Merge pull request #10863 from freqtrade/new_release

New release 2024.10

.devcontainer/Dockerfile

@@ -1,22 +0,0 @@
-FROM freqtradeorg/freqtrade:develop_freqairl
-
-USER root
-# Install dependencies
-COPY requirements-dev.txt /freqtrade/
-
-RUN apt-get update \
-    && apt-get -y install --no-install-recommends apt-utils dialog \
-    && apt-get -y install --no-install-recommends git 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 []

.devcontainer/devcontainer.json

@@ -1,42 +1,44 @@
 {
     "name": "freqtrade Develop",
-    "build": {
-        "dockerfile": "Dockerfile",
-        "context": ".."
-    },
+    "image": "ghcr.io/freqtrade/freqtrade-devcontainer:latest",
     // Use 'forwardPorts' to make a list of ports inside the container available locally.
     "forwardPorts": [
         8080
     ],
-    "mounts": [
-        "source=freqtrade-bashhistory,target=/home/ftuser/commandhistory,type=volume"
-    ],
     "workspaceMount": "source=${localWorkspaceFolder},target=/workspaces/freqtrade,type=bind,consistency=cached",
     // 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,
+                    "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",
             ],
         }
+    }
 }

.github/.devcontainer/Dockerfile

@@ -0,0 +1,21 @@
+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 []

.github/.devcontainer/devcontainer.json

@@ -0,0 +1,12 @@
+{
+    "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": {}
+    }
+}

.github/dependabot.yml

@@ -1,17 +1,34 @@
 version: 2
 updates:
 - package-ecosystem: docker
-  directory: "/"
+  directories:
+    - "/"
+    - "/docker"
   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
   target-branch: develop
+  groups:
+    types:
+      patterns:
+        - "types-*"
+    pytest:
+      patterns:
+        - "pytest*"
+    mkdocs:
+      patterns:
+        - "mkdocs*"
 
 - package-ecosystem: "github-actions"
   directory: "/"

.github/workflows/binance-lev-tier-update.yml

@@ -0,0 +1,47 @@
+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

.github/workflows/ci.yml

@@ -11,7 +11,7 @@ on:
     types: [published]
   pull_request:
   schedule:
-    - cron:  '0 5 * * 4'
+    - cron:  '0 3 * * 4'
 
 concurrency:
   group: "${{ github.workflow }}-${{ github.ref }}-${{ github.event_name }}"
@@ -19,32 +19,31 @@ concurrency:
 permissions:
   repository-projects: read
 jobs:
-  build_linux:
+  build-linux:
 
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ ubuntu-20.04, ubuntu-22.04 ]
-        python-version: ["3.9", "3.10", "3.11"]
+        os: [ "ubuntu-20.04", "ubuntu-22.04", "ubuntu-24.04" ]
+        python-version: ["3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
 
     - name: Cache_dependencies
-      uses: actions/cache@v3
+      uses: actions/cache@v4
       id: cache
       with:
         path: ~/dependencies/
         key: ${{ runner.os }}-dependencies
 
     - name: pip cache (linux)
-      uses: actions/cache@v3
-      if: runner.os == 'Linux'
+      uses: actions/cache@v4
       with:
         path: ~/.cache/pip
         key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -55,21 +54,31 @@ jobs:
         cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
 
     - name: Installation - *nix
-      if: runner.os == 'Linux'
       run: |
         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
         pip install -r requirements-dev.txt
+        pip install -e ft_client/
         pip install -e .
 
-    - name: Tests
+    - name: Check for version alignment
       run: |
-        pytest --random-order --cov=freqtrade --cov-config=.coveragerc
+        python build_helpers/freqtrade_client_version_align.py
+
+    - name: Tests
+      if: (!(runner.os == 'Linux' && matrix.python-version == '3.12' && matrix.os == 'ubuntu-22.04'))
+      run: |
+        pytest --random-order
+
+    - name: Tests with Coveralls
+      if: (runner.os == 'Linux' && matrix.python-version == '3.12' && matrix.os == 'ubuntu-22.04')
+      run: |
+        pytest --random-order --cov=freqtrade --cov=freqtrade_client --cov-config=.coveragerc
 
     - name: Coveralls
-      if: (runner.os == 'Linux' && matrix.python-version == '3.10' && matrix.os == 'ubuntu-22.04')
+      if: (runner.os == 'Linux' && matrix.python-version == '3.12' && matrix.os == 'ubuntu-22.04')
       env:
         # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
         COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@@ -77,6 +86,11 @@ 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: Check for repository changes
       run: |
         if [ -n "$(git status --porcelain)" ]; then
@@ -108,7 +122,11 @@ jobs:
 
     - name: Run Ruff
       run: |
-        ruff check --output-format=github .
+        ruff check --output-format=github
+
+    - name: Run Ruff format check
+      run: |
+        ruff format --check
 
     - name: Mypy
       run: |
@@ -122,43 +140,41 @@ jobs:
           details: Freqtrade CI failed on ${{ matrix.os }}
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
-  build_macos:
+  build-macos:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ macos-latest ]
-        python-version: ["3.9", "3.10", "3.11"]
+        os: [ "macos-13", "macos-14", "macos-15" ]
+        python-version: ["3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
         check-latest: true
 
     - name: Cache_dependencies
-      uses: actions/cache@v3
+      uses: actions/cache@v4
       id: cache
       with:
         path: ~/dependencies/
-        key: ${{ runner.os }}-dependencies
+        key: ${{ matrix.os }}-dependencies
 
     - name: pip cache (macOS)
-      uses: actions/cache@v3
-      if: runner.os == 'macOS'
+      uses: actions/cache@v4
       with:
         path: ~/Library/Caches/pip
-        key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
+        key: ${{ matrix.os }}-${{ 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
-      if: runner.os == 'macOS'
+    - name: Installation - macOS (Brew)
       run: |
         # brew update
         # TODO: Should be the brew upgrade
@@ -166,21 +182,30 @@ jobs:
         # 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 hdf5 c-blosc
+        brew install hdf5 c-blosc libomp
+
+    - name: Installation (python)
+      run: |
         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
         pip install -r requirements-dev.txt
+        pip install -e ft_client/
         pip install -e .
 
     - name: Tests
@@ -217,7 +242,11 @@ jobs:
 
     - name: Run Ruff
       run: |
-        ruff check --output-format=github .
+        ruff check --output-format=github
+
+    - name: Run Ruff format check
+      run: |
+        ruff format --check
 
     - name: Mypy
       run: |
@@ -231,24 +260,24 @@ jobs:
           details: Test Succeeded!
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
-  build_windows:
+  build-windows:
 
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
 
     - name: Pip cache (Windows)
-      uses: actions/cache@v3
+      uses: actions/cache@v4
       with:
         path: ~\AppData\Local\pip\Cache
         key: ${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -287,12 +316,27 @@ jobs:
 
     - name: Run Ruff
       run: |
-        ruff check --output-format=github .
+        ruff check --output-format=github
+
+    - name: Run Ruff format check
+      run: |
+        ruff format --check
 
     - 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)
@@ -301,15 +345,15 @@ jobs:
           details: Test Failed
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
-  mypy_version_check:
+  mypy-version-check:
     runs-on: ubuntu-22.04
     steps:
     - uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
-        python-version: "3.10"
+        python-version: "3.12"
 
     - name: pre-commit dependencies
       run: |
@@ -321,12 +365,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
-    - uses: actions/setup-python@v4
+    - uses: actions/setup-python@v5
       with:
-        python-version: "3.10"
-    - uses: pre-commit/action@v3.0.0
+        python-version: "3.12"
+    - uses: pre-commit/action@v3.0.1
 
-  docs_check:
+  docs-check:
     runs-on: ubuntu-22.04
     steps:
     - uses: actions/checkout@v4
@@ -336,14 +380,13 @@ jobs:
         ./tests/test_docs.sh
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
-        python-version: "3.11"
+        python-version: "3.12"
 
     - name: Documentation build
       run: |
         pip install -r docs/requirements-docs.txt
-        pip install mkdocs
         mkdocs build
 
     - name: Discord notification
@@ -355,27 +398,26 @@ jobs:
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
 
-  build_linux_online:
+  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@v4
+      uses: actions/setup-python@v5
       with:
-        python-version: "3.9"
+        python-version: "3.12"
 
     - name: Cache_dependencies
-      uses: actions/cache@v3
+      uses: actions/cache@v4
       id: cache
       with:
         path: ~/dependencies/
         key: ${{ runner.os }}-dependencies
 
     - name: pip cache (linux)
-      uses: actions/cache@v3
-      if: runner.os == 'Linux'
+      uses: actions/cache@v4
       with:
         path: ~/.cache/pip
         key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -386,36 +428,36 @@ jobs:
         cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
 
     - name: Installation - *nix
-      if: runner.os == 'Linux'
       run: |
         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
         pip install -r requirements-dev.txt
+        pip install -e ft_client/
         pip install -e .
 
     - name: Tests incl. ccxt compatibility tests
       env:
         CI_WEB_PROXY: http://152.67.78.211:13128
       run: |
-        pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
+        pytest --random-order --longrun --durations 20 -n auto
 
 
-  # Notify only once - when CI completes (and after deploy) in case it's successfull
+  # Notify only once - when CI completes (and after deploy) in case it's successful
   notify-complete:
     needs: [
-      build_linux,
-      build_macos,
-      build_windows,
-      docs_check,
-      mypy_version_check,
+      build-linux,
+      build-macos,
+      build-windows,
+      docs-check,
+      mypy-version-check,
       pre-commit,
-      build_linux_online
+      build-linux-online
     ]
     runs-on: ubuntu-22.04
     # Discord notification can't handle schedule events
-    if: (github.event_name != 'schedule')
+    if: github.event_name != 'schedule' && github.repository == 'freqtrade/freqtrade'
     permissions:
       repository-projects: read
     steps:
@@ -436,8 +478,78 @@ jobs:
           details: Test Completed!
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
-  deploy:
-    needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
+  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-pypi:
+    name: "Deploy to PyPI"
+    needs: [ build ]
+    runs-on: ubuntu-22.04
+    if: (github.event_name == 'release')
+    environment:
+      name: release
+      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 (Test)
+      uses: pypa/gh-action-pypi-publish@v1.10.3
+      with:
+        repository-url: https://test.pypi.org/legacy/
+
+    - name: Publish to PyPI
+      uses: pypa/gh-action-pypi-publish@v1.10.3
+
+
+  deploy-docker:
+    needs: [ build-linux, build-macos, build-windows, docs-check, mypy-version-check, pre-commit ]
     runs-on: ubuntu-22.04
 
     if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
@@ -446,34 +558,15 @@ jobs:
     - uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
-        python-version: "3.11"
+        python-version: "3.12"
 
     - name: Extract branch name
-      shell: bash
-      run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF##*/})"
-      id: extract_branch
-
-    - name: Build distribution
+      id: extract-branch
       run: |
-        pip install -U setuptools wheel
-        python setup.py sdist bdist_wheel
-
-    - name: Publish to PyPI (Test)
-      uses: pypa/gh-action-pypi-publish@v1.8.10
-      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@v1.8.10
-      if: (github.event_name == 'release')
-      with:
-        user: __token__
-        password: ${{ secrets.pypi_password }}
+        echo "GITHUB_REF='${GITHUB_REF}'"
+        echo "branch=${GITHUB_REF##*/}" >> "$GITHUB_OUTPUT"
 
     - name: Dockerhub login
       env:
@@ -490,26 +583,27 @@ 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: crazy-max/ghaction-docker-buildx@v3.3.1
-      with:
-        buildx-version: latest
-        qemu-version: latest
+      uses: docker/setup-buildx-action@v3
 
     - 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 }}
+        BRANCH_NAME: ${{ steps.extract-branch.outputs.branch }}
       run: |
         build_helpers/publish_docker_multi.sh
 
-  deploy_arm:
+  deploy-arm:
+    name: "Deploy Docker"
     permissions:
       packages: write
-    needs: [ deploy ]
+    needs: [ deploy-docker ]
     # 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'
@@ -518,9 +612,10 @@ jobs:
     - uses: actions/checkout@v4
 
     - name: Extract branch name
-      shell: bash
-      run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF##*/})"
-      id: extract_branch
+      id: extract-branch
+      run: |
+        echo "GITHUB_REF='${GITHUB_REF}'"
+        echo "branch=${GITHUB_REF##*/}" >> "$GITHUB_OUTPUT"
 
     - name: Dockerhub login
       env:
@@ -531,7 +626,7 @@ jobs:
 
     - name: Build and test and push docker images
       env:
-        BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
+        BRANCH_NAME: ${{ steps.extract-branch.outputs.branch }}
         GHCR_USERNAME: ${{ github.actor }}
         GHCR_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       run: |

.github/workflows/deploy-docs.yml

@@ -0,0 +1,55 @@
+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

.github/workflows/devcontainer-build.yml

@@ -0,0 +1,45 @@
+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

.github/workflows/docker-update-readme.yml

@@ -0,0 +1,18 @@
+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

.github/workflows/docker_update_readme.yml

@@ -1,17 +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@v3
-      env:
-        DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }}
-        DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
-        DOCKERHUB_REPOSITORY: freqtradeorg/freqtrade

.github/workflows/pre-commit-update.yml

@@ -0,0 +1,41 @@
+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

.gitignore

@@ -111,7 +111,8 @@ target/
 #exceptions
 !*.gitkeep
 !config_examples/config_binance.example.json
-!config_examples/config_bittrex.example.json
 !config_examples/config_full.example.json
 !config_examples/config_kraken.example.json
 !config_examples/config_freqai.example.json
+
+docker-compose-*.yml

.pre-commit-config.yaml

@@ -2,27 +2,28 @@
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
   - repo: https://github.com/pycqa/flake8
-    rev: "6.0.0"
+    rev: "7.1.1"
     hooks:
       - id: flake8
+        additional_dependencies: [Flake8-pyproject]
         # stages: [push]
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v1.7.0"
+    rev: "v1.13.0"
     hooks:
       - id: mypy
         exclude: build_helpers
         additional_dependencies:
-          - types-cachetools==5.3.0.7
+          - types-cachetools==5.5.0.20240820
           - types-filelock==3.2.7
-          - types-requests==2.31.0.10
-          - types-tabulate==0.9.0.3
-          - types-python-dateutil==2.8.19.14
-          - SQLAlchemy==2.0.23
+          - types-requests==2.32.0.20241016
+          - types-tabulate==0.9.0.20240106
+          - types-python-dateutil==2.9.0.20241003
+          - SQLAlchemy==2.0.36
         # stages: [push]
 
   - repo: https://github.com/pycqa/isort
-    rev: "5.12.0"
+    rev: "5.13.2"
     hooks:
       - id: isort
         name: isort (python)
@@ -30,12 +31,13 @@ repos:
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     # Ruff version.
-    rev: 'v0.1.1'
+    rev: 'v0.7.1'
     hooks:
       - id: ruff
+      - id: ruff-format
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v5.0.0
     hooks:
       - id: end-of-file-fixer
         exclude: |
@@ -53,3 +55,10 @@ repos:
           (?x)^(
                 .*\.md
             )$
+
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.3.0
+    hooks:
+    - id: codespell
+      additional_dependencies:
+        - tomli

.vscode/extensions.json

@@ -0,0 +1,11 @@
+{
+    "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",
+    ]
+}

CONTRIBUTING.md

@@ -48,7 +48,7 @@ pytest tests/test_<file_name>.py::test_<method_name>
 #### Run Ruff
 
 ```bash
-ruff .
+ruff check .
 ```
 
 We receive a lot of code that fails the `ruff` checks.
@@ -72,12 +72,12 @@ you can manually run pre-commit with `pre-commit run -a`.
 mypy freqtrade
 ```
 
-### 4. Ensure all imports are correct
+### 4. Ensure formatting is correct
 
-#### Run isort
+#### Run ruff
 
 ``` bash
-isort .
+ruff format .
 ```
 
 ## (Core)-Committer Guide

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.11.6-slim-bookworm as base
+FROM python:3.12.7-slim-bookworm as base
 
 # Setup env
 ENV LANG C.UTF-8
@@ -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 \
+RUN  pip install --user --no-cache-dir "numpy<2.0" \
   && pip install --user --no-cache-dir -r requirements-hyperopt.txt
 
 # Copy dependencies to runtime-image

MANIFEST.in

@@ -5,3 +5,5 @@ 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

README.md

@@ -29,8 +29,10 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
 
 - [X] [Binance](https://www.binance.com/)
 - [X] [Bitmart](https://bitmart.com/)
+- [X] [BingX](https://bingx.com/invite/0EM9RX)
+- [X] [Bybit](https://bybit.com/)
 - [X] [Gate.io](https://www.gate.io/ref/6266643)
-- [X] [Huobi](http://huobi.com/)
+- [X] [HTX](https://www.htx.com/) (Former Huobi)
 - [X] [Kraken](https://kraken.com/)
 - [X] [OKX](https://okx.com/) (Former OKEX)
 - [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
@@ -59,7 +61,7 @@ Please find the complete documentation on the [freqtrade website](https://www.fr
 
 ## Features
 
-- [x] **Based on Python 3.9+**: For botting on any operating system - Windows, macOS and Linux.
+- [x] **Based on Python 3.10+**: 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.
@@ -85,41 +87,50 @@ For further (native) installation methods, please refer to the [Installation doc
 
 ```
 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}
+                 {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-freqaimodels,list-timeframes,show-trades,test-pairlist,convert-db,install-ui,plot-dataframe,plot-profit,webserver,strategy-updater,lookahead-analysis,recursive-analysis}
                  ...
 
 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,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-freqaimodels,list-timeframes,show-trades,test-pairlist,convert-db,install-ui,plot-dataframe,plot-profit,webserver,strategy-updater,lookahead-analysis,recursive-analysis}
     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-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.
 
-optional arguments:
+options:
   -h, --help            show this help message and exit
   -V, --version         show program's version number and exit
 
@@ -207,7 +218,7 @@ To run this bot we recommend you a cloud instance with a minimum of:
 
 ### Software requirements
 
-- [Python >= 3.9](http://docs.python-guide.org/en/latest/starting/installation/)
+- [Python >= 3.10](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/)

build_helpers/binance_update_lev_tiers.py

@@ -0,0 +1,23 @@
+#!/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)

build_helpers/extract_config_json_schema.py

@@ -0,0 +1,17 @@
+"""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()

build_helpers/freqtrade_client_version_align.py

@@ -0,0 +1,15 @@
+#!/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()

build_helpers/pre_commit_update.py

@@ -1,4 +1,4 @@
-# File used in CI to ensure pre-commit dependencies are kept uptodate.
+# File used in CI to ensure pre-commit dependencies are kept up-to-date.
 
 import sys
 from pathlib import Path
@@ -6,28 +6,30 @@ 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")
+require = Path("requirements.txt")
 
-with require_dev.open('r') as rfile:
+with require_dev.open("r") as rfile:
     requirements = rfile.readlines()
 
-with require.open('r') as rfile:
+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-") or r.startswith("SQLAlchemy")
+]
 
-with pre_commit_file.open('r') as file:
-    f = yaml.load(file, Loader=yaml.FullLoader)
+with pre_commit_file.open("r") as file:
+    f = yaml.load(file, Loader=yaml.SafeLoader)
 
 
-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:

config_examples/config_binance.example.json

@@ -1,4 +1,5 @@
 {
+    "$schema": "https://schema.freqtrade.io/schema.json",
     "max_open_trades": 3,
     "stake_currency": "USDT",
     "stake_amount": 0.05,

config_examples/config_freqai.example.json

@@ -1,4 +1,5 @@
 {
+    "$schema": "https://schema.freqtrade.io/schema.json",
     "trading_mode": "futures",
     "margin_mode": "isolated",
     "max_open_trades": 5,
@@ -52,7 +53,7 @@
         "train_period_days": 15,
         "backtest_period_days": 7,
         "live_retrain_hours": 0,
-        "identifier": "uniqe-id",
+        "identifier": "unique-id",
         "feature_parameters": {
             "include_timeframes": [
                 "3m",

config_examples/config_full.example.json

@@ -1,4 +1,5 @@
 {
+    "$schema": "https://schema.freqtrade.io/schema.json",
     "max_open_trades": 3,
     "stake_currency": "BTC",
     "stake_amount": 0.05,

config_examples/config_kraken.example.json

@@ -1,4 +1,5 @@
 {
+    "$schema": "https://schema.freqtrade.io/schema.json",
     "max_open_trades": 5,
     "stake_currency": "EUR",
     "stake_amount": 10,

docker-compose.yml

@@ -1,5 +1,4 @@
 ---
-version: '3'
 services:
   freqtrade:
     image: freqtradeorg/freqtrade:stable

docker/Dockerfile.armhf

@@ -1,4 +1,4 @@
-FROM python:3.11.6-slim-bookworm as base
+FROM python:3.11.10-slim-bookworm as base
 
 # Setup env
 ENV LANG C.UTF-8
@@ -35,7 +35,7 @@ COPY build_helpers/* /tmp/
 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==0.4.28 \
+  && 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

docker/Dockerfile.jupyter

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

docker/docker-compose-freqai.yml

@@ -1,5 +1,4 @@
 ---
-version: '3'
 services:
   freqtrade:
     image: freqtradeorg/freqtrade:stable_freqaitorch

docker/docker-compose-jupyter.yml

@@ -1,12 +1,11 @@
 ---
-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:

docs/advanced-backtesting.md

@@ -18,15 +18,13 @@ 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 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.
+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.
 
 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` file in the
-`user_data/backtest_results` folder.
+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.
 
 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:
@@ -36,7 +34,7 @@ freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4 5
 ```
 
 This command will read from the last backtesting results. The `--analysis-groups` option is
-used to specify the various tabular outputs showing the profit fo each group or trade,
+used to specify the various tabular outputs showing the profit of 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
@@ -103,21 +101,72 @@ The indicators have to be present in your strategy's main DataFrame (either for
 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      :** maxiumum 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 volumne
+- **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
 

docs/advanced-hyperopt.md

@@ -30,11 +30,17 @@ 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],
+    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:
+        **kwargs,
+    ) -> float:
         """
         Objective function, returns smaller number for better results
         This is the legacy algorithm (used until now in freqtrade).

docs/advanced-orderflow.md

@@ -0,0 +1,152 @@
+# 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).
+
+!!! 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"] # Price level of stacked bid imbalance 
+dataframe["stacked_imbalances_ask"] # Price level of stacked ask imbalance  
+```
+
+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
+    }
+}
+```

docs/advanced-setup.md

@@ -114,8 +114,46 @@ 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
 

docs/backtesting.md

@@ -252,37 +252,37 @@ The most important in the backtesting is to understand the result.
 A backtesting result will look like that:
 
 ```
-========================================================= 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 |
-====================================================== 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 |
+================================================ 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 ====================
 | 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 |
@@ -293,6 +293,7 @@ A backtesting result will look like that:
 |-----------------------------+---------------------|
 | 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         |
@@ -358,7 +359,7 @@ 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 while the column `Cum Profit %` sums up all the profits/losses.
+The column `Avg Profit %` shows the average profit for all trades made.
 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%`.
 
@@ -398,6 +399,7 @@ 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         |
@@ -452,6 +454,7 @@ 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.
@@ -464,7 +467,7 @@ It contains some useful key metrics about performance of your strategy on backte
 - `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 `Cum Profit %`.
+- `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Tot 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).
@@ -522,21 +525,22 @@ 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, 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.
+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.
 
 ## 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
-- All orders are filled at the requested price (no slippage, no unfilled orders)
+- 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
 - 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
+  - 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)
 - Stoploss exits happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
@@ -587,7 +591,7 @@ 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 viceversa?).
+One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or vice-versa?).
 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.
@@ -618,22 +622,22 @@ 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.
+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.
 
 ``` 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-<strategy>.json`, injecting the strategy-name into the target filename.
+This will save the results to `user_data/backtest_results/backtest-result-<datetime>.json`, including results for both `Strategy001` and `Strategy002`.
 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    |  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 |
+================================================== 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 |
 ```
 
 ## Next step

docs/bot-basics.md

@@ -33,7 +33,6 @@ For spot pairs, naming will be `base/quote` (e.g. `ETH/USDT`).
 
 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.
@@ -50,7 +49,9 @@ By default, the bot loop runs every few seconds (`internals.process_throttle_sec
   * Call `populate_indicators()`
   * Call `populate_entry_trend()`
   * Call `populate_exit_trend()`
-* Check timeouts for open orders.
+* 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.
@@ -86,8 +87,10 @@ 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

docs/bot-usage.md

@@ -12,41 +12,50 @@ This page explains the different parameters of the bot and how to run it.
 
 ```
 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}
+                 {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-freqaimodels,list-timeframes,show-trades,test-pairlist,convert-db,install-ui,plot-dataframe,plot-profit,webserver,strategy-updater,lookahead-analysis,recursive-analysis}
                  ...
 
 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,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-freqaimodels,list-timeframes,show-trades,test-pairlist,convert-db,install-ui,plot-dataframe,plot-profit,webserver,strategy-updater,lookahead-analysis,recursive-analysis}
     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-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.
 
-optional arguments:
+options:
   -h, --help            show this help message and exit
   -V, --version         show program's version number and exit
 

docs/configuration.md

@@ -14,7 +14,7 @@ You can specify a different configuration file used by the bot with the `-c/--co
 If you used the [Quick start](docker_quickstart.md#docker-quick-start) method for installing
 the bot, the installation script should have already created the default configuration file (`config.json`) for you.
 
-If the default configuration file is not created we recommend to use `freqtrade new-config --config config.json` to generate a basic configuration file.
+If the default configuration file is not created we recommend to use `freqtrade new-config --config user_data/config.json` to generate a basic configuration file.
 
 The Freqtrade configuration file is to be written in JSON format.
 
@@ -49,6 +49,13 @@ FREQTRADE__EXCHANGE__SECRET=<yourExchangeSecret>
 !!! Note
     Environment variables detected are logged at startup - so if you can't find why a value is not what you think it should be based on the configuration, make sure it's not loaded from an environment variable.
 
+!!! Tip "Validate combined result"
+    You can use the [show-config subcommand](utils.md#show-config) to see the final, combined configuration.
+
+??? Warning "Loading sequence"
+    Environment variables are loaded after the initial configuration. As such, you cannot provide the path to the configuration through environment variables. Please use `--config path/to/config.json` for that.
+    This also applies to user_dir to some degree. while the user directory can be set through environment variables - the configuration will **not** be loaded from that location.
+
 ### Multiple configuration files
 
 Multiple configuration files can be specified and used by the bot or the bot can read its configuration parameters from the process standard input stream.
@@ -56,6 +63,9 @@ Multiple configuration files can be specified and used by the bot or the bot can
 You can specify additional configuration files in `add_config_files`. Files specified in this parameter will be loaded and merged with the initial config file. The files are resolved relative to the initial configuration file.
 This is similar to using multiple `--config` parameters, but simpler in usage as you don't have to specify all files for all commands.
 
+!!! Tip "Validate combined result"
+    You can use the [show-config subcommand](utils.md#show-config) to see the final, combined configuration.
+
 !!! Tip "Use multiple configuration files to keep secrets secret"
     You can use a 2nd configuration file containing your secrets. That way you can share your "primary" configuration file, while still keeping your API keys for yourself.
     The 2nd file should only specify what you intend to override.
@@ -113,6 +123,19 @@ This is similar to using multiple `--config` parameters, but simpler in usage as
 
     If multiple files are in the `add_config_files` section, then they will be assumed to be at identical levels, having the last occurrence override the earlier config (unless a parent already defined such a key).
 
+## Editor autocomplete and validation
+
+If you are using an editor that supports JSON schema, you can use the schema provided by Freqtrade to get autocompletion and validation of your configuration file by adding the following line to the top of your configuration file:
+
+``` json
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+}
+```
+
+??? Note "Develop version"
+    The develop schema is available as `https://schema.freqtrade.io/schema_dev.json` - though we recommend to stick to the stable version for the best experience.
+
 ## Configuration parameters
 
 The table below will list all configuration parameters available.
@@ -187,18 +210,18 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `position_adjustment_enable` | Enables the strategy to use position adjustments (additional buys or sells). [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*<br> **Datatype:** Boolean
 | `max_entry_position_adjustment` | Maximum additional order(s) for each open trade on top of the first entry Order. Set it to `-1` for unlimited additional orders. [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `-1`.*<br> **Datatype:** Positive Integer or -1
 | | **Exchange**
-| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
+| `exchange.name` | **Required.** Name of the exchange class to use. <br> **Datatype:** String
 | `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.uid` | API uid to use for the exchange. Only required when you are in production mode and for exchanges that use uid for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Supports regex pairs as `.*/BTC`. Not used by VolumePairList. [More information](plugins.md#pairlists-and-pairlist-handlers). <br> **Datatype:** List
 | `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting. [More information](plugins.md#pairlists-and-pairlist-handlers). <br> **Datatype:** List
-| `exchange.ccxt_config` | Additional CCXT parameters passed to both ccxt instances (sync and async). This is usually the correct place for additional ccxt configurations. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation). Please avoid adding exchange secrets here (use the dedicated fields instead), as they may be contained in logs. <br> **Datatype:** Dict
-| `exchange.ccxt_sync_config` | Additional CCXT parameters passed to the regular (sync) ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
-| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
+| `exchange.ccxt_config` | Additional CCXT parameters passed to both ccxt instances (sync and async). This is usually the correct place for additional ccxt configurations. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://docs.ccxt.com/#/README?id=overriding-exchange-properties-upon-instantiation). Please avoid adding exchange secrets here (use the dedicated fields instead), as they may be contained in logs. <br> **Datatype:** Dict
+| `exchange.ccxt_sync_config` | Additional CCXT parameters passed to the regular (sync) ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://docs.ccxt.com/#/README?id=overriding-exchange-properties-upon-instantiation) <br> **Datatype:** Dict
+| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://docs.ccxt.com/#/README?id=overriding-exchange-properties-upon-instantiation) <br> **Datatype:** Dict
+| `exchange.enable_ws` | Enable the usage of Websockets for the exchange. <br>[More information](#consuming-exchange-websockets).<br>*Defaults to `true`.* <br> **Datatype:** Boolean
 | `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
-| `exchange.skip_pair_validation` | Skip pairlist validation on startup.<br>*Defaults to `false`*<br> **Datatype:** Boolean
 | `exchange.skip_open_order_update` | Skips open order updates on startup should the exchange cause problems. Only relevant in live conditions.<br>*Defaults to `false`*<br> **Datatype:** Boolean
 | `exchange.unknown_fee_rate` | Fallback value to use when calculating trading fees. This can be useful for exchanges which have fees in non-tradable currencies. The value provided here will be multiplied with the "fee cost".<br>*Defaults to `None`<br> **Datatype:** float
 | `exchange.log_responses` | Log relevant exchange responses. For debug mode only - use with care.<br>*Defaults to `false`*<br> **Datatype:** Boolean
@@ -206,7 +229,6 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | | **Plugins**
 | `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation of all possible configuration options.
 | `pairlists` | Define one or more pairlists to be used. [More information](plugins.md#pairlists-and-pairlist-handlers). <br>*Defaults to `StaticPairList`.*  <br> **Datatype:** List of Dicts
-| `protections` | Define one or more protections to be used. [More information](plugins.md#protections). <br> **Datatype:** List of Dicts
 | | **Telegram**
 | `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
 | `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
@@ -242,7 +264,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `disable_dataframe_checks` | Disable checking the OHLCV dataframe returned from the strategy methods for correctness. Only use when intentionally changing the dataframe and understand what you are doing. [Strategy Override](#parameters-in-the-strategy).<br> *Defaults to `False`*. <br> **Datatype:** Boolean
 | `internals.process_throttle_secs` | Set the process throttle, or minimum loop duration for one bot iteration loop. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
 | `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
-| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
+| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](advanced-setup.md#configure-the-bot-running-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
 | `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
 | `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
 | `recursive_strategy_search` | Set to `true` to recursively search sub-directories inside `user_data/strategies` for a strategy. <br> **Datatype:** Boolean
@@ -321,11 +343,13 @@ For example, if you have 10 ETH available in your wallet on the exchange and `tr
 To fully utilize compounding profits when using multiple bots on the same exchange account, you'll want to limit each bot to a certain starting balance.
 This can be accomplished by setting `available_capital` to the desired starting balance.
 
-Assuming your account has 10.000 USDT and you want to run 2 different strategies on this exchange.
+Assuming your account has 10000 USDT and you want to run 2 different strategies on this exchange.
 You'd set `available_capital=5000` - granting each bot an initial capital of 5000 USDT.
 The bot will then split this starting balance equally into `max_open_trades` buckets.
 Profitable trades will result in increased stake-sizes for this bot - without affecting the stake-sizes of the other bot.
 
+Adjusting `available_capital` requires reloading the configuration to take effect. Adjusting the `available_capital` adds the difference between the previous `available_capital` and the new `available_capital`. Decreasing the available capital when trades are open doesn't exit the trades. The difference is returned to the wallet when the trades conclude. The outcome of this differs depending on the price movement between the adjustment and exiting the trades.
+
 !!! Warning "Incompatible with `tradable_balance_ratio`"
     Setting this option will replace any configuration of `tradable_balance_ratio`.
 
@@ -358,7 +382,7 @@ This setting works in combination with `max_open_trades`. The maximum capital en
 For example, the bot will at most use (0.05 BTC x 3) = 0.15 BTC, assuming a configuration of `max_open_trades=3` and `stake_amount=0.05`.
 
 !!! Note
-    This setting respects the [available balance configuration](#available-balance).
+    This setting respects the [available balance configuration](#tradable-balance).
 
 #### Dynamic stake amount
 
@@ -397,6 +421,8 @@ Or another example if your position adjustment assumes it can do 1 additional bu
 
 --8<-- "includes/pricing.md"
 
+## Further Configuration details
+
 ### Understand minimal_roi
 
 The `minimal_roi` configuration parameter is a JSON object where the key is a duration
@@ -503,13 +529,13 @@ Configuration:
     Please carefully read the section [Market order pricing](#market-order-pricing) section when using market orders.
 
 !!! Note "Stoploss on exchange"
-    `stoploss_on_exchange_interval` is not mandatory. Do not change its value if you are
+    `order_types.stoploss_on_exchange_interval` is not mandatory. Do not change its value if you are
     unsure of what you are doing. For more information about how stoploss works please
     refer to [the stoploss documentation](stoploss.md).
 
-    If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new stoploss order.
+    If `order_types.stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new stoploss order.
 
-!!! Warning "Warning: stoploss_on_exchange failures"
+!!! Warning "Warning: order_types.stoploss_on_exchange failures"
     If stoploss on exchange creation fails for some reason, then an "emergency exit" is initiated. By default, this will exit the trade using a market order. The order-type for the emergency-exit can be changed by setting the `emergency_exit` value in the `order_types` dictionary - however, this is not advised.
 
 ### Understand order_time_in_force
@@ -535,7 +561,7 @@ is automatically cancelled by the exchange.
 **PO (Post only):**
 
 Post only order. The order is either placed as a maker order, or it is canceled.
-This means the order must be placed on orderbook for at at least time in an unfilled state.
+This means the order must be placed on orderbook for at least time in an unfilled state.
 
 #### time_in_force config
 
@@ -556,7 +582,14 @@ The possible values are: `GTC` (default), `FOK` or `IOC`.
     This is ongoing work. For now, it is supported only for binance, gate and kucoin.
     Please don't change the default value unless you know what you are doing and have researched the impact of using different values for your particular exchange.
 
-### What values can be used for fiat_display_currency?
+### Fiat conversion
+
+Freqtrade uses the Coingecko API to convert the coin value to it's corresponding fiat value for the Telegram reports.
+The FIAT currency can be set in the configuration file as `fiat_display_currency`.
+
+Removing `fiat_display_currency` completely from the configuration will skip initializing coingecko, and will not show any FIAT currency conversion. This has no importance for the correct functioning of the bot.
+
+#### What values can be used for fiat_display_currency?
 
 The `fiat_display_currency` configuration parameter sets the base currency to use for the
 conversion from coin to fiat in the bot Telegram reports.
@@ -572,9 +605,53 @@ In addition to fiat currencies, a range of crypto currencies is supported.
 The valid values are:
 
 ```json
-"BTC", "ETH", "XRP", "LTC", "BCH", "USDT"
+"BTC", "ETH", "XRP", "LTC", "BCH", "BNB"
 ```
 
+#### Coingecko Rate limit problems
+
+On some IP ranges, coingecko is heavily rate-limiting.
+In such cases, you may want to add your coingecko API key to the configuration.
+
+``` json
+{
+    "fiat_display_currency": "USD",
+    "coingecko": {
+        "api_key": "your-api",
+        "is_demo": true
+    }
+}
+```
+
+Freqtrade supports both Demo and Pro coingecko API keys.
+
+The Coingecko API key is NOT required for the bot to function correctly.
+It is only used for the conversion of coin to fiat in the Telegram reports, which usually also work without API key.
+
+## Consuming exchange Websockets
+
+Freqtrade can consume websockets through ccxt.pro.
+
+Freqtrade aims ensure data is available at all times.
+Should the websocket connection fail (or be disabled), the bot will fall back to REST API calls.
+
+Should you experience problems you suspect are caused by websockets, you can disable these via the setting `exchange.enable_ws`, which defaults to true.
+
+```jsonc
+"exchange": {
+    // ...
+    "enable_ws": false,
+    // ...
+}
+```
+
+Should you be required to use a proxy, please refer to the [proxy section](#using-proxy-with-freqtrade) for more information.
+
+!!! Info "Rollout"
+    We're implementing this out slowly, ensuring stability of your bots.
+    Currently, usage is limited to ohlcv data streams.
+    It's also limited to a few exchanges, with new exchanges being added on an ongoing basis.
+
 ## Using Dry-run mode
 
 We recommend starting the bot in the Dry-run mode to see how your bot will
@@ -611,9 +688,9 @@ Once you will be happy with your bot performance running in the Dry-run mode, yo
 * API-keys may or may not be provided. Only Read-Only operations (i.e. operations that do not alter account state) on the exchange are performed in dry-run mode.
 * Wallets (`/balance`) are simulated based on `dry_run_wallet`.
 * Orders are simulated, and will not be posted to the exchange.
-* Market orders fill based on orderbook volume the moment the order is placed.
+* Market orders fill based on orderbook volume the moment the order is placed, with a maximum slippage of 5%.
 * Limit orders fill once the price reaches the defined level - or time out based on `unfilledtimeout` settings.
-* Limit orders will be converted to market orders if they cross the price by more than 1%.
+* Limit orders will be converted to market orders if they cross the price by more than 1%, and will be filled immediately based regular market order rules (see point about Market orders above).
 * In combination with `stoploss_on_exchange`, the stop_loss price is assumed to be filled.
 * Open orders (not trades, which are stored in the database) are kept open after bot restarts, with the assumption that they were not filled while being offline.
 
@@ -663,7 +740,7 @@ You should also make sure to read the [Exchanges](exchanges.md) section of the d
 
     **NEVER** share your private configuration file or your exchange keys with anyone!
 
-### Using proxy with Freqtrade
+## Using a proxy with Freqtrade
 
 To use a proxy with freqtrade, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values.
 This will have the proxy settings applied to everything (telegram, coingecko, ...) **except** for exchange requests.
@@ -674,7 +751,7 @@ export HTTPS_PROXY="http://addr:port"
 freqtrade
 ```
 
-#### Proxy exchange requests
+### Proxy exchange requests
 
 To use a proxy for exchange connections - you will have to define the proxies as part of the ccxt configuration.
 
@@ -683,6 +760,7 @@ To use a proxy for exchange connections - you will have to define the proxies as
   "exchange": {
     "ccxt_config": {
       "httpsProxy": "http://addr:port",
+      "wsProxy": "http://addr:port",
     }
   }
 }

docs/data-download.md

@@ -24,10 +24,10 @@ usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                [--days INT] [--new-pairs-days INT]
                                [--include-inactive-pairs]
                                [--timerange TIMERANGE] [--dl-trades]
-                               [--exchange EXCHANGE]
+                               [--convert] [--exchange EXCHANGE]
                                [-t TIMEFRAMES [TIMEFRAMES ...]] [--erase]
                                [--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
-                               [--data-format-trades {json,jsongz,hdf5,feather}]
+                               [--data-format-trades {json,jsongz,hdf5,feather,parquet}]
                                [--trading-mode {spot,margin,futures}]
                                [--prepend]
 
@@ -48,6 +48,11 @@ options:
   --dl-trades           Download trades instead of OHLCV data. The bot will
                         resample trades to the desired timeframe as specified
                         as --timeframes/-t.
+  --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
@@ -57,7 +62,7 @@ options:
   --data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
                         Storage format for downloaded candle (OHLCV) data.
                         (default: `feather`).
-  --data-format-trades {json,jsongz,hdf5,feather}
+  --data-format-trades {json,jsongz,hdf5,feather,parquet}
                         Storage format for downloaded trades data. (default:
                         `feather`).
   --trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
@@ -418,7 +423,8 @@ You can get a list of downloaded data using the `list-data` sub-command.
 usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                            [--userdir PATH] [--exchange EXCHANGE]
                            [--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
-                           [-p PAIRS [PAIRS ...]]
+                           [--data-format-trades {json,jsongz,hdf5,feather,parquet}]
+                           [--trades] [-p PAIRS [PAIRS ...]]
                            [--trading-mode {spot,margin,futures}]
                            [--show-timerange]
 
@@ -428,6 +434,10 @@ options:
   --data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
                         Storage format for downloaded candle (OHLCV) data.
                         (default: `feather`).
+  --data-format-trades {json,jsongz,hdf5,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.
@@ -460,26 +470,47 @@ Common arguments:
 ```bash
 > freqtrade list-data --userdir ~/.freqtrade/user_data/
 
-Found 33 pair / timeframe combinations.
-pairs       timeframe
-----------  -----------------------------------------
-ADA/BTC     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d
-ADA/ETH     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d
-ETH/BTC     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d
-ETH/USDT    5m, 15m, 30m, 1h, 2h, 4h
+              Found 33 pair / timeframe combinations.
+┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━┓
+┃          Pair ┃                                 Timeframe ┃ Type ┃
+┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━┩
+│       ADA/BTC │     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d │ spot │
+│       ADA/ETH │     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d │ spot │
+│       ETH/BTC │     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d │ spot │
+│      ETH/USDT │                  5m, 15m, 30m, 1h, 2h, 4h │ spot │
+└───────────────┴───────────────────────────────────────────┴──────┘
+
+```
+
+Show all trades data including from/to timerange
+
+``` bash
+> freqtrade list-data --show --trades
+                     Found trades data for 1 pair.                     
+┏━━━━━━━━━┳━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
+┃    Pair ┃ Type ┃                From ┃                  To ┃ Trades ┃
+┡━━━━━━━━━╇━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
+│ XRP/ETH │ spot │ 2019-10-11 00:00:11 │ 2019-10-13 11:19:28 │  12477 │
+└─────────┴──────┴─────────────────────┴─────────────────────┴────────┘
+
 ```
 
 ## Trades (tick) data
 
-By default, `download-data` sub-command downloads Candles (OHLCV) data. Some exchanges also provide historic trade-data via their API.
+By default, `download-data` sub-command downloads Candles (OHLCV) data. Most exchanges also provide historic trade-data via their API.
 This data can be useful if you need many different timeframes, since it is only downloaded once, and then resampled locally to the desired timeframes.
 
-Since this data is large by default, the files use the feather fileformat by default. They are stored in your data-directory with the naming convention of `<pair>-trades.feather` (`ETH_BTC-trades.feather`). Incremental mode is also supported, as for historic OHLCV data, so downloading the data once per week with `--days 8` will create an incremental data-repository.
+Since this data is large by default, the files use the feather file format by default. They are stored in your data-directory with the naming convention of `<pair>-trades.feather` (`ETH_BTC-trades.feather`). Incremental mode is also supported, as for historic OHLCV data, so downloading the data once per week with `--days 8` will create an incremental data-repository.
 
-To use this mode, simply add `--dl-trades` to your call. This will swap the download method to download trades, and resamples the data locally.
+To use this mode, simply add `--dl-trades` to your call. This will swap the download method to download trades.
+If `--convert` is also provided, the resample step will happen automatically and overwrite eventually existing OHLCV data for the given pair/timeframe combinations.
 
-!!! Warning "do not use"
-    You should not use this unless you're a kraken user. Most other exchanges provide OHLCV data with sufficient history.
+!!! Warning "Do not use"
+    You should not use this unless you're a kraken user (Kraken does not provide historic OHLCV data).  
+    Most other exchanges provide OHLCV data with sufficient history, so downloading multiple timeframes through that method will still proof to be a lot faster than downloading trades data.
+
+!!! Note "Kraken user"
+    Kraken users should read [this](exchanges.md#historic-kraken-data) before starting to download data.
 
 Example call:
 
@@ -490,12 +521,6 @@ freqtrade download-data --exchange kraken --pairs XRP/EUR ETH/EUR --days 20 --dl
 !!! Note
     While this method uses async calls, it will be slow, since it requires the result of the previous call to generate the next request to the exchange.
 
-!!! Warning
-    The historic trades are not available during Freqtrade dry-run and live trade modes because all exchanges tested provide this data with a delay of few 100 candles, so it's not suitable for real-time trading.
-
-!!! Note "Kraken user"
-    Kraken users should read [this](exchanges.md#historic-kraken-data) before starting to download data.
-
 ## Next step
 
-Great, you now have backtest data downloaded, so you can now start [backtesting](backtesting.md) your strategy.
+Great, you now have some data downloaded, so you can now start [backtesting](backtesting.md) your strategy.

docs/deprecated.md

@@ -75,7 +75,10 @@ Webhook terminology changed from "sell" to "exit", and from "buy" to "entry", re
 * `webhooksellfill`, `webhookexitfill` -> `exit_fill`
 * `webhooksellcancel`, `webhookexitcancel` -> `exit_cancel`
 
-
 ## Removal of `populate_any_indicators`
 
 version 2023.3 saw the removal of `populate_any_indicators` in favor of split methods for feature engineering and targets. Please read the [migration document](strategy_migration.md#freqai-strategy) for full details.
+
+## Removal of `protections` from configuration
+
+ Setting protections from the configuration via `"protections": [],` has been removed in 2024.10, after having raised deprecation warnings for over 3 years.

docs/developer.md

@@ -22,7 +22,7 @@ This will spin up a local server (usually on port 8000) so you can see if everyt
 ## Developer setup
 
 To configure a development environment, you can either use the provided [DevContainer](#devcontainer-setup), or use the `setup.sh` script and answer "y" when asked "Do you want to install dependencies for dev [y/N]? ".
-Alternatively (e.g. if your system is not supported by the setup.sh script), follow the manual installation process and run `pip3 install -e .[all]`.
+Alternatively (e.g. if your system is not supported by the setup.sh script), follow the manual installation process and run `pip3 install -r requirements-dev.txt` - followed by `pip3 install -e .[all]`.
 
 This will install all required tools for development, including `pytest`, `ruff`, `mypy`, and `coveralls`.
 
@@ -83,7 +83,7 @@ Details will obviously vary between setups - but this should work to get you sta
 ``` json
 {
     "name": "freqtrade trade",
-    "type": "python",
+    "type": "debugpy",
     "request": "launch",
     "module": "freqtrade",
     "console": "integratedTerminal",
@@ -116,7 +116,7 @@ A similar setup can also be taken for Pycharm - using `freqtrade` as module name
     ![Pycharm debug configuration](assets/pycharm_debug.png)
 
 !!! Note "Startup directory"
-    This assumes that you have the repository checked out, and the editor is started at the repository root level (so setup.py is at the top level of your repository).
+    This assumes that you have the repository checked out, and the editor is started at the repository root level (so pyproject.toml is at the top level of your repository).
 
 ## ErrorHandling
 
@@ -129,6 +129,8 @@ Below is an outline of exception inheritance hierarchy:
 + FreqtradeException
 |
 +---+ OperationalException
+|   |
+|   +---+ ConfigurationError
 |
 +---+ DependencyException
 |   |
@@ -203,7 +205,7 @@ This is called with each iteration of the bot (only if the Pairlist Handler is a
 
 It must return the resulting pairlist (which may then be passed into the chain of Pairlist Handlers).
 
-Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filtering. Use this if you limit your result to a certain number of pairs - so the end-result is not shorter than expected.
+Validations are optional, the parent class exposes a `verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filtering. Use this if you limit your result to a certain number of pairs - so the end-result is not shorter than expected.
 
 #### filter_pairlist
 
@@ -217,7 +219,7 @@ The default implementation in the base class simply calls the `_validate_pair()`
 
 If overridden, it must return the resulting pairlist (which may then be passed into the next Pairlist Handler in the chain).
 
-Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filters. Use this if you limit your result to a certain number of pairs - so the end result is not shorter than expected.
+Validations are optional, the parent class exposes a `verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filters. Use this if you limit your result to a certain number of pairs - so the end result is not shorter than expected.
 
 In `VolumePairList`, this implements different methods of sorting, does early validation so only the expected number of pairs is returned.
 
@@ -239,7 +241,6 @@ No protection should use datetime directly, but use the provided `date_now` vari
 
 !!! Tip "Writing a new Protection"
     Best copy one of the existing Protections to have a good example.
-    Don't forget to register your protection in `constants.py` under the variable `AVAILABLE_PROTECTIONS` - otherwise it will not be selectable.
 
 #### Implementation of a new protection
 
@@ -259,7 +260,7 @@ For that reason, they must implement the following methods:
 
 The `until` portion should be calculated using the provided `calculate_lock_end()` method.
 
-All Protections should use `"stop_duration"` / `"stop_duration_candles"` to define how long a a pair (or all pairs) should be locked.
+All Protections should use `"stop_duration"` / `"stop_duration_candles"` to define how long a pair (or all pairs) should be locked.
 The content of this is made available as `self._stop_duration` to the each Protection.
 
 If your protection requires a look-back period, please use `"lookback_period"` / `"lockback_period_candles"` to keep all protections aligned.
@@ -303,7 +304,7 @@ The `IProtection` parent class provides a helper method for this in `calculate_l
 
 Most exchanges supported by CCXT should work out of the box.
 
-To quickly test the public endpoints of an exchange, add a configuration for your exchange to `test_ccxt_compat.py` and run these tests with `pytest --longrun tests/exchange/test_ccxt_compat.py`.
+To quickly test the public endpoints of an exchange, add a configuration for your exchange to `tests/exchange_online/conftest.py` and run these tests with `pytest --longrun tests/exchange_online/test_ccxt_compat.py`.
 Completing these tests successfully a good basis point (it's a requirement, actually), however these won't guarantee correct exchange functioning, as this only tests public endpoints, but no private endpoint (like generate order or similar).
 
 Also try to use `freqtrade download-data` for an extended timerange (multiple months) and verify that the data downloaded correctly (no holes, the specified timerange was actually downloaded).
@@ -376,7 +377,7 @@ from pathlib import Path
 
 exchange = ccxt.binance({
     'apiKey': '<apikey>',
-    'secret': '<secret>'
+    'secret': '<secret>',
     'options': {'defaultType': 'swap'}
     })
 _ = exchange.load_markets()
@@ -419,6 +420,9 @@ This part of the documentation is aimed at maintainers, and shows how to create
 
 ### Create release branch
 
+!!! Note
+    Make sure that the `stable` branch is up-to-date!
+
 First, pick a commit that's about one week old (to not include latest additions to releases).
 
 ``` bash
@@ -431,14 +435,11 @@ Determine if crucial bugfixes have been made between this commit and the current
 * Merge the release branch (stable) into this branch.
 * Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
 * Commit this part.
-* push that branch to the remote and create a PR against the stable branch.
+* Push that branch to the remote and create a PR against the **stable branch**.
 * Update develop version to next version following the pattern `2019.8-dev`.
 
 ### Create changelog from git commits
 
-!!! Note
-    Make sure that the `stable` branch is up-to-date!
-
 ``` bash
 # Needs to be done before merging / pulling that branch.
 git log --oneline --no-decorate --no-merges stable..new_release
@@ -479,21 +480,24 @@ Once the PR against stable is merged (best right after merging):
 
 ### pypi
 
-!!! Note
-    This process is now automated as part of Github Actions.
+!!! Warning "Manual Releases"
+    This process is automated as part of Github Actions.  
+    Manual pypi pushes should not be necessary.
 
-To create a pypi release, please run the following commands:
+??? example "Manual release"
+    To manually create a pypi release, please run the following commands:
 
-Additional requirement: `wheel`, `twine` (for uploading), account on pypi with proper permissions.
+    Additional requirement: `wheel`, `twine` (for uploading), account on pypi with proper permissions.
 
-``` bash
-python setup.py sdist bdist_wheel
+    ``` bash
+    pip install -U build
+    python -m build --sdist --wheel
 
-# For pypi test (to check if some change to the installation did work)
-twine upload --repository-url https://test.pypi.org/legacy/ dist/*
+    # For pypi test (to check if some change to the installation did work)
+    twine upload --repository-url https://test.pypi.org/legacy/ dist/*
 
-# For production:
-twine upload dist/*
-```
+    # For production:
+    twine upload dist/*
+    ```
 
-Please don't push non-releases to the productive / real pypi instance.
+    Please don't push non-releases to the productive / real pypi instance.

docs/edge.md

@@ -137,7 +137,7 @@ $$ R = \frac{\text{average_profit}}{\text{average_loss}} = \frac{\mu_{win}}{\mu_
 
 ### Expectancy
 
-By combining the Win Rate $W$ and and the Risk Reward ratio $R$ to create an expectancy ratio $E$. A expectance ratio is the expected return of the investment made in a trade. We can compute the value of $E$ as follows:
+By combining the Win Rate $W$ and the Risk Reward ratio $R$ to create an expectancy ratio $E$. A expectance ratio is the expected return of the investment made in a trade. We can compute the value of $E$ as follows:
 
 $$E = R * W - L$$
 

docs/exchanges.md

@@ -68,6 +68,8 @@ Binance supports [time_in_force](configuration.md#understand-order_time_in_force
 For Binance, it is suggested to add `"BNB/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `BNB` on the account or unless you're willing to disable using `BNB` for fees.
 Binance accounts may use `BNB` for fees, and if a trade happens to be on `BNB`, further trades may consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
 
+If not enough `BNB` is available to cover transaction fees, then fees will not be covered by `BNB` and no fee reduction will occur. Freqtrade will never buy BNB to cover for fees. BNB needs to be bought and monitored manually to this end.
+
 ### Binance sites
 
 Binance has been split into 2, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
@@ -125,8 +127,17 @@ These settings will be checked on startup, and freqtrade will show an error if t
 
 Freqtrade will not attempt to change these settings.
 
+## Bingx
+
+BingX supports [time_in_force](configuration.md#understand-order_time_in_force) with settings "GTC" (good till cancelled), "IOC" (immediate-or-cancel) and "PO" (Post only) settings.
+
+!!! Tip "Stoploss on Exchange"
+    Bingx supports `stoploss_on_exchange` and can use both stop-limit and stop-market orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
+
 ## Kraken
 
+Kraken supports [time_in_force](configuration.md#understand-order_time_in_force) with settings "GTC" (good till cancelled), "IOC" (immediate-or-cancel) and "PO" (Post only) settings.
+
 !!! Tip "Stoploss on Exchange"
     Kraken supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
     You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
@@ -181,48 +192,6 @@ freqtrade download-data --exchange kraken --dl-trades -p BTC/EUR BCH/EUR
     Please pay attention that rateLimit configuration entry holds delay in milliseconds between requests, NOT requests\sec rate.
     So, in order to mitigate Kraken API "Rate limit exceeded" exception, this configuration should be increased, NOT decreased.
 
-## Bittrex
-
-### Order types
-
-Bittrex does not support market orders. If you have a message at the bot startup about this, you should change order type values set in your configuration and/or in the strategy from `"market"` to `"limit"`. See some more details on this [here in the FAQ](faq.md#im-getting-the-exchange-bittrex-does-not-support-market-orders-message-and-cannot-run-my-strategy).
-
-Bittrex also does not support `VolumePairlist` due to limited / split API constellation at the moment.
-Please use `StaticPairlist`. Other pairlists (other than `VolumePairlist`) should not be affected.
-
-### Volume pairlist
-
-Bittrex does not support the direct usage of VolumePairList. This can however be worked around by using the advanced mode with `lookback_days: 1` (or more), which will emulate 24h volume.
-
-Read more in the [pairlist documentation](plugins.md#volumepairlist-advanced-mode).
-
-### Restricted markets
-
-Bittrex split its exchange into US and International versions.
-The International version has more pairs available, however the API always returns all pairs, so there is currently no automated way to detect if you're affected by the restriction.
-
-If you have restricted pairs in your whitelist, you'll get a warning message in the log on Freqtrade startup for each restricted pair.
-
-The warning message will look similar to the following:
-
-``` output
-[...] Message: bittrex {"success":false,"message":"RESTRICTED_MARKET","result":null,"explanation":null}"
-```
-
-If you're an "International" customer on the Bittrex exchange, then this warning will probably not impact you.
-If you're a US customer, the bot will fail to create orders for these pairs, and you should remove them from your whitelist.
-
-You can get a list of restricted markets by using the following snippet:
-
-``` python
-import ccxt
-ct = ccxt.bittrex()
-lm = ct.load_markets()
-
-res = [p for p, x in lm.items() if 'US' in x['info']['prohibitedIn']]
-print(res)
-```
-
 ## Kucoin
 
 Kucoin requires a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:
@@ -248,10 +217,10 @@ Kucoin supports [time_in_force](configuration.md#understand-order_time_in_force)
 For Kucoin, it is suggested to add `"KCS/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `KCS` on the account or unless you're willing to disable using `KCS` for fees. 
 Kucoin accounts may use `KCS` for fees, and if a trade happens to be on `KCS`, further trades may consume this position and make the initial `KCS` trade unsellable as the expected amount is not there anymore.
 
-## Huobi
+## HTX (formerly Huobi)
 
 !!! Tip "Stoploss on Exchange"
-    Huobi supports `stoploss_on_exchange` and uses `stop-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
+    HTX supports `stoploss_on_exchange` and uses `stop-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
 
 ## OKX (former OKEX)
 
@@ -286,18 +255,24 @@ The configuration parameter `exchange.unknown_fee_rate` can be used to specify t
 ## Bybit
 
 Futures trading on bybit is currently supported for USDT markets, and will use isolated futures mode.
-Users with unified accounts (there's no way back) can create a Sub-account which will start as "non-unified", and can therefore use isolated futures.
-On startup, freqtrade will set the position mode to "One-way Mode" for the whole (sub)account. This avoids making this call over and over again (slowing down bot operations), but means that changes to this setting may result in exceptions and errors
+
+On startup, freqtrade will set the position mode to "One-way Mode" for the whole (sub)account. This avoids making this call over and over again (slowing down bot operations), but means that changes to this setting may result in exceptions and errors.
 
 As bybit doesn't provide funding rate history, the dry-run calculation is used for live trades as well.
 
-API Keys for live futures trading (Subaccount on non-unified) must have the following permissions:
+API Keys for live futures trading must have the following permissions:
 * Read-write
 * Contract - Orders
 * Contract - Positions
 
 We do strongly recommend to limit all API keys to the IP you're going to use it from.
 
+!!! Warning "Unified accounts"
+    Freqtrade assumes accounts to be dedicated to the bot.
+    We therefore recommend the usage of one subaccount per bot. This is especially important when using unified accounts.  
+    Other configurations (multiple bots on one account, manual non-bot trades on the bot account) are not supported and may lead to unexpected behavior.
+
+
 !!! Tip "Stoploss on Exchange"
     Bybit (futures only) supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
     On futures, Bybit supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
@@ -337,7 +312,7 @@ $ pip3 install web3
 Most exchanges return current incomplete candle via their OHLCV/klines API interface.
 By default, Freqtrade assumes that incomplete candle is fetched from the exchange and removes the last candle assuming it's the incomplete candle.
 
-Whether your exchange returns incomplete candles or not can be checked using [the helper script](developer.md#Incomplete-candles) from the Contributor documentation.
+Whether your exchange returns incomplete candles or not can be checked using [the helper script](developer.md#incomplete-candles) from the Contributor documentation.
 
 Due to the danger of repainting, Freqtrade does not allow you to use this incomplete candle.
 

docs/faq.md

@@ -2,7 +2,7 @@
 
 ## Supported Markets
 
-Freqtrade supports spot trading, as well as (isolated) futures trading for some selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an uptodate list of supported exchanges.
+Freqtrade supports spot trading, as well as (isolated) futures trading for some selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an up-to-date list of supported exchanges.
 
 ### Can my bot open short positions?
 
@@ -14,7 +14,7 @@ In spot markets, you can in some cases use leveraged spot tokens, which reflect
 
 ### Can my bot trade options or futures?
 
-Futures trading is supported for selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an uptodate list of supported exchanges.
+Futures trading is supported for selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an up-to-date list of supported exchanges.
 
 ## Beginner Tips & Tricks
 
@@ -128,15 +128,9 @@ This warning can point to one of the below problems:
 * Barely traded pair -> Check the pair on the exchange webpage, look at the timeframe your strategy uses. If the pair does not have any volume in some candles (usually visualized with a "volume 0" bar, and a "_" as candle), this pair did not have any trades in this timeframe. These pairs should ideally be avoided, as they can cause problems with order-filling.
 * API problem -> API returns wrong data (this only here for completeness, and should not happen with supported exchanges).
 
-### I'm getting the "RESTRICTED_MARKET" message in the log
-
-Currently known to happen for US Bittrex users.
-
-Read [the Bittrex section about restricted markets](exchanges.md#restricted-markets) for more information.
-
 ### I'm getting the "Exchange XXX does not support market orders." message and cannot run my strategy
 
-As the message says, your exchange does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Your strategy was probably written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Bittrex and Gate.io).
+As the message says, your exchange does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Your strategy was probably written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Gate.io).
 
 To fix this, redefine order types in the strategy to use "limit" instead of "market":
 

docs/freq-ui.md

@@ -0,0 +1,84 @@
+# FreqUI
+
+Freqtrade provides a builtin webserver, which can serve [FreqUI](https://github.com/freqtrade/frequi), the freqtrade frontend.
+
+By default, the UI is automatically installed as part of the installation (script, docker).
+freqUI can also be manually installed by using the `freqtrade install-ui` command.
+This same command can also be used to update freqUI to new new releases.
+
+Once the bot is started in trade / dry-run mode (with `freqtrade trade`) - the UI will be available under the configured API port (by default `http://127.0.0.1:8080`).
+
+??? Note "Looking to contribute to freqUI?"
+    Developers should not use this method, but instead clone the corresponding use the method described in the [freqUI repository](https://github.com/freqtrade/frequi) to get the source-code of freqUI. A working installation of node will be required to build the frontend.
+
+!!! tip "freqUI is not required to run freqtrade"
+    freqUI is an optional component of freqtrade, and is not required to run the bot.
+    It is a frontend that can be used to monitor the bot and to interact with it - but freqtrade itself will work perfectly fine without it.
+
+## Configuration
+
+FreqUI does not have it's own configuration file - but assumes a working setup for the [rest-api](rest-api.md) is available.
+Please refer to the corresponding documentation page to get setup with freqUI
+
+## UI
+
+FreqUI is a modern, responsive web application that can be used to monitor and interact with your bot.
+
+FreqUI provides a light, as well as a dark theme.
+Themes can be easily switched via a prominent button at the top of the page.
+The theme of the screenshots on this page will adapt to the selected documentation Theme, so to see the dark (or light) version, please switch the theme of the Documentation.
+
+### Login
+
+The below screenshot shows the login screen of freqUI.
+
+![FreqUI - login](assets/frequi-login-CORS.png#only-dark)
+![FreqUI - login](assets/frequi-login-CORS-light.png#only-light)
+
+!!! Hint "CORS"
+    The Cors error shown in this screenshot is due to the fact that the UI is running on a different port than the API, and [CORS](#cors) has not been setup correctly yet.
+
+### Trade view
+
+The trade view allows you to visualize the trades that the bot is making and to interact with the bot.
+On this page, you can also interact with the bot by starting and stopping it and - if configured - force trade entries and exits.
+
+![FreqUI - trade view](assets/freqUI-trade-pane-dark.png#only-dark)
+![FreqUI - trade view](assets/freqUI-trade-pane-light.png#only-light)
+
+### Plot Configurator
+
+FreqUI Plots can be configured either via a `plot_config` configuration object in the strategy (which can be loaded via "from strategy" button) or via the UI.
+Multiple plot configurations can be created and switched at will - allowing for flexible, different views into your charts.
+
+The plot configuration can be accessed via the "Plot Configurator" (Cog icon) button in the top right corner of the trade view.
+
+![FreqUI - plot configuration](assets/freqUI-plot-configurator-dark.png#only-dark)
+![FreqUI - plot configuration](assets/freqUI-plot-configurator-light.png#only-light)
+
+### Settings
+
+Several UI related settings can be changed by accessing the settings page.
+
+Things you can change (among others):
+
+* Timezone of the UI
+* Visualization of open trades as part of the favicon (browser tab)
+* Candle colors (up/down -> red/green)
+* Enable / disable in-app notification types
+
+![FreqUI - Settings view](assets/frequi-settings-dark.png#only-dark)
+![FreqUI - Settings view](assets/frequi-settings-light.png#only-light)
+
+## Backtesting
+
+When freqtrade is started in [webserver mode](utils.md#webserver-mode) (freqtrade started with `freqtrade webserver`), the backtesting view becomes available.
+This view allows you to backtest strategies and visualize the results.
+
+You can also load and visualize previous backtest results, as well as compare the results with each other.
+
+![FreqUI - Backtesting](assets/freqUI-backtesting-dark.png#only-dark)
+![FreqUI - Backtesting](assets/freqUI-backtesting-light.png#only-light)
+
+
+--8<-- "includes/cors.md"

docs/freqai-configuration.md

@@ -32,6 +32,9 @@ FreqAI is configured through the typical [Freqtrade config file](configuration.m
 
 A full example config is available in `config_examples/config_freqai.example.json`.
 
+!!! Note
+    The `identifier` is commonly overlooked by newcomers, however, this value plays an important role in your configuration. This value is a unique ID that you choose to describe one of your runs. Keeping it the same allows you to maintain crash resilience as well as faster backtesting. As soon as you want to try a new run (new features, new model, etc.), you should change this value (or delete the `user_data/models/unique-id` folder. More details available in the [parameter table](freqai-parameter-table.md#feature-parameters).
+
 ## Building a FreqAI strategy
 
 The FreqAI strategy requires including the following lines of code in the standard [Freqtrade strategy](strategy-customization.md):
@@ -162,7 +165,8 @@ Below are the values you can expect to include/use inside a typical strategy dat
 | `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
 | `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
 | `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
-| `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
+| `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%` (see details below). <br> **Datatype:** Depends on the feature created by the user.
+| `df['%%*']` | Any dataframe column prepended with `%%` in `feature_engineering_*()` is treated as a training feature, just the same as the above `%` prepend. However, in this case, the features are returned back to the strategy for FreqUI/plot-dataframe plotting and monitoring in Dry/Live/Backtesting <br> **Datatype:** Depends on the feature created by the user. Please note that features created in `feature_engineering_expand()` will have automatic FreqAI naming schemas depending on the expansions that you configured (i.e. `include_timeframes`, `include_corr_pairlist`, `indicators_periods_candles`, `include_shifted_candles`). So if you want to plot `%%-rsi` from `feature_engineering_expand_all()`, the final naming scheme for your plotting config would be: `%%-rsi-period_10_ETH/USDT:USDT_1h` for the `rsi` feature with `period=10`, `timeframe=1h`, and `pair=ETH/USDT:USDT` (the `:USDT` is added if you are using futures pairs). It is useful to simply add `print(dataframe.columns)` in your `populate_indicators()` after `self.freqai.start()` to see the full list of available features that are returned to the strategy for plotting purposes.
 
 ## Setting the `startup_candle_count`
 

docs/freqai-feature-engineering.md

@@ -224,7 +224,7 @@ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. B
 
 ## Building the data pipeline
 
-By default, FreqAI builds a dynamic pipeline based on user congfiguration settings. The default settings are robust and designed to work with a variety of methods. These two steps are a `MinMaxScaler(-1,1)` and a `VarianceThreshold` which removes any column that has 0 variance. Users can activate other steps with more configuration parameters. For example if users add `use_SVM_to_remove_outliers: true` to the `freqai` config, then FreqAI will automatically add the [`SVMOutlierExtractor`](#identifying-outliers-using-a-support-vector-machine-svm) to the pipeline. Likewise, users can add `principal_component_analysis: true` to the `freqai` config to activate PCA. The [DissimilarityIndex](#identifying-outliers-with-the-dissimilarity-index-di) is activated with `DI_threshold: 1`. Finally, noise can also be added to the data with `noise_standard_deviation: 0.1`. Finally, users can add [DBSCAN](#identifying-outliers-with-dbscan) outlier removal with `use_DBSCAN_to_remove_outliers: true`.
+By default, FreqAI builds a dynamic pipeline based on user configuration settings. The default settings are robust and designed to work with a variety of methods. These two steps are a `MinMaxScaler(-1,1)` and a `VarianceThreshold` which removes any column that has 0 variance. Users can activate other steps with more configuration parameters. For example if users add `use_SVM_to_remove_outliers: true` to the `freqai` config, then FreqAI will automatically add the [`SVMOutlierExtractor`](#identifying-outliers-using-a-support-vector-machine-svm) to the pipeline. Likewise, users can add `principal_component_analysis: true` to the `freqai` config to activate PCA. The [DissimilarityIndex](#identifying-outliers-with-the-dissimilarity-index-di) is activated with `DI_threshold: 1`. Finally, noise can also be added to the data with `noise_standard_deviation: 0.1`. Finally, users can add [DBSCAN](#identifying-outliers-with-dbscan) outlier removal with `use_DBSCAN_to_remove_outliers: true`.
 
 !!! note "More information available"
     Please review the [parameter table](freqai-parameter-table.md) for more information on these parameters.
@@ -235,7 +235,7 @@ By default, FreqAI builds a dynamic pipeline based on user congfiguration settin
 Users are encouraged to customize the data pipeline to their needs by building their own data pipeline. This can be done by simply setting `dk.feature_pipeline` to their desired `Pipeline` object inside their `IFreqaiModel` `train()` function, or if they prefer not to touch the `train()` function, they can override `define_data_pipeline`/`define_label_pipeline` functions in their `IFreqaiModel`:
 
 !!! note "More information available"
-    FreqAI uses the the [`DataSieve`](https://github.com/emergentmethods/datasieve) pipeline, which follows the SKlearn pipeline API, but adds, among other features, coherence between the X, y, and sample_weight vector point removals, feature removal, feature name following. 
+    FreqAI uses the [`DataSieve`](https://github.com/emergentmethods/datasieve) pipeline, which follows the SKlearn pipeline API, but adds, among other features, coherence between the X, y, and sample_weight vector point removals, feature removal, feature name following. 
 
 ```python
 from datasieve.transforms import SKLearnWrapper, DissimilarityIndex
@@ -391,3 +391,18 @@ Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters
 ![dbscan](assets/freqai_dbscan.jpg)
 
 FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (external website)) with `min_samples` ($N$) taken as 1/4 of the no. of time points (candles) in the feature set. `eps` ($\varepsilon$) is computed automatically as the elbow point in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set.
+
+
+### Data dimensionality reduction with Principal Component Analysis
+
+You can reduce the dimensionality of your features by activating the principal_component_analysis in the config:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "principal_component_analysis": true
+        }
+    }
+```
+
+This will perform PCA on the features and reduce their dimensionality so that the explained variance of the data set is >= 0.999. Reducing data dimensionality makes training the model faster and hence allows for more up-to-date models.

docs/freqai-parameter-table.md

@@ -22,6 +22,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
 | `data_kitchen_thread_count` | <br> Designate the number of threads you want to use for data processing (outlier methods, normalization, etc.). This has no impact on the number of threads used for training. If user does not set it (default), FreqAI will use max number of threads - 2 (leaving 1 physical core available for Freqtrade bot and FreqUI) <br> **Datatype:** Positive integer.
 | `activate_tensorboard` | <br> Indicate whether or not to activate tensorboard for the tensorboard enabled modules (currently Reinforcment Learning, XGBoost, Catboost, and PyTorch). Tensorboard needs Torch installed, which means you will need the torch/RL docker image or you need to answer "yes" to the install question about whether or not you wish to install Torch. <br> **Datatype:** Boolean. <br> Default: `True`.
+| `wait_for_training_iteration_on_reload` | <br> When using /reload or ctrl-c, wait for the current training iteration to finish before completing graceful shutdown. If set to `False`, FreqAI will break the current training iteration, allowing you to shutdown gracefully more quickly, but you will lose your current training iteration. <br> **Datatype:** Boolean. <br> Default: `True`.
 
 ### Feature parameters
 
@@ -31,12 +32,12 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
 | `include_timeframes` | A list of timeframes that all indicators in `feature_engineering_expand_*()` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
 | `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `feature_engineering_expand_*()` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
-| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `feature_engineering_expand_all()` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
+| `label_period_candles` | Number of candles into the future that the labels are created for. This can be used in `set_freqai_targets()` (see `templates/FreqaiExampleStrategy.py` for detailed usage). This parameter is not necessarily required, you can create custom labels and choose whether to make use of this parameter or not. Please see `templates/FreqaiExampleStrategy.py` to see the example usage. <br> **Datatype:** Positive integer.
 | `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
 | `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)). <br> **Datatype:** Positive float (typically < 1).
 | `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `feature_engineering_*()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
 | `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset. <br> **Datatype:** List of positive integers.
-| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. <br> Default: `False`.
+| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis) <br> **Datatype:** Boolean. <br> Default: `False`.
 | `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features. Plot is stored in `user_data/models/<identifier>/sub-train-<COIN>_<timestamp>.html`. <br> **Datatype:** Integer. <br> Default: `0`.
 | `DI_threshold` | Activates the use of the Dissimilarity Index for outlier detection when set to > 0. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Positive float (typically < 1).
 | `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
@@ -55,7 +56,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 |  |  **Data split parameters within the `freqai.data_split_parameters` sub dictionary**
 | `data_split_parameters` | Include any additional parameters available from scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
 | `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
-| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Defaut: `False`.
+| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Default: `False`.
 
 ### Model training parameters
 
@@ -75,7 +76,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `rl_config` | A dictionary containing the control parameters for a Reinforcement Learning model. <br> **Datatype:** Dictionary.
 | `train_cycles` | Training time steps will be set based on the `train_cycles * number of training data points. <br> **Datatype:** Integer.
 | `max_trade_duration_candles`| Guides the agent training to keep trades below desired length. Example usage shown in `prediction_models/ReinforcementLearner.py` within the customizable `calculate_reward()` function. <br> **Datatype:** int.
-| `model_type` | Model string from stable_baselines3 or SBcontrib. Available strings include: `'TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO', 'PPO', 'A2C', 'DQN'`. User should ensure that `model_training_parameters` match those available to the corresponding stable_baselines3 model by visiting their documentaiton. [PPO doc](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) (external website) <br> **Datatype:** string.
+| `model_type` | Model string from stable_baselines3 or SBcontrib. Available strings include: `'TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO', 'PPO', 'A2C', 'DQN'`. User should ensure that `model_training_parameters` match those available to the corresponding stable_baselines3 model by visiting their documentation. [PPO doc](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) (external website) <br> **Datatype:** string.
 | `policy_type` | One of the available policy types from stable_baselines3 <br> **Datatype:** string.
 | `max_training_drawdown_pct` | The maximum drawdown that the agent is allowed to experience during training. <br> **Datatype:** float. <br> Default: 0.8
 | `cpu_count` | Number of threads/cpus to dedicate to the Reinforcement Learning training process (depending on if `ReinforcementLearning_multiproc` is selected or not). Recommended to leave this untouched, by default, this value is set to the total number of physical cores minus 1. <br> **Datatype:** int. 

docs/freqai-reinforcement-learning.md

@@ -142,7 +142,7 @@ Parameter details can be found [here](freqai-parameter-table.md), but in general
 As you begin to modify the strategy and the prediction model, you will quickly realize some important differences between the Reinforcement Learner and the Regressors/Classifiers. Firstly, the strategy does not set a target value (no labels!). Instead, you set the `calculate_reward()` function inside the `MyRLEnv` class (see below). A default `calculate_reward()` is provided inside `prediction_models/ReinforcementLearner.py` to demonstrate the necessary building blocks for creating rewards, but this is *not* designed for production. Users *must* create their own custom reinforcement learning model class or use a pre-built one from outside the Freqtrade source code and save it to `user_data/freqaimodels`. It is inside the `calculate_reward()` where creative theories about the market can be expressed. For example, you can reward your agent when it makes a winning trade, and penalize the agent when it makes a losing trade. Or perhaps, you wish to reward the agent for entering trades, and penalize the agent for sitting in trades too long. Below we show examples of how these rewards are all calculated:
 
 !!! note "Hint"
-    The best reward functions are ones that are continuously differentiable, and well scaled. In other words, adding a single large negative penalty to a rare event is not a good idea, and the neural net will not be able to learn that function. Instead, it is better to add a small negative penalty to a common event. This will help the agent learn faster. Not only this, but you can help improve the continuity of your rewards/penalties by having them scale with severity according to some linear/exponential functions. In other words, you'd slowly scale the penalty as the duration of the trade increases. This is better than a single large penalty occuring at a single point in time.
+    The best reward functions are ones that are continuously differentiable, and well scaled. In other words, adding a single large negative penalty to a rare event is not a good idea, and the neural net will not be able to learn that function. Instead, it is better to add a small negative penalty to a common event. This will help the agent learn faster. Not only this, but you can help improve the continuity of your rewards/penalties by having them scale with severity according to some linear/exponential functions. In other words, you'd slowly scale the penalty as the duration of the trade increases. This is better than a single large penalty occurring at a single point in time.
 
 ```python
 from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner

docs/freqai-running.md

@@ -41,11 +41,11 @@ FreqAI stores new model files after each successful training. These files become
 
 ```json
     "freqai": {
-        "purge_old_models": true,
+        "purge_old_models": 4,
     }
 ```
 
-This will automatically purge all models older than the two most recently trained ones to save disk space.
+This will automatically purge all models older than the four most recently trained ones to save disk space. Inputing "0" will never purge any models.
 
 ## Backtesting
 
@@ -68,28 +68,31 @@ Backtesting mode requires [downloading the necessary data](#downloading-data-to-
     This way, you can return to using any model you wish by simply specifying the `identifier`.
 
 !!! Note
-    Backtesting calls `set_freqai_targets()` one time for each backtest window (where the number of windows is the full backtest timerange divided by the `backtest_period_days` parameter). Doing this means that the targets simulate dry/live behavior without look ahead bias. However, the definition of the features in `feature_engineering_*()` is performed once on the entire backtest timerange. This means that you should be sure that features do look-ahead into the future.
+    Backtesting calls `set_freqai_targets()` one time for each backtest window (where the number of windows is the full backtest timerange divided by the `backtest_period_days` parameter). Doing this means that the targets simulate dry/live behavior without look ahead bias. However, the definition of the features in `feature_engineering_*()` is performed once on the entire training timerange. This means that you should be sure that features do not look-ahead into the future.
     More details about look-ahead bias can be found in [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies).
 
 ---
 
-### Saving prediction data
+### Saving backtesting prediction data
 
 To allow for tweaking your strategy (**not** the features!), FreqAI will automatically save the predictions during backtesting so that they can be reused for future backtests and live runs using the same `identifier` model. This provides a performance enhancement geared towards enabling **high-level hyperopting** of entry/exit criteria.
 
-An additional directory called `backtesting_predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder.
+An additional directory called `backtesting_predictions`, which contains all the predictions stored in `feather` format, will be created in the `unique-id` folder.
 
 To change your **features**, you **must** set a new `identifier` in the config to signal to FreqAI to train new models.
 
 To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config.
 
+!!! Note
+    To ensure that the model can be reused, freqAI will call your strategy with a dataframe of length 1. 
+    If your strategy requires more data than this to generate the same features, you can't reuse backtest predictions for live deployment and need to update your `identifier` for each new backtest.
+
 ### Backtest live collected predictions
 
 FreqAI allow you to reuse live historic predictions through the backtest parameter `--freqai-backtest-live-models`. This can be useful when you want to reuse predictions generated in dry/run for comparison or other study.
 
 The `--timerange` parameter must not be informed, as it will be automatically calculated through the data in the historic predictions file.
 
-
 ### Downloading data to cover the full backtest period
 
 For live/dry deployments, FreqAI will download the necessary data automatically. However, to use backtesting functionality, you need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). You need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that there is a sufficient amount of training data *before* the start of the backtesting time range. The amount of additional data can be roughly estimated by moving the start date of the time range backwards by `train_period_days` and the `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters) from the beginning of the desired backtesting time range. 

docs/freqai.md

@@ -114,6 +114,11 @@ Here we compile some external materials that provide deeper looks into various c
 - [Real-time head-to-head: Adaptive modeling of financial market data using XGBoost and CatBoost](https://emergentmethods.medium.com/real-time-head-to-head-adaptive-modeling-of-financial-market-data-using-xgboost-and-catboost-995a115a7495)
 - [FreqAI - from price to prediction](https://emergentmethods.medium.com/freqai-from-price-to-prediction-6fadac18b665)
 
+
+## Support
+
+You can find support for FreqAI in a variety of places, including the [Freqtrade discord](https://discord.gg/Jd8JYeWHc4), the dedicated [FreqAI discord](https://discord.gg/7AMWACmbjT), and in [github issues](https://github.com/freqtrade/freqtrade/issues).
+
 ## Credits
 
 FreqAI is developed by a group of individuals who all contribute specific skillsets to the project.

docs/hyperopt.md

@@ -14,8 +14,7 @@ To learn how to get data for the pairs and exchange you're interested in, head o
 
 !!! Note
     Since 2021.4 release you no longer have to write a separate hyperopt class, but can configure the parameters directly in the strategy.
-    The legacy method is still supported, but it is no longer the recommended way of setting up hyperopt. 
-    The legacy documentation is available at [Legacy Hyperopt](advanced-hyperopt.md#legacy-hyperopt).
+    The legacy method was supported up to 2021.8 and has been removed in 2021.9.
 
 ## Install hyperopt dependencies
 
@@ -439,14 +438,13 @@ While this strategy is most likely too simple to provide consistent profit, it s
 ??? Hint "Performance tip"
     During normal hyperopting, indicators are calculated once and supplied to each epoch, linearly increasing RAM usage as a factor of increasing cores. As this also has performance implications, there are two alternatives to reduce RAM usage
 
-    * Move `ema_short` and `ema_long` calculations from `populate_indicators()` to `populate_entry_trend()`. Since `populate_entry_trend()` gonna be calculated every epochs, you don't need to use `.range` functionality.
+    * Move `ema_short` and `ema_long` calculations from `populate_indicators()` to `populate_entry_trend()`. Since `populate_entry_trend()` will be calculated every epoch, you don't need to use `.range` functionality.
     * hyperopt provides `--analyze-per-epoch` which will move the execution of `populate_indicators()` to the epoch process, calculating a single value per parameter per epoch instead of using the `.range` functionality. In this case, `.range` functionality will only return the actually used value.
 
     These alternatives will reduce RAM usage, but increase CPU usage. However, your hyperopting run will be less likely to fail due to Out Of Memory (OOM) issues.
 
     Whether you are using `.range` functionality or the alternatives above, you should try to use space ranges as small as possible since this will improve CPU/RAM usage.
 
-
 ## Optimizing protections
 
 Freqtrade can also optimize protections. How you optimize protections is up to you, and the following should be considered as example only.
@@ -590,14 +588,15 @@ Currently, the following loss functions are builtin:
 
 * `ShortTradeDurHyperOptLoss` - (default legacy Freqtrade hyperoptimization loss function) - Mostly for short trade duration and avoiding losses.
 * `OnlyProfitHyperOptLoss` - takes only amount of profit into consideration.
-* `SharpeHyperOptLoss` - optimizes Sharpe Ratio calculated on trade returns relative to standard deviation.
-* `SharpeHyperOptLossDaily` - optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation.
-* `SortinoHyperOptLoss` - optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation.
+* `SharpeHyperOptLoss` - Optimizes Sharpe Ratio calculated on trade returns relative to standard deviation.
+* `SharpeHyperOptLossDaily` - Optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation.
+* `SortinoHyperOptLoss` - Optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation.
 * `SortinoHyperOptLossDaily` - optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation.
 * `MaxDrawDownHyperOptLoss` - Optimizes Maximum absolute drawdown.
 * `MaxDrawDownRelativeHyperOptLoss` -  Optimizes both maximum absolute drawdown while also adjusting for maximum relative drawdown.
 * `CalmarHyperOptLoss` - Optimizes Calmar Ratio calculated on trade returns relative to max drawdown.
 * `ProfitDrawDownHyperOptLoss` - Optimizes by max Profit & min Drawdown objective. `DRAWDOWN_MULT` variable within the hyperoptloss file can be adjusted to be stricter or more flexible on drawdown purposes.
+* `MultiMetricHyperOptLoss` -  Optimizes by several key metrics to achieve balanced performance. The primary focus is on maximizing Profit and minimizing Drawdown, while also considering additional metrics such as Profit Factor, Expectancy Ratio and Winrate. Moreover, it applies a penalty for epochs with a low number of trades, encouraging strategies with adequate trade frequency.
 
 Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
 
@@ -765,7 +764,7 @@ Override the `roi_space()` method if you need components of the ROI tables to va
 A sample for these methods can be found in the [overriding pre-defined spaces section](advanced-hyperopt.md#overriding-pre-defined-spaces).
 
 !!! Note "Reduced search space"
-    To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#pverriding-pre-defined-spaces) to change this to your needs.
+    To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#overriding-pre-defined-spaces) to change this to your needs.
 
 ### Understand Hyperopt Stoploss results
 
@@ -807,7 +806,7 @@ If you have the `stoploss_space()` method in your custom hyperopt file, remove i
 Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in the [overriding pre-defined spaces section](advanced-hyperopt.md#overriding-pre-defined-spaces).
 
 !!! Note "Reduced search space"
-    To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#pverriding-pre-defined-spaces) to change this to your needs.
+    To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#overriding-pre-defined-spaces) to change this to your needs.
 
 ### Understand Hyperopt Trailing Stop results
 
@@ -926,6 +925,12 @@ Once the optimized strategy has been implemented into your strategy, you should
 
 To achieve same the results (number of trades, their durations, profit, etc.) as during Hyperopt, please use the same configuration and parameters (timerange, timeframe, ...) used for hyperopt `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
 
-Should results not match, please double-check to make sure you transferred all conditions correctly.
-Pay special care to the stoploss, max_open_trades and trailing stoploss parameters, as these are often set in configuration files, which override changes to the strategy.
-You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss`, `max_open_trades` or `trailing_stop`).
+### Why do my backtest results not match my hyperopt results?
+Should results not match, check the following factors:
+
+* You may have added parameters to hyperopt in `populate_indicators()` where they will be calculated only once **for all epochs**. If you are, for example, trying to optimise multiple SMA timeperiod values, the hyperoptable timeperiod parameter should be placed in `populate_entry_trend()` which is calculated every epoch. See [Optimizing an indicator parameter](https://www.freqtrade.io/en/stable/hyperopt/#optimizing-an-indicator-parameter).
+* If you have disabled the auto-export of hyperopt parameters into the JSON parameters file, double-check to make sure you transferred all hyperopted values into your strategy correctly.
+* Check the logs to verify what parameters are being set and what values are being used.
+* Pay special care to the stoploss, max_open_trades and trailing stoploss parameters, as these are often set in configuration files, which override changes to the strategy. Check the logs of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss`, `max_open_trades` or `trailing_stop`).
+* Verify that you do not have an unexpected parameters JSON file overriding the parameters or the default hyperopt settings in your strategy.
+* Verify that any protections that are enabled in backtesting are also enabled when hyperopting, and vice versa. When using `--space protection`, protections are auto-enabled for hyperopting.

docs/includes/cors.md

@@ -0,0 +1,43 @@
+## CORS
+
+This whole section is only necessary in cross-origin cases (where you multiple bot API's running on `localhost:8081`, `localhost:8082`, ...), and want to combine them into one FreqUI instance.
+
+??? info "Technical explanation"
+    All web-based front-ends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
+    Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
+    Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
+
+Users can allow access from different origin URL's to the bot API via the `CORS_origins` configuration setting.
+It consists of a list of allowed URL's that are allowed to consume resources from the bot's API.
+
+Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - this would mean that the following configuration becomes necessary:
+
+```jsonc
+{
+    //...
+    "jwt_secret_key": "somethingrandom",
+    "CORS_origins": ["https://frequi.freqtrade.io"],
+    //...
+}
+```
+
+In the following (pretty common) case, FreqUI is accessible on `http://localhost:8080/trade` (this is what you see in your navbar when navigating to freqUI).
+![freqUI url](assets/frequi_url.png)
+
+The correct configuration for this case is `http://localhost:8080` - the main part of the URL including the port.
+
+```jsonc
+{
+    //...
+    "jwt_secret_key": "somethingrandom",
+    "CORS_origins": ["http://localhost:8080"],
+    //...
+}
+```
+
+!!! Tip "trailing Slash"
+    The trailing slash is not allowed in the `CORS_origins` configuration (e.g. `"http://localhots:8080/"`).
+    Such a configuration will not take effect, and the cors errors will remain.
+
+!!! Note
+    We strongly recommend to also set `jwt_secret_key` to something random and known only to yourself to avoid unauthorized access to your bot.

docs/includes/pairlists.md

@@ -2,11 +2,11 @@
 
 Pairlist Handlers define the list of pairs (pairlist) that the bot should trade. They are configured in the `pairlists` section of the configuration settings.
 
-In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list) Pairlist Handler).
+In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list) and [`PercentChangePairList`](#percent-change-pair-list) Pairlist Handlers).
 
 Additionally, [`AgeFilter`](#agefilter), [`PrecisionFilter`](#precisionfilter), [`PriceFilter`](#pricefilter), [`ShuffleFilter`](#shufflefilter), [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) act as Pairlist Filters, removing certain pairs and/or moving their positions in the pairlist.
 
-If multiple Pairlist Handlers are used, they are chained and a combination of all Pairlist Handlers forms the resulting pairlist the bot uses for trading and backtesting. Pairlist Handlers are executed in the sequence they are configured. You should always configure either `StaticPairList` or `VolumePairList` as the starting Pairlist Handler.
+If multiple Pairlist Handlers are used, they are chained and a combination of all Pairlist Handlers forms the resulting pairlist the bot uses for trading and backtesting. Pairlist Handlers are executed in the sequence they are configured. You can define either `StaticPairList`, `VolumePairList`, `ProducerPairList`, `RemotePairList`, `MarketCapPairList` or `PercentChangePairList` as the starting Pairlist Handler.
 
 Inactive markets are always removed from the resulting pairlist. Explicitly blacklisted pairs (those in the `pair_blacklist` configuration setting) are also always removed from the resulting pairlist.
 
@@ -22,8 +22,10 @@ You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged
 
 * [`StaticPairList`](#static-pair-list) (default, if not configured differently)
 * [`VolumePairList`](#volume-pair-list)
+* [`PercentChangePairList`](#percent-change-pair-list)
 * [`ProducerPairList`](#producerpairlist)
 * [`RemotePairList`](#remotepairlist)
+* [`MarketCapPairList`](#marketcappairlist)
 * [`AgeFilter`](#agefilter)
 * [`FullTradesFilter`](#fulltradesfilter)
 * [`OffsetFilter`](#offsetfilter)
@@ -53,7 +55,6 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
 By default, only currently enabled pairs are allowed.
 To skip pair validation against active markets, set `"allow_inactive": true` within the `StaticPairList` configuration.
 This can be useful for backtesting expired pairs (like quarterly spot-markets).
-This option must be configured along with `exchange.skip_pair_validation` in the exchange configuration.
 
 When used in a "follow-up" position (e.g. after VolumePairlist), all pairs in `'pair_whitelist'` will be added to the end of the pairlist.
 
@@ -67,7 +68,7 @@ When used in the leading position of the chain of Pairlist Handlers, the `pair_w
 
 The `refresh_period` setting allows to define the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
 The pairlist cache (`refresh_period`) on `VolumePairList` is only applicable to generating pairlists.
-Filtering instances (not the first position in the list) will not apply any cache and will always use up-to-date data.
+Filtering instances (not the first position in the list) will not apply any cache (beyond caching candles for the duration of the candle in advanced mode) and will always use up-to-date data.
 
 `VolumePairList` is per default based on the ticker data from exchange, as reported by the ccxt library:
 
@@ -80,12 +81,14 @@ Filtering instances (not the first position in the list) will not apply any cach
         "number_assets": 20,
         "sort_key": "quoteVolume",
         "min_value": 0,
+        "max_value": 8000000,
         "refresh_period": 1800
     }
 ],
 ```
 
 You can define a minimum volume with `min_value` - which will filter out pairs with a volume lower than the specified value in the specified timerange.
+In addition to that, you can also define a maximum volume with `max_value` - which will filter out pairs with a volume higher than the specified value in the specified timerange.
 
 ##### VolumePairList Advanced mode
 
@@ -112,8 +115,8 @@ For convenience `lookback_days` can be specified, which will imply that 1d candl
 !!! Warning "Performance implications when using lookback range"
     If used in first position in combination with lookback, the computation of the range based volume can be time and resource consuming, as it downloads candles for all tradable pairs. Hence it's highly advised to use the standard approach with `VolumeFilter` to narrow the pairlist down for further range volume calculation.
 
-??? Tip "Unsupported exchanges (Bittrex, Gemini)"
-    On some exchanges (like Bittrex and Gemini), regular VolumePairList does not work as the api does not natively provide 24h volume. This can be worked around by using candle data to build the volume.
+??? Tip "Unsupported exchanges"
+    On some exchanges (like Gemini), regular VolumePairList does not work as the api does not natively provide 24h volume. This can be worked around by using candle data to build the volume.
     To roughly simulate 24h volume, you can use the following configuration.
     Please note that These pairlists will only refresh once per day.
 
@@ -149,6 +152,89 @@ More sophisticated approach can be used, by using `lookback_timeframe` for candl
 !!! Note
     `VolumePairList` does not support backtesting mode.
 
+#### Percent Change Pair List
+
+`PercentChangePairList` filters and sorts pairs based on the percentage change in their price over the last 24 hours or any defined timeframe as part of advanced options. This allows traders to focus on assets that have experienced significant price movements, either positive or negative.
+
+**Configuration Options**
+
+* `number_assets`: Specifies the number of top pairs to select based on the 24-hour percentage change.
+* `min_value`: Sets a minimum percentage change threshold. Pairs with a percentage change below this value will be filtered out.
+* `max_value`: Sets a maximum percentage change threshold. Pairs with a percentage change above this value will be filtered out.
+* `sort_direction`: Specifies the order in which pairs are sorted based on their percentage change. Accepts two values: `asc` for ascending order and `desc` for descending order.
+* `refresh_period`: Defines the interval (in seconds) at which the pairlist will be refreshed. The default is 1800 seconds (30 minutes).
+* `lookback_days`: Number of days to look back. When `lookback_days` is selected, the `lookback_timeframe` is defaulted to 1 day.
+* `lookback_timeframe`: Timeframe to use for the lookback period.
+* `lookback_period`: Number of periods to look back at. 
+
+When PercentChangePairList is used after other Pairlist Handlers, it will operate on the outputs of those handlers. If it is the leading Pairlist Handler, it will select pairs from all available markets with the specified stake currency.
+
+`PercentChangePairList` uses ticker data from the exchange, provided via the ccxt library:
+The percentage change is calculated as the change in price over the last 24 hours.
+
+??? Note "Unsupported exchanges"
+    On some exchanges (like HTX), regular PercentChangePairList does not work as the api does not natively provide 24h percent change in price. This can be worked around by using candle data to calculate the percentage change. To roughly simulate 24h percent change, you can use the following configuration. Please note that these pairlists will only refresh once per day.
+    ```json
+    "pairlists": [
+        {
+            "method": "PercentChangePairList",
+            "number_assets": 20,
+            "min_value": 0,
+            "refresh_period": 86400,
+            "lookback_days": 1
+        }
+    ],
+    ```
+
+**Example Configuration to Read from Ticker**
+
+```json
+"pairlists": [
+    {
+        "method": "PercentChangePairList",
+        "number_assets": 15,
+        "min_value": -10,
+        "max_value": 50
+    }
+],
+```
+
+In this configuration:
+
+1. The top 15 pairs are selected based on the highest percentage change in price over the last 24 hours.
+2. Only pairs with a percentage change between -10% and 50% are considered.
+
+**Example Configuration to Read from Candles**
+
+```json
+"pairlists": [
+    {
+        "method": "PercentChangePairList",
+        "number_assets": 15,
+        "sort_key": "percentage",
+        "min_value": 0,
+        "refresh_period": 3600,
+        "lookback_timeframe": "1h",
+        "lookback_period": 72
+    }
+],
+```
+
+This example builds the percent change pairs based on a rolling period of 3 days of 1-hour candles by using `lookback_timeframe` for candle size and `lookback_period` which specifies the number of candles.
+
+The percent change in price is calculated using the following formula, which expresses the percentage difference between the current candle's close price and the previous candle's close price, as defined by the specified timeframe and lookback period:
+
+$$ Percent Change = (\frac{Current Close - Previous Close}{Previous Close}) * 100 $$
+
+!!! Warning "Range look back and refresh period"
+    When used in conjunction with `lookback_days` and `lookback_timeframe` the `refresh_period` can not be smaller than the candle size in seconds. As this will result in unnecessary requests to the exchanges API.
+
+!!! Warning "Performance implications when using lookback range"
+    If used in first position in combination with lookback, the computation of the range-based percent change can be time and resource consuming, as it downloads candles for all tradable pairs. Hence it's highly advised to use the standard approach with `PercentChangePairList` to narrow the pairlist down for further percent-change calculation.
+
+!!! Note "Backtesting"
+    `PercentChangePairList` does not support backtesting mode.
+
 #### ProducerPairList
 
 With `ProducerPairList`, you can reuse the pairlist from a [Producer](producer-consumer.md) without explicitly defining the pairlist on each consumer.
@@ -192,14 +278,15 @@ The RemotePairList is defined in the pairlists section of the configuration sett
         "refresh_period": 1800,
         "keep_pairlist_on_failure": true,
         "read_timeout": 60,
-        "bearer_token": "my-bearer-token"
+        "bearer_token": "my-bearer-token",
+        "save_to_file": "user_data/filename.json" 
     }
 ]
 ```
 
 The optional `mode` option specifies if the pairlist should be used as a `blacklist` or as a `whitelist`. The default value is "whitelist".
 
-The optional `processing_mode` option in the RemotePairList configuration determines how the retrieved pairlist is processed. It can have two values: "filter" or "append".
+The optional `processing_mode` option in the RemotePairList configuration determines how the retrieved pairlist is processed. It can have two values: "filter" or "append". The default value is "filter".
 
 In "filter" mode, the retrieved pairlist is used as a filter. Only the pairs present in both the original pairlist and the retrieved pairlist are included in the final pairlist. Other pairs are filtered out.
 
@@ -207,6 +294,42 @@ In "append" mode, the retrieved pairlist is added to the original pairlist. All
 
 The `pairlist_url` option specifies the URL of the remote server where the pairlist is located, or the path to a local file (if file:/// is prepended). This allows the user to use either a remote server or a local file as the source for the pairlist.
 
+The `save_to_file` option, when provided with a valid filename, saves the processed pairlist to that file in JSON format. This option is optional, and by default, the pairlist is not saved to a file.
+
+??? Example "Multi bot with shared pairlist example"
+    
+    `save_to_file` can be used to save the pairlist to a file with Bot1:
+
+    ```json
+    "pairlists": [
+        {
+            "method": "RemotePairList",
+            "mode": "whitelist",
+            "pairlist_url": "https://example.com/pairlist",
+            "number_assets": 10,
+            "refresh_period": 1800,
+            "keep_pairlist_on_failure": true,
+            "read_timeout": 60,
+            "save_to_file": "user_data/filename.json" 
+        }
+    ]
+    ```
+
+    This saved pairlist file can be loaded by Bot2, or any additional bot with this configuration:
+
+    ```json
+    "pairlists": [
+        {
+            "method": "RemotePairList",
+            "mode": "whitelist",
+            "pairlist_url": "file:///user_data/filename.json",
+            "number_assets": 10,
+            "refresh_period": 10,
+            "keep_pairlist_on_failure": true,
+        }
+    ]
+    ```    
+
 The user is responsible for providing a server or local file that returns a JSON object with the following structure:
 
 ```json
@@ -227,6 +350,32 @@ The optional `bearer_token` will be included in the requests Authorization Heade
 !!! Note
     In case of a server error the last received pairlist will be kept if `keep_pairlist_on_failure` is set to true, when set to false a empty pairlist is returned.
 
+#### MarketCapPairList
+
+`MarketCapPairList` employs sorting/filtering of pairs by their marketcap rank based of CoinGecko. It will only recognize coins up to the coin placed at rank 250. The returned pairlist will be sorted based of their marketcap ranks.
+
+```json
+"pairlists": [
+    {
+        "method": "MarketCapPairList",
+        "number_assets": 20,
+        "max_rank": 50,
+        "refresh_period": 86400,
+        "categories": ["layer-1"]
+    }
+]
+```
+
+`number_assets` defines the maximum number of pairs returned by the pairlist. `max_rank` will determine the maximum rank used in creating/filtering the pairlist. It's expected that some coins within the top `max_rank` marketcap will not be included in the resulting pairlist since not all pairs will have active trading pairs in your preferred market/stake/exchange combination.
+
+The `refresh_period` setting defines the interval (in seconds) at which the marketcap rank data will be refreshed. The default is 86,400 seconds (1 day). The pairlist cache (`refresh_period`) applies to both generating pairlists (when in the first position in the list) and filtering instances (when not in the first position in the list).
+
+The `categories` setting specifies the [coingecko categories](https://www.coingecko.com/en/categories) from which to select coins from. The default is an empty list `[]`, meaning no category filtering is applied.
+If an incorrect category string is chosen, the plugin will print the available categories from CoinGecko and fail. The category should be the ID of the category, for example, for `https://www.coingecko.com/en/categories/layer-1`, the category ID would be `layer-1`. You can pass multiple categories such as `["layer-1", "meme-token"]` to select from several categories.
+
+!!! Warning "Many categories"
+    Each added category corresponds to one API call to CoinGecko. The more categories you add, the longer the pairlist generation will take, potentially causing rate limit issues.
+
 #### AgeFilter
 
 Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`) or more than `max_days_listed` days (defaults `None` mean infinity).
@@ -312,6 +461,11 @@ As this Filter uses past performance of the bot, it'll have some startup-period
 
 Filters low-value coins which would not allow setting stoplosses.
 
+Namely, pairs are blacklisted if a variance of one percent or more in the stop price would be caused by precision rounding on the exchange, i.e. `rounded(stop_price) <= rounded(stop_price * 0.99)`. The idea is to avoid coins with a value VERY close to their lower trading boundary, not allowing setting of proper stoploss.
+
+!!! Tip "PrecisionFilter is pointless for futures trading"
+    The above does not apply to shorts. And for longs, in theory the trade will be liquidated first.
+
 !!! Warning "Backtesting"
     `PrecisionFilter` does not support backtesting mode using multiple strategies.
 
@@ -393,6 +547,8 @@ If the trading range over the last 10 days is <1% or >99%, remove the pair from
 ]
 ```
 
+Adding `"sort_direction": "asc"` or `"sort_direction": "desc"` enables sorting for this pairlist.
+
 !!! Tip
     This Filter can be used to automatically remove stable coin pairs, which have a very low trading range, and are therefore extremely difficult to trade with profit.
     Additionally, it can also be used to automatically remove pairs with extreme high/low variance over a given amount of time.
@@ -420,6 +576,8 @@ If the volatility over the last 10 days is not in the range of 0.05-0.50, remove
 ]
 ```
 
+Adding `"sort_direction": "asc"` or `"sort_direction": "desc"` enables sorting mode for this pairlist.
+
 ### Full example of Pairlist Handlers
 
 The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#pricefilter), filtering all assets where 1 price unit is > 1%. Then the [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) is applied and pairs are finally shuffled with the random seed set to some predefined value.

docs/includes/protections.md

@@ -1,24 +1,16 @@
 ## Protections
 
-!!! Warning "Beta feature"
-    This feature is still in it's testing phase. Should you notice something you think is wrong please let us know via Discord or via Github Issue.
-
 Protections will protect your strategy from unexpected events and market conditions by temporarily stop trading for either one pair, or for all pairs.
 All protection end times are rounded up to the next candle to avoid sudden, unexpected intra-candle buys.
 
-!!! Note
+!!! Tip "Usage tips"
     Not all Protections will work for all strategies, and parameters will need to be tuned for your strategy to improve performance.  
 
-!!! Tip
     Each Protection can be configured multiple times with different parameters, to allow different levels of protection (short-term / long-term).
 
 !!! Note "Backtesting"
     Protections are supported by backtesting and hyperopt, but must be explicitly enabled by using the `--enable-protections` flag.
 
-!!! Warning "Setting protections from the configuration"
-    Setting protections from the configuration via `"protections": [],` key should be considered deprecated and will be removed in a future version.
-    It is also no longer guaranteed that your protections apply to the strategy in cases where the strategy defines [protections as property](hyperopt.md#optimizing-protections).
-
 ### Available Protections
 
 * [`StoplossGuard`](#stoploss-guard) Stop trading if a certain amount of stoploss occurred within a certain time window.
@@ -36,6 +28,7 @@ All protection end times are rounded up to the next candle to avoid sudden, unex
 | `lookback_period_candles` | Only trades that completed within the last `lookback_period_candles` candles will be considered. This setting may be ignored by some Protections. <br> **Datatype:** Positive integer (in candles).
 | `lookback_period` | Only trades that completed after `current_time - lookback_period` will be considered. <br>Cannot be used together with `lookback_period_candles`. <br>This setting may be ignored by some Protections. <br> **Datatype:**  Float (in minutes)
 | `trade_limit` | Number of trades required at minimum (not used by all Protections). <br> **Datatype:** Positive integer
+| `unlock_at` | Time when trading will be unlocked regularly (not used by all Protections). <br> **Datatype:** string <br>**Input Format:** "HH:MM" (24-hours)
 
 !!! Note "Durations"
     Durations (`stop_duration*` and `lookback_period*` can be defined in either minutes or candles).
@@ -44,7 +37,7 @@ All protection end times are rounded up to the next candle to avoid sudden, unex
 #### Stoploss Guard
 
 `StoplossGuard` selects all trades within `lookback_period` in minutes (or in candles when using `lookback_period_candles`).
-If `trade_limit` or more trades resulted in stoploss, trading will stop for `stop_duration` in minutes (or in candles when using `stop_duration_candles`).
+If `trade_limit` or more trades resulted in stoploss, trading will stop for `stop_duration` in minutes (or in candles when using `stop_duration_candles`, or until the set time when using `unlock_at`).
 
 This applies across all pairs, unless `only_per_pair` is set to true, which will then only look at one pair at a time.
 
@@ -97,7 +90,7 @@ def protections(self):
 #### Low Profit Pairs
 
 `LowProfitPairs` uses all trades for a pair within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the overall profit ratio.
-If that ratio is below `required_profit`, that pair will be locked for `stop_duration` in minutes (or in candles when using `stop_duration_candles`).
+If that ratio is below `required_profit`, that pair will be locked for `stop_duration` in minutes (or in candles when using `stop_duration_candles`, or until the set time when using `unlock_at`).
 
 For futures bots, setting `only_per_side` will make the bot only consider one side, and will then only lock this one side, allowing for example shorts to continue after a series of long losses.
 
@@ -120,7 +113,7 @@ def protections(self):
 
 #### Cooldown Period
 
-`CooldownPeriod` locks a pair for `stop_duration` in minutes (or in candles when using `stop_duration_candles`) after selling, avoiding a re-entry for this pair for `stop_duration` minutes.
+`CooldownPeriod` locks a pair for `stop_duration` in minutes (or in candles when using `stop_duration_candles`, or until the set time when using `unlock_at`) after exiting, avoiding a re-entry for this pair for `stop_duration` minutes.
 
 The below example will stop trading a pair for 2 candles after closing a trade, allowing this pair to "cool down".
 

docs/includes/showcase.md

@@ -5,7 +5,7 @@ This section will highlight a few projects from members of the community.
 - [Example freqtrade strategies](https://github.com/freqtrade/freqtrade-strategies/)
 - [FrequentHippo - Grafana dashboard with dry/live runs and backtests](http://frequenthippo.ddns.net:3000/) (by hippocritical).
 - [Online pairlist generator](https://remotepairlist.com/) (by Blood4rc).
-- [Freqtrade Backtesting Project](https://bt.robot.co.network/) (by Blood4rc).
+- [Freqtrade Backtesting Project](https://strat.ninja/) (by Blood4rc).
 - [Freqtrade analysis notebook](https://github.com/froggleston/freqtrade_analysis_notebook) (by Froggleston).
 - [TUI for freqtrade](https://github.com/froggleston/freqtrade-frogtrade9000) (by Froggleston).
 - [Bot Academy](https://botacademy.ddns.net/) (by stash86) - Blog about crypto bot projects.

docs/includes/strategy-imports.md

@@ -0,0 +1,45 @@
+## Imports necessary for a strategy
+
+When creating a strategy, you will need to import the necessary modules and classes. The following imports are required for a strategy:
+
+By default, we recommend the following imports as a base line for your strategy:
+This will cover all imports necessary for freqtrade functions to work.
+Obviously you can add more imports as needed for your strategy.
+
+``` python
+# flake8: noqa: F401
+# isort: skip_file
+# --- Do not remove these imports ---
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta, timezone
+from pandas import DataFrame
+from typing import Dict, Optional, Union, Tuple
+
+from freqtrade.strategy import (
+    IStrategy,
+    Trade, 
+    Order,
+    PairLocks,
+    informative,  # @informative decorator
+    # Hyperopt Parameters
+    BooleanParameter,
+    CategoricalParameter,
+    DecimalParameter,
+    IntParameter,
+    RealParameter,
+    # timeframe helpers
+    timeframe_to_minutes,
+    timeframe_to_next_date,
+    timeframe_to_prev_date,
+    # Strategy helper functions
+    merge_informative_pair,
+    stoploss_from_absolute,
+    stoploss_from_open,
+)
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+from technical import qtpylib
+```

docs/index.md

@@ -41,8 +41,10 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
 
 - [X] [Binance](https://www.binance.com/)
 - [X] [Bitmart](https://bitmart.com/)
+- [X] [BingX](https://bingx.com/invite/0EM9RX)
+- [X] [Bybit](https://bybit.com/)
 - [X] [Gate.io](https://www.gate.io/ref/6266643)
-- [X] [Huobi](http://huobi.com/)
+- [X] [HTX](https://www.htx.com/) (Former Huobi)
 - [X] [Kraken](https://kraken.com/)
 - [X] [OKX](https://okx.com/) (Former OKEX)
 - [ ] [potentially many others through <img alt="ccxt" width="30px" src="assets/ccxt-logo.svg" />](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
@@ -83,7 +85,7 @@ To run this bot we recommend you a linux cloud instance with a minimum of:
 
 Alternatively
 
-- Python 3.9+
+- Python 3.10+
 - pip (pip3)
 - git
 - TA-Lib

docs/installation.md

@@ -24,7 +24,7 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
     The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
 
 !!! Note
-    Python3.9 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.  
+    Python3.10 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.  
     Also, python headers (`python<yourversion>-dev` / `python<yourversion>-devel`) must be available for the installation to complete successfully.
 
 !!! Warning "Up-to-date clock"
@@ -42,7 +42,7 @@ These requirements apply to both [Script Installation](#script-installation) and
 
 ### Install guide
 
-* [Python >= 3.9](http://docs.python-guide.org/en/latest/starting/installation/)
+* [Python >= 3.10](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)
 * [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
@@ -51,10 +51,10 @@ These requirements apply to both [Script Installation](#script-installation) and
 ### Install code
 
 We've included/collected install instructions for Ubuntu, MacOS, and Windows. These are guidelines and your success may vary with other distros.
-OS Specific steps are listed first, the [Common](#common) section below is necessary for all systems.
+OS Specific steps are listed first, the common section below is necessary for all systems.
 
 !!! Note
-    Python3.9 or higher and the corresponding pip are assumed to be available.
+    Python3.10 or higher and the corresponding pip are assumed to be available.
 
 === "Debian/Ubuntu"
     #### Install necessary dependencies
@@ -69,7 +69,7 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
 
 === "RaspberryPi/Raspbian"
     The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/).
-    This image comes with python3.9 preinstalled, making it easy to get freqtrade up and running.
+    This image comes with python3.11 preinstalled, making it easy to get freqtrade up and running.
 
     Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
 
@@ -169,7 +169,7 @@ You can as well update, configure and reset the codebase of your bot with `./scr
 ** --install **
 
 With this option, the script will install the bot and most dependencies:
-You will need to have git and python3.9+ installed beforehand for this to work.
+You will need to have git and python3.10+ installed beforehand for this to work.
 
 * Mandatory software as: `ta-lib`
 * Setup your virtualenv under `.venv/`
@@ -286,7 +286,7 @@ cd freqtrade
 #### Freqtrade install: Conda Environment
 
 ```bash
-conda create --name freqtrade python=3.11
+conda create --name freqtrade python=3.12
 ```
 
 !!! Note "Creating Conda Environment"

docs/leverage.md

@@ -17,7 +17,7 @@ If you already have an existing strategy, please read the [strategy migration gu
 
 ## Shorting
 
-Shorting is not possible when trading with [`trading_mode`](#understand-tradingmode) set to `spot`. To short trade, `trading_mode` must be set to `margin`(currently unavailable) or [`futures`](#futures), with [`margin_mode`](#margin-mode) set to `cross`(currently unavailable) or [`isolated`](#isolated-margin-mode)
+Shorting is not possible when trading with [`trading_mode`](#leverage-trading-modes) set to `spot`. To short trade, `trading_mode` must be set to `margin`(currently unavailable) or [`futures`](#futures), with [`margin_mode`](#margin-mode) set to `cross`(currently unavailable) or [`isolated`](#isolated-margin-mode)
 
 For a strategy to short, the strategy class must set the class variable `can_short = True`
 

docs/lookahead-analysis.md

@@ -23,6 +23,7 @@ It also supports the lookahead-analysis of freqai strategies.
 - `--max-open-trades` is forced to be at least equal to the number of pairs.
 - `--dry-run-wallet` is forced to be basically infinite (1 billion).
 - `--stake-amount` is forced to be a static 10000 (10k).
+- `--enable-protections` is forced to be off.
 
 Those are set to avoid users accidentally generating false positives.
 
@@ -40,7 +41,6 @@ usage: freqtrade lookahead-analysis [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                     [--max-open-trades INT]
                                     [--stake-amount STAKE_AMOUNT]
                                     [--fee FLOAT] [-p PAIRS [PAIRS ...]]
-                                    [--enable-protections]
                                     [--dry-run-wallet DRY_RUN_WALLET]
                                     [--timeframe-detail TIMEFRAME_DETAIL]
                                     [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
@@ -101,3 +101,4 @@ This could lead to a false-negative (the strategy will then be reported as non-b
 - `lookahead-analysis` has access to everything that backtesting has too.
 Please don't provoke any configs like enabling position stacking.
 If you decide to do so, then make doubly sure that you won't ever run out of `max_open_trades` amount and neither leftover money in your wallet.
+- In the results table, the `biased_indicators` column will falsely flag FreqAI target indicators defined in `set_freqai_targets()` as biased. These are not biased and can safely be ignored.

docs/plotting.md

@@ -2,6 +2,14 @@
 
 This page explains how to plot prices, indicators and profits.
 
+!!! Warning "Deprecated"
+    The commands described in this page (`plot-dataframe`, `plot-profit`) should be considered deprecated and are in maintenance mode.
+    This is mostly for the performance problems even medium sized plots can cause, but also because "store a file and open it in a browser" isn't very intuitive from a UI perspective.
+
+    While there are no immediate plans to remove them, they are not actively maintained - and may be removed short-term should major changes be required to keep them working.
+    
+    Please use [FreqUI](freq-ui.md) for plotting needs, which doesn't struggle with the same performance problems.
+
 ## Installation / Setup
 
 Plotting modules use the Plotly library. You can install / upgrade this by running the following command:

docs/requirements-docs.txt

@@ -1,6 +1,7 @@
-markdown==3.5.1
-mkdocs==1.5.3
-mkdocs-material==9.4.14
+markdown==3.7
+mkdocs==1.6.1
+mkdocs-material==9.5.42
 mdx_truly_sane_lists==1.3
-pymdown-extensions==10.5
-jinja2==3.1.2
+pymdown-extensions==10.11.2
+jinja2==3.1.4
+mike==2.1.3

docs/rest-api.md

@@ -1,16 +1,8 @@
-# REST API & FreqUI
+# REST API
 
 ## FreqUI
 
-Freqtrade provides a builtin webserver, which can serve [FreqUI](https://github.com/freqtrade/frequi), the freqtrade UI.
-
-By default, the UI is not included in the installation (except for docker images), and must be installed explicitly with `freqtrade install-ui`.
-This same command can also be used to update freqUI, should there be a new release.
-
-Once the bot is started in trade / dry-run mode (with `freqtrade trade`) - the UI will be available under the configured port below (usually `http://127.0.0.1:8080`).
-
-!!! Note "developers"
-    Developers should not use this method, but instead use the method described in the [freqUI repository](https://github.com/freqtrade/frequi) to get the source-code of freqUI.
+FreqUI now has it's own dedicated [documentation section](freq-ui.md) - please refer to that section for all information regarding the FreqUI.
 
 ## Configuration
 
@@ -89,17 +81,20 @@ Make sure that the following 2 lines are available in your docker-compose file:
 ```
 
 !!! Danger "Security warning"
-    By using `8080:8080` in the docker port mapping, the API will be available to everyone connecting to the server under the correct port, so others may be able to control your bot.
+    By using `"8080:8080"` (or `"0.0.0.0:8080:8080"`) in the docker port mapping, the API will be available to everyone connecting to the server under the correct port, so others may be able to control your bot.
+    This **may** be safe if you're running the bot in a secure environment (like your home network), but it's not recommended to expose the API to the internet.
 
 ## Rest API
 
 ### Consuming the API
 
-You can consume the API by using the script `scripts/rest_client.py`.
-The client script only requires the `requests` module, so Freqtrade does not need to be installed on the system.
+You can consume the API by using `freqtrade-client` (also available as `scripts/rest_client.py`).
+This command can be installed independent of the bot by using `pip install freqtrade-client`.
+
+This module is designed to be lightweight, and only depends on the `requests` and `python-rapidjson` modules, skipping all heavy dependencies freqtrade otherwise needs.
 
 ``` bash
-python3 scripts/rest_client.py <command> [optional parameters]
+freqtrade-client <command> [optional parameters]
 ```
 
 By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be used, however you can specify a configuration file to override this behaviour.
@@ -120,9 +115,35 @@ By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be use
 ```
 
 ``` bash
-python3 scripts/rest_client.py --config rest_config.json <command> [optional parameters]
+freqtrade-client --config rest_config.json <command> [optional parameters]
 ```
 
+Commands with many arguments may require keyword arguments (for clarity) - which can be provided as follows:
+
+``` bash
+freqtrade-client --config rest_config.json forceenter BTC/USDT long enter_tag=GutFeeling
+```
+
+This method will work for all arguments - check the "show" command for a list of available parameters.
+
+??? Note "Programmatic use"
+    The `freqtrade-client` package (installable independent of freqtrade) can be used in your own scripts to interact with the freqtrade API.
+    to do so, please use the following:
+
+    ``` python
+    from freqtrade_client import FtRestClient
+    
+
+    client = FtRestClient(server_url, username, password)
+
+    # Get the status of the bot
+    ping = client.ping()
+    print(ping)
+    # ... 
+    ```
+
+    For a full list of available commands, please refer to the list below.
+
 ### Available endpoints
 
 |  Command | Description |
@@ -146,8 +167,9 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 | `mix_tags [pair]` | Shows profit statistics for each combinations of enter tag + exit reasons for given pair (or all pairs if pair isn't given). Pair is optional.
 | `locks` | Displays currently locked pairs.
 | `delete_lock <lock_id>` | Deletes (disables) the lock by id.
+| `locks add <pair>, <until>, [side], [reason]` | Locks a pair until "until". (Until will be rounded up to the nearest timeframe).
 | `profit` | Display a summary of your profit/loss from close trades and some stats about your performance.
-| `forceexit <trade_id>` | Instantly exits the given trade  (Ignoring `minimum_roi`).
+| `forceexit <trade_id> [order_type] [amount]` | Instantly exits the given trade (ignoring `minimum_roi`), using the given order type ("market" or "limit", uses your config setting if not specified), and the chosen amount (full sell if not specified).
 | `forceexit all` | Instantly exits all open trades (Ignoring `minimum_roi`).
 | `forceenter <pair> [rate]` | Instantly enters the given pair. Rate is optional. (`force_entry_enable` must be set to True)
 | `forceenter <pair> <side> [rate]` | Instantly longs or shorts the given pair. Rate is optional. (`force_entry_enable` must be set to True)
@@ -176,7 +198,7 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 Possible commands can be listed from the rest-client script using the `help` command.
 
 ``` bash
-python3 scripts/rest_client.py help
+freqtrade-client help
 ```
 
 ``` output
@@ -433,7 +455,7 @@ To properly configure your reverse proxy (securely), please consult it's documen
 - **Caddy**: Caddy v2 supports websockets out of the box, see the [documentation](https://caddyserver.com/docs/v2-upgrade#proxy)
 
 !!! Tip "SSL certificates"
-    You can use tools like certbot to setup ssl certificates to access your bot's UI through encrypted connection by using any fo the above reverse proxies.
+    You can use tools like certbot to setup ssl certificates to access your bot's UI through encrypted connection by using any of the above reverse proxies.
     While this will protect your data in transit, we do not recommend to run the freqtrade API outside of your private network (VPN, SSH tunnel).
 
 ### OpenAPI interface
@@ -466,42 +488,4 @@ Since the access token has a short timeout (15 min) - the `token/refresh` reques
 {"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk5NzQsIm5iZiI6MTU4OTExOTk3NCwianRpIjoiMDBjNTlhMWUtMjBmYS00ZTk0LTliZjAtNWQwNTg2MTdiZDIyIiwiZXhwIjoxNTg5MTIwODc0LCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.1seHlII3WprjjclY6DpRhen0rqdF4j6jbvxIhUFaSbs"}
 ```
 
-### CORS
-
-This whole section is only necessary in cross-origin cases (where you multiple bot API's running on `localhost:8081`, `localhost:8082`, ...), and want to combine them into one FreqUI instance.
-
-??? info "Technical explanation"
-    All web-based front-ends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
-    Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
-    Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
-
-Users can allow access from different origin URL's to the bot API via the `CORS_origins` configuration setting.
-It consists of a list of allowed URL's that are allowed to consume resources from the bot's API.
-
-Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - this would mean that the following configuration becomes necessary:
-
-```jsonc
-{
-    //...
-    "jwt_secret_key": "somethingrandom",
-    "CORS_origins": ["https://frequi.freqtrade.io"],
-    //...
-}
-```
-
-In the following (pretty common) case, FreqUI is accessible on `http://localhost:8080/trade` (this is what you see in your navbar when navigating to freqUI).
-![freqUI url](assets/frequi_url.png)
-
-The correct configuration for this case is `http://localhost:8080` - the main part of the URL including the port.
-
-```jsonc
-{
-    //...
-    "jwt_secret_key": "somethingrandom",
-    "CORS_origins": ["http://localhost:8080"],
-    //...
-}
-```
-
-!!! Note
-    We strongly recommend to also set `jwt_secret_key` to something random and known only to yourself to avoid unauthorized access to your bot.
+--8<-- "includes/cors.md"

docs/sql_cheatsheet.md

@@ -1,6 +1,13 @@
 # SQL Helper
 
-This page contains some help if you want to edit your sqlite db.
+This page contains some help if you want to query your sqlite db.
+
+!!! Tip "Other Database systems"
+    To use other Database Systems like PostgreSQL or MariaDB, you can use the same queries, but you need to use the respective client for the database system. [Click here](advanced-setup.md#use-a-different-database-system) to learn how to setup a different database system with freqtrade.
+
+!!! Warning
+    If you are not familiar with SQL, you should be very careful when running queries on your database.  
+    Always make sure to have a backup of your database before running any queries.
 
 ## Install sqlite3
 
@@ -43,13 +50,25 @@ sqlite3
 .schema <table_name>
 ```
 
-## Get all trades in the table
+### Get all trades in the table
 
 ```sql
 SELECT * FROM trades;
 ```
 
-## Fix trade still open after a manual exit on the exchange
+## Destructive queries
+
+Queries that write to the database.
+These queries should usually not be necessary as freqtrade tries to handle all database operations itself - or exposes them via API or telegram commands.
+
+!!! Warning
+    Please make sure you have a backup of your database before running any of the below queries.
+
+!!! Danger
+    You should also **never** run any writing query (`update`, `insert`, `delete`) while a bot is connected to the database.
+    This can and will lead to data corruption - most likely, without the possibility of recovery.
+
+### Fix trade still open after a manual exit on the exchange
 
 !!! Warning
     Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, /forceexit <tradeid> should be used to accomplish the same thing.  
@@ -69,7 +88,7 @@ SET is_open=0,
 WHERE id=<trade_ID_to_update>;
 ```
 
-### Example
+#### Example
 
 ```sql
 UPDATE trades
@@ -82,7 +101,7 @@ SET is_open=0,
 WHERE id=31;
 ```
 
-## Remove trade from the database
+### Remove trade from the database
 
 !!! Tip "Use RPC Methods to delete trades"
     Consider using `/delete <tradeid>` via telegram or rest API. That's the recommended way to deleting trades.
@@ -100,39 +119,3 @@ DELETE FROM trades WHERE id = 31;
 
 !!! Warning
     This will remove this trade from the database. Please make sure you got the correct id and **NEVER** run this query without the `where` clause.
-
-## 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>`

docs/stoploss.md

@@ -30,7 +30,8 @@ The Order-type will be ignored if only one mode is available.
 |----------|-------------|
 | Binance  | limit |
 | Binance Futures  | market, limit |
-| Huobi    | limit |
+| Bingx  | market, limit |
+| HTX (former Huobi)    | limit |
 | kraken   | market, limit |
 | Gate     | limit |
 | Okx      | limit |
@@ -158,7 +159,7 @@ You could also have a default stop loss when you are in the red with your buy (b
 For example, your default stop loss is -10%, but once you have more than 0% profit (example 0.1%) a different trailing stoploss will be used.
 
 !!! Note
-    If you want the stoploss to only be changed when you break even of making a profit (what most users want) please refer to next section with [offset enabled](#Trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset).
+    If you want the stoploss to only be changed when you break even of making a profit (what most users want) please refer to next section with [offset enabled](#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset).
 
 Both values require `trailing_stop` to be set to true and `trailing_stop_positive` with a value.
 
@@ -240,7 +241,7 @@ When using leverage, the same principle is applied - with stoploss defining the
 
 Therefore, a stoploss of 10% on a 10x trade would trigger on a 1% price move.
 If your stake amount (own capital) was 100$ - this trade would be 1000$ at 10x (after leverage).
-If price moves 1% - you've lost 10$ of your own capital - therfore stoploss will trigger in this case.
+If price moves 1% - you've lost 10$ of your own capital - therefore stoploss will trigger in this case.
 
 Make sure to be aware of this, and avoid using too tight stoploss (at 10x leverage, 10% risk may be too little to allow the trade to "breath" a little).
 

docs/strategy-101.md

@@ -0,0 +1,196 @@
+# Freqtrade Strategies 101: A Quick Start for Strategy Development
+
+For the purposes of this quick start, we are assuming you are familiar with the basics of trading, and have read the 
+[Freqtrade basics](bot-basics.md) page.
+
+## Required Knowledge
+
+A strategy in Freqtrade is a Python class that defines the logic for buying and selling cryptocurrency `assets`.
+
+Assets are defined as `pairs`, which represent the `coin` and the `stake`. The coin is the asset you are trading using another currency as the stake.
+
+Data is supplied by the exchange in the form of `candles`, which are made up of a six values: `date`, `open`, `high`, `low`, `close` and `volume`.
+
+`Technical analysis` functions analyse the candle data using various computational and statistical formulae, and produce secondary values called `indicators`.
+
+Indicators are analysed on the asset pair candles to generate `signals`.
+
+Signals are turned into `orders` on a cryptocurrency `exchange`, i.e. `trades`.
+
+We use the terms `entry` and `exit` instead of `buying` and `selling` because Freqtrade supports both `long` and `short` trades.
+
+- **long**: You buy the coin based on a stake, e.g. buying the coin BTC using USDT as your stake, and you make a profit by selling the coin at a higher rate than you paid for. In long trades, profits are made by the coin value going up versus the stake.
+- **short**: You borrow capital from the exchange in the form of the coin, and you pay back the stake value of the coin later. In short trades profits are made by the coin value going down versus the stake (you pay the loan off at a lower rate).
+
+Whilst Freqtrade supports spot and futures markets for certain exchanges, for simplicity we will focus on spot (long) trades only.
+
+## Structure of a Basic Strategy
+
+### Main dataframe
+
+Freqtrade strategies use a tabular data structure with rows and columns known as a `dataframe` to generate signals to enter and exit trades.
+
+Each pair in your configured pairlist has its own dataframe. Dataframes are indexed by the `date` column, e.g. `2024-06-31 12:00`.
+
+The next 5 columns represent the `open`, `high`, `low`, `close` and `volume` (OHLCV) data.
+
+### Populate indicator values
+
+The `populate_indicators` function adds columns to the dataframe that represent the technical analysis indicator values.
+
+Examples of common indicators include Relative Strength Index, Bollinger Bands, Money Flow Index, Moving Average, and Average True Range.
+
+Columns are added to the dataframe by calling technical analysis functions, e.g. ta-lib's RSI function `ta.RSI()`, and assigning them to a column name, e.g. `rsi`
+
+```python
+dataframe['rsi'] = ta.RSI(dataframe)
+```
+
+??? Hint "Technical Analysis libraries"
+    Different libraries work in different ways to generate indicator values. Please check the documentation of each library to understand
+    how to integrate it into your strategy. You can also check the [Freqtrade example strategies](https://github.com/freqtrade/freqtrade-strategies) to give you ideas.
+
+### Populate entry signals
+
+The `populate_entry_trend` function defines conditions for an entry signal.
+
+The dataframe column `enter_long` is added to the dataframe, and when a value of `1` is in this column, Freqtrade sees an entry signal.
+
+??? Hint "Shorting"
+    To enter short trades, use the `enter_short` column.
+
+### Populate exit signals
+
+The `populate_exit_trend` function defines conditions for an exit signal.
+
+The dataframe column `exit_long` is added to the dataframe, and when a value of `1` is in this column, Freqtrade sees an exit signal.
+
+??? Hint "Shorting"
+    To exit short trades, use the `exit_short` column.
+
+## A simple strategy
+
+Here is a minimal example of a Freqtrade strategy:
+
+```python
+from freqtrade.strategy import IStrategy
+from pandas import DataFrame
+import talib.abstract as ta
+
+class MyStrategy(IStrategy):
+
+    # set the initial stoploss to -10%
+    stoploss = -0.10
+
+    # exit profitable positions at any time when the profit is greater than 1%
+    minimal_roi = {"0": 0.01}
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # generate values for technical analysis indicators
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # generate entry signals based on indicator values
+        dataframe.loc[
+            (dataframe['rsi'] < 30),
+            'enter_long'] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # generate exit signals based on indicator values
+        dataframe.loc[
+            (dataframe['rsi'] > 70),
+            'exit_long'] = 1
+
+        return dataframe
+```
+
+## Making trades
+
+When a signal is found (a `1` in an entry or exit column), Freqtrade will attempt to make an order, i.e. a `trade` or `position`.
+
+Each new trade position takes up a `slot`. Slots represent the maximum number of concurrent new trades that can be opened.
+
+The number of slots is defined by the `max_open_trades` [configuration](configuration.md) option.
+
+However, there can be a range of scenarios where generating a signal does not always create a trade order. These include:
+
+- not enough remaining stake to buy an asset, or funds in your wallet to sell an asset (including any fees)
+- not enough remaining free slots for a new trade to be opened (the number of positions you have open equals the `max_open_trades` option)
+- there is already an open trade for a pair (Freqtrade cannot stack positions - however it can [adjust existing positions](strategy-callbacks.md#adjust-trade-position))
+- if an entry and exit signal is present on the same candle, they are considered as [colliding](strategy-customization.md#colliding-signals), and no order will be raised
+- the strategy actively rejects the trade order due to logic you specify by using one of the relevant [entry](strategy-callbacks.md#trade-entry-buy-order-confirmation) or [exit](strategy-callbacks.md#trade-exit-sell-order-confirmation) callbacks
+
+Read through the [strategy customization](strategy-customization.md) documentation for more details.
+
+## Backtesting and forward testing
+
+Strategy development can be a long and frustrating process, as turning our human "gut instincts" into a working computer-controlled
+("algo") strategy is not always straightforward.
+
+Therefore a strategy should be tested to verify that it is going to work as intended.
+
+Freqtrade has two testing modes:
+
+- **backtesting**: using historical data that you [download from an exchange](data-download.md), backtesting is a quick way to assess performance of a strategy. However, it can be very easy to distort results so a strategy will look a lot more profitable than it really is. Check the [backtesting documentation](backtesting.md) for more information.
+- **dry run**: often referred to as _forward testing_, dry runs use real time data from the exchange. However, any signals that would result in trades are tracked as normal by Freqtrade, but do not have any trades opened on the exchange itself. Forward testing runs in real time, so whilst it takes longer to get results it is a much more reliable indicator of **potential** performance than backtesting.
+
+Dry runs are enabled by setting `dry_run` to true in your [configuration](configuration.md#using-dry-run-mode).
+
+!!! Warning "Backtests can be very inaccurate"
+    There are many reasons why backtest results may not match reality. Please check the [backtesting assumptions](backtesting.md#assumptions-made-by-backtesting) and [common strategy mistakes](strategy-customization.md#common-mistakes-when-developing-strategies) documentation.
+    Some websites that list and rank Freqtrade strategies show impressive backtest results. Do not assume these results are achieveable or realistic.
+
+??? Hint "Useful commands"
+    Freqtrade includes two useful commands to check for basic flaws in strategies: [lookahead-analysis](lookahead-analysis.md) and [recursive-analysis](recursive-analysis.md).
+
+### Assessing backtesting and dry run results
+
+Always dry run your strategy after backtesting it to see if backtesting and dry run results are sufficiently similar.
+
+If there is any significant difference, verify that your entry and exit signals are consistent and appear on the same candles between the two modes. However, there will always be differences between dry runs and backtests:
+
+- Backtesting assumes all orders fill. In dry runs this might not be the case if using limit orders or there is no volume on the exchange.
+- Following an entry signal on candle close, backtesting assumes trades enter at the next candle's open price (unless you have custom pricing callbacks in your strategy). In dry runs, there is often a delay between signals and trades opening.
+  This is because when new candles come in on your main timeframe, e.g. every 5 minutes, it takes time for Freqtrade to analyse all pair dataframes. Therefore, Freqtrade will attempt to open trades a few seconds (ideally a small a delay as possible)
+  after candle open.
+- As entry rates in dry runs might not match backtesting, this means profit calculations will also differ. Therefore, it is normal if ROI, stoploss, trailing stoploss and callback exits are not identical.
+- The more computational "lag" you have between new candles coming in and your signals being raised and trades being opened will result in greater price unpredictability. Make sure your computer is powerful enough to process the data for the number 
+  of pairs you have in your pairlist within a reasonable time. Freqtrade will warn you in the logs if there are significant data processing delays.
+
+## Controlling or monitoring a running bot
+
+Once your bot is running in dry or live mode, Freqtrade has five mechanisms to control or monitor a running bot:
+
+- **[FreqUI](freq-ui.md)**: The easiest to get started with, FreqUI is a web interface to see and control current activity of your bot.
+- **[Telegram](telegram-usage.md)**: On mobile devices, Telegram integration is available to get alerts about your bot activity and to control certain aspects.
+- **[FTUI](https://github.com/freqtrade/ftui)**: FTUI is a terminal (command line) interface to Freqtrade, and allows monitoring of a running bot only.
+- **[REST API](rest-api.md)**: The REST API allows programmers to develop their own tools to interact with a Freqtrade bot.
+- **[Webhooks](webhook-config.md)**: Freqtrade can send information to other services, e.g. discord, by webhooks.
+
+### Logs
+
+Freqtrade generates extensive debugging logs to help you understand what's happening. Please familiarise yourself with the information and error messages you might see in your bot logs.
+
+## Final Thoughts
+
+Algo trading is difficult, and most public strategies are not good performers due to the time and effort to make a strategy work profitably in multiple scenarios.
+
+Therefore, taking public strategies and using backtests as a way to assess performance is often problematic. However, Freqtrade provides useful ways to help you make decisions and do your due diligence.
+
+There are many different ways to achieve profitability, and there is no one single tip, trick or config option that will fix a poorly performing strategy.
+
+Freqtrade is an open source platform with a large and helpful community - make sure to visit our [discord channel](https://discord.gg/p7nuUNVfP7) to discuss your strategy with others!
+
+As always, only invest what you are willing to lose.
+
+## Conclusion
+
+Developing a strategy in Freqtrade involves defining entry and exit signals based on technical indicators. By following the structure and methods outlined above, you can create and test your own trading strategies.
+
+Common questions and answers are available on our [FAQ](faq.md).
+
+To continue, refer to the more in-depth [Freqtrade strategy customization documentation](strategy-customization.md).

docs/strategy-advanced.md

@@ -11,14 +11,109 @@ The call sequence of the methods described here is covered under [bot execution
 !!! Tip
     Start off with a strategy template containing all available callback methods by running `freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced`
 
-## Storing information
+## Storing information (Persistent)
 
-Storing information can be accomplished by creating a new dictionary within the strategy class.
+Freqtrade allows storing/retrieving user custom information associated with a specific trade in the database.
 
-The name of the variable can be chosen at will, but should be prefixed with `custom_` to avoid naming collisions with predefined strategy variables.
+Using a trade object, information can be stored using `trade.set_custom_data(key='my_key', value=my_value)` and retrieved using `trade.get_custom_data(key='my_key')`. Each data entry is associated with a trade and a user supplied key (of type `string`). This means that this can only be used in callbacks that also provide a trade object.
+
+For the data to be able to be stored within the database, freqtrade must serialized the data. This is done by converting the data to a JSON formatted string.
+Freqtrade will attempt to reverse this action on retrieval, so from a strategy perspective, this should not be relevant.
 
 ```python
+from freqtrade.persistence import Trade
+from datetime import timedelta
+
 class AwesomeStrategy(IStrategy):
+
+    def bot_loop_start(self, **kwargs) -> None:
+        for trade in Trade.get_open_order_trades():
+            fills = trade.select_filled_orders(trade.entry_side)
+            if trade.pair == 'ETH/USDT':
+                trade_entry_type = trade.get_custom_data(key='entry_type')
+                if trade_entry_type is None:
+                    trade_entry_type = 'breakout' if 'entry_1' in trade.enter_tag else 'dip'
+                elif fills > 1:
+                    trade_entry_type = 'buy_up'
+                trade.set_custom_data(key='entry_type', value=trade_entry_type)
+        return super().bot_loop_start(**kwargs)
+
+    def adjust_entry_price(self, trade: Trade, order: Optional[Order], pair: str,
+                           current_time: datetime, proposed_rate: float, current_order_rate: float,
+                           entry_tag: Optional[str], side: str, **kwargs) -> float:
+        # Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
+        if (
+            pair == 'BTC/USDT' 
+            and entry_tag == 'long_sma200' 
+            and side == 'long' 
+            and (current_time - timedelta(minutes=10)) > trade.open_date_utc 
+            and order.filled == 0.0
+        ):
+            dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+            current_candle = dataframe.iloc[-1].squeeze()
+            # store information about entry adjustment
+            existing_count = trade.get_custom_data('num_entry_adjustments', default=0)
+            if not existing_count:
+                existing_count = 1
+            else:
+                existing_count += 1
+            trade.set_custom_data(key='num_entry_adjustments', value=existing_count)
+
+            # adjust order price
+            return current_candle['sma_200']
+
+        # default: maintain existing order
+        return current_order_rate
+
+    def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs):
+
+        entry_adjustment_count = trade.get_custom_data(key='num_entry_adjustments')
+        trade_entry_type = trade.get_custom_data(key='entry_type')
+        if entry_adjustment_count is None:
+            if current_profit > 0.01 and (current_time - timedelta(minutes=100) > trade.open_date_utc):
+                return True, 'exit_1'
+        else
+            if entry_adjustment_count > 0 and if current_profit > 0.05:
+                return True, 'exit_2'
+            if trade_entry_type == 'breakout' and current_profit > 0.1:
+                return True, 'exit_3
+
+        return False, None
+```
+
+The above is a simple example - there are simpler ways to retrieve trade data like entry-adjustments.
+
+!!! Note
+    It is recommended that simple data types are used `[bool, int, float, str]` to ensure no issues when serializing the data that needs to be stored.
+    Storing big junks of data may lead to unintended side-effects, like a database becoming big (and as a consequence, also slow).
+
+!!! Warning "Non-serializable data"
+    If supplied data cannot be serialized a warning is logged and the entry for the specified `key` will contain `None` as data.
+
+??? Note "All attributes"
+    custom-data has the following accessors through the Trade object (assumed as `trade` below):
+
+    * `trade.get_custom_data(key='something', default=0)` - Returns the actual value given in the type provided.
+    * `trade.get_custom_data_entry(key='something')` - Returns the entry - including metadata. The value is accessible via `.value` property.
+    * `trade.set_custom_data(key='something', value={'some': 'value'})` - set or update the corresponding key for this trade. Value must be serializable - and we recommend to keep the stored data relatively small.
+
+    "value" can be any type (both in setting and receiving) - but must be json serializable.
+
+## Storing information (Non-Persistent)
+
+!!! Warning "Deprecated"
+    This method of storing information is deprecated and we do advise against using non-persistent storage.  
+    Please use [Persistent Storage](#storing-information-persistent) instead.
+
+    It's content has therefore been collapsed.
+
+??? Abstract "Storing information"
+    Storing information can be accomplished by creating a new dictionary within the strategy class.
+
+    The name of the variable can be chosen at will, but should be prefixed with `custom_` to avoid naming collisions with predefined strategy variables.
+
+    ```python
+    class AwesomeStrategy(IStrategy):
         # Create custom dictionary
         custom_info = {}
 
@@ -32,12 +127,12 @@ class AwesomeStrategy(IStrategy):
                 self.custom_info[metadata["pair"]]["crosstime"] += 1
             else:
                 self.custom_info[metadata["pair"]]["crosstime"] = 1
-```
+    ```
 
-!!! Warning
+    !!! Warning
         The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
 
-!!! Note
+    !!! Note
         If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
 
 ## Dataframe access
@@ -114,7 +209,7 @@ def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_r
 
 ## Exit tag
 
-Similar to [Buy Tagging](#buy-tag), you can also specify a sell tag.
+Similar to [Entry Tagging](#enter-tag), you can also specify an exit tag.
 
 ``` python
 def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -231,4 +326,4 @@ for val in self.buy_ema_short.range:
 dataframe = pd.concat(frames, axis=1)
 ```
 
-Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existant.
+Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existent.

docs/strategy-callbacks.md

@@ -19,10 +19,13 @@ Currently available callbacks:
 * [`adjust_trade_position()`](#adjust-trade-position)
 * [`adjust_entry_price()`](#adjust-entry-price)
 * [`leverage()`](#leverage-callback)
+* [`order_filled()`](#order-filled-callback)
 
 !!! Tip "Callback calling sequence"
     You can find the callback calling sequence in [bot-basics](bot-basics.md#bot-execution-logic)
 
+--8<-- "includes/strategy-imports.md"
+
 ## Bot start
 
 A simple callback which is called once when the strategy is loaded.
@@ -40,10 +43,10 @@ class AwesomeStrategy(IStrategy):
         Called only once after bot instantiation.
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         """
-        if self.config['runmode'].value in ('live', 'dry_run'):
+        if self.config["runmode"].value in ("live", "dry_run"):
             # Assign this to the class by using self.*
             # can then be used by populate_* methods
-            self.custom_remote_data = requests.get('https://some_remote_source.example.com')
+            self.custom_remote_data = requests.get("https://some_remote_source.example.com")
 
 ```
 
@@ -56,6 +59,7 @@ seconds, unless configured differently) or once per candle in backtest/hyperopt
 This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
 
 ``` python
+# Default imports
 import requests
 
 class AwesomeStrategy(IStrategy):
@@ -70,10 +74,10 @@ class AwesomeStrategy(IStrategy):
         :param current_time: datetime object, containing the current datetime
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         """
-        if self.config['runmode'].value in ('live', 'dry_run'):
+        if self.config["runmode"].value in ("live", "dry_run"):
             # Assign this to the class by using self.*
             # can then be used by populate_* methods
-            self.remote_data = requests.get('https://some_remote_source.example.com')
+            self.remote_data = requests.get("https://some_remote_source.example.com")
 
 ```
 
@@ -82,6 +86,8 @@ class AwesomeStrategy(IStrategy):
 Called before entering a trade, makes it possible to manage your position size when placing a new trade.
 
 ```python
+# Default imports
+
 class AwesomeStrategy(IStrategy):
     def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                             proposed_stake: float, min_stake: Optional[float], max_stake: float,
@@ -91,13 +97,13 @@ class AwesomeStrategy(IStrategy):
         dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
         current_candle = dataframe.iloc[-1].squeeze()
 
-        if current_candle['fastk_rsi_1h'] > current_candle['fastd_rsi_1h']:
-            if self.config['stake_amount'] == 'unlimited':
+        if current_candle["fastk_rsi_1h"] > current_candle["fastd_rsi_1h"]:
+            if self.config["stake_amount"] == "unlimited":
                 # Use entire available wallet during favorable conditions when in compounding mode.
                 return max_stake
             else:
                 # Compound profits during favorable conditions instead of using a static stake.
-                return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
+                return self.wallets.get_total_stake_amount() / self.config["max_open_trades"]
 
         # Use default stake amount.
         return proposed_stake
@@ -128,25 +134,27 @@ Using `custom_exit()` signals in place of stoploss though *is not recommended*.
 An example of how we can use different indicators depending on the current profit and also exit trades that were open longer than one day:
 
 ``` python
+# Default imports
+
 class AwesomeStrategy(IStrategy):
-    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
+    def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
                     current_profit: float, **kwargs):
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         last_candle = dataframe.iloc[-1].squeeze()
 
         # Above 20% profit, sell when rsi < 80
         if current_profit > 0.2:
-            if last_candle['rsi'] < 80:
-                return 'rsi_below_80'
+            if last_candle["rsi"] < 80:
+                return "rsi_below_80"
 
         # Between 2% and 10%, sell if EMA-long above EMA-short
         if 0.02 < current_profit < 0.1:
-            if last_candle['emalong'] > last_candle['emashort']:
-                return 'ema_long_below_80'
+            if last_candle["emalong"] > last_candle["emashort"]:
+                return "ema_long_below_80"
 
         # Sell any positions at a loss if they are held for more than one day.
         if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
-            return 'unclog'
+            return "unclog"
 ```
 
 See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
@@ -164,9 +172,10 @@ E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoplo
 During backtesting, `current_rate` (and `current_profit`) are provided against the candle's high (or low for short trades) - while the resulting stoploss is evaluated against the candle's low (or high for short trades).
 
 The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
-Returning None will be interpreted as "no desire to change", and is the only safe way to return when you'd like to not modify the stoploss.
+Returning `None` will be interpreted as "no desire to change", and is the only safe way to return when you'd like to not modify the stoploss.
+`NaN` and `inf` values are considered invalid and will be ignored (identical to `None`).
 
-Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchange-freqtrade)).
+Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchangefreqtrade)).
 
 !!! Note "Use of dates"
     All time-based calculations should be done based on `current_time` - using `datetime.now()` or `datetime.utcnow()` is discouraged, as this will break backtesting support.
@@ -193,9 +202,7 @@ Of course, many more things are possible, and all examples can be combined at wi
 To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
 
 ``` python
-# additional imports required
-from datetime import datetime
-from freqtrade.persistence import Trade
+# Default imports
 
 class AwesomeStrategy(IStrategy):
 
@@ -203,7 +210,7 @@ class AwesomeStrategy(IStrategy):
 
     use_custom_stoploss = True
 
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                         current_rate: float, current_profit: float, after_fill: bool, 
                         **kwargs) -> Optional[float]:
         """
@@ -233,8 +240,7 @@ class AwesomeStrategy(IStrategy):
 Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
 
 ``` python
-from datetime import datetime, timedelta
-from freqtrade.persistence import Trade
+# Default imports
 
 class AwesomeStrategy(IStrategy):
 
@@ -242,7 +248,7 @@ class AwesomeStrategy(IStrategy):
 
     use_custom_stoploss = True
 
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                         current_rate: float, current_profit: float, after_fill: bool, 
                         **kwargs) -> Optional[float]:
 
@@ -260,8 +266,7 @@ Use the initial stoploss for the first 60 minutes, after this change to 10% trai
 If an additional order fills, set stoploss to -10% below the new `open_rate` ([Averaged across all entries](#position-adjust-calculations)).
 
 ``` python
-from datetime import datetime, timedelta
-from freqtrade.persistence import Trade
+# Default imports
 
 class AwesomeStrategy(IStrategy):
 
@@ -269,7 +274,7 @@ class AwesomeStrategy(IStrategy):
 
     use_custom_stoploss = True
 
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                         current_rate: float, current_profit: float, after_fill: bool, 
                         **kwargs) -> Optional[float]:
 
@@ -290,8 +295,7 @@ Use a different stoploss depending on the pair.
 In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
 
 ``` python
-from datetime import datetime
-from freqtrade.persistence import Trade
+# Default imports
 
 class AwesomeStrategy(IStrategy):
 
@@ -299,13 +303,13 @@ class AwesomeStrategy(IStrategy):
 
     use_custom_stoploss = True
 
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                         current_rate: float, current_profit: float, after_fill: bool,
                         **kwargs) -> Optional[float]:
 
-        if pair in ('ETH/BTC', 'XRP/BTC'):
+        if pair in ("ETH/BTC", "XRP/BTC"):
             return -0.10
-        elif pair in ('LTC/BTC'):
+        elif pair in ("LTC/BTC"):
             return -0.05
         return -0.15
 ```
@@ -317,8 +321,7 @@ Use the initial stoploss until the profit is above 4%, then use a trailing stopl
 Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
 
 ``` python
-from datetime import datetime, timedelta
-from freqtrade.persistence import Trade
+# Default imports
 
 class AwesomeStrategy(IStrategy):
 
@@ -326,12 +329,12 @@ class AwesomeStrategy(IStrategy):
 
     use_custom_stoploss = True
 
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                         current_rate: float, current_profit: float, after_fill: bool,
                         **kwargs) -> Optional[float]:
 
         if current_profit < 0.04:
-            return -1 # return a value bigger than the initial stoploss to keep using the initial stoploss
+            return None # return None to keep using the initial stoploss
 
         # After reaching the desired offset, allow the stoploss to trail by half the profit
         desired_stoploss = current_profit / 2
@@ -350,9 +353,7 @@ Instead of continuously trailing behind the current price, this example sets fix
 * Once profit is > 40% - set stoploss to 25% above open price.
 
 ``` python
-from datetime import datetime
-from freqtrade.persistence import Trade
-from freqtrade.strategy import stoploss_from_open
+# Default imports
 
 class AwesomeStrategy(IStrategy):
 
@@ -360,7 +361,7 @@ class AwesomeStrategy(IStrategy):
 
     use_custom_stoploss = True
 
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                         current_rate: float, current_profit: float, after_fill: bool,
                         **kwargs) -> Optional[float]:
 
@@ -381,15 +382,17 @@ class AwesomeStrategy(IStrategy):
 Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
 
 ``` python
+# Default imports
+
 class AwesomeStrategy(IStrategy):
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         # <...>
-        dataframe['sar'] = ta.SAR(dataframe)
+        dataframe["sar"] = ta.SAR(dataframe)
 
     use_custom_stoploss = True
 
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                         current_rate: float, current_profit: float, after_fill: bool,
                         **kwargs) -> Optional[float]:
 
@@ -397,7 +400,7 @@ class AwesomeStrategy(IStrategy):
         last_candle = dataframe.iloc[-1].squeeze()
 
         # Use parabolic sar as absolute stoploss price
-        stoploss_price = last_candle['sar']
+        stoploss_price = last_candle["sar"]
 
         # Convert absolute price to percentage relative to current_rate
         if stoploss_price < current_rate:
@@ -426,10 +429,7 @@ Stoploss values returned from `custom_stoploss()` must specify a percentage rela
 
 
     ``` python
-
-    from datetime import datetime
-    from freqtrade.persistence import Trade
-    from freqtrade.strategy import IStrategy, stoploss_from_open
+    # Default imports
 
     class AwesomeStrategy(IStrategy):
 
@@ -437,7 +437,7 @@ Stoploss values returned from `custom_stoploss()` must specify a percentage rela
 
         use_custom_stoploss = True
 
-        def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+        def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                             current_rate: float, current_profit: float, after_fill: bool,
                             **kwargs) -> Optional[float]:
 
@@ -449,7 +449,7 @@ Stoploss values returned from `custom_stoploss()` must specify a percentage rela
 
     ```
 
-    Full examples can be found in the [Custom stoploss](strategy-advanced.md#custom-stoploss) section of the Documentation.
+    Full examples can be found in the [Custom stoploss](strategy-callbacks.md#custom-stoploss) section of the Documentation.
 
 !!! Note
     Providing invalid input to `stoploss_from_open()` may produce "CustomStoploss function did not return valid stoploss" warnings.
@@ -466,37 +466,34 @@ The helper function `stoploss_from_absolute()` can be used to convert from an ab
 
 ??? Example "Returning a stoploss using absolute price from the custom stoploss function"
 
-    If we want to trail a stop price at 2xATR below current price we can call `stoploss_from_absolute(current_rate + (side * candle['atr'] * 2), current_rate, is_short=trade.is_short, leverage=trade.leverage)`.
+    If we want to trail a stop price at 2xATR below current price we can call `stoploss_from_absolute(current_rate + (side * candle["atr"] * 2), current_rate=current_rate, is_short=trade.is_short, leverage=trade.leverage)`.
     For futures, we need to adjust the direction (up or down), as well as adjust for leverage, since the [`custom_stoploss`](strategy-callbacks.md#custom-stoploss) callback  returns the ["risk for this trade"](stoploss.md#stoploss-and-leverage) - not the relative price movement.
 
     ``` python
-
-    from datetime import datetime
-    from freqtrade.persistence import Trade
-    from freqtrade.strategy import IStrategy, stoploss_from_absolute, timeframe_to_prev_date
+    # Default imports
 
     class AwesomeStrategy(IStrategy):
 
         use_custom_stoploss = True
 
-        def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-            dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
+        def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
             return dataframe
 
-        def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+        def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                             current_rate: float, current_profit: float, after_fill: bool,
                             **kwargs) -> Optional[float]:
             dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
             trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
             candle = dataframe.iloc[-1].squeeze()
-            sign = 1 if trade.is_short else -1
-            return stoploss_from_absolute(current_rate + (side * candle['atr'] * 2), 
-                                          current_rate, is_short=trade.is_short,
+            side = 1 if trade.is_short else -1
+            return stoploss_from_absolute(current_rate + (side * candle["atr"] * 2), 
+                                          current_rate=current_rate, 
+                                          is_short=trade.is_short,
                                           leverage=trade.leverage)
 
     ```
 
-
 ---
 
 ## Custom order price rules
@@ -516,19 +513,18 @@ Each of these methods are called right before placing an order on the exchange.
 ### Custom order entry and exit price example
 
 ``` python
-from datetime import datetime, timedelta, timezone
-from freqtrade.persistence import Trade
+# Default imports
 
 class AwesomeStrategy(IStrategy):
 
     # ... populate_* methods
 
-    def custom_entry_price(self, pair: str, trade: Optional['Trade'], current_time: datetime, proposed_rate: float,
+    def custom_entry_price(self, pair: str, trade: Optional[Trade], current_time: datetime, proposed_rate: float,
                            entry_tag: Optional[str], side: str, **kwargs) -> float:
 
         dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
                                                                 timeframe=self.timeframe)
-        new_entryprice = dataframe['bollinger_10_lowerband'].iat[-1]
+        new_entryprice = dataframe["bollinger_10_lowerband"].iat[-1]
 
         return new_entryprice
 
@@ -538,7 +534,7 @@ class AwesomeStrategy(IStrategy):
 
         dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
                                                                 timeframe=self.timeframe)
-        new_exitprice = dataframe['bollinger_10_upperband'].iat[-1]
+        new_exitprice = dataframe["bollinger_10_upperband"].iat[-1]
 
         return new_exitprice
 
@@ -575,8 +571,7 @@ It applies a tight timeout for higher priced assets, while allowing more time to
 The function must return either `True` (cancel order) or `False` (keep order alive).
 
 ``` python
-from datetime import datetime, timedelta
-from freqtrade.persistence import Trade, Order
+    # Default imports
 
 class AwesomeStrategy(IStrategy):
 
@@ -584,11 +579,11 @@ class AwesomeStrategy(IStrategy):
 
     # Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
     unfilledtimeout = {
-        'entry': 60 * 25,
-        'exit': 60 * 25
+        "entry": 60 * 25,
+        "exit": 60 * 25
     }
 
-    def check_entry_timeout(self, pair: str, trade: 'Trade', order: 'Order',
+    def check_entry_timeout(self, pair: str, trade: Trade, order: Order,
                             current_time: datetime, **kwargs) -> bool:
         if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
             return True
@@ -599,7 +594,7 @@ class AwesomeStrategy(IStrategy):
         return False
 
 
-    def check_exit_timeout(self, pair: str, trade: Trade, order: 'Order',
+    def check_exit_timeout(self, pair: str, trade: Trade, order: Order,
                            current_time: datetime, **kwargs) -> bool:
         if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
             return True
@@ -616,8 +611,7 @@ class AwesomeStrategy(IStrategy):
 ### Custom order timeout example (using additional data)
 
 ``` python
-from datetime import datetime
-from freqtrade.persistence import Trade, Order
+    # Default imports
 
 class AwesomeStrategy(IStrategy):
 
@@ -625,24 +619,24 @@ class AwesomeStrategy(IStrategy):
 
     # Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
     unfilledtimeout = {
-        'entry': 60 * 25,
-        'exit': 60 * 25
+        "entry": 60 * 25,
+        "exit": 60 * 25
     }
 
-    def check_entry_timeout(self, pair: str, trade: 'Trade', order: 'Order',
+    def check_entry_timeout(self, pair: str, trade: Trade, order: Order,
                             current_time: datetime, **kwargs) -> bool:
         ob = self.dp.orderbook(pair, 1)
-        current_price = ob['bids'][0][0]
+        current_price = ob["bids"][0][0]
         # Cancel buy order if price is more than 2% above the order.
         if current_price > order.price * 1.02:
             return True
         return False
 
 
-    def check_exit_timeout(self, pair: str, trade: 'Trade', order: 'Order',
+    def check_exit_timeout(self, pair: str, trade: Trade, order: Order,
                            current_time: datetime, **kwargs) -> bool:
         ob = self.dp.orderbook(pair, 1)
-        current_price = ob['asks'][0][0]
+        current_price = ob["asks"][0][0]
         # Cancel sell order if price is more than 2% below the order.
         if current_price < order.price * 0.98:
             return True
@@ -661,6 +655,8 @@ This are the last methods that will be called before an order is placed.
 `confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
 
 ``` python
+# Default imports
+
 class AwesomeStrategy(IStrategy):
 
     # ... populate_* methods
@@ -685,7 +681,7 @@ class AwesomeStrategy(IStrategy):
         :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
         :param current_time: datetime object, containing the current datetime
         :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
-        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :param side: "long" or "short" - indicating the direction of the proposed trade
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         :return bool: When True is returned, then the buy-order is placed on the exchange.
             False aborts the process
@@ -707,8 +703,7 @@ The exit-reasons (if applicable) will be in the following sequence:
 * `trailing_stop_loss`
 
 ``` python
-from freqtrade.persistence import Trade
-
+# Default imports
 
 class AwesomeStrategy(IStrategy):
 
@@ -734,14 +729,14 @@ class AwesomeStrategy(IStrategy):
                      or current rate for market orders.
         :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
         :param exit_reason: Exit reason.
-            Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
-                           'exit_signal', 'force_exit', 'emergency_exit']
+            Can be any of ["roi", "stop_loss", "stoploss_on_exchange", "trailing_stop_loss",
+                           "exit_signal", "force_exit", "emergency_exit"]
         :param current_time: datetime object, containing the current datetime
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         :return bool: When True, then the exit-order is placed on the exchange.
             False aborts the process
         """
-        if exit_reason == 'force_exit' and trade.calc_profit_ratio(rate) < 0:
+        if exit_reason == "force_exit" and trade.calc_profit_ratio(rate) < 0:
             # Reject force-sells with negative profit
             # This is just a sample, please adjust to your needs
             # (this does not necessarily make sense, assuming you know when you're force-selling)
@@ -760,28 +755,38 @@ The `position_adjustment_enable` strategy property enables the usage of `adjust_
 For performance reasons, it's disabled by default and freqtrade will show a warning message on startup if enabled.
 `adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging) or to increase or decrease positions.
 
-`max_entry_position_adjustment` property is used to limit the number of additional entries per trade (on top of the first entry order) that the bot can execute. By default, the value is -1 which means the bot have no limit on number of adjustment entries.
-
-The strategy is expected to return a stake_amount (in stake currency) between `min_stake` and `max_stake` if and when an additional entry order should be made (position is increased -> buy order for long trades, sell order for short trades).
-If there are not enough funds in the wallet (the return value is above `max_stake`) then the signal will be ignored.
 Additional orders also result in additional fees and those orders don't count towards `max_open_trades`.
 
 This callback is **not** called when there is an open order (either buy or sell) waiting for execution.
 
 `adjust_trade_position()` is called very frequently for the duration of a trade, so you must keep your implementation as performant as possible.
 
-Additional entries are ignored once you have reached the maximum amount of extra entries that you have set on `max_entry_position_adjustment`, but the callback is called anyway looking for partial exits.
-
 Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position (negative values will decrease your position), no matter if it's a long or short trade.
+Adjustment orders can be assigned with a tag by returning a 2 element Tuple, with the first element being the adjustment amount, and the 2nd element the tag (e.g. `return 250, "increase_favorable_conditions"`).
 
 Modifications to leverage are not possible, and the stake-amount returned is assumed to be before applying leverage.
 
+### Increase position
+
+The strategy is expected to return a positive **stake_amount** (in stake currency) between `min_stake` and `max_stake` if and when an additional entry order should be made (position is increased -> buy order for long trades, sell order for short trades).
+
+If there are not enough funds in the wallet (the return value is above `max_stake`) then the signal will be ignored.
+`max_entry_position_adjustment` property is used to limit the number of additional entries per trade (on top of the first entry order) that the bot can execute. By default, the value is -1 which means the bot have no limit on number of adjustment entries.
+
+Additional entries are ignored once you have reached the maximum amount of extra entries that you have set on `max_entry_position_adjustment`, but the callback is called anyway looking for partial exits.
+
+### Decrease position
+
+The strategy is expected to return a negative stake_amount (in stake currency) for a partial exit.
+Returning the full owned stake at that point (`-trade.stake_amount`) results in a full exit.  
+Returning a value more than the above (so remaining stake_amount would become negative) will result in the bot ignoring the signal.
+
 !!! Note "About stake size"
     Using fixed stake size means it will be the amount used for the first order, just like without position adjustment.
     If you wish to buy additional orders with DCA, then make sure to leave enough funds in the wallet for that.
-    Using 'unlimited' stake amount with DCA orders requires you to also implement the `custom_stake_amount()` callback to avoid allocating all funds to the initial order.
+    Using `"unlimited"` stake amount with DCA orders requires you to also implement the `custom_stake_amount()` callback to avoid allocating all funds to the initial order.
 
-!!! Warning
+!!! Warning "Stoploss calculation"
     Stoploss is still calculated from the initial opening price, not averaged price.
     Regular stoploss rules still apply (cannot move down).
 
@@ -791,9 +796,13 @@ Modifications to leverage are not possible, and the stake-amount returned is ass
     During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
     This can also cause deviating results between live and backtesting, since backtesting can adjust the trade only once per candle, whereas live could adjust the trade multiple times per candle.
 
-``` python
-from freqtrade.persistence import Trade
+!!! Warning "Performance with many position adjustments"
+    Position adjustments can be a good approach to increase a strategy's output - but it can also have drawbacks if using this feature extensively.  
+    Each of the orders will be attached to the trade object for the duration of the trade - hence increasing memory usage.
+    Trades with long duration and 10s or even 100ds of position adjustments are therefore not recommended, and should be closed at regular intervals to not affect performance.
 
+``` python
+# Default imports
 
 class DigDeeperStrategy(IStrategy):
 
@@ -824,7 +833,8 @@ class DigDeeperStrategy(IStrategy):
                               min_stake: Optional[float], max_stake: float,
                               current_entry_rate: float, current_exit_rate: float,
                               current_entry_profit: float, current_exit_profit: float,
-                              **kwargs) -> Optional[float]:
+                              **kwargs
+                              ) -> Union[Optional[float], Tuple[Optional[float], Optional[str]]]:
         """
         Custom trade adjustment logic, returning the stake amount that a trade should be
         increased or decreased.
@@ -850,11 +860,12 @@ class DigDeeperStrategy(IStrategy):
         :return float: Stake amount to adjust your trade,
                        Positive values to increase position, Negative values to decrease position.
                        Return None for no action.
+                       Optionally, return a tuple with a 2nd element with an order reason
         """
 
         if current_profit > 0.05 and trade.nr_of_successful_exits == 0:
             # Take half of the profit at +5%
-            return -(trade.stake_amount / 2)
+            return -(trade.stake_amount / 2), "half_profit_5%"
 
         if current_profit > -0.05:
             return None
@@ -864,7 +875,7 @@ class DigDeeperStrategy(IStrategy):
         # Only buy when not actively falling price.
         last_candle = dataframe.iloc[-1].squeeze()
         previous_candle = dataframe.iloc[-2].squeeze()
-        if last_candle['close'] < previous_candle['close']:
+        if last_candle["close"] < previous_candle["close"]:
             return None
 
         filled_entries = trade.select_filled_orders(trade.entry_side)
@@ -882,7 +893,7 @@ class DigDeeperStrategy(IStrategy):
             stake_amount = filled_entries[0].stake_amount
             # This then calculates current safety order size
             stake_amount = stake_amount * (1 + (count_of_entries * 0.25))
-            return stake_amount
+            return stake_amount, "1/3rd_increase"
         except Exception as exception:
             return None
 
@@ -929,8 +940,7 @@ If the cancellation of the original order fails, then the order will not be repl
     Entry Orders that are cancelled via the above methods will not have this callback called. Be sure to update timeout values to match your expectations.
 
 ```python
-from freqtrade.persistence import Trade
-from datetime import timedelta
+# Default imports
 
 class AwesomeStrategy(IStrategy):
 
@@ -955,13 +965,18 @@ class AwesomeStrategy(IStrategy):
         :param proposed_rate: Rate, calculated based on pricing settings in entry_pricing.
         :param current_order_rate: Rate of the existing order in place.
         :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
-        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :param side: "long" or "short" - indicating the direction of the proposed trade
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         :return float: New entry price value if provided
 
         """
         # Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
-        if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10)) > trade.open_date_utc:
+        if (
+            pair == "BTC/USDT" 
+            and entry_tag == "long_sma200" 
+            and side == "long" 
+            and (current_time - timedelta(minutes=10)) <= trade.open_date_utc
+        ):
             # just cancel the order if it has been filled more than half of the amount
             if order.filled > order.remaining:
                 return None
@@ -969,7 +984,7 @@ class AwesomeStrategy(IStrategy):
                 dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
                 current_candle = dataframe.iloc[-1].squeeze()
                 # desired price
-                return current_candle['sma_200']
+                return current_candle["sma_200"]
         # default: maintain existing order
         return current_order_rate
 ```
@@ -984,6 +999,8 @@ Values that are above `max_leverage` will be adjusted to `max_leverage`.
 For markets / exchanges that don't support leverage, this method is ignored.
 
 ``` python
+# Default imports
+
 class AwesomeStrategy(IStrategy):
     def leverage(self, pair: str, current_time: datetime, current_rate: float,
                  proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
@@ -997,7 +1014,7 @@ class AwesomeStrategy(IStrategy):
         :param proposed_leverage: A leverage proposed by the bot.
         :param max_leverage: Max leverage allowed on this pair
         :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
-        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :param side: "long" or "short" - indicating the direction of the proposed trade
         :return: A leverage amount, which is between 1.0 and max_leverage.
         """
         return 1.0
@@ -1005,3 +1022,35 @@ class AwesomeStrategy(IStrategy):
 
 All profit calculations include leverage. Stoploss / ROI also include leverage in their calculation.
 Defining a stoploss of 10% at 10x leverage would trigger the stoploss with a 1% move to the downside.
+
+## Order filled Callback
+
+The `order_filled()` callback may be used to perform specific actions based on the current trade state after an order is filled.
+It will be called independent of the order type (entry, exit, stoploss or position adjustment).
+
+Assuming that your strategy needs to store the high value of the candle at trade entry, this is possible with this callback as the following example show.
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+    def order_filled(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None:
+        """
+        Called right after an order fills. 
+        Will be called for all order types (entry, exit, stoploss, position adjustment).
+        :param pair: Pair for trade
+        :param trade: trade object.
+        :param order: Order object.
+        :param current_time: datetime object, containing the current datetime
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        # Obtain pair dataframe (just to show how to access it)
+        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+        
+        if (trade.nr_of_successful_entries == 1) and (order.ft_order_side == trade.entry_side):
+            trade.set_custom_data(key="entry_candle_high", value=last_candle["high"])
+
+        return None
+
+```

docs/strategy-customization.md

@@ -2,52 +2,93 @@
 
 This page explains how to customize your strategies, add new indicators and set up trading rules.
 
-Please familiarize yourself with [Freqtrade basics](bot-basics.md) first, which provides overall info on how the bot operates.
+If you haven't already, please familiarize yourself with:
+
+- the [Freqtrade strategy 101](freqtrade-101.md), which provides a quick start to strategy development
+- the [Freqtrade bot basics](bot-basics.md), which provides overall info on how the bot operates
 
 ## Develop your own strategy
 
 The bot includes a default strategy file.
+
 Also, several other strategies are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
 
 You will however most likely have your own idea for a strategy.
-This document intends to help you convert your strategy idea into your own strategy.
 
-To get started, use `freqtrade new-strategy --strategy AwesomeStrategy` (you can obviously use your own naming for your strategy).
-This will create a new strategy file from a template, which will be located under `user_data/strategies/AwesomeStrategy.py`.
+This document intends to help you convert your ideas into a working strategy.
+
+### Generating a strategy template
+
+To get started, you can use the command:
+
+```bash
+freqtrade new-strategy --strategy AwesomeStrategy
+```
+
+This will create a new strategy called `AwesomeStrategy` from a template, which will be located using the filename `user_data/strategies/AwesomeStrategy.py`.
 
 !!! Note
-    This is just a template file, which will most likely not be profitable out of the box.
+    There is a difference between the *name* of the strategy and the filename. In most commands, Freqtrade uses the *name* of the strategy, *not the filename*.
+
+!!! Note
+    The `new-strategy` command generates starting examples which will not be profitable out of the box.
 
 ??? Hint "Different template levels"
-    `freqtrade new-strategy` has an additional parameter, `--template`, which controls the amount of pre-build information you get in the created strategy. Use `--template minimal` to get an empty strategy without any indicator examples, or `--template advanced` to get a template with most callbacks defined.
+    `freqtrade new-strategy` has an additional parameter, `--template`, which controls the amount of pre-build information you get in the created strategy. Use `--template minimal` to get an empty strategy without any indicator examples, or `--template advanced` to get a template with more complicated features defined.
 
 ### Anatomy of a strategy
 
-A strategy file contains all the information needed to build a good strategy:
+A strategy file contains all the information needed to build the strategy logic:
 
+- Candle data in OHLCV format
 - Indicators
-- Entry strategy rules
-- Exit strategy rules
-- Minimal ROI recommended
-- Stoploss strongly recommended
+- Entry logic
+  - Signals
+- Exit logic
+  - Signals
+  - Minimal ROI
+  - Callbacks ("custom functions")
+- Stoploss
+  - Fixed/absolute
+  - Trailing
+  - Callbacks ("custom functions")
+- Pricing [optional]
+- Position adjustment [optional]
 
-The bot also include a sample strategy called `SampleStrategy` you can update: `user_data/strategies/sample_strategy.py`.
-You can test it with the parameter: `--strategy SampleStrategy`
+The bot includes a sample strategy called `SampleStrategy` that you can use as a basis: `user_data/strategies/sample_strategy.py`.
+You can test it with the parameter: `--strategy SampleStrategy`. Remember that you use the strategy class name, not the filename.
 
 Additionally, there is an attribute called `INTERFACE_VERSION`, which defines the version of the strategy interface the bot should use.
 The current version is 3 - which is also the default when it's not set explicitly in the strategy.
 
-Future versions will require this to be set.
+You may see older strategies set to interface version 2, and these will need to be updated to v3 terminology as future versions will require this to be set.
+
+Starting the bot in dry or live mode is accomplished using the `trade` command:
 
 ```bash
 freqtrade trade --strategy AwesomeStrategy
 ```
 
+### Bot modes
+
+Freqtrade strategies can be processed by the Freqtrade bot in 5 main modes:
+
+- backtesting
+- hyperopting
+- dry ("forward testing")
+- live
+- FreqAI (not covered here)
+
+Check the [configuration documentation](configuration.md) about how to set the bot to dry or live mode.
+
+**Always use dry mode when testing as this gives you an idea of how your strategy will work in reality without risking capital.**
+
+## Diving in deeper
 **For the following section we will use the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py)
 file as reference.**
 
 !!! Note "Strategies and Backtesting"
-    To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware
+    To avoid problems and unexpected differences between backtesting and dry/live modes, please be aware
     that during backtesting the full time range is passed to the `populate_*()` methods at once.
     It is therefore best to use vectorized operations (across the whole dataframe, not loops) and
     avoid index referencing (`df.iloc[-1]`), but instead use `df.shift()` to get to the previous candle.
@@ -57,14 +98,22 @@ file as reference.**
     needs to take care to avoid having the strategy utilize data from the future.
     Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
 
+??? Hint "Lookahead and recursive analysis"
+    Freqtrade includes two helpful commands to help assess common lookahead (using future data) and 
+    recursive bias (variance in indicator values) issues. Before running a strategy in dry or live more, 
+    you should always use these commands first. Please check the relevant documentation for 
+    [lookahead](lookahead-analysis.md) and [recursive](recursive-analysis.md) analysis.
+
 ### Dataframe
 
 Freqtrade uses [pandas](https://pandas.pydata.org/) to store/provide the candlestick (OHLCV) data.
-Pandas is a great library developed for processing large amounts of data.
+Pandas is a great library developed for processing large amounts of data in tabular format.
 
-Each row in a dataframe corresponds to one candle on a chart, with the latest candle always being the last in the dataframe (sorted by date).
+Each row in a dataframe corresponds to one candle on a chart, with the latest complete candle always being the last in the dataframe (sorted by date).
 
-``` output
+If we were to look at the first few rows of the main dataframe using the pandas `head()` function, we would see:
+
+```output
 > dataframe.head()
                        date      open      high       low     close     volume
 0 2021-11-09 23:25:00+00:00  67279.67  67321.84  67255.01  67300.97   44.62253
@@ -74,20 +123,16 @@ Each row in a dataframe corresponds to one candle on a chart, with the latest ca
 4 2021-11-09 23:45:00+00:00  67160.48  67160.48  66901.26  66943.37  111.39292
 ```
 
-Pandas provides fast ways to calculate metrics. To benefit from this speed, it's advised to not use loops, but use vectorized methods instead.
-
-Vectorized operations perform calculations across the whole range of data and are therefore, compared to looping through each row, a lot faster when calculating indicators.
-
-As a dataframe is a table, simple python comparisons like the following will not work
+A dataframe is a table where columns are not single values, but a series of data values. As such, simple python comparisons like the following will not work:
 
 ``` python
     if dataframe['rsi'] > 30:
         dataframe['enter_long'] = 1
 ```
 
-The above section will fail with `The truth value of a Series is ambiguous. [...]`.
+The above section will fail with `The truth value of a Series is ambiguous [...]`.
 
-This must instead be written in a pandas-compatible way, so the operation is performed across the whole dataframe.
+This must instead be written in a pandas-compatible way, so the operation is performed across the whole dataframe, i.e. `vectorisation`.
 
 ``` python
     dataframe.loc[
@@ -97,13 +142,38 @@ This must instead be written in a pandas-compatible way, so the operation is per
 
 With this section, you have a new column in your dataframe, which has `1` assigned whenever RSI is above 30.
 
+Freqtrade uses this new column as an entry signal, where it is assumed that a trade will subsequently open on the next open candle.
+
+Pandas provides fast ways to calculate metrics, i.e. "vectorisation". To benefit from this speed, it is advised to not use loops, but use vectorized methods instead.
+
+Vectorized operations perform calculations across the whole range of data and are therefore, compared to looping through each row, a lot faster when calculating indicators.
+
+??? Hint "Signals vs Trades"
+    - Signals are generated from indicators at candle close, and are intentions to enter a trade.
+    - Trades are orders that are executed (on the exchange in live mode) where a trade will then open as close to next candle open as possible.
+
+!!! Warning "Trade order assumptions"
+    In backtesting, signals are generated on candle close. Trades are then initiated immeditely on next candle open.
+    
+    In dry and live, this may be delayed due to all pair dataframes needing to be analysed first, then trade processing 
+    for each of those pairs happens. This means that in dry/live you need to be mindful of having as low a computation 
+    delay as possible, usually by running a low number of pairs and having a CPU with a good clock speed.
+
+#### Why can't I see "real time" candle data?
+
+Freqtrade does not store incomplete/unfinished candles in the dataframe.
+
+The use of incomplete data for making strategy decisions is called "repainting" and you might see other platforms allow this.
+
+Freqtrade does not. Only complete/finished candle data is available in the dataframe.
+
 ### Customize Indicators
 
-Buy and sell signals need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
+Entry and exit signals need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
 
 You should only add the indicators used in either `populate_entry_trend()`, `populate_exit_trend()`, or to populate another indicator, otherwise performance may suffer.
 
-It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
+It's important to always return the dataframe from these three functions without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
 
 Sample:
 
@@ -124,7 +194,7 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
     stoch = ta.STOCHF(dataframe)
     dataframe['fastd'] = stoch['fastd']
     dataframe['fastk'] = stoch['fastk']
-    dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
+    dataframe['bb_lower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
     dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
     dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
     dataframe['mfi'] = ta.MFI(dataframe)
@@ -145,6 +215,8 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
     dataframe['plus_di'] = ta.PLUS_DI(dataframe)
     dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
     dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+    # remember to always return the dataframe
     return dataframe
 ```
 
@@ -156,19 +228,21 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
 
 Out of the box, freqtrade installs the following technical libraries:
 
-* [ta-lib](http://mrjbq7.github.io/ta-lib/)
-* [pandas-ta](https://twopirllc.github.io/pandas-ta/)
-* [technical](https://github.com/freqtrade/technical/)
+- [ta-lib](https://ta-lib.github.io/ta-lib-python/)
+- [pandas-ta](https://twopirllc.github.io/pandas-ta/)
+- [technical](https://technical.freqtrade.io)
 
 Additional technical libraries can be installed as necessary, or custom indicators may be written / invented by the strategy author.
 
 ### Strategy startup period
 
-Most indicators have an instable startup period, in which they are either not available (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
+Some indicators have an unstable startup period in which there isn't enough candle data to calculate any values (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this unstable period is and uses whatever indicator values are in the dataframe.
+
 To account for this, the strategy can be assigned the `startup_candle_count` attribute.
+
 This should be set to the maximum number of candles that the strategy requires to calculate stable indicators. In the case where a user includes higher timeframes with informative pairs, the `startup_candle_count` does not necessarily change. The value is the maximum period (in candles) that any of the informatives timeframes need to compute stable indicators.
 
-You can use [recursive-analysis](recursive-analysis.md) to check and find the correct `startup_candle_count` to be used.
+You can use [recursive-analysis](recursive-analysis.md) to check and find the correct `startup_candle_count` to be used. When recursive analysis shows a variance of 0%, then you can be sure that you have enough startup candle data.
 
 In this example strategy, this should be set to 400 (`startup_candle_count = 400`), since the minimum needed history for ema100 calculation to make sure the value is correct is 400 candles.
 
@@ -195,19 +269,22 @@ Let's try to backtest 1 month (January 2019) of 5m candles using an example stra
 freqtrade backtesting --timerange 20190101-20190201 --timeframe 5m
 ```
 
-Assuming `startup_candle_count` is set to 400, backtesting knows it needs 400 candles to generate valid buy signals. It will load data from `20190101 - (400 * 5m)` - which is ~2018-12-30 11:40:00.
-If this data is available, indicators will be calculated with this extended timerange. The instable startup period (up to 2019-01-01 00:00:00) will then be removed before starting backtesting.
+Assuming `startup_candle_count` is set to 400, backtesting knows it needs 400 candles to generate valid entry signals. It will load data from `20190101 - (400 * 5m)` - which is ~2018-12-30 11:40:00.
 
-!!! Note
-    If data for the startup period is not available, then the timerange will be adjusted to account for this startup period - so Backtesting would start at 2019-01-02 09:20:00.
+If this data is available, indicators will be calculated with this extended timerange. The unstable startup period (up to 2019-01-01 00:00:00) will then be removed before backtesting is carried out.
+
+!!! Note "Unavailable startup candle data"
+    If data for the startup period is not available, then the timerange will be adjusted to account for this startup period. In our example, backtesting would then start from 2019-01-02 09:20:00.
 
 ### Entry signal rules
 
 Edit the method `populate_entry_trend()` in your strategy file to update your entry strategy.
 
-It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
+It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected. The strategy may then produce invalid values, or cease to work entirely.
 
-This method will also define a new column, `"enter_long"` (`"enter_short"` for shorts), which needs to contain 1 for entries, and 0 for "no action". `enter_long` is a mandatory column that must be set even if the strategy is shorting only.
+This method will also define a new column, `"enter_long"` (`"enter_short"` for shorts), which needs to contain `1` for entries, and `0` for "no action". `enter_long` is a mandatory column that must be set even if the strategy is shorting only.
+
+You can name your entry signals by using the `"enter_tag"` column, which can help debug and assess your strategy later. 
 
 Sample from `user_data/strategies/sample_strategy.py`:
 
@@ -232,12 +309,15 @@ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFram
 ```
 
 ??? Note "Enter short trades"
-    Short-entries can be created by setting `enter_short` (corresponds to `enter_long` for long trades).
+    Short entries can be created by setting `enter_short` (corresponds to `enter_long` for long trades).
     The `enter_tag` column remains identical.
-    Short-trades need to be supported by your exchange and market configuration!
-    Please make sure to set [`can_short`]() appropriately on your strategy if you intend to short.
+    Shorting needs to be supported by your exchange and market configuration!
+    Also, make sure you set [`can_short`](#can-short) appropriately on your strategy if you intend to short.
 
     ```python
+    # allow both long and short trades
+    can_short = True
+
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         dataframe.loc[
             (
@@ -261,17 +341,21 @@ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFram
     ```
 
 !!! Note
-    Buying requires sellers to buy from - therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods.
+    Buying requires sellers to buy from. Therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods.
 
 ### Exit signal rules
 
 Edit the method `populate_exit_trend()` into your strategy file to update your exit strategy.
+
 The exit-signal can be suppressed by setting `use_exit_signal` to false in the configuration or strategy.
+
 `use_exit_signal` will not influence [signal collision rules](#colliding-signals) - which will still apply and can prevent entries.
 
-It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
+It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected. The strategy may then produce invalid values, or cease to work entirely.
 
-This method will also define a new column, `"exit_long"` (`"exit_short"` for shorts), which needs to contain 1 for exits, and 0 for "no action".
+This method will also define a new column, `"exit_long"` (`"exit_short"` for shorts), which needs to contain `1` for exits, and `0` for "no action".
+
+You can name your exit signals by using the `"exit_tag"` column, which can help debug and assess your strategy later.
 
 Sample from `user_data/strategies/sample_strategy.py`:
 
@@ -295,11 +379,15 @@ def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
 ```
 
 ??? Note "Exit short trades"
-    Short-exits can be created by setting `exit_short` (corresponds to `exit_long`).
+    Short exits can be created by setting `exit_short` (corresponds to `exit_long`).
     The `exit_tag` column remains identical.
-    Short-trades need to be supported by your exchange and market configuration!
+    Shorting needs to be supported by your exchange and market configuration!
+    Also, make sure you set [`can_short`](#can-short) appropriately on your strategy if you intend to short.
 
     ```python
+    # allow both long and short trades
+    can_short = True
+
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         dataframe.loc[
             (
@@ -322,9 +410,9 @@ def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
 
 ### Minimal ROI
 
-This dict defines the minimal Return On Investment (ROI) a trade should reach before exiting, independent from the exit signal.
+The `minimal_roi` strategy variable defines the minimal Return On Investment (ROI) a trade should reach before exiting, independent from the exit signal.
 
-It is of the following format, with the dict key (left side of the colon) being the minutes passed since the trade opened, and the value (right side of the colon) being the percentage.
+It is of the following format, i.e. a python `dict`, with the dict key (left side of the colon) being the minutes passed since the trade opened, and the value (right side of the colon) being the percentage.
 
 ```python
 minimal_roi = {
@@ -344,14 +432,19 @@ The above configuration would therefore mean:
 
 The calculation does include fees.
 
+#### Disabling minimal ROI
+
 To disable ROI completely, set it to an empty dictionary:
 
 ```python
 minimal_roi = {}
 ```
 
+#### Using calculations in minimal ROI
+
 To use times based on candle duration (timeframe), the following snippet can be handy.
-This will allow you to change the timeframe for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
+
+This will allow you to change the timeframe for the strategy, but the minimal ROI times will still be set as candles, e.g. after 3 candles.
 
 ``` python
 from freqtrade.exchange import timeframe_to_minutes
@@ -367,6 +460,11 @@ class AwesomeStrategy(IStrategy):
     }
 ```
 
+??? info "Orders that don't fill immediately"
+    `minimal_roi` will take the `trade.open_date` as reference, which is the time the trade was initialized, i.e. when the first order for this trade was placed.
+    This will also hold true for limit orders that don't fill immediately (usually in combination with "off-spot" prices through `custom_entry_price()`), as well as for cases where the initial order price is replaced through `adjust_entry_price()`.
+    The time used will still be from the initial `trade.open_date` (when the initial order was first placed), not from the newly placed or adjusted order date.
+
 ### Stoploss
 
 Setting a stoploss is highly recommended to protect your capital from strong moves against you.
@@ -381,33 +479,44 @@ For the full documentation on stoploss features, look at the dedicated [stoploss
 
 ### Timeframe
 
-This is the set of candles the bot should download and use for the analysis.
+This is the periodicity of candles the bot should use in the strategy.
+
 Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
 
-Please note that the same entry/exit signals may work well with one timeframe, but not with the others.
+Please note that the same entry/exit signals may work well with one timeframe, but not with others.
 
 This setting is accessible within the strategy methods as the `self.timeframe` attribute.
 
 ### Can short
 
-To use short signals in futures markets, you will have to let us know to do so by setting `can_short=True`.
+To use short signals in futures markets, you will have to set `can_short = True`.
+
 Strategies which enable this will fail to load on spot markets.
-Disabling of this will have short signals ignored (also in futures markets).
+
+If you have `1` values in the `enter_short` column to raise short signals, setting `can_short = False` (which is the default) will mean that these short signals are ignored, even if you have specified futures markets in your configuration.
 
 ### Metadata dict
 
-The metadata-dict (available for `populate_entry_trend`, `populate_exit_trend`, `populate_indicators`) contains additional information.
-Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
+The `metadata` dict (available for `populate_entry_trend`, `populate_exit_trend`, `populate_indicators`) contains additional information.
+Currently this is `pair`, which can be accessed using `metadata['pair']`, and will return a pair in the format `XRP/BTC` (or `XRP/BTC:BTC` for futures markets).
 
-The Metadata-dict should not be modified and does not persist information across multiple calls.
-Instead, have a look at the [Storing information](strategy-advanced.md#Storing-information) section.
+The metadata dict should not be modified and does not persist information across multiple functions in your strategy.
+
+Instead, please check the [Storing information](strategy-advanced.md#storing-information-persistent) section.
+
+--8<-- "includes/strategy-imports.md"
 
 ## Strategy file loading
 
-By default, freqtrade will attempt to load strategies from all `.py` files within `user_data/strategies`.
+By default, freqtrade will attempt to load strategies from all `.py` files within the `userdir` (default `user_data/strategies`).
 
-Assuming your strategy is called `AwesomeStrategy`, stored in the file `user_data/strategies/AwesomeStrategy.py`, then you can start freqtrade with `freqtrade trade --strategy AwesomeStrategy`.
-Note that we're using the class-name, not the file name.
+Assuming your strategy is called `AwesomeStrategy`, stored in the file `user_data/strategies/AwesomeStrategy.py`, then you can start freqtrade in dry (or live, depending on your configuration) mode with:
+
+```bash
+    freqtrade trade --strategy AwesomeStrategy`
+```
+
+Note that we're using the class name, not the file name.
 
 You can use `freqtrade list-strategies` to see a list of all strategies Freqtrade is able to load (all strategies in the correct folder).
 It will also include a "status" field, highlighting potential problems.
@@ -419,9 +528,11 @@ It will also include a "status" field, highlighting potential problems.
 
 ### Get data for non-tradeable pairs
 
-Data for additional, informative pairs (reference pairs) can be beneficial for some strategies.
+Data for additional, informative pairs (reference pairs) can be beneficial for some strategies to see data on a wider timeframe.
+
 OHLCV data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see below).
-These parts will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting.
+
+These pairs will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting, e.g. `VolumePairlist`.
 
 The pairs need to be specified as tuples in the format `("pair", "timeframe")`, with pair as the first and timeframe as the second argument.
 
@@ -461,10 +572,13 @@ A full sample can be found [in the DataProvider section](#complete-data-provider
 
 ### Informative pairs decorator (`@informative()`)
 
-In most common case it is possible to easily define informative pairs by using a decorator. All decorated `populate_indicators_*` methods run in isolation,
-not having access to data from other informative pairs, in the end all informative dataframes are merged and passed to main `populate_indicators()` method.
-When hyperopting, use of hyperoptable parameter `.value` attribute is not supported. Please use `.range` attribute. See [optimizing an indicator parameter](hyperopt.md#optimizing-an-indicator-parameter)
-for more information.
+To easily define informative pairs, use the `@informative` decorator. All decorated `populate_indicators_*` methods run in isolation,
+and do not have access to data from other informative pairs. However, all informative dataframes for each pair are merged and passed to main `populate_indicators()` method.
+
+!!! Note
+    Do not use the `@informative` decorator if you need to use data from one informative pair when generating another informative pair. Instead, define informative pairs manually as described [in the DataProvider section](#complete-data-provider-sample).
+
+When hyperopting, use of the hyperoptable parameter `.value` attribute is not supported. Please use the `.range` attribute. See [optimizing an indicator parameter](hyperopt.md#optimizing-an-indicator-parameter) for more information.
 
 ??? info "Full documentation"
     ``` python
@@ -546,8 +660,8 @@ for more information.
     
         # Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
         # column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom
-        # formatting. Available in populate_indicators and other methods as 'rsi_upper'.
-        @informative('1h', 'BTC/{stake}', '{column}')
+        # formatting. Available in populate_indicators and other methods as 'rsi_upper_1h'.
+        @informative('1h', 'BTC/{stake}', '{column}_{timeframe}')
         def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
             dataframe['rsi_upper'] = ta.RSI(dataframe, timeperiod=14)
             return dataframe
@@ -561,10 +675,6 @@ for more information.
 
     ```
 
-!!! Note
-    Do not use `@informative` decorator if you need to use data of one informative pair when generating another informative pair. Instead, define informative pairs
-    manually as described [in the DataProvider section](#complete-data-provider-sample).
-
 !!! Note
     Use string formatting when accessing informative dataframes of other pairs. This will allow easily changing stake currency in config without having to adjust strategy code.
 
@@ -585,18 +695,15 @@ for more information.
     Alternatively column renaming may be used to remove stake currency from column names: `@informative('1h', 'BTC/{stake}', fmt='{base}_{column}_{timeframe}')`.
 
 !!! Warning "Duplicate method names"
-    Methods tagged with `@informative()` decorator must always have unique names! Re-using same name (for example when copy-pasting already defined informative method)
-    will overwrite previously defined method and not produce any errors due to limitations of Python programming language. In such cases you will find that indicators
-    created in earlier-defined methods are not available in the dataframe. Carefully review method names and make sure they are unique!
+    Methods tagged with the `@informative()` decorator must always have unique names! Reusing the same name (for example when copy-pasting already defined informative methods) will overwrite previously defined methods and not produce any errors due to limitations of Python programming language. In such cases you will find that indicators created in methods higher up in the strategy file are not available in the dataframe. Carefully review method names and make sure they are unique!
 
 ### *merge_informative_pair()*
 
-This method helps you merge an informative pair to a regular dataframe without lookahead bias.
-It's there to help you merge the dataframe in a safe and consistent way.
+This method helps you merge an informative pair to the regular main dataframe safely and consistently, without lookahead bias.
 
 Options:
 
-- Rename the columns for you to create unique columns
+- Rename the columns to create unique columns
 - Merge the dataframe without lookahead bias
 - Forward-fill (optional)
 
@@ -647,20 +754,20 @@ All columns of the informative dataframe will be available on the returning data
     ```
 
 !!! Warning "Informative timeframe < timeframe"
-    Using informative timeframes smaller than the dataframe timeframe is not recommended with this method, as it will not use any of the additional information this would provide.
-    To use the more detailed information properly, more advanced methods should be applied (which are out of scope for freqtrade documentation, as it'll depend on the respective need).
+    Using informative timeframes smaller than the main dataframe timeframe is not recommended with this method, as it will not use any of the additional information this would provide.
+    To use the more detailed information properly, more advanced methods should be applied (which are out of scope for this documentation).
 
 ## Additional data (DataProvider)
 
 The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
 
-All methods return `None` in case of failure (do not raise an exception).
+All methods return `None` in case of failure, i.e. failures do not raise an exception.
 
-Please always check the mode of operation to select the correct method to get data (samples see below).
+Please always check the mode of operation to select the correct method to get data (see below for examples).
 
-!!! Warning "Hyperopt"
-    Dataprovider is available during hyperopt, however it can only be used in `populate_indicators()` within a strategy.
-    It is not available in `populate_buy()` and `populate_sell()` methods, nor in `populate_indicators()`, if this method located in the hyperopt file.
+!!! Warning "Hyperopt Limitations"
+    The DataProvider is available during hyperopt, however it can only be used in `populate_indicators()` **within a strategy**, not within a hyperopt class file.
+    It is also not available in `populate_entry_trend()` and `populate_exit_trend()` methods.
 
 ### Possible options for DataProvider
 
@@ -686,30 +793,31 @@ for pair, timeframe in self.dp.available_pairs:
 
 ### *current_whitelist()*
 
-Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume.
+Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 exchange pairs by volume.
 
-The strategy might look something like this:
+The strategy logic might look something like this:
 
-*Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to buy and sell.*
+*Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to enter and exit.*
 
-Due to the limited available data, it's very difficult to resample `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500-1000 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
+Due to the limited available data, it's very difficult to resample `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit users to just 500-1000 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
 
-Since we can't resample the data we will have to use an informative pair; and since the whitelist will be dynamic we don't know which pair(s) to use.
+Since we can't resample the data we will have to use an informative pair, and since the whitelist will be dynamic we don't know which pair(s) to use! We have a problem!
 
-This is where calling `self.dp.current_whitelist()` comes in handy.
+This is where calling `self.dp.current_whitelist()` comes in handy to retrieve only those pairs in the whitelist.
 
 ```python
     def informative_pairs(self):
 
         # get access to all pairs available in whitelist.
         pairs = self.dp.current_whitelist()
-        # Assign tf to each pair so they can be downloaded and cached for strategy.
+        # Assign timeframe to each pair so they can be downloaded and cached for strategy.
         informative_pairs = [(pair, '1d') for pair in pairs]
         return informative_pairs
 ```
 
 ??? Note "Plotting with current_whitelist"
-    Current whitelist is not supported for `plot-dataframe`, as this command is usually used by providing an explicit pairlist - and would therefore make the return values of this method misleading.
+    Current whitelist is not supported for `plot-dataframe`, as this command is usually used by providing an explicit pairlist and would therefore make the return values of this method misleading.
+    It's also not supported for FreqUI visualization in [webserver mode](utils.md#webserver-mode), as the configuration for webserver mode doesn't require a pairlist to be set.
 
 ### *get_pair_dataframe(pair, timeframe)*
 
@@ -750,7 +858,7 @@ if self.dp.runmode.value in ('live', 'dry_run'):
     dataframe['best_ask'] = ob['asks'][0][0]
 ```
 
-The orderbook structure is aligned with the order structure from [ccxt](https://github.com/ccxt/ccxt/wiki/Manual#order-book-structure), so the result will look as follows:
+The orderbook structure is aligned with the order structure from [ccxt](https://github.com/ccxt/ccxt/wiki/Manual#order-book-structure), so the result will be formatted as follows:
 
 ``` js
 {
@@ -768,10 +876,10 @@ The orderbook structure is aligned with the order structure from [ccxt](https://
 }
 ```
 
-Therefore, using `ob['bids'][0][0]` as demonstrated above will result in using the best bid price. `ob['bids'][0][1]` would look at the amount at this orderbook position.
+Therefore, using `ob['bids'][0][0]` as demonstrated above will use the best bid price. `ob['bids'][0][1]` would look at the amount at this orderbook position.
 
 !!! Warning "Warning about backtesting"
-    The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return uptodate values.
+    The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return up-to-date values.
 
 ### *ticker(pair)*
 
@@ -785,12 +893,12 @@ if self.dp.runmode.value in ('live', 'dry_run'):
 
 !!! Warning
     Although the ticker data structure is a part of the ccxt Unified Interface, the values returned by this method can
-    vary for different exchanges. For instance, many exchanges do not return `vwap` values, some exchanges
-    does not always fills in the `last` field (so it can be None), etc. So you need to carefully verify the ticker
+    vary for different exchanges. For instance, many exchanges do not return `vwap` values, and some exchanges
+    do not always fill in the `last` field (so it can be None), etc. So you need to carefully verify the ticker
     data returned from the exchange and add appropriate error handling / defaults.
 
 !!! Warning "Warning about backtesting"
-    This method will always return up-to-date values - so usage during backtesting / hyperopt without runmode checks will lead to wrong results.
+    This method will always return up-to-date / real-time values. As such, usage during backtesting / hyperopt without runmode checks will lead to wrong results, e.g. your whole dataframe will contain the same single value in all rows.
 
 ### Send Notification
 
@@ -809,7 +917,7 @@ Notifications will only be sent in trading modes (Live/Dry-run) - so this method
 !!! Warning "Spamming"
     You can spam yourself pretty good by setting `always_send=True` in this method. Use this with great care and only in conditions you know will not happen throughout a candle to avoid a message every 5 seconds.
 
-### Complete Data-provider sample
+### Complete DataProvider sample
 
 ```python
 from freqtrade.strategy import IStrategy, merge_informative_pair
@@ -876,14 +984,14 @@ class SampleStrategy(IStrategy):
 
 ## Additional data (Wallets)
 
-The strategy provides access to the `wallets` object. This contains the current balances on the exchange.
+The strategy provides access to the `wallets` object. This contains the current balances of your wallets/accounts on the exchange.
 
 !!! Note "Backtesting / Hyperopt"
-    Wallets behaves differently depending on the function it's called.
+    Wallets behaves differently depending on the function from which it is called.
     Within `populate_*()` methods, it'll return the full wallet as configured.
     Within [callbacks](strategy-callbacks.md), you'll get the wallet state corresponding to the actual simulated wallet at that point in the simulation process.
 
-Please always check if `wallets` is available to avoid failures during backtesting.
+Always check if `wallets` is available to avoid failures during backtesting.
 
 ``` python
 if self.wallets:
@@ -902,15 +1010,15 @@ if self.wallets:
 
 ## Additional data (Trades)
 
-A history of Trades can be retrieved in the strategy by querying the database.
+A history of trades can be retrieved in the strategy by querying the database.
 
-At the top of the file, import Trade.
+At the top of the file, import the required object:
 
 ```python
 from freqtrade.persistence import Trade
 ```
 
-The following example queries for the current pair and trades from today, however other filters can easily be added.
+The following example queries trades from today for the current pair (`metadata['pair']`). Other filters can easily be added.
 
 ``` python
 trades = Trade.get_trades_proxy(pair=metadata['pair'],
@@ -928,7 +1036,9 @@ For a full list of available methods, please consult the [Trade object](trade-ob
 
 ## Prevent trades from happening for a specific pair
 
-Freqtrade locks pairs automatically for the current candle (until that candle is over) when a pair is sold, preventing an immediate re-buy of that pair.
+Freqtrade locks pairs automatically for the current candle (until that candle is over) when a pair exits, preventing an immediate re-entry of that pair.
+
+This is to prevent "waterfalls" of many and frequent trades within a single candle.
 
 Locked pairs will show the message `Pair <pair> is currently locked.`.
 
@@ -939,7 +1049,7 @@ Sometimes it may be desired to lock a pair after certain events happen (e.g. mul
 Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until, [reason])`.
 `until` must be a datetime object in the future, after which trading will be re-enabled for that pair, while `reason` is an optional string detailing why the pair was locked.
 
-Locks can also be lifted manually, by calling `self.unlock_pair(pair)` or `self.unlock_reason(<reason>)` - providing reason the pair was locked with.
+Locks can also be lifted manually, by calling `self.unlock_pair(pair)` or `self.unlock_reason(<reason>)`, providing the reason the pair was unlocked.
 `self.unlock_reason(<reason>)` will unlock all pairs currently locked with the provided reason.
 
 To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
@@ -948,7 +1058,7 @@ To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
     Locked pairs will always be rounded up to the next candle. So assuming a `5m` timeframe, a lock with `until` set to 10:18 will lock the pair until the candle from 10:15-10:20 will be finished.
 
 !!! Warning
-    Manually locking pairs is not available during backtesting, only locks via Protections are allowed.
+    Manually locking pairs is not available during backtesting. Only locks via Protections are allowed.
 
 #### Pair locking example
 
@@ -958,7 +1068,7 @@ from datetime import timedelta, datetime, timezone
 # Put the above lines a the top of the strategy file, next to all the other imports
 # --------
 
-# Within populate indicators (or populate_buy):
+# Within populate indicators (or populate_entry_trend):
 if self.config['runmode'].value in ('live', 'dry_run'):
     # fetch closed trades for the last 2 days
     trades = Trade.get_trades_proxy(
@@ -971,9 +1081,9 @@ if self.config['runmode'].value in ('live', 'dry_run'):
         self.lock_pair(metadata['pair'], until=datetime.now(timezone.utc) + timedelta(hours=12))
 ```
 
-## Print created dataframe
+## Print the main dataframe
 
-To inspect the created dataframe, you can issue a print-statement in either `populate_entry_trend()` or `populate_exit_trend()`.
+To inspect the current main dataframe, you can issue a print-statement in either `populate_entry_trend()` or `populate_exit_trend()`.
 You may also want to print the pair so it's clear what data is currently shown.
 
 ``` python
@@ -993,29 +1103,30 @@ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFram
     return dataframe
 ```
 
-Printing more than a few rows is also possible (simply use  `print(dataframe)` instead of `print(dataframe.tail())`), however not recommended, as that will be very verbose (~500 lines per pair every 5 seconds).
+Printing more than a few rows is also possible by using `print(dataframe)` instead of `print(dataframe.tail())`. However this is not recommended, as can results in a lot of output (~500 lines per pair every 5 seconds).
 
 ## Common mistakes when developing strategies
 
-### Peeking into the future while backtesting
+### Looking into the future while backtesting
 
-Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.
-This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods, since they all use data which is not available during dry/live runs, so these strategies will perform well during backtesting, but will fail / perform badly in real conditions.
+Backtesting analyzes the whole dataframe timerange at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not lookahead into the future, i.e. using data that would not be available in dry or live mode.
 
-The following lists some common patterns which should be avoided to prevent frustration:
+This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods. Strategies that look into the future will perform well during backtesting, often with incredible profits or winrates, but will fail or perform badly in real conditions.
 
-- don't use `shift(-1)`. This uses data from the future, which is not available.
-- don't use `.iloc[-1]` or any other absolute position in the dataframe, this will be different between dry-run and backtesting.
-- don't use `dataframe['volume'].mean()`. This uses the full DataFrame for backtesting, including data from the future. Use `dataframe['volume'].rolling(<window>).mean()` instead
-- don't use `.resample('1h')`. This uses the left border of the interval, so moves data from an hour to the start of the hour. Use `.resample('1h', label='right')` instead.
+The following list contains some common patterns which should be avoided to prevent frustration:
+
+- don't use `shift(-1)` or other negative values. This uses data from the future in backtesting, which is not available in dry or live modes.
+- don't use `.iloc[-1]` or any other absolute position in the dataframe within `populate_` functions, as this will be different between dry-run and backtesting. Absolute `iloc` indexing is safe to use in callbacks however - see [Strategy Callbacks](strategy-callbacks.md).
+- don't use functions that use all dataframe or column values, e.g. `dataframe['mean_volume'] = dataframe['volume'].mean()`. As backtesting uses the full dataframe, at any point in the dataframe, the `'mean_volume'` series would include data from the future. Use rolling() calculations instead, e.g. `dataframe['volume'].rolling(<window>).mean()`.
+- don't use `.resample('1h')`. This uses the left border of the period interval, so moves data from an hour boundary to the start of the hour. Use `.resample('1h', label='right')` instead.
 
 !!! Tip "Identifying problems"
-    You may also want to check the 2 helper commands [lookahead-analysis](lookahead-analysis.md) and [recursive-analysis](recursive-analysis.md), which can each help you figure out problems with your strategy in different ways.
-    Please treat them as what they are - helpers to identify most common problems. A negative result of each does not guarantee that there's none of the above errors included.
+    You should always use the two helper commands [lookahead-analysis](lookahead-analysis.md) and [recursive-analysis](recursive-analysis.md), which can each help you figure out problems with your strategy in different ways.
+    Please treat them as what they are - helpers to identify most common problems. A negative result of each does not guarantee that there are none of the above errors included.
 
 ### Colliding signals
 
-When conflicting signals collide (e.g. both `'enter_long'` and `'exit_long'` are 1), freqtrade will do nothing and ignore the entry signal. This will avoid trades that enter, and exit immediately. Obviously, this can potentially lead to missed entries.
+When conflicting signals collide (e.g. both `'enter_long'` and `'exit_long'` are set to `1`), freqtrade will do nothing and ignore the entry signal. This will avoid trades that enter, and exit immediately. Obviously, this can potentially lead to missed entries.
 
 The following rules apply, and entry signals will be ignored if more than one of the 3 signals is set:
 
@@ -1024,11 +1135,11 @@ The following rules apply, and entry signals will be ignored if more than one of
 
 ## Further strategy ideas
 
-To get additional Ideas for strategies, head over to the [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
-Feel free to use any of them as inspiration for your own strategies.
-We're happy to accept Pull Requests containing new Strategies to that repo.
+To get additional ideas for strategies, head over to the [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as examples, but results will depend on the current market situation, pairs used, etc. Therefore, these strategies should be considered only for learning purposes, not real world trading. Please backtest the strategy for your exchange/desired pairs first, then dry run to evaluate carefully, and use at your own risk.
 
-## Next step
+Feel free to use any of them as inspiration for your own strategies. We're happy to accept Pull Requests containing new strategies to the repository.
+
+## Next steps
 
 Now you have a perfect strategy you probably want to backtest it.
-Your next step is to learn [How to use the Backtesting](backtesting.md).
+Your next step is to learn [how to use backtesting](backtesting.md).

docs/strategy_analysis_example.md

@@ -13,19 +13,22 @@ Please follow the [documentation](https://www.freqtrade.io/en/stable/data-downlo
 import os
 from pathlib import Path
 
+
 # Change directory
 # Modify this cell to insure that the output shows the correct path.
 # Define all paths relative to the project root shown in the cell output
 project_root = "somedir/freqtrade"
-i=0
+i = 0
 try:
-    os.chdirdir(project_root)
-    assert Path('LICENSE').is_file()
-except:
-    while i<4 and (not Path('LICENSE').is_file()):
-        os.chdir(Path(Path.cwd(), '../'))
-        i+=1
+    os.chdir(project_root)
+    if not Path("LICENSE").is_file():
+        i = 0
+        while i < 4 and (not Path("LICENSE").is_file()):
+            os.chdir(Path(Path.cwd(), "../"))
+            i += 1
         project_root = Path.cwd()
+except FileNotFoundError:
+    print("Please define the project root relative to the current directory")
 print(Path.cwd())
 ```
 
@@ -35,6 +38,7 @@ print(Path.cwd())
 ```python
 from freqtrade.configuration import Configuration
 
+
 # Customize these according to your needs.
 
 # Initialize empty configuration object
@@ -58,12 +62,14 @@ pair = "BTC/USDT"
 from freqtrade.data.history import load_pair_history
 from freqtrade.enums import CandleType
 
-candles = load_pair_history(datadir=data_location,
+
+candles = load_pair_history(
+    datadir=data_location,
     timeframe=config["timeframe"],
     pair=pair,
-                            data_format = "json",  # Make sure to update this to your data
+    data_format="json",  # Make sure to update this to your data
     candle_type=CandleType.SPOT,
-                            )
+)
 
 # Confirm success
 print(f"Loaded {len(candles)} rows of data for {pair} from {data_location}")
@@ -76,14 +82,16 @@ candles.head()
 
 ```python
 # Load strategy using values set above
-from freqtrade.resolvers import StrategyResolver
 from freqtrade.data.dataprovider import DataProvider
+from freqtrade.resolvers import StrategyResolver
+
+
 strategy = StrategyResolver.load_strategy(config)
 strategy.dp = DataProvider(config, None, None)
 strategy.ft_bot_start()
 
 # Generate buy/sell signals using strategy
-df = strategy.analyze_ticker(candles, {'pair': pair})
+df = strategy.analyze_ticker(candles, {"pair": pair})
 df.tail()
 ```
 
@@ -102,7 +110,7 @@ df.tail()
 ```python
 # Report results
 print(f"Generated {df['enter_long'].sum()} entry signals")
-data = df.set_index('date', drop=False)
+data = df.set_index("date", drop=False)
 data.tail()
 ```
 
@@ -119,10 +127,13 @@ Analyze a trades dataframe (also used below for plotting)
 ```python
 from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats
 
+
 # if backtest_dir points to a directory, it'll automatically load the last backtest file.
 backtest_dir = config["user_data_dir"] / "backtest_results"
 # backtest_dir can also point to a specific file
-# backtest_dir = config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"
+# backtest_dir = (
+#   config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"
+# )
 ```
 
 
@@ -131,24 +142,24 @@ backtest_dir = config["user_data_dir"] / "backtest_results"
 # This contains all information used to generate the backtest result.
 stats = load_backtest_stats(backtest_dir)
 
-strategy = 'SampleStrategy'
-# All statistics are available per strategy, so if `--strategy-list` was used during backtest, this will be reflected here as well.
+strategy = "SampleStrategy"
+# All statistics are available per strategy, so if `--strategy-list` was used during backtest,
+# this will be reflected here as well.
 # Example usages:
-print(stats['strategy'][strategy]['results_per_pair'])
+print(stats["strategy"][strategy]["results_per_pair"])
 # Get pairlist used for this backtest
-print(stats['strategy'][strategy]['pairlist'])
+print(stats["strategy"][strategy]["pairlist"])
 # Get market change (average change of all pairs from start to end of the backtest period)
-print(stats['strategy'][strategy]['market_change'])
+print(stats["strategy"][strategy]["market_change"])
 # Maximum drawdown ()
-print(stats['strategy'][strategy]['max_drawdown'])
+print(stats["strategy"][strategy]["max_drawdown"])
 # Maximum drawdown start and end
-print(stats['strategy'][strategy]['drawdown_start'])
-print(stats['strategy'][strategy]['drawdown_end'])
+print(stats["strategy"][strategy]["drawdown_start"])
+print(stats["strategy"][strategy]["drawdown_end"])
 
 
 # Get strategy comparison (only relevant if multiple strategies were compared)
-print(stats['strategy_comparison'])
-
+print(stats["strategy_comparison"])
 ```
 
 
@@ -166,24 +177,25 @@ trades.groupby("pair")["exit_reason"].value_counts()
 ```python
 # Plotting equity line (starting with 0 on day 1 and adding daily profit for each backtested day)
 
+import pandas as pd
+import plotly.express as px
+
 from freqtrade.configuration import Configuration
 from freqtrade.data.btanalysis import load_backtest_stats
-import plotly.express as px
-import pandas as pd
+
 
 # strategy = 'SampleStrategy'
 # config = Configuration.from_files(["user_data/config.json"])
 # backtest_dir = config["user_data_dir"] / "backtest_results"
 
 stats = load_backtest_stats(backtest_dir)
-strategy_stats = stats['strategy'][strategy]
+strategy_stats = stats["strategy"][strategy]
 
-df = pd.DataFrame(columns=['dates','equity'], data=strategy_stats['daily_profit'])
-df['equity_daily'] = df['equity'].cumsum()
+df = pd.DataFrame(columns=["dates", "equity"], data=strategy_stats["daily_profit"])
+df["equity_daily"] = df["equity"].cumsum()
 
 fig = px.line(df, x="dates", y="equity_daily")
 fig.show()
-
 ```
 
 ### Load live trading results into a pandas dataframe
@@ -194,6 +206,7 @@ In case you did already some trading and want to analyze your performance
 ```python
 from freqtrade.data.btanalysis import load_trades_from_db
 
+
 # Fetch trades from database
 trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
 
@@ -210,8 +223,9 @@ This can be useful to find the best `max_open_trades` parameter, when used with
 ```python
 from freqtrade.data.btanalysis import analyze_trade_parallelism
 
+
 # Analyze the above
-parallel_trades = analyze_trade_parallelism(trades, '5m')
+parallel_trades = analyze_trade_parallelism(trades, "5m")
 
 parallel_trades.plot()
 ```
@@ -223,22 +237,22 @@ Freqtrade offers interactive plotting capabilities based on plotly.
 
 ```python
 from freqtrade.plot.plotting import generate_candlestick_graph
+
+
 # Limit graph period to keep plotly quick and reactive
 
 # Filter trades to one pair
-trades_red = trades.loc[trades['pair'] == pair]
+trades_red = trades.loc[trades["pair"] == pair]
 
-data_red = data['2019-06-01':'2019-06-10']
+data_red = data["2019-06-01":"2019-06-10"]
 # Generate candlestick graph
-graph = generate_candlestick_graph(pair=pair,
+graph = generate_candlestick_graph(
+    pair=pair,
     data=data_red,
     trades=trades_red,
-                                   indicators1=['sma20', 'ema50', 'ema55'],
-                                   indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
-                                  )
-
-
-
+    indicators1=["sma20", "ema50", "ema55"],
+    indicators2=["rsi", "macd", "macdsignal", "macdhist"],
+)
 ```
 
 
@@ -248,7 +262,6 @@ graph = generate_candlestick_graph(pair=pair,
 
 # Render graph in a separate window
 graph.show(renderer="browser")
-
 ```
 
 ## Plot average profit per trade as distribution graph
@@ -257,12 +270,12 @@ graph.show(renderer="browser")
 ```python
 import plotly.figure_factory as ff
 
+
 hist_data = [trades.profit_ratio]
-group_labels = ['profit_ratio']  # name of the dataset
+group_labels = ["profit_ratio"]  # name of the dataset
 
 fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)
 fig.show()
-
 ```
 
 Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.

docs/stylesheets/ft.extra.css

@@ -11,3 +11,7 @@
 .rst-versions .rst-other-versions {
     color: white;
 }
+
+.md-version__list {
+    font-weight: 500 !important;
+}

docs/telegram-usage.md

@@ -53,13 +53,13 @@ You can use bots in telegram groups by just adding them to the group. You can fi
 }
 ```
 
-For the Freqtrade configuration, you can then use the the full value (including `-` if it's there) as string:
+For the Freqtrade configuration, you can then use the full value (including `-` if it's there) as string:
 
 ```json
    "chat_id": "-1001332619709"
 ```
 !!! Warning "Using telegram groups"
-    When using telegram groups, you're giving every member of the telegram group access to your freqtrade bot and to all commands possible via telegram. Please make sure that you can trust everyone in the telegram group to avoid unpleasent surprises.
+    When using telegram groups, you're giving every member of the telegram group access to your freqtrade bot and to all commands possible via telegram. Please make sure that you can trust everyone in the telegram group to avoid unpleasant surprises.
 
 ## Control telegram noise
 
@@ -181,6 +181,7 @@ official commands. You can ask at any moment for help with `/help`.
 | `/locks` | Show currently locked pairs.
 | `/unlock <pair or lock_id>` | Remove the lock for this pair (or for this lock id).
 | `/marketdir [long | short | even | none]` | Updates the user managed variable that represents the current market direction. If no direction is provided, the currently set direction will be displayed.
+| `/list_custom_data <trade_id> [key]` | List custom_data for Trade ID & Key combination. If no Key is supplied it will list all key-value pairs found for that Trade ID.
 | **Modify Trade states** |
 | `/forceexit <trade_id> | /fx <tradeid>` | Instantly exits the given trade  (Ignoring `minimum_roi`).
 | `/forceexit all | /fx all` | Instantly exits all open trades (Ignoring `minimum_roi`).

docs/trade-object.md

@@ -13,28 +13,28 @@ The following attributes / properties are available for each individual trade -
 
 |  Attribute | DataType | Description |
 |------------|-------------|-------------|
-`pair`| string | Pair of this trade
-`is_open`| boolean | Is the trade currently open, or has it been concluded
-`open_rate`| float | Rate this trade was entered at (Avg. entry rate in case of trade-adjustments)
-`close_rate`| float | Close rate - only set when is_open = False
-`stake_amount`| float | Amount in Stake (or Quote) currency.
-`amount`| float | Amount in Asset / Base currency that is currently owned.
-`open_date`| datetime | Timestamp when trade was opened **use `open_date_utc` instead**
-`open_date_utc`| datetime | Timestamp when trade was opened - in UTC
-`close_date`| datetime | Timestamp when trade was closed **use `close_date_utc` instead**
-`close_date_utc`| datetime | Timestamp when trade was closed - in UTC
-`close_profit`| float | Relative profit at the time of trade closure. `0.01` == 1%
-`close_profit_abs`| float | Absolute profit (in stake currency) at the time of trade closure.
-`leverage` | float | Leverage used for this trade - defaults to 1.0 in spot markets.
-`enter_tag`| string | Tag provided on entry via the `enter_tag` column in the dataframe
-`is_short` | boolean | True for short trades, False otherwise
-`orders` | Order[] | List of order objects attached to this trade (includes both filled and cancelled orders)
-`date_last_filled_utc` | datetime | Time of the last filled order
-`entry_side` | "buy" / "sell" | Order Side the trade was entered
-`exit_side` | "buy" / "sell" | Order Side that will result in a trade exit / position reduction.
-`trade_direction` | "long" / "short" | Trade direction in text - long or short.
-`nr_of_successful_entries` | int | Number of successful (filled) entry orders
-`nr_of_successful_exits` | int | Number of successful (filled) exit orders
+| `pair` | string | Pair of this trade. |
+| `is_open` | boolean | Is the trade currently open, or has it been concluded. |
+| `open_rate` | float | Rate this trade was entered at (Avg. entry rate in case of trade-adjustments). |
+| `close_rate` | float | Close rate - only set when is_open = False. |
+| `stake_amount` | float | Amount in Stake (or Quote) currency. |
+| `amount` | float | Amount in Asset / Base currency that is currently owned. Will be 0.0 until the initial order fills. |
+| `open_date` | datetime | Timestamp when trade was opened **use `open_date_utc` instead** |
+| `open_date_utc` | datetime | Timestamp when trade was opened - in UTC. |
+| `close_date` | datetime | Timestamp when trade was closed **use `close_date_utc` instead** |
+| `close_date_utc` | datetime | Timestamp when trade was closed - in UTC. |
+| `close_profit` | float | Relative profit at the time of trade closure. `0.01` == 1% |
+| `close_profit_abs` | float | Absolute profit (in stake currency) at the time of trade closure. |
+| `leverage` | float | Leverage used for this trade - defaults to 1.0 in spot markets. |
+| `enter_tag` | string | Tag provided on entry via the `enter_tag` column in the dataframe. |
+| `is_short` | boolean | True for short trades, False otherwise. |
+| `orders` | Order[] | List of order objects attached to this trade (includes both filled and cancelled orders). |
+| `date_last_filled_utc` | datetime | Time of the last filled order. |
+| `entry_side` | "buy" / "sell" | Order Side the trade was entered. |
+| `exit_side` | "buy" / "sell" | Order Side that will result in a trade exit / position reduction. |
+| `trade_direction` | "long" / "short" | Trade direction in text - long or short. |
+| `nr_of_successful_entries` | int | Number of successful (filled) entry orders. |
+| `nr_of_successful_exits` | int | Number of successful (filled) exit orders. |
 
 ## Class methods
 
@@ -126,24 +126,24 @@ An `Order` object will always be tied to it's corresponding [`Trade`](#trade-obj
 ### Order - Available attributes
 
 an Order object is typically attached to a trade.
-Most properties here can be None as they are dependant on the exchange response.
+Most properties here can be None as they are dependent on the exchange response.
 
 |  Attribute | DataType | Description |
 |------------|-------------|-------------|
-`trade` | Trade | Trade object this order is attached to
-`ft_pair` | string | Pair this order is for
-`ft_is_open` | boolean | is the order filled?
-`order_type` | string | Order type as defined on the exchange - usually market, limit or stoploss
-`status` | string | Status as defined by ccxt. Usually open, closed, expired or canceled
-`side` | string | Buy or Sell
-`price` | float | Price the order was placed at
-`average` | float | Average price the order filled at
-`amount` | float | Amount in base currency
-`filled` | float | Filled amount (in base currency)
-`remaining` | float | Remaining amount
-`cost` | float | Cost of the order - usually average * filled (*Exchange dependant on futures, may contain the cost with or without leverage and may be in contracts.*)
-`stake_amount` | float | Stake amount used for this order. *Added in 2023.7.*
-`order_date` | datetime | Order creation date **use `order_date_utc` instead**
-`order_date_utc` | datetime | Order creation date (in UTC)
-`order_fill_date` | datetime |  Order fill date **use `order_fill_utc` instead**
-`order_fill_date_utc` | datetime | Order fill date
+| `trade` | Trade | Trade object this order is attached to |
+| `ft_pair` | string | Pair this order is for |
+| `ft_is_open` | boolean | is the order filled? |
+| `order_type` | string | Order type as defined on the exchange - usually market, limit or stoploss |
+| `status` | string | Status as defined by ccxt. Usually open, closed, expired or canceled |
+| `side` | string | Buy or Sell |
+| `price` | float | Price the order was placed at |
+| `average` | float | Average price the order filled at |
+| `amount` | float | Amount in base currency |
+| `filled` | float | Filled amount (in base currency) |
+| `remaining` | float | Remaining amount |
+| `cost` | float | Cost of the order - usually average * filled (*Exchange dependent on futures, may contain the cost with or without leverage and may be in contracts.*) |
+| `stake_amount` | float | Stake amount used for this order. *Added in 2023.7.* |
+| `order_date` | datetime | Order creation date **use `order_date_utc` instead** |
+| `order_date_utc` | datetime | Order creation date (in UTC) |
+| `order_fill_date` | datetime |  Order fill date **use `order_fill_utc` instead** |
+| `order_fill_date_utc` | datetime | Order fill date |