatahandler import JsonDataHandler
+        return JsonDataHandler
+    elif datatype == 'jsongz':
+        from .jsondatahandler import JsonGzDataHandler
+        return JsonGzDataHandler
+    else:
+        raise ValueError(f"No datahandler for datatype {datatype} available.")
+
+
+def get_datahandler(datadir: Path, data_format: str = None,
+                    data_handler: IDataHandler = None) -> IDataHandler:
+    """
+    :param datadir: Folder to save data
+    :data_format: dataformat to use
+    :data_handler: returns this datahandler if it exists or initializes a new one
+    """
+
+    if not data_handler:
+        HandlerClass = get_datahandlerclass(data_format or 'json')
+        data_handler = HandlerClass(datadir)
+    return data_handler

freqtrade/data/history/jsondatahandler.py

@@ -0,0 +1,179 @@
+import re
+from pathlib import Path
+from typing import Dict, List, Optional
+
+import numpy as np
+from pandas import DataFrame, read_json, to_datetime
+
+from freqtrade import misc
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
+
+from .idatahandler import IDataHandler
+
+
+class JsonDataHandler(IDataHandler):
+
+    _use_zip = False
+    _columns = DEFAULT_DATAFRAME_COLUMNS
+
+    @classmethod
+    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
+        """
+        Returns a list of all pairs with ohlcv data available in this datadir
+        for the specified timeframe
+        :param datadir: Directory to search for ohlcv files
+        :param timeframe: Timeframe to search pairs for
+        :return: List of Pairs
+        """
+
+        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + '.json)', p.name)
+                for p in datadir.glob(f"*{timeframe}.{cls._get_file_extension()}")]
+        # Check if regex found something and only return these results
+        return [match[0].replace('_', '/') for match in _tmp if match]
+
+    def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :timeframe: Timeframe - used to generate filename
+        :data: Dataframe containing OHLCV data
+        :return: None
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
+        _data = data.copy()
+        # Convert date to int
+        _data['date'] = _data['date'].astype(np.int64) // 1000 // 1000
+
+        # Reset index, select only appropriate columns and save as json
+        _data.reset_index(drop=True).loc[:, self._columns].to_json(
+            filename, orient="values",
+            compression='gzip' if self._use_zip else None)
+
+    def _ohlcv_load(self, pair: str, timeframe: str,
+                    timerange: Optional[TimeRange] = None,
+                    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
+        if not filename.exists():
+            return DataFrame(columns=self._columns)
+        pairdata = read_json(filename, orient='values')
+        pairdata.columns = self._columns
+        pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
+                                          'low': 'float', 'close': 'float', 'volume': 'float'})
+        pairdata['date'] = to_datetime(pairdata['date'],
+                                       unit='ms',
+                                       utc=True,
+                                       infer_datetime_format=True)
+        return pairdata
+
+    def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :param timeframe: Timeframe (e.g. "5m")
+        :return: True when deleted, false if file did not exist.
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
+
+    def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        """
+        raise NotImplementedError()
+
+    @classmethod
+    def trades_get_pairs(cls, datadir: Path) -> List[str]:
+        """
+        Returns a list of all pairs for which trade data is available in this
+        :param datadir: Directory to search for ohlcv files
+        :return: List of Pairs
+        """
+        _tmp = [re.search(r'^(\S+)(?=\-trades.json)', p.name)
+                for p in datadir.glob(f"*trades.{cls._get_file_extension()}")]
+        # Check if regex found something and only return these results to avoid exceptions.
+        return [match[0].replace('_', '/') for match in _tmp if match]
+
+    def trades_store(self, pair: str, data: List[Dict]) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: List of Dicts containing trade data
+        """
+        filename = self._pair_trades_filename(self._datadir, pair)
+        misc.file_dump_json(filename, data, is_zip=self._use_zip)
+
+    def trades_append(self, pair: str, data: List[Dict]):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: List of Dicts containing trade data
+        """
+        raise NotImplementedError()
+
+    def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> List[Dict]:
+        """
+        Load a pair from file, either .json.gz or .json
+        # TODO: respect timerange ...
+        :param pair: Load trades for this pair
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+        filename = self._pair_trades_filename(self._datadir, pair)
+        tradesdata = misc.file_load_json(filename)
+        if not tradesdata:
+            return []
+
+        return tradesdata
+
+    def trades_purge(self, pair: str) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :return: True when deleted, false if file did not exist.
+        """
+        filename = self._pair_trades_filename(self._datadir, pair)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
+
+    @classmethod
+    def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
+        pair_s = misc.pair_to_filename(pair)
+        filename = datadir.joinpath(f'{pair_s}-{timeframe}.{cls._get_file_extension()}')
+        return filename
+
+    @classmethod
+    def _get_file_extension(cls):
+        return "json.gz" if cls._use_zip else "json"
+
+    @classmethod
+    def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
+        pair_s = misc.pair_to_filename(pair)
+        filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}')
+        return filename
+
+
+class JsonGzDataHandler(JsonDataHandler):
+
+    _use_zip = True

freqtrade/edge/__init__.py

@@ -1,455 +1 @@
-# pragma pylint: disable=W0603
+from .edge_positioning import Edge, PairInfo  # noqa: F401
-""" Edge positioning package """
-import logging
-from pathlib import Path
-from typing import Any, Dict, NamedTuple
-
-import arrow
-import numpy as np
-import utils_find_1st as utf1st
-from pandas import DataFrame
-
-from freqtrade import constants, OperationalException
-from freqtrade.configuration import TimeRange
-from freqtrade.data import history
-from freqtrade.strategy.interface import SellType
-
-
-logger = logging.getLogger(__name__)
-
-
-class PairInfo(NamedTuple):
-    stoploss: float
-    winrate: float
-    risk_reward_ratio: float
-    required_risk_reward: float
-    expectancy: float
-    nb_trades: int
-    avg_trade_duration: float
-
-
-class Edge:
-    """
-    Calculates Win Rate, Risk Reward Ratio, Expectancy
-    against historical data for a give set of markets and a strategy
-    it then adjusts stoploss and position size accordingly
-    and force it into the strategy
-    Author: https://github.com/mishaker
-    """
-
-    config: Dict = {}
-    _cached_pairs: Dict[str, Any] = {}  # Keeps a list of pairs
-
-    def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
-
-        self.config = config
-        self.exchange = exchange
-        self.strategy = strategy
-
-        self.edge_config = self.config.get('edge', {})
-        self._cached_pairs: Dict[str, Any] = {}  # Keeps a list of pairs
-        self._final_pairs: list = []
-
-        # checking max_open_trades. it should be -1 as with Edge
-        # the number of trades is determined by position size
-        if self.config['max_open_trades'] != float('inf'):
-            logger.critical('max_open_trades should be -1 in config !')
-
-        if self.config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT:
-            raise OperationalException('Edge works only with unlimited stake amount')
-
-        self._capital_percentage: float = self.edge_config.get('capital_available_percentage')
-        self._allowed_risk: float = self.edge_config.get('allowed_risk')
-        self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14)
-        self._last_updated: int = 0  # Timestamp of pairs last updated time
-        self._refresh_pairs = True
-
-        self._stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
-        self._stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
-        self._stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
-
-        # calculating stoploss range
-        self._stoploss_range = np.arange(
-            self._stoploss_range_min,
-            self._stoploss_range_max,
-            self._stoploss_range_step
-        )
-
-        self._timerange: TimeRange = TimeRange.parse_timerange("%s-" % arrow.now().shift(
-            days=-1 * self._since_number_of_days).format('YYYYMMDD'))
-        if config.get('fee'):
-            self.fee = config['fee']
-        else:
-            self.fee = self.exchange.get_fee()
-
-    def calculate(self) -> bool:
-        pairs = self.config['exchange']['pair_whitelist']
-        heartbeat = self.edge_config.get('process_throttle_secs')
-
-        if (self._last_updated > 0) and (
-                self._last_updated + heartbeat > arrow.utcnow().timestamp):
-            return False
-
-        data: Dict[str, Any] = {}
-        logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
-        logger.info('Using local backtesting data (using whitelist in given config) ...')
-
-        data = history.load_data(
-            datadir=Path(self.config['datadir']),
-            pairs=pairs,
-            ticker_interval=self.strategy.ticker_interval,
-            refresh_pairs=self._refresh_pairs,
-            exchange=self.exchange,
-            timerange=self._timerange
-        )
-
-        if not data:
-            # Reinitializing cached pairs
-            self._cached_pairs = {}
-            logger.critical("No data found. Edge is stopped ...")
-            return False
-
-        preprocessed = self.strategy.tickerdata_to_dataframe(data)
-
-        # Print timeframe
-        min_date, max_date = history.get_timeframe(preprocessed)
-        logger.info(
-            'Measuring data from %s up to %s (%s days) ...',
-            min_date.isoformat(),
-            max_date.isoformat(),
-            (max_date - min_date).days
-        )
-        headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
-
-        trades: list = []
-        for pair, pair_data in preprocessed.items():
-            # Sorting dataframe by date and reset index
-            pair_data = pair_data.sort_values(by=['date'])
-            pair_data = pair_data.reset_index(drop=True)
-
-            ticker_data = self.strategy.advise_sell(
-                self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
-
-            trades += self._find_trades_for_stoploss_range(ticker_data, pair, self._stoploss_range)
-
-        # If no trade found then exit
-        if len(trades) == 0:
-            logger.info("No trades found.")
-            return False
-
-        # Fill missing, calculable columns, profit, duration , abs etc.
-        trades_df = self._fill_calculable_fields(DataFrame(trades))
-        self._cached_pairs = self._process_expectancy(trades_df)
-        self._last_updated = arrow.utcnow().timestamp
-
-        return True
-
-    def stake_amount(self, pair: str, free_capital: float,
-                     total_capital: float, capital_in_trade: float) -> float:
-        stoploss = self.stoploss(pair)
-        available_capital = (total_capital + capital_in_trade) * self._capital_percentage
-        allowed_capital_at_risk = available_capital * self._allowed_risk
-        max_position_size = abs(allowed_capital_at_risk / stoploss)
-        position_size = min(max_position_size, free_capital)
-        if pair in self._cached_pairs:
-            logger.info(
-                'winrate: %s, expectancy: %s, position size: %s, pair: %s,'
-                ' capital in trade: %s, free capital: %s, total capital: %s,'
-                ' stoploss: %s, available capital: %s.',
-                self._cached_pairs[pair].winrate,
-                self._cached_pairs[pair].expectancy,
-                position_size, pair,
-                capital_in_trade, free_capital, total_capital,
-                stoploss, available_capital
-            )
-        return round(position_size, 15)
-
-    def stoploss(self, pair: str) -> float:
-        if pair in self._cached_pairs:
-            return self._cached_pairs[pair].stoploss
-        else:
-            logger.warning('tried to access stoploss of a non-existing pair, '
-                           'strategy stoploss is returned instead.')
-            return self.strategy.stoploss
-
-    def adjust(self, pairs) -> list:
-        """
-        Filters out and sorts "pairs" according to Edge calculated pairs
-        """
-        final = []
-        for pair, info in self._cached_pairs.items():
-            if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
-                info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)) and \
-                    pair in pairs:
-                final.append(pair)
-
-        if self._final_pairs != final:
-            self._final_pairs = final
-            if self._final_pairs:
-                logger.info(
-                    'Minimum expectancy and minimum winrate are met only for %s,'
-                    ' so other pairs are filtered out.',
-                    self._final_pairs
-                    )
-            else:
-                logger.info(
-                    'Edge removed all pairs as no pair with minimum expectancy '
-                    'and minimum winrate was found !'
-                    )
-
-        return self._final_pairs
-
-    def accepted_pairs(self) -> list:
-        """
-        return a list of accepted pairs along with their winrate, expectancy and stoploss
-        """
-        final = []
-        for pair, info in self._cached_pairs.items():
-            if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
-                 info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)):
-                final.append({
-                    'Pair': pair,
-                    'Winrate': info.winrate,
-                    'Expectancy': info.expectancy,
-                    'Stoploss': info.stoploss,
-                })
-        return final
-
-    def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
-        """
-        The result frame contains a number of columns that are calculable
-        from other columns. These are left blank till all rows are added,
-        to be populated in single vector calls.
-
-        Columns to be populated are:
-        - Profit
-        - trade duration
-        - profit abs
-        :param result Dataframe
-        :return: result Dataframe
-        """
-
-        # stake and fees
-        # stake = 0.015
-        # 0.05% is 0.0005
-        # fee = 0.001
-
-        # we set stake amount to an arbitrary amount.
-        # as it doesn't change the calculation.
-        # all returned values are relative. they are percentages.
-        stake = 0.015
-        fee = self.fee
-        open_fee = fee / 2
-        close_fee = fee / 2
-
-        result['trade_duration'] = result['close_time'] - result['open_time']
-
-        result['trade_duration'] = result['trade_duration'].map(
-            lambda x: int(x.total_seconds() / 60))
-
-        # Spends, Takes, Profit, Absolute Profit
-
-        # Buy Price
-        result['buy_vol'] = stake / result['open_rate']  # How many target are we buying
-        result['buy_fee'] = stake * open_fee
-        result['buy_spend'] = stake + result['buy_fee']  # How much we're spending
-
-        # Sell price
-        result['sell_sum'] = result['buy_vol'] * result['close_rate']
-        result['sell_fee'] = result['sell_sum'] * close_fee
-        result['sell_take'] = result['sell_sum'] - result['sell_fee']
-
-        # profit_percent
-        result['profit_percent'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
-
-        # Absolute profit
-        result['profit_abs'] = result['sell_take'] - result['buy_spend']
-
-        return result
-
-    def _process_expectancy(self, results: DataFrame) -> Dict[str, Any]:
-        """
-        This calculates WinRate, Required Risk Reward, Risk Reward and Expectancy of all pairs
-        The calulation will be done per pair and per strategy.
-        """
-        # Removing pairs having less than min_trades_number
-        min_trades_number = self.edge_config.get('min_trade_number', 10)
-        results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
-        ###################################
-
-        # Removing outliers (Only Pumps) from the dataset
-        # The method to detect outliers is to calculate standard deviation
-        # Then every value more than (standard deviation + 2*average) is out (pump)
-        #
-        # Removing Pumps
-        if self.edge_config.get('remove_pumps', False):
-            results = results.groupby(['pair', 'stoploss']).apply(
-                lambda x: x[x['profit_abs'] < 2 * x['profit_abs'].std() + x['profit_abs'].mean()])
-        ##########################################################################
-
-        # Removing trades having a duration more than X minutes (set in config)
-        max_trade_duration = self.edge_config.get('max_trade_duration_minute', 1440)
-        results = results[results.trade_duration < max_trade_duration]
-        #######################################################################
-
-        if results.empty:
-            return {}
-
-        groupby_aggregator = {
-            'profit_abs': [
-                ('nb_trades', 'count'),  # number of all trades
-                ('profit_sum', lambda x: x[x > 0].sum()),  # cumulative profit of all winning trades
-                ('loss_sum', lambda x: abs(x[x < 0].sum())),  # cumulative loss of all losing trades
-                ('nb_win_trades', lambda x: x[x > 0].count())  # number of winning trades
-            ],
-            'trade_duration': [('avg_trade_duration', 'mean')]
-        }
-
-        # Group by (pair and stoploss) by applying above aggregator
-        df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
-            groupby_aggregator).reset_index(col_level=1)
-
-        # Dropping level 0 as we don't need it
-        df.columns = df.columns.droplevel(0)
-
-        # Calculating number of losing trades, average win and average loss
-        df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
-        df['average_win'] = df['profit_sum'] / df['nb_win_trades']
-        df['average_loss'] = df['loss_sum'] / df['nb_loss_trades']
-
-        # Win rate = number of profitable trades / number of trades
-        df['winrate'] = df['nb_win_trades'] / df['nb_trades']
-
-        # risk_reward_ratio = average win / average loss
-        df['risk_reward_ratio'] = df['average_win'] / df['average_loss']
-
-        # required_risk_reward = (1 / winrate) - 1
-        df['required_risk_reward'] = (1 / df['winrate']) - 1
-
-        # expectancy = (risk_reward_ratio * winrate) - (lossrate)
-        df['expectancy'] = (df['risk_reward_ratio'] * df['winrate']) - (1 - df['winrate'])
-
-        # sort by expectancy and stoploss
-        df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
-            'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
-
-        final = {}
-        for x in df.itertuples():
-            final[x.pair] = PairInfo(
-                x.stoploss,
-                x.winrate,
-                x.risk_reward_ratio,
-                x.required_risk_reward,
-                x.expectancy,
-                x.nb_trades,
-                x.avg_trade_duration
-            )
-
-        # Returning a list of pairs in order of "expectancy"
-        return final
-
-    def _find_trades_for_stoploss_range(self, ticker_data, pair, stoploss_range):
-        buy_column = ticker_data['buy'].values
-        sell_column = ticker_data['sell'].values
-        date_column = ticker_data['date'].values
-        ohlc_columns = ticker_data[['open', 'high', 'low', 'close']].values
-
-        result: list = []
-        for stoploss in stoploss_range:
-            result += self._detect_next_stop_or_sell_point(
-                buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
-            )
-
-        return result
-
-    def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
-                                        ohlc_columns, stoploss, pair):
-        """
-        Iterate through ohlc_columns in order to find the next trade
-        Next trade opens from the first buy signal noticed to
-        The sell or stoploss signal after it.
-        It then cuts OHLC, buy_column, sell_column and date_column.
-        Cut from (the exit trade index) + 1.
-
-        Author: https://github.com/mishaker
-        """
-
-        result: list = []
-        start_point = 0
-
-        while True:
-            open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal)
-
-            # Return empty if we don't find trade entry (i.e. buy==1) or
-            # we find a buy but at the end of array
-            if open_trade_index == -1 or open_trade_index == len(buy_column) - 1:
-                break
-            else:
-                # When a buy signal is seen,
-                # trade opens in reality on the next candle
-                open_trade_index += 1
-
-            stop_price_percentage = stoploss + 1
-            open_price = ohlc_columns[open_trade_index, 0]
-            stop_price = (open_price * stop_price_percentage)
-
-            # Searching for the index where stoploss is hit
-            stop_index = utf1st.find_1st(
-                ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller)
-
-            # If we don't find it then we assume stop_index will be far in future (infinite number)
-            if stop_index == -1:
-                stop_index = float('inf')
-
-            # Searching for the index where sell is hit
-            sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal)
-
-            # If we don't find it then we assume sell_index will be far in future (infinite number)
-            if sell_index == -1:
-                sell_index = float('inf')
-
-            # Check if we don't find any stop or sell point (in that case trade remains open)
-            # It is not interesting for Edge to consider it so we simply ignore the trade
-            # And stop iterating there is no more entry
-            if stop_index == sell_index == float('inf'):
-                break
-
-            if stop_index <= sell_index:
-                exit_index = open_trade_index + stop_index
-                exit_type = SellType.STOP_LOSS
-                exit_price = stop_price
-            elif stop_index > sell_index:
-                # If exit is SELL then we exit at the next candle
-                exit_index = open_trade_index + sell_index + 1
-
-                # Check if we have the next candle
-                if len(ohlc_columns) - 1 < exit_index:
-                    break
-
-                exit_type = SellType.SELL_SIGNAL
-                exit_price = ohlc_columns[exit_index, 0]
-
-            trade = {'pair': pair,
-                     'stoploss': stoploss,
-                     'profit_percent': '',
-                     'profit_abs': '',
-                     'open_time': date_column[open_trade_index],
-                     'close_time': date_column[exit_index],
-                     'open_index': start_point + open_trade_index,
-                     'close_index': start_point + exit_index,
-                     'trade_duration': '',
-                     'open_rate': round(open_price, 15),
-                     'close_rate': round(exit_price, 15),
-                     'exit_type': exit_type
-                     }
-
-            result.append(trade)
-
-            # Giving a view of exit_index till the end of array
-            buy_column = buy_column[exit_index:]
-            sell_column = sell_column[exit_index:]
-            date_column = date_column[exit_index:]
-            ohlc_columns = ohlc_columns[exit_index:]
-            start_point += exit_index
-
-        return result

freqtrade/edge/edge_positioning.py

@@ -0,0 +1,465 @@
+# pragma pylint: disable=W0603
+""" Edge positioning package """
+import logging
+from typing import Any, Dict, List, NamedTuple
+
+import arrow
+import numpy as np
+import utils_find_1st as utf1st
+from pandas import DataFrame
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
+from freqtrade.exceptions import OperationalException
+from freqtrade.data.history import get_timerange, load_data, refresh_data
+from freqtrade.strategy.interface import SellType
+
+logger = logging.getLogger(__name__)
+
+
+class PairInfo(NamedTuple):
+    stoploss: float
+    winrate: float
+    risk_reward_ratio: float
+    required_risk_reward: float
+    expectancy: float
+    nb_trades: int
+    avg_trade_duration: float
+
+
+class Edge:
+    """
+    Calculates Win Rate, Risk Reward Ratio, Expectancy
+    against historical data for a give set of markets and a strategy
+    it then adjusts stoploss and position size accordingly
+    and force it into the strategy
+    Author: https://github.com/mishaker
+    """
+
+    config: Dict = {}
+    _cached_pairs: Dict[str, Any] = {}  # Keeps a list of pairs
+
+    def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
+
+        self.config = config
+        self.exchange = exchange
+        self.strategy = strategy
+
+        self.edge_config = self.config.get('edge', {})
+        self._cached_pairs: Dict[str, Any] = {}  # Keeps a list of pairs
+        self._final_pairs: list = []
+
+        # checking max_open_trades. it should be -1 as with Edge
+        # the number of trades is determined by position size
+        if self.config['max_open_trades'] != float('inf'):
+            logger.critical('max_open_trades should be -1 in config !')
+
+        if self.config['stake_amount'] != UNLIMITED_STAKE_AMOUNT:
+            raise OperationalException('Edge works only with unlimited stake amount')
+
+        # Deprecated capital_available_percentage. Will use tradable_balance_ratio in the future.
+        self._capital_percentage: float = self.edge_config.get(
+            'capital_available_percentage', self.config['tradable_balance_ratio'])
+        self._allowed_risk: float = self.edge_config.get('allowed_risk')
+        self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14)
+        self._last_updated: int = 0  # Timestamp of pairs last updated time
+        self._refresh_pairs = True
+
+        self._stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
+        self._stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
+        self._stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
+
+        # calculating stoploss range
+        self._stoploss_range = np.arange(
+            self._stoploss_range_min,
+            self._stoploss_range_max,
+            self._stoploss_range_step
+        )
+
+        self._timerange: TimeRange = TimeRange.parse_timerange("%s-" % arrow.now().shift(
+            days=-1 * self._since_number_of_days).format('YYYYMMDD'))
+        if config.get('fee'):
+            self.fee = config['fee']
+        else:
+            self.fee = self.exchange.get_fee(symbol=self.config['exchange']['pair_whitelist'][0])
+
+    def calculate(self) -> bool:
+        pairs = self.config['exchange']['pair_whitelist']
+        heartbeat = self.edge_config.get('process_throttle_secs')
+
+        if (self._last_updated > 0) and (
+                self._last_updated + heartbeat > arrow.utcnow().timestamp):
+            return False
+
+        data: Dict[str, Any] = {}
+        logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
+        logger.info('Using local backtesting data (using whitelist in given config) ...')
+
+        if self._refresh_pairs:
+            refresh_data(
+                datadir=self.config['datadir'],
+                pairs=pairs,
+                exchange=self.exchange,
+                timeframe=self.strategy.ticker_interval,
+                timerange=self._timerange,
+            )
+
+        data = load_data(
+            datadir=self.config['datadir'],
+            pairs=pairs,
+            timeframe=self.strategy.ticker_interval,
+            timerange=self._timerange,
+            startup_candles=self.strategy.startup_candle_count,
+            data_format=self.config.get('dataformat_ohlcv', 'json'),
+        )
+
+        if not data:
+            # Reinitializing cached pairs
+            self._cached_pairs = {}
+            logger.critical("No data found. Edge is stopped ...")
+            return False
+
+        preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
+
+        # Print timeframe
+        min_date, max_date = get_timerange(preprocessed)
+        logger.info(
+            'Measuring data from %s up to %s (%s days) ...',
+            min_date.isoformat(),
+            max_date.isoformat(),
+            (max_date - min_date).days
+        )
+        headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
+
+        trades: list = []
+        for pair, pair_data in preprocessed.items():
+            # Sorting dataframe by date and reset index
+            pair_data = pair_data.sort_values(by=['date'])
+            pair_data = pair_data.reset_index(drop=True)
+
+            df_analyzed = self.strategy.advise_sell(
+                self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
+
+            trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
+
+        # If no trade found then exit
+        if len(trades) == 0:
+            logger.info("No trades found.")
+            return False
+
+        # Fill missing, calculable columns, profit, duration , abs etc.
+        trades_df = self._fill_calculable_fields(DataFrame(trades))
+        self._cached_pairs = self._process_expectancy(trades_df)
+        self._last_updated = arrow.utcnow().timestamp
+
+        return True
+
+    def stake_amount(self, pair: str, free_capital: float,
+                     total_capital: float, capital_in_trade: float) -> float:
+        stoploss = self.stoploss(pair)
+        available_capital = (total_capital + capital_in_trade) * self._capital_percentage
+        allowed_capital_at_risk = available_capital * self._allowed_risk
+        max_position_size = abs(allowed_capital_at_risk / stoploss)
+        position_size = min(max_position_size, free_capital)
+        if pair in self._cached_pairs:
+            logger.info(
+                'winrate: %s, expectancy: %s, position size: %s, pair: %s,'
+                ' capital in trade: %s, free capital: %s, total capital: %s,'
+                ' stoploss: %s, available capital: %s.',
+                self._cached_pairs[pair].winrate,
+                self._cached_pairs[pair].expectancy,
+                position_size, pair,
+                capital_in_trade, free_capital, total_capital,
+                stoploss, available_capital
+            )
+        return round(position_size, 15)
+
+    def stoploss(self, pair: str) -> float:
+        if pair in self._cached_pairs:
+            return self._cached_pairs[pair].stoploss
+        else:
+            logger.warning('tried to access stoploss of a non-existing pair, '
+                           'strategy stoploss is returned instead.')
+            return self.strategy.stoploss
+
+    def adjust(self, pairs: List[str]) -> list:
+        """
+        Filters out and sorts "pairs" according to Edge calculated pairs
+        """
+        final = []
+        for pair, info in self._cached_pairs.items():
+            if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
+                info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)) and \
+                    pair in pairs:
+                final.append(pair)
+
+        if self._final_pairs != final:
+            self._final_pairs = final
+            if self._final_pairs:
+                logger.info(
+                    'Minimum expectancy and minimum winrate are met only for %s,'
+                    ' so other pairs are filtered out.',
+                    self._final_pairs
+                    )
+            else:
+                logger.info(
+                    'Edge removed all pairs as no pair with minimum expectancy '
+                    'and minimum winrate was found !'
+                    )
+
+        return self._final_pairs
+
+    def accepted_pairs(self) -> list:
+        """
+        return a list of accepted pairs along with their winrate, expectancy and stoploss
+        """
+        final = []
+        for pair, info in self._cached_pairs.items():
+            if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
+                 info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)):
+                final.append({
+                    'Pair': pair,
+                    'Winrate': info.winrate,
+                    'Expectancy': info.expectancy,
+                    'Stoploss': info.stoploss,
+                })
+        return final
+
+    def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
+        """
+        The result frame contains a number of columns that are calculable
+        from other columns. These are left blank till all rows are added,
+        to be populated in single vector calls.
+
+        Columns to be populated are:
+        - Profit
+        - trade duration
+        - profit abs
+        :param result Dataframe
+        :return: result Dataframe
+        """
+
+        # stake and fees
+        # stake = 0.015
+        # 0.05% is 0.0005
+        # fee = 0.001
+
+        # we set stake amount to an arbitrary amount.
+        # as it doesn't change the calculation.
+        # all returned values are relative.
+        # they are defined as ratios.
+        stake = 0.015
+        fee = self.fee
+        open_fee = fee / 2
+        close_fee = fee / 2
+
+        result['trade_duration'] = result['close_time'] - result['open_time']
+
+        result['trade_duration'] = result['trade_duration'].map(
+            lambda x: int(x.total_seconds() / 60))
+
+        # Spends, Takes, Profit, Absolute Profit
+
+        # Buy Price
+        result['buy_vol'] = stake / result['open_rate']  # How many target are we buying
+        result['buy_fee'] = stake * open_fee
+        result['buy_spend'] = stake + result['buy_fee']  # How much we're spending
+
+        # Sell price
+        result['sell_sum'] = result['buy_vol'] * result['close_rate']
+        result['sell_fee'] = result['sell_sum'] * close_fee
+        result['sell_take'] = result['sell_sum'] - result['sell_fee']
+
+        # profit_ratio
+        result['profit_ratio'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
+
+        # Absolute profit
+        result['profit_abs'] = result['sell_take'] - result['buy_spend']
+
+        return result
+
+    def _process_expectancy(self, results: DataFrame) -> Dict[str, Any]:
+        """
+        This calculates WinRate, Required Risk Reward, Risk Reward and Expectancy of all pairs
+        The calulation will be done per pair and per strategy.
+        """
+        # Removing pairs having less than min_trades_number
+        min_trades_number = self.edge_config.get('min_trade_number', 10)
+        results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
+        ###################################
+
+        # Removing outliers (Only Pumps) from the dataset
+        # The method to detect outliers is to calculate standard deviation
+        # Then every value more than (standard deviation + 2*average) is out (pump)
+        #
+        # Removing Pumps
+        if self.edge_config.get('remove_pumps', False):
+            results = results.groupby(['pair', 'stoploss']).apply(
+                lambda x: x[x['profit_abs'] < 2 * x['profit_abs'].std() + x['profit_abs'].mean()])
+        ##########################################################################
+
+        # Removing trades having a duration more than X minutes (set in config)
+        max_trade_duration = self.edge_config.get('max_trade_duration_minute', 1440)
+        results = results[results.trade_duration < max_trade_duration]
+        #######################################################################
+
+        if results.empty:
+            return {}
+
+        groupby_aggregator = {
+            'profit_abs': [
+                ('nb_trades', 'count'),  # number of all trades
+                ('profit_sum', lambda x: x[x > 0].sum()),  # cumulative profit of all winning trades
+                ('loss_sum', lambda x: abs(x[x < 0].sum())),  # cumulative loss of all losing trades
+                ('nb_win_trades', lambda x: x[x > 0].count())  # number of winning trades
+            ],
+            'trade_duration': [('avg_trade_duration', 'mean')]
+        }
+
+        # Group by (pair and stoploss) by applying above aggregator
+        df = results.groupby(['pair', 'stoploss'])[['profit_abs', 'trade_duration']].agg(
+            groupby_aggregator).reset_index(col_level=1)
+
+        # Dropping level 0 as we don't need it
+        df.columns = df.columns.droplevel(0)
+
+        # Calculating number of losing trades, average win and average loss
+        df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
+        df['average_win'] = df['profit_sum'] / df['nb_win_trades']
+        df['average_loss'] = df['loss_sum'] / df['nb_loss_trades']
+
+        # Win rate = number of profitable trades / number of trades
+        df['winrate'] = df['nb_win_trades'] / df['nb_trades']
+
+        # risk_reward_ratio = average win / average loss
+        df['risk_reward_ratio'] = df['average_win'] / df['average_loss']
+
+        # required_risk_reward = (1 / winrate) - 1
+        df['required_risk_reward'] = (1 / df['winrate']) - 1
+
+        # expectancy = (risk_reward_ratio * winrate) - (lossrate)
+        df['expectancy'] = (df['risk_reward_ratio'] * df['winrate']) - (1 - df['winrate'])
+
+        # sort by expectancy and stoploss
+        df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
+            'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
+
+        final = {}
+        for x in df.itertuples():
+            final[x.pair] = PairInfo(
+                x.stoploss,
+                x.winrate,
+                x.risk_reward_ratio,
+                x.required_risk_reward,
+                x.expectancy,
+                x.nb_trades,
+                x.avg_trade_duration
+            )
+
+        # Returning a list of pairs in order of "expectancy"
+        return final
+
+    def _find_trades_for_stoploss_range(self, df, pair, stoploss_range):
+        buy_column = df['buy'].values
+        sell_column = df['sell'].values
+        date_column = df['date'].values
+        ohlc_columns = df[['open', 'high', 'low', 'close']].values
+
+        result: list = []
+        for stoploss in stoploss_range:
+            result += self._detect_next_stop_or_sell_point(
+                buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
+            )
+
+        return result
+
+    def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
+                                        ohlc_columns, stoploss, pair):
+        """
+        Iterate through ohlc_columns in order to find the next trade
+        Next trade opens from the first buy signal noticed to
+        The sell or stoploss signal after it.
+        It then cuts OHLC, buy_column, sell_column and date_column.
+        Cut from (the exit trade index) + 1.
+
+        Author: https://github.com/mishaker
+        """
+
+        result: list = []
+        start_point = 0
+
+        while True:
+            open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal)
+
+            # Return empty if we don't find trade entry (i.e. buy==1) or
+            # we find a buy but at the end of array
+            if open_trade_index == -1 or open_trade_index == len(buy_column) - 1:
+                break
+            else:
+                # When a buy signal is seen,
+                # trade opens in reality on the next candle
+                open_trade_index += 1
+
+            open_price = ohlc_columns[open_trade_index, 0]
+            stop_price = (open_price * (stoploss + 1))
+
+            # Searching for the index where stoploss is hit
+            stop_index = utf1st.find_1st(
+                ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller)
+
+            # If we don't find it then we assume stop_index will be far in future (infinite number)
+            if stop_index == -1:
+                stop_index = float('inf')
+
+            # Searching for the index where sell is hit
+            sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal)
+
+            # If we don't find it then we assume sell_index will be far in future (infinite number)
+            if sell_index == -1:
+                sell_index = float('inf')
+
+            # Check if we don't find any stop or sell point (in that case trade remains open)
+            # It is not interesting for Edge to consider it so we simply ignore the trade
+            # And stop iterating there is no more entry
+            if stop_index == sell_index == float('inf'):
+                break
+
+            if stop_index <= sell_index:
+                exit_index = open_trade_index + stop_index
+                exit_type = SellType.STOP_LOSS
+                exit_price = stop_price
+            elif stop_index > sell_index:
+                # If exit is SELL then we exit at the next candle
+                exit_index = open_trade_index + sell_index + 1
+
+                # Check if we have the next candle
+                if len(ohlc_columns) - 1 < exit_index:
+                    break
+
+                exit_type = SellType.SELL_SIGNAL
+                exit_price = ohlc_columns[exit_index, 0]
+
+            trade = {'pair': pair,
+                     'stoploss': stoploss,
+                     'profit_ratio': '',
+                     'profit_abs': '',
+                     'open_time': date_column[open_trade_index],
+                     'close_time': date_column[exit_index],
+                     'open_index': start_point + open_trade_index,
+                     'close_index': start_point + exit_index,
+                     'trade_duration': '',
+                     'open_rate': round(open_price, 15),
+                     'close_rate': round(exit_price, 15),
+                     'exit_type': exit_type
+                     }
+
+            result.append(trade)
+
+            # Giving a view of exit_index till the end of array
+            buy_column = buy_column[exit_index:]
+            sell_column = sell_column[exit_index:]
+            date_column = date_column[exit_index:]
+            ohlc_columns = ohlc_columns[exit_index:]
+            start_point += exit_index
+
+        return result

freqtrade/exceptions.py

@@ -0,0 +1,37 @@
+
+
+class FreqtradeException(Exception):
+    """
+    Freqtrade base exception. Handled at the outermost level.
+    All other exception types are subclasses of this exception type.
+    """
+
+
+class OperationalException(FreqtradeException):
+    """
+    Requires manual intervention and will stop the bot.
+    Most of the time, this is caused by an invalid Configuration.
+    """
+
+
+class DependencyException(FreqtradeException):
+    """
+    Indicates that an assumed dependency is not met.
+    This could happen when there is currently not enough money on the account.
+    """
+
+
+class InvalidOrderException(FreqtradeException):
+    """
+    This is returned when the order is not valid. Example:
+    If stoploss on exchange order is hit, then trying to cancel the order
+    should return this exception.
+    """
+
+
+class TemporaryError(FreqtradeException):
+    """
+    Temporary network or exchange related error.
+    This could happen when an exchange is congested, unavailable, or the user
+    has networking problems. Usually resolves itself after a time.
+    """

freqtrade/exchange/__init__.py

@@ -1,16 +1,20 @@
-from freqtrade.exchange.exchange import Exchange, MAP_EXCHANGE_CHILDCLASS  # noqa: F401
+# flake8: noqa: F401
-from freqtrade.exchange.exchange import (get_exchange_bad_reason,  # noqa: F401
+from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS
+from freqtrade.exchange.exchange import Exchange
+from freqtrade.exchange.exchange import (get_exchange_bad_reason,
                                          is_exchange_bad,
                                          is_exchange_known_ccxt,
                                          is_exchange_officially_supported,
                                          ccxt_exchanges,
                                          available_exchanges)
-from freqtrade.exchange.exchange import (timeframe_to_seconds,  # noqa: F401
+from freqtrade.exchange.exchange import (timeframe_to_seconds,
                                          timeframe_to_minutes,
                                          timeframe_to_msecs,
                                          timeframe_to_next_date,
                                          timeframe_to_prev_date)
-from freqtrade.exchange.exchange import (market_is_active,  # noqa: F401
+from freqtrade.exchange.exchange import (market_is_active,
                                          symbol_is_pair)
-from freqtrade.exchange.kraken import Kraken  # noqa: F401
+from freqtrade.exchange.kraken import Kraken
-from freqtrade.exchange.binance import Binance  # noqa: F401
+from freqtrade.exchange.binance import Binance
+from freqtrade.exchange.bibox import Bibox
+from freqtrade.exchange.ftx import Ftx

freqtrade/exchange/bibox.py

@@ -0,0 +1,22 @@
+""" Bibox exchange subclass """
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+logger = logging.getLogger(__name__)
+
+
+class Bibox(Exchange):
+    """
+    Bibox exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+
+    Please note that this exchange is not included in the list of exchanges
+    officially supported by the Freqtrade development team. So some features
+    may still not work as expected.
+    """
+
+    # fetchCurrencies API point requires authentication for Bibox,
+    # so switch it off for Freqtrade load_markets()
+    _ccxt_config: Dict = {"has": {"fetchCurrencies": False}}

freqtrade/exchange/binance.py

@@ -4,7 +4,7 @@ from typing import Dict
 
 import ccxt
 
-from freqtrade import (DependencyException, InvalidOrderException,
+from freqtrade.exceptions import (DependencyException, InvalidOrderException,
                                   OperationalException, TemporaryError)
 from freqtrade.exchange import Exchange
 
@@ -32,16 +32,26 @@ class Binance(Exchange):
 
         return super().get_order_book(pair, limit)
 
-    def stoploss_limit(self, pair: str, amount: float, stop_price: float, rate: float) -> Dict:
+    def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
+        """
+        Verify stop_loss against stoploss-order value (limit or price)
+        Returns True if adjustment is necessary.
+        """
+        return order['type'] == 'stop_loss_limit' and stop_loss > float(order['info']['stopPrice'])
+
+    def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
         """
         creates a stoploss limit order.
         this stoploss-limit is binance-specific.
         It may work with a limited number of other exchanges, but this has not been tested yet.
-
         """
+        # Limit price threshold: As limit price should always be below stop-price
+        limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
+        rate = stop_price * limit_price_pct
+
         ordertype = "stop_loss_limit"
 
-        stop_price = self.symbol_price_prec(pair, stop_price)
+        stop_price = self.price_to_precision(pair, stop_price)
 
         # Ensure rate is less than stop price
         if stop_price <= rate:
@@ -57,12 +67,12 @@ class Binance(Exchange):
             params = self._params.copy()
             params.update({'stopPrice': stop_price})
 
-            amount = self.symbol_amount_prec(pair, amount)
+            amount = self.amount_to_precision(pair, amount)
 
-            rate = self.symbol_price_prec(pair, rate)
+            rate = self.price_to_precision(pair, rate)
 
-            order = self._api.create_order(pair, ordertype, 'sell',
+            order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
-                                           amount, rate, params)
+                                           amount=amount, price=stop_price, params=params)
             logger.info('stoploss limit order added for %s. '
                         'stop price: %s. limit: %s', pair, stop_price, rate)
             return order

freqtrade/exchange/common.py

@@ -0,0 +1,124 @@
+import logging
+
+from freqtrade.exceptions import DependencyException, TemporaryError
+
+logger = logging.getLogger(__name__)
+
+
+API_RETRY_COUNT = 4
+BAD_EXCHANGES = {
+    "bitmex": "Various reasons.",
+    "bitstamp": "Does not provide history. "
+                "Details in https://github.com/freqtrade/freqtrade/issues/1983",
+    "hitbtc": "This API cannot be used with Freqtrade. "
+              "Use `hitbtc2` exchange id to access this exchange.",
+    **dict.fromkeys([
+        'adara',
+        'anxpro',
+        'bigone',
+        'coinbase',
+        'coinexchange',
+        'coinmarketcap',
+        'lykke',
+        'xbtce',
+    ], "Does not provide timeframes. ccxt fetchOHLCV: False"),
+    **dict.fromkeys([
+        'bcex',
+        'bit2c',
+        'bitbay',
+        'bitflyer',
+        'bitforex',
+        'bithumb',
+        'bitso',
+        'bitstamp1',
+        'bl3p',
+        'braziliex',
+        'btcbox',
+        'btcchina',
+        'btctradeim',
+        'btctradeua',
+        'bxinth',
+        'chilebit',
+        'coincheck',
+        'coinegg',
+        'coinfalcon',
+        'coinfloor',
+        'coingi',
+        'coinmate',
+        'coinone',
+        'coinspot',
+        'coolcoin',
+        'crypton',
+        'deribit',
+        'exmo',
+        'exx',
+        'flowbtc',
+        'foxbit',
+        'fybse',
+        # 'hitbtc',
+        'ice3x',
+        'independentreserve',
+        'indodax',
+        'itbit',
+        'lakebtc',
+        'latoken',
+        'liquid',
+        'livecoin',
+        'luno',
+        'mixcoins',
+        'negociecoins',
+        'nova',
+        'paymium',
+        'southxchange',
+        'stronghold',
+        'surbitcoin',
+        'therock',
+        'tidex',
+        'vaultoro',
+        'vbtc',
+        'virwox',
+        'yobit',
+        'zaif',
+    ], "Does not provide timeframes. ccxt fetchOHLCV: emulated"),
+}
+
+MAP_EXCHANGE_CHILDCLASS = {
+    'binanceus': 'binance',
+    'binanceje': 'binance',
+}
+
+
+def retrier_async(f):
+    async def wrapper(*args, **kwargs):
+        count = kwargs.pop('count', API_RETRY_COUNT)
+        try:
+            return await f(*args, **kwargs)
+        except (TemporaryError, DependencyException) as ex:
+            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
+            if count > 0:
+                count -= 1
+                kwargs.update({'count': count})
+                logger.warning('retrying %s() still for %s times', f.__name__, count)
+                return await wrapper(*args, **kwargs)
+            else:
+                logger.warning('Giving up retrying: %s()', f.__name__)
+                raise ex
+    return wrapper
+
+
+def retrier(f):
+    def wrapper(*args, **kwargs):
+        count = kwargs.pop('count', API_RETRY_COUNT)
+        try:
+            return f(*args, **kwargs)
+        except (TemporaryError, DependencyException) as ex:
+            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
+            if count > 0:
+                count -= 1
+                kwargs.update({'count': count})
+                logger.warning('retrying %s() still for %s times', f.__name__, count)
+                return wrapper(*args, **kwargs)
+            else:
+                logger.warning('Giving up retrying: %s()', f.__name__)
+                raise ex
+    return wrapper

freqtrade/exchange/exchange.py

@@ -7,148 +7,37 @@ import inspect
 import logging
 from copy import deepcopy
 from datetime import datetime, timezone
-from math import ceil, floor
+from math import ceil
 from random import randint
 from typing import Any, Dict, List, Optional, Tuple
 
 import arrow
 import ccxt
 import ccxt.async_support as ccxt_async
-from ccxt.base.decimal_to_precision import ROUND_UP, ROUND_DOWN
+from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE,
+                                            TRUNCATE, decimal_to_precision)
 from pandas import DataFrame
 
-from freqtrade import (DependencyException, InvalidOrderException,
+from freqtrade.data.converter import ohlcv_to_dataframe
-                       OperationalException, TemporaryError, constants)
+from freqtrade.exceptions import (DependencyException, InvalidOrderException,
-from freqtrade.data.converter import parse_ticker_dataframe
+                                  OperationalException, TemporaryError)
+from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
 from freqtrade.misc import deep_merge_dicts
 
 
+CcxtModuleType = Any
+
+
 logger = logging.getLogger(__name__)
 
 
-API_RETRY_COUNT = 4
-BAD_EXCHANGES = {
-    "bitmex": "Various reasons.",
-    "bitstamp": "Does not provide history. "
-                "Details in https://github.com/freqtrade/freqtrade/issues/1983",
-    "hitbtc": "This API cannot be used with Freqtrade. "
-              "Use `hitbtc2` exchange id to access this exchange.",
-    **dict.fromkeys([
-        'adara',
-        'anxpro',
-        'bigone',
-        'coinbase',
-        'coinexchange',
-        'coinmarketcap',
-        'lykke',
-        'xbtce',
-        ], "Does not provide timeframes. ccxt fetchOHLCV: False"),
-    **dict.fromkeys([
-        'bcex',
-        'bit2c',
-        'bitbay',
-        'bitflyer',
-        'bitforex',
-        'bithumb',
-        'bitso',
-        'bitstamp1',
-        'bl3p',
-        'braziliex',
-        'btcbox',
-        'btcchina',
-        'btctradeim',
-        'btctradeua',
-        'bxinth',
-        'chilebit',
-        'coincheck',
-        'coinegg',
-        'coinfalcon',
-        'coinfloor',
-        'coingi',
-        'coinmate',
-        'coinone',
-        'coinspot',
-        'coolcoin',
-        'crypton',
-        'deribit',
-        'exmo',
-        'exx',
-        'flowbtc',
-        'foxbit',
-        'fybse',
-        # 'hitbtc',
-        'ice3x',
-        'independentreserve',
-        'indodax',
-        'itbit',
-        'lakebtc',
-        'latoken',
-        'liquid',
-        'livecoin',
-        'luno',
-        'mixcoins',
-        'negociecoins',
-        'nova',
-        'paymium',
-        'southxchange',
-        'stronghold',
-        'surbitcoin',
-        'therock',
-        'tidex',
-        'vaultoro',
-        'vbtc',
-        'virwox',
-        'yobit',
-        'zaif',
-        ], "Does not provide timeframes. ccxt fetchOHLCV: emulated"),
-    }
-
-MAP_EXCHANGE_CHILDCLASS = {
-    'binanceus': 'binance',
-    'binanceje': 'binance',
-}
-
-
-def retrier_async(f):
-    async def wrapper(*args, **kwargs):
-        count = kwargs.pop('count', API_RETRY_COUNT)
-        try:
-            return await f(*args, **kwargs)
-        except (TemporaryError, DependencyException) as ex:
-            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
-            if count > 0:
-                count -= 1
-                kwargs.update({'count': count})
-                logger.warning('retrying %s() still for %s times', f.__name__, count)
-                return await wrapper(*args, **kwargs)
-            else:
-                logger.warning('Giving up retrying: %s()', f.__name__)
-                raise ex
-    return wrapper
-
-
-def retrier(f):
-    def wrapper(*args, **kwargs):
-        count = kwargs.pop('count', API_RETRY_COUNT)
-        try:
-            return f(*args, **kwargs)
-        except (TemporaryError, DependencyException) as ex:
-            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
-            if count > 0:
-                count -= 1
-                kwargs.update({'count': count})
-                logger.warning('retrying %s() still for %s times', f.__name__, count)
-                return wrapper(*args, **kwargs)
-            else:
-                logger.warning('Giving up retrying: %s()', f.__name__)
-                raise ex
-    return wrapper
-
-
 class Exchange:
 
     _config: Dict = {}
 
+    # Parameters to add directly to ccxt sync/async initialization.
+    _ccxt_config: Dict = {}
+
     # Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
     _params: Dict = {}
 
@@ -166,7 +55,7 @@ class Exchange:
     }
     _ft_has: Dict = {}
 
-    def __init__(self, config: dict, validate: bool = True) -> None:
+    def __init__(self, config: Dict[str, Any], validate: bool = True) -> None:
         """
         Initializes this module with the given config,
         it does basic validation whether the specified exchange and pairs are valid.
@@ -177,8 +66,6 @@ class Exchange:
 
         self._config.update(config)
 
-        self._cached_ticker: Dict[str, Any] = {}
-
         # Holds last candle refreshed time of each pair
         self._pairs_last_refresh_time: Dict[Tuple[str, str], int] = {}
         # Timestamp of last markets refresh
@@ -210,10 +97,17 @@ class Exchange:
         self._trades_pagination_arg = self._ft_has['trades_pagination_arg']
 
         # Initialize ccxt objects
+        ccxt_config = self._ccxt_config.copy()
+        ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}),
+                                       ccxt_config)
         self._api = self._init_ccxt(
-            exchange_config, ccxt_kwargs=exchange_config.get('ccxt_config'))
+            exchange_config, ccxt_kwargs=ccxt_config)
+
+        ccxt_async_config = self._ccxt_config.copy()
+        ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_async_config', {}),
+                                             ccxt_async_config)
         self._api_async = self._init_ccxt(
-            exchange_config, ccxt_async, ccxt_kwargs=exchange_config.get('ccxt_async_config'))
+            exchange_config, ccxt_async, ccxt_kwargs=ccxt_async_config)
 
         logger.info('Using Exchange "%s"', self.name)
 
@@ -225,9 +119,11 @@ class Exchange:
             self._load_markets()
 
             # Check if all pairs are available
+            self.validate_stakecurrency(config['stake_currency'])
             self.validate_pairs(config['exchange']['pair_whitelist'])
             self.validate_ordertypes(config.get('order_types', {}))
             self.validate_order_time_in_force(config.get('order_time_in_force', {}))
+            self.validate_required_startup_candles(config.get('startup_candle_count', 0))
 
         # Converts the interval provided in minutes in config to seconds
         self.markets_refresh_interval: int = exchange_config.get(
@@ -241,7 +137,7 @@ class Exchange:
         if self._api_async and inspect.iscoroutinefunction(self._api_async.close):
             asyncio.get_event_loop().run_until_complete(self._api_async.close())
 
-    def _init_ccxt(self, exchange_config: dict, ccxt_module=ccxt,
+    def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt,
                    ccxt_kwargs: dict = None) -> ccxt.Exchange:
         """
         Initialize ccxt with given config and return valid
@@ -296,6 +192,11 @@ class Exchange:
             self._load_markets()
         return self._api.markets
 
+    @property
+    def precisionMode(self) -> str:
+        """exchange ccxt precisionMode"""
+        return self._api.precisionMode
+
     def get_markets(self, base_currencies: List[str] = None, quote_currencies: List[str] = None,
                     pairs_only: bool = False, active_only: bool = False) -> Dict:
         """
@@ -318,13 +219,32 @@ class Exchange:
             markets = {k: v for k, v in markets.items() if market_is_active(v)}
         return markets
 
-    def klines(self, pair_interval: Tuple[str, str], copy=True) -> DataFrame:
+    def get_quote_currencies(self) -> List[str]:
+        """
+        Return a list of supported quote currencies
+        """
+        markets = self.markets
+        return sorted(set([x['quote'] for _, x in markets.items()]))
+
+    def get_pair_quote_currency(self, pair: str) -> str:
+        """
+        Return a pair's quote currency
+        """
+        return self.markets.get(pair, {}).get('quote', '')
+
+    def get_pair_base_currency(self, pair: str) -> str:
+        """
+        Return a pair's quote currency
+        """
+        return self.markets.get(pair, {}).get('base', '')
+
+    def klines(self, pair_interval: Tuple[str, str], copy: bool = True) -> DataFrame:
         if pair_interval in self._klines:
             return self._klines[pair_interval].copy() if copy else self._klines[pair_interval]
         else:
             return DataFrame()
 
-    def set_sandbox(self, api, exchange_config: dict, name: str):
+    def set_sandbox(self, api: ccxt.Exchange, exchange_config: dict, name: str) -> None:
         if exchange_config.get('sandbox'):
             if api.urls.get('test'):
                 api.urls['api'] = api.urls['test']
@@ -334,7 +254,7 @@ class Exchange:
                                      "Please check your config.json")
                 raise OperationalException(f'Exchange {name} does not provide a sandbox api')
 
-    def _load_async_markets(self, reload=False) -> None:
+    def _load_async_markets(self, reload: bool = False) -> None:
         try:
             if self._api_async:
                 asyncio.get_event_loop().run_until_complete(
@@ -367,18 +287,30 @@ class Exchange:
         except ccxt.BaseError:
             logger.exception("Could not reload markets.")
 
+    def validate_stakecurrency(self, stake_currency: str) -> None:
+        """
+        Checks stake-currency against available currencies on the exchange.
+        :param stake_currency: Stake-currency to validate
+        :raise: OperationalException if stake-currency is not available.
+        """
+        quote_currencies = self.get_quote_currencies()
+        if stake_currency not in quote_currencies:
+            raise OperationalException(
+                f"{stake_currency} is not available as stake on {self.name}. "
+                f"Available currencies are: {', '.join(quote_currencies)}")
+
     def validate_pairs(self, pairs: List[str]) -> None:
         """
         Checks if all given pairs are tradable on the current exchange.
-        Raises OperationalException if one pair is not available.
         :param pairs: list of pairs
+        :raise: OperationalException if one pair is not available
         :return: None
         """
 
         if not self.markets:
             logger.warning('Unable to validate pairs (assuming they are correct).')
             return
-
+        invalid_pairs = []
         for pair in pairs:
             # Note: ccxt has BaseCurrency/QuoteCurrency format for pairs
             # TODO: add a support for having coins in BTC/USDT format
@@ -386,14 +318,29 @@ class Exchange:
                 raise OperationalException(
                     f'Pair {pair} is not available on {self.name}. '
                     f'Please remove {pair} from your whitelist.')
-            elif self.markets[pair].get('info', {}).get('IsRestricted', False):
+
+                # From ccxt Documentation:
+                # markets.info: An associative array of non-common market properties,
+                # including fees, rates, limits and other general market information.
+                # The internal info array is different for each particular market,
+                # its contents depend on the exchange.
+                # It can also be a string or similar ... so we need to verify that first.
+            elif (isinstance(self.markets[pair].get('info', None), dict)
+                  and self.markets[pair].get('info', {}).get('IsRestricted', False)):
                 # Warn users about restricted pairs in whitelist.
                 # We cannot determine reliably if Users are affected.
                 logger.warning(f"Pair {pair} is restricted for some users on this exchange."
                                f"Please check if you are impacted by this restriction "
                                f"on the exchange and eventually remove {pair} from your whitelist.")
+            if (self._config['stake_currency'] and
+                    self.get_pair_quote_currency(pair) != self._config['stake_currency']):
+                invalid_pairs.append(pair)
+        if invalid_pairs:
+            raise OperationalException(
+                f"Stake-currency '{self._config['stake_currency']}' not compatible with "
+                f"pair-whitelist. Please remove the following pairs: {invalid_pairs}")
 
-    def get_valid_pair_combination(self, curr_1, curr_2) -> str:
+    def get_valid_pair_combination(self, curr_1: str, curr_2: str) -> str:
         """
         Get valid pair combination of curr_1 and curr_2 by trying both combinations.
         """
@@ -404,7 +351,7 @@ class Exchange:
 
     def validate_timeframes(self, timeframe: Optional[str]) -> None:
         """
-        Checks if ticker interval from config is a supported timeframe on the exchange
+        Check if timeframe from config is a supported timeframe on the exchange
         """
         if not hasattr(self._api, "timeframes") or self._api.timeframes is None:
             # If timeframes attribute is missing (or is None), the exchange probably
@@ -417,7 +364,11 @@ class Exchange:
 
         if timeframe and (timeframe not in self.timeframes):
             raise OperationalException(
-                f"Invalid ticker interval '{timeframe}'. This exchange supports: {self.timeframes}")
+                f"Invalid timeframe '{timeframe}'. This exchange supports: {self.timeframes}")
+
+        if timeframe and timeframe_to_minutes(timeframe) < 1:
+            raise OperationalException(
+                f"Timeframes < 1m are currently not supported by Freqtrade.")
 
     def validate_ordertypes(self, order_types: Dict) -> None:
         """
@@ -443,6 +394,16 @@ class Exchange:
             raise OperationalException(
                 f'Time in force policies are not supported for {self.name} yet.')
 
+    def validate_required_startup_candles(self, startup_candles: int) -> None:
+        """
+        Checks if required startup_candles is more than ohlcv_candle_limit.
+        Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
+        """
+        if startup_candles + 5 > self._ft_has['ohlcv_candle_limit']:
+            raise OperationalException(
+                f"This strategy requires {startup_candles} candles to start. "
+                f"{self.name} only provides {self._ft_has['ohlcv_candle_limit']}.")
+
     def exchange_has(self, endpoint: str) -> bool:
         """
         Checks if exchange implements a specific API endpoint.
@@ -452,23 +413,40 @@ class Exchange:
         """
         return endpoint in self._api.has and self._api.has[endpoint]
 
-    def symbol_amount_prec(self, pair, amount: float):
+    def amount_to_precision(self, pair: str, amount: float) -> float:
         '''
         Returns the amount to buy or sell to a precision the Exchange accepts
-        Rounded down
+        Reimplementation of ccxt internal methods - ensuring we can test the result is correct
+        based on our definitions.
         '''
         if self.markets[pair]['precision']['amount']:
-            symbol_prec = self.markets[pair]['precision']['amount']
+            amount = float(decimal_to_precision(amount, rounding_mode=TRUNCATE,
-            big_amount = amount * pow(10, symbol_prec)
+                                                precision=self.markets[pair]['precision']['amount'],
-            amount = floor(big_amount) / pow(10, symbol_prec)
+                                                counting_mode=self.precisionMode,
+                                                ))
+
         return amount
 
-    def symbol_price_prec(self, pair, price: float):
+    def price_to_precision(self, pair: str, price: float) -> float:
         '''
-        Returns the price buying or selling with to the precision the Exchange accepts
+        Returns the price rounded up to the precision the Exchange accepts.
+        Partial Reimplementation of ccxt internal method decimal_to_precision(),
+        which does not support rounding up
+        TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and
+        align with amount_to_precision().
         Rounds up
         '''
         if self.markets[pair]['precision']['price']:
+            # price = float(decimal_to_precision(price, rounding_mode=ROUND,
+            #                                    precision=self.markets[pair]['precision']['price'],
+            #                                    counting_mode=self.precisionMode,
+            #                                    ))
+            if self.precisionMode == TICK_SIZE:
+                precision = self.markets[pair]['precision']['price']
+                missing = price % precision
+                if missing != 0:
+                    price = price - missing + precision
+            else:
                 symbol_prec = self.markets[pair]['precision']['price']
                 big_price = price * pow(10, symbol_prec)
                 price = ceil(big_price) / pow(10, symbol_prec)
@@ -477,15 +455,16 @@ class Exchange:
     def dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
                       rate: float, params: Dict = {}) -> Dict[str, Any]:
         order_id = f'dry_run_{side}_{randint(0, 10**6)}'
+        _amount = self.amount_to_precision(pair, amount)
         dry_order = {
             "id": order_id,
             'pair': pair,
             'price': rate,
-            'amount': amount,
+            'amount': _amount,
-            "cost": amount * rate,
+            "cost": _amount * rate,
             'type': ordertype,
             'side': side,
-            'remaining': amount,
+            'remaining': _amount,
             'datetime': arrow.utcnow().isoformat(),
             'status': "closed" if ordertype == "market" else "open",
             'fee': None,
@@ -511,13 +490,13 @@ class Exchange:
                      rate: float, params: Dict = {}) -> Dict:
         try:
             # Set the precision for amount and price(rate) as accepted by the exchange
-            amount = self.symbol_amount_prec(pair, amount)
+            amount = self.amount_to_precision(pair, amount)
             needs_price = (ordertype != 'market'
                            or self._api.options.get("createMarketBuyOrderRequiresPrice", False))
-            rate = self.symbol_price_prec(pair, rate) if needs_price else None
+            rate_for_order = self.price_to_precision(pair, rate) if needs_price else None
 
             return self._api.create_order(pair, ordertype, side,
-                                          amount, rate, params)
+                                          amount, rate_for_order, params)
 
         except ccxt.InsufficientFunds as e:
             raise DependencyException(
@@ -536,7 +515,7 @@ class Exchange:
             raise OperationalException(e) from e
 
     def buy(self, pair: str, ordertype: str, amount: float,
-            rate: float, time_in_force) -> Dict:
+            rate: float, time_in_force: str) -> Dict:
 
         if self._config['dry_run']:
             dry_order = self.dry_run_order(pair, ordertype, "buy", amount, rate)
@@ -549,7 +528,7 @@ class Exchange:
         return self.create_order(pair, ordertype, 'buy', amount, rate, params)
 
     def sell(self, pair: str, ordertype: str, amount: float,
-             rate: float, time_in_force='gtc') -> Dict:
+             rate: float, time_in_force: str = 'gtc') -> Dict:
 
         if self._config['dry_run']:
             dry_order = self.dry_run_order(pair, ordertype, "sell", amount, rate)
@@ -561,9 +540,17 @@ class Exchange:
 
         return self.create_order(pair, ordertype, 'sell', amount, rate, params)
 
-    def stoploss_limit(self, pair: str, amount: float, stop_price: float, rate: float) -> Dict:
+    def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
         """
-        creates a stoploss limit order.
+        Verify stop_loss against stoploss-order value (limit or price)
+        Returns True if adjustment is necessary.
+        """
+        raise OperationalException(f"stoploss is not implemented for {self.name}.")
+
+    def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
+        """
+        creates a stoploss order.
+        The precise ordertype is determined by the order_types dict or exchange default.
         Since ccxt does not unify stoploss-limit orders yet, this needs to be implemented in each
         exchange's subclass.
         The exception below should never raise, since we disallow
@@ -571,12 +558,12 @@ class Exchange:
         Note: Changes to this interface need to be applied to all sub-classes too.
         """
 
-        raise OperationalException(f"stoploss_limit is not implemented for {self.name}.")
+        raise OperationalException(f"stoploss is not implemented for {self.name}.")
 
     @retrier
     def get_balance(self, currency: str) -> float:
         if self._config['dry_run']:
-            return constants.DRY_RUN_WALLET
+            return self._config['dry_run_wallet']
 
         # ccxt exception is already handled by get_balances
         balances = self.get_balances()
@@ -621,159 +608,152 @@ class Exchange:
             raise OperationalException(e) from e
 
     @retrier
-    def get_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
+    def fetch_ticker(self, pair: str) -> dict:
-        if refresh or pair not in self._cached_ticker.keys():
         try:
             if pair not in self._api.markets or not self._api.markets[pair].get('active'):
                 raise DependencyException(f"Pair {pair} not available")
             data = self._api.fetch_ticker(pair)
-                try:
-                    self._cached_ticker[pair] = {
-                        'bid': float(data['bid']),
-                        'ask': float(data['ask']),
-                    }
-                except KeyError:
-                    logger.debug("Could not cache ticker data for %s", pair)
             return data
         except (ccxt.NetworkError, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not load ticker due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
-        else:
-            logger.info("returning cached ticker-data for %s", pair)
-            return self._cached_ticker[pair]
 
-    def get_historic_ohlcv(self, pair: str, ticker_interval: str,
+    def get_historic_ohlcv(self, pair: str, timeframe: str,
                            since_ms: int) -> List:
         """
-        Gets candle history using asyncio and returns the list of candles.
+        Get candle history using asyncio and returns the list of candles.
-        Handles all async doing.
+        Handles all async work for this.
-        Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
+        Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
         :param pair: Pair to download
-        :param ticker_interval: Interval to get
+        :param timeframe: Timeframe to get data for
         :param since_ms: Timestamp in milliseconds to get history from
-        :returns List of tickers
+        :returns List with candle (OHLCV) data
         """
         return asyncio.get_event_loop().run_until_complete(
-            self._async_get_historic_ohlcv(pair=pair, ticker_interval=ticker_interval,
+            self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
                                            since_ms=since_ms))
 
     async def _async_get_historic_ohlcv(self, pair: str,
-                                        ticker_interval: str,
+                                        timeframe: str,
                                         since_ms: int) -> List:
 
-        one_call = timeframe_to_msecs(ticker_interval) * self._ohlcv_candle_limit
+        one_call = timeframe_to_msecs(timeframe) * self._ohlcv_candle_limit
         logger.debug(
             "one_call: %s msecs (%s)",
             one_call,
             arrow.utcnow().shift(seconds=one_call // 1000).humanize(only_distance=True)
         )
         input_coroutines = [self._async_get_candle_history(
-            pair, ticker_interval, since) for since in
+            pair, timeframe, since) for since in
             range(since_ms, arrow.utcnow().timestamp * 1000, one_call)]
 
-        tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
+        results = await asyncio.gather(*input_coroutines, return_exceptions=True)
 
-        # Combine tickers
+        # Combine gathered results
         data: List = []
-        for p, ticker_interval, ticker in tickers:
+        for p, timeframe, res in results:
             if p == pair:
-                data.extend(ticker)
+                data.extend(res)
         # Sort data again after extending the result - above calls return in "async order"
         data = sorted(data, key=lambda x: x[0])
-        logger.info("downloaded %s with length %s.", pair, len(data))
+        logger.info("Downloaded data for %s with length %s.", pair, len(data))
         return data
 
     def refresh_latest_ohlcv(self, pair_list: List[Tuple[str, str]]) -> List[Tuple[str, List]]:
         """
-        Refresh in-memory ohlcv asynchronously and set `_klines` with the result
+        Refresh in-memory OHLCV asynchronously and set `_klines` with the result
         Loops asynchronously over pair_list and downloads all pairs async (semi-parallel).
+        Only used in the dataprovider.refresh() method.
         :param pair_list: List of 2 element tuples containing pair, interval to refresh
-        :return: Returns a List of ticker-dataframes.
+        :return: TODO: return value is only used in the tests, get rid of it
         """
-        logger.debug("Refreshing ohlcv data for %d pairs", len(pair_list))
+        logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
 
         input_coroutines = []
 
         # Gather coroutines to run
-        for pair, ticker_interval in set(pair_list):
+        for pair, timeframe in set(pair_list):
-            if (not ((pair, ticker_interval) in self._klines)
+            if (not ((pair, timeframe) in self._klines)
-                    or self._now_is_time_to_refresh(pair, ticker_interval)):
+                    or self._now_is_time_to_refresh(pair, timeframe)):
-                input_coroutines.append(self._async_get_candle_history(pair, ticker_interval))
+                input_coroutines.append(self._async_get_candle_history(pair, timeframe))
             else:
                 logger.debug(
-                    "Using cached ohlcv data for pair %s, interval %s ...",
+                    "Using cached candle (OHLCV) data for pair %s, timeframe %s ...",
-                    pair, ticker_interval
+                    pair, timeframe
                 )
 
-        tickers = asyncio.get_event_loop().run_until_complete(
+        results = asyncio.get_event_loop().run_until_complete(
             asyncio.gather(*input_coroutines, return_exceptions=True))
 
         # handle caching
-        for res in tickers:
+        for res in results:
             if isinstance(res, Exception):
                 logger.warning("Async code raised an exception: %s", res.__class__.__name__)
                 continue
             pair = res[0]
-            ticker_interval = res[1]
+            timeframe = res[1]
             ticks = res[2]
             # keeping last candle time as last refreshed time of the pair
             if ticks:
-                self._pairs_last_refresh_time[(pair, ticker_interval)] = ticks[-1][0] // 1000
+                self._pairs_last_refresh_time[(pair, timeframe)] = ticks[-1][0] // 1000
             # keeping parsed dataframe in cache
-            self._klines[(pair, ticker_interval)] = parse_ticker_dataframe(
+            self._klines[(pair, timeframe)] = ohlcv_to_dataframe(
-                ticks, ticker_interval, pair=pair, fill_missing=True,
+                ticks, timeframe, pair=pair, fill_missing=True,
                 drop_incomplete=self._ohlcv_partial_candle)
-        return tickers
 
-    def _now_is_time_to_refresh(self, pair: str, ticker_interval: str) -> bool:
+        return results
-        # Calculating ticker interval in seconds
-        interval_in_sec = timeframe_to_seconds(ticker_interval)
 
-        return not ((self._pairs_last_refresh_time.get((pair, ticker_interval), 0)
+    def _now_is_time_to_refresh(self, pair: str, timeframe: str) -> bool:
+        # Timeframe in seconds
+        interval_in_sec = timeframe_to_seconds(timeframe)
+
+        return not ((self._pairs_last_refresh_time.get((pair, timeframe), 0)
                      + interval_in_sec) >= arrow.utcnow().timestamp)
 
     @retrier_async
-    async def _async_get_candle_history(self, pair: str, ticker_interval: str,
+    async def _async_get_candle_history(self, pair: str, timeframe: str,
                                         since_ms: Optional[int] = None) -> Tuple[str, str, List]:
         """
-        Asynchronously gets candle histories using fetch_ohlcv
+        Asynchronously get candle history data using fetch_ohlcv
-        returns tuple: (pair, ticker_interval, ohlcv_list)
+        returns tuple: (pair, timeframe, ohlcv_list)
         """
         try:
-            # fetch ohlcv asynchronously
+            # Fetch OHLCV asynchronously
             s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
             logger.debug(
                 "Fetching pair %s, interval %s, since %s %s...",
-                pair, ticker_interval, since_ms, s
+                pair, timeframe, since_ms, s
             )
 
-            data = await self._api_async.fetch_ohlcv(pair, timeframe=ticker_interval,
+            data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
                                                      since=since_ms)
 
-            # Because some exchange sort Tickers ASC and other DESC.
+            # Some exchanges sort OHLCV in ASC order and others in DESC.
-            # Ex: Bittrex returns a list of tickers ASC (oldest first, newest last)
+            # Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
-            # when GDAX returns a list of tickers DESC (newest first, oldest last)
+            # while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
             # Only sort if necessary to save computing time
             try:
                 if data and data[0][0] > data[-1][0]:
                     data = sorted(data, key=lambda x: x[0])
             except IndexError:
                 logger.exception("Error loading %s. Result was %s.", pair, data)
-                return pair, ticker_interval, []
+                return pair, timeframe, []
-            logger.debug("Done fetching pair %s, interval %s ...", pair, ticker_interval)
+            logger.debug("Done fetching pair %s, interval %s ...", pair, timeframe)
-            return pair, ticker_interval, data
+            return pair, timeframe, data
 
         except ccxt.NotSupported as e:
             raise OperationalException(
-                f'Exchange {self._api.name} does not support fetching historical candlestick data.'
+                f'Exchange {self._api.name} does not support fetching historical '
-                f'Message: {e}') from e
+                f'candle (OHLCV) data. Message: {e}') from e
         except (ccxt.NetworkError, ccxt.ExchangeError) as e:
-            raise TemporaryError(f'Could not load ticker history due to {e.__class__.__name__}. '
+            raise TemporaryError(f'Could not fetch historical candle (OHLCV) data '
+                                 f'for pair {pair} due to {e.__class__.__name__}. '
                                  f'Message: {e}') from e
         except ccxt.BaseError as e:
-            raise OperationalException(f'Could not fetch ticker data. Msg: {e}') from e
+            raise OperationalException(f'Could not fetch historical candle (OHLCV) data '
+                                       f'for pair {pair}. Message: {e}') from e
 
     @retrier_async
     async def _async_fetch_trades(self, pair: str,
@@ -906,15 +886,14 @@ class Exchange:
                             until: Optional[int] = None,
                             from_id: Optional[str] = None) -> Tuple[str, List]:
         """
-        Gets candle history using asyncio and returns the list of candles.
+        Get trade history data using asyncio.
-        Handles all async doing.
+        Handles all async work and returns the list of candles.
-        Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
+        Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
         :param pair: Pair to download
-        :param ticker_interval: Interval to get
         :param since: Timestamp in milliseconds to get history from
         :param until: Timestamp in milliseconds. Defaults to current timestamp if not defined.
         :param from_id: Download data starting with ID (if id is known)
-        :returns List of tickers
+        :returns List of trade data
         """
         if not self.exchange_has("fetchTrades"):
             raise OperationalException("This exchange does not suport downloading Trades.")
@@ -983,6 +962,22 @@ class Exchange:
 
     @retrier
     def get_trades_for_order(self, order_id: str, pair: str, since: datetime) -> List:
+        """
+        Fetch Orders using the "fetch_my_trades" endpoint and filter them by order-id.
+        The "since" argument passed in is coming from the database and is in UTC,
+        as timezone-native datetime object.
+        From the python documentation:
+            > Naive datetime instances are assumed to represent local time
+        Therefore, calling "since.timestamp()" will get the UTC timestamp, after applying the
+        transformation from local timezone to UTC.
+        This works for timezones UTC+ since then the result will contain trades from a few hours
+        instead of from the last 5 seconds, however fails for UTC- timezones,
+        since we're then asking for trades with a "since" argument in the future.
+
+        :param order_id order_id: Order-id as given when creating the order
+        :param pair: Pair the order is for
+        :param since: datetime object of the order creation time. Assumes object is in UTC.
+        """
         if self._config['dry_run']:
             return []
         if not self.exchange_has('fetchMyTrades'):
@@ -990,7 +985,8 @@ class Exchange:
         try:
             # Allow 5s offset to catch slight time offsets (discovered in #1185)
             # since needs to be int in milliseconds
-            my_trades = self._api.fetch_my_trades(pair, int((since.timestamp() - 5) * 1000))
+            my_trades = self._api.fetch_my_trades(
+                pair, int((since.replace(tzinfo=timezone.utc).timestamp() - 5) * 1000))
             matched_trades = [trade for trade in my_trades if trade['order'] == order_id]
 
             return matched_trades
@@ -1002,8 +998,8 @@ class Exchange:
             raise OperationalException(e) from e
 
     @retrier
-    def get_fee(self, symbol='ETH/BTC', type='', side='', amount=1,
+    def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
-                price=1, taker_or_maker='maker') -> float:
+                price: float = 1, taker_or_maker: str = 'maker') -> float:
         try:
             # validate that markets are loaded before trying to get fee
             if self._api.markets is None or len(self._api.markets) == 0:
@@ -1026,22 +1022,22 @@ def get_exchange_bad_reason(exchange_name: str) -> str:
     return BAD_EXCHANGES.get(exchange_name, "")
 
 
-def is_exchange_known_ccxt(exchange_name: str, ccxt_module=None) -> bool:
+def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
     return exchange_name in ccxt_exchanges(ccxt_module)
 
 
 def is_exchange_officially_supported(exchange_name: str) -> bool:
-    return exchange_name in ['bittrex', 'binance']
+    return exchange_name in ['bittrex', 'binance', 'kraken']
 
 
-def ccxt_exchanges(ccxt_module=None) -> List[str]:
+def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
     """
     Return the list of all exchanges known to ccxt
     """
     return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
 
 
-def available_exchanges(ccxt_module=None) -> List[str]:
+def available_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
     """
     Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
     """
@@ -1049,27 +1045,27 @@ def available_exchanges(ccxt_module=None) -> List[str]:
     return [x for x in exchanges if not is_exchange_bad(x)]
 
 
-def timeframe_to_seconds(ticker_interval: str) -> int:
+def timeframe_to_seconds(timeframe: str) -> int:
     """
     Translates the timeframe interval value written in the human readable
     form ('1m', '5m', '1h', '1d', '1w', etc.) to the number
     of seconds for one timeframe interval.
     """
-    return ccxt.Exchange.parse_timeframe(ticker_interval)
+    return ccxt.Exchange.parse_timeframe(timeframe)
 
 
-def timeframe_to_minutes(ticker_interval: str) -> int:
+def timeframe_to_minutes(timeframe: str) -> int:
     """
     Same as timeframe_to_seconds, but returns minutes.
     """
-    return ccxt.Exchange.parse_timeframe(ticker_interval) // 60
+    return ccxt.Exchange.parse_timeframe(timeframe) // 60
 
 
-def timeframe_to_msecs(ticker_interval: str) -> int:
+def timeframe_to_msecs(timeframe: str) -> int:
     """
     Same as timeframe_to_seconds, but returns milliseconds.
     """
-    return ccxt.Exchange.parse_timeframe(ticker_interval) * 1000
+    return ccxt.Exchange.parse_timeframe(timeframe) * 1000
 
 
 def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
@@ -1101,7 +1097,8 @@ def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
     return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
 
 
-def symbol_is_pair(market_symbol: str, base_currency: str = None, quote_currency: str = None):
+def symbol_is_pair(market_symbol: str, base_currency: str = None,
+                   quote_currency: str = None) -> bool:
     """
     Check if the market symbol is a pair, i.e. that its symbol consists of the base currency and the
     quote currency separated by '/' character. If base_currency and/or quote_currency is passed,
@@ -1114,7 +1111,7 @@ def symbol_is_pair(market_symbol: str, base_currency: str = None, quote_currency
             (symbol_parts[1] == quote_currency if quote_currency else len(symbol_parts[1]) > 0))
 
 
-def market_is_active(market):
+def market_is_active(market: Dict) -> bool:
     """
     Return True if the market is active.
     """

freqtrade/exchange/ftx.py

@@ -0,0 +1,14 @@
+""" FTX exchange subclass """
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+logger = logging.getLogger(__name__)
+
+
+class Ftx(Exchange):
+
+    _ft_has: Dict = {
+        "ohlcv_candle_limit": 1500,
+    }

freqtrade/exchange/kraken.py

@@ -4,7 +4,8 @@ from typing import Dict
 
 import ccxt
 
-from freqtrade import OperationalException, TemporaryError
+from freqtrade.exceptions import (DependencyException, InvalidOrderException,
+                                  OperationalException, TemporaryError)
 from freqtrade.exchange import Exchange
 from freqtrade.exchange.exchange import retrier
 
@@ -15,6 +16,7 @@ class Kraken(Exchange):
 
     _params: Dict = {"trading_agreement": "agree"}
     _ft_has: Dict = {
+        "stoploss_on_exchange": True,
         "trades_pagination": "id",
         "trades_pagination_arg": "since",
     }
@@ -48,3 +50,51 @@ class Kraken(Exchange):
                 f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
+
+    def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
+        """
+        Verify stop_loss against stoploss-order value (limit or price)
+        Returns True if adjustment is necessary.
+        """
+        return order['type'] == 'stop-loss' and stop_loss > float(order['price'])
+
+    def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
+        """
+        Creates a stoploss market order.
+        Stoploss market orders is the only stoploss type supported by kraken.
+        """
+
+        ordertype = "stop-loss"
+
+        stop_price = self.price_to_precision(pair, stop_price)
+
+        if self._config['dry_run']:
+            dry_order = self.dry_run_order(
+                pair, ordertype, "sell", amount, stop_price)
+            return dry_order
+
+        try:
+            params = self._params.copy()
+
+            amount = self.amount_to_precision(pair, amount)
+
+            order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
+                                           amount=amount, price=stop_price, params=params)
+            logger.info('stoploss order added for %s. '
+                        'stop price: %s.', pair, stop_price)
+            return order
+        except ccxt.InsufficientFunds as e:
+            raise DependencyException(
+                f'Insufficient funds to create {ordertype} sell order on market {pair}.'
+                f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
+                f'Message: {e}') from e
+        except ccxt.InvalidOrder as e:
+            raise InvalidOrderException(
+                f'Could not create {ordertype} sell order on market {pair}. '
+                f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
+                f'Message: {e}') from e
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e

freqtrade/loggers.py

@@ -1,9 +1,12 @@
 import logging
 import sys
 
-from logging.handlers import RotatingFileHandler
+from logging import Formatter
+from logging.handlers import RotatingFileHandler, SysLogHandler
 from typing import Any, Dict, List
 
+from freqtrade.exceptions import OperationalException
+
 
 logger = logging.getLogger(__name__)
 
@@ -33,11 +36,39 @@ def setup_logging(config: Dict[str, Any]) -> None:
     # Log level
     verbosity = config['verbosity']
 
-    # Log to stdout, not stderr
+    # Log to stderr
-    log_handlers: List[logging.Handler] = [logging.StreamHandler(sys.stdout)]
+    log_handlers: List[logging.Handler] = [logging.StreamHandler(sys.stderr)]
 
-    if config.get('logfile'):
+    logfile = config.get('logfile')
-        log_handlers.append(RotatingFileHandler(config['logfile'],
+    if logfile:
+        s = logfile.split(':')
+        if s[0] == 'syslog':
+            # Address can be either a string (socket filename) for Unix domain socket or
+            # a tuple (hostname, port) for UDP socket.
+            # Address can be omitted (i.e. simple 'syslog' used as the value of
+            # config['logfilename']), which defaults to '/dev/log', applicable for most
+            # of the systems.
+            address = (s[1], int(s[2])) if len(s) > 2 else s[1] if len(s) > 1 else '/dev/log'
+            handler = SysLogHandler(address=address)
+            # No datetime field for logging into syslog, to allow syslog
+            # to perform reduction of repeating messages if this is set in the
+            # syslog config. The messages should be equal for this.
+            handler.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
+            log_handlers.append(handler)
+        elif s[0] == 'journald':
+            try:
+                from systemd.journal import JournaldLogHandler
+            except ImportError:
+                raise OperationalException("You need the systemd python package be installed in "
+                                           "order to use logging to journald.")
+            handler = JournaldLogHandler()
+            # No datetime field for logging into journald, to allow syslog
+            # to perform reduction of repeating messages if this is set in the
+            # syslog config. The messages should be equal for this.
+            handler.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
+            log_handlers.append(handler)
+        else:
+            log_handlers.append(RotatingFileHandler(logfile,
                                                     maxBytes=1024 * 1024,  # 1Mb
                                                     backupCount=10))
 

freqtrade/main.py

@@ -4,6 +4,7 @@ Main Freqtrade bot script.
 Read the documentation to know what cli arguments you need.
 """
 
+from freqtrade.exceptions import FreqtradeException, OperationalException
 import sys
 # check min. python version
 if sys.version_info < (3, 6):
@@ -13,9 +14,7 @@ if sys.version_info < (3, 6):
 import logging
 from typing import Any, List
 
-from freqtrade import OperationalException
+from freqtrade.commands import Arguments
-from freqtrade.configuration import Arguments
-from freqtrade.worker import Worker
 
 
 logger = logging.getLogger('freqtrade')
@@ -28,35 +27,35 @@ def main(sysargv: List[str] = None) -> None:
     """
 
     return_code: Any = 1
-    worker = None
     try:
         arguments = Arguments(sysargv)
         args = arguments.get_parsed_arg()
 
-        # A subcommand has been issued.
+        # Call subcommand.
-        # Means if Backtesting or Hyperopt have been called we exit the bot
         if 'func' in args:
-            args['func'](args)
+            return_code = args['func'](args)
-            # TODO: fetch return_code as returned by the command function here
-            return_code = 0
         else:
-            # Load and run worker
+            # No subcommand was issued.
-            worker = Worker(args)
+            raise OperationalException(
-            worker.run()
+                "Usage of Freqtrade requires a subcommand to be specified.\n"
+                "To have the bot executing trades in live/dry-run modes, "
+                "depending on the value of the `dry_run` setting in the config, run Freqtrade "
+                "as `freqtrade trade [options...]`.\n"
+                "To see the full list of options available, please use "
+                "`freqtrade --help` or `freqtrade <command> --help`."
+                )
 
     except SystemExit as e:
         return_code = e
     except KeyboardInterrupt:
         logger.info('SIGINT received, aborting ...')
         return_code = 0
-    except OperationalException as e:
+    except FreqtradeException as e:
         logger.error(str(e))
         return_code = 2
     except Exception:
         logger.exception('Fatal exception!')
     finally:
-        if worker:
-            worker.exit()
         sys.exit(return_code)
 
 

freqtrade/misc.py

@@ -6,6 +6,7 @@ import logging
 import re
 from datetime import datetime
 from pathlib import Path
+from typing import Any
 from typing.io import IO
 
 import numpy as np
@@ -40,28 +41,30 @@ def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
     return dates.dt.to_pydatetime()
 
 
-def file_dump_json(filename: Path, data, is_zip=False) -> None:
+def file_dump_json(filename: Path, data: Any, is_zip: bool = False) -> None:
     """
     Dump JSON data into a file
     :param filename: file to create
     :param data: JSON Data to save
     :return:
     """
-    logger.info(f'dumping json to "{filename}"')
 
     if is_zip:
         if filename.suffix != '.gz':
             filename = filename.with_suffix('.gz')
+        logger.info(f'dumping json to "{filename}"')
+
         with gzip.open(filename, 'w') as fp:
             rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
     else:
+        logger.info(f'dumping json to "{filename}"')
         with open(filename, 'w') as fp:
             rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
 
     logger.debug(f'done json to "{filename}"')
 
 
-def json_load(datafile: IO):
+def json_load(datafile: IO) -> Any:
     """
     load data with rapidjson
     Use this to have a consistent experience,
@@ -78,18 +81,24 @@ def file_load_json(file):
         gzipfile = file
     # Try gzip file first, otherwise regular json file.
     if gzipfile.is_file():
-        logger.debug('Loading ticker data from file %s', gzipfile)
+        logger.debug(f"Loading historical data from file {gzipfile}")
-        with gzip.open(gzipfile) as tickerdata:
+        with gzip.open(gzipfile) as datafile:
-            pairdata = json_load(tickerdata)
+            pairdata = json_load(datafile)
     elif file.is_file():
-        logger.debug('Loading ticker data from file %s', file)
+        logger.debug(f"Loading historical data from file {file}")
-        with open(file) as tickerdata:
+        with open(file) as datafile:
-            pairdata = json_load(tickerdata)
+            pairdata = json_load(datafile)
     else:
         return None
     return pairdata
 
 
+def pair_to_filename(pair: str) -> str:
+    for ch in ['/', '-', ' ', '.', '@', '$', '+', ':']:
+        pair = pair.replace(ch, '_')
+    return pair
+
+
 def format_ms_time(date: int) -> str:
     """
     convert MS date to readable format.
@@ -125,5 +134,17 @@ def round_dict(d, n):
     return {k: (round(v, n) if isinstance(v, float) else v) for k, v in d.items()}
 
 
-def plural(num, singular: str, plural: str = None) -> str:
+def plural(num: float, singular: str, plural: str = None) -> str:
     return singular if (num == 1 or num == -1) else plural or singular + 's'
+
+
+def render_template(templatefile: str, arguments: dict = {}) -> str:
+
+    from jinja2 import Environment, PackageLoader, select_autoescape
+
+    env = Environment(
+        loader=PackageLoader('freqtrade', 'templates'),
+        autoescape=select_autoescape(['html', 'xml'])
+    )
+    template = env.get_template(templatefile)
+    return template.render(**arguments)

freqtrade/optimize/__init__.py

@@ -1,102 +0,0 @@
-import logging
-from typing import Any, Dict
-
-from freqtrade import DependencyException, constants, OperationalException
-from freqtrade.state import RunMode
-from freqtrade.utils import setup_utils_configuration
-
-
-logger = logging.getLogger(__name__)
-
-
-def setup_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
-    """
-    Prepare the configuration for the Hyperopt module
-    :param args: Cli args from Arguments()
-    :return: Configuration
-    """
-    config = setup_utils_configuration(args, method)
-
-    if method == RunMode.BACKTEST:
-        if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
-            raise DependencyException('stake amount could not be "%s" for backtesting' %
-                                      constants.UNLIMITED_STAKE_AMOUNT)
-
-    return config
-
-
-def start_backtesting(args: Dict[str, Any]) -> None:
-    """
-    Start Backtesting script
-    :param args: Cli args from Arguments()
-    :return: None
-    """
-    # Import here to avoid loading backtesting module when it's not used
-    from freqtrade.optimize.backtesting import Backtesting
-
-    # Initialize configuration
-    config = setup_configuration(args, RunMode.BACKTEST)
-
-    logger.info('Starting freqtrade in Backtesting mode')
-
-    # Initialize backtesting object
-    backtesting = Backtesting(config)
-    backtesting.start()
-
-
-def start_hyperopt(args: Dict[str, Any]) -> None:
-    """
-    Start hyperopt script
-    :param args: Cli args from Arguments()
-    :return: None
-    """
-    # Import here to avoid loading hyperopt module when it's not used
-    try:
-        from filelock import FileLock, Timeout
-        from freqtrade.optimize.hyperopt import Hyperopt
-    except ImportError as e:
-        raise OperationalException(
-            f"{e}. Please ensure that the hyperopt dependencies are installed.") from e
-    # Initialize configuration
-    config = setup_configuration(args, RunMode.HYPEROPT)
-
-    logger.info('Starting freqtrade in Hyperopt mode')
-
-    lock = FileLock(Hyperopt.get_lock_filename(config))
-
-    try:
-        with lock.acquire(timeout=1):
-
-            # Remove noisy log messages
-            logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
-            logging.getLogger('filelock').setLevel(logging.WARNING)
-
-            # Initialize backtesting object
-            hyperopt = Hyperopt(config)
-            hyperopt.start()
-
-    except Timeout:
-        logger.info("Another running instance of freqtrade Hyperopt detected.")
-        logger.info("Simultaneous execution of multiple Hyperopt commands is not supported. "
-                    "Hyperopt module is resource hungry. Please run your Hyperopts sequentially "
-                    "or on separate machines.")
-        logger.info("Quitting now.")
-        # TODO: return False here in order to help freqtrade to exit
-        # with non-zero exit code...
-        # Same in Edge and Backtesting start() functions.
-
-
-def start_edge(args: Dict[str, Any]) -> None:
-    """
-    Start Edge script
-    :param args: Cli args from Arguments()
-    :return: None
-    """
-    from freqtrade.optimize.edge_cli import EdgeCli
-    # Initialize configuration
-    config = setup_configuration(args, RunMode.EDGE)
-    logger.info('Starting freqtrade in Edge mode')
-
-    # Initialize Edge object
-    edge_cli = EdgeCli(config)
-    edge_cli.start()

freqtrade/optimize/backtesting.py

@@ -6,22 +6,24 @@ This module contains the backtesting logic
 import logging
 from copy import deepcopy
 from datetime import datetime, timedelta
-from pathlib import Path
+from typing import Any, Dict, List, NamedTuple, Optional, Tuple
-from typing import Any, Dict, List, NamedTuple, Optional
 
+import arrow
 from pandas import DataFrame
 
-from freqtrade import OperationalException
+from freqtrade.configuration import (TimeRange, remove_credentials,
-from freqtrade.configuration import TimeRange
+                                     validate_config_consistency)
 from freqtrade.data import history
+from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.dataprovider import DataProvider
-from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.exceptions import OperationalException
-from freqtrade.misc import file_dump_json
+from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
+from freqtrade.optimize.optimize_reports import (show_backtest_results,
+                                                 store_backtest_result)
 from freqtrade.persistence import Trade
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.state import RunMode
-from freqtrade.strategy.interface import IStrategy, SellType
+from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
-from tabulate import tabulate
 
 logger = logging.getLogger(__name__)
 
@@ -57,18 +59,14 @@ class Backtesting:
         self.config = config
 
         # Reset keys for backtesting
-        self.config['exchange']['key'] = ''
+        remove_credentials(self.config)
-        self.config['exchange']['secret'] = ''
-        self.config['exchange']['password'] = ''
-        self.config['exchange']['uid'] = ''
-        self.config['dry_run'] = True
         self.strategylist: List[IStrategy] = []
-        self.exchange = ExchangeResolver(self.config['exchange']['name'], self.config).exchange
+        self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
 
         if config.get('fee'):
             self.fee = config['fee']
         else:
-            self.fee = self.exchange.get_fee()
+            self.fee = self.exchange.get_fee(symbol=self.config['exchange']['pair_whitelist'][0])
 
         if self.config.get('runmode') != RunMode.HYPEROPT:
             self.dataprovider = DataProvider(self.config, self.exchange)
@@ -78,18 +76,22 @@ class Backtesting:
             for strat in list(self.config['strategy_list']):
                 stratconf = deepcopy(self.config)
                 stratconf['strategy'] = strat
-                self.strategylist.append(StrategyResolver(stratconf).strategy)
+                self.strategylist.append(StrategyResolver.load_strategy(stratconf))
+                validate_config_consistency(stratconf)
 
         else:
             # No strategy list specified, only one strategy
-            self.strategylist.append(StrategyResolver(self.config).strategy)
+            self.strategylist.append(StrategyResolver.load_strategy(self.config))
+            validate_config_consistency(self.config)
 
         if "ticker_interval" not in self.config:
-            raise OperationalException("Ticker-interval needs to be set in either configuration "
+            raise OperationalException("Timeframe (ticker interval) needs to be set in either "
-                                       "or as cli argument `--ticker-interval 5m`")
+                                       "configuration or as cli argument `--ticker-interval 5m`")
-        self.ticker_interval = str(self.config.get('ticker_interval'))
+        self.timeframe = str(self.config.get('ticker_interval'))
-        self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
+        self.timeframe_min = timeframe_to_minutes(self.timeframe)
 
+        # Get maximum required startup period
+        self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
         # Load one (first) strategy
         self._set_strategy(self.strategylist[0])
 
@@ -103,140 +105,103 @@ class Backtesting:
         # And the regular "stoploss" function would not apply to that case
         self.strategy.order_types['stoploss_on_exchange'] = False
 
-    def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame,
+    def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
-                             skip_nan: bool = False) -> str:
+        timerange = TimeRange.parse_timerange(None if self.config.get(
+            'timerange') is None else str(self.config.get('timerange')))
+
+        data = history.load_data(
+            datadir=self.config['datadir'],
+            pairs=self.config['exchange']['pair_whitelist'],
+            timeframe=self.timeframe,
+            timerange=timerange,
+            startup_candles=self.required_startup,
+            fail_without_data=True,
+            data_format=self.config.get('dataformat_ohlcv', 'json'),
+        )
+
+        min_date, max_date = history.get_timerange(data)
+
+        logger.info(
+            'Loading data from %s up to %s (%s days)..',
+            min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
+        )
+        # Adjust startts forward if not enough data is available
+        timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
+                                            self.required_startup, min_date)
+
+        return data, timerange
+
+    def _get_ohlcv_as_lists(self, processed: Dict) -> Dict[str, DataFrame]:
         """
-        Generates and returns a text table for the given backtest data and the results dataframe
+        Helper function to convert a processed dataframes into lists for performance reasons.
-        :return: pretty printed table with tabulate as str
-        """
-        stake_currency = str(self.config.get('stake_currency'))
-        max_open_trades = self.config.get('max_open_trades')
-
-        floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
-        tabular_data = []
-        headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
-                   'tot profit ' + stake_currency, 'tot profit %', 'avg duration',
-                   'profit', 'loss']
-        for pair in data:
-            result = results[results.pair == pair]
-            if skip_nan and result.profit_abs.isnull().all():
-                continue
-
-            tabular_data.append([
-                pair,
-                len(result.index),
-                result.profit_percent.mean() * 100.0,
-                result.profit_percent.sum() * 100.0,
-                result.profit_abs.sum(),
-                result.profit_percent.sum() * 100.0 / max_open_trades,
-                str(timedelta(
-                    minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
-                len(result[result.profit_abs > 0]),
-                len(result[result.profit_abs < 0])
-            ])
-
-        # Append Total
-        tabular_data.append([
-            'TOTAL',
-            len(results.index),
-            results.profit_percent.mean() * 100.0,
-            results.profit_percent.sum() * 100.0,
-            results.profit_abs.sum(),
-            results.profit_percent.sum() * 100.0 / max_open_trades,
-            str(timedelta(
-                minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
-            len(results[results.profit_abs > 0]),
-            len(results[results.profit_abs < 0])
-        ])
-        # Ignore type as floatfmt does allow tuples but mypy does not know that
-        return tabulate(tabular_data, headers=headers,
-                        floatfmt=floatfmt, tablefmt="pipe")  # type: ignore
-
-    def _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str:
-        """
-        Generate small table outlining Backtest results
-        """
-        tabular_data = []
-        headers = ['Sell Reason', 'Count']
-        for reason, count in results['sell_reason'].value_counts().iteritems():
-            tabular_data.append([reason.value,  count])
-        return tabulate(tabular_data, headers=headers, tablefmt="pipe")
-
-    def _generate_text_table_strategy(self, all_results: dict) -> str:
-        """
-        Generate summary table per strategy
-        """
-        stake_currency = str(self.config.get('stake_currency'))
-        max_open_trades = self.config.get('max_open_trades')
-
-        floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
-        tabular_data = []
-        headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
-                   'tot profit ' + stake_currency, 'tot profit %', 'avg duration',
-                   'profit', 'loss']
-        for strategy, results in all_results.items():
-            tabular_data.append([
-                strategy,
-                len(results.index),
-                results.profit_percent.mean() * 100.0,
-                results.profit_percent.sum() * 100.0,
-                results.profit_abs.sum(),
-                results.profit_percent.sum() * 100.0 / max_open_trades,
-                str(timedelta(
-                    minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
-                len(results[results.profit_abs > 0]),
-                len(results[results.profit_abs < 0])
-            ])
-        # Ignore type as floatfmt does allow tuples but mypy does not know that
-        return tabulate(tabular_data, headers=headers,
-                        floatfmt=floatfmt, tablefmt="pipe")  # type: ignore
-
-    def _store_backtest_result(self, recordfilename: Path, results: DataFrame,
-                               strategyname: Optional[str] = None) -> None:
-
-        records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
-                    t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
-                    t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
-                   for index, t in results.iterrows()]
-
-        if records:
-            if strategyname:
-                # Inject strategyname to filename
-                recordfilename = Path.joinpath(
-                    recordfilename.parent,
-                    f'{recordfilename.stem}-{strategyname}').with_suffix(recordfilename.suffix)
-            logger.info(f'Dumping backtest results to {recordfilename}')
-            file_dump_json(recordfilename, records)
-
-    def _get_ticker_list(self, processed) -> Dict[str, DataFrame]:
-        """
-        Helper function to convert a processed tickerlist into a list for performance reasons.
 
         Used by backtest() - so keep this optimized for performance.
         """
         headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
-        ticker: Dict = {}
+        data: Dict = {}
-        # Create ticker dict
+        # Create dict with data
         for pair, pair_data in processed.items():
-            pair_data['buy'], pair_data['sell'] = 0, 0  # cleanup from previous run
+            pair_data.loc[:, 'buy'] = 0  # cleanup from previous run
+            pair_data.loc[:, 'sell'] = 0  # cleanup from previous run
 
-            ticker_data = self.strategy.advise_sell(
+            df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
 
-            # to avoid using data from future, we buy/sell with signal from previous candle
+            # To avoid using data from future, we use buy/sell signals shifted
-            ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
+            # from the previous candle
-            ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
+            df_analyzed.loc[:, 'buy'] = df_analyzed['buy'].shift(1)
+            df_analyzed.loc[:, 'sell'] = df_analyzed['sell'].shift(1)
 
-            ticker_data.drop(ticker_data.head(1).index, inplace=True)
+            df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
 
             # Convert from Pandas to list for performance reasons
             # (Looping Pandas is slow.)
-            ticker[pair] = [x for x in ticker_data.itertuples()]
+            data[pair] = [x for x in df_analyzed.itertuples()]
-        return ticker
+        return data
+
+    def _get_close_rate(self, sell_row, trade: Trade, sell: SellCheckTuple,
+                        trade_dur: int) -> float:
+        """
+        Get close rate for backtesting result
+        """
+        # Special handling if high or low hit STOP_LOSS or ROI
+        if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
+            # Set close_rate to stoploss
+            return trade.stop_loss
+        elif sell.sell_type == (SellType.ROI):
+            roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur)
+            if roi is not None:
+                if roi == -1 and roi_entry % self.timeframe_min == 0:
+                    # When forceselling with ROI=-1, the roi time will always be equal to trade_dur.
+                    # If that entry is a multiple of the timeframe (so on candle open)
+                    # - we'll use open instead of close
+                    return sell_row.open
+
+                # - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
+                close_rate = - (trade.open_rate * roi + trade.open_rate *
+                                (1 + trade.fee_open)) / (trade.fee_close - 1)
+
+                if (trade_dur > 0 and trade_dur == roi_entry
+                        and roi_entry % self.timeframe_min == 0
+                        and sell_row.open > close_rate):
+                    # new ROI entry came into effect.
+                    # use Open rate if open_rate > calculated sell rate
+                    return sell_row.open
+
+                # Use the maximum between close_rate and low as we
+                # cannot sell outside of a candle.
+                # Applies when a new ROI setting comes in place and the whole candle is above that.
+                return max(close_rate, sell_row.low)
+
+            else:
+                # This should not be reached...
+                return sell_row.open
+        else:
+            return sell_row.open
 
     def _get_sell_trade_entry(
             self, pair: str, buy_row: DataFrame,
-            partial_ticker: List, trade_count_lock: Dict,
+            partial_ohlcv: List, trade_count_lock: Dict,
             stake_amount: float, max_open_trades: int) -> Optional[BacktestResult]:
 
         trade = Trade(
@@ -251,7 +216,7 @@ class Backtesting:
         )
         logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
         # calculate win/lose forwards from buy point
-        for sell_row in partial_ticker:
+        for sell_row in partial_ohlcv:
             if max_open_trades > 0:
                 # Increase trade_count_lock for every iteration
                 trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
@@ -260,29 +225,10 @@ class Backtesting:
                                              sell_row.sell, low=sell_row.low, high=sell_row.high)
             if sell.sell_flag:
                 trade_dur = int((sell_row.date - buy_row.date).total_seconds() // 60)
-                # Special handling if high or low hit STOP_LOSS or ROI
+                closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
-                if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
-                    # Set close_rate to stoploss
-                    closerate = trade.stop_loss
-                elif sell.sell_type == (SellType.ROI):
-                    roi = self.strategy.min_roi_reached_entry(trade_dur)
-                    if roi is not None:
-                        # - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
-                        closerate = - (trade.open_rate * roi + trade.open_rate *
-                                       (1 + trade.fee_open)) / (trade.fee_close - 1)
-
-                        # Use the maximum between closerate and low as we
-                        # cannot sell outside of a candle.
-                        # Applies when using {"xx": -1} as roi to force sells after xx minutes
-                        closerate = max(closerate, sell_row.low)
-                    else:
-                        # This should not be reached...
-                        closerate = sell_row.open
-                else:
-                    closerate = sell_row.open
 
                 return BacktestResult(pair=pair,
-                                      profit_percent=trade.calc_profit_percent(rate=closerate),
+                                      profit_percent=trade.calc_profit_ratio(rate=closerate),
                                       profit_abs=trade.calc_profit(rate=closerate),
                                       open_time=buy_row.date,
                                       close_time=sell_row.date,
@@ -294,11 +240,11 @@ class Backtesting:
                                       close_rate=closerate,
                                       sell_reason=sell.sell_type
                                       )
-        if partial_ticker:
+        if partial_ohlcv:
             # no sell condition found - trade stil open at end of backtest period
-            sell_row = partial_ticker[-1]
+            sell_row = partial_ohlcv[-1]
             bt_res = BacktestResult(pair=pair,
-                                    profit_percent=trade.calc_profit_percent(rate=sell_row.open),
+                                    profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
                                     profit_abs=trade.calc_profit(rate=sell_row.open),
                                     open_time=buy_row.date,
                                     close_time=sell_row.date,
@@ -318,50 +264,49 @@ class Backtesting:
             return bt_res
         return None
 
-    def backtest(self, args: Dict) -> DataFrame:
+    def backtest(self, processed: Dict, stake_amount: float,
+                 start_date: arrow.Arrow, end_date: arrow.Arrow,
+                 max_open_trades: int = 0, position_stacking: bool = False) -> DataFrame:
         """
-        Implements backtesting functionality
+        Implement backtesting functionality
 
         NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
         Of course try to not have ugly code. By some accessor are sometime slower than functions.
-        Avoid, logging on this method
+        Avoid extensive logging in this method and functions it calls.
 
-        :param args: a dict containing:
+        :param processed: a processed dictionary with format {pair, data}
-            stake_amount: btc amount to use for each trade
+        :param stake_amount: amount to use for each trade
-            processed: a processed dictionary with format {pair, data}
+        :param start_date: backtesting timerange start datetime
-            max_open_trades: maximum number of concurrent trades (default: 0, disabled)
+        :param end_date: backtesting timerange end datetime
-            position_stacking: do we allow position stacking? (default: False)
+        :param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
-        :return: DataFrame
+        :param position_stacking: do we allow position stacking?
+        :return: DataFrame with trades (results of backtesting)
         """
-        # Arguments are long and noisy, so this is commented out.
+        logger.debug(f"Run backtest, stake_amount: {stake_amount}, "
-        # Uncomment if you need to debug the backtest() method.
+                     f"start_date: {start_date}, end_date: {end_date}, "
-#        logger.debug(f"Start backtest, args: {args}")
+                     f"max_open_trades: {max_open_trades}, position_stacking: {position_stacking}"
-        processed = args['processed']
+                     )
-        stake_amount = args['stake_amount']
-        max_open_trades = args.get('max_open_trades', 0)
-        position_stacking = args.get('position_stacking', False)
-        start_date = args['start_date']
-        end_date = args['end_date']
         trades = []
         trade_count_lock: Dict = {}
 
-        # Dict of ticker-lists for performance (looping lists is a lot faster than dataframes)
+        # Use dict of lists with data for performance
-        ticker: Dict = self._get_ticker_list(processed)
+        # (looping lists is a lot faster than pandas DataFrames)
+        data: Dict = self._get_ohlcv_as_lists(processed)
 
         lock_pair_until: Dict = {}
         # Indexes per pair, so some pairs are allowed to have a missing start.
         indexes: Dict = {}
-        tmp = start_date + timedelta(minutes=self.ticker_interval_mins)
+        tmp = start_date + timedelta(minutes=self.timeframe_min)
 
         # Loop timerange and get candle for each pair at that point in time
         while tmp < end_date:
 
-            for i, pair in enumerate(ticker):
+            for i, pair in enumerate(data):
                 if pair not in indexes:
                     indexes[pair] = 0
 
                 try:
-                    row = ticker[pair][indexes[pair]]
+                    row = data[pair][indexes[pair]]
                 except IndexError:
                     # missing Data for one pair at the end.
                     # Warnings for this are shown during data loading
@@ -389,7 +334,7 @@ class Backtesting:
 
                 # since indexes has been incremented before, we need to go one step back to
                 # also check the buying candle for sell conditions.
-                trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]-1:],
+                trade_entry = self._get_sell_trade_entry(pair, row, data[pair][indexes[pair]-1:],
                                                          trade_count_lock, stake_amount,
                                                          max_open_trades)
 
@@ -403,85 +348,57 @@ class Backtesting:
                     lock_pair_until[pair] = end_date.datetime
 
             # Move time one configured time_interval ahead.
-            tmp += timedelta(minutes=self.ticker_interval_mins)
+            tmp += timedelta(minutes=self.timeframe_min)
         return DataFrame.from_records(trades, columns=BacktestResult._fields)
 
     def start(self) -> None:
         """
-        Run a backtesting end-to-end
+        Run backtesting end-to-end
         :return: None
         """
         data: Dict[str, Any] = {}
-        pairs = self.config['exchange']['pair_whitelist']
+
         logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
         logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
 
-        timerange = TimeRange.parse_timerange(None if self.config.get(
-            'timerange') is None else str(self.config.get('timerange')))
-        data = history.load_data(
-            datadir=Path(self.config['datadir']),
-            pairs=pairs,
-            ticker_interval=self.ticker_interval,
-            timerange=timerange,
-        )
-
-        if not data:
-            logger.critical("No data found. Terminating.")
-            return
         # Use max_open_trades in backtesting, except --disable-max-market-positions is set
         if self.config.get('use_max_market_positions', True):
             max_open_trades = self.config['max_open_trades']
         else:
             logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
             max_open_trades = 0
+        position_stacking = self.config.get('position_stacking', False)
+
+        data, timerange = self.load_bt_data()
+
         all_results = {}
-
-        min_date, max_date = history.get_timeframe(data)
-
-        logger.info(
-            'Backtesting with data from %s up to %s (%s days)..',
-            min_date.isoformat(),
-            max_date.isoformat(),
-            (max_date - min_date).days
-        )
-
         for strat in self.strategylist:
             logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
             self._set_strategy(strat)
 
             # need to reprocess data every time to populate signals
-            preprocessed = self.strategy.tickerdata_to_dataframe(data)
+            preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
 
+            # Trim startup period from analyzed dataframe
+            for pair, df in preprocessed.items():
+                preprocessed[pair] = trim_dataframe(df, timerange)
+            min_date, max_date = history.get_timerange(preprocessed)
+
+            logger.info(
+                'Backtesting with data from %s up to %s (%s days)..',
+                min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
+            )
             # Execute backtest and print results
             all_results[self.strategy.get_strategy_name()] = self.backtest(
-                {
+                processed=preprocessed,
-                    'stake_amount': self.config.get('stake_amount'),
+                stake_amount=self.config['stake_amount'],
-                    'processed': preprocessed,
+                start_date=min_date,
-                    'max_open_trades': max_open_trades,
+                end_date=max_date,
-                    'position_stacking': self.config.get('position_stacking', False),
+                max_open_trades=max_open_trades,
-                    'start_date': min_date,
+                position_stacking=position_stacking,
-                    'end_date': max_date,
-                }
             )
 
-        for strategy, results in all_results.items():
-
         if self.config.get('export', False):
-                self._store_backtest_result(Path(self.config['exportfilename']), results,
+            store_backtest_result(self.config['exportfilename'], all_results)
-                                            strategy if len(self.strategylist) > 1 else None)
+        # Show backtest results
-
+        show_backtest_results(self.config, data, all_results)
-            print(f"Result for strategy {strategy}")
-            print(' BACKTESTING REPORT '.center(133, '='))
-            print(self._generate_text_table(data, results))
-
-            print(' SELL REASON STATS '.center(133, '='))
-            print(self._generate_text_table_sell_reason(data, results))
-
-            print(' LEFT OPEN TRADES REPORT '.center(133, '='))
-            print(self._generate_text_table(data, results.loc[results.open_at_end], True))
-            print()
-        if len(all_results) > 1:
-            # Print Strategy summary table
-            print(' Strategy Summary '.center(133, '='))
-            print(self._generate_text_table_strategy(all_results))
-            print('\nFor more details, please look at the detail tables above')

freqtrade/optimize/edge_cli.py

@@ -4,14 +4,14 @@
 This module contains the edge backtesting interface
 """
 import logging
-from typing import Dict, Any
+from typing import Any, Dict
-from tabulate import tabulate
-from freqtrade import constants
-from freqtrade.edge import Edge
 
-from freqtrade.configuration import TimeRange
+from freqtrade import constants
-from freqtrade.exchange import Exchange
+from freqtrade.configuration import (TimeRange, remove_credentials,
-from freqtrade.resolvers import StrategyResolver
+                                     validate_config_consistency)
+from freqtrade.edge import Edge
+from freqtrade.optimize.optimize_reports import generate_edge_table
+from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 
 logger = logging.getLogger(__name__)
 
@@ -29,51 +29,22 @@ class EdgeCli:
         self.config = config
 
         # Reset keys for edge
-        self.config['exchange']['key'] = ''
+        remove_credentials(self.config)
-        self.config['exchange']['secret'] = ''
-        self.config['exchange']['password'] = ''
-        self.config['exchange']['uid'] = ''
         self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
-        self.config['dry_run'] = True
+        self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
-        self.exchange = Exchange(self.config)
+        self.strategy = StrategyResolver.load_strategy(self.config)
-        self.strategy = StrategyResolver(self.config).strategy
+
+        validate_config_consistency(self.config)
 
         self.edge = Edge(config, self.exchange, self.strategy)
         # Set refresh_pairs to false for edge-cli (it must be true for edge)
         self.edge._refresh_pairs = False
 
-        self.timerange = TimeRange.parse_timerange(None if self.config.get(
+        self.edge._timerange = TimeRange.parse_timerange(None if self.config.get(
             'timerange') is None else str(self.config.get('timerange')))
 
-        self.edge._timerange = self.timerange
-
-    def _generate_edge_table(self, results: dict) -> str:
-
-        floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', '.d')
-        tabular_data = []
-        headers = ['pair', 'stoploss', 'win rate', 'risk reward ratio',
-                   'required risk reward', 'expectancy', 'total number of trades',
-                   'average duration (min)']
-
-        for result in results.items():
-            if result[1].nb_trades > 0:
-                tabular_data.append([
-                    result[0],
-                    result[1].stoploss,
-                    result[1].winrate,
-                    result[1].risk_reward_ratio,
-                    result[1].required_risk_reward,
-                    result[1].expectancy,
-                    result[1].nb_trades,
-                    round(result[1].avg_trade_duration)
-                ])
-
-        # Ignore type as floatfmt does allow tuples but mypy does not know that
-        return tabulate(tabular_data, headers=headers,
-                        floatfmt=floatfmt, tablefmt="pipe")  # type: ignore
-
     def start(self) -> None:
         result = self.edge.calculate()
         if result:
             print('')  # blank line for readability
-            print(self._generate_edge_table(self.edge._cached_pairs))
+            print(generate_edge_table(self.edge._cached_pairs))

freqtrade/optimize/hyperopt.py

@@ -4,9 +4,12 @@
 This module contains the hyperopt logic
 """
 
+import locale
 import logging
+import random
 import sys
-
+import warnings
+from math import ceil
 from collections import OrderedDict
 from operator import itemgetter
 from pathlib import Path
@@ -14,22 +17,31 @@ from pprint import pprint
 from typing import Any, Dict, List, Optional
 
 import rapidjson
-
-from colorama import init as colorama_init
 from colorama import Fore, Style
-from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects, cpu_count
+from colorama import init as colorama_init
-from pandas import DataFrame
+from joblib import (Parallel, cpu_count, delayed, dump, load,
-from skopt import Optimizer
+                    wrap_non_picklable_objects)
-from skopt.space import Dimension
+from pandas import DataFrame, json_normalize, isna
+import tabulate
+from os import path
+import io
 
-from freqtrade.configuration import TimeRange
+from freqtrade.data.converter import trim_dataframe
-from freqtrade.data.history import load_data, get_timeframe
+from freqtrade.data.history import get_timerange
-from freqtrade.misc import round_dict
+from freqtrade.exceptions import OperationalException
+from freqtrade.misc import plural, round_dict
 from freqtrade.optimize.backtesting import Backtesting
 # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
-from freqtrade.optimize.hyperopt_interface import IHyperOpt  # noqa: F4
+from freqtrade.optimize.hyperopt_interface import IHyperOpt  # noqa: F401
-from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss  # noqa: F4
+from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss  # noqa: F401
-from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver, HyperOptLossResolver
+from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
+                                                   HyperOptResolver)
+
+# Suppress scikit-learn FutureWarnings from skopt
+with warnings.catch_warnings():
+    warnings.filterwarnings("ignore", category=FutureWarning)
+    from skopt import Optimizer
+    from skopt.space import Dimension
 
 
 logger = logging.getLogger(__name__)
@@ -52,19 +64,20 @@ class Hyperopt:
     hyperopt = Hyperopt(config)
     hyperopt.start()
     """
+
     def __init__(self, config: Dict[str, Any]) -> None:
         self.config = config
 
-        self.custom_hyperopt = HyperOptResolver(self.config).hyperopt
-
         self.backtesting = Backtesting(self.config)
 
-        self.custom_hyperoptloss = HyperOptLossResolver(self.config).hyperoptloss
+        self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
+
+        self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
         self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
 
         self.trials_file = (self.config['user_data_dir'] /
                             'hyperopt_results' / 'hyperopt_results.pickle')
-        self.tickerdata_pickle = (self.config['user_data_dir'] /
+        self.data_pickle_file = (self.config['user_data_dir'] /
                                  'hyperopt_results' / 'hyperopt_tickerdata.pkl')
         self.total_epochs = config.get('epochs', 0)
 
@@ -75,6 +88,8 @@ class Hyperopt:
         else:
             logger.info("Continuing on previous hyperopt results.")
 
+        self.num_trials_saved = 0
+
         # Previous evaluations
         self.trials: List = []
 
@@ -103,140 +118,336 @@ class Hyperopt:
                 self.config['ask_strategy'] = {}
             self.config['ask_strategy']['use_sell_signal'] = True
 
+        self.print_all = self.config.get('print_all', False)
+        self.hyperopt_table_header = 0
+        self.print_colorized = self.config.get('print_colorized', False)
+        self.print_json = self.config.get('print_json', False)
+
     @staticmethod
-    def get_lock_filename(config) -> str:
+    def get_lock_filename(config: Dict[str, Any]) -> str:
 
         return str(config['user_data_dir'] / 'hyperopt.lock')
 
-    def clean_hyperopt(self):
+    def clean_hyperopt(self) -> None:
         """
         Remove hyperopt pickle files to restart hyperopt.
         """
-        for f in [self.tickerdata_pickle, self.trials_file]:
+        for f in [self.data_pickle_file, self.trials_file]:
             p = Path(f)
             if p.is_file():
                 logger.info(f"Removing `{p}`.")
                 p.unlink()
 
-    def get_args(self, params):
+    def _get_params_dict(self, raw_params: List[Any]) -> Dict:
 
-        dimensions = self.dimensions
+        dimensions: List[Dimension] = self.dimensions
 
         # Ensure the number of dimensions match
-        # the number of parameters in the list x.
+        # the number of parameters in the list.
-        if len(params) != len(dimensions):
+        if len(raw_params) != len(dimensions):
-            raise ValueError('Mismatch in number of search-space dimensions. '
+            raise ValueError('Mismatch in number of search-space dimensions.')
-                             f'len(dimensions)=={len(dimensions)} and len(x)=={len(params)}')
 
-        # Create a dict where the keys are the names of the dimensions
+        # Return a dict where the keys are the names of the dimensions
-        # and the values are taken from the list of parameters x.
+        # and the values are taken from the list of parameters.
-        arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
+        return {d.name: v for d, v in zip(dimensions, raw_params)}
-        return arg_dict
 
-    def save_trials(self) -> None:
+    def save_trials(self, final: bool = False) -> None:
         """
         Save hyperopt trials to file
         """
-        if self.trials:
+        num_trials = len(self.trials)
-            logger.info("Saving %d evaluations to '%s'", len(self.trials), self.trials_file)
+        if num_trials > self.num_trials_saved:
+            logger.debug(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
             dump(self.trials, self.trials_file)
+            self.num_trials_saved = num_trials
+        if final:
+            logger.info(f"{num_trials} {plural(num_trials, 'epoch')} "
+                        f"saved to '{self.trials_file}'.")
 
-    def read_trials(self) -> List:
+    @staticmethod
+    def _read_trials(trials_file: Path) -> List:
         """
         Read hyperopt trials file
         """
-        logger.info("Reading Trials from '%s'", self.trials_file)
+        logger.info("Reading Trials from '%s'", trials_file)
-        trials = load(self.trials_file)
+        trials = load(trials_file)
-        self.trials_file.unlink()
         return trials
 
-    def log_trials_result(self) -> None:
+    def _get_params_details(self, params: Dict) -> Dict:
         """
-        Display Best hyperopt result
+        Return the params for each space
         """
-        results = sorted(self.trials, key=itemgetter('loss'))
+        result: Dict = {}
-        best_result = results[0]
-        params = best_result['params']
-        log_str = self.format_results_logstring(best_result)
-        print(f"\nBest result:\n\n{log_str}\n")
 
-        if self.config.get('print_json'):
-            result_dict: Dict = {}
-            if self.has_space('buy') or self.has_space('sell'):
-                result_dict['params'] = {}
         if self.has_space('buy'):
-                result_dict['params'].update({p.name: params.get(p.name)
+            result['buy'] = {p.name: params.get(p.name)
-                                             for p in self.hyperopt_space('buy')})
+                             for p in self.hyperopt_space('buy')}
         if self.has_space('sell'):
-                result_dict['params'].update({p.name: params.get(p.name)
+            result['sell'] = {p.name: params.get(p.name)
-                                             for p in self.hyperopt_space('sell')})
+                              for p in self.hyperopt_space('sell')}
         if self.has_space('roi'):
+            result['roi'] = self.custom_hyperopt.generate_roi_table(params)
+        if self.has_space('stoploss'):
+            result['stoploss'] = {p.name: params.get(p.name)
+                                  for p in self.hyperopt_space('stoploss')}
+        if self.has_space('trailing'):
+            result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
+
+        return result
+
+    @staticmethod
+    def print_epoch_details(results, total_epochs: int, print_json: bool,
+                            no_header: bool = False, header_str: str = None) -> None:
+        """
+        Display details of the hyperopt result
+        """
+        params = results.get('params_details', {})
+
+        # Default header string
+        if header_str is None:
+            header_str = "Best result"
+
+        if not no_header:
+            explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
+            print(f"\n{header_str}:\n\n{explanation_str}\n")
+
+        if print_json:
+            result_dict: Dict = {}
+            for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
+                Hyperopt._params_update_for_json(result_dict, params, s)
+            print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
+
+        else:
+            Hyperopt._params_pretty_print(params, 'buy', "Buy hyperspace params:")
+            Hyperopt._params_pretty_print(params, 'sell', "Sell hyperspace params:")
+            Hyperopt._params_pretty_print(params, 'roi', "ROI table:")
+            Hyperopt._params_pretty_print(params, 'stoploss', "Stoploss:")
+            Hyperopt._params_pretty_print(params, 'trailing', "Trailing stop:")
+
+    @staticmethod
+    def _params_update_for_json(result_dict, params, space: str) -> None:
+        if space in params:
+            space_params = Hyperopt._space_params(params, space)
+            if space in ['buy', 'sell']:
+                result_dict.setdefault('params', {}).update(space_params)
+            elif space == 'roi':
                 # Convert keys in min_roi dict to strings because
                 # rapidjson cannot dump dicts with integer keys...
                 # OrderedDict is used to keep the numeric order of the items
                 # in the dict.
                 result_dict['minimal_roi'] = OrderedDict(
-                    (str(k), v) for k, v in self.custom_hyperopt.generate_roi_table(params).items()
+                    (str(k), v) for k, v in space_params.items()
                 )
-            if self.has_space('stoploss'):
+            else:  # 'stoploss', 'trailing'
-                result_dict['stoploss'] = params.get('stoploss')
+                result_dict.update(space_params)
-            print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
-        else:
-            if self.has_space('buy'):
-                print('Buy hyperspace params:')
-                pprint({p.name: params.get(p.name) for p in self.hyperopt_space('buy')},
-                       indent=4)
-            if self.has_space('sell'):
-                print('Sell hyperspace params:')
-                pprint({p.name: params.get(p.name) for p in self.hyperopt_space('sell')},
-                       indent=4)
-            if self.has_space('roi'):
-                print("ROI table:")
-                # Round printed values to 5 digits after the decimal point
-                pprint(round_dict(self.custom_hyperopt.generate_roi_table(params), 5), indent=4)
-            if self.has_space('stoploss'):
-                # Also round to 5 digits after the decimal point
-                print(f"Stoploss: {round(params.get('stoploss'), 5)}")
 
-    def log_results(self, results) -> None:
+    @staticmethod
+    def _params_pretty_print(params, space: str, header: str) -> None:
+        if space in params:
+            space_params = Hyperopt._space_params(params, space, 5)
+            if space == 'stoploss':
+                print(header, space_params.get('stoploss'))
+            else:
+                print(header)
+                pprint(space_params, indent=4)
+
+    @staticmethod
+    def _space_params(params, space: str, r: int = None) -> Dict:
+        d = params[space]
+        # Round floats to `r` digits after the decimal point if requested
+        return round_dict(d, r) if r else d
+
+    @staticmethod
+    def is_best_loss(results, current_best_loss: float) -> bool:
+        return results['loss'] < current_best_loss
+
+    def print_results(self, results) -> None:
         """
         Log results if it is better than any previous evaluation
         """
-        print_all = self.config.get('print_all', False)
+        is_best = results['is_best']
-        is_best_loss = results['loss'] < self.current_best_loss
+        if not self.print_all:
-        if print_all or is_best_loss:
+            # Print '\n' after each 100th epoch to separate dots from the log messages.
-            if is_best_loss:
+            # Otherwise output is messy on a terminal.
-                self.current_best_loss = results['loss']
+            print('.', end='' if results['current_epoch'] % 100 != 0 else None)  # type: ignore
-            log_str = self.format_results_logstring(results)
-            # Colorize output
-            if self.config.get('print_colorized', False):
-                if results['total_profit'] > 0:
-                    log_str = Fore.GREEN + log_str
-                if print_all and is_best_loss:
-                    log_str = Style.BRIGHT + log_str
-            if print_all:
-                print(log_str)
-            else:
-                print('\n' + log_str)
-        else:
-            print('.', end='')
             sys.stdout.flush()
 
-    def format_results_logstring(self, results) -> str:
+        if self.print_all or is_best:
-        # Output human-friendly index here (starting from 1)
+            if not self.print_all:
-        current = results['current_epoch'] + 1
+                # Separate the results explanation string from dots
-        total = self.total_epochs
+                print("\n")
-        res = results['results_explanation']
+            self.print_result_table(self.config, results, self.total_epochs,
-        loss = results['loss']
+                                    self.print_all, self.print_colorized,
-        log_str = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
+                                    self.hyperopt_table_header)
-        log_str = f'*{log_str}' if results['is_initial_point'] else f' {log_str}'
+            self.hyperopt_table_header = 2
-        return log_str
+
+    @staticmethod
+    def print_results_explanation(results, total_epochs, highlight_best: bool,
+                                  print_colorized: bool) -> None:
+        """
+        Log results explanation string
+        """
+        explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
+        # Colorize output
+        if print_colorized:
+            if results['total_profit'] > 0:
+                explanation_str = Fore.GREEN + explanation_str
+            if highlight_best and results['is_best']:
+                explanation_str = Style.BRIGHT + explanation_str
+        print(explanation_str)
+
+    @staticmethod
+    def _format_explanation_string(results, total_epochs) -> str:
+        return (("*" if results['is_initial_point'] else " ") +
+                f"{results['current_epoch']:5d}/{total_epochs}: " +
+                f"{results['results_explanation']} " +
+                f"Objective: {results['loss']:.5f}")
+
+    @staticmethod
+    def print_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
+                           print_colorized: bool, remove_header: int) -> None:
+        """
+        Log result table
+        """
+        if not results:
+            return
+
+        tabulate.PRESERVE_WHITESPACE = True
+
+        trials = json_normalize(results, max_level=1)
+        trials['Best'] = ''
+        trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
+                         'results_metrics.avg_profit', 'results_metrics.total_profit',
+                         'results_metrics.profit', 'results_metrics.duration',
+                         'loss', 'is_initial_point', 'is_best']]
+        trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
+                          'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
+        trials['is_profit'] = False
+        trials.loc[trials['is_initial_point'], 'Best'] = '*'
+        trials.loc[trials['is_best'], 'Best'] = 'Best'
+        trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
+        trials['Trades'] = trials['Trades'].astype(str)
+
+        trials['Epoch'] = trials['Epoch'].apply(
+            lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
+        )
+        trials['Avg profit'] = trials['Avg profit'].apply(
+            lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+        )
+        trials['Avg duration'] = trials['Avg duration'].apply(
+            lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+        )
+        trials['Objective'] = trials['Objective'].apply(
+            lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
+        )
+
+        trials['Profit'] = trials.apply(
+            lambda x: '{:,.8f} {} {}'.format(
+                x['Total profit'], config['stake_currency'],
+                '({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
+            ).rjust(25+len(config['stake_currency']))
+            if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
+            axis=1
+        )
+        trials = trials.drop(columns=['Total profit'])
+
+        if print_colorized:
+            for i in range(len(trials)):
+                if trials.loc[i]['is_profit']:
+                    for j in range(len(trials.loc[i])-3):
+                        trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
+                                                           str(trials.loc[i][j]), Fore.RESET)
+                if trials.loc[i]['is_best'] and highlight_best:
+                    for j in range(len(trials.loc[i])-3):
+                        trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
+                                                           str(trials.loc[i][j]), Style.RESET_ALL)
+
+        trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
+        if remove_header > 0:
+            table = tabulate.tabulate(
+                trials.to_dict(orient='list'), tablefmt='orgtbl',
+                headers='keys', stralign="right"
+            )
+
+            table = table.split("\n", remove_header)[remove_header]
+        elif remove_header < 0:
+            table = tabulate.tabulate(
+                trials.to_dict(orient='list'), tablefmt='psql',
+                headers='keys', stralign="right"
+            )
+            table = "\n".join(table.split("\n")[0:remove_header])
+        else:
+            table = tabulate.tabulate(
+                trials.to_dict(orient='list'), tablefmt='psql',
+                headers='keys', stralign="right"
+            )
+        print(table)
+
+    @staticmethod
+    def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
+                        csv_file: str) -> None:
+        """
+        Log result to csv-file
+        """
+        if not results:
+            return
+
+        # Verification for overwrite
+        if path.isfile(csv_file):
+            logger.error("CSV-File already exists!")
+            return
+
+        try:
+            io.open(csv_file, 'w+').close()
+        except IOError:
+            logger.error("Filed to create CSV-File!")
+            return
+
+        trials = json_normalize(results, max_level=1)
+        trials['Best'] = ''
+        trials['Stake currency'] = config['stake_currency']
+        trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
+                         'results_metrics.avg_profit', 'results_metrics.total_profit',
+                         'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
+                         'loss', 'is_initial_point', 'is_best']]
+        trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
+                          'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
+        trials['is_profit'] = False
+        trials.loc[trials['is_initial_point'], 'Best'] = '*'
+        trials.loc[trials['is_best'], 'Best'] = 'Best'
+        trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
+        trials['Epoch'] = trials['Epoch'].astype(str)
+        trials['Trades'] = trials['Trades'].astype(str)
+
+        trials['Total profit'] = trials['Total profit'].apply(
+            lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
+        )
+        trials['Profit'] = trials['Profit'].apply(
+            lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
+        )
+        trials['Avg profit'] = trials['Avg profit'].apply(
+            lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
+        )
+        trials['Avg duration'] = trials['Avg duration'].apply(
+            lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
+        )
+        trials['Objective'] = trials['Objective'].apply(
+            lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
+        )
+
+        trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
+        trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
+        print("CSV-File created!")
 
     def has_space(self, space: str) -> bool:
         """
-        Tell if a space value is contained in the configuration
+        Tell if the space value is contained in the configuration
         """
+        # The 'trailing' space is not included in the 'default' set of spaces
+        if space == 'trailing':
             return any(s in self.config['spaces'] for s in [space, 'all'])
+        else:
+            return any(s in self.config['spaces'] for s in [space, 'all', 'default'])
 
     def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
         """
@@ -246,109 +457,133 @@ class Hyperopt:
         for all hyperspaces used.
         """
         spaces: List[Dimension] = []
+
         if space == 'buy' or (space is None and self.has_space('buy')):
             logger.debug("Hyperopt has 'buy' space")
             spaces += self.custom_hyperopt.indicator_space()
+
         if space == 'sell' or (space is None and self.has_space('sell')):
             logger.debug("Hyperopt has 'sell' space")
             spaces += self.custom_hyperopt.sell_indicator_space()
+
         if space == 'roi' or (space is None and self.has_space('roi')):
             logger.debug("Hyperopt has 'roi' space")
             spaces += self.custom_hyperopt.roi_space()
+
         if space == 'stoploss' or (space is None and self.has_space('stoploss')):
             logger.debug("Hyperopt has 'stoploss' space")
             spaces += self.custom_hyperopt.stoploss_space()
+
+        if space == 'trailing' or (space is None and self.has_space('trailing')):
+            logger.debug("Hyperopt has 'trailing' space")
+            spaces += self.custom_hyperopt.trailing_space()
+
         return spaces
 
-    def generate_optimizer(self, _params: Dict, iteration=None) -> Dict:
+    def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
         """
         Used Optimize function. Called once per epoch to optimize whatever is configured.
         Keep this function as optimized as possible!
         """
-        params = self.get_args(_params)
+        params_dict = self._get_params_dict(raw_params)
+        params_details = self._get_params_details(params_dict)
 
         if self.has_space('roi'):
             self.backtesting.strategy.minimal_roi = \
-                    self.custom_hyperopt.generate_roi_table(params)
+                self.custom_hyperopt.generate_roi_table(params_dict)
 
         if self.has_space('buy'):
             self.backtesting.strategy.advise_buy = \
-                    self.custom_hyperopt.buy_strategy_generator(params)
+                self.custom_hyperopt.buy_strategy_generator(params_dict)
 
         if self.has_space('sell'):
             self.backtesting.strategy.advise_sell = \
-                    self.custom_hyperopt.sell_strategy_generator(params)
+                self.custom_hyperopt.sell_strategy_generator(params_dict)
 
         if self.has_space('stoploss'):
-            self.backtesting.strategy.stoploss = params['stoploss']
+            self.backtesting.strategy.stoploss = params_dict['stoploss']
 
-        processed = load(self.tickerdata_pickle)
+        if self.has_space('trailing'):
+            d = self.custom_hyperopt.generate_trailing_params(params_dict)
+            self.backtesting.strategy.trailing_stop = d['trailing_stop']
+            self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
+            self.backtesting.strategy.trailing_stop_positive_offset = \
+                d['trailing_stop_positive_offset']
+            self.backtesting.strategy.trailing_only_offset_is_reached = \
+                d['trailing_only_offset_is_reached']
 
-        min_date, max_date = get_timeframe(processed)
+        processed = load(self.data_pickle_file)
 
-        results = self.backtesting.backtest(
+        min_date, max_date = get_timerange(processed)
-            {
+
-                'stake_amount': self.config['stake_amount'],
+        backtesting_results = self.backtesting.backtest(
-                'processed': processed,
+            processed=processed,
-                'max_open_trades': self.max_open_trades,
+            stake_amount=self.config['stake_amount'],
-                'position_stacking': self.position_stacking,
+            start_date=min_date,
-                'start_date': min_date,
+            end_date=max_date,
-                'end_date': max_date,
+            max_open_trades=self.max_open_trades,
-            }
+            position_stacking=self.position_stacking,
         )
-        results_explanation = self.format_results(results)
+        return self._get_results_dict(backtesting_results, min_date, max_date,
+                                      params_dict, params_details)
 
-        trade_count = len(results.index)
+    def _get_results_dict(self, backtesting_results, min_date, max_date,
-        total_profit = results.profit_abs.sum()
+                          params_dict, params_details):
+        results_metrics = self._calculate_results_metrics(backtesting_results)
+        results_explanation = self._format_results_explanation_string(results_metrics)
+
+        trade_count = results_metrics['trade_count']
+        total_profit = results_metrics['total_profit']
 
         # If this evaluation contains too short amount of trades to be
         # interesting -- consider it as 'bad' (assigned max. loss value)
         # in order to cast this hyperspace point away from optimization
         # path. We do not want to optimize 'hodl' strategies.
-        if trade_count < self.config['hyperopt_min_trades']:
+        loss: float = MAX_LOSS
-            return {
+        if trade_count >= self.config['hyperopt_min_trades']:
-                'loss': MAX_LOSS,
+            loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
-                'params': params,
-                'results_explanation': results_explanation,
-                'total_profit': total_profit,
-            }
-
-        loss = self.calculate_loss(results=results, trade_count=trade_count,
                                        min_date=min_date.datetime, max_date=max_date.datetime)
-
         return {
             'loss': loss,
-            'params': params,
+            'params_dict': params_dict,
+            'params_details': params_details,
+            'results_metrics': results_metrics,
             'results_explanation': results_explanation,
             'total_profit': total_profit,
         }
 
-    def format_results(self, results: DataFrame) -> str:
+    def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
+        return {
+            'trade_count': len(backtesting_results.index),
+            'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
+            'total_profit': backtesting_results.profit_abs.sum(),
+            'profit': backtesting_results.profit_percent.sum() * 100.0,
+            'duration': backtesting_results.trade_duration.mean(),
+        }
+
+    def _format_results_explanation_string(self, results_metrics: Dict) -> str:
         """
         Return the formatted results explanation in a string
         """
-        trades = len(results.index)
-        avg_profit = results.profit_percent.mean() * 100.0
-        total_profit = results.profit_abs.sum()
         stake_cur = self.config['stake_currency']
-        profit = results.profit_percent.sum() * 100.0
+        return (f"{results_metrics['trade_count']:6d} trades. "
-        duration = results.trade_duration.mean()
+                f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
+                f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
+                f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
+                f"Avg duration {results_metrics['duration']:5.1f} min."
+                ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
 
-        return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
+    def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
-                f'Total profit {total_profit: 11.8f} {stake_cur} '
-                f'({profit: 7.2f}Σ%). Avg duration {duration:5.1f} mins.')
-
-    def get_optimizer(self, dimensions, cpu_count) -> Optimizer:
         return Optimizer(
             dimensions,
             base_estimator="ET",
             acq_optimizer="auto",
             n_initial_points=INITIAL_POINTS,
             acq_optimizer_kwargs={'n_jobs': cpu_count},
-            random_state=self.config.get('hyperopt_random_state', None)
+            random_state=self.random_state,
         )
 
-    def fix_optimizer_models_list(self):
+    def fix_optimizer_models_list(self) -> None:
         """
         WORKAROUND: Since skopt is not actively supported, this resolves problems with skopt
         memory usage, see also: https://github.com/scikit-optimize/scikit-optimize/pull/746
@@ -369,78 +604,107 @@ class Hyperopt:
         return parallel(delayed(
                         wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
 
-    def load_previous_results(self):
+    @staticmethod
-        """ read trials file if we have one """
+    def load_previous_results(trials_file: Path) -> List:
-        if self.trials_file.is_file() and self.trials_file.stat().st_size > 0:
+        """
-            self.trials = self.read_trials()
+        Load data for epochs from the file if we have one
-            logger.info(
+        """
-                'Loaded %d previous evaluations from disk.',
+        trials: List = []
-                len(self.trials)
+        if trials_file.is_file() and trials_file.stat().st_size > 0:
-            )
+            trials = Hyperopt._read_trials(trials_file)
+            if trials[0].get('is_best') is None:
+                raise OperationalException(
+                    "The file with Hyperopt results is incompatible with this version "
+                    "of Freqtrade and cannot be loaded.")
+            logger.info(f"Loaded {len(trials)} previous evaluations from disk.")
+        return trials
+
+    def _set_random_state(self, random_state: Optional[int]) -> int:
+        return random_state or random.randint(1, 2**16 - 1)
 
     def start(self) -> None:
-        timerange = TimeRange.parse_timerange(None if self.config.get(
+        self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
-            'timerange') is None else str(self.config.get('timerange')))
+        logger.info(f"Using optimizer random state: {self.random_state}")
-        data = load_data(
+        self.hyperopt_table_header = -1
-            datadir=Path(self.config['datadir']),
+        data, timerange = self.backtesting.load_bt_data()
-            pairs=self.config['exchange']['pair_whitelist'],
-            ticker_interval=self.backtesting.ticker_interval,
-            timerange=timerange
-        )
 
-        if not data:
+        preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
-            logger.critical("No data found. Terminating.")
-            return
 
-        min_date, max_date = get_timeframe(data)
+        # Trim startup period from analyzed dataframe
+        for pair, df in preprocessed.items():
+            preprocessed[pair] = trim_dataframe(df, timerange)
+        min_date, max_date = get_timerange(data)
 
         logger.info(
             'Hyperopting with data from %s up to %s (%s days)..',
-            min_date.isoformat(),
+            min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
-            max_date.isoformat(),
-            (max_date - min_date).days
         )
-
+        dump(preprocessed, self.data_pickle_file)
-        preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
-
-        dump(preprocessed, self.tickerdata_pickle)
 
         # We don't need exchange instance anymore while running hyperopt
         self.backtesting.exchange = None  # type: ignore
 
-        self.load_previous_results()
+        self.trials = self.load_previous_results(self.trials_file)
 
         cpus = cpu_count()
         logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
         config_jobs = self.config.get('hyperopt_jobs', -1)
         logger.info(f'Number of parallel jobs set as: {config_jobs}')
 
-        self.dimensions = self.hyperopt_space()
+        self.dimensions: List[Dimension] = self.hyperopt_space()
         self.opt = self.get_optimizer(self.dimensions, config_jobs)
 
-        if self.config.get('print_colorized', False):
+        if self.print_colorized:
             colorama_init(autoreset=True)
 
         try:
             with Parallel(n_jobs=config_jobs) as parallel:
                 jobs = parallel._effective_n_jobs()
                 logger.info(f'Effective number of parallel workers used: {jobs}')
-                EVALS = max(self.total_epochs // jobs, 1)
+                EVALS = ceil(self.total_epochs / jobs)
                 for i in range(EVALS):
-                    asked = self.opt.ask(n_points=jobs)
+                    # Correct the number of epochs to be processed for the last
+                    # iteration (should not exceed self.total_epochs in total)
+                    n_rest = (i + 1) * jobs - self.total_epochs
+                    current_jobs = jobs - n_rest if n_rest > 0 else jobs
+
+                    asked = self.opt.ask(n_points=current_jobs)
                     f_val = self.run_optimizer_parallel(parallel, asked, i)
                     self.opt.tell(asked, [v['loss'] for v in f_val])
                     self.fix_optimizer_models_list()
-                    for j in range(jobs):
+
-                        current = i * jobs + j
+                    for j, val in enumerate(f_val):
-                        val = f_val[j]
+                        # Use human-friendly indexes here (starting from 1)
+                        current = i * jobs + j + 1
                         val['current_epoch'] = current
-                        val['is_initial_point'] = current < INITIAL_POINTS
+                        val['is_initial_point'] = current <= INITIAL_POINTS
-                        self.log_results(val)
-                        self.trials.append(val)
                         logger.debug(f"Optimizer epoch evaluated: {val}")
+
+                        is_best = self.is_best_loss(val, self.current_best_loss)
+                        # This value is assigned here and not in the optimization method
+                        # to keep proper order in the list of results. That's because
+                        # evaluations can take different time. Here they are aligned in the
+                        # order they will be shown to the user.
+                        val['is_best'] = is_best
+
+                        self.print_results(val)
+
+                        if is_best:
+                            self.current_best_loss = val['loss']
+                        self.trials.append(val)
+                        # Save results after each best epoch and every 100 epochs
+                        if is_best or current % 100 == 0:
+                            self.save_trials()
         except KeyboardInterrupt:
             print('User interrupted..')
 
-        self.save_trials()
+        self.save_trials(final=True)
-        self.log_trials_result()
+
+        if self.trials:
+            sorted_trials = sorted(self.trials, key=itemgetter('loss'))
+            results = sorted_trials[0]
+            self.print_epoch_details(results, self.total_epochs, self.print_json)
+        else:
+            # This is printed when Ctrl+C is pressed quickly, before first epochs have
+            # a chance to be evaluated.
+            print("No epochs evaluated yet, no best result.")

freqtrade/optimize/hyperopt_interface.py

@@ -1,21 +1,18 @@
 """
 IHyperOpt interface
-This module defines the interface to apply for hyperopts
+This module defines the interface to apply for hyperopt
 """
 import logging
 import math
+from abc import ABC
+from typing import Any, Callable, Dict, List
 
-from abc import ABC, abstractmethod
+from skopt.space import Categorical, Dimension, Integer, Real
-from typing import Dict, Any, Callable, List
 
-from pandas import DataFrame
+from freqtrade.exceptions import OperationalException
-from skopt.space import Dimension, Integer, Real
-
-from freqtrade import OperationalException
 from freqtrade.exchange import timeframe_to_minutes
 from freqtrade.misc import round_dict
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -28,8 +25,8 @@ def _format_exception_message(method: str, space: str) -> str:
 
 class IHyperOpt(ABC):
     """
-    Interface for freqtrade hyperopts
+    Interface for freqtrade hyperopt
-    Defines the mandatory structure must follow any custom hyperopts
+    Defines the mandatory structure must follow any custom hyperopt
 
     Class attributes you can use:
         ticker_interval -> int: value of the ticker interval to use for the strategy
@@ -42,15 +39,6 @@ class IHyperOpt(ABC):
         # Assign ticker_interval to be used in hyperopt
         IHyperOpt.ticker_interval = str(config['ticker_interval'])
 
-    @staticmethod
-    @abstractmethod
-    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
-        """
-        Populate indicators that will be used in the Buy and Sell strategy.
-        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe().
-        :return: A Dataframe with all mandatory indicators for the strategies.
-        """
-
     @staticmethod
     def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
         """
@@ -116,10 +104,10 @@ class IHyperOpt(ABC):
         roi_t_alpha = 1.0
         roi_p_alpha = 1.0
 
-        ticker_interval_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
+        timeframe_min = timeframe_to_minutes(IHyperOpt.ticker_interval)
 
         # We define here limits for the ROI space parameters automagically adapted to the
-        # ticker_interval used by the bot:
+        # timeframe used by the bot:
         #
         # * 'roi_t' (limits for the time intervals in the ROI tables) components
         #   are scaled linearly.
@@ -127,8 +115,8 @@ class IHyperOpt(ABC):
         #
         # The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
         # method for the 5m ticker interval.
-        roi_t_scale = ticker_interval_mins / 5
+        roi_t_scale = timeframe_min / 5
-        roi_p_scale = math.log1p(ticker_interval_mins) / math.log1p(5)
+        roi_p_scale = math.log1p(timeframe_min) / math.log1p(5)
         roi_limits = {
             'roi_t1_min': int(10 * roi_t_scale * roi_t_alpha),
             'roi_t1_max': int(120 * roi_t_scale * roi_t_alpha),
@@ -184,6 +172,47 @@ class IHyperOpt(ABC):
             Real(-0.35, -0.02, name='stoploss'),
         ]
 
+    @staticmethod
+    def generate_trailing_params(params: Dict) -> Dict:
+        """
+        Create dict with trailing stop parameters.
+        """
+        return {
+            'trailing_stop': params['trailing_stop'],
+            'trailing_stop_positive': params['trailing_stop_positive'],
+            'trailing_stop_positive_offset': (params['trailing_stop_positive'] +
+                                              params['trailing_stop_positive_offset_p1']),
+            'trailing_only_offset_is_reached': params['trailing_only_offset_is_reached'],
+        }
+
+    @staticmethod
+    def trailing_space() -> List[Dimension]:
+        """
+        Create a trailing stoploss space.
+
+        You may override it in your custom Hyperopt class.
+        """
+        return [
+            # It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
+            # is used. Otherwise hyperopt will vary other parameters that won't have effect if
+            # trailing_stop is set False.
+            # This parameter is included into the hyperspace dimensions rather than assigning
+            # it explicitly in the code in order to have it printed in the results along with
+            # other 'trailing' hyperspace parameters.
+            Categorical([True], name='trailing_stop'),
+
+            Real(0.01, 0.35, name='trailing_stop_positive'),
+
+            # 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
+            # so this intermediate parameter is used as the value of the difference between
+            # them. The value of the 'trailing_stop_positive_offset' is constructed in the
+            # generate_trailing_params() method.
+            # This is similar to the hyperspace dimensions used for constructing the ROI tables.
+            Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
+
+            Categorical([True, False], name='trailing_only_offset_is_reached'),
+        ]
+
     # This is needed for proper unpickling the class attribute ticker_interval
     # which is set to the actual value by the resolver.
     # Why do I still need such shamanic mantras in modern python?