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16 Commits

Author SHA1 Message Date
robcaulk
2be3ff6bcb fix footer 2022-12-13 00:45:16 +01:00
robcaulk
457b8d8761 remove @ from github handles in acknowledgements 2022-12-11 20:24:53 +01:00
Robert Caulk
70dfa1435b Add DOI to pandas citation 2022-12-07 16:42:25 +01:00
Emre
98fc5b6e65 Fix small typo 2022-11-26 21:07:27 +03:00
Emre
c126c26501 Fix typo 2022-11-26 20:59:48 +03:00
robcaulk
2159059b87 add longlong yu and add github handles 2022-11-26 10:25:56 +01:00
robcaulk
f0f4faca71 add ORCID for pascal schmidt 2022-11-26 00:55:24 +01:00
robcaulk
0bc647dbd9 add Emre Suzen (@aemr3) to acknowledgements 2022-11-26 00:53:02 +01:00
robcaulk
e3efb72efe add some changes recommended by @shagunsodhani 2022-10-31 17:51:06 +01:00
robcaulk
a9ef63cb20 add assets to joss sub-folder 2022-10-11 21:15:40 +02:00
robcaulk
3b0daff2a2 ensure compiled pdf is written to dir 2022-10-11 20:06:46 +02:00
robcaulk
67bd4f08e6 ensure paper compiles on push 2022-10-11 20:04:21 +02:00
robcaulk
4c2d291eaf add JOSS draft workflow 2022-10-11 20:01:17 +02:00
robcaulk
85df7faa98 add CNN prediction model 2022-10-11 19:55:28 +02:00
robcaulk
8d3ed03184 add JOSS paper sources 2022-10-11 19:46:25 +02:00
robcaulk
f5870a7540 add tensorflow interface 2022-09-26 21:55:23 +02:00
713 changed files with 72605 additions and 157621 deletions

21
.devcontainer/Dockerfile Normal file
View File

@@ -0,0 +1,21 @@
FROM freqtradeorg/freqtrade:develop
USER root
# Install dependencies
COPY requirements-dev.txt /freqtrade/
RUN apt-get update \
&& apt-get -y install git mercurial sudo vim build-essential \
&& apt-get clean \
&& mkdir -p /home/ftuser/.vscode-server /home/ftuser/.vscode-server-insiders /home/ftuser/commandhistory \
&& echo "export PROMPT_COMMAND='history -a'" >> /home/ftuser/.bashrc \
&& echo "export HISTFILE=~/commandhistory/.bash_history" >> /home/ftuser/.bashrc \
&& chown ftuser:ftuser -R /home/ftuser/.local/ \
&& chown ftuser: -R /home/ftuser/
USER ftuser
RUN pip install --user autopep8 -r docs/requirements-docs.txt -r requirements-dev.txt --no-cache-dir
# Empty the ENTRYPOINT to allow all commands
ENTRYPOINT []

View File

@@ -1,44 +1,39 @@
{
"name": "freqtrade Develop",
"image": "ghcr.io/freqtrade/freqtrade-devcontainer:latest",
"build": {
"dockerfile": "Dockerfile",
"context": ".."
},
// Use 'forwardPorts' to make a list of ports inside the container available locally.
"forwardPorts": [
8080
],
"workspaceMount": "source=${localWorkspaceFolder},target=/workspaces/freqtrade,type=bind,consistency=cached",
"mounts": [
"source=freqtrade-bashhistory,target=/home/ftuser/commandhistory,type=volume"
],
// Uncomment to connect as a non-root user if you've added one. See https://aka.ms/vscode-remote/containers/non-root.
"remoteUser": "ftuser",
"onCreateCommand": "pip install --user -e .",
"postCreateCommand": "freqtrade create-userdir --userdir user_data/",
"workspaceFolder": "/workspaces/freqtrade",
"customizations": {
"vscode": {
"settings": {
"terminal.integrated.shell.linux": "/bin/bash",
"editor.insertSpaces": true,
"files.trimTrailingWhitespace": true,
"[markdown]": {
"files.trimTrailingWhitespace": false
},
"python.pythonPath": "/usr/local/bin/python",
"[python]": {
"editor.codeActionsOnSave": {
"source.organizeImports": "explicit"
},
"editor.formatOnSave": true,
"editor.defaultFormatter": "charliermarsh.ruff"
}
},
// Add the IDs of extensions you want installed when the container is created.
"extensions": [
"ms-python.python",
"ms-python.vscode-pylance",
"charliermarsh.ruff",
"davidanson.vscode-markdownlint",
"ms-azuretools.vscode-docker",
"vscode-icons-team.vscode-icons",
"github.vscode-github-actions",
],
}
}
"workspaceFolder": "/freqtrade/",
"settings": {
"terminal.integrated.shell.linux": "/bin/bash",
"editor.insertSpaces": true,
"files.trimTrailingWhitespace": true,
"[markdown]": {
"files.trimTrailingWhitespace": false,
},
"python.pythonPath": "/usr/local/bin/python",
},
// Add the IDs of extensions you want installed when the container is created.
"extensions": [
"ms-python.python",
"ms-python.vscode-pylance",
"davidanson.vscode-markdownlint",
"ms-azuretools.vscode-docker",
"vscode-icons-team.vscode-icons",
],
}

View File

@@ -1,21 +0,0 @@
FROM freqtradeorg/freqtrade:develop_freqairl
USER root
# Install dependencies
COPY requirements-dev.txt /freqtrade/
ARG USERNAME=ftuser
RUN apt-get update \
&& apt-get -y install --no-install-recommends apt-utils dialog git ssh vim build-essential zsh \
&& apt-get clean \
&& mkdir -p /home/${USERNAME}/.vscode-server /home/${USERNAME}/.vscode-server-insiders /home/${USERNAME}/commandhistory \
&& chown ${USERNAME}:${USERNAME} -R /home/${USERNAME}/.local/ \
&& chown ${USERNAME}: -R /home/${USERNAME}/
USER ftuser
RUN pip install --user autopep8 -r docs/requirements-docs.txt -r requirements-dev.txt --no-cache-dir
# Empty the ENTRYPOINT to allow all commands
ENTRYPOINT []

View File

@@ -1,12 +0,0 @@
{
"name": "freqtrade Dev container image builder",
"build": {
"dockerfile": "Dockerfile",
"context": "../../"
},
"features": {
"ghcr.io/devcontainers/features/common-utils:2": {
},
"ghcr.io/stuartleeks/dev-container-features/shell-history:0.0.3": {}
}
}

View File

@@ -20,7 +20,7 @@ Please do not use bug reports to request new features.
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
* Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
Note: All issues other than enhancement requests will be closed without further comment if the above template is deleted or not filled out.

View File

@@ -18,7 +18,7 @@ Have you search for this feature before requesting it? It's highly likely that a
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
* Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
## Describe the enhancement

View File

@@ -18,7 +18,7 @@ Please do not use the question template to report bugs or to request new feature
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
* Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
## Your question

View File

@@ -1,34 +1,17 @@
version: 2
updates:
- package-ecosystem: docker
directories:
- "/"
- "/docker"
directory: "/"
schedule:
interval: daily
ignore:
- dependency-name: "*"
update-types: ["version-update:semver-major"]
open-pull-requests-limit: 10
- package-ecosystem: pip
directory: "/"
schedule:
interval: weekly
time: "03:00"
timezone: "Etc/UTC"
open-pull-requests-limit: 15
open-pull-requests-limit: 10
target-branch: develop
groups:
types:
patterns:
- "types-*"
pytest:
patterns:
- "pytest*"
mkdocs:
patterns:
- "mkdocs*"
- package-ecosystem: "github-actions"
directory: "/"

View File

@@ -1,47 +0,0 @@
name: Binance Leverage tiers update
on:
schedule:
- cron: "0 3 * * 4"
# on demand
workflow_dispatch:
permissions:
contents: read
jobs:
auto-update:
runs-on: ubuntu-latest
environment:
name: develop
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.12"
- name: Install ccxt
run: pip install ccxt
- name: Run leverage tier update
env:
CI_WEB_PROXY: ${{ secrets.CI_WEB_PROXY }}
FREQTRADE__EXCHANGE__KEY: ${{ secrets.BINANCE_EXCHANGE_KEY }}
FREQTRADE__EXCHANGE__SECRET: ${{ secrets.BINANCE_EXCHANGE_SECRET }}
run: python build_helpers/binance_update_lev_tiers.py
- uses: peter-evans/create-pull-request@v7
with:
token: ${{ secrets.REPO_SCOPED_TOKEN }}
add-paths: freqtrade/exchange/binance_leverage_tiers.json
labels: |
Tech maintenance
Dependencies
branch: update/binance-leverage-tiers
title: Update Binance Leverage Tiers
commit-message: "chore: update pre-commit hooks"
committer: Freqtrade Bot <noreply@github.com>
body: Update binance leverage tiers.
delete-branch: true

View File

@@ -11,77 +11,70 @@ on:
types: [published]
pull_request:
schedule:
- cron: '0 3 * * 4'
- cron: '0 5 * * 4'
concurrency:
group: "${{ github.workflow }}-${{ github.ref }}-${{ github.event_name }}"
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
permissions:
repository-projects: read
jobs:
build-linux:
build_linux:
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ "ubuntu-20.04", "ubuntu-22.04", "ubuntu-24.04" ]
python-version: ["3.10", "3.11", "3.12"]
os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
python-version: ["3.8", "3.9", "3.10.6"]
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v5
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Install uv
uses: astral-sh/setup-uv@v5
with:
enable-cache: true
python-version: ${{ matrix.python-version }}
cache-dependency-glob: "requirements**.txt"
cache-suffix: "${{ matrix.python-version }}"
prune-cache: false
- name: Cache_dependencies
uses: actions/cache@v4
uses: actions/cache@v3
id: cache
with:
path: ~/dependencies/
key: ${{ runner.os }}-dependencies
- name: pip cache (linux)
uses: actions/cache@v3
if: runner.os == 'Linux'
with:
path: ~/.cache/pip
key: test-${{ 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 ${HOME}/dependencies/; cd ..
- name: Installation - *nix
if: runner.os == 'Linux'
run: |
uv pip install --upgrade wheel
python -m pip install --upgrade pip wheel
export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
export TA_LIBRARY_PATH=${HOME}/dependencies/lib
export TA_INCLUDE_PATH=${HOME}/dependencies/include
uv pip install -r requirements-dev.txt
uv pip install -e ft_client/
uv pip install -e .
- name: Check for version alignment
run: |
python build_helpers/freqtrade_client_version_align.py
pip install -r requirements-dev.txt
pip install -e .
- name: Tests
if: (!(runner.os == 'Linux' && matrix.python-version == '3.12' && matrix.os == 'ubuntu-22.04'))
run: |
pytest --random-order
pytest --random-order --cov=freqtrade --cov-config=.coveragerc
if: matrix.python-version != '3.9' || matrix.os != 'ubuntu-22.04'
- name: Tests with Coveralls
if: (runner.os == 'Linux' && matrix.python-version == '3.12' && matrix.os == 'ubuntu-22.04')
- name: Tests incl. ccxt compatibility tests
run: |
pytest --random-order --cov=freqtrade --cov=freqtrade_client --cov-config=.coveragerc
pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
if: matrix.python-version == '3.9' && matrix.os == 'ubuntu-22.04'
- name: Coveralls
if: (runner.os == 'Linux' && matrix.python-version == '3.12' && matrix.os == 'ubuntu-22.04')
if: (runner.os == 'Linux' && matrix.python-version == '3.9')
env:
# Coveralls token. Not used as secret due to github not providing secrets to forked repositories
COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@@ -89,30 +82,9 @@ jobs:
# Allow failure for coveralls
coveralls || true
- name: Run json schema extract
# This should be kept before the repository check to ensure that the schema is up-to-date
run: |
python build_helpers/extract_config_json_schema.py
- name: Run command docs partials extract
# This should be kept before the repository check to ensure that the docs are up-to-date
run: |
python build_helpers/create_command_partials.py
- name: Check for repository changes
run: |
if [ -n "$(git status --porcelain)" ]; then
echo "Repository is dirty, changes detected:"
git status
git diff
exit 1
else
echo "Repository is clean, no changes detected."
fi
- name: Backtesting (multi)
run: |
cp tests/testdata/config.tests.json config.json
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade new-strategy -s AwesomeStrategy
freqtrade new-strategy -s AwesomeStrategyMin --template minimal
@@ -120,22 +92,18 @@ jobs:
- name: Hyperopt
run: |
cp tests/testdata/config.tests.json config.json
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 6 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |
flake8
- name: Sort imports (isort)
run: |
isort --check .
- name: Run Ruff
run: |
ruff check --output-format=github
- name: Run Ruff format check
run: |
ruff format --check
- name: Mypy
run: |
mypy freqtrade scripts tests
@@ -148,117 +116,77 @@ jobs:
details: Freqtrade CI failed on ${{ matrix.os }}
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
build-macos:
build_macos:
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ "macos-13", "macos-14", "macos-15" ]
python-version: ["3.10", "3.11", "3.12"]
os: [ macos-latest ]
python-version: ["3.8", "3.9", "3.10.6"]
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v5
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
check-latest: true
- name: Install uv
uses: astral-sh/setup-uv@v5
with:
enable-cache: true
python-version: ${{ matrix.python-version }}
cache-dependency-glob: "requirements**.txt"
cache-suffix: "${{ matrix.python-version }}"
prune-cache: false
- name: Cache_dependencies
uses: actions/cache@v4
uses: actions/cache@v3
id: cache
with:
path: ~/dependencies/
key: ${{ matrix.os }}-dependencies
key: ${{ runner.os }}-dependencies
- name: pip cache (macOS)
uses: actions/cache@v3
if: runner.os == 'macOS'
with:
path: ~/Library/Caches/pip
key: test-${{ 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 ${HOME}/dependencies/; cd ..
- name: Installation - macOS (Brew)
- name: Installation - macOS
if: runner.os == 'macOS'
run: |
# brew update
# TODO: Should be the brew upgrade
# homebrew fails to update python due to unlinking failures
# https://github.com/actions/runner-images/issues/6817
rm /usr/local/bin/2to3 || true
rm /usr/local/bin/2to3-3.11 || true
rm /usr/local/bin/2to3-3.12 || true
rm /usr/local/bin/idle3 || true
rm /usr/local/bin/idle3.11 || true
rm /usr/local/bin/idle3.12 || true
rm /usr/local/bin/pydoc3 || true
rm /usr/local/bin/pydoc3.11 || true
rm /usr/local/bin/pydoc3.12 || true
rm /usr/local/bin/python3 || true
rm /usr/local/bin/python3.11 || true
rm /usr/local/bin/python3.12 || true
rm /usr/local/bin/python3-config || true
rm /usr/local/bin/python3.11-config || true
rm /usr/local/bin/python3.12-config || true
brew install libomp
- name: Installation (python)
run: |
uv pip install wheel
brew update
brew install hdf5 c-blosc
python -m pip install --upgrade pip wheel
export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
export TA_LIBRARY_PATH=${HOME}/dependencies/lib
export TA_INCLUDE_PATH=${HOME}/dependencies/include
uv pip install -r requirements-dev.txt
uv pip install -e ft_client/
uv pip install -e .
pip install -r requirements-dev.txt
pip install -e .
- name: Tests
run: |
pytest --random-order
- name: Check for repository changes
run: |
if [ -n "$(git status --porcelain)" ]; then
echo "Repository is dirty, changes detected:"
git status
git diff
exit 1
else
echo "Repository is clean, no changes detected."
fi
- name: Backtesting
run: |
cp tests/testdata/config.tests.json config.json
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade new-strategy -s AwesomeStrategyAdv --template advanced
freqtrade backtesting --datadir tests/testdata --strategy AwesomeStrategyAdv
- name: Hyperopt
run: |
cp tests/testdata/config.tests.json config.json
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |
flake8
- name: Sort imports (isort)
run: |
isort --check .
- name: Run Ruff
run: |
ruff check --output-format=github
- name: Run Ruff format check
run: |
ruff format --check
- name: Mypy
run: |
mypy freqtrade scripts
@@ -271,89 +199,56 @@ jobs:
details: Test Succeeded!
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
build-windows:
build_windows:
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ windows-latest ]
python-version: ["3.10", "3.11", "3.12"]
python-version: ["3.8", "3.9", "3.10.6"]
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v5
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Install uv
uses: astral-sh/setup-uv@v5
- name: Pip cache (Windows)
uses: actions/cache@v3
with:
enable-cache: true
python-version: ${{ matrix.python-version }}
cache-dependency-glob: "requirements**.txt"
cache-suffix: "${{ matrix.python-version }}"
prune-cache: false
path: ~\AppData\Local\pip\Cache
key: ${{ matrix.os }}-${{ matrix.python-version }}-pip
- name: Installation
run: |
function uvpipFunction { uv pip $args }
Set-Alias -name pip -value uvpipFunction
./build_helpers/install_windows.ps1
- name: Tests
run: |
pytest --random-order --durations 20 -n auto
- name: Check for repository changes
run: |
if (git status --porcelain) {
Write-Host "Repository is dirty, changes detected:"
git status
git diff
exit 1
}
else {
Write-Host "Repository is clean, no changes detected."
}
pytest --random-order
- name: Backtesting
run: |
cp tests/testdata/config.tests.json config.json
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
- name: Hyperopt
run: |
cp tests/testdata/config.tests.json config.json
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Run Ruff
- name: Flake8
run: |
ruff check --output-format=github
- name: Run Ruff format check
run: |
ruff format --check
flake8
- name: Mypy
run: |
mypy freqtrade scripts tests
- name: Run Pester tests (PowerShell)
run: |
$PSVersionTable
Set-PSRepository psgallery -InstallationPolicy trusted
Install-Module -Name Pester -RequiredVersion 5.3.1 -Confirm:$false -Force -SkipPublisherCheck
$Error.clear()
Invoke-Pester -Path "tests" -CI
if ($Error.Length -gt 0) {exit 1}
shell: powershell
- name: Discord notification
uses: rjstone/discord-webhook-notify@v1
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
@@ -362,15 +257,15 @@ jobs:
details: Test Failed
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
mypy-version-check:
runs-on: ubuntu-22.04
mypy_version_check:
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v5
uses: actions/setup-python@v4
with:
python-version: "3.12"
python-version: "3.10"
- name: pre-commit dependencies
run: |
@@ -380,30 +275,31 @@ jobs:
pre-commit:
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@v3
- uses: actions/setup-python@v5
- uses: actions/setup-python@v4
with:
python-version: "3.12"
- uses: pre-commit/action@v3.0.1
python-version: "3.10"
- uses: pre-commit/action@v3.0.0
docs-check:
runs-on: ubuntu-22.04
docs_check:
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@v3
- name: Documentation syntax
run: |
./tests/test_docs.sh
- name: Set up Python
uses: actions/setup-python@v5
uses: actions/setup-python@v4
with:
python-version: "3.12"
python-version: "3.10"
- name: Documentation build
run: |
pip install -r docs/requirements-docs.txt
pip install mkdocs
mkdocs build
- name: Discord notification
@@ -414,71 +310,12 @@ jobs:
details: Freqtrade doc test failed!
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
build-linux-online:
# Run pytest with "live" checks
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.12"
- name: Install uv
uses: astral-sh/setup-uv@v5
with:
enable-cache: true
python-version: "3.12"
cache-dependency-glob: "requirements**.txt"
cache-suffix: "3.12"
prune-cache: false
- name: Cache_dependencies
uses: actions/cache@v4
id: cache
with:
path: ~/dependencies/
key: ${{ runner.os }}-dependencies
- name: TA binary *nix
if: steps.cache.outputs.cache-hit != 'true'
run: |
cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
- name: Installation - *nix
run: |
uv pip install --upgrade wheel
export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
export TA_LIBRARY_PATH=${HOME}/dependencies/lib
export TA_INCLUDE_PATH=${HOME}/dependencies/include
uv pip install -r requirements-dev.txt
uv pip install -e ft_client/
uv pip install -e .
- name: Tests incl. ccxt compatibility tests
env:
CI_WEB_PROXY: http://152.67.78.211:13128
run: |
pytest --random-order --longrun --durations 20 -n auto
# Notify only once - when CI completes (and after deploy) in case it's successful
# Notify only once - when CI completes (and after deploy) in case it's successfull
notify-complete:
needs: [
build-linux,
build-macos,
build-windows,
docs-check,
mypy-version-check,
pre-commit,
build-linux-online
]
runs-on: ubuntu-22.04
needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
runs-on: ubuntu-20.04
# Discord notification can't handle schedule events
if: github.event_name != 'schedule' && github.repository == 'freqtrade/freqtrade'
if: (github.event_name != 'schedule')
permissions:
repository-projects: read
steps:
@@ -499,116 +336,44 @@ jobs:
details: Test Completed!
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
build:
name: "Build"
needs: [ build-linux, build-macos, build-windows, docs-check, mypy-version-check, pre-commit ]
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.12"
- name: Build distribution
run: |
pip install -U build
python -m build --sdist --wheel
- name: Upload artifacts 📦
uses: actions/upload-artifact@v4
with:
name: freqtrade-build
path: |
dist
retention-days: 10
- name: Build Client distribution
run: |
pip install -U build
python -m build --sdist --wheel ft_client
- name: Upload artifacts 📦
uses: actions/upload-artifact@v4
with:
name: freqtrade-client-build
path: |
ft_client/dist
retention-days: 10
deploy-test-pypi:
name: "Publish Python 🐍 distribution 📦 to TestPyPI"
needs: [ build ]
runs-on: ubuntu-22.04
if: (github.event_name == 'release')
environment:
name: testpypi
url: https://test.pypi.org/p/freqtrade
permissions:
id-token: write
steps:
- uses: actions/checkout@v4
- name: Download artifact 📦
uses: actions/download-artifact@v4
with:
pattern: freqtrade*-build
path: dist
merge-multiple: true
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@v1.12.4
with:
repository-url: https://test.pypi.org/legacy/
deploy-pypi:
name: "Publish Python 🐍 distribution 📦 to PyPI"
needs: [ build ]
runs-on: ubuntu-22.04
if: (github.event_name == 'release')
environment:
name: pypi
url: https://pypi.org/p/freqtrade
permissions:
id-token: write
steps:
- uses: actions/checkout@v4
- name: Download artifact 📦
uses: actions/download-artifact@v4
with:
pattern: freqtrade*-build
path: dist
merge-multiple: true
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@v1.12.4
deploy-docker:
needs: [ build-linux, build-macos, build-windows, docs-check, mypy-version-check, pre-commit ]
runs-on: ubuntu-22.04
deploy:
needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
runs-on: ubuntu-20.04
if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v5
uses: actions/setup-python@v4
with:
python-version: "3.12"
python-version: "3.9"
- name: Extract branch name
id: extract-branch
shell: bash
run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF##*/})"
id: extract_branch
- name: Build distribution
run: |
echo "GITHUB_REF='${GITHUB_REF}'"
echo "branch=${GITHUB_REF##*/}" >> "$GITHUB_OUTPUT"
pip install -U setuptools wheel
python setup.py sdist bdist_wheel
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@v1.5.1
if: (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@v1.5.1
if: (github.event_name == 'release')
with:
user: __token__
password: ${{ secrets.pypi_password }}
- name: Dockerhub login
env:
@@ -625,39 +390,36 @@ jobs:
sudo systemctl restart docker
docker version -f '{{.Server.Experimental}}'
- name: Set up QEMU
uses: docker/setup-qemu-action@v3
- name: Set up Docker Buildx
id: buildx
uses: docker/setup-buildx-action@v3
uses: crazy-max/ghaction-docker-buildx@v3.3.1
with:
buildx-version: latest
qemu-version: latest
- name: Available platforms
run: echo ${{ steps.buildx.outputs.platforms }}
- name: Build and test and push docker images
env:
BRANCH_NAME: ${{ steps.extract-branch.outputs.branch }}
IMAGE_NAME: freqtradeorg/freqtrade
BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
run: |
build_helpers/publish_docker_multi.sh
deploy-arm:
name: "Deploy Docker"
permissions:
packages: write
needs: [ deploy-docker ]
deploy_arm:
needs: [ deploy ]
# Only run on 64bit machines
runs-on: [self-hosted, linux, ARM64]
if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@v3
- name: Extract branch name
id: extract-branch
run: |
echo "GITHUB_REF='${GITHUB_REF}'"
echo "branch=${GITHUB_REF##*/}" >> "$GITHUB_OUTPUT"
shell: bash
run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF##*/})"
id: extract_branch
- name: Dockerhub login
env:
@@ -668,9 +430,8 @@ jobs:
- name: Build and test and push docker images
env:
BRANCH_NAME: ${{ steps.extract-branch.outputs.branch }}
GHCR_USERNAME: ${{ github.actor }}
GHCR_TOKEN: ${{ secrets.GITHUB_TOKEN }}
IMAGE_NAME: freqtradeorg/freqtrade
BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
run: |
build_helpers/publish_docker_arm64.sh
@@ -680,4 +441,4 @@ jobs:
with:
severity: info
details: Deploy Succeeded!
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}

View File

@@ -1,55 +0,0 @@
name: Build Documentation
on:
push:
branches:
- develop
release:
types: [published]
# disable permissions for all of the available permissions
permissions: {}
jobs:
build-docs:
permissions:
contents: write # for mike to push
name: Deploy Docs through mike
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.12'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r docs/requirements-docs.txt
- name: Fetch gh-pages branch
run: |
git fetch origin gh-pages --depth=1
- name: Configure Git user
run: |
git config --local user.email "github-actions[bot]@users.noreply.github.com"
git config --local user.name "github-actions[bot]"
- name: Build and push Mike
if: ${{ github.event_name == 'push' }}
run: |
mike deploy ${{ github.ref_name }} latest --push --update-aliases
- name: Build and push Mike - Release
if: ${{ github.event_name == 'release' }}
run: |
mike deploy ${{ github.ref_name }} stable --push --update-aliases
- name: Show mike versions
run: |
mike list

View File

@@ -1,45 +0,0 @@
name: Devcontainer Pre-Build
on:
workflow_dispatch:
schedule:
- cron: "0 3 * * 0"
# push:
# branches:
# - "master"
# tags:
# - "v*.*.*"
# pull_requests:
# branches:
# - "master"
concurrency:
group: "${{ github.workflow }}"
cancel-in-progress: true
permissions:
packages: write
jobs:
build-and-push:
runs-on: ubuntu-latest
steps:
-
name: Checkout
id: checkout
uses: actions/checkout@v4
-
name: Login to GitHub Container Registry
uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
-
name: Pre-build dev container image
uses: devcontainers/ci@v0.3
with:
subFolder: .github
imageName: ghcr.io/${{ github.repository }}-devcontainer
cacheFrom: ghcr.io/${{ github.repository }}-devcontainer
push: always

View File

@@ -1,18 +0,0 @@
name: Update Docker Hub Description
on:
push:
branches:
- stable
jobs:
dockerHubDescription:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Docker Hub Description
uses: peter-evans/dockerhub-description@v4
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_PASSWORD }}
repository: freqtradeorg/freqtrade

View File

@@ -0,0 +1,17 @@
name: Update Docker Hub Description
on:
push:
branches:
- stable
jobs:
dockerHubDescription:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Docker Hub Description
uses: peter-evans/dockerhub-description@v3
env:
DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }}
DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
DOCKERHUB_REPOSITORY: freqtradeorg/freqtrade

23
.github/workflows/draft-pdf.yml vendored Normal file
View File

@@ -0,0 +1,23 @@
on: [push]
jobs:
paper:
runs-on: ubuntu-latest
name: Paper Draft
steps:
- name: Checkout
uses: actions/checkout@v2
- name: Build draft PDF
uses: openjournals/openjournals-draft-action@master
with:
journal: joss
# This should be the path to the paper within your repo.
paper-path: docs/JOSS_paper/paper.md
- name: Upload
uses: actions/upload-artifact@v1
with:
name: paper
# This is the output path where Pandoc will write the compiled
# PDF. Note, this should be the same directory as the input
# paper.md
path: docs/JOSS_paper/paper.pdf

View File

@@ -1,41 +0,0 @@
name: Pre-commit auto-update
on:
schedule:
- cron: "0 3 * * 2"
# on demand
workflow_dispatch:
permissions:
contents: read
jobs:
auto-update:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.12"
- name: Install pre-commit
run: pip install pre-commit
- name: Run auto-update
run: pre-commit autoupdate
- uses: peter-evans/create-pull-request@v7
with:
token: ${{ secrets.REPO_SCOPED_TOKEN }}
add-paths: .pre-commit-config.yaml
labels: |
Tech maintenance
Dependencies
branch: update/pre-commit-hooks
title: Update pre-commit hooks
commit-message: "chore: update pre-commit hooks"
committer: Freqtrade Bot <noreply@github.com>
body: Update versions of pre-commit hooks to latest version.
delete-branch: true

8
.gitignore vendored
View File

@@ -83,9 +83,6 @@ instance/
# Scrapy stuff:
.scrapy
# memray
memray-*
# Sphinx documentation
docs/_build/
# Mkdocs documentation
@@ -111,8 +108,9 @@ target/
#exceptions
!*.gitkeep
!config_examples/config_binance.example.json
!config_examples/config_bittrex.example.json
!config_examples/config_ftx.example.json
!config_examples/config_full.example.json
!config_examples/config_kraken.example.json
!config_examples/config_freqai.example.json
docker-compose-*.yml
!config_examples/config_freqai-rl.example.json

View File

@@ -2,42 +2,33 @@
# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/pycqa/flake8
rev: "7.1.1"
rev: "4.0.1"
hooks:
- id: flake8
additional_dependencies: [Flake8-pyproject]
# stages: [push]
- repo: https://github.com/pre-commit/mirrors-mypy
rev: "v1.14.1"
rev: "v0.942"
hooks:
- id: mypy
exclude: build_helpers
additional_dependencies:
- types-cachetools==5.5.0.20240820
- types-cachetools==5.2.1
- types-filelock==3.2.7
- types-requests==2.32.0.20241016
- types-tabulate==0.9.0.20241207
- types-python-dateutil==2.9.0.20241206
- SQLAlchemy==2.0.37
- types-requests==2.28.11
- types-tabulate==0.8.11
- types-python-dateutil==2.8.19
# stages: [push]
- repo: https://github.com/pycqa/isort
rev: "6.0.0"
rev: "5.10.1"
hooks:
- id: isort
name: isort (python)
# stages: [push]
- repo: https://github.com/charliermarsh/ruff-pre-commit
# Ruff version.
rev: 'v0.9.3'
hooks:
- id: ruff
- id: ruff-format
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v5.0.0
rev: v2.4.0
hooks:
- id: end-of-file-fixer
exclude: |
@@ -55,15 +46,3 @@ repos:
(?x)^(
.*\.md
)$
- repo: https://github.com/stefmolin/exif-stripper
rev: 0.6.1
hooks:
- id: strip-exif
- repo: https://github.com/codespell-project/codespell
rev: v2.4.0
hooks:
- id: codespell
additional_dependencies:
- tomli

View File

@@ -1,14 +1,8 @@
# .readthedocs.yml
version: 2
build:
os: "ubuntu-22.04"
tools:
python: "3.11"
image: latest
python:
install:
- requirements: docs/requirements-docs.txt
mkdocs:
configuration: mkdocs.yml
version: 3.8
setup_py_install: false

View File

@@ -1,11 +0,0 @@
{
"recommendations": [
"ms-python.python",
"ms-python.vscode-pylance",
"charliermarsh.ruff",
"davidanson.vscode-markdownlint",
"ms-azuretools.vscode-docker",
"vscode-icons-team.vscode-icons",
"github.vscode-github-actions",
]
}

View File

@@ -45,17 +45,16 @@ pytest tests/test_<file_name>.py::test_<method_name>
### 2. Test if your code is PEP8 compliant
#### Run Ruff
#### Run Flake8
```bash
ruff check .
flake8 freqtrade tests scripts
```
We receive a lot of code that fails the `ruff` checks.
We receive a lot of code that fails the `flake8` checks.
To help with that, we encourage you to install the git pre-commit
hook that will warn you when you try to commit code that fails these checks.
you can manually run pre-commit with `pre-commit run -a`.
hook that will warn you when you try to commit code that fails these checks.
Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using-hooks.html).
##### Additional styles applied
@@ -72,12 +71,12 @@ you can manually run pre-commit with `pre-commit run -a`.
mypy freqtrade
```
### 4. Ensure formatting is correct
### 4. Ensure all imports are correct
#### Run ruff
#### Run isort
``` bash
ruff format .
isort .
```
## (Core)-Committer Guide
@@ -125,7 +124,7 @@ 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. Both 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.

View File

@@ -1,4 +1,4 @@
FROM python:3.12.7-slim-bookworm as base
FROM python:3.10.7-slim-bullseye as base
# Setup env
ENV LANG C.UTF-8
@@ -11,7 +11,7 @@ ENV FT_APP_ENV="docker"
# Prepare environment
RUN mkdir /freqtrade \
&& apt-get update \
&& apt-get -y install sudo libatlas3-base curl sqlite3 libgomp1 \
&& apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev libgomp1 \
&& apt-get clean \
&& useradd -u 1000 -G sudo -U -m -s /bin/bash ftuser \
&& chown ftuser:ftuser /freqtrade \
@@ -25,7 +25,7 @@ FROM base as python-deps
RUN apt-get update \
&& apt-get -y install build-essential libssl-dev git libffi-dev libgfortran5 pkg-config cmake gcc \
&& apt-get clean \
&& pip install --upgrade pip wheel
&& pip install --upgrade pip
# Install TA-lib
COPY build_helpers/* /tmp/
@@ -35,7 +35,7 @@ ENV LD_LIBRARY_PATH /usr/local/lib
# Install dependencies
COPY --chown=ftuser:ftuser requirements.txt requirements-hyperopt.txt /freqtrade/
USER ftuser
RUN pip install --user --no-cache-dir "numpy<2.0" \
RUN pip install --user --no-cache-dir numpy \
&& pip install --user --no-cache-dir -r requirements-hyperopt.txt
# Copy dependencies to runtime-image

View File

@@ -5,5 +5,3 @@ recursive-include freqtrade/templates/ *.j2 *.ipynb
include freqtrade/exchange/binance_leverage_tiers.json
include freqtrade/rpc/api_server/ui/fallback_file.html
include freqtrade/rpc/api_server/ui/favicon.ico
prune tests

View File

@@ -1,7 +1,6 @@
# ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade_poweredby.svg)
[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.04864/status.svg)](https://doi.org/10.21105/joss.04864)
[![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)
@@ -28,24 +27,19 @@ hesitate to read the source code and understand the mechanism of this bot.
Please read the [exchange specific notes](docs/exchanges.md) to learn about eventual, special configurations needed for each exchange.
- [X] [Binance](https://www.binance.com/)
- [X] [Bitmart](https://bitmart.com/)
- [X] [BingX](https://bingx.com/invite/0EM9RX)
- [X] [Bybit](https://bybit.com/)
- [X] [Bittrex](https://bittrex.com/)
- [X] [FTX](https://ftx.com/#a=2258149)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [HTX](https://www.htx.com/)
- [X] [Hyperliquid](https://hyperliquid.xyz/) (A decentralized exchange, or DEX)
- [X] [Huobi](http://huobi.com/)
- [X] [Kraken](https://kraken.com/)
- [X] [OKX](https://okx.com/)
- [X] [MyOKX](https://okx.com/) (OKX EEA)
- [X] [OKX](https://okx.com/) (Former OKEX)
- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
### Supported Futures Exchanges (experimental)
- [X] [Binance](https://www.binance.com/)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [Hyperliquid](https://hyperliquid.xyz/) (A decentralized exchange, or DEX)
- [X] [OKX](https://okx.com/)
- [X] [Bybit](https://bybit.com/)
- [X] [OKX](https://okx.com/).
Please make sure to read the [exchange specific notes](docs/exchanges.md), as well as the [trading with leverage](docs/leverage.md) documentation before diving in.
@@ -64,7 +58,7 @@ Please find the complete documentation on the [freqtrade website](https://www.fr
## Features
- [x] **Based on Python 3.10+**: For botting on any operating system - Windows, macOS and Linux.
- [x] **Based on Python 3.8+**: For botting on any operating system - Windows, macOS and Linux.
- [x] **Persistence**: Persistence is achieved through sqlite.
- [x] **Dry-run**: Run the bot without paying money.
- [x] **Backtesting**: Run a simulation of your buy/sell strategy.
@@ -90,53 +84,44 @@ For further (native) installation methods, please refer to the [Installation doc
```
usage: freqtrade [-h] [-V]
{trade,create-userdir,new-config,show-config,new-strategy,download-data,convert-data,convert-trade-data,trades-to-ohlcv,list-data,backtesting,backtesting-show,backtesting-analysis,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-markets,list-pairs,list-strategies,list-hyperoptloss,list-freqaimodels,list-timeframes,show-trades,test-pairlist,convert-db,install-ui,plot-dataframe,plot-profit,webserver,strategy-updater,lookahead-analysis,recursive-analysis}
{trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
...
Free, open source crypto trading bot
positional arguments:
{trade,create-userdir,new-config,show-config,new-strategy,download-data,convert-data,convert-trade-data,trades-to-ohlcv,list-data,backtesting,backtesting-show,backtesting-analysis,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-markets,list-pairs,list-strategies,list-hyperoptloss,list-freqaimodels,list-timeframes,show-trades,test-pairlist,convert-db,install-ui,plot-dataframe,plot-profit,webserver,strategy-updater,lookahead-analysis,recursive-analysis}
{trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
trade Trade module.
create-userdir Create user-data directory.
new-config Create new config
show-config Show resolved config
new-strategy Create new strategy
download-data Download backtesting data.
convert-data Convert candle (OHLCV) data from one format to
another.
convert-trade-data Convert trade data from one format to another.
trades-to-ohlcv Convert trade data to OHLCV data.
list-data List downloaded data.
backtesting Backtesting module.
backtesting-show Show past Backtest results
backtesting-analysis
Backtest Analysis module.
edge Edge module.
hyperopt Hyperopt module.
hyperopt-list List Hyperopt results
hyperopt-show Show details of Hyperopt results
list-exchanges Print available exchanges.
list-hyperopts Print available hyperopt classes.
list-markets Print markets on exchange.
list-pairs Print pairs on exchange.
list-strategies Print available strategies.
list-hyperoptloss Print available hyperopt loss functions.
list-freqaimodels Print available freqAI models.
list-timeframes Print available timeframes for the exchange.
show-trades Show trades.
test-pairlist Test your pairlist configuration.
convert-db Migrate database to different system
install-ui Install FreqUI
plot-dataframe Plot candles with indicators.
plot-profit Generate plot showing profits.
webserver Webserver module.
strategy-updater updates outdated strategy files to the current version
lookahead-analysis Check for potential look ahead bias.
recursive-analysis Check for potential recursive formula issue.
options:
optional arguments:
-h, --help show this help message and exit
-V, --version show program's version number and exit
```
### Telegram RPC commands
@@ -179,10 +164,6 @@ first. If it hasn't been reported, please
ensure you follow the template guide so that the team can assist you as
quickly as possible.
For every [issue](https://github.com/freqtrade/freqtrade/issues/new/choose) created, kindly follow up and mark satisfaction or reminder to close issue when equilibrium ground is reached.
--Maintain github's [community policy](https://docs.github.com/en/site-policy/github-terms/github-community-code-of-conduct)--
### [Feature Requests](https://github.com/freqtrade/freqtrade/labels/enhancement)
Have you a great idea to improve the bot you want to share? Please,
@@ -221,9 +202,9 @@ To run this bot we recommend you a cloud instance with a minimum of:
### Software requirements
- [Python >= 3.10](http://docs.python-guide.org/en/latest/starting/installation/)
- [Python >= 3.8](http://docs.python-guide.org/en/latest/starting/installation/)
- [pip](https://pip.pypa.io/en/stable/installing/)
- [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
- [TA-Lib](https://ta-lib.github.io/ta-lib-python/)
- [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)
- [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
- [Docker](https://www.docker.com/products/docker) (Recommended)

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@@ -1,23 +0,0 @@
#!/usr/bin/env python3
import json
import os
from pathlib import Path
import ccxt
key = os.environ.get("FREQTRADE__EXCHANGE__KEY")
secret = os.environ.get("FREQTRADE__EXCHANGE__SECRET")
proxy = os.environ.get("CI_WEB_PROXY")
exchange = ccxt.binance(
{"apiKey": key, "secret": secret, "httpsProxy": proxy, "options": {"defaultType": "swap"}}
)
_ = exchange.load_markets()
lev_tiers = exchange.fetch_leverage_tiers()
# Assumes this is running in the root of the repository.
file = Path("freqtrade/exchange/binance_leverage_tiers.json")
json.dump(dict(sorted(lev_tiers.items())), file.open("w"), indent=2)

View File

@@ -1,53 +0,0 @@
import subprocess
from pathlib import Path
subcommands = [
"trade",
"create-userdir",
"new-config",
"show-config",
"new-strategy",
"download-data",
"convert-data",
"convert-trade-data",
"trades-to-ohlcv",
"list-data",
"backtesting",
"backtesting-show",
"backtesting-analysis",
"edge",
"hyperopt",
"hyperopt-list",
"hyperopt-show",
"list-exchanges",
"list-markets",
"list-pairs",
"list-strategies",
"list-hyperoptloss",
"list-freqaimodels",
"list-timeframes",
"show-trades",
"test-pairlist",
"convert-db",
"install-ui",
"plot-dataframe",
"plot-profit",
"webserver",
"strategy-updater",
"lookahead-analysis",
"recursive-analysis",
]
result = subprocess.run(["freqtrade", "--help"], capture_output=True, text=True)
with Path("docs/commands/main.md").open("w") as f:
f.write(f"```\n{result.stdout}\n```\n")
for command in subcommands:
print(f"Running for {command}")
result = subprocess.run(["freqtrade", command, "--help"], capture_output=True, text=True)
with Path(f"docs/commands/{command}.md").open("w") as f:
f.write(f"```\n{result.stdout}\n```\n")

View File

@@ -1,17 +0,0 @@
"""Script to extract the configuration json schema from config_schema.py file."""
from pathlib import Path
import rapidjson
from freqtrade.configuration.config_schema import CONF_SCHEMA
def extract_config_json_schema():
schema_filename = Path(__file__).parent / "schema.json"
with schema_filename.open("w") as f:
rapidjson.dump(CONF_SCHEMA, f, indent=2)
if __name__ == "__main__":
extract_config_json_schema()

View File

@@ -1,15 +0,0 @@
#!/usr/bin/env python3
from freqtrade import __version__ as ft_version
from freqtrade_client import __version__ as client_version
def main():
if ft_version != client_version:
print(f"Versions do not match: \nft: {ft_version} \nclient: {client_version}")
exit(1)
print(f"Versions match: ft: {ft_version}, client: {client_version}")
exit(0)
if __name__ == "__main__":
main()

View File

@@ -8,9 +8,8 @@ if [ -n "$2" ] || [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
tar zxvf ta-lib-0.4.0-src.tar.gz
cd ta-lib \
&& sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \
&& echo "Downloading gcc config.guess and config.sub" \
&& curl -s 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.guess' -o config.guess \
&& curl -s 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.sub' -o config.sub \
&& curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess \
&& curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub \
&& ./configure --prefix=${INSTALL_LOC}/ \
&& make
if [ $? -ne 0 ]; then

View File

@@ -1,10 +1,18 @@
# vendored Wheels compiled via https://github.com/xmatthias/ta-lib-python/tree/ta_bundled_040
# Downloads don't work automatically, since the URL is regenerated via javascript.
# Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
python -m pip install --upgrade pip
python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
python -m pip install --upgrade pip wheel
pip install -U wheel "numpy<2"
pip install --only-binary ta-lib --find-links=build_helpers\ ta-lib
$pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
if ($pyv -eq '3.8') {
pip install build_helpers\TA_Lib-0.4.25-cp38-cp38-win_amd64.whl
}
if ($pyv -eq '3.9') {
pip install build_helpers\TA_Lib-0.4.25-cp39-cp39-win_amd64.whl
}
if ($pyv -eq '3.10') {
pip install build_helpers\TA_Lib-0.4.25-cp310-cp310-win_amd64.whl
}
pip install -r requirements-dev.txt
pip install -e .

View File

@@ -1,4 +1,4 @@
# File used in CI to ensure pre-commit dependencies are kept up-to-date.
# File used in CI to ensure pre-commit dependencies are kept uptodate.
import sys
from pathlib import Path
@@ -6,30 +6,23 @@ from pathlib import Path
import yaml
pre_commit_file = Path(".pre-commit-config.yaml")
require_dev = Path("requirements-dev.txt")
require = Path("requirements.txt")
pre_commit_file = Path('.pre-commit-config.yaml')
require_dev = Path('requirements-dev.txt')
with require_dev.open("r") as rfile:
with require_dev.open('r') as rfile:
requirements = rfile.readlines()
with require.open("r") as rfile:
requirements.extend(rfile.readlines())
# Extract types only
type_reqs = [
r.strip("\n") for r in requirements if r.startswith("types-") or r.startswith("SQLAlchemy")
]
type_reqs = [r.strip('\n') for r in requirements if r.startswith('types-')]
with pre_commit_file.open("r") as file:
f = yaml.load(file, Loader=yaml.SafeLoader)
with pre_commit_file.open('r') as file:
f = yaml.load(file, Loader=yaml.FullLoader)
mypy_repo = [
repo for repo in f["repos"] if repo["repo"] == "https://github.com/pre-commit/mirrors-mypy"
]
mypy_repo = [repo for repo in f['repos'] if repo['repo']
== 'https://github.com/pre-commit/mirrors-mypy']
hooks = mypy_repo[0]["hooks"][0]["additional_dependencies"]
hooks = mypy_repo[0]['hooks'][0]['additional_dependencies']
errors = []
for hook in hooks:

View File

@@ -3,22 +3,16 @@
# Use BuildKit, otherwise building on ARM fails
export DOCKER_BUILDKIT=1
IMAGE_NAME=freqtradeorg/freqtrade
CACHE_IMAGE=freqtradeorg/freqtrade_cache
GHCR_IMAGE_NAME=ghcr.io/freqtrade/freqtrade
# Replace / with _ to create a valid tag
TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
TAG_PLOT=${TAG}_plot
TAG_FREQAI=${TAG}_freqai
TAG_FREQAI_RL=${TAG_FREQAI}rl
TAG_FREQAI_TORCH=${TAG_FREQAI}torch
TAG_PI="${TAG}_pi"
TAG_ARM=${TAG}_arm
TAG_PLOT_ARM=${TAG_PLOT}_arm
TAG_FREQAI_ARM=${TAG_FREQAI}_arm
TAG_FREQAI_RL_ARM=${TAG_FREQAI_RL}_arm
CACHE_IMAGE=freqtradeorg/freqtrade_cache
echo "Running for ${TAG}"
@@ -42,19 +36,17 @@ if [ $? -ne 0 ]; then
echo "failed building multiarch images"
return 1
fi
docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_PLOT_ARM} -f docker/Dockerfile.plot .
docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_ARM} -f docker/Dockerfile.freqai .
docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG_FREQAI_ARM} -t freqtrade:${TAG_FREQAI_RL_ARM} -f docker/Dockerfile.freqai_rl .
# Tag image for upload and next build step
docker tag freqtrade:$TAG_ARM ${CACHE_IMAGE}:$TAG_ARM
docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_PLOT_ARM} -f docker/Dockerfile.plot .
docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_ARM} -f docker/Dockerfile.freqai .
docker tag freqtrade:$TAG_PLOT_ARM ${CACHE_IMAGE}:$TAG_PLOT_ARM
docker tag freqtrade:$TAG_FREQAI_ARM ${CACHE_IMAGE}:$TAG_FREQAI_ARM
docker tag freqtrade:$TAG_FREQAI_RL_ARM ${CACHE_IMAGE}:$TAG_FREQAI_RL_ARM
# Run backtest
docker run --rm -v $(pwd)/tests/testdata/config.tests.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG_ARM} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3
docker run --rm -v $(pwd)/config_examples/config_bittrex.example.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG_ARM} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3
if [ $? -ne 0 ]; then
echo "failed running backtest"
@@ -63,9 +55,9 @@ fi
docker images
# docker push ${IMAGE_NAME}
docker push ${CACHE_IMAGE}:$TAG_PLOT_ARM
docker push ${CACHE_IMAGE}:$TAG_FREQAI_ARM
docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL_ARM
docker push ${CACHE_IMAGE}:$TAG_ARM
# Create multi-arch image
@@ -73,47 +65,22 @@ docker push ${CACHE_IMAGE}:$TAG_ARM
# Otherwise installation might fail.
echo "create manifests"
docker manifest create ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI}
docker manifest create --amend ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
docker manifest push -p ${IMAGE_NAME}:${TAG}
docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM}
docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM} ${CACHE_IMAGE}:${TAG_PLOT}
docker manifest push -p ${IMAGE_NAME}:${TAG_PLOT}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM} ${CACHE_IMAGE}:${TAG_FREQAI}
docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM}
docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI_RL}
# Create special Torch tag - which is identical to the RL tag.
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_TORCH} ${CACHE_IMAGE}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM}
docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI_TORCH}
# copy images to ghcr.io
alias crane="docker run --rm -i -v $(pwd)/.crane:/home/nonroot/.docker/ gcr.io/go-containerregistry/crane"
mkdir .crane
chmod a+rwx .crane
echo "${GHCR_TOKEN}" | crane auth login ghcr.io -u "${GHCR_USERNAME}" --password-stdin
crane copy ${IMAGE_NAME}:${TAG_FREQAI_RL} ${GHCR_IMAGE_NAME}:${TAG_FREQAI_RL}
crane copy ${IMAGE_NAME}:${TAG_FREQAI_RL} ${GHCR_IMAGE_NAME}:${TAG_FREQAI_TORCH}
crane copy ${IMAGE_NAME}:${TAG_FREQAI} ${GHCR_IMAGE_NAME}:${TAG_FREQAI}
crane copy ${IMAGE_NAME}:${TAG_PLOT} ${GHCR_IMAGE_NAME}:${TAG_PLOT}
crane copy ${IMAGE_NAME}:${TAG} ${GHCR_IMAGE_NAME}:${TAG}
# Tag as latest for develop builds
if [ "${TAG}" = "develop" ]; then
echo 'Tagging image as latest'
docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
docker manifest push -p ${IMAGE_NAME}:latest
crane copy ${IMAGE_NAME}:latest ${GHCR_IMAGE_NAME}:latest
fi
docker images
rm -rf .crane
# Cleanup old images from arm64 node.
docker image prune -a --force --filter "until=24h"

View File

@@ -2,17 +2,15 @@
# The below assumes a correctly setup docker buildx environment
IMAGE_NAME=freqtradeorg/freqtrade
CACHE_IMAGE=freqtradeorg/freqtrade_cache
# Replace / with _ to create a valid tag
TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
TAG_PLOT=${TAG}_plot
TAG_FREQAI=${TAG}_freqai
TAG_FREQAI_RL=${TAG_FREQAI}rl
TAG_PI="${TAG}_pi"
PI_PLATFORM="linux/arm/v7"
echo "Running for ${TAG}"
CACHE_IMAGE=freqtradeorg/freqtrade_cache
CACHE_TAG=${CACHE_IMAGE}:${TAG_PI}_cache
# Add commit and commit_message to docker container
@@ -27,10 +25,7 @@ if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
--cache-to=type=registry,ref=${CACHE_TAG} \
-f docker/Dockerfile.armhf \
--platform ${PI_PLATFORM} \
-t ${IMAGE_NAME}:${TAG_PI} \
--push \
--provenance=false \
.
-t ${IMAGE_NAME}:${TAG_PI} --push .
else
echo "event ${GITHUB_EVENT_NAME}: building with cache"
# Build regular image
@@ -39,16 +34,12 @@ else
# Pull last build to avoid rebuilding the whole image
# docker pull --platform ${PI_PLATFORM} ${IMAGE_NAME}:${TAG}
# disable provenance due to https://github.com/docker/buildx/issues/1509
docker buildx build \
--cache-from=type=registry,ref=${CACHE_TAG} \
--cache-to=type=registry,ref=${CACHE_TAG} \
-f docker/Dockerfile.armhf \
--platform ${PI_PLATFORM} \
-t ${IMAGE_NAME}:${TAG_PI} \
--push \
--provenance=false \
.
-t ${IMAGE_NAME}:${TAG_PI} --push .
fi
if [ $? -ne 0 ]; then
@@ -58,16 +49,14 @@ fi
# Tag image for upload and next build step
docker tag freqtrade:$TAG ${CACHE_IMAGE}:$TAG
docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG} -t freqtrade:${TAG_PLOT} -f docker/Dockerfile.plot .
docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG} -t freqtrade:${TAG_FREQAI} -f docker/Dockerfile.freqai .
docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG_FREQAI} -t freqtrade:${TAG_FREQAI_RL} -f docker/Dockerfile.freqai_rl .
docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG} -t freqtrade:${TAG_PLOT} -f docker/Dockerfile.plot .
docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG} -t freqtrade:${TAG_FREQAI} -f docker/Dockerfile.freqai .
docker tag freqtrade:$TAG_PLOT ${CACHE_IMAGE}:$TAG_PLOT
docker tag freqtrade:$TAG_FREQAI ${CACHE_IMAGE}:$TAG_FREQAI
docker tag freqtrade:$TAG_FREQAI_RL ${CACHE_IMAGE}:$TAG_FREQAI_RL
# Run backtest
docker run --rm -v $(pwd)/tests/testdata/config.tests.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3
docker run --rm -v $(pwd)/config_examples/config_bittrex.example.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3
if [ $? -ne 0 ]; then
echo "failed running backtest"
@@ -76,10 +65,11 @@ fi
docker images
docker push ${CACHE_IMAGE}:$TAG
docker push ${CACHE_IMAGE}
docker push ${CACHE_IMAGE}:$TAG_PLOT
docker push ${CACHE_IMAGE}:$TAG_FREQAI
docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL
docker push ${CACHE_IMAGE}:$TAG
docker images

File diff suppressed because it is too large Load Diff

View File

@@ -1,7 +1,6 @@
{
"$schema": "https://schema.freqtrade.io/schema.json",
"max_open_trades": 3,
"stake_currency": "USDT",
"stake_currency": "BTC",
"stake_amount": 0.05,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
@@ -37,29 +36,43 @@
"ccxt_async_config": {
},
"pair_whitelist": [
"ALGO/USDT",
"ATOM/USDT",
"BAT/USDT",
"BCH/USDT",
"BRD/USDT",
"EOS/USDT",
"ETH/USDT",
"IOTA/USDT",
"LINK/USDT",
"LTC/USDT",
"NEO/USDT",
"NXS/USDT",
"XMR/USDT",
"XRP/USDT",
"XTZ/USDT"
"ALGO/BTC",
"ATOM/BTC",
"BAT/BTC",
"BCH/BTC",
"BRD/BTC",
"EOS/BTC",
"ETH/BTC",
"IOTA/BTC",
"LINK/BTC",
"LTC/BTC",
"NEO/BTC",
"NXS/BTC",
"XMR/BTC",
"XRP/BTC",
"XTZ/BTC"
],
"pair_blacklist": [
"BNB/.*"
"BNB/BTC"
]
},
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

View File

@@ -29,11 +29,14 @@
"order_book_top": 1
},
"exchange": {
"name": "binance",
"name": "bittrex",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"ccxt_config": {},
"ccxt_async_config": {},
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 500
},
"pair_whitelist": [
"ETH/BTC",
"LTC/BTC",
@@ -53,6 +56,20 @@
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

View File

@@ -1,5 +1,4 @@
{
"$schema": "https://schema.freqtrade.io/schema.json",
"trading_mode": "futures",
"margin_mode": "isolated",
"max_open_trades": 5,
@@ -19,11 +18,16 @@
"name": "binance",
"key": "",
"secret": "",
"ccxt_config": {},
"ccxt_async_config": {},
"ccxt_config": {
"enableRateLimit": true
},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 200
},
"pair_whitelist": [
"1INCH/USDT:USDT",
"ALGO/USDT:USDT"
"1INCH/USDT",
"ALGO/USDT"
],
"pair_blacklist": []
},
@@ -49,11 +53,11 @@
],
"freqai": {
"enabled": true,
"purge_old_models": 2,
"purge_old_models": true,
"train_period_days": 15,
"backtest_period_days": 7,
"live_retrain_hours": 0,
"identifier": "unique-id",
"identifier": "uniqe-id",
"feature_parameters": {
"include_timeframes": [
"3m",
@@ -61,8 +65,8 @@
"1h"
],
"include_corr_pairlist": [
"BTC/USDT:USDT",
"ETH/USDT:USDT"
"BTC/USDT",
"ETH/USDT"
],
"label_period_candles": 20,
"include_shifted_candles": 2,
@@ -80,7 +84,9 @@
"test_size": 0.33,
"random_state": 1
},
"model_training_parameters": {}
"model_training_parameters": {
"n_estimators": 1000
}
},
"bot_name": "",
"force_entry_enable": true,

View File

@@ -1,15 +1,15 @@
{
"max_open_trades": 3,
"stake_currency": "BTC",
"stake_amount": 0.05,
"stake_currency": "USD",
"stake_amount": 50,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
"timeframe": "5m",
"dry_run": true,
"cancel_open_orders_on_exit": false,
"unfilledtimeout": {
"entry": 5,
"exit": 5,
"entry": 10,
"exit": 10,
"exit_timeout_count": 0,
"unit": "minutes"
},
@@ -23,33 +23,55 @@
"bids_to_ask_delta": 1
}
},
"exit_pricing":{
"exit_pricing": {
"price_side": "same",
"use_order_book": true,
"order_book_top": 1
},
"exchange": {
"name": "gate",
"name": "ftx",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"ccxt_config": {},
"ccxt_async_config": {},
"pair_whitelist": [
"ETH/BTC",
"LTC/BTC",
"ETC/BTC",
"XLM/BTC",
"XRP/BTC",
"ADA/BTC",
"DOT/BTC"
"BTC/USD",
"ETH/USD",
"BNB/USD",
"USDT/USD",
"LTC/USD",
"SRM/USD",
"SXP/USD",
"XRP/USD",
"DOGE/USD",
"1INCH/USD",
"CHZ/USD",
"MATIC/USD",
"LINK/USD",
"OXY/USD",
"SUSHI/USD"
],
"pair_blacklist": [
"DOGE/BTC"
"FTT/USD"
]
},
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

View File

@@ -1,5 +1,4 @@
{
"$schema": "https://schema.freqtrade.io/schema.json",
"max_open_trades": 3,
"stake_currency": "BTC",
"stake_amount": 0.05,
@@ -61,7 +60,6 @@
"force_entry": "market",
"stoploss": "market",
"stoploss_on_exchange": false,
"stoploss_price_type": "last",
"stoploss_on_exchange_interval": 60,
"stoploss_on_exchange_limit_ratio": 0.99
},
@@ -71,7 +69,6 @@
},
"pairlists": [
{"method": "StaticPairList"},
{"method": "FullTradesFilter"},
{
"method": "VolumePairList",
"number_assets": 20,
@@ -91,6 +88,7 @@
],
"exchange": {
"name": "binance",
"sandbox": false,
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"password": "",
@@ -206,7 +204,6 @@
"strategy_path": "user_data/strategies/",
"recursive_strategy_search": false,
"add_config_files": [],
"reduce_df_footprint": false,
"dataformat_ohlcv": "feather",
"dataformat_trades": "feather"
"dataformat_ohlcv": "json",
"dataformat_trades": "jsongz"
}

View File

@@ -1,5 +1,4 @@
{
"$schema": "https://schema.freqtrade.io/schema.json",
"max_open_trades": 5,
"stake_currency": "EUR",
"stake_amount": 10,
@@ -65,6 +64,20 @@
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

View File

@@ -1,19 +1,11 @@
---
version: '3'
services:
freqtrade:
image: freqtradeorg/freqtrade:stable
# image: freqtradeorg/freqtrade:develop
# Use plotting image
# image: freqtradeorg/freqtrade:develop_plot
# # Enable GPU Image and GPU Resources (only relevant for freqAI)
# # Make sure to uncomment the whole deploy section
# deploy:
# resources:
# reservations:
# devices:
# - driver: nvidia
# count: 1
# capabilities: [gpu]
# Build step - only needed when additional dependencies are needed
# build:
# context: .
@@ -24,7 +16,7 @@ services:
- "./user_data:/freqtrade/user_data"
# Expose api on port 8080 (localhost only)
# Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
# for more information.
# before enabling this.
ports:
- "127.0.0.1:8080:8080"
# Default command used when running `docker compose up`

View File

@@ -1,4 +1,4 @@
FROM python:3.11.10-slim-bookworm as base
FROM python:3.9.12-slim-bullseye as base
# Setup env
ENV LANG C.UTF-8
@@ -11,13 +11,12 @@ ENV FT_APP_ENV="docker"
# Prepare environment
RUN mkdir /freqtrade \
&& apt-get update \
&& apt-get -y install sudo libatlas3-base libopenblas-dev curl sqlite3 libutf8proc-dev libsnappy-dev \
&& apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-dev \
&& apt-get clean \
&& useradd -u 1000 -G sudo -U -m ftuser \
&& chown ftuser:ftuser /freqtrade \
# Allow sudoers
&& echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers \
&& pip install --upgrade pip
&& echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers
WORKDIR /freqtrade
@@ -26,16 +25,18 @@ FROM base as python-deps
RUN apt-get update \
&& apt-get -y install build-essential libssl-dev libffi-dev libgfortran5 pkg-config cmake gcc \
&& apt-get clean \
&& pip install --upgrade pip \
&& echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > /etc/pip.conf
# Install TA-lib
COPY build_helpers/* /tmp/
RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
ENV LD_LIBRARY_PATH /usr/local/lib
# Install dependencies
COPY --chown=ftuser:ftuser requirements.txt /freqtrade/
USER ftuser
RUN pip install --user --no-cache-dir numpy \
&& pip install --user --no-index --find-links /tmp/ pyarrow TA-Lib \
&& pip install --user --no-cache-dir -r requirements.txt
# Copy dependencies to runtime-image

View File

@@ -1,8 +0,0 @@
ARG sourceimage=freqtradeorg/freqtrade
ARG sourcetag=develop_freqai
FROM ${sourceimage}:${sourcetag}
# Install dependencies
COPY requirements-freqai.txt requirements-freqai-rl.txt /freqtrade/
RUN pip install -r requirements-freqai-rl.txt --user --no-cache-dir

View File

@@ -1,8 +1,8 @@
FROM freqtradeorg/freqtrade:develop_plot
# Pin prompt-toolkit to avoid questionary version conflict
RUN pip install jupyterlab "prompt-toolkit<=3.0.36" jupyter-client --user --no-cache-dir
# Pin jupyter-client to avoid tornado version conflict
RUN pip install jupyterlab jupyter-client==7.3.4 --user --no-cache-dir
# Empty the ENTRYPOINT to allow all commands
ENTRYPOINT []

View File

@@ -1,35 +0,0 @@
---
services:
freqtrade:
image: freqtradeorg/freqtrade:stable_freqaitorch
# # Enable GPU Image and GPU Resources
# # Make sure to uncomment the whole deploy section
# deploy:
# resources:
# reservations:
# devices:
# - driver: nvidia
# count: 1
# capabilities: [gpu]
# Build step - only needed when additional dependencies are needed
# build:
# context: .
# dockerfile: "./docker/Dockerfile.custom"
restart: unless-stopped
container_name: freqtrade
volumes:
- "./user_data:/freqtrade/user_data"
# Expose api on port 8080 (localhost only)
# Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
# for more information.
ports:
- "127.0.0.1:8080:8080"
# Default command used when running `docker compose up`
command: >
trade
--logfile /freqtrade/user_data/logs/freqtrade.log
--db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
--config /freqtrade/user_data/config.json
--freqaimodel XGBoostRegressor
--strategy FreqaiExampleStrategy

View File

@@ -1,11 +1,12 @@
---
version: '3'
services:
ft_jupyterlab:
build:
context: ..
dockerfile: docker/Dockerfile.jupyter
restart: unless-stopped
# container_name: freqtrade
container_name: freqtrade
ports:
- "127.0.0.1:8888:8888"
volumes:

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@@ -0,0 +1,15 @@
Dear Editors,
We present a paper for ``FreqAI`` a machine learning sandbox for researchers and citizen scientists alike.
There are a large number of authors, however all have contributed in a significant way to this paper.
For clarity the contribution of each author is outlined:
- Robert Caulk : Conception and software development
- Elin Tornquist : Theoretical brainstorming, data analysis, tool dev
- Matthias Voppichler : Software architecture and code review
- Andrew R. Lawless : Extensive testing, feature brainstorming
- Ryan McMullan : Extensive testing, feature brainstorming
- Wagner Costa Santos : Major backtesting developments, extensive testing
- Pascal Schmidt : Extensive testing, feature brainstorming
- Timothy C. Pogue : Webhooks forecast sharing
- Stefan P. Gehring : Extensive testing, feature brainstorming
- Johan van der Vlugt : Extensive testing, feature brainstorming

207
docs/JOSS_paper/paper.bib Normal file
View File

@@ -0,0 +1,207 @@
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year = {2018},
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address = {Red Hook, NY, USA},
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booktitle = {Proceedings of the 32nd International Conference on Neural Information Processing Systems},
pages = {66396649},
numpages = {11},
location = {Montr\'{e}al, Canada},
series = {NIPS'18}
}
@article{lightgbm,
title={Lightgbm: A highly efficient gradient boosting decision tree},
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doi = {10.1145/2939672.2939785},
acmid = {2939785},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {large-scale machine learning},
}
@article{stable-baselines3,
author = {Antonin Raffin and Ashley Hill and Adam Gleave and Anssi Kanervisto and Maximilian Ernestus and Noah Dormann},
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url = {http://jmlr.org/papers/v22/20-1364.html}
}
@misc{openai,
title={OpenAI Gym},
author={Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba},
year={2016},
eprint={1606.01540},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@misc{tensorflow,
title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
url={https://www.tensorflow.org/},
note={Software available from tensorflow.org},
author={
Mart\'{i}n~Abadi and
Ashish~Agarwal and
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Zhifeng~Chen and
Craig~Citro and
Greg~S.~Corrado and
Andy~Davis and
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Manjunath~Kudlur and
Josh~Levenberg and
Dandelion~Man\'{e} and
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Derek~Murray and
Chris~Olah and
Mike~Schuster and
Jonathon~Shlens and
Benoit~Steiner and
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year={2015},
}
@incollection{pytorch,
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
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}
@ARTICLE{scipy,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
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Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
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adsurl = {https://rdcu.be/b08Wh},
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}
@Article{numpy,
title = {Array programming with {NumPy}},
author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J.
van der Walt and Ralf Gommers and Pauli Virtanen and David
Cournapeau and Eric Wieser and Julian Taylor and Sebastian
Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del
R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre
G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and
Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
Travis E. Oliphant},
year = {2020},
month = sep,
journal = {Nature},
volume = {585},
number = {7825},
pages = {357--362},
doi = {10.1038/s41586-020-2649-2},
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url = {https://doi.org/10.1038/s41586-020-2649-2}
}
@inproceedings{pandas,
title={Data structures for statistical computing in python},
author={McKinney, Wes and others},
booktitle={Proceedings of the 9th Python in Science Conference},
volume={445},
pages={51--56},
year={2010},
organization={Austin, TX},
doi={10.25080/Majora-92bf1922-00a}
}
@online{finrl,
title = {AI4Finance-Foundation},
year = 2022,
url = {https://github.com/AI4Finance-Foundation/FinRL},
urldate = {2022-09-30}
}
@online{tensortrade,
title = {tensortrade},
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urldate = {2022-09-30}
}

941
docs/JOSS_paper/paper.jats Normal file
View File

@@ -0,0 +1,941 @@
<?xml version="1.0" encoding="utf-8" ?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN"
"JATS-publishing1.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.2" article-type="other">
<front>
<journal-meta>
<journal-id></journal-id>
<journal-title-group>
<journal-title>Journal of Open Source Software</journal-title>
<abbrev-journal-title>JOSS</abbrev-journal-title>
</journal-title-group>
<issn publication-format="electronic">2475-9066</issn>
<publisher>
<publisher-name>Open Journals</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">0</article-id>
<article-id pub-id-type="doi">N/A</article-id>
<title-group>
<article-title><monospace>FreqAI</monospace>: generalizing adaptive
modeling for chaotic time-series market forecasts</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">0000-0001-5618-8629</contrib-id>
<name>
<surname>Ph.D</surname>
<given-names>Robert A. Caulk</given-names>
</name>
<xref ref-type="aff" rid="aff-1"/>
<xref ref-type="aff" rid="aff-2"/>
</contrib>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">0000-0003-3289-8604</contrib-id>
<name>
<surname>Ph.D</surname>
<given-names>Elin Törnquist</given-names>
</name>
<xref ref-type="aff" rid="aff-1"/>
<xref ref-type="aff" rid="aff-2"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Voppichler</surname>
<given-names>Matthias</given-names>
</name>
<xref ref-type="aff" rid="aff-2"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Lawless</surname>
<given-names>Andrew R.</given-names>
</name>
<xref ref-type="aff" rid="aff-2"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>McMullan</surname>
<given-names>Ryan</given-names>
</name>
<xref ref-type="aff" rid="aff-2"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Santos</surname>
<given-names>Wagner Costa</given-names>
</name>
<xref ref-type="aff" rid="aff-1"/>
<xref ref-type="aff" rid="aff-2"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Pogue</surname>
<given-names>Timothy C.</given-names>
</name>
<xref ref-type="aff" rid="aff-1"/>
<xref ref-type="aff" rid="aff-2"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>van der Vlugt</surname>
<given-names>Johan</given-names>
</name>
<xref ref-type="aff" rid="aff-2"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Gehring</surname>
<given-names>Stefan P.</given-names>
</name>
<xref ref-type="aff" rid="aff-2"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Schmidt</surname>
<given-names>Pascal</given-names>
</name>
<xref ref-type="aff" rid="aff-2"/>
</contrib>
<aff id="aff-1">
<institution-wrap>
<institution>Emergent Methods LLC, Arvada Colorado, 80005,
USA</institution>
</institution-wrap>
</aff>
<aff id="aff-2">
<institution-wrap>
<institution>Freqtrade open source project</institution>
</institution-wrap>
</aff>
</contrib-group>
<volume>¿VOL?</volume>
<issue>¿ISSUE?</issue>
<fpage>¿PAGE?</fpage>
<permissions>
<copyright-statement>Authors of papers retain copyright and release the
work under a Creative Commons Attribution 4.0 International License (CC
BY 4.0)</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>The article authors</copyright-holder>
<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
<license-p>Authors of papers retain copyright and release the work under
a Creative Commons Attribution 4.0 International License (CC BY
4.0)</license-p>
</license>
</permissions>
<kwd-group kwd-group-type="author">
<kwd>Python</kwd>
<kwd>Machine Learning</kwd>
<kwd>adaptive modeling</kwd>
<kwd>chaotic systems</kwd>
<kwd>time-series forecasting</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="statement-of-need">
<title>Statement of need</title>
<p>Forecasting chaotic time-series based systems, such as
equity/cryptocurrency markets, requires a broad set of tools geared
toward testing a wide range of hypotheses. Fortunately, a recent
maturation of robust machine learning libraries
(e.g. <monospace>scikit-learn</monospace>), has opened up a wide range
of research possibilities. Scientists from a diverse range of fields
can now easily prototype their studies on an abundance of established
machine learning algorithms. Similarly, these user-friendly libraries
enable “citzen scientists” to use their basic Python skills for
data-exploration. However, leveraging these machine learning libraries
on historical and live chaotic data sources can be logistically
difficult and expensive. Additionally, robust data-collection,
storage, and handling presents a disparate challenge.
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/"><monospace>FreqAI</monospace></ext-link>
aims to provide a generalized and extensible open-sourced framework
geared toward live deployments of adaptive modeling for market
forecasting. The <monospace>FreqAI</monospace> framework is
effectively a sandbox for the rich world of open-source machine
learning libraries. Inside the <monospace>FreqAI</monospace> sandbox,
users find they can combine a wide variety of third-party libraries to
test creative hypotheses on a free live 24/7 chaotic data source -
cryptocurrency exchange data.</p>
</sec>
<sec id="summary">
<title>Summary</title>
<p><ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/"><monospace>FreqAI</monospace></ext-link>
evolved from a desire to test and compare a range of adaptive
time-series forecasting methods on chaotic data. Cryptocurrency
markets provide a unique data source since they are operational 24/7
and the data is freely available. Luckily, an existing open-source
software,
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/stable/"><monospace>Freqtrade</monospace></ext-link>,
had already matured under a range of talented developers to support
robust data collection/storage, as well as robust live environmental
interactions for standard algorithmic trading.
<monospace>Freqtrade</monospace> also provides a set of data
analysis/visualization tools for the evaluation of historical
performance as well as live environmental feedback.
<monospace>FreqAI</monospace> builds on top of
<monospace>Freqtrade</monospace> to include a user-friendly well
tested interface for integrating external machine learning libraries
for adaptive time-series forecasting. Beyond enabling the integration
of existing libraries, <monospace>FreqAI</monospace> hosts a range of
custom algorithms and methodologies aimed at improving computational
and predictive performances. Thus, <monospace>FreqAI</monospace>
contains a range of unique features which can be easily tested in
combination with all the existing Python-accessible machine learning
libraries to generate novel research on live and historical data.</p>
<p>The high-level overview of the software is depicted in Figure
1.</p>
<p><named-content content-type="image">freqai-algo</named-content>
<italic>Abstracted overview of FreqAI algorithm</italic></p>
<sec id="connecting-machine-learning-libraries">
<title>Connecting machine learning libraries</title>
<p>Although the <monospace>FreqAI</monospace> framework is designed
to accommodate any Python library in the “Model training” and
“Feature set engineering” portions of the software (Figure 1), it
already boasts a wide range of well documented examples based on
various combinations of:</p>
<list list-type="bullet">
<list-item>
<p>scikit-learn
(<xref alt="Pedregosa et al., 2011" rid="ref-scikit-learn" ref-type="bibr">Pedregosa
et al., 2011</xref>), Catboost
(<xref alt="Prokhorenkova et al., 2018" rid="ref-catboost" ref-type="bibr">Prokhorenkova
et al., 2018</xref>), LightGBM
(<xref alt="Ke et al., 2017" rid="ref-lightgbm" ref-type="bibr">Ke
et al., 2017</xref>), XGBoost
(<xref alt="Chen &amp; Guestrin, 2016" rid="ref-xgboost" ref-type="bibr">Chen
&amp; Guestrin, 2016</xref>), stable_baselines3
(<xref alt="Raffin et al., 2021" rid="ref-stable-baselines3" ref-type="bibr">Raffin
et al., 2021</xref>), openai gym
(<xref alt="Brockman et al., 2016" rid="ref-openai" ref-type="bibr">Brockman
et al., 2016</xref>), tensorflow
(<xref alt="Abadi et al., 2015" rid="ref-tensorflow" ref-type="bibr">Abadi
et al., 2015</xref>), pytorch
(<xref alt="Paszke et al., 2019" rid="ref-pytorch" ref-type="bibr">Paszke
et al., 2019</xref>), Scipy
(<xref alt="Virtanen et al., 2020" rid="ref-scipy" ref-type="bibr">Virtanen
et al., 2020</xref>), Numpy
(<xref alt="Harris et al., 2020" rid="ref-numpy" ref-type="bibr">Harris
et al., 2020</xref>), and pandas
(<xref alt="McKinney &amp; others, 2010" rid="ref-pandas" ref-type="bibr">McKinney
&amp; others, 2010</xref>).</p>
</list-item>
</list>
<p>These mature projects contain a wide range of peer-reviewed and
industry standard methods, including:</p>
<list list-type="bullet">
<list-item>
<p>Regression, Classification, Neural Networks, Reinforcement
Learning, Support Vector Machines, Principal Component Analysis,
point clustering, and much more.</p>
</list-item>
</list>
<p>which are all leveraged in <monospace>FreqAI</monospace> for
users to use as templates or extend with their own methods.</p>
</sec>
<sec id="furnishing-novel-methods-and-features">
<title>Furnishing novel methods and features</title>
<p>Beyond the industry standard methods available through external
libraries - <monospace>FreqAI</monospace> includes novel methods
which are not available anywhere else in the open-source (or
scientific) world. For example, <monospace>FreqAI</monospace>
provides :</p>
<list list-type="bullet">
<list-item>
<p>a custom algorithm/methodology for adaptive modeling</p>
</list-item>
<list-item>
<p>rapid and self-monitored feature engineering tools</p>
</list-item>
<list-item>
<p>unique model features/indicators</p>
</list-item>
<list-item>
<p>optimized data collection algorithms</p>
</list-item>
<list-item>
<p>safely integrated outlier detection methods</p>
</list-item>
<list-item>
<p>websocket communicated forecasts</p>
</list-item>
</list>
<p>Of particular interest for researchers,
<monospace>FreqAI</monospace> provides the option of large scale
experimentation via an optimized websocket communications
interface.</p>
</sec>
<sec id="optimizing-the-back-end">
<title>Optimizing the back-end</title>
<p><monospace>FreqAI</monospace> aims to make it simple for users to
combine all the above tools to run studies based in two distinct
modules:</p>
<list list-type="bullet">
<list-item>
<p>backtesting studies</p>
</list-item>
<list-item>
<p>live-deployments</p>
</list-item>
</list>
<p>Both of these modules and their respective data management
systems are built on top of
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/"><monospace>Freqtrade</monospace></ext-link>,
a mature and actively developed cryptocurrency trading software.
This means that <monospace>FreqAI</monospace> benefits from a wide
range of tangential/disparate feature developments such as:</p>
<list list-type="bullet">
<list-item>
<p>FreqUI, a graphical interface for backtesting and live
monitoring</p>
</list-item>
<list-item>
<p>telegram control</p>
</list-item>
<list-item>
<p>robust database handling</p>
</list-item>
<list-item>
<p>futures/leverage trading</p>
</list-item>
<list-item>
<p>dollar cost averaging</p>
</list-item>
<list-item>
<p>trading strategy handling</p>
</list-item>
<list-item>
<p>a variety of free data sources via CCXT (FTX, Binance, Kucoin
etc.)</p>
</list-item>
</list>
<p>These features derive from a strong external developer community
that shares in the benefit and stability of a communal CI
(Continuous Integration) system. Beyond the developer community,
<monospace>FreqAI</monospace> benefits strongly from the userbase of
<monospace>Freqtrade</monospace>, where most
<monospace>FreqAI</monospace> beta-testers/developers originated.
This symbiotic relationship between <monospace>Freqtrade</monospace>
and <monospace>FreqAI</monospace> ignited a thoroughly tested
<ext-link ext-link-type="uri" xlink:href="https://github.com/freqtrade/freqtrade/pull/6832"><monospace>beta</monospace></ext-link>,
which demanded a four month beta and
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/">comprehensive
documentation</ext-link> containing:</p>
<list list-type="bullet">
<list-item>
<p>numerous example scripts</p>
</list-item>
<list-item>
<p>a full parameter table</p>
</list-item>
<list-item>
<p>methodological descriptions</p>
</list-item>
<list-item>
<p>high-resolution diagrams/figures</p>
</list-item>
<list-item>
<p>detailed parameter setting recommendations</p>
</list-item>
</list>
</sec>
<sec id="providing-a-reproducible-foundation-for-researchers">
<title>Providing a reproducible foundation for researchers</title>
<p><monospace>FreqAI</monospace> provides an extensible, robust,
framework for researchers and citizen data scientists. The
<monospace>FreqAI</monospace> sandbox enables rapid conception and
testing of exotic hypotheses. From a research perspective,
<monospace>FreqAI</monospace> handles the multitude of logistics
associated with live deployments, historical backtesting, and
feature engineering. With <monospace>FreqAI</monospace>, researchers
can focus on their primary interests of feature engineering and
hypothesis testing rather than figuring out how to collect and
handle data. Further - the well maintained and easily installed
open-source framework of <monospace>FreqAI</monospace> enables
reproducible scientific studies. This reproducibility component is
essential to general scientific advancement in time-series
forecasting for chaotic systems.</p>
</sec>
</sec>
<sec id="technical-details">
<title>Technical details</title>
<p>Typical users configure <monospace>FreqAI</monospace> via two
files:</p>
<list list-type="order">
<list-item>
<p>A <monospace>configuration</monospace> file
(<monospace>--config</monospace>) which provides access to the
full parameter list available
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/">here</ext-link>:</p>
</list-item>
</list>
<list list-type="bullet">
<list-item>
<p>control high-level feature engineering</p>
</list-item>
<list-item>
<p>customize adaptive modeling techniques</p>
</list-item>
<list-item>
<p>set any model training parameters available in third-party
libraries</p>
</list-item>
<list-item>
<p>manage adaptive modeling parameters (retrain frequency,
training window size, continual learning, etc.)</p>
</list-item>
</list>
<list list-type="order">
<list-item>
<label>2.</label>
<p>A strategy file (<monospace>--strategy</monospace>) where
users:</p>
</list-item>
</list>
<list list-type="bullet">
<list-item>
<p>list of the base training features</p>
</list-item>
<list-item>
<p>set standard technical-analysis strategies</p>
</list-item>
<list-item>
<p>control trade entry/exit criteria</p>
</list-item>
</list>
<p>With these two files, most users can exploit a wide range of
pre-existing integrations in <monospace>Catboost</monospace> and 7
other libraries with a simple command:</p>
<preformat>freqtrade trade --config config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel CatboostRegressor</preformat>
<p>Advanced users will edit one of the existing
<monospace>--freqaimodel</monospace> files, which are simply an
children of the <monospace>IFreqaiModel</monospace> (details below).
Within these files, advanced users can customize training procedures,
prediction procedures, outlier detection methods, data preparation,
data saving methods, etc. This is all configured in a way where they
can customize as little or as much as they want. This flexible
customization is owed to the foundational architecture in
<monospace>FreqAI</monospace>, which is comprised of three distinct
Python objects:</p>
<list list-type="bullet">
<list-item>
<p><monospace>IFreqaiModel</monospace></p>
<list list-type="bullet">
<list-item>
<p>A singular long-lived object containing all the necessary
logic to collect data, store data, process data, engineer
features, run training, and inference models.</p>
</list-item>
</list>
</list-item>
<list-item>
<p><monospace>FreqaiDataKitchen</monospace></p>
<list list-type="bullet">
<list-item>
<p>A short-lived object which is uniquely created for each
asset/model. Beyond metadata, it also contains a variety of
data processing tools.</p>
</list-item>
</list>
</list-item>
<list-item>
<p><monospace>FreqaiDataDrawer</monospace></p>
<list list-type="bullet">
<list-item>
<p>Singular long-lived object containing all the historical
predictions, models, and save/load methods.</p>
</list-item>
</list>
</list-item>
</list>
<p>These objects interact with one another with one goal in mind - to
provide a clean data set to machine learning experts/enthusiasts at
the user endpoint. These power-users interact with an inherited
<monospace>IFreqaiModel</monospace> that allows them to dig as deep or
as shallow as they wish into the inheritence tree. Typical power-users
focus their efforts on customizing training procedures and testing
exotic functionalities available in third-party libraries. Thus,
power-users are freed from the algorithmic weight associated with data
management, and can instead focus their energy on testing creative
hypotheses. Meanwhile, some users choose to override deeper
functionalities within <monospace>IFreqaiModel</monospace> to help
them craft unique data structures and training procedures.</p>
<p>The class structure and algorithmic details are depicted in the
following diagram:</p>
<p><named-content content-type="image">image</named-content>
<italic>Class diagram summarizing object interactions in
FreqAI</italic></p>
</sec>
<sec id="online-documentation">
<title>Online documentation</title>
<p>The documentation for
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/"><monospace>FreqAI</monospace></ext-link>
is available online at
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/">https://www.freqtrade.io/en/latest/freqai/</ext-link>
and covers a wide range of materials:</p>
<list list-type="bullet">
<list-item>
<p>Quick-start with a single command and example files -
(beginners)</p>
</list-item>
<list-item>
<p>Introduction to the feature engineering interface and basic
configurations - (intermediate users)</p>
</list-item>
<list-item>
<p>Parameter table with indepth descriptions and default parameter
setting recommendations - (intermediate users)</p>
</list-item>
<list-item>
<p>Data analysis and post-processing - (advanced users)</p>
</list-item>
<list-item>
<p>Methodological considerations complemented by high resolution
figures - (advanced users)</p>
</list-item>
<list-item>
<p>Instructions for integrating third party machine learning
libraries into custom prediction models - (advanced users)</p>
</list-item>
<list-item>
<p>Software architectural description with class diagram -
(developers)</p>
</list-item>
<list-item>
<p>File structure descriptions - (developers)</p>
</list-item>
</list>
<p>The docs direct users to a variety of pre-made examples which
integrate <monospace>Catboost</monospace>,
<monospace>LightGBM</monospace>, <monospace>XGBoost</monospace>,
<monospace>Sklearn</monospace>,
<monospace>stable_baselines3</monospace>,
<monospace>torch</monospace>, <monospace>tensorflow</monospace>.
Meanwhile, developers will also find thorough docstrings and type
hinting throughout the source code to aid in code readability and
customization.</p>
<p><monospace>FreqAI</monospace> also benefits from a strong support
network of users and developers on the
<ext-link ext-link-type="uri" xlink:href="https://discord.gg/w6nDM6cM4y"><monospace>Freqtrade</monospace>
discord</ext-link> as well as on the
<ext-link ext-link-type="uri" xlink:href="https://discord.gg/xE4RMg4QYw"><monospace>FreqAI</monospace>
discord</ext-link>. Within the <monospace>FreqAI</monospace> discord,
users will find a deep and easily searched knowledge base containing
common errors. But more importantly, users in the
<monospace>FreqAI</monospace> discord share anectdotal and
quantitative observations which compare performance between various
third-party libraries and methods.</p>
</sec>
<sec id="state-of-the-field">
<title>State of the field</title>
<p>There are two other open-source tools which are geared toward
helping users build models for time-series forecasts on market based
data. However, each of these tools suffer from a non-generalized
frameworks that do not permit comparison of methods and libraries.
Additionally, they do not permit easy live-deployments or
adaptive-modeling methods. For example, two open-sourced projects
called
<ext-link ext-link-type="uri" xlink:href="https://tensortradex.readthedocs.io/en/latest/"><monospace>tensortrade</monospace></ext-link>
(<xref alt="Tensortrade, 2022" rid="ref-tensortrade" ref-type="bibr"><italic>Tensortrade</italic>,
2022</xref>) and
<ext-link ext-link-type="uri" xlink:href="https://github.com/AI4Finance-Foundation/FinRL"><monospace>FinRL</monospace></ext-link>
(<xref alt="AI4Finance-Foundation, 2022" rid="ref-finrl" ref-type="bibr"><italic>AI4Finance-Foundation</italic>,
2022</xref>) limit users to the exploration of reinforcement learning
on historical data. These softwares also do not provide robust live
deployments, they do not furnish novel feature engineering algorithms,
and they do not provide custom data analysis tools.
<monospace>FreqAI</monospace> fills the gap.</p>
</sec>
<sec id="on-going-research">
<title>On-going research</title>
<p>Emergent Methods, based in Arvada CO, is actively using
<monospace>FreqAI</monospace> to perform large scale experiments aimed
at comparing machine learning libraries in live and historical
environments. Past projects include backtesting parametric sweeps,
while active projects include a 3 week live deployment comparison
between <monospace>CatboosRegressor</monospace>,
<monospace>LightGBMRegressor</monospace>, and
<monospace>XGBoostRegressor</monospace>. Results from these studies
are on track for publication in scientific journals as well as more
general data science blogs (e.g. Medium).</p>
</sec>
<sec id="installing-and-running-freqai">
<title>Installing and running <monospace>FreqAI</monospace></title>
<p><monospace>FreqAI</monospace> is automatically installed with
<monospace>Freqtrade</monospace> using the following commands on linux
systems:</p>
<preformat>git clone git@github.com:freqtrade/freqtrade.git
cd freqtrade
./setup.sh -i</preformat>
<p>However, <monospace>FreqAI</monospace> also benefits from
<monospace>Freqtrade</monospace> docker distributions, and can be run
with docker by pulling the stable or develop images from
<monospace>Freqtrade</monospace> distributions.</p>
</sec>
<sec id="funding-sources">
<title>Funding sources</title>
<p><ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/"><monospace>FreqAI</monospace></ext-link>
has had no official sponsors, and is entirely grass roots. All
donations into the project (e.g. the GitHub sponsor system) are kept
inside the project to help support development of open-sourced and
communally beneficial features.</p>
</sec>
<sec id="acknowledgements">
<title>Acknowledgements</title>
<p>We would like to acknowledge various beta testers of
<monospace>FreqAI</monospace>:</p>
<list list-type="bullet">
<list-item>
<p>Richárd Józsa</p>
</list-item>
<list-item>
<p>Juha Nykänen</p>
</list-item>
<list-item>
<p>Salah Lamkadem</p>
</list-item>
</list>
<p>As well as various <monospace>Freqtrade</monospace>
<ext-link ext-link-type="uri" xlink:href="https://github.com/freqtrade/freqtrade/graphs/contributors">developers</ext-link>
maintaining tangential, yet essential, modules.</p>
</sec>
</body>
<back>
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---
title: '`FreqAI`: generalizing adaptive modeling for chaotic time-series market forecasts'
tags:
- Python
- Machine Learning
- adaptive modeling
- chaotic systems
- time-series forecasting
authors:
- name: Robert A. Caulk
orcid: 0000-0001-5618-8629
affiliation: 1, 2
- name: Elin Törnquist
orcid: 0000-0003-3289-8604
affiliation: 1, 2
- name: Matthias Voppichler
orcid:
affiliation: 2
- name: Andrew R. Lawless
orcid:
affiliation: 2
- name: Ryan McMullan
orcid:
affiliation: 2
- name: Wagner Costa Santos
orcid:
affiliation: 1, 2
- name: Timothy C. Pogue
orcid:
affiliation: 1, 2
- name: Johan van der Vlugt
orcid:
affiliation: 2
- name: Stefan P. Gehring
orcid:
affiliation: 2
- name: Pascal Schmidt
orcid: 0000-0001-9328-4345
affiliation: 2
<!-- affiliation: "1, 2" # (Multiple affiliations must be quoted) -->
affiliations:
- name: Emergent Methods LLC, Arvada Colorado, 80005, USA
index: 1
- name: Freqtrade open source project
index: 2
date: October 2022
bibliography: paper.bib
---
# Statement of need
Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`), has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citizen scientists" to use their basic Python skills for data-exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data-collection, storage, and handling presents a disparate challenge. [`FreqAI`](https://www.freqtrade.io/en/latest/freqai/) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data.
# Summary
[`FreqAI`](https://www.freqtrade.io/en/latest/freqai/) evolved from a desire to test and compare a range of adaptive time-series forecasting methods on chaotic data. Cryptocurrency markets provide a unique data source since they are operational 24/7 and the data is freely available via a variety of open-sourced [exchange APIs](https://docs.ccxt.com/en/latest/manual.html#exchange-structure). Luckily, an existing open-source software, [`Freqtrade`](https://www.freqtrade.io/en/stable/), had already matured under a range of talented developers to support robust data collection/storage, as well as robust live environmental interactions for standard algorithmic trading. `Freqtrade` also provides a set of data analysis/visualization tools for the evaluation of historical performance as well as live environmental feedback. `FreqAI` builds on top of `Freqtrade` to include a user-friendly well tested interface for integrating external machine learning libraries for adaptive time-series forecasting. Beyond enabling the integration of existing libraries, `FreqAI` hosts a range of custom algorithms and methodologies aimed at improving computational and predictive performances. Thus, `FreqAI` contains a range of unique features which can be easily tested in combination with all the existing Python-accessible machine learning libraries to generate novel research on live and historical data.
The high-level overview of the software is depicted in Figure 1.
![freqai-algo](assets/freqai_algo.jpg)
*Abstracted overview of FreqAI algorithm*
## Connecting machine learning libraries
Although the `FreqAI` framework is designed to accommodate any Python library in the "Model training" and "Feature set engineering" portions of the software (Figure 1), it already boasts a wide range of well documented examples based on various combinations of:
* scikit-learn [@scikit-learn], Catboost [@catboost], LightGBM [@lightgbm], XGBoost [@xgboost], stable_baselines3 [@stable-baselines3], openai gym [@openai], tensorflow [@tensorflow], pytorch [@pytorch], Scipy [@scipy], Numpy [@numpy], and pandas [@pandas].
These mature projects contain a wide range of peer-reviewed and industry standard methods, including:
* Regression, Classification, Neural Networks, Reinforcement Learning, Support Vector Machines, Principal Component Analysis, point clustering, and much more.
which are all leveraged in `FreqAI` for users to use as templates or extend with their own methods.
## Furnishing novel methods and features
Beyond the industry standard methods available through external libraries - `FreqAI` includes novel methods which are not available anywhere else in the open-source (or scientific) world. For example, `FreqAI` provides :
* a custom algorithm/methodology for adaptive modeling details [here](https://www.freqtrade.io/en/stable/freqai/#general-approach) and [here](https://www.freqtrade.io/en/stable/freqai-developers/#project-architecture)
* rapid and self-monitored feature engineering tools, details [here](https://www.freqtrade.io/en/stable/freqai-feature-engineering/#feature-engineering)
* unique model features/indicators, such as the [inlier metric](https://www.freqtrade.io/en/stable/freqai-feature-engineering/#inlier-metric)
* optimized data collection/storage algorithms, all code shown [here](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/freqai/data_drawer.py)
* safely integrated outlier detection methods, details [here](https://www.freqtrade.io/en/stable/freqai-feature-engineering/#outlier-detection)
* websocket communicated forecasts, details [here](https://www.freqtrade.io/en/stable/producer-consumer/)
Of particular interest for researchers, `FreqAI` provides the option of large scale experimentation via an optimized [websocket communications interface](https://www.freqtrade.io/en/stable/producer-consumer/).
## Optimizing the back-end
`FreqAI` aims to make it simple for users to combine all the above tools to run studies based in two distinct modules:
* backtesting studies
* live-deployments
Both of these modules and their respective data management systems are built on top of [`Freqtrade`](https://www.freqtrade.io/en/latest/), a mature and actively developed cryptocurrency trading software. This means that `FreqAI` benefits from a wide range of tangential/disparate feature developments such as:
* FreqUI, a graphical interface for backtesting and live monitoring
* telegram control
* robust database handling
* futures/leverage trading
* dollar cost averaging
* trading strategy handling
* a variety of free data sources via [CCXT](https://docs.ccxt.com/en/latest/manual.html#exchange-structure) (FTX, Binance, Kucoin etc.)
These features derive from a strong external developer community that shares in the benefit and stability of a communal CI (Continuous Integration) system. Beyond the developer community, `FreqAI` benefits strongly from the userbase of `Freqtrade`, where most `FreqAI` beta-testers/developers originated. This symbiotic relationship between `Freqtrade` and `FreqAI` ignited a thoroughly tested [`beta`](https://github.com/freqtrade/freqtrade/pull/6832), which demanded a four month beta and [comprehensive documentation](https://www.freqtrade.io/en/latest/freqai/) containing:
* numerous example scripts
* a full parameter table
* methodological descriptions
* high-resolution diagrams/figures
* detailed parameter setting recommendations
## Providing a reproducible foundation for researchers
`FreqAI` provides an extensible, robust, framework for researchers and citizen data scientists. The `FreqAI` sandbox enables rapid conception and testing of exotic hypotheses. From a research perspective, `FreqAI` handles the multitude of logistics associated with live deployments, historical backtesting, and feature engineering. With `FreqAI`, researchers can focus on their primary interests of feature engineering and hypothesis testing rather than figuring out how to collect and handle data. Further - the well maintained and easily installed open-source framework of `FreqAI` enables reproducible scientific studies. This reproducibility component is essential to general scientific advancement in time-series forecasting for chaotic systems.
# Technical details
Typical users configure `FreqAI` via two files:
1. A `configuration` file (`--config`) which provides access to the full parameter list available [here](https://www.freqtrade.io/en/latest/freqai/):
* control high-level feature engineering
* customize adaptive modeling techniques
* set any model training parameters available in third-party libraries
* manage adaptive modeling parameters (retrain frequency, training window size, continual learning, etc.)
2. A strategy file (`--strategy`) where users:
* list of the base training features
* set standard technical-analysis strategies
* control trade entry/exit criteria
With these two files, most users can exploit a wide range of pre-existing integrations in `Catboost` and 7 other libraries with a simple command:
```
freqtrade trade --config config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel CatboostRegressor
```
Advanced users will edit one of the existing `--freqaimodel` files, which are simply an children of the `IFreqaiModel` (details below). Within these files, advanced users can customize training procedures, prediction procedures, outlier detection methods, data preparation, data saving methods, etc. This is all configured in a way where they can customize as little or as much as they want. This flexible customization is owed to the foundational architecture in `FreqAI`, which is comprised of three distinct Python objects:
* `IFreqaiModel`
* A singular long-lived object containing all the necessary logic to collect data, store data, process data, engineer features, run training, and inference models.
* `FreqaiDataKitchen`
* A short-lived object which is uniquely created for each asset/model. Beyond metadata, it also contains a variety of data processing tools.
* `FreqaiDataDrawer`
* Singular long-lived object containing all the historical predictions, models, and save/load methods.
These objects interact with one another with one goal in mind - to provide a clean data set to machine learning experts/enthusiasts at the user endpoint. These power-users interact with an inherited `IFreqaiModel` that allows them to dig as deep or as shallow as they wish into the inheritence tree. Typical power-users focus their efforts on customizing training procedures and testing exotic functionalities available in third-party libraries. Thus, power-users are freed from the algorithmic weight associated with data management, and can instead focus their energy on testing creative hypotheses. Meanwhile, some users choose to override deeper functionalities within `IFreqaiModel` to help them craft unique data structures and training procedures.
The class structure and algorithmic details are depicted in the following diagram:
![image](assets/freqai_algorithm-diagram.jpg)
*Class diagram summarizing object interactions in FreqAI*
# Online documentation
The documentation for [`FreqAI`](https://www.freqtrade.io/en/latest/freqai/) is available online at [https://www.freqtrade.io/en/latest/freqai/](https://www.freqtrade.io/en/latest/freqai/) and covers a wide range of materials:
* Quick-start with a single command and example files - (beginners)
* Introduction to the feature engineering interface and basic configurations - (intermediate users)
* Parameter table with indepth descriptions and default parameter setting recommendations - (intermediate users)
* Data analysis and post-processing - (advanced users)
* Methodological considerations complemented by high resolution figures - (advanced users)
* Instructions for integrating third party machine learning libraries into custom prediction models - (advanced users)
* Software architectural description with class diagram - (developers)
* File structure descriptions - (developers)
The docs direct users to a variety of pre-made examples which integrate `Catboost`, `LightGBM`, `XGBoost`, `Sklearn`, `stable_baselines3`, `torch`, `tensorflow`. Meanwhile, developers will also find thorough docstrings and type hinting throughout the source code to aid in code readability and customization.
`FreqAI` also benefits from a strong support network of users and developers on the [`Freqtrade` discord](https://discord.gg/w6nDM6cM4y) as well as on the [`FreqAI` discord](https://discord.gg/xE4RMg4QYw). Within the `FreqAI` discord, users will find a deep and easily searched knowledge base containing common errors. But more importantly, users in the `FreqAI` discord share anectdotal and quantitative observations which compare performance between various third-party libraries and methods.
# State of the field
There are two other open-source tools which are geared toward helping users build models for time-series forecasts on market based data. However, each of these tools suffer from a non-generalized frameworks that do not permit comparison of methods and libraries. Additionally, they do not permit easy live-deployments or adaptive-modeling methods. For example, two open-sourced projects called [`tensortrade`](https://tensortradex.readthedocs.io/en/latest/) [@tensortrade] and [`FinRL`](https://github.com/AI4Finance-Foundation/FinRL) [@finrl] limit users to the exploration of reinforcement learning on historical data. These softwares also do not provide robust live deployments, they do not furnish novel feature engineering algorithms, and they do not provide custom data analysis tools. `FreqAI` fills the gap.
# On-going research
Emergent Methods, based in Arvada CO, is actively using `FreqAI` to perform large scale experiments aimed at comparing machine learning libraries in live and historical environments. Past projects include backtesting parametric sweeps, while active projects include a 3 week live deployment comparison between `CatboostRegressor`, `LightGBMRegressor`, and `XGBoostRegressor`. Results from these studies are planned for submission to scientific journals as well as more general data science blogs (e.g. Medium).
# Installing and running `FreqAI`
`FreqAI` is automatically installed with `Freqtrade` using the following commands on linux systems:
```
git clone git@github.com:freqtrade/freqtrade.git
cd freqtrade
./setup.sh -i
```
However, `FreqAI` also benefits from `Freqtrade` docker distributions, and can be run with docker by pulling the stable or develop images from `Freqtrade` distributions.
# Funding sources
[`FreqAI`](https://www.freqtrade.io/en/latest/freqai/) has had no official sponsors, and is entirely grass roots. All donations into the project (e.g. the GitHub sponsor system) are kept inside the project to help support development of open-sourced and communally beneficial features.
# Acknowledgements
We would like to acknowledge various beta testers of `FreqAI`:
- Longlong Yu (lolongcovas)
- Richárd Józsa (richardjozsa)
- Juha Nykänen (suikula)
- Emre Suzen (aemr3)
- Salah Lamkadem (ikonx)
As well as various `Freqtrade` [developers](https://github.com/freqtrade/freqtrade/graphs/contributors) maintaining tangential, yet essential, modules.
# References

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@@ -18,31 +18,31 @@ freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_nam
```
This will tell freqtrade to output a pickled dictionary of strategy, pairs and corresponding
DataFrame of the candles that resulted in entry and exit signals.
Depending on how many entries your strategy makes, this file may get quite large, so periodically check your `user_data/backtest_results` folder to delete old exports.
DataFrame of the candles that resulted in buy signals. Depending on how many buys your strategy
makes, this file may get quite large, so periodically check your `user_data/backtest_results`
folder to delete old exports.
Before running your next backtest, make sure you either delete your old backtest results or run
backtesting with the `--cache none` option to make sure no cached results are used.
If all goes well, you should now see a `backtest-result-{timestamp}_signals.pkl` and `backtest-result-{timestamp}_exited.pkl` files in the `user_data/backtest_results` folder.
If all goes well, you should now see a `backtest-result-{timestamp}_signals.pkl` file in the
`user_data/backtest_results` folder.
To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-analysis` command
with `--analysis-groups` option provided with space-separated arguments:
with `--analysis-groups` option provided with space-separated arguments (default `0 1 2`):
``` bash
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4 5
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4
```
This command will read from the last backtesting results. The `--analysis-groups` option is
used to specify the various tabular outputs showing the profit of each group or trade,
used to specify the various tabular outputs showing the profit fo each group or trade,
ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4):
* 0: overall winrate and profit summary by enter_tag
* 1: profit summaries grouped by enter_tag
* 2: profit summaries grouped by enter_tag and exit_tag
* 3: profit summaries grouped by pair and enter_tag
* 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
* 5: profit summaries grouped by exit_tag
More options are available by running with the `-h` option.
@@ -100,119 +100,3 @@ freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 2 --enter-re
The indicators have to be present in your strategy's main DataFrame (either for your main
timeframe or for informative timeframes) otherwise they will simply be ignored in the script
output.
!!! Note "Indicator List"
The indicator values will be displayed for both entry and exit points. If `--indicator-list all` is specified,
only the indicators at the entry point will be shown to avoid excessively large lists, which could occur depending on the strategy.
There are a range of candle and trade-related fields that are included in the analysis so are
automatically accessible by including them on the indicator-list, and these include:
- **open_date :** trade open datetime
- **close_date :** trade close datetime
- **min_rate :** minimum price seen throughout the position
- **max_rate :** maximum price seen throughout the position
- **open :** signal candle open price
- **close :** signal candle close price
- **high :** signal candle high price
- **low :** signal candle low price
- **volume :** signal candle volume
- **profit_ratio :** trade profit ratio
- **profit_abs :** absolute profit return of the trade
#### Sample Output for Indicator Values
```bash
freqtrade backtesting-analysis -c user_data/config.json --analysis-groups 0 --indicator-list chikou_span tenkan_sen
```
In this example,
we aim to display the `chikou_span` and `tenkan_sen` indicator values at both the entry and exit points of trades.
A sample output for indicators might look like this:
| pair | open_date | enter_reason | exit_reason | chikou_span (entry) | tenkan_sen (entry) | chikou_span (exit) | tenkan_sen (exit) |
|-----------|---------------------------|--------------|-------------|---------------------|--------------------|--------------------|-------------------|
| DOGE/USDT | 2024-07-06 00:35:00+00:00 | | exit_signal | 0.105 | 0.106 | 0.105 | 0.107 |
| BTC/USDT | 2024-08-05 14:20:00+00:00 | | roi | 54643.440 | 51696.400 | 54386.000 | 52072.010 |
As shown in the table, `chikou_span (entry)` represents the indicator value at the time of trade entry,
while `chikou_span (exit)` reflects its value at the time of exit.
This detailed view of indicator values enhances the analysis.
The `(entry)` and `(exit)` suffixes are added to indicators
to distinguish the values at the entry and exit points of the trade.
!!! Note "Trade-wide Indicators"
Certain trade-wide indicators do not have the `(entry)` or `(exit)` suffix. These indicators include: `pair`, `stake_amount`,
`max_stake_amount`, `amount`, `open_date`, `close_date`, `open_rate`, `close_rate`, `fee_open`, `fee_close`, `trade_duration`,
`profit_ratio`, `profit_abs`, `exit_reason`,`initial_stop_loss_abs`, `initial_stop_loss_ratio`, `stop_loss_abs`, `stop_loss_ratio`,
`min_rate`, `max_rate`, `is_open`, `enter_tag`, `leverage`, `is_short`, `open_timestamp`, `close_timestamp` and `orders`
#### Filtering Indicators Based on Entry or Exit Signals
The `--indicator-list` option, by default, displays indicator values for both entry and exit signals. To filter the indicator values exclusively for entry signals, you can use the `--entry-only` argument. Similarly, to display indicator values only at exit signals, use the `--exit-only` argument.
Example: Display indicator values at entry signals:
```bash
freqtrade backtesting-analysis -c user_data/config.json --analysis-groups 0 --indicator-list chikou_span tenkan_sen --entry-only
```
Example: Display indicator values at exit signals:
```bash
freqtrade backtesting-analysis -c user_data/config.json --analysis-groups 0 --indicator-list chikou_span tenkan_sen --exit-only
```
!!! note
When using these filters, the indicator names will not be suffixed with `(entry)` or `(exit)`.
### Filtering the trade output by date
To show only trades between dates within your backtested timerange, supply the usual `timerange` option in `YYYYMMDD-[YYYYMMDD]` format:
```
--timerange : Timerange to filter output trades, start date inclusive, end date exclusive. e.g. 20220101-20221231
```
For example, if your backtest timerange was `20220101-20221231` but you only want to output trades in January:
```bash
freqtrade backtesting-analysis -c <config.json> --timerange 20220101-20220201
```
### Printing out rejected signals
Use the `--rejected-signals` option to print out rejected signals.
```bash
freqtrade backtesting-analysis -c <config.json> --rejected-signals
```
### Writing tables to CSV
Some of the tabular outputs can become large, so printing them out to the terminal is not preferable.
Use the `--analysis-to-csv` option to disable printing out of tables to standard out and write them to CSV files.
```bash
freqtrade backtesting-analysis -c <config.json> --analysis-to-csv
```
By default this will write one file per output table you specified in the `backtesting-analysis` command, e.g.
```bash
freqtrade backtesting-analysis -c <config.json> --analysis-to-csv --rejected-signals --analysis-groups 0 1
```
This will write to `user_data/backtest_results`:
* rejected_signals.csv
* group_0.csv
* group_1.csv
To override where the files will be written, also specify the `--analysis-csv-path` option.
```bash
freqtrade backtesting-analysis -c <config.json> --analysis-to-csv --analysis-csv-path another/data/path/
```

View File

@@ -30,18 +30,11 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
"""
@staticmethod
def hyperopt_loss_function(
*,
results: DataFrame,
trade_count: int,
min_date: datetime,
max_date: datetime,
config: Config,
processed: dict[str, DataFrame],
backtest_stats: dict[str, Any],
starting_balance: float,
**kwargs,
) -> float:
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
config: Config, processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results
This is the legacy algorithm (used until now in freqtrade).
@@ -71,7 +64,6 @@ Currently, the arguments are:
* `config`: Config object used (Note: Not all strategy-related parameters will be updated here if they are part of a hyperopt space).
* `processed`: Dict of Dataframes with the pair as keys containing the data used for backtesting.
* `backtest_stats`: Backtesting statistics using the same format as the backtesting file "strategy" substructure. Available fields can be seen in `generate_strategy_stats()` in `optimize_reports.py`.
* `starting_balance`: Starting balance used for backtesting.
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.
@@ -83,11 +75,9 @@ This function needs to return a floating point number (`float`). Smaller numbers
## Overriding pre-defined spaces
To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`, `max_open_trades_space`), define a nested class called Hyperopt and define the required spaces as follows:
To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`), define a nested class called Hyperopt and define the required spaces as follows:
```python
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
# Define a custom stoploss space.
@@ -104,39 +94,6 @@ class MyAwesomeStrategy(IStrategy):
SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
]
def generate_roi_table(params: Dict) -> dict[int, float]:
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
def trailing_space() -> List[Dimension]:
# All parameters here are mandatory, you can only modify their type or the range.
return [
# Fixed to true, if optimizing trailing_stop we assume to use trailing stop at all times.
Categorical([True], name='trailing_stop'),
SKDecimal(0.01, 0.35, decimals=3, 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.
SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]
# Define a custom max_open_trades space
def max_open_trades_space(self) -> List[Dimension]:
return [
Integer(-1, 10, name='max_open_trades'),
]
```
!!! Note
@@ -144,7 +101,7 @@ class MyAwesomeStrategy(IStrategy):
### Dynamic parameters
Parameters can also be defined dynamically, but must be available to the instance once the [`bot_start()` callback](strategy-callbacks.md#bot-start) has been called.
Parameters can also be defined dynamically, but must be available to the instance once the * [`bot_start()` callback](strategy-callbacks.md#bot-start) has been called.
``` python

View File

@@ -1,153 +0,0 @@
# Orderflow data
This guide walks you through utilizing public trade data for advanced orderflow analysis in Freqtrade.
!!! Warning "Experimental Feature"
The orderflow feature is currently in beta and may be subject to changes in future releases. Please report any issues or feedback on the [Freqtrade GitHub repository](https://github.com/freqtrade/freqtrade/issues).
It's also currently not been tested with freqAI - and combining these two features is considered out of scope at this point.
!!! Warning "Performance"
Orderflow requires raw trades data. This data is rather large, and can cause a slow initial startup, when freqtrade needs to download the trades data for the last X candles. Additionally, enabling this feature will cause increased memory usage. Please ensure to have sufficient resources available.
## Getting Started
### Enable Public Trades
In your `config.json` file, set the `use_public_trades` option to true under the `exchange` section.
```json
"exchange": {
...
"use_public_trades": true,
}
```
### Configure Orderflow Processing
Define your desired settings for orderflow processing within the orderflow section of config.json. Here, you can adjust factors like:
- `cache_size`: How many previous orderflow candles are saved into cache instead of calculated every new candle
- `max_candles`: Filter how many candles would you like to get trades data for.
- `scale`: This controls the price bin size for the footprint chart.
- `stacked_imbalance_range`: Defines the minimum consecutive imbalanced price levels required for consideration.
- `imbalance_volume`: Filters out imbalances with volume below this threshold.
- `imbalance_ratio`: Filters out imbalances with a ratio (difference between ask and bid volume) lower than this value.
```json
"orderflow": {
"cache_size": 1000,
"max_candles": 1500,
"scale": 0.5,
"stacked_imbalance_range": 3, // needs at least this amount of imbalance next to each other
"imbalance_volume": 1, // filters out below
"imbalance_ratio": 3 // filters out ratio lower than
},
```
## Downloading Trade Data for Backtesting
To download historical trade data for backtesting, use the --dl-trades flag with the freqtrade download-data command.
```bash
freqtrade download-data -p BTC/USDT:USDT --timerange 20230101- --trading-mode futures --timeframes 5m --dl-trades
```
!!! Warning "Data availability"
Not all exchanges provide public trade data. For supported exchanges, freqtrade will warn you if public trade data is not available if you start downloading data with the `--dl-trades` flag.
## Accessing Orderflow Data
Once activated, several new columns become available in your dataframe:
``` python
dataframe["trades"] # Contains information about each individual trade.
dataframe["orderflow"] # Represents a footprint chart dict (see below)
dataframe["imbalances"] # Contains information about imbalances in the order flow.
dataframe["bid"] # Total bid volume
dataframe["ask"] # Total ask volume
dataframe["delta"] # Difference between ask and bid volume.
dataframe["min_delta"] # Minimum delta within the candle
dataframe["max_delta"] # Maximum delta within the candle
dataframe["total_trades"] # Total number of trades
dataframe["stacked_imbalances_bid"] # List of price levels of stacked bid imbalance range beginnings
dataframe["stacked_imbalances_ask"] # List of price levels of stacked ask imbalance range beginnings
```
You can access these columns in your strategy code for further analysis. Here's an example:
``` python
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Calculating cumulative delta
dataframe["cum_delta"] = cumulative_delta(dataframe["delta"])
# Accessing total trades
total_trades = dataframe["total_trades"]
...
def cumulative_delta(delta: Series):
cumdelta = delta.cumsum()
return cumdelta
```
### Footprint chart (`dataframe["orderflow"]`)
This column provides a detailed breakdown of buy and sell orders at different price levels, offering valuable insights into order flow dynamics. The `scale` parameter in your configuration determines the price bin size for this representation
The `orderflow` column contains a dict with the following structure:
``` output
{
"price": {
"bid_amount": 0.0,
"ask_amount": 0.0,
"bid": 0,
"ask": 0,
"delta": 0.0,
"total_volume": 0.0,
"total_trades": 0
}
}
```
#### Orderflow column explanation
- key: Price bin - binned at `scale` intervals
- `bid_amount`: Total volume bought at each price level.
- `ask_amount`: Total volume sold at each price level.
- `bid`: Number of buy orders at each price level.
- `ask`: Number of sell orders at each price level.
- `delta`: Difference between ask and bid volume at each price level.
- `total_volume`: Total volume (ask amount + bid amount) at each price level.
- `total_trades`: Total number of trades (ask + bid) at each price level.
By leveraging these features, you can gain valuable insights into market sentiment and potential trading opportunities based on order flow analysis.
### Raw trades data (`dataframe["trades"]`)
List with the individual trades that occurred during the candle. This data can be used for more granular analysis of order flow dynamics.
Each individual entry contains a dict with the following keys:
- `timestamp`: Timestamp of the trade.
- `date`: Date of the trade.
- `price`: Price of the trade.
- `amount`: Volume of the trade.
- `side`: Buy or sell.
- `id`: Unique identifier for the trade.
- `cost`: Total cost of the trade (price * amount).
### Imbalances (`dataframe["imbalances"]`)
This column provides a dict with information about imbalances in the order flow. An imbalance occurs when there is a significant difference between the ask and bid volume at a given price level.
Each row looks as follows - with price as index, and the corresponding bid and ask imbalance values as columns
``` output
{
"price": {
"bid_imbalance": False,
"ask_imbalance": False
}
}
```

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@@ -114,46 +114,8 @@ services:
--strategy SampleStrategy
```
You can use whatever naming convention you want, freqtrade1 and 2 are arbitrary. Note, that you will need to use different database files, port mappings and telegram configurations for each instance, as mentioned above.
## Use a different database system
Freqtrade is using SQLAlchemy, which supports multiple different database systems. As such, a multitude of database systems should be supported.
Freqtrade does not depend or install any additional database driver. Please refer to the [SQLAlchemy docs](https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls) on installation instructions for the respective database systems.
The following systems have been tested and are known to work with freqtrade:
* sqlite (default)
* PostgreSQL
* MariaDB
!!! Warning
By using one of the below database systems, you acknowledge that you know how to manage such a system. The freqtrade team will not provide any support with setup or maintenance (or backups) of the below database systems.
### PostgreSQL
Installation:
`pip install psycopg2-binary`
Usage:
`... --db-url postgresql+psycopg2://<username>:<password>@localhost:5432/<database>`
Freqtrade will automatically create the tables necessary upon startup.
If you're running different instances of Freqtrade, you must either setup one database per Instance or use different users / schemas for your connections.
### MariaDB / MySQL
Freqtrade supports MariaDB by using SQLAlchemy, which supports multiple different database systems.
Installation:
`pip install pymysql`
Usage:
`... --db-url mysql+pymysql://<username>:<password>@localhost:3306/<database>`
## Configure the bot running as a systemd service
@@ -230,7 +192,7 @@ $RepeatedMsgReduction on
### Logging to journald
This needs the `cysystemd` python package installed as dependency (`pip install cysystemd`), which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
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 `--logfile` command line option with the value in the following format:

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@@ -7,7 +7,103 @@ To learn how to get data for the pairs and exchange you're interested in, head o
## Backtesting command reference
--8<-- "commands/backtesting.md"
```
usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-i TIMEFRAME]
[--timerange TIMERANGE]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[-p PAIRS [PAIRS ...]] [--eps] [--dmmp]
[--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET]
[--timeframe-detail TIMEFRAME_DETAIL]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
[--export {none,trades,signals}]
[--export-filename PATH]
[--breakdown {day,week,month} [{day,week,month} ...]]
[--cache {none,day,week,month}]
optional arguments:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME
Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--data-format-ohlcv {json,jsongz,hdf5}
Storage format for downloaded candle (OHLCV) data.
(default: `json`).
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--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).
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
number).
--enable-protections, --enableprotections
Enable protections for backtesting.Will slow
backtesting down by a considerable amount, but will
include configured protections
--dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
Starting balance, used for backtesting / hyperopt and
dry-runs.
--timeframe-detail TIMEFRAME_DETAIL
Specify detail timeframe for backtesting (`1m`, `5m`,
`30m`, `1h`, `1d`).
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
Provide a space-separated list of strategies to
backtest. Please note that timeframe needs to be set
either in config or via command line. When using this
together with `--export trades`, the strategy-name is
injected into the filename (so `backtest-data.json`
becomes `backtest-data-SampleStrategy.json`
--export {none,trades,signals}
Export backtest results (default: trades).
--export-filename PATH, --backtest-filename PATH
Use this filename for backtest results.Requires
`--export` to be set as well. Example: `--export-filen
ame=user_data/backtest_results/backtest_today.json`
--breakdown {day,week,month} [{day,week,month} ...]
Show backtesting breakdown per [day, week, month].
--cache {none,day,week,month}
Load a cached backtest result no older than specified
age (default: day).
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.
```
## Test your strategy with Backtesting
@@ -74,11 +170,11 @@ freqtrade backtesting --strategy AwesomeStrategy --dry-run-wallet 1000
Using a different on-disk historical candle (OHLCV) data source
Assume you downloaded the history data from the Binance exchange and kept it in the `user_data/data/binance-20180101` directory.
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:
```bash
freqtrade backtesting --strategy AwesomeStrategy --datadir user_data/data/binance-20180101
freqtrade backtesting --strategy AwesomeStrategy --datadir user_data/data/bittrex-20180101
```
---
@@ -156,48 +252,46 @@ The most important in the backtesting is to understand the result.
A backtesting result will look like that:
```
================================================ BACKTESTING REPORT =================================================
| Pair | Trades | Avg Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins Draws Loss Win% |
|----------+--------+----------------+------------------+----------------+--------------+--------------------------|
| ADA/BTC | 35 | -0.11 | -0.00019428 | -1.94 | 4:35:00 | 14 0 21 40.0 |
| ARK/BTC | 11 | -0.41 | -0.00022647 | -2.26 | 2:03:00 | 3 0 8 27.3 |
| BTS/BTC | 32 | 0.31 | 0.00048938 | 4.89 | 5:05:00 | 18 0 14 56.2 |
| DASH/BTC | 13 | -0.08 | -0.00005343 | -0.53 | 4:39:00 | 6 0 7 46.2 |
| ENG/BTC | 18 | 1.36 | 0.00122807 | 12.27 | 2:50:00 | 8 0 10 44.4 |
| EOS/BTC | 36 | 0.08 | 0.00015304 | 1.53 | 3:34:00 | 16 0 20 44.4 |
| ETC/BTC | 26 | 0.37 | 0.00047576 | 4.75 | 6:14:00 | 11 0 15 42.3 |
| ETH/BTC | 33 | 0.30 | 0.00049856 | 4.98 | 7:31:00 | 16 0 17 48.5 |
| IOTA/BTC | 32 | 0.03 | 0.00005444 | 0.54 | 3:12:00 | 14 0 18 43.8 |
| LSK/BTC | 15 | 1.75 | 0.00131413 | 13.13 | 2:58:00 | 6 0 9 40.0 |
| LTC/BTC | 32 | -0.04 | -0.00006886 | -0.69 | 4:49:00 | 11 0 21 34.4 |
| NANO/BTC | 17 | 1.26 | 0.00107058 | 10.70 | 1:55:00 | 10 0 7 58.5 |
| NEO/BTC | 23 | 0.82 | 0.00094936 | 9.48 | 2:59:00 | 10 0 13 43.5 |
| REQ/BTC | 9 | 1.17 | 0.00052734 | 5.27 | 3:47:00 | 4 0 5 44.4 |
| XLM/BTC | 16 | 1.22 | 0.00097800 | 9.77 | 3:15:00 | 7 0 9 43.8 |
| XMR/BTC | 23 | -0.18 | -0.00020696 | -2.07 | 5:30:00 | 12 0 11 52.2 |
| XRP/BTC | 35 | 0.66 | 0.00114897 | 11.48 | 3:49:00 | 12 0 23 34.3 |
| ZEC/BTC | 22 | -0.46 | -0.00050971 | -5.09 | 2:22:00 | 7 0 15 31.8 |
| TOTAL | 429 | 0.36 | 0.00762792 | 76.20 | 4:12:00 | 186 0 243 43.4 |
============================================= LEFT OPEN TRADES REPORT =============================================
| Pair | Trades | Avg Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Win Draw Loss Win% |
|----------+---------+----------------+------------------+----------------+----------------+---------------------|
| ADA/BTC | 1 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 0 0 100 |
| LTC/BTC | 1 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 0 0 100 |
| TOTAL | 2 | 0.78 | 0.00007855 | 0.78 | 4:00:00 | 2 0 0 100 |
==================== EXIT REASON STATS ====================
========================================================= BACKTESTING REPORT =========================================================
| Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins Draws Loss Win% |
|:---------|--------:|---------------:|---------------:|-----------------:|---------------:|:-------------|-------------------------:|
| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 0 21 40.0 |
| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 0 8 27.3 |
| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 0 14 56.2 |
| DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 0 7 46.2 |
| ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 0 10 44.4 |
| EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 0 20 44.4 |
| ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 0 15 42.3 |
| ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 0 17 48.5 |
| IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 0 18 43.8 |
| LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 0 9 40.0 |
| LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 0 21 34.4 |
| NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 0 7 58.5 |
| NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 0 13 43.5 |
| REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 0 5 44.4 |
| XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 0 9 43.8 |
| XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 0 11 52.2 |
| XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 0 23 34.3 |
| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 0 15 31.8 |
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 0 243 43.4 |
========================================================= EXIT REASON STATS ==========================================================
| Exit Reason | Exits | Wins | Draws | Losses |
|--------------------+---------+-------+--------+---------|
|:-------------------|--------:|------:|-------:|--------:|
| trailing_stop_loss | 205 | 150 | 0 | 55 |
| stop_loss | 166 | 0 | 0 | 166 |
| exit_signal | 56 | 36 | 0 | 20 |
| force_exit | 2 | 0 | 0 | 2 |
====================================================== LEFT OPEN TRADES REPORT ======================================================
| Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Win Draw Loss Win% |
|:---------|---------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 0 0 100 |
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 0 0 100 |
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 0 0 100 |
================== SUMMARY METRICS ==================
| Metric | Value |
|-----------------------------+---------------------|
| Backtesting from | 2019-01-01 00:00:00 |
| Backtesting to | 2019-05-01 00:00:00 |
| Trading Mode | Spot |
| Max open trades | 3 |
| | |
| Total/Daily Avg Trades | 429 / 3.575 |
@@ -206,11 +300,7 @@ A backtesting result will look like that:
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| CAGR % | 460.87% |
| Sortino | 1.88 |
| Sharpe | 2.97 |
| Calmar | 6.29 |
| Profit factor | 1.11 |
| Expectancy (Ratio) | -0.15 (-0.05) |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
@@ -229,7 +319,6 @@ A backtesting result will look like that:
| Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| Max Consecutive Wins / Loss | 3 / 4 |
| Rejected Entry signals | 3089 |
| Entry/Exit Timeouts | 0 / 0 |
| Canceled Trade Entries | 34 |
@@ -263,7 +352,7 @@ here:
The bot has made `429` trades for an average duration of `4:12:00`, with a performance of `76.20%` (profit), that means it has
earned a total of `0.00762792 BTC` starting with a capital of 0.01 BTC.
The column `Avg Profit %` shows the average profit for all trades made.
The column `Avg Profit %` shows the average profit for all trades made while the column `Cum Profit %` sums up all the profits/losses.
The column `Tot Profit %` shows instead the total profit % in relation to the starting balance.
In the above results, we have a starting balance of 0.01 BTC and an absolute profit of 0.00762792 BTC - so the `Tot Profit %` will be `(0.00762792 / 0.01) * 100 ~= 76.2%`.
@@ -303,7 +392,6 @@ It contains some useful key metrics about performance of your strategy on backte
|-----------------------------+---------------------|
| Backtesting from | 2019-01-01 00:00:00 |
| Backtesting to | 2019-05-01 00:00:00 |
| Trading Mode | Spot |
| Max open trades | 3 |
| | |
| Total/Daily Avg Trades | 429 / 3.575 |
@@ -312,11 +400,7 @@ It contains some useful key metrics about performance of your strategy on backte
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| CAGR % | 460.87% |
| Sortino | 1.88 |
| Sharpe | 2.97 |
| Calmar | 6.29 |
| Profit factor | 1.11 |
| Expectancy (Ratio) | -0.15 (-0.05) |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
@@ -335,7 +419,6 @@ It contains some useful key metrics about performance of your strategy on backte
| Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| Max Consecutive Wins / Loss | 3 / 4 |
| Rejected Entry signals | 3089 |
| Entry/Exit Timeouts | 0 / 0 |
| Canceled Trade Entries | 34 |
@@ -358,25 +441,20 @@ It contains some useful key metrics about performance of your strategy on backte
- `Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
- `Max open trades`: Setting of `max_open_trades` (or `--max-open-trades`) - or number of pairs in the pairlist (whatever is lower).
- `Trading Mode`: Spot or Futures trading.
- `Total/Daily Avg Trades`: Identical to the total trades of the backtest output table / Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
- `Starting balance`: Start balance - as given by dry-run-wallet (config or command line).
- `Final balance`: Final balance - starting balance + absolute profit.
- `Absolute profit`: Profit made in stake currency.
- `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital Starting capital) / Starting capital`.
- `CAGR %`: Compound annual growth rate.
- `Sortino`: Annualized Sortino ratio.
- `Sharpe`: Annualized Sharpe ratio.
- `Calmar`: Annualized Calmar ratio.
- `Profit factor`: profit / loss.
- `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
- `Total trade volume`: Volume generated on the exchange to reach the above profit.
- `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Tot Profit %`.
- `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Cum Profit %`.
- `Best Trade` / `Worst Trade`: Biggest single winning trade and biggest single losing trade.
- `Best day` / `Worst day`: Best and worst day based on daily profit.
- `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
- `Max Consecutive Wins / Loss`: Maximum consecutive wins/losses in a row.
- `Rejected Entry signals`: Trade entry signals that could not be acted upon due to `max_open_trades` being reached.
- `Entry/Exit Timeouts`: Entry/exit orders which did not fill (only applicable if custom pricing is used).
- `Canceled Trade Entries`: Number of trades that have been canceled by user request via `adjust_entry_price`.
@@ -429,30 +507,28 @@ To save time, by default backtest will reuse a cached result from within the las
### Further backtest-result analysis
To further analyze your backtest results, freqtrade will export the trades to file by default.
You can then load the trades to perform further analysis as shown in the [data analysis](strategy_analysis_example.md#load-backtest-results-to-pandas-dataframe) backtesting section.
To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
You can then load the trades to perform further analysis as shown in the [data analysis](data-analysis.md#backtesting) backtesting section.
## Assumptions made by backtesting
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
- Exchange [trading limits](#trading-limits-in-backtesting) are respected
- Entries happen at open-price unless a custom price logic has been specified
- All orders are filled at the requested price (no slippage) as long as the price is within the candle's high/low range
- Entries happen at open-price
- All orders are filled at the requested price (no slippage, no unfilled orders)
- Exit-signal exits happen at open-price of the consecutive candle
- Exits free their trade slot for a new trade with a different pair
- Exit-signal is favored over Stoploss, because exit-signals are assumed to trigger on candle's open
- ROI
- Exits are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the exit will be at 2%)
- Exits are never "below the candle", so a ROI of 2% may result in a exit at 2.4% if low was at 2.4% profit
- ROI entries which came into effect on the triggering candle (e.g. `120: 0.02` for 1h candles, from `60: 0.05`) will use the candle's open as exit rate
- Force-exits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
- exits are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the exit will be at 2%)
- exits are never "below the candle", so a ROI of 2% may result in a exit at 2.4% if low was at 2.4% profit
- Forceexits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
- Stoploss exits happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
- Stoploss is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` exit reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes
- Low happens before high for stoploss, protecting capital first
- Trailing stoploss
- Trailing Stoploss is only adjusted if it's below the candle's low (otherwise it would be triggered)
- On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point. This rule is NOT applicable to custom-stoploss scenarios, since there's no information about the stoploss logic available.
- On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point
- High happens first - adjusting stoploss
- Low uses the adjusted stoploss (so exits with large high-low difference are backtested correctly)
- ROI applies before trailing-stop, ensuring profits are "top-capped" at ROI if both ROI and trailing stop applies
@@ -462,7 +538,6 @@ Since backtesting lacks some detailed information about what happens within a ca
- Stoploss
- ROI
- Trailing stoploss
- Position reversals (futures only) happen if an entry signal in the other direction than the closing trade triggers at the candle the existing trade closes.
Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.
Also, keep in mind that past results don't guarantee future success.
@@ -471,13 +546,13 @@ In addition to the above assumptions, strategy authors should carefully read the
### Trading limits in backtesting
Exchanges have certain trading limits, like minimum (and maximum) base currency, or minimum/maximum stake (quote) currency.
These limits are usually listed in the exchange documentation as "trading rules" or similar and can be quite different between different pairs.
Exchanges have certain trading limits, like minimum base currency, or minimum stake (quote) currency.
These limits are usually listed in the exchange documentation as "trading rules" or similar.
Backtesting (as well as live and dry-run) does honor these limits, and will ensure that a stoploss can be placed below this value - so the value will be slightly higher than what the exchange specifies.
Freqtrade has however no information about historic limits.
This can lead to situations where trading-limits are inflated by using a historic price, resulting in minimum amounts > 50\$.
This can lead to situations where trading-limits are inflated by using a historic price, resulting in minimum amounts > 50$.
For example:
@@ -496,7 +571,7 @@ These precision values are based on current exchange limits (as described in the
## Improved backtest accuracy
One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or vice-versa?).
One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or viceversa?).
So assuming you run backtesting with a 1h timeframe, there will be 4 prices for that candle (Open, High, Low, Close).
While backtesting does take some assumptions (read above) about this - this can never be perfect, and will always be biased in one way or the other.
@@ -508,13 +583,7 @@ To utilize this, you can append `--timeframe-detail 5m` to your regular backtest
freqtrade backtesting --strategy AwesomeStrategy --timeframe 1h --timeframe-detail 5m
```
This will load 1h data (the main timeframe) as well as 5m data (detail timeframe) for the selected timerange.
The strategy will be analyzed with the 1h timeframe.
Candles where activity may take place (there's an active signal, the pair is in a trade) are evaluated at the 5m timeframe.
This will allow for a more accurate simulation of intra-candle movements - and can lead to different results, especially on higher timeframes.
Entries will generally still happen at the main candle's open, however freed trade slots may be freed earlier (if the exit signal is triggered on the 5m candle), which can then be used for a new trade of a different pair.
This will load 1h data as well as 5m data for the timeframe. The strategy will be analyzed with the 1h timeframe - and for every "open trade candle" (candles where a trade is open) the 5m data will be used to simulate intra-candle movements.
All callback functions (`custom_exit()`, `custom_stoploss()`, ... ) will be running for each 5m candle once the trade is opened (so 12 times in the above example of 1h timeframe, and 5m detailed timeframe).
`--timeframe-detail` must be smaller than the original timeframe, otherwise backtesting will fail to start.
@@ -525,27 +594,6 @@ Also, data must be available / downloaded already.
!!! Tip
You can use this function as the last part of strategy development, to ensure your strategy is not exploiting one of the [backtesting assumptions](#assumptions-made-by-backtesting). Strategies that perform similarly well with this mode have a good chance to perform well in dry/live modes too (although only forward-testing (dry-mode) can really confirm a strategy).
??? Sample "Extreme Difference Example"
Using `--timeframe-detail` on an extreme example (all below pairs have the 10:00 candle with an entry signal) may lead to the following backtesting Trade sequence with 1 max_open_trades:
| Pair | Entry Time | Exit Time | Duration |
|------|------------|-----------| -------- |
| BTC/USDT | 2024-01-01 10:00:00 | 2021-01-01 10:05:00 | 5m |
| ETH/USDT | 2024-01-01 10:05:00 | 2021-01-01 10:15:00 | 10m |
| XRP/USDT | 2024-01-01 10:15:00 | 2021-01-01 10:30:00 | 15m |
| SOL/USDT | 2024-01-01 10:15:00 | 2021-01-01 11:05:00 | 50m |
| BTC/USDT | 2024-01-01 11:05:00 | 2021-01-01 12:00:00 | 55m |
Without timeframe-detail, this would look like:
| Pair | Entry Time | Exit Time | Duration |
|------|------------|-----------| -------- |
| BTC/USDT | 2024-01-01 10:00:00 | 2021-01-01 11:00:00 | 1h |
| BTC/USDT | 2024-01-01 11:00:00 | 2021-01-01 12:00:00 | 1h |
The difference is significant, as without detail data, only the first `max_open_trades` signals per candle are evaluated, and the trade slots are only freed at the end of the candle, allowing for a new trade to be opened at the next candle.
## Backtesting multiple strategies
To compare multiple strategies, a list of Strategies can be provided to backtesting.
@@ -553,22 +601,22 @@ To compare multiple strategies, a list of Strategies can be provided to backtest
This is limited to 1 timeframe 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, unless also `--recursive-strategy-search` is specified, where sub-directories within the strategy directory are also considered.
All listed Strategies need to be in the same directory.
``` bash
freqtrade backtesting --timerange 20180401-20180410 --timeframe 5m --strategy-list Strategy001 Strategy002 --export trades
```
This will save the results to `user_data/backtest_results/backtest-result-<datetime>.json`, including results for both `Strategy001` and `Strategy002`.
This will save the results to `user_data/backtest_results/backtest-result-<strategy>.json`, injecting the strategy-name into the target filename.
There will be an additional table comparing win/losses of the different strategies (identical to the "Total" row in the first table).
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 | Trades | Avg Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | Drawdown % |
|-------------+---------+----------------+------------------+----------------+----------------+-------+--------+--------+------------|
| Strategy1 | 429 | 0.36 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 | 45.2 |
| Strategy2 | 1487 | -0.13 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 825 | 241.68 |
=========================================================== STRATEGY SUMMARY ===========================================================================
| Strategy | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | Drawdown % |
|:------------|---------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|-----------:|
| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 | 45.2 |
| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 825 | 241.68 |
```
## Next step

View File

@@ -7,32 +7,16 @@ This page provides you some basic concepts on how Freqtrade works and operates.
* **Strategy**: Your trading strategy, telling the bot what to do.
* **Trade**: Open position.
* **Open Order**: Order which is currently placed on the exchange, and is not yet complete.
* **Pair**: Tradable pair, usually in the format of Base/Quote (e.g. `XRP/USDT` for spot, `XRP/USDT:USDT` for futures).
* **Pair**: Tradable pair, usually in the format of Base/Quote (e.g. XRP/USDT).
* **Timeframe**: Candle length to use (e.g. `"5m"`, `"1h"`, ...).
* **Indicators**: Technical indicators (SMA, EMA, RSI, ...).
* **Limit order**: Limit orders which execute at the defined limit price or better.
* **Market order**: Guaranteed to fill, may move price depending on the order size.
* **Current Profit**: Currently pending (unrealized) profit for this trade. This is mainly used throughout the bot and UI.
* **Realized Profit**: Already realized profit. Only relevant in combination with [partial exits](strategy-callbacks.md#adjust-trade-position) - which also explains the calculation logic for this.
* **Total Profit**: Combined realized and unrealized profit. The relative number (%) is calculated against the total investment in this trade.
## Fee handling
All profit calculations of Freqtrade include fees. For Backtesting / Hyperopt / Dry-run modes, the exchange default fee is used (lowest tier on the exchange). For live operations, fees are used as applied by the exchange (this includes BNB rebates etc.).
## Pair naming
Freqtrade follows the [ccxt naming convention](https://docs.ccxt.com/#/README?id=consistency-of-base-and-quote-currencies) for currencies.
Using the wrong naming convention in the wrong market will usually result in the bot not recognizing the pair, usually resulting in errors like "this pair is not available".
### Spot pair naming
For spot pairs, naming will be `base/quote` (e.g. `ETH/USDT`).
### Futures pair naming
For futures pairs, naming will be `base/quote:settle` (e.g. `ETH/USDT:USDT`).
## Bot execution logic
Starting freqtrade in dry-run or live mode (using `freqtrade trade`) will start the bot and start the bot iteration loop.
@@ -49,12 +33,10 @@ By default, the bot loop runs every few seconds (`internals.process_throttle_sec
* Call `populate_indicators()`
* Call `populate_entry_trend()`
* Call `populate_exit_trend()`
* Update trades open order state from exchange.
* Call `order_filled()` strategy callback for filled orders.
* Check timeouts for open orders.
* Calls `check_entry_timeout()` strategy callback for open entry orders.
* Calls `check_exit_timeout()` strategy callback for open exit orders.
* Calls `adjust_entry_price()` strategy callback for open entry orders.
* Check timeouts for open orders.
* Calls `check_entry_timeout()` strategy callback for open entry orders.
* Calls `check_exit_timeout()` strategy callback for open exit orders.
* Calls `adjust_entry_price()` strategy callback for open entry orders.
* Verifies existing positions and eventually places exit orders.
* Considers stoploss, ROI and exit-signal, `custom_exit()` and `custom_stoploss()`.
* Determine exit-price based on `exit_pricing` configuration setting or by using the `custom_exit_price()` callback.
@@ -75,10 +57,10 @@ This loop will be repeated again and again until the bot is stopped.
* Load historic data for configured pairlist.
* Calls `bot_start()` once.
* Calls `bot_loop_start()` once.
* Calculate indicators (calls `populate_indicators()` once per pair).
* Calculate entry / exit signals (calls `populate_entry_trend()` and `populate_exit_trend()` once per pair).
* Loops per candle simulating entry and exit points.
* Calls `bot_loop_start()` strategy callback.
* Check for Order timeouts, either via the `unfilledtimeout` configuration, or via `check_entry_timeout()` / `check_exit_timeout()` strategy callbacks.
* Calls `adjust_entry_price()` strategy callback for open entry orders.
* Check for trade entry signals (`enter_long` / `enter_short` columns).
@@ -87,15 +69,9 @@ This loop will be repeated again and again until the bot is stopped.
* In Margin and Futures mode, `leverage()` strategy callback is called to determine the desired leverage.
* Determine stake size by calling the `custom_stake_amount()` callback.
* Check position adjustments for open trades if enabled and call `adjust_trade_position()` to determine if an additional order is requested.
* Call `order_filled()` strategy callback for filled entry orders.
* Call `custom_stoploss()` and `custom_exit()` to find custom exit points.
* For exits based on exit-signal, custom-exit and partial exits: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle).
* Call `order_filled()` strategy callback for filled exit orders.
* Generate backtest report output
!!! Note
Both Backtesting and Hyperopt include exchange default Fees in the calculation. Custom fees can be passed to backtesting / hyperopt by specifying the `--fee` argument.
!!! Warning "Callback call frequency"
Backtesting will call each callback at max. once per candle (`--timeframe-detail` modifies this behavior to once per detailed candle).
Most callbacks will be called once per iteration in live (usually every ~5s) - which can cause backtesting mismatches.

View File

@@ -3,18 +3,99 @@
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 .venv/bin/activate`) before running freqtrade commands.
If you've used `setup.sh`, don't forget to activate your virtual environment (`source .env/bin/activate`) before running freqtrade commands.
!!! Warning "Up-to-date clock"
The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
## Bot commands
--8<-- "commands/main.md"
```
usage: freqtrade [-h] [-V]
{trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
...
Free, open source crypto trading bot
positional arguments:
{trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
trade Trade module.
create-userdir Create user-data directory.
new-config Create new config
new-strategy Create new strategy
download-data Download backtesting data.
convert-data Convert candle (OHLCV) data from one format to
another.
convert-trade-data Convert trade data from one format to another.
list-data List downloaded data.
backtesting Backtesting module.
edge Edge module.
hyperopt Hyperopt module.
hyperopt-list List Hyperopt results
hyperopt-show Show details of Hyperopt results
list-exchanges Print available exchanges.
list-hyperopts Print available hyperopt classes.
list-markets Print markets on exchange.
list-pairs Print pairs on exchange.
list-strategies Print available strategies.
list-timeframes Print available timeframes for the exchange.
show-trades Show trades.
test-pairlist Test your pairlist configuration.
install-ui Install FreqUI
plot-dataframe Plot candles with indicators.
plot-profit Generate plot showing profits.
webserver Webserver module.
optional arguments:
-h, --help show this help message and exit
-V, --version show program's version number and exit
```
### Bot trading commands
--8<-- "commands/trade.md"
```
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]
[--dry-run-wallet DRY_RUN_WALLET]
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).
--dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
Starting balance, used for backtesting / hyperopt and
dry-runs.
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.
```
### How to specify which configuration file be used?

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@@ -1,66 +0,0 @@
```
usage: freqtrade backtesting-analysis [-h] [-v] [--no-color] [--logfile FILE]
[-V] [-c PATH] [-d PATH]
[--userdir PATH]
[--export-filename PATH]
[--analysis-groups {0,1,2,3,4,5} [{0,1,2,3,4,5} ...]]
[--enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]]
[--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]]
[--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]]
[--entry-only] [--exit-only]
[--timerange TIMERANGE]
[--rejected-signals] [--analysis-to-csv]
[--analysis-csv-path ANALYSIS_CSV_PATH]
options:
-h, --help show this help message and exit
--export-filename PATH, --backtest-filename PATH
Use this filename for backtest results.Requires
`--export` to be set as well. Example: `--export-filen
ame=user_data/backtest_results/backtest_today.json`
--analysis-groups {0,1,2,3,4,5} [{0,1,2,3,4,5} ...]
grouping output - 0: simple wins/losses by enter tag,
1: by enter_tag, 2: by enter_tag and exit_tag, 3: by
pair and enter_tag, 4: by pair, enter_ and exit_tag
(this can get quite large), 5: by exit_tag
--enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]
Space separated list of entry signals to analyse.
Default: all. e.g. 'entry_tag_a entry_tag_b'
--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]
Space separated list of exit signals to analyse.
Default: all. e.g. 'exit_tag_a roi stop_loss
trailing_stop_loss'
--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]
Space separated list of indicators to analyse. e.g.
'close rsi bb_lowerband profit_abs'
--entry-only Only analyze entry signals.
--exit-only Only analyze exit signals.
--timerange TIMERANGE
Specify what timerange of data to use.
--rejected-signals Analyse rejected signals
--analysis-to-csv Save selected analysis tables to individual CSVs
--analysis-csv-path ANALYSIS_CSV_PATH
Specify a path to save the analysis CSVs if
--analysis-to-csv is enabled. Default:
user_data/basktesting_results/
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```

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@@ -1,36 +0,0 @@
```
usage: freqtrade backtesting-show [-h] [-v] [--no-color] [--logfile FILE] [-V]
[-c PATH] [-d PATH] [--userdir PATH]
[--export-filename PATH] [--show-pair-list]
[--breakdown {day,week,month} [{day,week,month} ...]]
options:
-h, --help show this help message and exit
--export-filename PATH, --backtest-filename PATH
Use this filename for backtest results.Requires
`--export` to be set as well. Example: `--export-filen
ame=user_data/backtest_results/backtest_today.json`
--show-pair-list Show backtesting pairlist sorted by profit.
--breakdown {day,week,month} [{day,week,month} ...]
Show backtesting breakdown per [day, week, month].
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```

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@@ -1,107 +0,0 @@
```
usage: freqtrade backtesting [-h] [-v] [--no-color] [--logfile FILE] [-V]
[-c PATH] [-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH]
[--recursive-strategy-search]
[--freqaimodel NAME] [--freqaimodel-path PATH]
[-i TIMEFRAME] [--timerange TIMERANGE]
[--data-format-ohlcv {json,jsongz,feather,parquet}]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[-p PAIRS [PAIRS ...]] [--eps]
[--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET]
[--timeframe-detail TIMEFRAME_DETAIL]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
[--export {none,trades,signals}]
[--export-filename PATH]
[--breakdown {day,week,month} [{day,week,month} ...]]
[--cache {none,day,week,month}]
[--freqai-backtest-live-models]
options:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME
Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--data-format-ohlcv {json,jsongz,feather,parquet}
Storage format for downloaded candle (OHLCV) data.
(default: `feather`).
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--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).
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
--enable-protections, --enableprotections
Enable protections for backtesting.Will slow
backtesting down by a considerable amount, but will
include configured protections
--dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
Starting balance, used for backtesting / hyperopt and
dry-runs.
--timeframe-detail TIMEFRAME_DETAIL
Specify detail timeframe for backtesting (`1m`, `5m`,
`30m`, `1h`, `1d`).
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
Provide a space-separated list of strategies to
backtest. Please note that timeframe needs to be set
either in config or via command line. When using this
together with `--export trades`, the strategy-name is
injected into the filename (so `backtest-data.json`
becomes `backtest-data-SampleStrategy.json`
--export {none,trades,signals}
Export backtest results (default: trades).
--export-filename PATH, --backtest-filename PATH
Use this filename for backtest results.Requires
`--export` to be set as well. Example: `--export-filen
ame=user_data/backtest_results/backtest_today.json`
--breakdown {day,week,month} [{day,week,month} ...]
Show backtesting breakdown per [day, week, month].
--cache {none,day,week,month}
Load a cached backtest result no older than specified
age (default: day).
--freqai-backtest-live-models
Run backtest with ready models.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir 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.
--recursive-strategy-search
Recursively search for a strategy in the strategies
folder.
--freqaimodel NAME Specify a custom freqaimodels.
--freqaimodel-path PATH
Specify additional lookup path for freqaimodels.
```

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@@ -1,52 +0,0 @@
```
usage: freqtrade convert-data [-h] [-v] [--no-color] [--logfile FILE] [-V]
[-c PATH] [-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz,feather,parquet} --format-to
{json,jsongz,feather,parquet} [--erase]
[--exchange EXCHANGE]
[-t TIMEFRAMES [TIMEFRAMES ...]]
[--trading-mode {spot,margin,futures}]
[--candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]]
options:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--format-from {json,jsongz,feather,parquet}
Source format for data conversion.
--format-to {json,jsongz,feather,parquet}
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
--exchange EXCHANGE Exchange name. Only valid if no config is provided.
-t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
--trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
Select Trading mode
--candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]
Select candle type to convert. Defaults to all
available types.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```

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@@ -1,12 +0,0 @@
```
usage: freqtrade convert-db [-h] [--db-url PATH] [--db-url-from PATH]
options:
-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).
--db-url-from PATH Source db url to use when migrating a database.
```

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@@ -1,41 +0,0 @@
```
usage: freqtrade convert-trade-data [-h] [-v] [--no-color] [--logfile FILE]
[-V] [-c PATH] [-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz,feather,parquet,kraken_csv}
--format-to {json,jsongz,feather,parquet}
[--erase] [--exchange EXCHANGE]
options:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--format-from {json,jsongz,feather,parquet,kraken_csv}
Source format for data conversion.
--format-to {json,jsongz,feather,parquet}
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
--exchange EXCHANGE Exchange name. Only valid if no config is provided.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```

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@@ -1,10 +0,0 @@
```
usage: freqtrade create-userdir [-h] [--userdir PATH] [--reset]
options:
-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.
```

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@@ -1,70 +0,0 @@
```
usage: freqtrade download-data [-h] [-v] [--no-color] [--logfile FILE] [-V]
[-c PATH] [-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] [--pairs-file FILE]
[--days INT] [--new-pairs-days INT]
[--include-inactive-pairs]
[--timerange TIMERANGE] [--dl-trades]
[--convert] [--exchange EXCHANGE]
[-t TIMEFRAMES [TIMEFRAMES ...]] [--erase]
[--data-format-ohlcv {json,jsongz,feather,parquet}]
[--data-format-trades {json,jsongz,feather,parquet}]
[--trading-mode {spot,margin,futures}]
[--prepend]
options:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--pairs-file FILE File containing a list of pairs. Takes precedence over
--pairs or pairs configured in the configuration.
--days INT Download data for given number of days.
--new-pairs-days INT Download data of new pairs for given number of days.
Default: `None`.
--include-inactive-pairs
Also download data from inactive pairs.
--timerange TIMERANGE
Specify what timerange of data to use.
--dl-trades Download trades instead of OHLCV data.
--convert Convert downloaded trades to OHLCV data. Only
applicable in combination with `--dl-trades`. Will be
automatic for exchanges which don't have historic
OHLCV (e.g. Kraken). If not provided, use `trades-to-
ohlcv` to convert trades data to OHLCV data.
--exchange EXCHANGE Exchange name. Only valid if no config is provided.
-t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
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,feather,parquet}
Storage format for downloaded candle (OHLCV) data.
(default: `feather`).
--data-format-trades {json,jsongz,feather,parquet}
Storage format for downloaded trades data. (default:
`feather`).
--trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
Select Trading mode
--prepend Allow data prepending. (Data-appending is disabled)
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```

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@@ -1,69 +0,0 @@
```
usage: freqtrade edge [-h] [-v] [--no-color] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [--recursive-strategy-search]
[--freqaimodel NAME] [--freqaimodel-path PATH]
[-i TIMEFRAME] [--timerange TIMERANGE]
[--data-format-ohlcv {json,jsongz,feather,parquet}]
[--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
[--fee FLOAT] [-p PAIRS [PAIRS ...]]
[--stoplosses STOPLOSS_RANGE]
options:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME
Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--data-format-ohlcv {json,jsongz,feather,parquet}
Storage format for downloaded candle (OHLCV) data.
(default: `feather`).
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--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).
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--stoplosses STOPLOSS_RANGE
Defines a range of stoploss values against which edge
will assess the strategy. The format is "min,max,step"
(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).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir 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.
--recursive-strategy-search
Recursively search for a strategy in the strategies
folder.
--freqaimodel NAME Specify a custom freqaimodels.
--freqaimodel-path PATH
Specify additional lookup path for freqaimodels.
```

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@@ -1,62 +0,0 @@
```
usage: freqtrade hyperopt-list [-h] [-v] [--no-color] [--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]
[--min-objective FLOAT] [--max-objective FLOAT]
[--print-json] [--no-details]
[--hyperopt-filename FILENAME]
[--export-csv FILE]
options:
-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 above average time.
--max-avg-time FLOAT Select epochs below average time.
--min-avg-profit FLOAT
Select epochs above average profit.
--max-avg-profit FLOAT
Select epochs below average profit.
--min-total-profit FLOAT
Select epochs above total profit.
--max-total-profit FLOAT
Select epochs below total profit.
--min-objective FLOAT
Select epochs above objective.
--max-objective FLOAT
Select epochs below objective.
--print-json Print output in JSON format.
--no-details Do not print best epoch details.
--hyperopt-filename FILENAME
Hyperopt result filename.Example: `--hyperopt-
filename=hyperopt_results_2020-09-27_16-20-48.pickle`
--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).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```

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@@ -1,43 +0,0 @@
```
usage: freqtrade hyperopt-show [-h] [-v] [--no-color] [--logfile FILE] [-V]
[-c PATH] [-d PATH] [--userdir PATH] [--best]
[--profitable] [-n INT] [--print-json]
[--hyperopt-filename FILENAME] [--no-header]
[--disable-param-export]
[--breakdown {day,week,month} [{day,week,month} ...]]
options:
-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 output in JSON format.
--hyperopt-filename FILENAME
Hyperopt result filename.Example: `--hyperopt-
filename=hyperopt_results_2020-09-27_16-20-48.pickle`
--no-header Do not print epoch details header.
--disable-param-export
Disable automatic hyperopt parameter export.
--breakdown {day,week,month} [{day,week,month} ...]
Show backtesting breakdown per [day, week, month].
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```

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@@ -1,121 +0,0 @@
```
usage: freqtrade hyperopt [-h] [-v] [--no-color] [--logfile FILE] [-V]
[-c PATH] [-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [--recursive-strategy-search]
[--freqaimodel NAME] [--freqaimodel-path PATH]
[-i TIMEFRAME] [--timerange TIMERANGE]
[--data-format-ohlcv {json,jsongz,feather,parquet}]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[-p PAIRS [PAIRS ...]] [--hyperopt-path PATH]
[--eps] [--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET]
[--timeframe-detail TIMEFRAME_DETAIL] [-e INT]
[--spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]]
[--print-all] [--print-json] [-j JOBS]
[--random-state INT] [--min-trades INT]
[--hyperopt-loss NAME] [--disable-param-export]
[--ignore-missing-spaces] [--analyze-per-epoch]
options:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME
Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--data-format-ohlcv {json,jsongz,feather,parquet}
Storage format for downloaded candle (OHLCV) data.
(default: `feather`).
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--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).
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--hyperopt-path PATH Specify additional lookup path for Hyperopt Loss
functions.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
--enable-protections, --enableprotections
Enable protections for backtesting.Will slow
backtesting down by a considerable amount, but will
include configured protections
--dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
Starting balance, used for backtesting / hyperopt and
dry-runs.
--timeframe-detail TIMEFRAME_DETAIL
Specify detail timeframe for backtesting (`1m`, `5m`,
`30m`, `1h`, `1d`).
-e INT, --epochs INT Specify number of epochs (default: 100).
--spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]
Specify which parameters to hyperopt. Space-separated
list.
--print-all Print all results, not only the best ones.
--print-json Print output in JSON format.
-j JOBS, --job-workers JOBS
The number of concurrently running jobs for
hyperoptimization (hyperopt worker processes). If -1
(default), all CPUs are used, for -2, all CPUs but one
are used, etc. If 1 is given, no parallel computing
code is used at all.
--random-state INT Set random state to some positive integer for
reproducible hyperopt results.
--min-trades INT Set minimal desired number of trades for evaluations
in the hyperopt optimization path (default: 1).
--hyperopt-loss NAME, --hyperoptloss NAME
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:
ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
SharpeHyperOptLoss, SharpeHyperOptLossDaily,
SortinoHyperOptLoss, SortinoHyperOptLossDaily,
CalmarHyperOptLoss, MaxDrawDownHyperOptLoss,
MaxDrawDownRelativeHyperOptLoss,
ProfitDrawDownHyperOptLoss, MultiMetricHyperOptLoss
--disable-param-export
Disable automatic hyperopt parameter export.
--ignore-missing-spaces, --ignore-unparameterized-spaces
Suppress errors for any requested Hyperopt spaces that
do not contain any parameters.
--analyze-per-epoch Run populate_indicators once per epoch.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir 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.
--recursive-strategy-search
Recursively search for a strategy in the strategies
folder.
--freqaimodel NAME Specify a custom freqaimodels.
--freqaimodel-path PATH
Specify additional lookup path for freqaimodels.
```

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@@ -1,11 +0,0 @@
```
usage: freqtrade install-ui [-h] [--erase] [--ui-version UI_VERSION]
options:
-h, --help show this help message and exit
--erase Clean UI folder, don't download new version.
--ui-version UI_VERSION
Specify a specific version of FreqUI to install. Not
specifying this installs the latest version.
```

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@@ -1,48 +0,0 @@
```
usage: freqtrade list-data [-h] [-v] [--no-color] [--logfile FILE] [-V]
[-c PATH] [-d PATH] [--userdir PATH]
[--exchange EXCHANGE]
[--data-format-ohlcv {json,jsongz,feather,parquet}]
[--data-format-trades {json,jsongz,feather,parquet}]
[--trades] [-p PAIRS [PAIRS ...]]
[--trading-mode {spot,margin,futures}]
[--show-timerange]
options:
-h, --help show this help message and exit
--exchange EXCHANGE Exchange name. Only valid if no config is provided.
--data-format-ohlcv {json,jsongz,feather,parquet}
Storage format for downloaded candle (OHLCV) data.
(default: `feather`).
--data-format-trades {json,jsongz,feather,parquet}
Storage format for downloaded trades data. (default:
`feather`).
--trades Work on trades data instead of OHLCV data.
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
Select Trading mode
--show-timerange Show timerange available for available data. (May take
a while to calculate).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```

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```
usage: freqtrade list-exchanges [-h] [-v] [--no-color] [--logfile FILE] [-V]
[-c PATH] [-d PATH] [--userdir PATH] [-1] [-a]
options:
-h, --help show this help message and exit
-1, --one-column Print output in one column.
-a, --all Print all exchanges known to the ccxt library.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```

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@@ -1,31 +0,0 @@
```
usage: freqtrade list-freqaimodels [-h] [-v] [--no-color] [--logfile FILE]
[-V] [-c PATH] [-d PATH] [--userdir PATH]
[--freqaimodel-path PATH] [-1]
options:
-h, --help show this help message and exit
--freqaimodel-path PATH
Specify additional lookup path for freqaimodels.
-1, --one-column Print output in one column.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```

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@@ -1,31 +0,0 @@
```
usage: freqtrade list-hyperoptloss [-h] [-v] [--no-color] [--logfile FILE]
[-V] [-c PATH] [-d PATH] [--userdir PATH]
[--hyperopt-path PATH] [-1]
options:
-h, --help show this help message and exit
--hyperopt-path PATH Specify additional lookup path for Hyperopt Loss
functions.
-1, --one-column Print output in one column.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file 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, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```

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