add noise feature, improve docstrings

closes #7252

.dependabot/config.yml

@@ -1,17 +0,0 @@
-version: 1
-
-update_configs:
-  - package_manager: "python"
-    directory: "/"
-    update_schedule: "weekly"
-    allowed_updates:
-    - match:
-        update_type: "all"
-    target_branch: "develop"
-
-  - package_manager: "docker"
-    directory: "/"
-    update_schedule: "daily"
-    allowed_updates:
-    - match:
-        update_type: "all"

.devcontainer/Dockerfile

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

.devcontainer/devcontainer.json

@@ -0,0 +1,39 @@
+{
+    "name": "freqtrade Develop",
+    "build": {
+        "dockerfile": "Dockerfile",
+        "context": ".."
+    },
+    // 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"
+    ],
+    // Uncomment to connect as a non-root user if you've added one. See https://aka.ms/vscode-remote/containers/non-root.
+    "remoteUser": "ftuser",
+
+    "postCreateCommand": "freqtrade create-userdir --userdir user_data/",
+
+    "workspaceFolder": "/freqtrade/",
+
+    "settings": {
+        "terminal.integrated.shell.linux": "/bin/bash",
+        "editor.insertSpaces": true,
+        "files.trimTrailingWhitespace": true,
+        "[markdown]": {
+            "files.trimTrailingWhitespace": false,
+        },
+        "python.pythonPath": "/usr/local/bin/python",
+    },
+
+    // Add the IDs of extensions you want installed when the container is created.
+    "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance",
+        "davidanson.vscode-markdownlint",
+        "ms-azuretools.vscode-docker",
+        "vscode-icons-team.vscode-icons",
+    ],
+}

.dockerignore

@@ -1,9 +1,9 @@
 .git
 .gitignore
 Dockerfile
+Dockerfile.armhf
 .dockerignore
-config.json*
-*.sqlite
+docker/
 .coveragerc
 .eggs
 .github
@@ -13,3 +13,13 @@ CONTRIBUTING.md
 MANIFEST.in
 README.md
 freqtrade.service
+freqtrade.egg-info
+
+config.json*
+*.sqlite
+user_data
+*.log
+
+.vscode
+.mypy_cache
+.ipynb_checkpoints

.gitattributes

@@ -0,0 +1,3 @@
+*.py    eol=lf
+*.sh    eol=lf
+*.ps1   eol=crlf

.github/FUNDING.yml

@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+github: [xmatthias]

.github/ISSUE_TEMPLATE/bug_report.md

@@ -9,7 +9,7 @@ assignees: ''
 <!-- 
 Have you searched for similar issues before posting it?
 
-If you have discovered a bug in the bot, please [search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue). 
+If you have discovered a bug in the bot, please [search the issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue). 
 If it hasn't been reported, please create a new issue.
 
 Please do not use bug reports to request new features.

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,6 @@
+---
+blank_issues_enabled: false
+contact_links:
+  - name: Discord Server
+    url: https://discord.gg/p7nuUNVfP7
+    about: Ask a question or get community support from our Discord server

.github/ISSUE_TEMPLATE/feature_request.md

@@ -24,4 +24,3 @@ Have you search for this feature before requesting it? It's highly likely that a
 ## Describe the enhancement
 
 *Explain the enhancement you would like*
-

.github/ISSUE_TEMPLATE/question.md

@@ -1,5 +1,5 @@
 ---
-name: BQuestion
+name: Question
 about: Ask a question you could not find an answer in the docs
 title: ''
 labels: "Question"
@@ -22,4 +22,4 @@ Please do not use the question template to report bugs or to request new feature
   
 ## Your question
 
-*Ask the question you have not been able to find an answer in our [Documentation](https://www.freqtrade.io/en/latest/)*
+*Ask the question you have not been able to find an answer in the [Documentation](https://www.freqtrade.io/en/latest/)*

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,15 +1,17 @@
-Thank you for sending your pull request. But first, have you included
+<!-- Thank you for sending your pull request. But first, have you included
 unit tests, and is your code PEP8 conformant? [More details](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
-
+-->
 ## Summary
-Explain in one sentence the goal of this PR
+
+<!-- Explain in one sentence the goal of this PR -->
 
 Solve the issue: #___
 
 ## Quick changelog
 
-- <change log #1>
-- <change log #2>
+- <change log 1>
+- <change log 1>
 
 ## What's new?
-*Explain in details what this PR solve or improve. You can include visuals.* 
+
+<!-- Explain in details what this PR solve or improve. You can include visuals. -->

.github/dependabot.yml

@@ -0,0 +1,21 @@
+version: 2
+updates:
+- package-ecosystem: docker
+  directory: "/"
+  schedule:
+    interval: daily
+  open-pull-requests-limit: 10
+
+- package-ecosystem: pip
+  directory: "/"
+  schedule:
+    interval: weekly
+  open-pull-requests-limit: 10
+  target-branch: develop
+
+- package-ecosystem: "github-actions"
+  directory: "/"
+  schedule:
+    interval: "weekly"
+  open-pull-requests-limit: 10
+  target-branch: develop

.github/workflows/ci.yml

@@ -3,62 +3,60 @@ name: Freqtrade CI
 on:
   push:
     branches:
-      - master
+      - stable
       - develop
-      - github_actions_tests
+      - ci/*
     tags:
-    release:
-      types: [published]
+  release:
+    types: [published]
   pull_request:
   schedule:
     - cron:  '0 5 * * 4'
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
-  build:
+  build_linux:
 
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ ubuntu-18.04, macos-latest ]
-        python-version: [3.7, 3.8]
+        os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
 
     - name: Set up Python
-      uses: actions/setup-python@v1
+      uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python-version }}
 
     - name: Cache_dependencies
-      uses: actions/cache@v1
+      uses: actions/cache@v3
       id: cache
       with:
         path: ~/dependencies/
         key: ${{ runner.os }}-dependencies
 
     - name: pip cache (linux)
-      uses: actions/cache@preview
-      if: startsWith(matrix.os, 'ubuntu')
+      uses: actions/cache@v3
+      if: runner.os == 'Linux'
       with:
         path: ~/.cache/pip
         key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
 
-    - name: pip cache (macOS)
-      uses: actions/cache@preview
-      if: startsWith(matrix.os, 'macOS')
-      with:
-        path: ~/Library/Caches/pip
-        key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
-
     - name: TA binary *nix
       if: steps.cache.outputs.cache-hit != 'true'
       run: |
         cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
 
     - name: Installation - *nix
+      if: runner.os == 'Linux'
       run: |
-        python -m pip install --upgrade pip
+        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
@@ -68,46 +66,138 @@ jobs:
     - name: Tests
       run: |
         pytest --random-order --cov=freqtrade --cov-config=.coveragerc
+      if: matrix.python-version != '3.9' || matrix.os != 'ubuntu-22.04'
+
+    - name: Tests incl. ccxt compatibility tests
+      run: |
+        pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
+      if: matrix.python-version == '3.9' && matrix.os == 'ubuntu-22.04'
 
     - name: Coveralls
-      if: (startsWith(matrix.os, 'ubuntu') && matrix.python-version == '3.8')
+      if: (runner.os == 'Linux' && matrix.python-version == '3.9')
       env:
         # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
         COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
       run: |
         # Allow failure for coveralls
-        coveralls -v  || true
+        coveralls || true
 
-    - name: Backtesting
+    - name: Backtesting (multi)
       run: |
-        cp config.json.example config.json
+        cp config_examples/config_bittrex.example.json config.json
         freqtrade create-userdir --userdir user_data
-        freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
+        freqtrade new-strategy -s AwesomeStrategy
+        freqtrade new-strategy -s AwesomeStrategyMin --template minimal
+        freqtrade backtesting --datadir tests/testdata --strategy-list AwesomeStrategy AwesomeStrategyMin -i 5m
 
     - name: Hyperopt
       run: |
-        cp config.json.example config.json
+        cp config_examples/config_bittrex.example.json config.json
         freqtrade create-userdir --userdir user_data
-        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
 
     - name: Flake8
       run: |
         flake8
 
+    - name: Sort imports (isort)
+      run: |
+        isort --check .
+
+    - name: Mypy
+      run: |
+        mypy freqtrade scripts tests
+
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
+      if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+          severity: error
+          details: Freqtrade CI failed on ${{ matrix.os }}
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
+
+  build_macos:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ macos-latest ]
+        python-version: ["3.8", "3.9", "3.10"]
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Cache_dependencies
+      uses: actions/cache@v3
+      id: cache
+      with:
+        path: ~/dependencies/
+        key: ${{ runner.os }}-dependencies
+
+    - name: pip cache (macOS)
+      uses: actions/cache@v3
+      if: runner.os == 'macOS'
+      with:
+        path: ~/Library/Caches/pip
+        key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
+
+    - name: TA binary *nix
+      if: steps.cache.outputs.cache-hit != 'true'
+      run: |
+        cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
+
+    - name: Installation - macOS
+      if: runner.os == 'macOS'
+      run: |
+        brew update
+        brew install hdf5 c-blosc
+        python -m pip install --upgrade pip wheel
+        export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
+        export TA_LIBRARY_PATH=${HOME}/dependencies/lib
+        export TA_INCLUDE_PATH=${HOME}/dependencies/include
+        pip install -r requirements-dev.txt
+        pip install -e .
+
+    - name: Tests
+      run: |
+        pytest --random-order
+
+    - name: Backtesting
+      run: |
+        cp config_examples/config_bittrex.example.json config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade new-strategy -s AwesomeStrategyAdv --template advanced
+        freqtrade backtesting --datadir tests/testdata --strategy AwesomeStrategyAdv
+
+    - name: Hyperopt
+      run: |
+        cp config_examples/config_bittrex.example.json config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
+
+    - name: Flake8
+      run: |
+        flake8
+
+    - name: Sort imports (isort)
+      run: |
+        isort --check .
+
     - name: Mypy
       run: |
         mypy freqtrade scripts
 
-    - name: Slack Notification
-      uses: homoluctus/slatify@v1.8.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade CI ${{ matrix.os }}*'
-        mention: 'here'
-        mention_if: 'failure'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+          severity: info
+          details: Test Succeeded!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   build_windows:
 
@@ -115,19 +205,18 @@ jobs:
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: [3.7, 3.8]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
 
     - name: Set up Python
-      uses: actions/setup-python@v1
+      uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python-version }}
 
     - name: Pip cache (Windows)
-      uses: actions/cache@preview
-      if: startsWith(runner.os, 'Windows')
+      uses: actions/cache@v3
       with:
         path: ~\AppData\Local\pip\Cache
         key: ${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -138,19 +227,19 @@ jobs:
 
     - name: Tests
       run: |
-        pytest --random-order --cov=freqtrade --cov-config=.coveragerc
+        pytest --random-order
 
     - name: Backtesting
       run: |
-        cp config.json.example config.json
+        cp config_examples/config_bittrex.example.json config.json
         freqtrade create-userdir --userdir user_data
         freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
 
     - name: Hyperopt
       run: |
-        cp config.json.example config.json
+        cp config_examples/config_bittrex.example.json config.json
         freqtrade create-userdir --userdir user_data
-        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
 
     - name: Flake8
       run: |
@@ -158,75 +247,102 @@ jobs:
 
     - name: Mypy
       run: |
-        mypy freqtrade scripts
+        mypy freqtrade scripts tests
 
-    - name: Slack Notification
-      uses: homoluctus/slatify@v1.8.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade CI windows*'
-        mention: 'here'
-        mention_if: 'failure'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+          severity: error
+          details: Test Failed
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
+
+  mypy_version_check:
+    runs-on: ubuntu-20.04
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.10"
+
+    - name: pre-commit dependencies
+      run: |
+        pip install pyaml
+        python build_helpers/pre_commit_update.py
 
   docs_check:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-20.04
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
 
     - name: Documentation syntax
       run: |
         ./tests/test_docs.sh
 
-    - name: Slack Notification
-      uses: homoluctus/slatify@v1.8.0
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.10"
+
+    - name: Documentation build
+      run: |
+        pip install -r docs/requirements-docs.txt
+        pip install mkdocs
+        mkdocs build
+
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade Docs*'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+          severity: error
+          details: Freqtrade doc test failed!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
-  cleanup-prior-runs:
-    runs-on: ubuntu-latest
-    steps:
-    - name: Cleanup previous runs on this branch
-      uses: rokroskar/workflow-run-cleanup-action@v0.2.2
-      if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/master' && github.repository == 'freqtrade/freqtrade'"
-      env:
-        GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
-
-  # Notify on slack 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 successfull
   notify-complete:
-    needs: [ build, build_windows, docs_check ]
-    runs-on: ubuntu-latest
+    needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check ]
+    runs-on: ubuntu-20.04
+    # Discord notification can't handle schedule events
+    if: (github.event_name != 'schedule')
+    permissions:
+      repository-projects: read
     steps:
-    - name: Slack Notification
-      uses: homoluctus/slatify@v1.8.0
-      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+
+    - name: Check user permission
+      id: check
+      uses: scherermichael-oss/action-has-permission@1.0.6
       with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade CI*'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+        required-permission: write
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
+      if: always() && steps.check.outputs.has-permission && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+          severity: info
+          details: Test Completed!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   deploy:
-    needs: [ build, build_windows, docs_check ]
-    runs-on: ubuntu-18.04
+    needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check ]
+    runs-on: ubuntu-20.04
+
     if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
+
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
 
     - name: Set up Python
-      uses: actions/setup-python@v1
+      uses: actions/setup-python@v4
       with:
-        python-version: 3.8
+        python-version: "3.9"
 
     - name: Extract branch name
       shell: bash
-      run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})"
+      run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF##*/})"
       id: extract_branch
 
     - name: Build distribution
@@ -235,16 +351,16 @@ jobs:
         python setup.py sdist bdist_wheel
 
     - name: Publish to PyPI (Test)
-      uses: pypa/gh-action-pypi-publish@master
-      if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
+      uses: pypa/gh-action-pypi-publish@v1.5.1
+      if: (github.event_name == 'release')
       with:
         user: __token__
         password: ${{ secrets.pypi_test_password }}
         repository_url: https://test.pypi.org/legacy/
 
     - name: Publish to PyPI
-      uses: pypa/gh-action-pypi-publish@master
-      if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
+      uses: pypa/gh-action-pypi-publish@v1.5.1
+      if: (github.event_name == 'release')
       with:
         user: __token__
         password: ${{ secrets.pypi_password }}
@@ -256,13 +372,6 @@ jobs:
       run: |
         echo "${DOCKER_PASSWORD}" | docker login --username ${DOCKER_USERNAME} --password-stdin
 
-    - name: Build and test and push docker image
-      env:
-        IMAGE_NAME: freqtradeorg/freqtrade
-        BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
-      run: |
-        build_helpers/publish_docker.sh
-
     # We need docker experimental to pull the ARM image.
     - name: Switch docker to experimental
       run: |
@@ -273,7 +382,7 @@ jobs:
 
     - name: Set up Docker Buildx
       id: buildx
-      uses: crazy-max/ghaction-docker-buildx@v1
+      uses: crazy-max/ghaction-docker-buildx@v3.3.1
       with:
         buildx-version: latest
         qemu-version: latest
@@ -281,22 +390,46 @@ jobs:
     - name: Available platforms
       run: echo ${{ steps.buildx.outputs.platforms }}
 
-    - name: Build Raspberry docker image
+    - name: Build and test and push docker images
       env:
         IMAGE_NAME: freqtradeorg/freqtrade
-        BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}_pi
+        BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
       run: |
-        build_helpers/publish_docker_pi.sh
+        build_helpers/publish_docker_multi.sh
 
-
-    - name: Slack Notification
-      uses: homoluctus/slatify@v1.8.0
-      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
+      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false) && (github.event_name != 'schedule')
       with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade CI Deploy*'
-        mention: 'here'
-        mention_if: 'failure'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+          severity: info
+          details: Deploy Succeeded!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
+
+  deploy_arm:
+    needs: [ deploy ]
+    # Only run on 64bit machines
+    runs-on: [self-hosted, linux, ARM64]
+    if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Extract branch name
+      shell: bash
+      run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF##*/})"
+      id: extract_branch
+
+    - name: Dockerhub login
+      env:
+        DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
+        DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
+      run: |
+        echo "${DOCKER_PASSWORD}" | docker login --username ${DOCKER_USERNAME} --password-stdin
+
+    - name: Build and test and push docker images
+      env:
+        IMAGE_NAME: freqtradeorg/freqtrade
+        BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
+      run: |
+        build_helpers/publish_docker_arm64.sh

.github/workflows/docker_update_readme.yml

@@ -2,17 +2,16 @@ name: Update Docker Hub Description
 on:
   push:
     branches:
-    - master
+    - stable
 
 jobs:
   dockerHubDescription:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v1
+    - uses: actions/checkout@v3
     - name: Docker Hub Description
-      uses: peter-evans/dockerhub-description@v2.1.0
+      uses: peter-evans/dockerhub-description@v3
       env:
         DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }}
         DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
         DOCKERHUB_REPOSITORY: freqtradeorg/freqtrade
-

.gitignore

@@ -1,13 +1,24 @@
 # Freqtrade rules
 config*.json
 *.sqlite
+*.sqlite-shm
+*.sqlite-wal
 logfile.txt
 user_data/*
 !user_data/strategy/sample_strategy.py
 !user_data/notebooks
+!user_data/models
+!user_data/freqaimodels
+user_data/freqaimodels/*
+user_data/models/*
 user_data/notebooks/*
 freqtrade-plot.html
 freqtrade-profit-plot.html
+freqtrade/rpc/api_server/ui/*
+build_helpers/ta-lib/*
+
+# Macos related
+.DS_Store
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -74,6 +85,8 @@ instance/
 
 # Sphinx documentation
 docs/_build/
+# Mkdocs documentation
+site/
 
 # PyBuilder
 target/
@@ -94,3 +107,9 @@ target/
 
 #exceptions
 !*.gitkeep
+!config_examples/config_binance.example.json
+!config_examples/config_bittrex.example.json
+!config_examples/config_ftx.example.json
+!config_examples/config_full.example.json
+!config_examples/config_kraken.example.json
+!config_examples/config_freqai.example.json

.pre-commit-config.yaml

@@ -0,0 +1,46 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+  - repo: https://github.com/pycqa/flake8
+    rev: "4.0.1"
+    hooks:
+      - id: flake8
+        # stages: [push]
+
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: "v0.942"
+    hooks:
+      - id: mypy
+        exclude: build_helpers
+        additional_dependencies:
+          - types-cachetools==5.2.1
+          - types-filelock==3.2.7
+          - types-requests==2.28.8
+          - types-tabulate==0.8.11
+          - types-python-dateutil==2.8.19
+        # stages: [push]
+
+  - repo: https://github.com/pycqa/isort
+    rev: "5.10.1"
+    hooks:
+      - id: isort
+        name: isort (python)
+        # stages: [push]
+
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.4.0
+    hooks:
+      - id: end-of-file-fixer
+        exclude: |
+            (?x)^(
+                tests/.*|
+                .*\.svg
+            )$
+      - id: mixed-line-ending
+      - id: debug-statements
+      - id: check-ast
+      - id: trailing-whitespace
+        exclude: |
+          (?x)^(
+                .*\.md
+            )$

.pylintrc

@@ -7,4 +7,3 @@ ignore=vendor
 
 [TYPECHECK]
 ignored-modules=numpy,talib,talib.abstract
-

.readthedocs.yml

@@ -4,5 +4,5 @@ build:
   image: latest
 
 python:
-  version: 3.6
-  setup_py_install: false
+  version: 3.8
+  setup_py_install: false

.travis.yml

@@ -1,61 +0,0 @@
-os:
-- linux
-dist: xenial
-language: python
-python:
-- 3.6
-services:
-  - docker
-env:
-  global:
-    - IMAGE_NAME=freqtradeorg/freqtrade
-install:
-- cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies; cd ..
-- export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
-- export TA_LIBRARY_PATH=${HOME}/dependencies/lib
-- export TA_INCLUDE_PATH=${HOME}/dependencies/include
-- pip install -r requirements-dev.txt
-- pip install -e .
-jobs:
-
-  include:
-    - stage: tests
-      script:
-      - pytest --random-order --cov=freqtrade --cov-config=.coveragerc
-      # Allow failure for coveralls
-      # - coveralls || true
-      name: pytest
-    - script:
-      - cp config.json.example config.json
-      - freqtrade create-userdir --userdir user_data
-      - freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
-      name: backtest
-    - script:
-      - cp config.json.example config.json
-      - freqtrade create-userdir --userdir user_data
-      - freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
-      name: hyperopt
-    - script: flake8
-      name: flake8
-    - script:
-      # Test Documentation boxes -
-      # !!! <TYPE>: is not allowed!
-      # !!! <TYPE> "title" - Title needs to be quoted!
-      - grep -Er '^!{3}\s\S+:|^!{3}\s\S+\s[^"]' docs/*; test $? -ne 0
-      name: doc syntax
-    - script: mypy freqtrade scripts
-      name: mypy
-
-    # - stage: docker
-    #   if: branch in (master, develop, feat/improve_travis) AND (type in (push, cron))
-    #   script:
-    #     - build_helpers/publish_docker.sh
-    #   name: "Build and test and push docker image"
-
-notifications:
-  slack:
-    secure: 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
-cache:
-  pip: True
-  directories:
-    - $HOME/dependencies

CONTRIBUTING.md

@@ -8,17 +8,17 @@ Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/
 
 Few pointers for contributions:
 
-- Create your PR against the `develop` branch, not `master`.
-- New features need to contain unit tests and must be PEP8 conformant (max-line-length = 100).
+- Create your PR against the `develop` branch, not `stable`.
+- New features need to contain unit tests, must conform to PEP8 (max-line-length = 100) and should be documented with the introduction PR.
+- PR's can be declared as `[WIP]` - which signify Work in Progress Pull Requests (which are not finished).
 
-If you are unsure, discuss the feature on our [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE)
-or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
+If you are unsure, discuss the feature on our [discord server](https://discord.gg/p7nuUNVfP7) or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a Pull Request.
 
 ## Getting started
 
 Best start by reading the [documentation](https://www.freqtrade.io/) to get a feel for what is possible with the bot, or head straight to the [Developer-documentation](https://www.freqtrade.io/en/latest/developer/) (WIP) which should help you getting started.
 
-## Before sending the PR:
+## Before sending the PR
 
 ### 1. Run unit tests
 
@@ -56,6 +56,13 @@ To help with that, we encourage you to install the git pre-commit
 hook that will warn you when you try to commit code that fails these checks. 
 Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using-hooks.html).
 
+##### Additional styles applied
+
+* Have docstrings on all public methods
+* Use double-quotes for docstrings
+* Multiline docstrings should be indented to the level of the first quote
+* Doc-strings should follow the reST format (`:param xxx: ...`, `:return: ...`, `:raises KeyError: ... `)
+
 ### 3. Test if all type-hints are correct
 
 #### Run mypy
@@ -64,6 +71,14 @@ Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using
 mypy freqtrade
 ```
 
+### 4. Ensure all imports are correct
+
+#### Run isort
+
+``` bash
+isort .
+```
+
 ## (Core)-Committer Guide
 
 ### Process: Pull Requests
@@ -114,6 +129,6 @@ Contributors may be given commit privileges. Preference will be given to those w
 1. Access to resources for cross-platform development and testing.
 1. Time to devote to the project regularly.
 
-Being a Committer does not grant write permission on `develop` or `master` for security reasons (Users trust Freqtrade with their Exchange API keys).
+Being a Committer does not grant write permission on `develop` or `stable` for security reasons (Users trust Freqtrade with their Exchange API keys).
 
 After being Committer for some time, a Committer may be named Core Committer and given full repository access.

Dockerfile

@@ -1,28 +1,58 @@
-FROM python:3.8.3-slim-buster
+FROM python:3.10.6-slim-bullseye as base
 
-RUN apt-get update \
-    && apt-get -y install curl build-essential libssl-dev \
-    && apt-get clean \
-    && pip install --upgrade pip
+# Setup env
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONFAULTHANDLER 1
+ENV PATH=/home/ftuser/.local/bin:$PATH
+ENV FT_APP_ENV="docker"
 
 # Prepare environment
-RUN mkdir /freqtrade
+RUN mkdir /freqtrade \
+  && apt-get update \
+  && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev  \
+  && apt-get clean \
+  && useradd -u 1000 -G sudo -U -m -s /bin/bash ftuser \
+  && chown ftuser:ftuser /freqtrade \
+  # Allow sudoers
+  && echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers
+
 WORKDIR /freqtrade
 
+# Install dependencies
+FROM base as python-deps
+RUN  apt-get update \
+  && apt-get -y install build-essential libssl-dev git libffi-dev libgfortran5 pkg-config cmake gcc \
+  && apt-get clean \
+  && pip install --upgrade pip
+
 # Install TA-lib
 COPY build_helpers/* /tmp/
 RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
-
 ENV LD_LIBRARY_PATH /usr/local/lib
 
 # Install dependencies
-COPY requirements.txt requirements-common.txt requirements-hyperopt.txt /freqtrade/
-RUN pip install numpy --no-cache-dir \
-  && pip install -r requirements-hyperopt.txt --no-cache-dir
+COPY --chown=ftuser:ftuser requirements.txt requirements-hyperopt.txt /freqtrade/
+USER ftuser
+RUN  pip install --user --no-cache-dir numpy \
+  && pip install --user --no-cache-dir -r requirements-hyperopt.txt
 
+# Copy dependencies to runtime-image
+FROM base as runtime-image
+COPY --from=python-deps /usr/local/lib /usr/local/lib
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+COPY --from=python-deps --chown=ftuser:ftuser /home/ftuser/.local /home/ftuser/.local
+
+USER ftuser
 # Install and execute
-COPY . /freqtrade/
-RUN pip install -e . --no-cache-dir
+COPY --chown=ftuser:ftuser . /freqtrade/
+
+RUN pip install -e . --user --no-cache-dir --no-build-isolation \
+  && mkdir /freqtrade/user_data/ \
+  && freqtrade install-ui
+
 ENTRYPOINT ["freqtrade"]
 # Default to trade mode
 CMD [ "trade" ]

Dockerfile.armhf

@@ -1,29 +0,0 @@
-FROM --platform=linux/arm/v7 python:3.7.7-slim-buster
-
-RUN apt-get update \
-  && apt-get -y install curl build-essential libssl-dev libatlas3-base libgfortran5 \
-  && apt-get clean \
-  && pip install --upgrade pip \
-  && echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > /etc/pip.conf
-
-# Prepare environment
-RUN mkdir /freqtrade
-WORKDIR /freqtrade
-
-# Install TA-lib
-COPY build_helpers/* /tmp/
-RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
-
-ENV LD_LIBRARY_PATH /usr/local/lib
-
-# Install dependencies
-COPY requirements.txt requirements-common.txt /freqtrade/
-RUN pip install numpy --no-cache-dir \
-  && pip install -r requirements.txt --no-cache-dir
-
-# Install and execute
-COPY . /freqtrade/
-RUN pip install -e . --no-cache-dir
-ENTRYPOINT ["freqtrade"]
-# Default to trade mode
-CMD [ "trade" ]

Dockerfile.technical

@@ -1,6 +0,0 @@
-FROM freqtradeorg/freqtrade:develop
-
-RUN apt-get update \
-    && apt-get -y install git \
-    && apt-get clean \
-    && pip install git+https://github.com/freqtrade/technical

MANIFEST.in

@@ -1,5 +1,7 @@
 include LICENSE
 include README.md
-include config.json.example
 recursive-include freqtrade *.py
 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

README.md

@@ -1,11 +1,11 @@
-# Freqtrade
+# ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade_poweredby.svg)
 
 [![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
 [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
 [![Documentation](https://readthedocs.org/projects/freqtrade/badge/)](https://www.freqtrade.io)
 [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
 
-Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
+Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or webUI. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
 
 ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade-screenshot.png)
 
@@ -22,46 +22,61 @@ expect.
 We strongly recommend you to have coding and Python knowledge. Do not
 hesitate to read the source code and understand the mechanism of this bot.
 
-## Exchange marketplaces supported
+## Supported Exchange marketplaces
 
+Please read the [exchange specific notes](docs/exchanges.md) to learn about eventual, special configurations needed for each exchange.
+
+- [X] [Binance](https://www.binance.com/)
 - [X] [Bittrex](https://bittrex.com/)
-- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#blacklists))
+- [X] [FTX](https://ftx.com/#a=2258149)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [Huobi](http://huobi.com/)
 - [X] [Kraken](https://kraken.com/)
-- [ ] [113 others to tests](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
+- [X] [OKX](https://okx.com/) (Former OKEX)
+- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
+
+### Supported Futures Exchanges (experimental)
+
+- [X] [Binance](https://www.binance.com/)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [OKX](https://okx.com/).
+
+Please make sure to read the [exchange specific notes](docs/exchanges.md), as well as the [trading with leverage](docs/leverage.md) documentation before diving in.
+
+### Community tested
+
+Exchanges confirmed working by the community:
+
+- [X] [Bitvavo](https://bitvavo.com/)
+- [X] [Kucoin](https://www.kucoin.com/)
 
 ## Documentation
 
 We invite you to read the bot documentation to ensure you understand how the bot is working.
 
-Please find the complete documentation on our [website](https://www.freqtrade.io).
+Please find the complete documentation on the [freqtrade website](https://www.freqtrade.io).
 
 ## Features
 
-- [x] **Based on Python 3.6+**: For botting on any operating system - Windows, macOS and Linux.
+- [x] **Based on Python 3.8+**: For botting on any operating system - Windows, macOS and Linux.
 - [x] **Persistence**: Persistence is achieved through sqlite.
-- [x] **Dry-run**: Run the bot without playing money.
+- [x] **Dry-run**: Run the bot without paying money.
 - [x] **Backtesting**: Run a simulation of your buy/sell strategy.
 - [x] **Strategy Optimization by machine learning**: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
-- [x] **Edge position sizing** Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market. [Learn more](https://www.freqtrade.io/en/latest/edge/).
+- [X] **Adaptive prediction modeling**: Build a smart strategy with FreqAI that self-trains to the market via adaptive machine learning methods. [Learn more](https://www.freqtrade.io/en/stable/freqai/)
+- [x] **Edge position sizing** Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market. [Learn more](https://www.freqtrade.io/en/stable/edge/).
 - [x] **Whitelist crypto-currencies**: Select which crypto-currency you want to trade or use dynamic whitelists.
 - [x] **Blacklist crypto-currencies**: Select which crypto-currency you want to avoid.
+- [x] **Builtin WebUI**: Builtin web UI to manage your bot.
 - [x] **Manageable via Telegram**: Manage the bot with Telegram.
-- [x] **Display profit/loss in fiat**: Display your profit/loss in 33 fiat.
-- [x] **Daily summary of profit/loss**: Provide a daily summary of your profit/loss.
+- [x] **Display profit/loss in fiat**: Display your profit/loss in fiat currency.
 - [x] **Performance status report**: Provide a performance status of your current trades.
 
 ## Quick start
 
-Freqtrade provides a Linux/macOS script to install all dependencies and help you to configure the bot.
+Please refer to the [Docker Quickstart documentation](https://www.freqtrade.io/en/stable/docker_quickstart/) on how to get started quickly.
 
-```bash
-git clone git@github.com:freqtrade/freqtrade.git
-cd freqtrade
-git checkout develop
-./setup.sh --install
-```
-
-For any other type of installation please refer to [Installation doc](https://www.freqtrade.io/en/latest/installation/).
+For further (native) installation methods, please refer to the [Installation documentation page](https://www.freqtrade.io/en/stable/installation/).
 
 ## Basic Usage
 
@@ -69,22 +84,22 @@ For any other type of installation please refer to [Installation doc](https://ww
 
 ```
 usage: freqtrade [-h] [-V]
-                 {trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
+                 {trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
                  ...
 
 Free, open source crypto trading bot
 
 positional arguments:
-  {trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
+  {trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
     trade               Trade module.
     create-userdir      Create user-data directory.
     new-config          Create new config
-    new-hyperopt        Create new hyperopt
     new-strategy        Create new strategy
     download-data       Download backtesting data.
     convert-data        Convert candle (OHLCV) data from one format to
                         another.
     convert-trade-data  Convert trade data from one format to another.
+    list-data           List downloaded data.
     backtesting         Backtesting module.
     edge                Edge module.
     hyperopt            Hyperopt module.
@@ -98,8 +113,10 @@ positional arguments:
     list-timeframes     Print available timeframes for the exchange.
     show-trades         Show trades.
     test-pairlist       Test your pairlist configuration.
+    install-ui          Install FreqUI
     plot-dataframe      Plot candles with indicators.
     plot-profit         Generate plot showing profits.
+    webserver           Webserver module.
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -109,45 +126,42 @@ optional arguments:
 
 ### Telegram RPC commands
 
-Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on our [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
+Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on the [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
 
-- `/start`: Starts the trader
-- `/stop`: Stops the trader
-- `/status [table]`: Lists all open trades
-- `/count`: Displays number of open trades
-- `/profit`: Lists cumulative profit from all finished trades
-- `/forcesell <trade_id>|all`: Instantly sells the given trade (Ignoring `minimum_roi`).
+- `/start`: Starts the trader.
+- `/stop`: Stops the trader.
+- `/stopbuy`: Stop entering new trades.
+- `/status <trade_id>|[table]`: Lists all or specific open trades.
+- `/profit [<n>]`: Lists cumulative profit from all finished trades, over the last n days.
+- `/forceexit <trade_id>|all`: Instantly exits the given trade (Ignoring `minimum_roi`).
+- `/fx <trade_id>|all`: Alias to `/forceexit`
 - `/performance`: Show performance of each finished trade grouped by pair
-- `/balance`: Show account balance per currency
-- `/daily <n>`: Shows profit or loss per day, over the last n days
-- `/help`: Show help message
-- `/version`: Show version
-
+- `/balance`: Show account balance per currency.
+- `/daily <n>`: Shows profit or loss per day, over the last n days.
+- `/help`: Show help message.
+- `/version`: Show version.
 
 ## Development branches
 
 The project is currently setup in two main branches:
 
-- `develop` - This branch has often new features, but might also cause breaking changes.
-- `master` - This branch contains the latest stable release. The bot 'should' be stable on this branch, and is generally well tested.
+- `develop` - This branch has often new features, but might also contain breaking changes. We try hard to keep this branch as stable as possible.
+- `stable` - This branch contains the latest stable release. This branch is generally well tested.
 - `feat/*` - These are feature branches, which are being worked on heavily. Please don't use these unless you want to test a specific feature.
 
 ## Support
 
-### Help / Slack
+### Help / Discord
 
-For any questions not covered by the documentation or for further
-information about the bot, we encourage you to join our slack channel.
-
-- [Click here to join Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE).
+For any questions not covered by the documentation or for further information about the bot, or to simply engage with like-minded individuals, we encourage you to join the Freqtrade [discord server](https://discord.gg/p7nuUNVfP7).
 
 ### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
 
 If you discover a bug in the bot, please
-[search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
+[search the issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
 first. If it hasn't been reported, please
-[create a new issue](https://github.com/freqtrade/freqtrade/issues/new) and
-ensure you follow the template guide so that our team can assist you as
+[create a new issue](https://github.com/freqtrade/freqtrade/issues/new/choose) and
+ensure you follow the template guide so that the team can assist you as
 quickly as possible.
 
 ### [Feature Requests](https://github.com/freqtrade/freqtrade/labels/enhancement)
@@ -155,32 +169,32 @@ quickly as possible.
 Have you a great idea to improve the bot you want to share? Please,
 first search if this feature was not [already discussed](https://github.com/freqtrade/freqtrade/labels/enhancement).
 If it hasn't been requested, please
-[create a new request](https://github.com/freqtrade/freqtrade/issues/new)
+[create a new request](https://github.com/freqtrade/freqtrade/issues/new/choose)
 and ensure you follow the template guide so that it does not get lost
 in the bug reports.
 
 ### [Pull Requests](https://github.com/freqtrade/freqtrade/pulls)
 
-Feel like our bot is missing a feature? We welcome your pull requests!
+Feel like the bot is missing a feature? We welcome your pull requests!
 
-Please read our
+Please read the
 [Contributing document](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
 to understand the requirements before sending your pull-requests.
 
-Coding is not a neccessity to contribute - maybe start with improving our documentation?
+Coding is not a necessity to contribute - maybe start with improving the documentation?
 Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.
 
-**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
+**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [discord](https://discord.gg/p7nuUNVfP7) (please use the #dev channel for this). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
 
-**Important:** Always create your PR against the `develop` branch, not `master`.
+**Important:** Always create your PR against the `develop` branch, not `stable`.
 
 ## Requirements
 
-### Uptodate clock
+### Up-to-date clock
 
-The clock must be accurate, syncronized to a NTP server very frequently to avoid problems with communication to the exchanges.
+The clock must be accurate, synchronized to a NTP server very frequently to avoid problems with communication to the exchanges.
 
-### Min hardware required
+### Minimum hardware required
 
 To run this bot we recommend you a cloud instance with a minimum of:
 
@@ -188,9 +202,9 @@ To run this bot we recommend you a cloud instance with a minimum of:
 
 ### Software requirements
 
-- [Python 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/)
+- [Python >= 3.8](http://docs.python-guide.org/en/latest/starting/installation/)
 - [pip](https://pip.pypa.io/en/stable/installing/)
 - [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
 - [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)
-- [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
+- [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
 - [Docker](https://www.docker.com/products/docker) (Recommended)

build_helpers/install_ta-lib.sh

@@ -4,14 +4,31 @@ else
   INSTALL_LOC=${1}
 fi
 echo "Installing to ${INSTALL_LOC}"
-if [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
+if [ -n "$2" ] || [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
   tar zxvf ta-lib-0.4.0-src.tar.gz
   cd ta-lib \
   && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \
+  && curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess \
+  && curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub \
   && ./configure --prefix=${INSTALL_LOC}/ \
-  && make \
-  && which sudo && sudo make install || make install \
-  && cd ..
+  && make
+  if [ $? -ne 0 ]; then
+    echo "Failed building ta-lib."
+    cd .. && rm -rf ./ta-lib/
+    exit 1
+  fi
+  if [ -z "$2" ]; then
+    which sudo && sudo make install || make install
+    if [ -x "$(command -v apt-get)" ]; then
+      echo "Updating library path using ldconfig"
+      sudo ldconfig
+    fi
+  else
+    # Don't install with sudo
+    make install
+  fi
+
+  cd .. && rm -rf ./ta-lib/
 else
   echo "TA-lib already installed, skipping installation"
 fi

build_helpers/install_windows.ps1

@@ -1,17 +1,18 @@
 # Downloads don't work automatically, since the URL is regenerated via javascript.
 # Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
-# Invoke-WebRequest -Uri "https://download.lfd.uci.edu/pythonlibs/xxxxxxx/TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl" -OutFile "TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl"
 
-python -m pip install --upgrade pip
+python -m pip install --upgrade pip wheel
 
 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
 
-if ($pyv -eq '3.7') {
-    pip install build_helpers\TA_Lib-0.4.18-cp37-cp37m-win_amd64.whl
-}
 if ($pyv -eq '3.8') {
-    pip install build_helpers\TA_Lib-0.4.18-cp38-cp38-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.24-cp38-cp38-win_amd64.whl
+}
+if ($pyv -eq '3.9') {
+    pip install build_helpers\TA_Lib-0.4.24-cp39-cp39-win_amd64.whl
+}
+if ($pyv -eq '3.10') {
+    pip install build_helpers\TA_Lib-0.4.24-cp310-cp310-win_amd64.whl
 }
-
 pip install -r requirements-dev.txt
 pip install -e .

build_helpers/pre_commit_update.py

@@ -0,0 +1,42 @@
+# File used in CI to ensure pre-commit dependencies are kept uptodate.
+
+import sys
+from pathlib import Path
+
+import yaml
+
+
+pre_commit_file = Path('.pre-commit-config.yaml')
+require_dev = Path('requirements-dev.txt')
+
+with require_dev.open('r') as rfile:
+    requirements = rfile.readlines()
+
+# Extract types only
+type_reqs = [r.strip('\n') for r in requirements if r.startswith('types-')]
+
+with pre_commit_file.open('r') as file:
+    f = yaml.load(file, Loader=yaml.FullLoader)
+
+
+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']
+
+errors = []
+for hook in hooks:
+    if hook not in type_reqs:
+        errors.append(f"{hook} is missing in requirements-dev.txt.")
+
+for req in type_reqs:
+    if req not in hooks:
+        errors.append(f"{req} is missing in pre-config file.")
+
+
+if errors:
+    for e in errors:
+        print(e)
+    sys.exit(1)
+
+sys.exit(0)

build_helpers/publish_docker.sh

@@ -1,52 +0,0 @@
-#!/bin/sh
-
-# Replace / with _ to create a valid tag
-TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
-echo "Running for ${TAG}"
-
-# Add commit and commit_message to docker container
-echo "${GITHUB_SHA}" > freqtrade_commit
-
-if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
-    echo "event ${GITHUB_EVENT_NAME}: full rebuild - skipping cache"
-    docker build -t freqtrade:${TAG} .
-else
-    echo "event ${GITHUB_EVENT_NAME}: building with cache"
-    # Pull last build to avoid rebuilding the whole image
-    docker pull ${IMAGE_NAME}:${TAG}
-    docker build --cache-from ${IMAGE_NAME}:${TAG} -t freqtrade:${TAG} .
-fi
-
-if [ $? -ne 0 ]; then
-    echo "failed building image"
-    return 1
-fi
-
-# Run backtest
-docker run --rm -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy DefaultStrategy
-
-if [ $? -ne 0 ]; then
-    echo "failed running backtest"
-    return 1
-fi
-
-# Tag image for upload
-docker tag freqtrade:$TAG ${IMAGE_NAME}:$TAG
-if [ $? -ne 0 ]; then
-    echo "failed tagging image"
-    return 1
-fi
-
-# Tag as latest for develop builds
-if [ "${TAG}" = "develop" ]; then
-    docker tag freqtrade:$TAG ${IMAGE_NAME}:latest
-fi
-
-# Show all available images
-docker images
-
-docker push ${IMAGE_NAME}
-if [ $? -ne 0 ]; then
-    echo "failed pushing repo"
-    return 1
-fi

build_helpers/publish_docker_arm64.sh

@@ -0,0 +1,86 @@
+#!/bin/sh
+
+# Use BuildKit, otherwise building on ARM fails
+export DOCKER_BUILDKIT=1
+
+# Replace / with _ to create a valid tag
+TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
+TAG_PLOT=${TAG}_plot
+TAG_FREQAI=${TAG}_freqai
+TAG_PI="${TAG}_pi"
+
+TAG_ARM=${TAG}_arm
+TAG_PLOT_ARM=${TAG_PLOT}_arm
+TAG_FREQAI_ARM=${TAG_FREQAI}_arm
+CACHE_IMAGE=freqtradeorg/freqtrade_cache
+
+echo "Running for ${TAG}"
+
+# Add commit and commit_message to docker container
+echo "${GITHUB_SHA}" > freqtrade_commit
+
+if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
+    echo "event ${GITHUB_EVENT_NAME}: full rebuild - skipping cache"
+    # Build regular image
+    docker build -t freqtrade:${TAG_ARM} .
+
+else
+    echo "event ${GITHUB_EVENT_NAME}: building with cache"
+    # Build regular image
+    docker pull ${IMAGE_NAME}:${TAG_ARM}
+    docker build --cache-from ${IMAGE_NAME}:${TAG_ARM} -t freqtrade:${TAG_ARM} .
+
+fi
+
+if [ $? -ne 0 ]; then
+    echo "failed building multiarch images"
+    return 1
+fi
+# Tag image for upload and next build step
+docker tag freqtrade:$TAG_ARM ${CACHE_IMAGE}:$TAG_ARM
+
+docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_PLOT_ARM} -f docker/Dockerfile.plot .
+docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_ARM} -f docker/Dockerfile.freqai .
+
+docker tag freqtrade:$TAG_PLOT_ARM ${CACHE_IMAGE}:$TAG_PLOT_ARM
+docker tag freqtrade:$TAG_FREQAI_ARM ${CACHE_IMAGE}:$TAG_FREQAI_ARM
+
+# Run backtest
+docker run --rm -v $(pwd)/config_examples/config_bittrex.example.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG_ARM} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3
+
+if [ $? -ne 0 ]; then
+    echo "failed running backtest"
+    return 1
+fi
+
+docker images
+
+# docker push ${IMAGE_NAME}
+docker push ${CACHE_IMAGE}:$TAG_PLOT_ARM
+docker push ${CACHE_IMAGE}:$TAG_FREQAI_ARM
+docker push ${CACHE_IMAGE}:$TAG_ARM
+
+# Create multi-arch image
+# Make sure that all images contained here are pushed to github first.
+# Otherwise installation might fail.
+echo "create manifests"
+
+docker manifest create --amend ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
+docker manifest push -p ${IMAGE_NAME}:${TAG}
+
+docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM} ${CACHE_IMAGE}:${TAG_PLOT}
+docker manifest push -p ${IMAGE_NAME}:${TAG_PLOT}
+
+docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM} ${CACHE_IMAGE}:${TAG_FREQAI}
+docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI}
+
+# Tag as latest for develop builds
+if [ "${TAG}" = "develop" ]; then
+    docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
+    docker manifest push -p ${IMAGE_NAME}:latest
+fi
+
+docker images
+
+# Cleanup old images from arm64 node.
+docker image prune -a --force --filter "until=24h"

build_helpers/publish_docker_multi.sh

@@ -0,0 +1,79 @@
+#!/bin/sh
+
+# The below assumes a correctly setup docker buildx environment
+
+# Replace / with _ to create a valid tag
+TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
+TAG_PLOT=${TAG}_plot
+TAG_FREQAI=${TAG}_freqai
+TAG_PI="${TAG}_pi"
+
+PI_PLATFORM="linux/arm/v7"
+echo "Running for ${TAG}"
+CACHE_IMAGE=freqtradeorg/freqtrade_cache
+CACHE_TAG=${CACHE_IMAGE}:${TAG_PI}_cache
+
+# Add commit and commit_message to docker container
+echo "${GITHUB_SHA}" > freqtrade_commit
+
+if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
+    echo "event ${GITHUB_EVENT_NAME}: full rebuild - skipping cache"
+    # Build regular image
+    docker build -t freqtrade:${TAG} .
+    # Build PI image
+    docker buildx build \
+        --cache-to=type=registry,ref=${CACHE_TAG} \
+        -f docker/Dockerfile.armhf \
+        --platform ${PI_PLATFORM} \
+        -t ${IMAGE_NAME}:${TAG_PI} --push .
+else
+    echo "event ${GITHUB_EVENT_NAME}: building with cache"
+    # Build regular image
+    docker pull ${IMAGE_NAME}:${TAG}
+    docker build --cache-from ${IMAGE_NAME}:${TAG} -t freqtrade:${TAG} .
+
+    # Pull last build to avoid rebuilding the whole image
+    # docker pull --platform ${PI_PLATFORM} ${IMAGE_NAME}:${TAG}
+    docker buildx build \
+        --cache-from=type=registry,ref=${CACHE_TAG} \
+        --cache-to=type=registry,ref=${CACHE_TAG} \
+        -f docker/Dockerfile.armhf \
+        --platform ${PI_PLATFORM} \
+        -t ${IMAGE_NAME}:${TAG_PI} --push .
+fi
+
+if [ $? -ne 0 ]; then
+    echo "failed building multiarch images"
+    return 1
+fi
+# Tag image for upload and next build step
+docker tag freqtrade:$TAG ${CACHE_IMAGE}:$TAG
+
+docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG} -t freqtrade:${TAG_PLOT} -f docker/Dockerfile.plot .
+docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG} -t freqtrade:${TAG_FREQAI} -f docker/Dockerfile.freqai .
+
+docker tag freqtrade:$TAG_PLOT ${CACHE_IMAGE}:$TAG_PLOT
+docker tag freqtrade:$TAG_FREQAI ${CACHE_IMAGE}:$TAG_FREQAI
+
+# Run backtest
+docker run --rm -v $(pwd)/config_examples/config_bittrex.example.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3
+
+if [ $? -ne 0 ]; then
+    echo "failed running backtest"
+    return 1
+fi
+
+docker images
+
+docker push ${CACHE_IMAGE}
+docker push ${CACHE_IMAGE}:$TAG_PLOT
+docker push ${CACHE_IMAGE}:$TAG_FREQAI
+docker push ${CACHE_IMAGE}:$TAG
+
+
+docker images
+
+if [ $? -ne 0 ]; then
+    echo "failed building image"
+    return 1
+fi

build_helpers/publish_docker_pi.sh

@@ -1,36 +0,0 @@
-#!/bin/sh
-
-# The below assumes a correctly setup docker buildx environment
-
-# Replace / with _ to create a valid tag
-TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
-PI_PLATFORM="linux/arm/v7"
-echo "Running for ${TAG}"
-CACHE_TAG=freqtradeorg/freqtrade_cache:${TAG}_cache
-
-# Add commit and commit_message to docker container
-echo "${GITHUB_SHA}" > freqtrade_commit
-
-if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
-    echo "event ${GITHUB_EVENT_NAME}: full rebuild - skipping cache"
-    docker buildx build \
-        --cache-to=type=registry,ref=${CACHE_TAG} \
-        -f Dockerfile.armhf \
-        --platform ${PI_PLATFORM} \
-        -t ${IMAGE_NAME}:${TAG} --push .
-else
-    echo "event ${GITHUB_EVENT_NAME}: building with cache"
-    # Pull last build to avoid rebuilding the whole image
-    # docker pull --platform ${PI_PLATFORM} ${IMAGE_NAME}:${TAG}
-    docker buildx build \
-        --cache-from=type=registry,ref=${CACHE_TAG} \
-        --cache-to=type=registry,ref=${CACHE_TAG} \
-        -f Dockerfile.armhf \
-        --platform ${PI_PLATFORM} \
-        -t ${IMAGE_NAME}:${TAG} --push .
-fi
-
-if [ $? -ne 0 ]; then
-    echo "failed building image"
-    return 1
-fi

config_examples/config_binance.example.json

@@ -7,36 +7,33 @@
     "timeframe": "5m",
     "dry_run": true,
     "cancel_open_orders_on_exit": false,
-    "trailing_stop": false,
     "unfilledtimeout": {
-        "buy": 10,
-        "sell": 30
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
-    "bid_strategy": {
-        "use_order_book": false,
-        "ask_last_balance": 0.0,
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
         "order_book_top": 1,
+        "price_last_balance": 0.0,
         "check_depth_of_market": {
             "enabled": false,
             "bids_to_ask_delta": 1
         }
     },
-    "ask_strategy":{
-        "use_order_book": false,
-        "order_book_min": 1,
-        "order_book_max": 1,
-        "use_sell_signal": true,
-        "sell_profit_only": false,
-        "ignore_roi_if_buy_signal": false
+    "exit_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
     },
     "exchange": {
         "name": "binance",
         "key": "your_exchange_key",
         "secret": "your_exchange_secret",
-        "ccxt_config": {"enableRateLimit": true},
+        "ccxt_config": {},
         "ccxt_async_config": {
-            "enableRateLimit": true,
-            "rateLimit": 200
         },
         "pair_whitelist": [
             "ALGO/BTC",
@@ -85,14 +82,15 @@
         "enabled": false,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
-        "verbosity": "info",
+        "verbosity": "error",
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
-        "username": "",
-        "password": ""
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
     },
+    "bot_name": "freqtrade",
     "initial_state": "running",
-    "forcebuy_enable": false,
+    "force_entry_enable": false,
     "internals": {
         "process_throttle_secs": 5
     }

config_examples/config_bittrex.example.json

@@ -5,29 +5,28 @@
     "tradable_balance_ratio": 0.99,
     "fiat_display_currency": "USD",
     "timeframe": "5m",
-    "dry_run": false,
+    "dry_run": true,
     "cancel_open_orders_on_exit": false,
-    "trailing_stop": false,
     "unfilledtimeout": {
-        "buy": 10,
-        "sell": 30
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
-    "bid_strategy": {
-        "ask_last_balance": 0.0,
-        "use_order_book": false,
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
         "order_book_top": 1,
+        "price_last_balance": 0.0,
         "check_depth_of_market": {
             "enabled": false,
             "bids_to_ask_delta": 1
         }
     },
-    "ask_strategy":{
-        "use_order_book": false,
-        "order_book_min": 1,
-        "order_book_max": 1,
-        "use_sell_signal": true,
-        "sell_profit_only": false,
-        "ignore_roi_if_buy_signal": false
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
     },
     "exchange": {
         "name": "bittrex",
@@ -42,13 +41,13 @@
             "ETH/BTC",
             "LTC/BTC",
             "ETC/BTC",
-            "DASH/BTC",
-            "ZEC/BTC",
+            "RVN/BTC",
+            "CRO/BTC",
             "XLM/BTC",
             "XRP/BTC",
             "TRX/BTC",
             "ADA/BTC",
-            "XMR/BTC"
+            "DOT/BTC"
         ],
         "pair_blacklist": [
             "DOGE/BTC"
@@ -80,14 +79,15 @@
         "enabled": false,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
-        "verbosity": "info",
+        "verbosity": "error",
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
-        "username": "",
-        "password": ""
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
     },
+    "bot_name": "freqtrade",
     "initial_state": "running",
-    "forcebuy_enable": false,
+    "force_entry_enable": false,
     "internals": {
         "process_throttle_secs": 5
     }

config_examples/config_freqai.example.json

@@ -0,0 +1,96 @@
+{
+    "trading_mode": "futures",
+    "margin_mode": "isolated",
+    "max_open_trades": 5,
+    "stake_currency": "USDT",
+    "stake_amount": 200,
+    "tradable_balance_ratio": 1,
+    "fiat_display_currency": "USD",
+    "dry_run": true,
+    "timeframe": "3m",
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": true,
+    "unfilledtimeout": {
+        "entry": 10,
+        "exit": 30
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "",
+        "secret": "",
+        "ccxt_config": {
+            "enableRateLimit": true
+        },
+        "ccxt_async_config": {
+            "enableRateLimit": true,
+            "rateLimit": 200
+        },
+        "pair_whitelist": [
+            "1INCH/USDT",
+            "ALGO/USDT"
+        ],
+        "pair_blacklist": []
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing": {
+        "price_side": "other",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList"
+        }
+    ],
+    "freqai": {
+        "enabled": true,
+        "startup_candles": 10000,
+        "purge_old_models": true,
+        "train_period_days": 15,
+        "backtest_period_days": 7,
+        "live_retrain_hours": 0,
+        "identifier": "uniqe-id",
+        "feature_parameters": {
+            "include_timeframes": [
+                "3m",
+                "15m",
+                "1h"
+            ],
+            "include_corr_pairlist": [
+                "BTC/USDT",
+                "ETH/USDT"
+            ],
+            "label_period_candles": 20,
+            "include_shifted_candles": 2,
+            "DI_threshold": 0.9,
+            "weight_factor": 0.9,
+            "principal_component_analysis": false,
+            "use_SVM_to_remove_outliers": true,
+            "stratify_training_data": 0,
+            "indicator_max_period_candles": 20,
+            "indicator_periods_candles": [10, 20]
+        },
+        "data_split_parameters": {
+            "test_size": 0.33,
+            "random_state": 1
+        },
+        "model_training_parameters": {
+            "n_estimators": 1000
+        }
+    },
+    "bot_name": "",
+    "force_entry_enable": true,
+    "initial_state": "running",
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}

config_examples/config_ftx.example.json

@@ -0,0 +1,96 @@
+{
+    "max_open_trades": 3,
+    "stake_currency": "USD",
+    "stake_amount": 50,
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "timeframe": "5m",
+    "dry_run": true,
+    "cancel_open_orders_on_exit": false,
+    "unfilledtimeout": {
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "ftx",
+        "key": "your_exchange_key",
+        "secret": "your_exchange_secret",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "BTC/USD",
+            "ETH/USD",
+            "BNB/USD",
+            "USDT/USD",
+            "LTC/USD",
+            "SRM/USD",
+            "SXP/USD",
+            "XRP/USD",
+            "DOGE/USD",
+            "1INCH/USD",
+            "CHZ/USD",
+            "MATIC/USD",
+            "LINK/USD",
+            "OXY/USD",
+            "SUSHI/USD"
+        ],
+        "pair_blacklist": [
+            "FTT/USD"
+        ]
+    },
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
+    "edge": {
+        "enabled": false,
+        "process_throttle_secs": 3600,
+        "calculate_since_number_of_days": 7,
+        "allowed_risk": 0.01,
+        "stoploss_range_min": -0.01,
+        "stoploss_range_max": -0.1,
+        "stoploss_range_step": -0.01,
+        "minimum_winrate": 0.60,
+        "minimum_expectancy": 0.20,
+        "min_trade_number": 10,
+        "max_trade_duration_minute": 1440,
+        "remove_pumps": false
+    },
+    "telegram": {
+        "enabled": false,
+        "token": "your_telegram_token",
+        "chat_id": "your_telegram_chat_id"
+    },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
+    },
+    "bot_name": "freqtrade",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}

config_examples/config_full.example.json

@@ -0,0 +1,192 @@
+{
+    "max_open_trades": 3,
+    "stake_currency": "BTC",
+    "stake_amount": 0.05,
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "amount_reserve_percent": 0.05,
+    "available_capital": 1000,
+    "amend_last_stake_amount": false,
+    "last_stake_amount_min_ratio": 0.5,
+    "dry_run": true,
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": false,
+    "timeframe": "5m",
+    "trailing_stop": false,
+    "trailing_stop_positive": 0.005,
+    "trailing_stop_positive_offset": 0.0051,
+    "trailing_only_offset_is_reached": false,
+    "use_exit_signal": true,
+    "exit_profit_only": false,
+    "exit_profit_offset": 0.0,
+    "ignore_roi_if_entry_signal": false,
+    "ignore_buying_expired_candle_after": 300,
+    "trading_mode": "spot",
+    "margin_mode": "",
+    "minimal_roi": {
+        "40":  0.0,
+        "30":  0.01,
+        "20":  0.02,
+        "0":  0.04
+    },
+    "stoploss": -0.10,
+    "unfilledtimeout": {
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0
+    },
+    "order_types": {
+        "entry": "limit",
+        "exit": "limit",
+        "emergency_exit": "market",
+        "force_exit": "market",
+        "force_entry": "market",
+        "stoploss": "market",
+        "stoploss_on_exchange": false,
+        "stoploss_on_exchange_interval": 60,
+        "stoploss_on_exchange_limit_ratio": 0.99
+    },
+    "order_time_in_force": {
+        "entry": "gtc",
+        "exit": "gtc"
+    },
+    "pairlists": [
+        {"method": "StaticPairList"},
+        {
+            "method": "VolumePairList",
+            "number_assets": 20,
+            "sort_key": "quoteVolume",
+            "refresh_period": 1800
+        },
+        {"method": "AgeFilter", "min_days_listed": 10},
+        {"method": "PrecisionFilter"},
+        {"method": "PriceFilter", "low_price_ratio": 0.01, "min_price": 0.00000010},
+        {"method": "SpreadFilter", "max_spread_ratio": 0.005},
+        {
+            "method": "RangeStabilityFilter",
+            "lookback_days": 10,
+            "min_rate_of_change": 0.01,
+            "refresh_period": 1440
+        }
+    ],
+    "exchange": {
+        "name": "binance",
+        "sandbox": false,
+        "key": "your_exchange_key",
+        "secret": "your_exchange_secret",
+        "password": "",
+        "log_responses": false,
+        // "unknown_fee_rate": 1,
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "ALGO/BTC",
+            "ATOM/BTC",
+            "BAT/BTC",
+            "BCH/BTC",
+            "BRD/BTC",
+            "EOS/BTC",
+            "ETH/BTC",
+            "IOTA/BTC",
+            "LINK/BTC",
+            "LTC/BTC",
+            "NEO/BTC",
+            "NXS/BTC",
+            "XMR/BTC",
+            "XRP/BTC",
+            "XTZ/BTC"
+        ],
+        "pair_blacklist": [
+            "DOGE/BTC"
+        ],
+        "outdated_offset": 5,
+        "markets_refresh_interval": 60
+    },
+    "edge": {
+        "enabled": false,
+        "process_throttle_secs": 3600,
+        "calculate_since_number_of_days": 7,
+        "allowed_risk": 0.01,
+        "stoploss_range_min": -0.01,
+        "stoploss_range_max": -0.1,
+        "stoploss_range_step": -0.01,
+        "minimum_winrate": 0.60,
+        "minimum_expectancy": 0.20,
+        "min_trade_number": 10,
+        "max_trade_duration_minute": 1440,
+        "remove_pumps": false
+    },
+    "telegram": {
+        "enabled": false,
+        "token": "your_telegram_token",
+        "chat_id": "your_telegram_chat_id",
+        "notification_settings": {
+            "status": "on",
+            "warning": "on",
+            "startup": "on",
+            "entry": "on",
+            "entry_fill": "on",
+            "exit": {
+                "roi": "off",
+                "emergency_exit": "off",
+                "force_exit": "off",
+                "exit_signal": "off",
+                "trailing_stop_loss": "off",
+                "stop_loss": "off",
+                "stoploss_on_exchange": "off",
+                "custom_exit": "off"
+            },
+            "exit_fill": "on",
+            "entry_cancel": "on",
+            "exit_cancel": "on",
+            "protection_trigger": "off",
+            "protection_trigger_global": "on",
+            "show_candle": "off"
+        },
+        "reload": true,
+        "balance_dust_level": 0.01
+    },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "enable_openapi": false,
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
+    },
+    "bot_name": "freqtrade",
+    "db_url": "sqlite:///tradesv3.sqlite",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 5,
+        "heartbeat_interval": 60
+    },
+    "disable_dataframe_checks": false,
+    "strategy": "SampleStrategy",
+    "strategy_path": "user_data/strategies/",
+    "recursive_strategy_search": false,
+    "add_config_files": [],
+    "dataformat_ohlcv": "json",
+    "dataformat_trades": "jsongz"
+}

config_examples/config_kraken.example.json

@@ -7,37 +7,33 @@
     "timeframe": "5m",
     "dry_run": true,
     "cancel_open_orders_on_exit": false,
-    "trailing_stop": false,
     "unfilledtimeout": {
-        "buy": 10,
-        "sell": 30
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
-    "bid_strategy": {
-        "use_order_book": false,
-        "ask_last_balance": 0.0,
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
         "order_book_top": 1,
+        "price_last_balance": 0.0,
         "check_depth_of_market": {
             "enabled": false,
             "bids_to_ask_delta": 1
         }
     },
-    "ask_strategy":{
-        "use_order_book": false,
-        "order_book_min": 1,
-        "order_book_max": 1,
-        "use_sell_signal": true,
-        "sell_profit_only": false,
-        "ignore_roi_if_buy_signal": false
-
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
     },
     "exchange": {
         "name": "kraken",
-        "key": "",
-        "secret": "",
-        "ccxt_config": {"enableRateLimit": true},
+        "key": "your_exchange_key",
+        "secret": "your_exchange_key",
+        "ccxt_config": {},
         "ccxt_async_config": {
-            "enableRateLimit": true,
-            "rateLimit": 1000
         },
         "pair_whitelist": [
             "ADA/EUR",
@@ -91,14 +87,15 @@
         "enabled": false,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
-        "verbosity": "info",
+        "verbosity": "error",
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
-        "username": "",
-        "password": ""
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
     },
+    "bot_name": "freqtrade",
     "initial_state": "running",
-    "forcebuy_enable": false,
+    "force_entry_enable": false,
     "internals": {
         "process_throttle_secs": 5
     },

config_full.json.example

@@ -1,143 +0,0 @@
-{
-    "max_open_trades": 3,
-    "stake_currency": "BTC",
-    "stake_amount": 0.05,
-    "tradable_balance_ratio": 0.99,
-    "fiat_display_currency": "USD",
-    "amount_reserve_percent": 0.05,
-    "amend_last_stake_amount": false,
-    "last_stake_amount_min_ratio": 0.5,
-    "dry_run": false,
-    "cancel_open_orders_on_exit": false,
-    "timeframe": "5m",
-    "trailing_stop": false,
-    "trailing_stop_positive": 0.005,
-    "trailing_stop_positive_offset": 0.0051,
-    "trailing_only_offset_is_reached": false,
-    "minimal_roi": {
-        "40":  0.0,
-        "30":  0.01,
-        "20":  0.02,
-        "0":  0.04
-    },
-    "stoploss": -0.10,
-    "unfilledtimeout": {
-        "buy": 10,
-        "sell": 30
-    },
-    "bid_strategy": {
-        "price_side": "bid",
-        "use_order_book": false,
-        "ask_last_balance": 0.0,
-        "order_book_top": 1,
-        "check_depth_of_market": {
-            "enabled": false,
-            "bids_to_ask_delta": 1
-        }
-    },
-    "ask_strategy":{
-        "price_side": "ask",
-        "use_order_book": false,
-        "order_book_min": 1,
-        "order_book_max": 1,
-        "use_sell_signal": true,
-        "sell_profit_only": false,
-        "ignore_roi_if_buy_signal": false
-    },
-    "order_types": {
-        "buy": "limit",
-        "sell": "limit",
-        "emergencysell": "market",
-        "stoploss": "market",
-        "stoploss_on_exchange": false,
-        "stoploss_on_exchange_interval": 60
-    },
-    "order_time_in_force": {
-        "buy": "gtc",
-        "sell": "gtc"
-    },
-    "pairlists": [
-        {"method": "StaticPairList"},
-        {
-            "method": "VolumePairList",
-            "number_assets": 20,
-            "sort_key": "quoteVolume",
-            "refresh_period": 1800
-        },
-        {"method": "AgeFilter", "min_days_listed": 10},
-        {"method": "PrecisionFilter"},
-        {"method": "PriceFilter", "low_price_ratio": 0.01},
-        {"method": "SpreadFilter", "max_spread_ratio": 0.005}
-    ],
-    "exchange": {
-        "name": "bittrex",
-        "sandbox": false,
-        "key": "your_exchange_key",
-        "secret": "your_exchange_secret",
-        "password": "",
-        "ccxt_config": {"enableRateLimit": true},
-        "ccxt_async_config": {
-            "enableRateLimit": false,
-            "rateLimit": 500,
-            "aiohttp_trust_env": false
-        },
-        "pair_whitelist": [
-            "ETH/BTC",
-            "LTC/BTC",
-            "ETC/BTC",
-            "DASH/BTC",
-            "ZEC/BTC",
-            "XLM/BTC",
-            "NXT/BTC",
-            "TRX/BTC",
-            "ADA/BTC",
-            "XMR/BTC"
-        ],
-        "pair_blacklist": [
-            "DOGE/BTC"
-        ],
-        "outdated_offset": 5,
-        "markets_refresh_interval": 60
-    },
-    "edge": {
-        "enabled": false,
-        "process_throttle_secs": 3600,
-        "calculate_since_number_of_days": 7,
-        "allowed_risk": 0.01,
-        "stoploss_range_min": -0.01,
-        "stoploss_range_max": -0.1,
-        "stoploss_range_step": -0.01,
-        "minimum_winrate": 0.60,
-        "minimum_expectancy": 0.20,
-        "min_trade_number": 10,
-        "max_trade_duration_minute": 1440,
-        "remove_pumps": false
-    },
-    "telegram": {
-        "enabled": true,
-        "token": "your_telegram_token",
-        "chat_id": "your_telegram_chat_id"
-    },
-    "api_server": {
-        "enabled": false,
-        "listen_ip_address": "127.0.0.1",
-        "listen_port": 8080,
-        "verbosity": "info",
-        "jwt_secret_key": "somethingrandom",
-        "CORS_origins": [],
-        "username": "freqtrader",
-        "password": "SuperSecurePassword"
-    },
-    "db_url": "sqlite:///tradesv3.sqlite",
-    "initial_state": "running",
-    "forcebuy_enable": false,
-    "internals": {
-        "process_throttle_secs": 5,
-        "heartbeat_interval": 60
-    },
-    "disable_dataframe_checks": false,
-    "strategy": "DefaultStrategy",
-    "strategy_path": "user_data/strategies/",
-    "dataformat_ohlcv": "json",
-    "dataformat_trades": "jsongz"
-}

docker-compose.develop.yml

@@ -1,20 +0,0 @@
----
-version: '3'
-services:
-  freqtrade_develop:
-    build:
-      context: .
-      dockerfile: "./Dockerfile.develop"
-    volumes:
-      - ".:/freqtrade"
-    entrypoint:
-      - "freqtrade"
-
-  freqtrade_bash:
-    build:
-      context: .
-      dockerfile: "./Dockerfile.develop"
-    volumes:
-      - ".:/freqtrade"
-    entrypoint:
-      - "/bin/bash"

docker-compose.yml

@@ -2,16 +2,23 @@
 version: '3'
 services:
   freqtrade:
-    image: freqtradeorg/freqtrade:master
+    image: freqtradeorg/freqtrade:stable
     # image: freqtradeorg/freqtrade:develop
+    # Use plotting image
+    # image: freqtradeorg/freqtrade:develop_plot
     # Build step - only needed when additional dependencies are needed
     # build:
     #   context: .
-    #   dockerfile: "./Dockerfile.technical"
+    #   dockerfile: "./docker/Dockerfile.custom"
     restart: unless-stopped
     container_name: freqtrade
     volumes:
       - "./user_data:/freqtrade/user_data"
+    # Expose api on port 8080 (localhost only)
+    # Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
+    # before enabling this.
+    ports:
+      - "127.0.0.1:8080:8080"
     # Default command used when running `docker compose up`
     command: >
       trade

docker/Dockerfile.armhf

@@ -0,0 +1,59 @@
+FROM python:3.9.12-slim-bullseye as base
+
+# Setup env
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONFAULTHANDLER 1
+ENV PATH=/home/ftuser/.local/bin:$PATH
+ENV FT_APP_ENV="docker"
+
+# Prepare environment
+RUN mkdir /freqtrade \
+  && apt-get update \
+  && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-dev \
+  && apt-get clean \
+  && useradd -u 1000 -G sudo -U -m ftuser \
+  && chown ftuser:ftuser /freqtrade \
+  # Allow sudoers
+  && echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers
+
+WORKDIR /freqtrade
+
+# Install dependencies
+FROM base as python-deps
+RUN  apt-get update \
+  && apt-get -y install build-essential libssl-dev libffi-dev libgfortran5 pkg-config cmake gcc \
+  && apt-get clean \
+  && pip install --upgrade pip \
+  && echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > /etc/pip.conf
+
+# Install TA-lib
+COPY build_helpers/* /tmp/
+RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+# Install dependencies
+COPY --chown=ftuser:ftuser requirements.txt /freqtrade/
+USER ftuser
+RUN  pip install --user --no-cache-dir numpy \
+  && pip install --user --no-cache-dir -r requirements.txt
+
+# Copy dependencies to runtime-image
+FROM base as runtime-image
+COPY --from=python-deps /usr/local/lib /usr/local/lib
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+COPY --from=python-deps --chown=ftuser:ftuser /home/ftuser/.local /home/ftuser/.local
+
+USER ftuser
+# Install and execute
+COPY --chown=ftuser:ftuser . /freqtrade/
+
+RUN pip install -e . --user --no-cache-dir --no-build-isolation\
+  && mkdir /freqtrade/user_data/ \
+  && freqtrade install-ui
+
+ENTRYPOINT ["freqtrade"]
+# Default to trade mode
+CMD [ "trade" ]

docker/Dockerfile.custom

@@ -0,0 +1,11 @@
+FROM freqtradeorg/freqtrade:develop
+
+# Switch user to root if you must install something from apt
+# Don't forget to switch the user back below!
+# USER root
+
+# The below dependency - pyti - serves as an example. Please use whatever you need!
+RUN pip install --user pyti
+
+# Switch back to user (only if you required root above)
+# USER ftuser

docker/Dockerfile.develop

@@ -2,8 +2,9 @@ FROM freqtradeorg/freqtrade:develop
 
 # Install dependencies
 COPY requirements-dev.txt /freqtrade/
-RUN pip install numpy --no-cache-dir \
-  && pip install -r requirements-dev.txt --no-cache-dir
+
+RUN pip install numpy --user --no-cache-dir \
+  && pip install -r requirements-dev.txt --user --no-cache-dir
 
 # Empty the ENTRYPOINT to allow all commands
 ENTRYPOINT []

docker/Dockerfile.freqai

@@ -0,0 +1,9 @@
+ARG sourceimage=freqtradeorg/freqtrade
+ARG sourcetag=develop
+FROM ${sourceimage}:${sourcetag}
+
+# Install dependencies
+COPY requirements-freqai.txt /freqtrade/
+
+RUN pip install -r requirements-freqai.txt --user --no-cache-dir
+

docker/Dockerfile.jupyter

@@ -0,0 +1,7 @@
+FROM freqtradeorg/freqtrade:develop_plot
+
+
+RUN pip install jupyterlab --user --no-cache-dir
+
+# Empty the ENTRYPOINT to allow all commands
+ENTRYPOINT []

docker/Dockerfile.plot

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

docker/docker-compose-jupyter.yml

@@ -0,0 +1,16 @@
+---
+version: '3'
+services:
+  ft_jupyterlab:
+    build:
+      context: ..
+      dockerfile: docker/Dockerfile.jupyter
+    restart: unless-stopped
+    container_name: freqtrade
+    ports:
+      - "127.0.0.1:8888:8888"
+    volumes:
+      - "./user_data:/freqtrade/user_data"
+    # Default command used when running `docker compose up`
+    command: >
+      jupyter lab --port=8888 --ip 0.0.0.0 --allow-root

docs/advanced-backtesting.md

@@ -0,0 +1,102 @@
+# Advanced Backtesting Analysis
+
+## Analyze the buy/entry and sell/exit tags
+
+It can be helpful to understand how a strategy behaves according to the buy/entry tags used to
+mark up different buy conditions. You might want to see more complex statistics about each buy and
+sell condition above those provided by the default backtesting output. You may also want to
+determine indicator values on the signal candle that resulted in a trade opening.
+
+!!! Note
+    The following buy reason analysis is only available for backtesting, *not hyperopt*.
+
+We need to run backtesting with the `--export` option set to `signals` to enable the exporting of
+signals **and** trades:
+
+``` bash
+freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_name> --timerange=<timerange> --export=signals
+```
+
+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.
+
+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.
+
+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 (default `0 1 2`):
+
+``` bash
+freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4
+```
+
+This command will read from the last backtesting results. The `--analysis-groups` option is
+used to specify the various tabular outputs showing the profit fo each group or trade,
+ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4):
+
+* 1: profit summaries grouped by enter_tag
+* 2: profit summaries grouped by enter_tag and exit_tag
+* 3: profit summaries grouped by pair and enter_tag
+* 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
+
+More options are available by running with the `-h` option.
+
+### Using export-filename
+
+Normally, `backtesting-analysis` uses the latest backtest results, but if you wanted to go
+back to a previous backtest output, you need to supply the `--export-filename` option.
+You can supply the same parameter to `backtest-analysis` with the name of the final backtest
+output file. This allows you to keep historical versions of backtest results and re-analyse
+them at a later date:
+
+``` bash
+freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_name> --timerange=<timerange> --export=signals --export-filename=/tmp/mystrat_backtest.json
+```
+
+You should see some output similar to below in the logs with the name of the timestamped
+filename that was exported:
+
+```
+2022-06-14 16:28:32,698 - freqtrade.misc - INFO - dumping json to "/tmp/mystrat_backtest-2022-06-14_16-28-32.json"
+```
+
+You can then use that filename in `backtesting-analysis`:
+
+```
+freqtrade backtesting-analysis -c <config.json> --export-filename=/tmp/mystrat_backtest-2022-06-14_16-28-32.json
+```
+
+### Tuning the buy tags and sell tags to display
+
+To show only certain buy and sell tags in the displayed output, use the following two options:
+
+```
+--enter-reason-list : Space-separated list of enter signals to analyse. Default: "all"
+--exit-reason-list : Space-separated list of exit signals to analyse. Default: "all"
+```
+
+For example:
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 2 --enter-reason-list enter_tag_a enter_tag_b --exit-reason-list roi custom_exit_tag_a stop_loss
+```
+
+### Outputting signal candle indicators
+
+The real power of `freqtrade backtesting-analysis` comes from the ability to print out the indicator
+values present on signal candles to allow fine-grained investigation and tuning of buy signal
+indicators. To print out a column for a given set of indicators, use the `--indicator-list`
+option:
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 2 --enter-reason-list enter_tag_a enter_tag_b --exit-reason-list roi custom_exit_tag_a stop_loss --indicator-list rsi rsi_1h bb_lowerband ema_9 macd macdsignal
+```
+
+The indicators have to be present in your strategy's main DataFrame (either for your main
+timeframe or for informative timeframes) otherwise they will simply be ignored in the script
+output.

docs/advanced-hyperopt.md

@@ -4,34 +4,6 @@ This page explains some advanced Hyperopt topics that may require higher
 coding skills and Python knowledge than creation of an ordinal hyperoptimization
 class.
 
-## Derived hyperopt classes
-
-Custom hyperop classes can be derived in the same way [it can be done for strategies](strategy-customization.md#derived-strategies).
-
-Applying to hyperoptimization, as an example, you may override how dimensions are defined in your optimization hyperspace:
-
-```python
-class MyAwesomeHyperOpt(IHyperOpt):
-    ...
-    # Uses default stoploss dimension
-
-class MyAwesomeHyperOpt2(MyAwesomeHyperOpt):
-    @staticmethod
-    def stoploss_space() -> List[Dimension]:
-        # Override boundaries for stoploss
-        return [
-            Real(-0.33, -0.01, name='stoploss'),
-        ]
-```
-
-and then quickly switch between hyperopt classes, running optimization process with hyperopt class you need in each particular case:
-
-```
-$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt ...
-or
-$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 ...
-```
-
 ## Creating and using a custom loss function
 
 To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
@@ -40,6 +12,11 @@ For the sample below, you then need to add the command line parameter `--hyperop
 A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in [userdata/hyperopts](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py).
 
 ``` python
+from datetime import datetime
+from typing import Any, Dict
+
+from pandas import DataFrame
+
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 TARGET_TRADES = 600
@@ -54,6 +31,8 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
+                               config: Dict, processed: Dict[str, DataFrame],
+                               backtest_stats: Dict[str, Any],
                                *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for better results
@@ -63,7 +42,7 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
         * 0.25: Avoiding trade loss
         * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
         """
-        total_profit = results['profit_percent'].sum()
+        total_profit = results['profit_ratio'].sum()
         trade_duration = results['trade_duration'].mean()
 
         trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
@@ -75,17 +54,138 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
 
 Currently, the arguments are:
 
-* `results`: DataFrame containing the result  
+* `results`: DataFrame containing the resulting trades.
     The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):  
-    `pair, profit_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reason`
+    `pair, profit_ratio, profit_abs, open_date, open_rate, fee_open, close_date, close_rate, fee_close, amount, trade_duration, is_open, exit_reason, stake_amount, min_rate, max_rate, stop_loss_ratio, stop_loss_abs`
 * `trade_count`: Amount of trades (identical to `len(results)`)
-* `min_date`: Start date of the hyperopting TimeFrame
-* `min_date`: End date of the hyperopting TimeFrame
+* `min_date`: Start date of the timerange used
+* `min_date`: End date of the timerange used
+* `config`: Config object used (Note: Not all strategy-related parameters will be updated here if they are part of a hyperopt space).
+* `processed`: Dict of Dataframes with the pair as keys containing the data used for backtesting.
+* `backtest_stats`: Backtesting statistics using the same format as the backtesting file "strategy" substructure. Available fields can be seen in `generate_strategy_stats()` in `optimize_reports.py`.
 
 This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
 
 !!! Note
-    This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
+    This function is called once per epoch - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
+
+!!! Note "`*args` and `**kwargs`"
+    Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface in the future.
+
+## Overriding pre-defined spaces
+
+To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`), define a nested class called Hyperopt and define the required spaces as follows:
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        # Define a custom stoploss space.
+        def stoploss_space():
+            return [SKDecimal(-0.05, -0.01, decimals=3, name='stoploss')]
+
+        # Define custom ROI space
+        def roi_space() -> List[Dimension]:
+            return [
+                Integer(10, 120, name='roi_t1'),
+                Integer(10, 60, name='roi_t2'),
+                Integer(10, 40, name='roi_t3'),
+                SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'),
+                SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
+                SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
+            ]
+```
 
 !!! Note
-    Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
+    All overrides are optional and can be mixed/matched as necessary.
+
+### Dynamic parameters
+
+Parameters can also be defined dynamically, but must be available to the instance once the * [`bot_start()` callback](strategy-callbacks.md#bot-start) has been called.
+
+``` python
+
+class MyAwesomeStrategy(IStrategy):
+
+    def bot_start(self, **kwargs) -> None:
+        self.buy_adx = IntParameter(20, 30, default=30, optimize=True)
+
+    # ...
+```
+
+!!! Warning
+    Parameters created this way will not show up in the `list-strategies` parameter count.
+
+### Overriding Base estimator
+
+You can define your own estimator for Hyperopt by implementing `generate_estimator()` in the Hyperopt subclass.
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
+            return "RF"
+
+```
+
+Possible values are either one of "GP", "RF", "ET", "GBRT" (Details can be found in the [scikit-optimize documentation](https://scikit-optimize.github.io/)), or "an instance of a class that inherits from `RegressorMixin` (from sklearn) and where the `predict` method has an optional `return_std` argument, which returns `std(Y | x)` along with `E[Y | x]`".
+
+Some research will be necessary to find additional Regressors.
+
+Example for `ExtraTreesRegressor` ("ET") with additional parameters:
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
+            from skopt.learning import ExtraTreesRegressor
+            # Corresponds to "ET" - but allows additional parameters.
+            return ExtraTreesRegressor(n_estimators=100)
+
+```
+
+The `dimensions` parameter is the list of `skopt.space.Dimension` objects corresponding to the parameters to be optimized. It can be used to create isotropic kernels for the `skopt.learning.GaussianProcessRegressor` estimator. Here's an example:
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
+            from skopt.utils import cook_estimator
+            from skopt.learning.gaussian_process.kernels import (Matern, ConstantKernel)
+            kernel_bounds = (0.0001, 10000)
+            kernel = (
+                ConstantKernel(1.0, kernel_bounds) * 
+                Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=2.5)
+            )
+            kernel += (
+                ConstantKernel(1.0, kernel_bounds) * 
+                Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=1.5)
+            )
+
+            return cook_estimator("GP", space=dimensions, kernel=kernel, n_restarts_optimizer=2)
+```
+
+!!! Note
+    While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
+    If you're unsure about this, best use one of the Defaults (`"ET"` has proven to be the most versatile) without further parameters.
+
+## Space options
+
+For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:
+
+* `Categorical` - Pick from a list of categories (e.g. `Categorical(['a', 'b', 'c'], name="cat")`)
+* `Integer` - Pick from a range of whole numbers (e.g. `Integer(1, 10, name='rsi')`)
+* `SKDecimal` - Pick from a range of decimal numbers with limited precision (e.g. `SKDecimal(0.1, 0.5, decimals=3, name='adx')`). *Available only with freqtrade*.
+* `Real` - Pick from a range of decimal numbers with full precision (e.g. `Real(0.1, 0.5, name='adx')`
+
+You can import all of these from `freqtrade.optimize.space`, although `Categorical`, `Integer` and `Real` are only aliases for their corresponding scikit-optimize Spaces. `SKDecimal` is provided by freqtrade for faster optimizations.
+
+``` python
+from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real  # noqa
+```
+
+!!! Hint "SKDecimal vs. Real"
+    We recommend to use `SKDecimal` instead of the `Real` space in almost all cases. While the Real space provides full accuracy (up to ~16 decimal places) - this precision is rarely needed, and leads to unnecessary long hyperopt times.
+
+    Assuming the definition of a rather small space (`SKDecimal(0.10, 0.15, decimals=2, name='xxx')`) - SKDecimal will have 5 possibilities (`[0.10, 0.11, 0.12, 0.13, 0.14, 0.15]`).
+
+    A corresponding real space `Real(0.10, 0.15 name='xxx')`  on the other hand has an almost unlimited number of possibilities (`[0.10, 0.010000000001, 0.010000000002, ... 0.014999999999, 0.01500000000]`).

docs/advanced-setup.md

@@ -52,6 +52,71 @@ freqtrade trade -c MyConfigUSDT.json -s MyCustomStrategy --db-url sqlite:///user
 
 For more information regarding usage of the sqlite databases, for example to manually enter or remove trades, please refer to the [SQL Cheatsheet](sql_cheatsheet.md).
 
+### Multiple instances using docker
+
+To run multiple instances of freqtrade using docker you will need to edit the docker-compose.yml file and add all the instances you want as separate services. Remember, you can separate your configuration into multiple files, so it's a good idea to think about making them modular, then if you need to edit something common to all bots, you can do that in a single config file. 
+``` yml
+---
+version: '3'
+services:
+  freqtrade1:
+    image: freqtradeorg/freqtrade:stable
+    # image: freqtradeorg/freqtrade:develop
+    # Use plotting image
+    # image: freqtradeorg/freqtrade:develop_plot
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./docker/Dockerfile.custom"
+    restart: always
+    container_name: freqtrade1
+    volumes:
+      - "./user_data:/freqtrade/user_data"
+    # Expose api on port 8080 (localhost only)
+    # Please read the https://www.freqtrade.io/en/latest/rest-api/ documentation
+    # before enabling this.
+     ports:
+     - "127.0.0.1:8080:8080"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/logs/freqtrade1.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3_freqtrade1.sqlite
+      --config /freqtrade/user_data/config.json
+      --config /freqtrade/user_data/config.freqtrade1.json
+      --strategy SampleStrategy
+  
+  freqtrade2:
+    image: freqtradeorg/freqtrade:stable
+    # image: freqtradeorg/freqtrade:develop
+    # Use plotting image
+    # image: freqtradeorg/freqtrade:develop_plot
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./docker/Dockerfile.custom"
+    restart: always
+    container_name: freqtrade2
+    volumes:
+      - "./user_data:/freqtrade/user_data"
+    # Expose api on port 8080 (localhost only)
+    # Please read the https://www.freqtrade.io/en/latest/rest-api/ documentation
+    # before enabling this.
+    ports:
+      - "127.0.0.1:8081:8080"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/logs/freqtrade2.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3_freqtrade2.sqlite
+      --config /freqtrade/user_data/config.json
+      --config /freqtrade/user_data/config.freqtrade2.json
+      --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. 
+
+
 ## Configure the bot running as a systemd service
 
 Copy the `freqtrade.service` file to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
@@ -111,12 +176,15 @@ Log messages are send to `syslog` with the `user` facility. So you can see them
 On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
 
 For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
+
 ```
 if $programname startswith "freqtrade" then -/var/log/freqtrade.log
 ```
+
 to one of the rsyslog configuration files, for example at the end of the `/etc/rsyslog.d/50-default.conf`.
 
 For `syslog` (`rsyslog`), the reduction mode can be switched on. This will reduce the number of repeating messages. For instance, multiple bot Heartbeat messages will be reduced to a single message when nothing else happens with the bot. To achieve this, set in `/etc/rsyslog.conf`:
+
 ```
 # Filter duplicated messages
 $RepeatedMsgReduction on

docs/assets/ccxt-logo.svg

@@ -0,0 +1,3 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" preserveAspectRatio="xMidYMid meet" viewBox="0 0 90 90" width="100" height="100"><defs><path d="M0 90L0 0L90 0L90 90L0 90ZM50 60L60 60L60 80L70 80L70 60L80 60L80 50L50 50L50 60ZM30 80L40 80L40 70L30 70L30 80ZM30 60L20 60L20 70L10 70L10 80L20 80L20 70L30 70L30 60L40 60L40 50L30 50L30 60ZM10 60L20 60L20 50L10 50L10 60ZM10 40L40 40L40 30L20 30L20 20L40 20L40 10L10 10L10 40ZM50 40L80 40L80 30L60 30L60 20L80 20L80 10L50 10L50 40Z" id="c6g67PWSoP"></path></defs><g><g><g><use xlink:href="#c6g67PWSoP" opacity="1" fill="#000000" fill-opacity="1"></use></g></g></g></svg>

docs/backtesting.md

@@ -5,57 +5,181 @@ This page explains how to validate your strategy performance by using Backtestin
 Backtesting requires historic data to be available.
 To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
 
+## Backtesting command reference
+
+```
+usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                             [-d PATH] [--userdir PATH] [-s NAME]
+                             [--strategy-path PATH] [-i TIMEFRAME]
+                             [--timerange TIMERANGE]
+                             [--data-format-ohlcv {json,jsongz,hdf5}]
+                             [--max-open-trades INT]
+                             [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
+                             [-p PAIRS [PAIRS ...]] [--eps] [--dmmp]
+                             [--enable-protections]
+                             [--dry-run-wallet DRY_RUN_WALLET]
+                             [--timeframe-detail TIMEFRAME_DETAIL]
+                             [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
+                             [--export {none,trades,signals}]
+                             [--export-filename PATH]
+                             [--breakdown {day,week,month} [{day,week,month} ...]]
+                             [--cache {none,day,week,month}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -i TIMEFRAME, --timeframe TIMEFRAME
+                        Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --data-format-ohlcv {json,jsongz,hdf5}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `json`).
+  --max-open-trades INT
+                        Override the value of the `max_open_trades`
+                        configuration setting.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
+  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
+                        entry and exit).
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --eps, --enable-position-stacking
+                        Allow buying the same pair multiple times (position
+                        stacking).
+  --dmmp, --disable-max-market-positions
+                        Disable applying `max_open_trades` during backtest
+                        (same as setting `max_open_trades` to a very high
+                        number).
+  --enable-protections, --enableprotections
+                        Enable protections for backtesting.Will slow
+                        backtesting down by a considerable amount, but will
+                        include configured protections
+  --dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
+                        Starting balance, used for backtesting / hyperopt and
+                        dry-runs.
+  --timeframe-detail TIMEFRAME_DETAIL
+                        Specify detail timeframe for backtesting (`1m`, `5m`,
+                        `30m`, `1h`, `1d`).
+  --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
+                        Provide a space-separated list of strategies to
+                        backtest. Please note that timeframe needs to be set
+                        either in config or via command line. When using this
+                        together with `--export trades`, the strategy-name is
+                        injected into the filename (so `backtest-data.json`
+                        becomes `backtest-data-SampleStrategy.json`
+  --export {none,trades,signals}
+                        Export backtest results (default: trades).
+  --export-filename PATH, --backtest-filename PATH
+                        Use this filename for backtest results.Requires
+                        `--export` to be set as well. Example: `--export-filen
+                        ame=user_data/backtest_results/backtest_today.json`
+  --breakdown {day,week,month} [{day,week,month} ...]
+                        Show backtesting breakdown per [day, week, month].
+  --cache {none,day,week,month}
+                        Load a cached backtest result no older than specified
+                        age (default: day).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+
+```
+
 ## Test your strategy with Backtesting
 
 Now you have good Buy and Sell strategies and some historic data, you want to test it against
-real data. This is what we call
-[backtesting](https://en.wikipedia.org/wiki/Backtesting).
+real data. This is what we call [backtesting](https://en.wikipedia.org/wiki/Backtesting).
 
-Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
+Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHLCV) data from `user_data/data/<exchange>` by default.
 If no data is available for the exchange / pair / timeframe combination, backtesting will ask you to download them first using `freqtrade download-data`.
 For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
 
 The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
 
+All profit calculations include fees, and freqtrade will use the exchange's default fees for the calculation.
+
 !!! Warning "Using dynamic pairlists for backtesting"
-    Using dynamic pairlists is possible, however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
-    Also, when using pairlists other than StaticPairlist, reproducability of backtesting-results cannot be guaranteed.
-    Please read the [pairlists documentation](configuration.md#pairlists) for more information.
+    Using dynamic pairlists is possible (not all of the handlers are allowed to be used in backtest mode), however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
+    Also, when using pairlists other than StaticPairlist, reproducibility of backtesting-results cannot be guaranteed.
+    Please read the [pairlists documentation](plugins.md#pairlists) for more information.
 
     To achieve reproducible results, best generate a pairlist via the [`test-pairlist`](utils.md#test-pairlist) command and use that as static pairlist.
 
-### Run a backtesting against the currencies listed in your config file
+!!! Note
+    By default, Freqtrade will export backtesting results to `user_data/backtest_results`.
+    The exported trades can be used for [further analysis](#further-backtest-result-analysis) or can be used by the [plotting sub-command](plotting.md#plot-price-and-indicators) (`freqtrade plot-dataframe`) in the scripts directory.
 
-#### With 5 min candle (OHLCV) data (per default)
+
+### Starting balance
+
+Backtesting will require a starting balance, which can be provided as `--dry-run-wallet <balance>` or `--starting-balance <balance>` command line argument, or via `dry_run_wallet` configuration setting.
+This amount must be higher than `stake_amount`, otherwise the bot will not be able to simulate any trade.
+
+### Dynamic stake amount
+
+Backtesting supports [dynamic stake amount](configuration.md#dynamic-stake-amount) by configuring `stake_amount` as `"unlimited"`, which will split the starting balance into `max_open_trades` pieces.
+Profits from early trades will result in subsequent higher stake amounts, resulting in compounding of profits over the backtesting period.
+
+### Example backtesting commands
+
+With 5 min candle (OHLCV) data (per default)
 
 ```bash
-freqtrade backtesting
+freqtrade backtesting --strategy AwesomeStrategy
 ```
 
-#### With 1 min candle (OHLCV) data
+Where `--strategy AwesomeStrategy` / `-s AwesomeStrategy` refers to the class name of the strategy, which is within a python file in the `user_data/strategies` directory.
+
+---
+
+With 1 min candle (OHLCV) data
 
 ```bash
-freqtrade backtesting --timeframe 1m
+freqtrade backtesting --strategy AwesomeStrategy --timeframe 1m
 ```
 
-#### Using a different on-disk historical candle (OHLCV) data source
+---
+
+Providing a custom starting balance of 1000 (in stake currency)
+
+```bash
+freqtrade backtesting --strategy AwesomeStrategy --dry-run-wallet 1000
+```
+
+---
+
+Using a different on-disk historical candle (OHLCV) data source
 
 Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory. 
 You can then use this data for backtesting as follows:
 
 ```bash
-freqtrade --datadir user_data/data/bittrex-20180101 backtesting
+freqtrade backtesting --strategy AwesomeStrategy --datadir user_data/data/bittrex-20180101 
 ```
 
-#### With a (custom) strategy file
+---
 
-```bash
-freqtrade backtesting -s SampleStrategy
-```
-
-Where `-s SampleStrategy` refers to the class name within the strategy file `sample_strategy.py` found in the `freqtrade/user_data/strategies` directory.
-
-#### Comparing multiple Strategies
+Comparing multiple Strategies
 
 ```bash
 freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timeframe 5m
@@ -63,23 +187,29 @@ freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timefram
 
 Where `SampleStrategy1` and `AwesomeStrategy` refer to class names of strategies.
 
-#### Exporting trades to file
+---
+
+Prevent exporting trades to file
 
 ```bash
-freqtrade backtesting --export trades
+freqtrade backtesting --strategy backtesting --export none --config config.json 
 ```
 
-The exported trades can be used for [further analysis](#further-backtest-result-analysis), or can be used by the plotting script `plot_dataframe.py` in the scripts directory.
+Only use this if you're sure you'll not want to plot or analyze your results further.
 
-#### Exporting trades to file specifying a custom filename
+---
+
+Exporting trades to file specifying a custom filename
 
 ```bash
-freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json
+freqtrade backtesting --strategy backtesting --export trades --export-filename=backtest_samplestrategy.json
 ```
 
 Please also read about the [strategy startup period](strategy-customization.md#strategy-startup-period).
 
-#### Supplying custom fee value
+---
+
+Supplying custom fee value
 
 Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.
 To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
@@ -94,26 +224,26 @@ freqtrade backtesting --fee 0.001
 !!! Note
     Only supply this option (or the corresponding configuration parameter) if you want to experiment with different fee values. By default, Backtesting fetches the default fee from the exchange pair/market info.
 
-#### Running backtest with smaller testset by using timerange
+---
 
-Use the `--timerange` argument to change how much of the testset you want to use.
+Running backtest with smaller test-set by using timerange
 
+Use the `--timerange` argument to change how much of the test-set you want to use.
 
-For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your inputdata.
+For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your input data.
 
 ```bash
 freqtrade backtesting --timerange=20190501-
 ```
 
-You can also specify particular dates or a range span indexed by start and stop.
+You can also specify particular date ranges.
 
 The full timerange specification:
 
-- Use tickframes till 2018/01/31: `--timerange=-20180131`
-- Use tickframes since 2018/01/31: `--timerange=20180131-`
-- Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
-- Use tickframes between POSIX timestamps 1527595200 1527618600:
-                                                `--timerange=1527595200-1527618600`
+- Use data until 2018/01/31: `--timerange=-20180131`
+- Use data since 2018/01/31: `--timerange=20180131-`
+- Use data since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
+- Use data between POSIX / epoch timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`
 
 ## Understand the backtesting result
 
@@ -122,69 +252,113 @@ The most important in the backtesting is to understand the result.
 A backtesting result will look like that:
 
 ```
-========================================================= BACKTESTING REPORT ========================================================
-| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws |  Losses |
-|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
-| ADA/BTC  |     35 |          -0.11 |          -3.88 |      -0.00019428 |          -1.94 | 4:35:00        |    14 |      0 |      21 |
-| ARK/BTC  |     11 |          -0.41 |          -4.52 |      -0.00022647 |          -2.26 | 2:03:00        |     3 |      0 |       8 |
-| BTS/BTC  |     32 |           0.31 |           9.78 |       0.00048938 |           4.89 | 5:05:00        |    18 |      0 |      14 |
-| DASH/BTC |     13 |          -0.08 |          -1.07 |      -0.00005343 |          -0.53 | 4:39:00        |     6 |      0 |       7 |
-| ENG/BTC  |     18 |           1.36 |          24.54 |       0.00122807 |          12.27 | 2:50:00        |     8 |      0 |      10 |
-| EOS/BTC  |     36 |           0.08 |           3.06 |       0.00015304 |           1.53 | 3:34:00        |    16 |      0 |      20 |
-| ETC/BTC  |     26 |           0.37 |           9.51 |       0.00047576 |           4.75 | 6:14:00        |    11 |      0 |      15 |
-| ETH/BTC  |     33 |           0.30 |           9.96 |       0.00049856 |           4.98 | 7:31:00        |    16 |      0 |      17 |
-| IOTA/BTC |     32 |           0.03 |           1.09 |       0.00005444 |           0.54 | 3:12:00        |    14 |      0 |      18 |
-| LSK/BTC  |     15 |           1.75 |          26.26 |       0.00131413 |          13.13 | 2:58:00        |     6 |      0 |       9 |
-| LTC/BTC  |     32 |          -0.04 |          -1.38 |      -0.00006886 |          -0.69 | 4:49:00        |    11 |      0 |      21 |
-| NANO/BTC |     17 |           1.26 |          21.39 |       0.00107058 |          10.70 | 1:55:00        |    10 |      0 |       7 |
-| NEO/BTC  |     23 |           0.82 |          18.97 |       0.00094936 |           9.48 | 2:59:00        |    10 |      0 |      13 |
-| REQ/BTC  |      9 |           1.17 |          10.54 |       0.00052734 |           5.27 | 3:47:00        |     4 |      0 |       5 |
-| XLM/BTC  |     16 |           1.22 |          19.54 |       0.00097800 |           9.77 | 3:15:00        |     7 |      0 |       9 |
-| XMR/BTC  |     23 |          -0.18 |          -4.13 |      -0.00020696 |          -2.07 | 5:30:00        |    12 |      0 |      11 |
-| XRP/BTC  |     35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00        |    12 |      0 |      23 |
-| ZEC/BTC  |     22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00        |     7 |      0 |      15 |
-| TOTAL    |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |     243 |
-========================================================= SELL REASON STATS =========================================================
-| Sell Reason        |   Sells |  Wins |  Draws |  Losses |
+========================================================= BACKTESTING REPORT ==========================================================
+| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration |  Wins Draws Loss   Win%  |
+|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:-------------|-------------------------:|
+| ADA/BTC  |     35 |          -0.11 |          -3.88 |      -0.00019428 |          -1.94 | 4:35:00      |    14     0    21   40.0 |
+| ARK/BTC  |     11 |          -0.41 |          -4.52 |      -0.00022647 |          -2.26 | 2:03:00      |     3     0     8   27.3 |
+| BTS/BTC  |     32 |           0.31 |           9.78 |       0.00048938 |           4.89 | 5:05:00      |    18     0    14   56.2 |
+| DASH/BTC |     13 |          -0.08 |          -1.07 |      -0.00005343 |          -0.53 | 4:39:00      |     6     0     7   46.2 |
+| ENG/BTC  |     18 |           1.36 |          24.54 |       0.00122807 |          12.27 | 2:50:00      |     8     0    10   44.4 |
+| EOS/BTC  |     36 |           0.08 |           3.06 |       0.00015304 |           1.53 | 3:34:00      |    16     0    20   44.4 |
+| ETC/BTC  |     26 |           0.37 |           9.51 |       0.00047576 |           4.75 | 6:14:00      |    11     0    15   42.3 |
+| ETH/BTC  |     33 |           0.30 |           9.96 |       0.00049856 |           4.98 | 7:31:00      |    16     0    17   48.5 |
+| IOTA/BTC |     32 |           0.03 |           1.09 |       0.00005444 |           0.54 | 3:12:00      |    14     0    18   43.8 |
+| LSK/BTC  |     15 |           1.75 |          26.26 |       0.00131413 |          13.13 | 2:58:00      |     6     0     9   40.0 |
+| LTC/BTC  |     32 |          -0.04 |          -1.38 |      -0.00006886 |          -0.69 | 4:49:00      |    11     0    21   34.4 |
+| NANO/BTC |     17 |           1.26 |          21.39 |       0.00107058 |          10.70 | 1:55:00      |    10     0     7   58.5 |
+| NEO/BTC  |     23 |           0.82 |          18.97 |       0.00094936 |           9.48 | 2:59:00      |    10     0    13   43.5 |
+| REQ/BTC  |      9 |           1.17 |          10.54 |       0.00052734 |           5.27 | 3:47:00      |     4     0     5   44.4 |
+| XLM/BTC  |     16 |           1.22 |          19.54 |       0.00097800 |           9.77 | 3:15:00      |     7     0     9   43.8 |
+| XMR/BTC  |     23 |          -0.18 |          -4.13 |      -0.00020696 |          -2.07 | 5:30:00      |    12     0    11   52.2 |
+| XRP/BTC  |     35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00      |    12     0    23   34.3 |
+| ZEC/BTC  |     22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00      |     7     0    15   31.8 |
+| TOTAL    |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00      |   186     0   243   43.4 |
+========================================================= EXIT REASON STATS ==========================================================
+| Exit Reason        |   Sells |  Wins |  Draws |  Losses |
 |:-------------------|--------:|------:|-------:|--------:|
 | trailing_stop_loss |     205 |   150 |      0 |      55 |
 | stop_loss          |     166 |     0 |      0 |     166 |
-| sell_signal        |      56 |    36 |      0 |      20 |
-| force_sell         |       2 |     0 |      0 |       2 |
+| exit_signal        |      56 |    36 |      0 |      20 |
+| force_exit         |       2 |     0 |      0 |       2 |
 ====================================================== LEFT OPEN TRADES REPORT ======================================================
-| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws |  Losses |
-|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
-| ADA/BTC  |      1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |     1 |      0 |       0 |
-| LTC/BTC  |      1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |     1 |      0 |       0 |
-| TOTAL    |      2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |     2 |      0 |       0 |
+| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Win Draw Loss Win% |
+|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
+| ADA/BTC  |      1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |    1    0    0  100 |
+| LTC/BTC  |      1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |    1    0    0  100 |
+| TOTAL    |      2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |    2    0    0  100 |
+================== SUMMARY METRICS ==================
+| Metric                      | Value               |
+|-----------------------------+---------------------|
+| Backtesting from            | 2019-01-01 00:00:00 |
+| Backtesting to              | 2019-05-01 00:00:00 |
+| Max open trades             | 3                   |
+|                             |                     |
+| Total/Daily Avg Trades      | 429 / 3.575         |
+| Starting balance            | 0.01000000 BTC      |
+| Final balance               | 0.01762792 BTC      |
+| Absolute profit             | 0.00762792 BTC      |
+| Total profit %              | 76.2%               |
+| CAGR %                      | 460.87%             |
+| Profit factor               | 1.11                |
+| Avg. stake amount           | 0.001      BTC      |
+| Total trade volume          | 0.429      BTC      |
+|                             |                     |
+| Long / Short                | 352 / 77            |
+| Total profit Long %         | 1250.58%            |
+| Total profit Short %        | -15.02%             |
+| Absolute profit Long        | 0.00838792 BTC      |
+| Absolute profit Short       | -0.00076 BTC        |
+|                             |                     |
+| Best Pair                   | LSK/BTC 26.26%      |
+| Worst Pair                  | ZEC/BTC -10.18%     |
+| Best Trade                  | LSK/BTC 4.25%       |
+| Worst Trade                 | ZEC/BTC -10.25%     |
+| Best day                    | 0.00076 BTC         |
+| Worst day                   | -0.00036 BTC        |
+| Days win/draw/lose          | 12 / 82 / 25        |
+| Avg. Duration Winners       | 4:23:00             |
+| Avg. Duration Loser         | 6:55:00             |
+| Rejected Entry signals      | 3089                |
+| Entry/Exit Timeouts         | 0 / 0               |
+| Canceled Trade Entries      | 34                  |
+| Canceled Entry Orders       | 123                 |
+| Replaced Entry Orders       | 89                  |
+|                             |                     |
+| Min balance                 | 0.00945123 BTC      |
+| Max balance                 | 0.01846651 BTC      |
+| Max % of account underwater | 25.19%              |
+| Absolute Drawdown (Account) | 13.33%              |
+| Drawdown                    | 0.0015 BTC          |
+| Drawdown high               | 0.0013 BTC          |
+| Drawdown low                | -0.0002 BTC         |
+| Drawdown Start              | 2019-02-15 14:10:00 |
+| Drawdown End                | 2019-04-11 18:15:00 |
+| Market change               | -5.88%              |
+=====================================================
 ```
 
+### Backtesting report table
+
 The 1st table contains all trades the bot made, including "left open trades".
 
-The 2nd table contains a recap of sell reasons.
-This table can tell you which area needs some additional work (i.e. all `sell_signal` trades are losses, so we should disable the sell-signal or work on improving that).
-
-The 3rd table contains all trades the bot had to `forcesell` at the end of the backtest period to present a full picture.
-This is necessary to simulate realistic behaviour, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
-These trades are also included in the first table, but are extracted separately for clarity.
-
 The last line will give you the overall performance of your strategy,
 here:
 
 ```
-| TOTAL    |         429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |      186 |    243 |
+| TOTAL    |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00      |   186     0   243   43.4 |
 ```
 
 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 `tot profit %` shows instead the total profit % in relation to allocated capital (`max_open_trades * stake_amount`).
-In the above results we have `max_open_trades=2` and `stake_amount=0.005` in config  so `tot_profit %` will be `(76.20/100) * (0.005 * 2) =~ 0.00762792 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 `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%`.
 
-Your strategy performance is influenced by your buy strategy, your sell strategy, and also by the `minimal_roi` and `stop_loss` you have set.
+Your strategy performance is influenced by your buy strategy, your exit strategy, and also by the `minimal_roi` and `stop_loss` you have set.
 
-For example, if your `minimal_roi` is only `"0":  0.01` you cannot expect the bot to make more profit than 1% (because it will sell every time a trade reaches 1%).
+For example, if your `minimal_roi` is only `"0":  0.01` you cannot expect the bot to make more profit than 1% (because it will exit every time a trade reaches 1%).
 
 ```json
 "minimal_roi": {
@@ -196,33 +370,221 @@ On the other hand, if you set a too high `minimal_roi` like `"0":  0.55`
 (55%), there is almost no chance that the bot will ever reach this profit.
 Hence, keep in mind that your performance is an integral mix of all different elements of the strategy, your configuration, and the crypto-currency pairs you have set up.
 
-### Assumptions made by backtesting
+### Exit reasons table
+
+The 2nd table contains a recap of exit reasons.
+This table can tell you which area needs some additional work (e.g. all or many of the `exit_signal` trades are losses, so you should work on improving the exit signal, or consider disabling it).
+
+### Left open trades table
+
+The 3rd table contains all trades the bot had to `force_exit` at the end of the backtesting period to present you the full picture.
+This is necessary to simulate realistic behavior, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
+These trades are also included in the first table, but are also shown separately in this table for clarity.
+
+### Summary metrics
+
+The last element of the backtest report is the summary metrics table.
+It contains some useful key metrics about performance of your strategy on backtesting data.
+
+```
+================== SUMMARY METRICS ==================
+| Metric                      | Value               |
+|-----------------------------+---------------------|
+| Backtesting from            | 2019-01-01 00:00:00 |
+| Backtesting to              | 2019-05-01 00:00:00 |
+| Max open trades             | 3                   |
+|                             |                     |
+| Total/Daily Avg Trades      | 429 / 3.575         |
+| Starting balance            | 0.01000000 BTC      |
+| Final balance               | 0.01762792 BTC      |
+| Absolute profit             | 0.00762792 BTC      |
+| Total profit %              | 76.2%               |
+| CAGR %                      | 460.87%             |
+| Profit factor               | 1.11                |
+| Avg. stake amount           | 0.001      BTC      |
+| Total trade volume          | 0.429      BTC      |
+|                             |                     |
+| Long / Short                | 352 / 77            |
+| Total profit Long %         | 1250.58%            |
+| Total profit Short %        | -15.02%             |
+| Absolute profit Long        | 0.00838792 BTC      |
+| Absolute profit Short       | -0.00076 BTC        |
+|                             |                     |
+| Best Pair                   | LSK/BTC 26.26%      |
+| Worst Pair                  | ZEC/BTC -10.18%     |
+| Best Trade                  | LSK/BTC 4.25%       |
+| Worst Trade                 | ZEC/BTC -10.25%     |
+| Best day                    | 0.00076 BTC         |
+| Worst day                   | -0.00036 BTC        |
+| Days win/draw/lose          | 12 / 82 / 25        |
+| Avg. Duration Winners       | 4:23:00             |
+| Avg. Duration Loser         | 6:55:00             |
+| Rejected Entry signals      | 3089                |
+| Entry/Exit Timeouts         | 0 / 0               |
+| Canceled Trade Entries      | 34                  |
+| Canceled Entry Orders       | 123                 |
+| Replaced Entry Orders       | 89                  |
+|                             |                     |
+| Min balance                 | 0.00945123 BTC      |
+| Max balance                 | 0.01846651 BTC      |
+| Max % of account underwater | 25.19%              |
+| Absolute Drawdown (Account) | 13.33%              |
+| Drawdown                    | 0.0015 BTC          |
+| Drawdown high               | 0.0013 BTC          |
+| Drawdown low                | -0.0002 BTC         |
+| Drawdown Start              | 2019-02-15 14:10:00 |
+| Drawdown End                | 2019-04-11 18:15:00 |
+| Market change               | -5.88%              |
+=====================================================
+
+```
+
+- `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).
+- `Total/Daily Avg Trades`: Identical to the total trades of the backtest output table / Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
+- `Starting balance`: Start balance - as given by dry-run-wallet (config or command line).
+- `Final balance`: Final balance - starting balance + absolute profit.
+- `Absolute profit`: Profit made in stake currency.
+- `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
+- `CAGR %`: Compound annual growth rate.
+- `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 Trade` / `Worst Trade`: Biggest single winning trade and biggest single losing trade.
+- `Best day` / `Worst day`: Best and worst day based on daily profit.
+- `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
+- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
+- `Rejected Entry signals`: Trade entry signals that could not be acted upon due to `max_open_trades` being reached.
+- `Entry/Exit Timeouts`: Entry/exit orders which did not fill (only applicable if custom pricing is used).
+- `Canceled Trade Entries`: Number of trades that have been canceled by user request via `adjust_entry_price`.
+- `Canceled Entry Orders`: Number of entry orders that have been canceled by user request via `adjust_entry_price`.
+- `Replaced Entry Orders`: Number of entry orders that have been replaced by user request via `adjust_entry_price`.
+- `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
+- `Max % of account underwater`: Maximum percentage your account has decreased from the top since the simulation started.
+Calculated as the maximum of `(Max Balance - Current Balance) / (Max Balance)`.
+- `Absolute Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
+- `Drawdown`: Maximum, absolute drawdown experienced. Difference between Drawdown High and Subsequent Low point.
+- `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
+- `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
+- `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
+- `Long / Short`: Split long/short values (Only shown when short trades were made).
+- `Total profit Long %` / `Absolute profit Long`: Profit long trades only (Only shown when short trades were made).
+- `Total profit Short %` / `Absolute profit Short`: Profit short trades only (Only shown when short trades were made).
+
+### Daily / Weekly / Monthly breakdown
+
+You can get an overview over daily / weekly or monthly results by using the `--breakdown <>` switch.
+
+To visualize daily and weekly breakdowns, you can use the following:
+
+``` bash
+freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day week
+```
+
+``` output
+======================== DAY BREAKDOWN =========================
+|        Day |   Tot Profit USDT |   Wins |   Draws |   Losses |
+|------------+-------------------+--------+---------+----------|
+| 03/07/2021 |           200.0   |      2 |       0 |        0 |
+| 04/07/2021 |           -50.31  |      0 |       0 |        2 |
+| 05/07/2021 |           220.611 |      3 |       2 |        0 |
+| 06/07/2021 |           150.974 |      3 |       0 |        2 |
+| 07/07/2021 |           -70.193 |      1 |       0 |        2 |
+| 08/07/2021 |           212.413 |      2 |       0 |        3 |
+
+```
+
+The output will show a table containing the realized absolute Profit (in stake currency) for the given timeperiod, as well as wins, draws and losses that materialized (closed) on this day. Below that there will be a second table for the summarized values of weeks indicated by the date of the closing Sunday. The same would apply to a monthly breakdown indicated by the last day of the month.
+
+### Backtest result caching
+
+To save time, by default backtest will reuse a cached result from within the last day when the backtested strategy and config match that of a previous backtest. To force a new backtest despite existing result for an identical run specify `--cache none` parameter.
+
+!!! Warning
+    Caching is automatically disabled for open-ended timeranges (`--timerange 20210101-`), as freqtrade cannot ensure reliably that the underlying data didn't change. It can also use cached results where it shouldn't if the original backtest had missing data at the end, which was fixed by downloading more data.
+    In this instance, please use `--cache none` once to force a fresh backtest.
+
+### 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.
+
+## 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
 - Buys happen at open-price
-- Sell signal sells happen at open-price of the following candle
-- Low happens before high for stoploss, protecting capital first
+- All orders are filled at the requested price (no slippage, no unfilled orders)
+- Exit-signal exits happen at open-price of the consecutive candle
+- Exit-signal is favored over Stoploss, because exit-signals are assumed to trigger on candle's open
 - ROI
-  - sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
-  - sells are never "below the candle", so a ROI of 2% may result in a sell at 2.4% if low was at 2.4% profit
-  - Forcesells caused by `<N>=-1` ROI entries use low as sell value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
-- Stoploss sells happen exactly at stoploss price, even if low was lower
+  - exits are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the exit will be at 2%)
+  - exits are never "below the candle", so a ROI of 2% may result in a exit at 2.4% if low was at 2.4% profit
+  - Forceexits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
+- Stoploss exits happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
+- Stoploss is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` exit reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes
+- Low happens before high for stoploss, protecting capital first
 - Trailing stoploss
+  - Trailing Stoploss is only adjusted if it's below the candle's low (otherwise it would be triggered)
+  - On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point
   - High happens first - adjusting stoploss
-  - Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
-- Sell-reason does not explain if a trade was positive or negative, just what triggered the sell (this can look odd if negative ROI values are used)
-- Stoploss (and trailing stoploss) is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` and/or `trailing_stop` sell reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes.
+  - Low uses the adjusted stoploss (so exits with large high-low difference are backtested correctly)
+  - ROI applies before trailing-stop, ensuring profits are "top-capped" at ROI if both ROI and trailing stop applies
+- Exit-reason does not explain if a trade was positive or negative, just what triggered the exit (this can look odd if negative ROI values are used)
+- Evaluation sequence (if multiple signals happen on the same candle)
+  - Exit-signal
+  - Stoploss
+  - ROI
+  - Trailing stoploss
 
 Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.
 Also, keep in mind that past results don't guarantee future success.
 
 In addition to the above assumptions, strategy authors should carefully read the [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies) section, to avoid using data in backtesting which is not available in real market conditions.
 
-### Further backtest-result analysis
+### Trading limits in backtesting
 
-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 our [data analysis](data-analysis.md#backtesting) backtesting section.
+Exchanges have certain trading limits, like minimum base currency, or minimum stake (quote) currency.
+These limits are usually listed in the exchange documentation as "trading rules" or similar.
+
+Backtesting (as well as live and dry-run) does honor these limits, and will ensure that a stoploss can be placed below this value - so the value will be slightly higher than what the exchange specifies.
+Freqtrade has however no information about historic limits.
+
+This can lead to situations where trading-limits are inflated by using a historic price, resulting in minimum amounts > 50$.
+
+For example:
+
+BTC minimum tradable amount is 0.001.
+BTC trades at 22.000\$ today (0.001 BTC is related to this) - but the backtesting period includes prices as high as 50.000\$.
+Today's minimum would be `0.001 * 22_000` - or 22\$.  
+However the limit could also be 50$ - based on `0.001 * 50_000` in some historic setting.
+
+## 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?).
+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.
+To mitigate this, freqtrade can use a lower (faster) timeframe to simulate intra-candle movements.
+
+To utilize this, you can append `--timeframe-detail 5m` to your regular backtesting command.
+
+``` bash
+freqtrade backtesting --strategy AwesomeStrategy --timeframe 1h --timeframe-detail 5m
+```
+
+This will load 1h data as well as 5m data for the timeframe. The strategy will be analyzed with the 1h timeframe - and for every "open trade candle" (candles where a trade is open) the 5m data will be used to simulate intra-candle movements.
+All callback functions (`custom_exit()`, `custom_stoploss()`, ... ) will be running for each 5m candle once the trade is opened (so 12 times in the above example of 1h timeframe, and 5m detailed timeframe).
+
+`--timeframe-detail` must be smaller than the original timeframe, otherwise backtesting will fail to start.
+
+Obviously this will require more memory (5m data is bigger than 1h data), and will also impact runtime (depending on the amount of trades and trade durations).
+Also, data must be available / downloaded already.
+
+!!! Tip
+    You can use this function as the last part of strategy development, to ensure your strategy is not exploiting one of the [backtesting assumptions](#assumptions-made-by-backtesting). Strategies that perform similarly well with this mode have a good chance to perform well in dry/live modes too (although only forward-testing (dry-mode) can really confirm a strategy).
 
 ## Backtesting multiple strategies
 
@@ -242,15 +604,14 @@ There will be an additional table comparing win/losses of the different strategi
 Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
 
 ```
-=========================================================== STRATEGY SUMMARY ===========================================================
-| Strategy    |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws | Losses |
-|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|
-| Strategy1   |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |    243 |
-| Strategy2   |   1487 |          -0.13 |        -197.58 |      -0.00988917 |         -98.79 | 4:43:00        |   662 |      0 |    825 |
+=========================================================== STRATEGY SUMMARY =========================================================================
+| Strategy    |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws | Losses | Drawdown % |
+|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|-----------:|
+| Strategy1   |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |    243 |       45.2 |
+| Strategy2   |   1487 |          -0.13 |        -197.58 |      -0.00988917 |         -98.79 | 4:43:00        |   662 |      0 |    825 |     241.68 |
 ```
 
 ## Next step
 
-Great, your strategy is profitable. What if the bot can give your the
-optimal parameters to use for your strategy?
+Great, your strategy is profitable. What if the bot can give your the optimal parameters to use for your strategy?
 Your next step is to learn [how to find optimal parameters with Hyperopt](hyperopt.md)

docs/bot-basics.md

@@ -0,0 +1,77 @@
+# Freqtrade basics
+
+This page provides you some basic concepts on how Freqtrade works and operates.
+
+## Freqtrade terminology
+
+* **Strategy**: Your trading strategy, telling the bot what to do.
+* **Trade**: Open position.
+* **Open Order**: Order which is currently placed on the exchange, and is not yet complete.
+* **Pair**: Tradable pair, usually in the format of Base/Quote (e.g. XRP/USDT).
+* **Timeframe**: Candle length to use (e.g. `"5m"`, `"1h"`, ...).
+* **Indicators**: Technical indicators (SMA, EMA, RSI, ...).
+* **Limit order**: Limit orders which execute at the defined limit price or better.
+* **Market order**: Guaranteed to fill, may move price depending on the order size.
+
+## Fee handling
+
+All profit calculations of Freqtrade include fees. For Backtesting / Hyperopt / Dry-run modes, the exchange default fee is used (lowest tier on the exchange). For live operations, fees are used as applied by the exchange (this includes BNB rebates etc.).
+
+## Bot execution logic
+
+Starting freqtrade in dry-run or live mode (using `freqtrade trade`) will start the bot and start the bot iteration loop.
+This will also run the `bot_start()` callback.
+
+By default, the bot loop runs every few seconds (`internals.process_throttle_secs`) and performs the following actions:
+
+* Fetch open trades from persistence.
+* Calculate current list of tradable pairs.
+* Download OHLCV data for the pairlist including all [informative pairs](strategy-customization.md#get-data-for-non-tradeable-pairs)  
+  This step is only executed once per Candle to avoid unnecessary network traffic.
+* Call `bot_loop_start()` strategy callback.
+* Analyze strategy per pair.
+  * Call `populate_indicators()`
+  * Call `populate_entry_trend()`
+  * Call `populate_exit_trend()`
+* Check timeouts for open orders.
+  * Calls `check_entry_timeout()` strategy callback for open entry orders.
+  * Calls `check_exit_timeout()` strategy callback for open exit orders.
+  * Calls `adjust_entry_price()` strategy callback for open entry orders.
+* Verifies existing positions and eventually places exit orders.
+  * Considers stoploss, ROI and exit-signal, `custom_exit()` and `custom_stoploss()`.
+  * Determine exit-price based on `exit_pricing` configuration setting or by using the `custom_exit_price()` callback.
+  * Before a exit order is placed, `confirm_trade_exit()` strategy callback is called.
+* Check position adjustments for open trades if enabled by calling `adjust_trade_position()` and place additional order if required.
+* Check if trade-slots are still available (if `max_open_trades` is reached).
+* Verifies entry signal trying to enter new positions.
+  * Determine entry-price based on `entry_pricing` configuration setting, or by using the `custom_entry_price()` callback.
+  * 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.
+  * Before an entry order is placed, `confirm_trade_entry()` strategy callback is called.
+
+This loop will be repeated again and again until the bot is stopped.
+
+## Backtesting / Hyperopt execution logic
+
+[backtesting](backtesting.md) or [hyperopt](hyperopt.md) do only part of the above logic, since most of the trading operations are fully simulated.
+
+* Load historic data for configured pairlist.
+* Calls `bot_start()` once.
+* Calls `bot_loop_start()` once.
+* Calculate indicators (calls `populate_indicators()` once per pair).
+* Calculate entry / exit signals (calls `populate_entry_trend()` and `populate_exit_trend()` once per pair).
+* Loops per candle simulating entry and exit points.
+  * Check for Order timeouts, either via the `unfilledtimeout` configuration, or via `check_entry_timeout()` / `check_exit_timeout()` strategy callbacks.
+  * Calls `adjust_entry_price()` strategy callback for open entry orders.
+  * Check for trade entry signals (`enter_long` / `enter_short` columns).
+  * Confirm trade entry / exits (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy).
+  * Call `custom_entry_price()` (if implemented in the strategy) to determine entry price (Prices are moved to be within the opening candle).
+  * 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 `custom_stoploss()` and `custom_exit()` to find custom exit points.
+  * For exits based on exit-signal and custom-exit: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle).
+* Generate backtest report output
+
+!!! Note
+    Both Backtesting and Hyperopt include exchange default Fees in the calculation. Custom fees can be passed to backtesting / hyperopt by specifying the `--fee` argument.

docs/bot-usage.md

@@ -5,26 +5,29 @@ This page explains the different parameters of the bot and how to run it.
 !!! Note
     If you've used `setup.sh`, don't forget to activate your virtual environment (`source .env/bin/activate`) before running freqtrade commands.
 
+!!! Warning "Up-to-date clock"
+    The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
+
 ## Bot commands
 
 ```
 usage: freqtrade [-h] [-V]
-                 {trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
+                 {trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
                  ...
 
 Free, open source crypto trading bot
 
 positional arguments:
-  {trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
+  {trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
     trade               Trade module.
     create-userdir      Create user-data directory.
     new-config          Create new config
-    new-hyperopt        Create new hyperopt
     new-strategy        Create new strategy
     download-data       Download backtesting data.
     convert-data        Convert candle (OHLCV) data from one format to
                         another.
     convert-trade-data  Convert trade data from one format to another.
+    list-data           List downloaded data.
     backtesting         Backtesting module.
     edge                Edge module.
     hyperopt            Hyperopt module.
@@ -38,8 +41,10 @@ positional arguments:
     list-timeframes     Print available timeframes for the exchange.
     show-trades         Show trades.
     test-pairlist       Test your pairlist configuration.
+    install-ui          Install FreqUI
     plot-dataframe      Plot candles with indicators.
     plot-profit         Generate plot showing profits.
+    webserver           Webserver module.
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -53,6 +58,7 @@ optional arguments:
 usage: freqtrade trade [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                        [--userdir PATH] [-s NAME] [--strategy-path PATH]
                        [--db-url PATH] [--sd-notify] [--dry-run]
+                       [--dry-run-wallet DRY_RUN_WALLET]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -63,6 +69,9 @@ optional arguments:
   --sd-notify           Notify systemd service manager.
   --dry-run             Enforce dry-run for trading (removes Exchange secrets
                         and simulates trades).
+  --dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
+                        Starting balance, used for backtesting / hyperopt and
+                        dry-runs.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -202,245 +211,6 @@ in production mode. Example command:
 freqtrade trade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
 ```
 
-## Backtesting commands
-
-Backtesting also uses the config specified via `-c/--config`.
-
-```
-usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                             [-d PATH] [--userdir PATH] [-s NAME]
-                             [--strategy-path PATH] [-i TIMEFRAME]
-                             [--timerange TIMERANGE] [--max-open-trades INT]
-                             [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
-                             [--eps] [--dmmp]
-                             [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
-                             [--export EXPORT] [--export-filename PATH]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
-                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
-                        `1d`).
-  --timerange TIMERANGE
-                        Specify what timerange of data to use.
-  --max-open-trades INT
-                        Override the value of the `max_open_trades`
-                        configuration setting.
-  --stake-amount STAKE_AMOUNT
-                        Override the value of the `stake_amount` configuration
-                        setting.
-  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
-                        entry and exit).
-  --eps, --enable-position-stacking
-                        Allow buying the same pair multiple times (position
-                        stacking).
-  --dmmp, --disable-max-market-positions
-                        Disable applying `max_open_trades` during backtest
-                        (same as setting `max_open_trades` to a very high
-                        number).
-  --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
-                        Provide a space-separated list of strategies to
-                        backtest. Please note that ticker-interval needs to be
-                        set either in config or via command line. When using
-                        this together with `--export trades`, the strategy-
-                        name is injected into the filename (so `backtest-
-                        data.json` becomes `backtest-data-
-                        DefaultStrategy.json`
-  --export EXPORT       Export backtest results, argument are: trades.
-                        Example: `--export=trades`
-  --export-filename PATH
-                        Save backtest results to the file with this filename.
-                        Requires `--export` to be set as well. Example:
-                        `--export-filename=user_data/backtest_results/backtest
-                        _today.json`
-
-Common arguments:
-  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are:
-                        'syslog', 'journald'. See the documentation for more
-                        details.
-  -V, --version         show program's version number and exit
-  -c PATH, --config PATH
-                        Specify configuration file (default:
-                        `userdir/config.json` or `config.json` whichever
-                        exists). Multiple --config options may be used. Can be
-                        set to `-` to read config from stdin.
-  -d PATH, --datadir PATH
-                        Path to directory with historical backtesting data.
-  --userdir PATH, --user-data-dir PATH
-                        Path to userdata directory.
-
-Strategy arguments:
-  -s NAME, --strategy NAME
-                        Specify strategy class name which will be used by the
-                        bot.
-  --strategy-path PATH  Specify additional strategy lookup path.
-
-```
-
-### Getting historic data for backtesting
-
-The first time your run Backtesting, you will need to download some historic data first.
-This can be accomplished by using `freqtrade download-data`.  
-Check the corresponding [Data Downloading](data-download.md) section for more details
-
-## Hyperopt commands
-
-To optimize your strategy, you can use hyperopt parameter hyperoptimization
-to find optimal parameter values for your strategy.
-
-```
-usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
-                          [--userdir PATH] [-s NAME] [--strategy-path PATH]
-                          [-i TIMEFRAME] [--timerange TIMERANGE]
-                          [--max-open-trades INT]
-                          [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
-                          [--hyperopt NAME] [--hyperopt-path PATH] [--eps]
-                          [-e INT]
-                          [--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]]
-                          [--dmmp] [--print-all] [--no-color] [--print-json]
-                          [-j JOBS] [--random-state INT] [--min-trades INT]
-                          [--continue] [--hyperopt-loss NAME]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
-                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
-                        `1d`).
-  --timerange TIMERANGE
-                        Specify what timerange of data to use.
-  --max-open-trades INT
-                        Override the value of the `max_open_trades`
-                        configuration setting.
-  --stake-amount STAKE_AMOUNT
-                        Override the value of the `stake_amount` configuration
-                        setting.
-  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
-                        entry and exit).
-  --hyperopt NAME       Specify hyperopt class name which will be used by the
-                        bot.
-  --hyperopt-path PATH  Specify additional lookup path for Hyperopt and
-                        Hyperopt Loss functions.
-  --eps, --enable-position-stacking
-                        Allow buying the same pair multiple times (position
-                        stacking).
-  -e INT, --epochs INT  Specify number of epochs (default: 100).
-  --spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]
-                        Specify which parameters to hyperopt. Space-separated
-                        list.
-  --dmmp, --disable-max-market-positions
-                        Disable applying `max_open_trades` during backtest
-                        (same as setting `max_open_trades` to a very high
-                        number).
-  --print-all           Print all results, not only the best ones.
-  --no-color            Disable colorization of hyperopt results. May be
-                        useful if you are redirecting output to a file.
-  --print-json          Print output in JSON format.
-  -j JOBS, --job-workers JOBS
-                        The number of concurrently running jobs for
-                        hyperoptimization (hyperopt worker processes). If -1
-                        (default), all CPUs are used, for -2, all CPUs but one
-                        are used, etc. If 1 is given, no parallel computing
-                        code is used at all.
-  --random-state INT    Set random state to some positive integer for
-                        reproducible hyperopt results.
-  --min-trades INT      Set minimal desired number of trades for evaluations
-                        in the hyperopt optimization path (default: 1).
-  --continue            Continue hyperopt from previous runs. By default,
-                        temporary files will be removed and hyperopt will
-                        start from scratch.
-  --hyperopt-loss NAME  Specify the class name of the hyperopt loss function
-                        class (IHyperOptLoss). Different functions can
-                        generate completely different results, since the
-                        target for optimization is different. Built-in
-                        Hyperopt-loss-functions are: DefaultHyperOptLoss,
-                        OnlyProfitHyperOptLoss, SharpeHyperOptLoss,
-                        SharpeHyperOptLossDaily, SortinoHyperOptLoss,
-                        SortinoHyperOptLossDaily.(default:
-                        `DefaultHyperOptLoss`).
-
-Common arguments:
-  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are:
-                        'syslog', 'journald'. See the documentation for more
-                        details.
-  -V, --version         show program's version number and exit
-  -c PATH, --config PATH
-                        Specify configuration file (default:
-                        `userdir/config.json` or `config.json` whichever
-                        exists). Multiple --config options may be used. Can be
-                        set to `-` to read config from stdin.
-  -d PATH, --datadir PATH
-                        Path to directory with historical backtesting data.
-  --userdir PATH, --user-data-dir PATH
-                        Path to userdata directory.
-
-Strategy arguments:
-  -s NAME, --strategy NAME
-                        Specify strategy class name which will be used by the
-                        bot.
-  --strategy-path PATH  Specify additional strategy lookup path.
-
-```
-
-## Edge commands
-
-To know your trade expectancy and winrate against historical data, you can use Edge.
-
-```
-usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
-                      [--userdir PATH] [-s NAME] [--strategy-path PATH]
-                      [-i TIMEFRAME] [--timerange TIMERANGE]
-                      [--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
-                      [--fee FLOAT] [--stoplosses STOPLOSS_RANGE]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
-                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
-                        `1d`).
-  --timerange TIMERANGE
-                        Specify what timerange of data to use.
-  --max-open-trades INT
-                        Override the value of the `max_open_trades`
-                        configuration setting.
-  --stake-amount STAKE_AMOUNT
-                        Override the value of the `stake_amount` configuration
-                        setting.
-  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
-                        entry and exit).
-  --stoplosses STOPLOSS_RANGE
-                        Defines a range of stoploss values against which edge
-                        will assess the strategy. The format is "min,max,step"
-                        (without any space). Example:
-                        `--stoplosses=-0.01,-0.1,-0.001`
-
-Common arguments:
-  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are:
-                        'syslog', 'journald'. See the documentation for more
-                        details.
-  -V, --version         show program's version number and exit
-  -c PATH, --config PATH
-                        Specify configuration file (default:
-                        `userdir/config.json` or `config.json` whichever
-                        exists). Multiple --config options may be used. Can be
-                        set to `-` to read config from stdin.
-  -d PATH, --datadir PATH
-                        Path to directory with historical backtesting data.
-  --userdir PATH, --user-data-dir PATH
-                        Path to userdata directory.
-
-Strategy arguments:
-  -s NAME, --strategy NAME
-                        Specify strategy class name which will be used by the
-                        bot.
-  --strategy-path PATH  Specify additional strategy lookup path.
-
-```
-
-To understand edge and how to read the results, please read the [edge documentation](edge.md).
-
 ## Next step
 
 The optimal strategy of the bot will change with time depending of the market trends. The next step is to

docs/data-analysis.md

@@ -1,12 +1,22 @@
-# Analyzing bot data with Jupyter notebooks  
+# Analyzing bot data with Jupyter notebooks
 
-You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/`.  
+You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.
 
-## Pro tips  
+## Quick start with docker
+
+Freqtrade provides a docker-compose file which starts up a jupyter lab server.
+You can run this server using the following command: `docker-compose -f docker/docker-compose-jupyter.yml up`
+
+This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
+Please use the link that's printed in the console after startup for simplified login.
+
+For more information, Please visit the [Data analysis with Docker](docker_quickstart.md#data-analayis-using-docker-compose) section.
+
+### Pro tips
 
 * See [jupyter.org](https://jupyter.org/documentation) for usage instructions.
 * Don't forget to start a Jupyter notebook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
-* Copy the example notebook before use so your changes don't get clobbered with the next freqtrade update.
+* Copy the example notebook before use so your changes don't get overwritten with the next freqtrade update.
 
 ### Using virtual environment with system-wide Jupyter installation
 
@@ -28,37 +38,38 @@ ipython kernel install --user --name=freqtrade
 !!! Note
     This section is provided for completeness, the Freqtrade Team won't provide full support for problems with this setup and will recommend to install Jupyter in the virtual environment directly, as that is the easiest way to get jupyter notebooks up and running. For help with this setup please refer to the [Project Jupyter](https://jupyter.org/) [documentation](https://jupyter.org/documentation) or [help channels](https://jupyter.org/community).
 
-    
-## Fine print  
+!!! Warning
+    Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.
 
-Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.
+## Recommended workflow
 
-## Recommended workflow  
-
-| Task | Tool |  
-  --- | ---  
-Bot operations | CLI  
+| Task | Tool |
+  --- | ---
+Bot operations | CLI
 Repetitive tasks | Shell scripts
-Data analysis & visualization | Notebook  
+Data analysis & visualization | Notebook
 
 1. Use the CLI to
+
     * download historical data
     * run a backtest
     * run with real-time data
-    * export results  
+    * export results
 
 1. Collect these actions in shell scripts
+
     * save complicated commands with arguments
-    * execute multi-step operations  
+    * execute multi-step operations
     * automate testing strategies and preparing data for analysis
 
 1. Use a notebook to
+
     * visualize data
-    * munge and plot to generate insights
+    * mangle and plot to generate insights
 
-## Example utility snippets  
+## Example utility snippets
 
-### Change directory to root  
+### Change directory to root
 
 Jupyter notebooks execute from the notebook directory. The following snippet searches for the project root, so relative paths remain consistent.
 
@@ -111,5 +122,6 @@ Best avoid relative paths, since this starts at the storage location of the jupy
 
 * [Strategy debugging](strategy_analysis_example.md) - also available as Jupyter notebook (`user_data/notebooks/strategy_analysis_example.ipynb`)
 * [Plotting](plotting.md)
+* [Tag Analysis](advanced-backtesting.md)
 
 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/data-download.md

@@ -8,130 +8,62 @@ If no additional parameter is specified, freqtrade will download data for `"1m"`
 Exchange and pairs will come from `config.json` (if specified using `-c/--config`).
 Otherwise `--exchange` becomes mandatory.
 
+You can use a relative timerange (`--days 20`) or an absolute starting point (`--timerange 20200101-`). For incremental downloads, the relative approach should be used.
+
 !!! Tip "Tip: Updating existing data"
-    If you already have backtesting data available in your data-directory and would like to refresh this data up to today, use `--days xx` with a number slightly higher than the missing number of days. Freqtrade will keep the available data and only download the missing data.
-    Be carefull though: If the number is too small (which would result in a few missing days), the whole dataset will be removed and only xx days will be downloaded.
+    If you already have backtesting data available in your data-directory and would like to refresh this data up to today, freqtrade will automatically calculate the data missing for the existing pairs and the download will occur from the latest available point until "now", neither --days or --timerange parameters are required. Freqtrade will keep the available data and only download the missing data.
+    If you are updating existing data after inserting new pairs that you have no data for, use `--new-pairs-days xx` parameter. Specified number of days will be downloaded for new pairs while old pairs will be updated with missing data only.
+    If you use `--days xx` parameter alone - data for specified number of days will be downloaded for _all_ pairs. Be careful, if specified number of days is smaller than gap between now and last downloaded candle - freqtrade will delete all existing data to avoid gaps in candle data.
 
 ### Usage
 
 ```
-usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
-                               [--pairs-file FILE] [--days INT] [--dl-trades] [--exchange EXCHANGE]
-                               [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
-                               [--erase] [--data-format-ohlcv {json,jsongz}] [--data-format-trades {json,jsongz}]
+usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                               [-d PATH] [--userdir PATH]
+                               [-p PAIRS [PAIRS ...]] [--pairs-file FILE]
+                               [--days INT] [--new-pairs-days INT]
+                               [--include-inactive-pairs]
+                               [--timerange TIMERANGE] [--dl-trades]
+                               [--exchange EXCHANGE]
+                               [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
+                               [--erase]
+                               [--data-format-ohlcv {json,jsongz,hdf5}]
+                               [--data-format-trades {json,jsongz,hdf5}]
+                               [--trading-mode {spot,margin,futures}]
+                               [--prepend]
 
 optional arguments:
   -h, --help            show this help message and exit
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
-                        Show profits for only these pairs. Pairs are space-separated.
+                        Limit command to these pairs. Pairs are space-
+                        separated.
   --pairs-file FILE     File containing a list of pairs to download.
   --days INT            Download data for given number of days.
-  --dl-trades           Download trades instead of OHLCV data. The bot will resample trades to the desired timeframe as specified as
-                        --timeframes/-t.
-  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no config is provided.
-  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
-                        Specify which tickers to download. Space-separated list. Default: `1m 5m`.
-  --erase               Clean all existing data for the selected exchange/pairs/timeframes.
-  --data-format-ohlcv {json,jsongz}
-                        Storage format for downloaded candle (OHLCV) data. (default: `json`).
-  --data-format-trades {json,jsongz}
-                        Storage format for downloaded trades data. (default: `jsongz`).
-
-Common arguments:
-  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more details.
-  -V, --version         show program's version number and exit
-  -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
-                        to read config from stdin.
-  -d PATH, --datadir PATH
-                        Path to directory with historical backtesting data.
-  --userdir PATH, --user-data-dir PATH
-                        Path to userdata directory.
-```
-
-### Data format
-
-Freqtrade currently supports 2 dataformats, `json` (plain "text" json files) and `jsongz` (a gzipped version of json files).
-By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
-
-This can be changed via the `--data-format-ohlcv` and `--data-format-trades` parameters respectivly.
-
-If the default dataformat has been changed during download, then the keys `dataformat_ohlcv` and `dataformat_trades` in the configuration file need to be adjusted to the selected dataformat as well.
-
-!!! Note
-    You can convert between data-formats using the [convert-data](#subcommand-convert-data) and [convert-trade-data](#subcommand-convert-trade-data) methods.
-
-#### Subcommand convert data
-
-```
-usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                              [-d PATH] [--userdir PATH]
-                              [-p PAIRS [PAIRS ...]] --format-from
-                              {json,jsongz} --format-to {json,jsongz}
-                              [--erase]
-                              [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
-                        Show profits for only these pairs. Pairs are space-
-                        separated.
-  --format-from {json,jsongz}
-                        Source format for data conversion.
-  --format-to {json,jsongz}
-                        Destination format for data conversion.
-  --erase               Clean all existing data for the selected
-                        exchange/pairs/timeframes.
-  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
+  --new-pairs-days INT  Download data of new pairs for given number of days.
+                        Default: `None`.
+  --include-inactive-pairs
+                        Also download data from inactive pairs.
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --dl-trades           Download trades instead of OHLCV data. The bot will
+                        resample trades to the desired timeframe as specified
+                        as --timeframes/-t.
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
+                        config is provided.
+  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
                         Specify which tickers to download. Space-separated
                         list. Default: `1m 5m`.
-
-Common arguments:
-  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are:
-                        'syslog', 'journald'. See the documentation for more
-                        details.
-  -V, --version         show program's version number and exit
-  -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
-  -d PATH, --datadir PATH
-                        Path to directory with historical backtesting data.
-  --userdir PATH, --user-data-dir PATH
-                        Path to userdata directory.
-```
-
-##### Example converting data
-
-The following command will convert all candle (OHLCV) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
-It'll also remove original json data files (`--erase` parameter).
-
-``` bash
-freqtrade convert-data --format-from json --format-to jsongz --datadir ~/.freqtrade/data/binance -t 5m 15m --erase
-```
-
-#### Subcommand convert-trade data
-
-```
-usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                                    [-d PATH] [--userdir PATH]
-                                    [-p PAIRS [PAIRS ...]] --format-from
-                                    {json,jsongz} --format-to {json,jsongz}
-                                    [--erase]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
-                        Show profits for only these pairs. Pairs are space-
-                        separated.
-  --format-from {json,jsongz}
-                        Source format for data conversion.
-  --format-to {json,jsongz}
-                        Destination format for data conversion.
   --erase               Clean all existing data for the selected
                         exchange/pairs/timeframes.
+  --data-format-ohlcv {json,jsongz,hdf5}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `json`).
+  --data-format-trades {json,jsongz,hdf5}
+                        Storage format for downloaded trades data. (default:
+                        `jsongz`).
+  --trading-mode {spot,margin,futures}
+                        Select Trading mode
+  --prepend             Allow data prepending.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -140,28 +72,25 @@ Common arguments:
                         details.
   -V, --version         show program's version number and exit
   -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
   -d PATH, --datadir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
+
 ```
 
-##### Example converting trades
+!!! Note "Startup period"
+    `download-data` is a strategy-independent command. The idea is to download a big chunk of data once, and then iteratively increase the amount of data stored.
 
-The following command will convert all available trade-data in `~/.freqtrade/data/kraken` from jsongz to json.
-It'll also remove original jsongz data files (`--erase` parameter).
-
-``` bash
-freqtrade convert-trade-data --format-from jsongz --format-to json --datadir ~/.freqtrade/data/kraken --erase
-```
+    For that reason, `download-data` does not care about the "startup-period" defined in a strategy. It's up to the user to download additional days if the backtest should start at a specific point in time (while respecting startup period).
 
 ### Pairs file
 
 In alternative to the whitelist from `config.json`, a `pairs.json` file can be used.
-
 If you are using Binance for example:
 
 - create a directory `user_data/data/binance` and copy or create the `pairs.json` file in that directory.
@@ -169,7 +98,7 @@ If you are using Binance for example:
 
 ```bash
 mkdir -p user_data/data/binance
-cp freqtrade/tests/testdata/pairs.json user_data/data/binance
+touch user_data/data/binance/pairs.json
 ```
 
 The format of the `pairs.json` file is a simple json list.
@@ -184,6 +113,24 @@ Mixing different stake-currencies is allowed for this file, since it's only used
 ]
 ```
 
+!!! Tip "Downloading all data for one quote currency"
+    Often, you'll want to download data for all pairs of a specific quote-currency. In such cases, you can use the following shorthand:
+    `freqtrade download-data --exchange binance --pairs .*/USDT <...>`. The provided "pairs" string will be expanded to contain all active pairs on the exchange.
+    To also download data for inactive (delisted) pairs, add `--include-inactive-pairs` to the command.
+
+??? Note "Permission denied errors"
+    If your configuration directory `user_data` was made by docker, you may get the following error:
+
+    ```
+    cp: cannot create regular file 'user_data/data/binance/pairs.json': Permission denied
+    ```
+
+    You can fix the permissions of your user-data directory as follows:
+
+    ```
+    sudo chown -R $UID:$GID user_data
+    ```
+
 ### Start download
 
 Then run:
@@ -194,28 +141,301 @@ freqtrade download-data --exchange binance
 
 This will download historical candle (OHLCV) data for all the currency pairs you defined in `pairs.json`.
 
+Alternatively, specify the pairs directly
+
+```bash
+freqtrade download-data --exchange binance --pairs ETH/USDT XRP/USDT BTC/USDT
+```
+
+or as regex (to download all active USDT pairs)
+
+```bash
+freqtrade download-data --exchange binance --pairs .*/USDT
+```
+
 ### Other Notes
 
 - To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
-- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
+- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust rate limits etc.)
 - To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
 - To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
+- To download historical candle (OHLCV) data from a fixed starting point, use `--timerange 20200101-` - which will download all data from January 1st, 2020.
 - Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
 - To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
 
+#### Download additional data before the current timerange
+
+Assuming you downloaded all data from 2022 (`--timerange 20220101-`) - but you'd now like to also backtest with earlier data.
+You can do so by using the `--prepend` flag, combined with `--timerange` - specifying an end-date.
+
+``` bash
+freqtrade download-data --exchange binance --pairs ETH/USDT XRP/USDT BTC/USDT --prepend --timerange 20210101-20220101
+```
+
+!!! Note
+    Freqtrade will ignore the end-date in this mode if data is available, updating the end-date to the existing data start point.
+
+### Data format
+
+Freqtrade currently supports 3 data-formats for both OHLCV and trades data:
+
+* `json` (plain "text" json files)
+* `jsongz` (a gzip-zipped version of json files)
+* `hdf5` (a high performance datastore)
+
+By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
+
+This can be changed via the `--data-format-ohlcv` and `--data-format-trades` command line arguments respectively.
+To persist this change, you can should also add the following snippet to your configuration, so you don't have to insert the above arguments each time:
+
+``` jsonc
+    // ...
+    "dataformat_ohlcv": "hdf5",
+    "dataformat_trades": "hdf5",
+    // ...
+```
+
+If the default data-format has been changed during download, then the keys `dataformat_ohlcv` and `dataformat_trades` in the configuration file need to be adjusted to the selected dataformat as well.
+
+!!! Note
+    You can convert between data-formats using the [convert-data](#sub-command-convert-data) and [convert-trade-data](#sub-command-convert-trade-data) methods.
+
+#### Sub-command convert data
+
+```
+usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                              [-d PATH] [--userdir PATH]
+                              [-p PAIRS [PAIRS ...]] --format-from
+                              {json,jsongz,hdf5} --format-to
+                              {json,jsongz,hdf5} [--erase]
+                              [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
+                              [--exchange EXCHANGE]
+                              [--trading-mode {spot,margin,futures}]
+                              [--candle-types {spot,,futures,mark,index,premiumIndex,funding_rate} [{spot,,futures,mark,index,premiumIndex,funding_rate} ...]]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --format-from {json,jsongz,hdf5}
+                        Source format for data conversion.
+  --format-to {json,jsongz,hdf5}
+                        Destination format for data conversion.
+  --erase               Clean all existing data for the selected
+                        exchange/pairs/timeframes.
+  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
+                        Specify which tickers to download. Space-separated
+                        list. Default: `1m 5m`.
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
+                        config is provided.
+  --trading-mode {spot,margin,futures}
+                        Select Trading mode
+  --candle-types {spot,,futures,mark,index,premiumIndex,funding_rate} [{spot,,futures,mark,index,premiumIndex,funding_rate} ...]
+                        Select candle type to use
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
+
+##### Example converting data
+
+The following command will convert all candle (OHLCV) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
+It'll also remove original json data files (`--erase` parameter).
+
+``` bash
+freqtrade convert-data --format-from json --format-to jsongz --datadir ~/.freqtrade/data/binance -t 5m 15m --erase
+```
+
+#### Sub-command convert trade data
+
+```
+usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                    [-d PATH] [--userdir PATH]
+                                    [-p PAIRS [PAIRS ...]] --format-from
+                                    {json,jsongz,hdf5} --format-to
+                                    {json,jsongz,hdf5} [--erase]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Show profits for only these pairs. Pairs are space-
+                        separated.
+  --format-from {json,jsongz,hdf5}
+                        Source format for data conversion.
+  --format-to {json,jsongz,hdf5}
+                        Destination format for data conversion.
+  --erase               Clean all existing data for the selected
+                        exchange/pairs/timeframes.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
+
+##### Example converting trades
+
+The following command will convert all available trade-data in `~/.freqtrade/data/kraken` from jsongz to json.
+It'll also remove original jsongz data files (`--erase` parameter).
+
+``` bash
+freqtrade convert-trade-data --format-from jsongz --format-to json --datadir ~/.freqtrade/data/kraken --erase
+```
+
+### Sub-command trades to ohlcv
+
+When you need to use `--dl-trades` (kraken only) to download data, conversion of trades data to ohlcv data is the last step.
+This command will allow you to repeat this last step for additional timeframes without re-downloading the data.
+
+```
+usage: freqtrade trades-to-ohlcv [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                 [-d PATH] [--userdir PATH]
+                                 [-p PAIRS [PAIRS ...]]
+                                 [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
+                                 [--exchange EXCHANGE]
+                                 [--data-format-ohlcv {json,jsongz,hdf5}]
+                                 [--data-format-trades {json,jsongz,hdf5}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
+                        Specify which tickers to download. Space-separated
+                        list. Default: `1m 5m`.
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
+                        config is provided.
+  --data-format-ohlcv {json,jsongz,hdf5}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `json`).
+  --data-format-trades {json,jsongz,hdf5}
+                        Storage format for downloaded trades data. (default:
+                        `jsongz`).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
+
+#### Example trade-to-ohlcv conversion
+
+``` bash
+freqtrade trades-to-ohlcv --exchange kraken -t 5m 1h 1d --pairs BTC/EUR ETH/EUR
+```
+
+### Sub-command list-data
+
+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}]
+                           [-p PAIRS [PAIRS ...]]
+                           [--trading-mode {spot,margin,futures}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
+                        config is provided.
+  --data-format-ohlcv {json,jsongz,hdf5}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `json`).
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --trading-mode {spot,margin,futures}
+                        Select Trading mode
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
+
+#### Example list-data
+
+```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
+```
+
 ### Trades (tick) data
 
-By default, `download-data` subcommand downloads Candles (OHLCV) data. Some exchanges also provide historic trade-data via their API.
+By default, `download-data` sub-command downloads Candles (OHLCV) data. Some 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 gzip by default. They are stored in your data-directory with the naming convention of `<pair>-trades.json.gz` (`ETH_BTC-trades.json.gz`). 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.
 
+!!! Warning "do not use"
+    You should not use this unless you're a kraken user. Most other exchanges provide OHLCV data with sufficient history.
+
 Example call:
 
 ```bash
-freqtrade download-data --exchange binance --pairs XRP/ETH ETH/BTC --days 20 --dl-trades
+freqtrade download-data --exchange kraken --pairs XRP/EUR ETH/EUR --days 20 --dl-trades
 ```
 
 !!! Note

docs/deprecated.md

@@ -9,21 +9,24 @@ and are no longer supported. Please avoid their usage in your configuration.
 ### the `--refresh-pairs-cached` command line option
 
 `--refresh-pairs-cached` in the context of backtesting, hyperopt and edge allows to refresh candle data for backtesting.
-Since this leads to much confusion, and slows down backtesting (while not being part of backtesting) this has been singled out 
-as a seperate freqtrade subcommand `freqtrade download-data`.
+Since this leads to much confusion, and slows down backtesting (while not being part of backtesting) this has been singled out as a separate freqtrade sub-command `freqtrade download-data`.
 
-This command line option was deprecated in 2019.7-dev (develop branch) and removed in 2019.9 (master branch).
+This command line option was deprecated in 2019.7-dev (develop branch) and removed in 2019.9.
 
 ### The **--dynamic-whitelist** command line option
 
-This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch)
-and in freqtrade 2019.7 (master branch).
+This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch) and in freqtrade 2019.7.
+Please refer to [pairlists](plugins.md#pairlists-and-pairlist-handlers) instead.
 
 ### the `--live` command line option
 
 `--live` in the context of backtesting allowed to download the latest tick data for backtesting.
 Did only download the latest 500 candles, so was ineffective in getting good backtest data.
-Removed in 2019-7-dev (develop branch) and in freqtrade 2019-8 (master branch)
+Removed in 2019-7-dev (develop branch) and in freqtrade 2019.8.
+
+### `ticker_interval` (now `timeframe`)
+
+Support for `ticker_interval` terminology was deprecated in 2020.6 in favor of `timeframe` - and compatibility code was removed in 2022.3.
 
 ### Allow running multiple pairlists in sequence
 
@@ -31,6 +34,43 @@ The former `"pairlist"` section in the configuration has been removed, and is re
 
 The old section of configuration parameters (`"pairlist"`) has been deprecated in 2019.11 and has been removed in 2020.4.
 
-### deprecation of bidVolume and askVolume from volumepairlist
+### deprecation of bidVolume and askVolume from volume-pairlist
 
-Since only quoteVolume can be compared between assets, the other options (bidVolume, askVolume) have been deprecated in 2020.4.
+Since only quoteVolume can be compared between assets, the other options (bidVolume, askVolume) have been deprecated in 2020.4, and have been removed in 2020.9.
+
+### Using order book steps for exit price
+
+Using `order_book_min` and `order_book_max` used to allow stepping the orderbook and trying to find the next ROI slot - trying to place sell-orders early.
+As this does however increase risk and provides no benefit, it's been removed for maintainability purposes in 2021.7.
+
+### Legacy Hyperopt mode
+
+Using separate hyperopt files was deprecated in 2021.4 and was removed in 2021.9.
+Please switch to the new [Parametrized Strategies](hyperopt.md) to benefit from the new hyperopt interface.
+
+## Strategy changes between V2 and V3
+
+Isolated Futures / short trading was introduced in 2022.4. This required major changes to configuration settings, strategy interfaces, ...
+
+We have put a great effort into keeping compatibility with existing strategies, so if you just want to continue using freqtrade in spot markets, there are no changes necessary.
+While we may drop support for the current interface sometime in the future, we will announce this separately and have an appropriate transition period.
+
+Please follow the [Strategy migration](strategy_migration.md) guide to migrate your strategy to the new format to start using the new functionalities.
+
+### webhooks - changes with 2022.4
+
+#### `buy_tag` has been renamed to `enter_tag`
+
+This should apply only to your strategy and potentially to webhooks.
+We will keep a compatibility layer for 1-2 versions (so both `buy_tag` and `enter_tag` will still work), but support for this in webhooks will disappear after that.
+
+#### Naming changes
+
+Webhook terminology changed from "sell" to "exit", and from "buy" to "entry".
+
+* `webhookbuy` -> `webhookentry`
+* `webhookbuyfill` -> `webhookentryfill`
+* `webhookbuycancel` -> `webhookentrycancel`
+* `webhooksell` -> `webhookexit`
+* `webhooksellfill` -> `webhookexitfill`
+* `webhooksellcancel` -> `webhookexitcancel`

docs/developer.md

@@ -2,21 +2,48 @@
 
 This page is intended for developers of Freqtrade, people who want to contribute to the Freqtrade codebase or documentation, or people who want to understand the source code of the application they're running.
 
-All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel in [slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) where you can ask questions.
+All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel on [discord](https://discord.gg/p7nuUNVfP7) where you can ask questions.
 
 ## Documentation
 
 Documentation is available at [https://freqtrade.io](https://www.freqtrade.io/) and needs to be provided with every new feature PR.
 
-Special fields for the documentation (like Note boxes, ...) can be found [here](https://squidfunk.github.io/mkdocs-material/extensions/admonition/).
+Special fields for the documentation (like Note boxes, ...) can be found [here](https://squidfunk.github.io/mkdocs-material/reference/admonitions/).
+
+To test the documentation locally use the following commands.
+
+``` bash
+pip install -r docs/requirements-docs.txt
+mkdocs serve
+```
+
+This will spin up a local server (usually on port 8000) so you can see if everything looks as you'd like it to.
 
 ## Developer setup
 
-To configure a development environment, best use the `setup.sh` script and answer "y" when asked "Do you want to install dependencies for dev [y/N]? ".
-Alternatively (if your system is not supported by the setup.sh script), follow the manual installation process and run `pip3 install -e .[all]`.
+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]`.
 
 This will install all required tools for development, including `pytest`, `flake8`, `mypy`, and `coveralls`.
 
+Then install the git hook scripts by running `pre-commit install`, so your changes will be verified locally before committing.
+This avoids a lot of waiting for CI already, as some basic formatting checks are done locally on your machine.
+
+Before opening a pull request, please familiarize yourself with our [Contributing Guidelines](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md).
+
+### Devcontainer setup
+
+The fastest and easiest way to get started is to use [VSCode](https://code.visualstudio.com/) with the Remote container extension.
+This gives developers the ability to start the bot with all required dependencies *without* needing to install any freqtrade specific dependencies on your local machine.
+
+#### Devcontainer dependencies
+
+* [VSCode](https://code.visualstudio.com/)
+* [docker](https://docs.docker.com/install/)
+* [Remote container extension documentation](https://code.visualstudio.com/docs/remote)
+
+For more information about the [Remote container extension](https://code.visualstudio.com/docs/remote), best consult the documentation.
+
 ### Tests
 
 New code should be covered by basic unittests. Depending on the complexity of the feature, Reviewers may request more in-depth unittests.
@@ -41,53 +68,72 @@ def test_method_to_test(caplog):
 
 ```
 
-### Local docker usage
+### Debug configuration
 
-The fastest and easiest way to start up is to use docker-compose.develop which gives developers the ability to start the bot up with all the required dependencies, *without* needing to install any freqtrade specific dependencies on your local machine.
+To debug freqtrade, we recommend VSCode with the following launch configuration (located in `.vscode/launch.json`).
+Details will obviously vary between setups - but this should work to get you started.
 
-#### Install
-
-* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
-* [docker](https://docs.docker.com/install/)
-* [docker-compose](https://docs.docker.com/compose/install/)
-
-#### Starting the bot
-##### Use the develop dockerfile
-
-``` bash
-rm docker-compose.yml && mv docker-compose.develop.yml docker-compose.yml
+``` json
+{
+    "name": "freqtrade trade",
+    "type": "python",
+    "request": "launch",
+    "module": "freqtrade",
+    "console": "integratedTerminal",
+    "args": [
+        "trade",
+        // Optional:
+        // "--userdir", "user_data",
+        "--strategy", 
+        "MyAwesomeStrategy",
+    ]
+},
 ```
 
-#### Docker Compose
+Command line arguments can be added in the `"args"` array.
+This method can also be used to debug a strategy, by setting the breakpoints within the strategy.
 
-##### Starting
+A similar setup can also be taken for Pycharm - using `freqtrade` as module name, and setting the command line arguments as "parameters".
 
-``` bash
-docker-compose up
+!!! 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).
+
+## ErrorHandling
+
+Freqtrade Exceptions all inherit from `FreqtradeException`.
+This general class of error should however not be used directly. Instead, multiple specialized sub-Exceptions exist.
+
+Below is an outline of exception inheritance hierarchy:
+
+```
++ FreqtradeException
+|
++---+ OperationalException
+|
++---+ DependencyException
+|   |
+|   +---+ PricingError
+|   |
+|   +---+ ExchangeError
+|       |
+|       +---+ TemporaryError
+|       |
+|       +---+ DDosProtection
+|       |
+|       +---+ InvalidOrderException
+|           |
+|           +---+ RetryableOrderError
+|           |
+|           +---+ InsufficientFundsError
+|
++---+ StrategyError
 ```
 
-![Docker compose up](https://user-images.githubusercontent.com/419355/65456322-47f63a80-de06-11e9-90c6-3c74d1bad0b8.png)
+---
 
-##### Rebuilding
+## Plugins
 
-``` bash
-docker-compose build
-```
-
-##### Execing (effectively SSH into the container)
-
-The `exec` command requires that the container already be running, if you want to start it
-that can be effected by `docker-compose up` or `docker-compose run freqtrade_develop`
-
-``` bash
-docker-compose exec freqtrade_develop /bin/bash
-```
-
-![image](https://user-images.githubusercontent.com/419355/65456522-ba671a80-de06-11e9-9598-df9ca0d8dcac.png)
-
-## Modules
-
-### Dynamic Pairlist
+### Pairlists
 
 You have a great idea for a new pair selection algorithm you would like to try out? Great.
 Hopefully you also want to contribute this back upstream.
@@ -98,7 +144,7 @@ First of all, have a look at the [VolumePairList](https://github.com/freqtrade/f
 
 This is a simple Handler, which however serves as a good example on how to start developing.
 
-Next, modify the classname of the Handler (ideally align this with the module filename).
+Next, modify the class-name of the Handler (ideally align this with the module filename).
 
 The base-class provides an instance of the exchange (`self._exchange`) the pairlist manager (`self._pairlistmanager`), as well as the main configuration (`self._config`), the pairlist dedicated configuration (`self._pairlistconfig`) and the absolute position within the list of pairlists.
 
@@ -110,6 +156,9 @@ The base-class provides an instance of the exchange (`self._exchange`) the pairl
         self._pairlist_pos = pairlist_pos
 ```
 
+!!! Tip
+    Don't forget to register your pairlist in `constants.py` under the variable `AVAILABLE_PAIRLISTS` - otherwise it will not be selectable.
+
 Now, let's step through the methods which require actions:
 
 #### Pairlist configuration
@@ -118,7 +167,7 @@ Configuration for the chain of Pairlist Handlers is done in the bot configuratio
 
 By convention, `"number_assets"` is used to specify the maximum number of pairs to keep in the pairlist. Please follow this to ensure a consistent user experience.
 
-Additional parameters can be configured as needed. For instance, `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successfull and dynamic.
+Additional parameters can be configured as needed. For instance, `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successful and dynamic.
 
 #### short_desc
 
@@ -134,7 +183,7 @@ 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 endresult 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
 
@@ -142,13 +191,13 @@ This method is called for each Pairlist Handler in the chain by the pairlist man
 
 This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
 
-It get's passed a pairlist (which can be the result of previous pairlists) as well as `tickers`, a pre-fetched version of `get_tickers()`.
+It gets passed a pairlist (which can be the result of previous pairlists) as well as `tickers`, a pre-fetched version of `get_tickers()`.
 
 The default implementation in the base class simply calls the `_validate_pair()` method for each pair in the pairlist, but you may override it. So you should either implement the `_validate_pair()` in your Pairlist Handler or override `filter_pairlist()` to do something else.
 
 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 endresult 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.
 
@@ -161,18 +210,105 @@ In `VolumePairList`, this implements different methods of sorting, does early va
         return pairs
 ```
 
+### Protections
+
+Best read the [Protection documentation](plugins.md#protections) to understand protections.
+This Guide is directed towards Developers who want to develop a new protection.
+
+No protection should use datetime directly, but use the provided `date_now` variable for date calculations. This preserves the ability to backtest protections.
+
+!!! 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
+
+All Protection implementations must have `IProtection` as parent class.
+For that reason, they must implement the following methods:
+
+* `short_desc()`
+* `global_stop()`
+* `stop_per_pair()`.
+
+`global_stop()` and `stop_per_pair()` must return a ProtectionReturn object, which consists of:
+
+* lock pair - boolean
+* lock until - datetime - until when should the pair be locked (will be rounded up to the next new candle)
+* reason - string, used for logging and storage in the database
+* lock_side - long, short or '*'.
+
+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.
+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.
+
+#### Global vs. local stops
+
+Protections can have 2 different ways to stop trading for a limited :
+
+* Per pair (local)
+* For all Pairs (globally)
+
+##### Protections - per pair
+
+Protections that implement the per pair approach must set `has_local_stop=True`.
+The method `stop_per_pair()` will be called whenever a trade closed (exit order completed).
+
+##### Protections - global protection
+
+These Protections should do their evaluation across all pairs, and consequently will also lock all pairs from trading (called a global PairLock).
+Global protection must set `has_global_stop=True` to be evaluated for global stops.
+The method `global_stop()` will be called whenever a trade closed (exit order completed).
+
+##### Protections - calculating lock end time
+
+Protections should calculate the lock end time based on the last trade it considers.
+This avoids re-locking should the lookback-period be longer than the actual lock period.
+
+The `IProtection` parent class provides a helper method for this in `calculate_lock_end()`.
+
+---
+
 ## Implement a new Exchange (WIP)
 
 !!! Note
     This section is a Work in Progress and is not a complete guide on how to test a new exchange with Freqtrade.
 
+!!! Note
+    Make sure to use an up-to-date version of CCXT before running any of the below tests.
+    You can get the latest version of ccxt by running `pip install -U ccxt` with activated virtual environment.
+    Native docker is not supported for these tests, however the available dev-container will support all required actions and eventually necessary changes.
+
 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`.
+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).
+
+These are prerequisites to have an exchange listed as either Supported or Community tested (listed on the homepage).
+The below are "extras", which will make an exchange better (feature-complete) - but are not absolutely necessary for either of the 2 categories.
+
+Additional tests / steps to complete:
+
+* Verify data provided by `fetch_ohlcv()` - and eventually adjust `ohlcv_candle_limit` for this exchange
+* Check L2 orderbook limit range (API documentation) - and eventually set as necessary
+* Check if balance shows correctly (*)
+* Create market order (*)
+* Create limit order (*)
+* Complete trade (enter + exit) (*)
+  * Compare result calculation between exchange and bot
+  * Ensure fees are applied correctly (check the database against the exchange)
+
+(*) Requires API keys and Balance on the exchange.
+
 ### Stoploss On Exchange
 
 Check if the new exchange supports Stoploss on Exchange orders through their API.
 
-Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need to implement the exchange-specific parameters ourselfs. Best look at `binance.py` for an example implementation of this. You'll need to dig through the documentation of the Exchange's API on how exactly this can be done. [CCXT Issues](https://github.com/ccxt/ccxt/issues) may also provide great help, since others may have implemented something similar for their projects.
+Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need to implement the exchange-specific parameters ourselves. Best look at `binance.py` for an example implementation of this. You'll need to dig through the documentation of the Exchange's API on how exactly this can be done. [CCXT Issues](https://github.com/ccxt/ccxt/issues) may also provide great help, since others may have implemented something similar for their projects.
 
 ### Incomplete candles
 
@@ -208,6 +344,32 @@ The output will show the last entry from the Exchange as well as the current UTC
 If the day shows the same day, then the last candle can be assumed as incomplete and should be dropped (leave the setting `"ohlcv_partial_candle"` from the exchange-class untouched / True). Otherwise, set `"ohlcv_partial_candle"` to `False` to not drop Candles (shown in the example above).
 Another way is to run this command multiple times in a row and observe if the volume is changing (while the date remains the same).
 
+### Update binance cached leverage tiers
+
+Updating leveraged tiers should be done regularly - and requires an authenticated account with futures enabled.
+
+``` python
+import ccxt
+import json
+from pathlib import Path
+
+exchange = ccxt.binance({
+    'apiKey': '<apikey>',
+    'secret': '<secret>'
+    'options': {'defaultType': 'future'}
+    })
+_ = 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)
+
+```
+
+This file should then be contributed upstream, so others can benefit from this, too.
+
 ## Updating example notebooks
 
 To keep the jupyter notebooks aligned with the documentation, the following should be ran after updating a example notebook.
@@ -222,13 +384,13 @@ jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown freqtrade
 This documents some decisions taken for the CI Pipeline.
 
 * CI runs on all OS variants, Linux (ubuntu), macOS and Windows.
-* Docker images are build for the branches `master` and `develop`.
-* Raspberry PI Docker images are postfixed with `_pi` - so tags will be `:master_pi` and `develop_pi`.
+* Docker images are build for the branches `stable` and `develop`, and are built as multiarch builds, supporting multiple platforms via the same tag.
+* Docker images containing Plot dependencies are also available as `stable_plot` and `develop_plot`.
 * Docker images contain a file, `/freqtrade/freqtrade_commit` containing the commit this image is based of.
 * Full docker image rebuilds are run once a week via schedule.
 * Deployments run on ubuntu.
 * ta-lib binaries are contained in the build_helpers directory to avoid fails related to external unavailability.
-* All tests must pass for a PR to be merged to `master` or `develop`.
+* All tests must pass for a PR to be merged to `stable` or `develop`.
 
 ## Creating a release
 
@@ -245,21 +407,22 @@ git checkout -b new_release <commitid>
 
 Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
 
+* 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 master branch
+* push that branch to the remote and create a PR against the stable branch
 
 ### Create changelog from git commits
 
 !!! Note
-    Make sure that the master branch is uptodate!
+    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 master..new_release
+git log --oneline --no-decorate --no-merges stable..new_release
 ```
 
-To keep the release-log short, best wrap the full git changelog into a collapsible details secction.
+To keep the release-log short, best wrap the full git changelog into a collapsible details section.
 
 ```markdown
 <details>
@@ -272,17 +435,20 @@ To keep the release-log short, best wrap the full git changelog into a collapsib
 
 ### Create github release / tag
 
-Once the PR against master is merged (best right after merging):
+Once the PR against stable is merged (best right after merging):
 
 * Use the button "Draft a new release" in the Github UI (subsection releases).
 * Use the version-number specified as tag.
-* Use "master" as reference (this step comes after the above PR is merged).
+* Use "stable" as reference (this step comes after the above PR is merged).
 * Use the above changelog as release comment (as codeblock)
 
 ## Releases
 
 ### pypi
 
+!!! Note
+    This process is now automated as part of Github Actions.
+
 To create a pypi release, please run the following commands:
 
 Additional requirement: `wheel`, `twine` (for uploading), account on pypi with proper permissions.

docs/docker.md

@@ -1,341 +0,0 @@
-# Using Freqtrade with Docker
-
-## Install Docker
-
-Start by downloading and installing Docker CE for your platform:
-
-* [Mac](https://docs.docker.com/docker-for-mac/install/)
-* [Windows](https://docs.docker.com/docker-for-windows/install/)
-* [Linux](https://docs.docker.com/install/)
-
-Optionally, [docker-compose](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
-
-Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
-
-## Freqtrade with docker-compose
-
-Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
-
-!!! Note
-    The following section assumes that docker and docker-compose is installed and available to the logged in user.
-
-!!! Note
-    All below comands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
-
-!!! Note "Docker on Raspberry"
-    If you're running freqtrade on a Raspberry PI, you must change the image from `freqtradeorg/freqtrade:master` to `freqtradeorg/freqtrade:master_pi` or `freqtradeorg/freqtrade:develop_pi`, otherwise the image will not work.
-
-### Docker quick start
-
-Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
-
-``` bash
-mkdir ft_userdata
-cd ft_userdata/
-# Download the docker-compose file from the repository
-curl https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docker-compose.yml -o docker-compose.yml
-
-# Pull the freqtrade image
-docker-compose pull
-
-# Create user directory structure
-docker-compose run --rm freqtrade create-userdir --userdir user_data
-
-# Create configuration - Requires answering interactive questions
-docker-compose run --rm freqtrade new-config --config user_data/config.json
-```
-
-The above snippet creates a new directory called "ft_userdata", downloads the latest compose file and pulls the freqtrade image.
-The last 2 steps in the snippet create the directory with user-data, as well as (interactively) the default configuration based on your selections.
-
-!!! Note
-    You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
-
-#### Adding your strategy
-
-The configuration is now available as `user_data/config.json`.
-You should now copy your strategy to `user_data/strategies/` - and add the Strategy class name to the `docker-compose.yml` file, replacing `SampleStrategy`. If you wish to run the bot with the SampleStrategy, just leave it as it is.
-
-!!! Warning
-    The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
-    Please always backtest the strategy and use dry-run for some time before risking real money!
-
-Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
-
-``` bash
-docker-compose up -d
-```
-
-#### Docker-compose logs
-
-Logs will be written to `user_data/logs/freqtrade.log`. 
-Alternatively, you can check the latest logs using `docker-compose logs -f`.
-
-#### Database
-
-The database will be in the user_data directory as well, and will be called `user_data/tradesv3.sqlite`.
-
-#### Updating freqtrade with docker-compose
-
-To update freqtrade when using docker-compose is as simple as running the following 2 commands:
-
-``` bash
-# Download the latest image
-docker-compose pull
-# Restart the image
-docker-compose up -d
-```
-
-This will first pull the latest image, and will then restart the container with the just pulled version.
-
-!!! Note
-    You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
-
-#### Going from here
-
-Advanced users may edit the docker-compose file further to include all possible options or arguments.
-
-All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
-
-!!! Note "`docker-compose run --rm`"
-    Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
-
-##### Example: Download data with docker-compose
-
-Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
-
-``` bash
-docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
-```
-
-Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
-
-##### Example: Backtest with docker-compose
-
-Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
-
-``` bash
-docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
-```
-
-Head over to the [Backtesting Documentation](backtesting.md) to learn more.
-
-#### Additional dependencies with docker-compose
-
-If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
-For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) for an example).
-
-You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
-
-``` yaml
-    image: freqtrade_custom
-    build:
-      context: .
-      dockerfile: "./Dockerfile.<yourextension>"
-```
-
-You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
-
-## Freqtrade with docker without docker-compose
-
-!!! Warning
-    The below documentation is provided for completeness and assumes that you are somewhat familiar with running docker containers. If you're just starting out with docker, we recommend to follow the [Freqtrade with docker-compose](#freqtrade-with-docker-compose) instructions.
-
-### Download the official Freqtrade docker image
-
-Pull the image from docker hub.
-
-Branches / tags available can be checked out on [Dockerhub tags page](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
-
-```bash
-docker pull freqtradeorg/freqtrade:develop
-# Optionally tag the repository so the run-commands remain shorter
-docker tag freqtradeorg/freqtrade:develop freqtrade
-```
-
-To update the image, simply run the above commands again and restart your running container.
-
-Should you require additional libraries, please [build the image yourself](#build-your-own-docker-image).
-
-!!! Note "Docker image update frequency"
-    The official docker images with tags `master`, `develop` and `latest` are automatically rebuild once a week to keep the base image uptodate.
-    In addition to that, every merge to `develop` will trigger a rebuild for `develop` and `latest`.
-
-### Prepare the configuration files
-
-Even though you will use docker, you'll still need some files from the github repository.
-
-#### Clone the git repository
-
-Linux/Mac/Windows with WSL
-
-```bash
-git clone https://github.com/freqtrade/freqtrade.git
-```
-
-Windows with docker
-
-```bash
-git clone --config core.autocrlf=input https://github.com/freqtrade/freqtrade.git
-```
-
-#### Copy `config.json.example` to `config.json`
-
-```bash
-cd freqtrade
-cp -n config.json.example config.json
-```
-
-> To understand the configuration options, please refer to the [Bot Configuration](configuration.md) page.
-
-#### Create your database file
-
-Production
-
-```bash
-touch tradesv3.sqlite
-````
-
-Dry-Run
-
-```bash
-touch tradesv3.dryrun.sqlite
-```
-
-!!! Note
-    Make sure to use the path to this file when starting the bot in docker.
-
-### Build your own Docker image
-
-Best start by pulling the official docker image from dockerhub as explained [here](#download-the-official-docker-image) to speed up building.
-
-To add additional libraries to your docker image, best check out [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) which adds the [technical](https://github.com/freqtrade/technical) module to the image.
-
-```bash
-docker build -t freqtrade -f Dockerfile.technical .
-```
-
-If you are developing using Docker, use `Dockerfile.develop` to build a dev Docker image, which will also set up develop dependencies:
-
-```bash
-docker build -f Dockerfile.develop -t freqtrade-dev .
-```
-
-!!! Note
-    For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see the "5. Run a restartable docker image" section) to keep it between  updates.
-
-#### Verify the Docker image
-
-After the build process you can verify that the image was created with:
-
-```bash
-docker images
-```
-
-The output should contain the freqtrade image.
-
-### Run the Docker image
-
-You can run a one-off container that is immediately deleted upon exiting with the following command (`config.json` must be in the current working directory):
-
-```bash
-docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
-```
-
-!!! Warning
-    In this example, the database will be created inside the docker instance and will be lost when you will refresh your image.
-
-#### Adjust timezone
-
-By default, the container will use UTC timezone.
-Should you find this irritating please add the following to your docker commands:
-
-##### Linux
-
-``` bash
--v /etc/timezone:/etc/timezone:ro
-
-# Complete command:
-docker run --rm -v /etc/timezone:/etc/timezone:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
-```
-
-##### MacOS
-
-There is known issue in OSX Docker versions after 17.09.1, whereby `/etc/localtime` cannot be shared causing Docker to not start. A work-around for this is to start with the following cmd.
-
-```bash
-docker run --rm -e TZ=`ls -la /etc/localtime | cut -d/ -f8-9` -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
-```
-
-More information on this docker issue and work-around can be read [here](https://github.com/docker/for-mac/issues/2396).
-
-### Run a restartable docker image
-
-To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem).
-
-#### Move your config file and database
-
-The following will assume that you place your configuration / database files to `~/.freqtrade`, which is a hidden directory in your home directory. Feel free to use a different directory and replace the directory in the upcomming commands.
-
-```bash
-mkdir ~/.freqtrade
-mv config.json ~/.freqtrade
-mv tradesv3.sqlite ~/.freqtrade
-```
-
-#### Run the docker image
-
-```bash
-docker run -d \
-  --name freqtrade \
-  -v ~/.freqtrade/config.json:/freqtrade/config.json \
-  -v ~/.freqtrade/user_data/:/freqtrade/user_data \
-  -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-  freqtrade trade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
-```
-
-!!! Note
-    When using docker, it's best to specify `--db-url` explicitly to ensure that the database URL and the mounted database file match.
-
-!!! Note
-    All available bot command line parameters can be added to the end of the `docker run` command.
-
-!!! Note
-    You can define a [restart policy](https://docs.docker.com/config/containers/start-containers-automatically/) in docker. It can be useful in some cases to use the `--restart unless-stopped` flag (crash of freqtrade or reboot of your system).
-
-### Monitor your Docker instance
-
-You can use the following commands to monitor and manage your container:
-
-```bash
-docker logs freqtrade
-docker logs -f freqtrade
-docker restart freqtrade
-docker stop freqtrade
-docker start freqtrade
-```
-
-For more information on how to operate Docker, please refer to the [official Docker documentation](https://docs.docker.com/).
-
-!!! Note
-    You do not need to rebuild the image for configuration changes, it will suffice to edit `config.json` and restart the container.
-
-### Backtest with docker
-
-The following assumes that the download/setup of the docker image have been completed successfully.
-Also, backtest-data should be available at `~/.freqtrade/user_data/`.
-
-```bash
-docker run -d \
-  --name freqtrade \
-  -v /etc/localtime:/etc/localtime:ro \
-  -v ~/.freqtrade/config.json:/freqtrade/config.json \
-  -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-  -v ~/.freqtrade/user_data/:/freqtrade/user_data/ \
-  freqtrade backtesting --strategy AwsomelyProfitableStrategy
-```
-
-Head over to the [Backtesting Documentation](backtesting.md) for more details.
-
-!!! Note
-    Additional bot command line parameters can be appended after the image name (`freqtrade` in the above example).

docs/docker_quickstart.md

@@ -0,0 +1,217 @@
+# Using Freqtrade with Docker
+
+This page explains how to run the bot with Docker. It is not meant to work out of the box. You'll still need to read through the documentation and understand how to properly configure it.
+
+## Install Docker
+
+Start by downloading and installing Docker CE for your platform:
+
+* [Mac](https://docs.docker.com/docker-for-mac/install/)
+* [Windows](https://docs.docker.com/docker-for-windows/install/)
+* [Linux](https://docs.docker.com/install/)
+
+To simplify running freqtrade, [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the below [docker quick start guide](#docker-quick-start).
+
+## Freqtrade with docker-compose
+
+Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/stable/docker-compose.yml) ready for usage.
+
+!!! Note
+    - The following section assumes that `docker` and `docker-compose` are installed and available to the logged in user.
+    - All below commands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
+
+### Docker quick start
+
+Create a new directory and place the [docker-compose file](https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml) in this directory.
+
+``` bash
+mkdir ft_userdata
+cd ft_userdata/
+# Download the docker-compose file from the repository
+curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml
+
+# Pull the freqtrade image
+docker-compose pull
+
+# Create user directory structure
+docker-compose run --rm freqtrade create-userdir --userdir user_data
+
+# Create configuration - Requires answering interactive questions
+docker-compose run --rm freqtrade new-config --config user_data/config.json
+```
+
+The above snippet creates a new directory called `ft_userdata`, downloads the latest compose file and pulls the freqtrade image.
+The last 2 steps in the snippet create the directory with `user_data`, as well as (interactively) the default configuration based on your selections.
+
+!!! Question "How to edit the bot configuration?"
+    You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
+
+    You can also change the both Strategy and commands by editing the command section of your `docker-compose.yml` file.
+
+#### Adding a custom strategy
+
+1. The configuration is now available as `user_data/config.json`
+2. Copy a custom strategy to the directory `user_data/strategies/`
+3. Add the Strategy' class name to the `docker-compose.yml` file
+
+The `SampleStrategy` is run by default.
+
+!!! Danger "`SampleStrategy` is just a demo!"
+    The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
+    Please always backtest your strategy and use dry-run for some time before risking real money!
+    You will find more information about Strategy development in the [Strategy documentation](strategy-customization.md).
+
+Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
+
+``` bash
+docker-compose up -d
+```
+
+!!! Warning "Default configuration"
+    While the configuration generated will be mostly functional, you will still need to verify that all options correspond to what you want (like Pricing, pairlist, ...) before starting the bot.
+
+#### Accessing the UI
+
+If you've selected to enable FreqUI in the `new-config` step, you will have freqUI available at port `localhost:8080`.
+
+You can now access the UI by typing localhost:8080 in your browser.
+
+??? Note "UI Access on a remote servers"
+    If you're running on a VPS, you should consider using either a ssh tunnel, or setup a VPN (openVPN, wireguard) to connect to your bot.
+    This will ensure that freqUI is not directly exposed to the internet, which is not recommended for security reasons (freqUI does not support https out of the box).
+    Setup of these tools is not part of this tutorial, however many good tutorials can be found on the internet.
+    Please also read the [API configuration with docker](rest-api.md#configuration-with-docker) section to learn more about this configuration.
+
+#### Monitoring the bot
+
+You can check for running instances with `docker-compose ps`.
+This should list the service `freqtrade` as `running`. If that's not the case, best check the logs (see next point).
+
+#### Docker-compose logs
+
+Logs will be written to: `user_data/logs/freqtrade.log`.  
+You can also check the latest log with the command `docker-compose logs -f`.
+
+#### Database
+
+The database will be located at: `user_data/tradesv3.sqlite`
+
+#### Updating freqtrade with docker-compose
+
+Updating freqtrade when using `docker-compose` is as simple as running the following 2 commands:
+
+``` bash
+# Download the latest image
+docker-compose pull
+# Restart the image
+docker-compose up -d
+```
+
+This will first pull the latest image, and will then restart the container with the just pulled version.
+
+!!! Warning "Check the Changelog"
+    You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
+
+### Editing the docker-compose file
+
+Advanced users may edit the docker-compose file further to include all possible options or arguments.
+
+All freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
+
+!!! Warning "`docker-compose` for trade commands"
+    Trade commands (`freqtrade trade <...>`) should not be ran via `docker-compose run` - but should use `docker-compose up -d` instead.
+    This makes sure that the container is properly started (including port forwardings) and will make sure that the container will restart after a system reboot.
+    If you intend to use freqUI, please also ensure to adjust the [configuration accordingly](rest-api.md#configuration-with-docker), otherwise the UI will not be available.
+
+!!! Note "`docker-compose run --rm`"
+    Including `--rm` will remove the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
+
+??? Note "Using docker without docker-compose"
+    "`docker-compose run --rm`" will require a compose file to be provided.
+    Some freqtrade commands that don't require authentication such as `list-pairs` can be run with "`docker run --rm`" instead.  
+    For example `docker run --rm freqtradeorg/freqtrade:stable list-pairs --exchange binance --quote BTC --print-json`.  
+    This can be useful for fetching exchange information to add to your `config.json` without affecting your running containers.
+
+#### Example: Download data with docker-compose
+
+Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
+
+``` bash
+docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
+```
+
+Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
+
+#### Example: Backtest with docker-compose
+
+Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
+
+``` bash
+docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
+```
+
+Head over to the [Backtesting Documentation](backtesting.md) to learn more.
+
+### Additional dependencies with docker-compose
+
+If your strategy requires dependencies not included in the default image - it will be necessary to build the image on your host.
+For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [docker/Dockerfile.custom](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.custom) for an example).
+
+You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
+
+``` yaml
+    image: freqtrade_custom
+    build:
+      context: .
+      dockerfile: "./Dockerfile.<yourextension>"
+```
+
+You can then run `docker-compose build --pull` to build the docker image, and run it using the commands described above.
+
+### Plotting with docker-compose
+
+Commands `freqtrade plot-profit` and `freqtrade plot-dataframe` ([Documentation](plotting.md)) are available by changing the image to `*_plot` in your docker-compose.yml file.
+You can then use these commands as follows:
+
+``` bash
+docker-compose run --rm freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --timerange=20180801-20180805
+```
+
+The output will be stored in the `user_data/plot` directory, and can be opened with any modern browser.
+
+### Data analysis using docker compose
+
+Freqtrade provides a docker-compose file which starts up a jupyter lab server.
+You can run this server using the following command:
+
+``` bash
+docker-compose -f docker/docker-compose-jupyter.yml up
+```
+
+This will create a docker-container running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
+Please use the link that's printed in the console after startup for simplified login.
+
+Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) up-to-date.
+
+``` bash
+docker-compose -f docker/docker-compose-jupyter.yml build --no-cache
+```
+
+## Troubleshooting
+
+### Docker on Windows
+
+* Error: `"Timestamp for this request is outside of the recvWindow."`
+  * The market api requests require a synchronized clock but the time in the docker container shifts a bit over time into the past.
+    To fix this issue temporarily you need to run `wsl --shutdown` and restart docker again (a popup on windows 10 will ask you to do so).
+    A permanent solution is either to host the docker container on a linux host or restart the wsl from time to time with the scheduler.
+
+    ``` bash
+    taskkill /IM "Docker Desktop.exe" /F
+    wsl --shutdown
+    start "" "C:\Program Files\Docker\Docker\Docker Desktop.exe"
+    ```
+
+!!! Warning
+    Due to the above, we do not recommend the usage of docker on windows for production setups, but only for experimentation, datadownload and backtesting.
+    Best use a linux-VPS for running freqtrade reliably.

docs/edge.md

@@ -1,95 +1,161 @@
 # Edge positioning
 
-This page explains how to use Edge Positioning module in your bot in order to enter into a trade only if the trade has a reasonable win rate and risk reward ratio, and consequently adjust your position size and stoploss.
+The `Edge Positioning` module uses probability to calculate your win rate and risk reward ratio. It will use these statistics to control your strategy trade entry points, position size and, stoploss.
 
 !!! Warning
-    Edge positioning is not compatible with dynamic (volume-based) whitelist.
+    When using `Edge positioning` with a dynamic whitelist (VolumePairList), make sure to also use `AgeFilter` and set it to at least `calculate_since_number_of_days` to avoid problems with missing data.
 
 !!! Note
-    Edge does not consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else are ignored in its calculation.
+    `Edge Positioning` only considers *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
+    `Edge Positioning` improves the performance of some trading strategies and *decreases* the performance of others.
+
 
 ## Introduction
 
-Trading is all about probability. No one can claim that he has a strategy working all the time. You have to assume that sometimes you lose.
+Trading strategies are not perfect. They are frameworks that are susceptible to the market and its indicators. Because the market is not at all predictable, sometimes a strategy will win and sometimes the same strategy will lose.
 
-But it doesn't mean there is no rule, it only means rules should work "most of the time". Let's play a game: we toss a coin, heads: I give you 10$, tails: you give me 10$. Is it an interesting game? No, it's quite boring, isn't it?
+To obtain an edge in the market, a strategy has to make more money than it loses. Making money in trading is not only about *how often* the strategy makes or loses money.
 
-But let's say the probability that we have heads is 80% (because our coin has the displaced distribution of mass or other defect), and the probability that we have tails is 20%. Now it is becoming interesting...
+!!! tip "It doesn't matter how often, but how much!"
+    A bad strategy might make 1 penny in *ten* transactions but lose 1 dollar in *one* transaction. If one only checks the number of winning trades, it would be misleading to think that the strategy is actually making a profit.
 
-That means 10$ X 80% versus 10$ X 20%. 8$ versus 2$. That means over time you will win 8$ risking only 2$ on each toss of coin.
+The Edge Positioning module seeks to improve a strategy's winning probability and the money that the strategy will make *on the long run*. 
 
-Let's complicate it more: you win 80% of the time but only 2$, I win 20% of the time but 8$. The calculation is: 80% X 2$ versus 20% X 8$. It is becoming boring again because overtime you win $1.6$ (80% X 2$) and me $1.6 (20% X 8$) too.
+We raise the following question[^1]:
 
-The question is: How do you calculate that? How do you know if you wanna play?
+!!! Question "Which trade is a better option?"
+    a) A trade with 80% of chance of losing 100\$ and 20% chance of winning 200\$<br/>
+    b) A trade with 100% of chance of losing 30\$
 
-The answer comes to two factors:
+???+ Info "Answer"
+    The expected value of *a)* is smaller than the expected value of *b)*.<br/>
+    Hence, *b*) represents a smaller loss in the long run.<br/>
+    However, the answer is: *it depends*
 
-- Win Rate
-- Risk Reward Ratio
+Another way to look at it is to ask a similar question:
 
-### Win Rate
+!!! Question "Which trade is a better option?"
+    a) A trade with 80% of chance of winning 100\$ and 20% chance of losing 200\$<br/>
+    b) A trade with 100% of chance of winning 30\$
 
-Win Rate (*W*) is is the mean over some amount of trades (*N*) what is the percentage of winning trades to total number of trades (note that we don't consider how much you gained but only if you won or not).
+Edge positioning tries to answer the hard questions about risk/reward and position size automatically, seeking to minimizes the chances of losing of a given strategy.
 
-```
-W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N
-```
+### Trading, winning and losing
 
-Complementary Loss Rate (*L*) is defined as
+Let's call $o$ the return of a single transaction $o$ where $o \in \mathbb{R}$. The collection $O = \{o_1, o_2, ..., o_N\}$ is the set of all returns of transactions made during a trading session. We say that $N$ is the cardinality of $O$, or, in lay terms, it is the number of transactions made in a trading session.
 
-```
-L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N
-```
+!!! Example
+    In a session where a strategy made three transactions we can say that $O = \{3.5, -1, 15\}$. That means that $N = 3$ and $o_1 = 3.5$, $o_2 = -1$, $o_3 = 15$.
 
-or, which is the same, as
+A winning trade is a trade where a strategy *made* money. Making money means that the strategy closed the position in a value that returned a profit, after all deducted fees. Formally, a winning trade will have a return $o_i > 0$. Similarly, a losing trade will have a return $o_j \leq 0$. With that, we can discover the set of all winning trades, $T_{win}$, as follows:
 
-```
-L = 1 – W
-```
+$$ T_{win} = \{ o \in O | o > 0 \} $$
+
+Similarly, we can discover the set of losing trades $T_{lose}$ as follows:
+
+$$ T_{lose} = \{o \in O | o \leq 0\} $$
+
+!!! Example
+    In a section where a strategy made four transactions $O = \{3.5, -1, 15, 0\}$:<br>
+    $T_{win} = \{3.5, 15\}$<br>
+    $T_{lose} = \{-1, 0\}$<br>
+
+### Win Rate and Lose Rate
+
+The win rate $W$ is the proportion of winning trades with respect to all the trades made by a strategy. We use the following function to compute the win rate:
+
+$$W = \frac{|T_{win}|}{N}$$
+
+Where $W$ is the win rate, $N$ is the number of trades and, $T_{win}$ is the set of all trades where the strategy made money.
+
+Similarly, we can compute the rate of losing trades:
+
+$$ 
+    L = \frac{|T_{lose}|}{N} 
+$$
+
+Where $L$ is the lose rate, $N$ is the amount of trades made and, $T_{lose}$ is the set of all trades where the strategy lost money. Note that the above formula is the same as calculating $L = 1 – W$ or $W = 1 – L$
 
 ### Risk Reward Ratio
 
-Risk Reward Ratio (*R*) is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose:
+Risk Reward Ratio ($R$) is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose. Formally:
 
-```
-R = Profit / Loss
-```
+$$ R = \frac{\text{potential_profit}}{\text{potential_loss}} $$
 
-Over time, on many trades, you can calculate your risk reward by dividing your average profit on winning trades by your average loss on losing trades:
+???+ Example "Worked example of $R$ calculation"
+    Let's say that you think that the price of *stonecoin* today is 10.0\$. You believe that, because they will start mining stonecoin, it will go up to 15.0\$ tomorrow. There is the risk that the stone is too hard, and the GPUs can't mine it, so the price might go to 0\$ tomorrow. You are planning to invest 100\$, which will give you 10 shares (100 / 10).
 
-```
-Average profit = (Sum of profits) / (Number of winning trades)
+    Your potential profit is calculated as:
 
-Average loss = (Sum of losses) / (Number of losing trades)
+    $\begin{aligned} 
+        \text{potential_profit} &= (\text{potential_price} - \text{entry_price}) * \frac{\text{investment}}{\text{entry_price}} \\
+                                &= (15 - 10) * (100 / 10) \\
+                                &= 50
+    \end{aligned}$
 
-R = (Average profit) / (Average loss)
-```
+    Since the price might go to 0\$, the 100\$ dollars invested could turn into 0.
+
+    We do however use a stoploss of 15% - so in the worst case, we'll sell 15% below entry price (or at 8.5$\).
+
+    $\begin{aligned}
+        \text{potential_loss} &= (\text{entry_price} - \text{stoploss}) * \frac{\text{investment}}{\text{entry_price}} \\
+                                &= (10 - 8.5) * (100 / 10)\\
+                                &= 15
+    \end{aligned}$
+
+    We can compute the Risk Reward Ratio as follows:
+
+    $\begin{aligned}
+        R   &= \frac{\text{potential_profit}}{\text{potential_loss}}\\
+            &= \frac{50}{15}\\
+            &= 3.33
+    \end{aligned}$<br>
+    What it effectively means is that the strategy have the potential to make 3.33\$ for each 1\$ invested.
+
+On a long horizon, that is, on many trades, we can calculate the risk reward by dividing the strategy' average profit on winning trades by the strategy' average loss on losing trades. We can calculate the average profit, $\mu_{win}$, as follows:
+
+$$ \text{average_profit} = \mu_{win} = \frac{\text{sum_of_profits}}{\text{count_winning_trades}} = \frac{\sum^{o \in T_{win}} o}{|T_{win}|} $$
+
+Similarly, we can calculate the average loss, $\mu_{lose}$, as follows:
+
+$$ \text{average_loss} = \mu_{lose} = \frac{\text{sum_of_losses}}{\text{count_losing_trades}} = \frac{\sum^{o \in T_{lose}} o}{|T_{lose}|} $$
+
+Finally, we can calculate the Risk Reward ratio, $R$, as follows:
+
+$$ R = \frac{\text{average_profit}}{\text{average_loss}} = \frac{\mu_{win}}{\mu_{lose}}\\ $$
+
+
+???+ Example "Worked example of $R$ calculation using mean profit/loss"
+    Let's say the strategy that we are using makes an average win $\mu_{win} = 2.06$ and an average loss $\mu_{loss} = 4.11$.<br>
+    We calculate the risk reward ratio as follows:<br>
+    $R = \frac{\mu_{win}}{\mu_{loss}} = \frac{2.06}{4.11} = 0.5012...$
+    
 
 ### Expectancy
 
-At this point we can combine *W* and *R* to create an expectancy ratio. This is a simple process of multiplying the risk reward ratio by the percentage of winning trades and subtracting the percentage of losing trades, which is calculated as follows:
+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:
 
-```
-Expectancy Ratio = (Risk Reward Ratio X Win Rate) – Loss Rate = (R X W) – L
-```
+$$E = R * W - L$$
 
-So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
+!!! Example "Calculating $E$"
+    Let's say that a strategy has a win rate $W = 0.28$ and a risk reward ratio $R = 5$. What this means is that the strategy is expected to make 5 times the investment around on 28% of the trades it makes. Working out the example:<br>
+    $E = R * W - L = 5 * 0.28 - 0.72 = 0.68$
+    <br>
 
-```
-Expectancy = (5 X 0.28) – 0.72 = 0.68
-```
+The expectancy worked out in the example above means that, on average, this strategy' trades will return 1.68 times the size of its losses. Said another way, the strategy makes 1.68\$ for every 1\$ it loses, on average. 
 
-Superficially, this means that on average you expect this strategy’s trades to return 1.68 times the size of your loses. Said another way, you can expect to win $1.68 for every $1 you lose. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
+This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
 
 It is important to remember that any system with an expectancy greater than 0 is profitable using past data. The key is finding one that will be profitable in the future.
 
 You can also use this value to evaluate the effectiveness of modifications to this system.
 
-**NOTICE:** It's important to keep in mind that Edge is testing your expectancy using historical data, there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology, but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
+!!! Note
+    It's important to keep in mind that Edge is testing your expectancy using historical data, there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
 
 ## How does it work?
 
-If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over *N* trades for each stoploss. Here is an example:
+Edge combines dynamic stoploss, dynamic positions, and whitelist generation into one isolated module which is then applied to the trading strategy. If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over *N* trades for each stoploss. Here is an example:
 
 | Pair   |      Stoploss      |  Win Rate | Risk Reward Ratio | Expectancy |
 |----------|:-------------:|-------------:|------------------:|-----------:|
@@ -98,13 +164,13 @@ If enabled in config, Edge will go through historical data with a range of stopl
 | XZC/ETH  |  -0.03        |   0.52       |1.359670           | 0.228      |
 | XZC/ETH  |  -0.04        |   0.51       |1.234539           | 0.117      |
 
-The goal here is to find the best stoploss for the strategy in order to have the maximum expectancy. In the above example stoploss at 3% leads to the maximum expectancy according to historical data.
+The goal here is to find the best stoploss for the strategy in order to have the maximum expectancy. In the above example stoploss at $3%$ leads to the maximum expectancy according to historical data.
 
 Edge module then forces stoploss value it evaluated to your strategy dynamically.
 
 ### Position size
 
-Edge also dictates the stake amount for each trade to the bot according to the following factors:
+Edge dictates the amount at stake for each trade to the bot according to the following factors:
 
 - Allowed capital at risk
 - Stoploss
@@ -115,9 +181,9 @@ Allowed capital at risk is calculated as follows:
 Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade)
 ```
 
-Stoploss is calculated as described above against historical data.
+Stoploss is calculated as described above with respect to historical data.
 
-Your position size then will be:
+The position size is calculated as follows:
 
 ```
 Position size = (Allowed capital at risk) / Stoploss
@@ -125,19 +191,84 @@ Position size = (Allowed capital at risk) / Stoploss
 
 Example:
 
-Let's say the stake currency is ETH and you have 10 ETH on the exchange, your capital available percentage is 50% and you would allow 1% of risk for each trade. thus your available capital for trading is **10 x 0.5 = 5 ETH** and allowed capital at risk would be **5 x 0.01 = 0.05 ETH**.
+Let's say the stake currency is **ETH** and there is $10$ **ETH** on the wallet. The capital available percentage is $50%$ and the allowed risk per trade is $1\%$. Thus, the available capital for trading is $10 * 0.5 = 5$ **ETH** and the allowed capital at risk would be $5 * 0.01 = 0.05$ **ETH**.
 
-Let's assume Edge has calculated that for **XLM/ETH** market your stoploss should be at 2%. So your position size will be **0.05 / 0.02 = 2.5 ETH**.
+-   **Trade 1:** The strategy detects a new buy signal in the **XLM/ETH** market. `Edge Positioning` calculates a stoploss of $2\%$ and a position of $0.05 / 0.02 = 2.5$ **ETH**. The bot takes a position of $2.5$ **ETH** in the **XLM/ETH** market.
 
-Bot takes a position of 2.5 ETH on XLM/ETH (call it trade 1). Up next, you receive another buy signal while trade 1 is still open. This time on **BTC/ETH** market. Edge calculated stoploss for this market at 4%. So your position size would be 0.05 / 0.04 = 1.25 ETH (call it trade 2).
+-   **Trade 2:** The strategy detects a buy signal on the **BTC/ETH** market while **Trade 1** is still open. `Edge Positioning` calculates the stoploss of $4\%$ on this market. Thus, **Trade 2** position size is $0.05 / 0.04 = 1.25$ **ETH**.
 
-Note that available capital for trading didn’t change for trade 2 even if you had already trade 1. The available capital doesn’t mean the free amount on your wallet.
+!!! Tip "Available Capital $\neq$ Available in wallet"
+    The available capital for trading didn't change in **Trade 2** even with **Trade 1** still open. The available capital **is not** the free amount in the wallet.
 
-Now you have two trades open. The bot receives yet another buy signal for another market: **ADA/ETH**. This time the stoploss is calculated at 1%. So your position size is **0.05 / 0.01 = 5 ETH**. But there are already 3.75 ETH blocked in two previous trades. So the position size for this third trade would be **5 – 3.75 = 1.25 ETH**.
+-   **Trade 3:** The strategy detects a buy signal in the **ADA/ETH** market. `Edge Positioning` calculates a stoploss of $1\%$ and a position of $0.05 / 0.01 = 5$ **ETH**. Since **Trade 1** has $2.5$ **ETH** blocked and **Trade 2** has $1.25$ **ETH** blocked, there is only $5 - 1.25 - 2.5 = 1.25$ **ETH** available. Hence, the position size of **Trade 3** is $1.25$ **ETH**. 
 
-Available capital doesn’t change before a position is sold. Let’s assume that trade 1 receives a sell signal and it is sold with a profit of 1 ETH. Your total capital on exchange would be 11 ETH and the available capital for trading becomes 5.5 ETH.
+!!! Tip "Available Capital Updates"
+    The available capital does not change before a position is sold. After a trade is closed the Available Capital goes up if the trade was profitable or goes down if the trade was a loss.
 
-So the Bot receives another buy signal for trade 4 with a stoploss at 2% then your position size would be **0.055 / 0.02 = 2.75 ETH**.
+-   The strategy detects a sell signal in the **XLM/ETH** market. The bot exits **Trade 1** for a profit of $1$ **ETH**. The total capital in the wallet becomes $11$ **ETH** and the available capital for trading becomes $5.5$ **ETH**.
+
+-   **Trade 4** The strategy detects a new buy signal int the **XLM/ETH** market. `Edge Positioning` calculates the stoploss of $2\%$, and the position size of $0.055 / 0.02 = 2.75$ **ETH**.
+
+## Edge command reference
+
+```
+usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
+                      [--userdir PATH] [-s NAME] [--strategy-path PATH]
+                      [-i TIMEFRAME] [--timerange TIMERANGE]
+                      [--data-format-ohlcv {json,jsongz,hdf5}]
+                      [--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
+                      [--fee FLOAT] [-p PAIRS [PAIRS ...]]
+                      [--stoplosses STOPLOSS_RANGE]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -i TIMEFRAME, --timeframe TIMEFRAME
+                        Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --data-format-ohlcv {json,jsongz,hdf5}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `None`).
+  --max-open-trades INT
+                        Override the value of the `max_open_trades`
+                        configuration setting.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
+  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
+                        entry and exit).
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --stoplosses STOPLOSS_RANGE
+                        Defines a range of stoploss values against which edge
+                        will assess the strategy. The format is "min,max,step"
+                        (without any space). Example:
+                        `--stoplosses=-0.01,-0.1,-0.001`
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+
+```
 
 ## Configurations
 
@@ -153,7 +284,7 @@ Edge module has following configuration options:
 | `stoploss_range_max` | Maximum stoploss. <br>*Defaults to `-0.10`.* <br> **Datatype:** Float
 | `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br> **Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br> **Datatype:** Float
 | `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
-| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
+| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10\$ on a trade you expect a 12\$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
 | `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
 | `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
 | `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> **Datatype:** Boolean
@@ -168,23 +299,29 @@ freqtrade edge
 
 An example of its output:
 
-| pair      |   stoploss |   win rate |   risk reward ratio |   required risk reward |   expectancy |   total number of trades |   average duration (min) |
-|:----------|-----------:|-----------:|--------------------:|-----------------------:|-------------:|-------------------------:|-------------------------:|
-| AGI/BTC   |      -0.02 |       0.64 |                5.86 |                   0.56 |         3.41 |                       14 |                       54 |
-| NXS/BTC   |      -0.03 |       0.64 |                2.99 |                   0.57 |         1.54 |                       11 |                       26 |
-| LEND/BTC  |      -0.02 |       0.82 |                2.05 |                   0.22 |         1.50 |                       11 |                       36 |
-| VIA/BTC   |      -0.01 |       0.55 |                3.01 |                   0.83 |         1.19 |                       11 |                       48 |
-| MTH/BTC   |      -0.09 |       0.56 |                2.82 |                   0.80 |         1.12 |                       18 |                       52 |
-| ARDR/BTC  |      -0.04 |       0.42 |                3.14 |                   1.40 |         0.73 |                       12 |                       42 |
-| BCPT/BTC  |      -0.01 |       0.71 |                1.34 |                   0.40 |         0.67 |                       14 |                       30 |
-| WINGS/BTC |      -0.02 |       0.56 |                1.97 |                   0.80 |         0.65 |                       27 |                       42 |
-| VIBE/BTC  |      -0.02 |       0.83 |                0.91 |                   0.20 |         0.59 |                       12 |                       35 |
-| MCO/BTC   |      -0.02 |       0.79 |                0.97 |                   0.27 |         0.55 |                       14 |                       31 |
-| GNT/BTC   |      -0.02 |       0.50 |                2.06 |                   1.00 |         0.53 |                       18 |                       24 |
-| HOT/BTC   |      -0.01 |       0.17 |                7.72 |                   4.81 |         0.50 |                      209 |                        7 |
-| SNM/BTC   |      -0.03 |       0.71 |                1.06 |                   0.42 |         0.45 |                       17 |                       38 |
-| APPC/BTC  |      -0.02 |       0.44 |                2.28 |                   1.27 |         0.44 |                       25 |                       43 |
-| NEBL/BTC  |      -0.03 |       0.63 |                1.29 |                   0.58 |         0.44 |                       19 |                       59 |
+| **pair**     |   **stoploss** |   **win rate** |   **risk reward ratio** |   **required risk reward** |   **expectancy** |   **total number of trades** |   **average duration (min)** |
+|:----------|-----------:|-----------:|--------------------:|-----------------------:|-------------:|-----------------:|---------------:|
+| **AGI/BTC**   |      -0.02 |       0.64 |                5.86 |                   0.56 |         3.41 |                       14 |                       54 |
+| **NXS/BTC**   |      -0.03 |       0.64 |                2.99 |                   0.57 |         1.54 |                       11 |                       26 |
+| **LEND/BTC**  |      -0.02 |       0.82 |                2.05 |                   0.22 |         1.50 |                       11 |                       36 |
+| **VIA/BTC**   |      -0.01 |       0.55 |                3.01 |                   0.83 |         1.19 |                       11 |                       48 |
+| **MTH/BTC**   |      -0.09 |       0.56 |                2.82 |                   0.80 |         1.12 |                       18 |                       52 |
+| **ARDR/BTC**  |      -0.04 |       0.42 |                3.14 |                   1.40 |         0.73 |                       12 |                       42 |
+| **BCPT/BTC**  |      -0.01 |       0.71 |                1.34 |                   0.40 |         0.67 |                       14 |                       30 |
+| **WINGS/BTC** |      -0.02 |       0.56 |                1.97 |                   0.80 |         0.65 |                       27 |                       42 |
+| **VIBE/BTC**  |      -0.02 |       0.83 |                0.91 |                   0.20 |         0.59 |                       12 |                       35 |
+| **MCO/BTC**   |      -0.02 |       0.79 |                0.97 |                   0.27 |         0.55 |                       14 |                       31 |
+| **GNT/BTC**   |      -0.02 |       0.50 |                2.06 |                   1.00 |         0.53 |                       18 |                       24 |
+| **HOT/BTC**   |      -0.01 |       0.17 |                7.72 |                   4.81 |         0.50 |                      209 |                        7 |
+| **SNM/BTC**   |      -0.03 |       0.71 |                1.06 |                   0.42 |         0.45 |                       17 |                       38 |
+| **APPC/BTC**  |      -0.02 |       0.44 |                2.28 |                   1.27 |         0.44 |                       25 |                       43 |
+| **NEBL/BTC**  |      -0.03 |       0.63 |                1.29 |                   0.58 |         0.44 |                       19 |                       59 |
+
+Edge produced the above table by comparing `calculate_since_number_of_days` to `minimum_expectancy` to find `min_trade_number` historical information based on the config file. The timerange Edge uses for its comparisons can be further limited by using the `--timerange` switch.
+
+In live and dry-run modes, after the `process_throttle_secs` has passed, Edge will again process `calculate_since_number_of_days` against `minimum_expectancy` to find `min_trade_number`. If no `min_trade_number` is found, the bot will return "whitelist empty". Depending on the trade strategy being deployed, "whitelist empty" may be return much of the time - or *all* of the time. The use of Edge may also cause trading to occur in bursts, though this is rare.
+
+If you encounter "whitelist empty" a lot, condsider tuning `calculate_since_number_of_days`, `minimum_expectancy`  and `min_trade_number` to align to the trading frequency of your strategy.
 
 ### Update cached pairs with the latest data
 
@@ -211,3 +348,6 @@ The full timerange specification:
 * Use tickframes since 2018/01/31: `--timerange=20180131-`
 * Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
 * Use tickframes between POSIX timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`
+
+
+[^1]: Question extracted from MIT Opencourseware S096 - Mathematics with applications in Finance: https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/

docs/exchanges.md

@@ -1,29 +1,103 @@
 # Exchange-specific Notes
 
-This page combines common gotchas and informations which are exchange-specific and most likely don't apply to other exchanges.
+This page combines common gotchas and Information which are exchange-specific and most likely don't apply to other exchanges.
+
+## Exchange configuration
+
+Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports over 100 cryptocurrency
+exchange markets and trading APIs. The complete up-to-date list can be found in the
+[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
+However, the bot was tested by the development team with only a few exchanges.
+A current list of these can be found in the "Home" section of this documentation.
+
+Feel free to test other exchanges and submit your feedback or PR to improve the bot or confirm exchanges that work flawlessly..
+
+Some exchanges require special configuration, which can be found below.
+
+### Sample exchange configuration
+
+A exchange configuration for "binance" would look as follows:
+
+```json
+"exchange": {
+    "name": "binance",
+    "key": "your_exchange_key",
+    "secret": "your_exchange_secret",
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+    // ... 
+```
+
+### Setting rate limits
+
+Usually, rate limits set by CCXT are reliable and work well.
+In case of problems related to rate-limits (usually DDOS Exceptions in your logs), it's easy to change rateLimit settings to other values.
+
+```json
+"exchange": {
+    "name": "kraken",
+    "key": "your_exchange_key",
+    "secret": "your_exchange_secret",
+    "ccxt_config": {"enableRateLimit": true},
+    "ccxt_async_config": {
+        "enableRateLimit": true,
+        "rateLimit": 3100
+    },
+```
+
+This configuration enables kraken, as well as rate-limiting to avoid bans from the exchange.
+`"rateLimit": 3100` defines a wait-event of 3.1s between each call. This can also be completely disabled by setting `"enableRateLimit"` to false.
+
+!!! Note
+    Optimal settings for rate-limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
+    We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that `"enableRateLimit"` is enabled and increase the `"rateLimit"` parameter step by step.
 
 ## Binance
 
-!!! Tip "Stoploss on Exchange"
-    Binance supports `stoploss_on_exchange` and uses stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
+Binance supports [time_in_force](configuration.md#understand-order_time_in_force).
 
-### Blacklists
+!!! Tip "Stoploss on Exchange"
+    Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange..
+
+### Binance Blacklist
 
 For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
-Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB order unsellable as the expected amount is not there anymore.
+Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
+
+### Binance Futures
+
+Binance has specific (unfortunately complex) [Futures Trading Quantitative Rules](https://www.binance.com/en/support/faq/4f462ebe6ff445d4a170be7d9e897272) which need to be followed, and which prohibit a too low stake-amount (among others) for too many orders.
+Violating these rules will result in a trading restriction.
+
+When trading on Binance Futures market, orderbook must be used because there is no price ticker data for futures.
+
+``` jsonc
+  "entry_pricing": {
+      "use_order_book": true,
+      "order_book_top": 1,
+      "check_depth_of_market": {
+          "enabled": false,
+          "bids_to_ask_delta": 1
+      }
+  },
+  "exit_pricing": {
+      "use_order_book": true,
+      "order_book_top": 1
+  },
+```
 
 ### Binance sites
 
-Binance has been split into 3, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
+Binance has been split into 2, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
 
 * [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
 * [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
-* [binance.je](https://www.binance.je/) - Binance Jersey, trading fiat currencies. Use exchange id: `binanceje`.
 
 ## Kraken
 
 !!! Tip "Stoploss on Exchange"
-    Kraken supports `stoploss_on_exchange` and uses stop-loss-market orders. It provides great advantages, so we recommend to benefit from it, however since the resulting order is a stoploss-market order, sell-rates are not guaranteed, which makes this feature less secure than on other exchanges. This limitation is based on kraken's policy [source](https://blog.kraken.com/post/1234/announcement-delisting-pairs-and-temporary-suspension-of-advanced-order-types/) and [source2](https://blog.kraken.com/post/1494/kraken-enables-advanced-orders-and-adds-10-currency-pairs/) - which has stoploss-limit orders disabled.
+    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.
 
 ### Historic Kraken data
 
@@ -39,12 +113,29 @@ Due to the heavy rate-limiting applied by Kraken, the following configuration se
     },
 ```
 
+!!! Warning "Downloading data from kraken"
+    Downloading kraken data will require significantly more memory (RAM) than any other exchange, as the trades-data needs to be converted into candles on your machine.
+    It will also take a long time, as freqtrade will need to download every single trade that happened on the exchange for the pair / timerange combination, therefore please be patient.
+
+!!! Warning "rateLimit tuning"
+    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.
@@ -66,8 +157,9 @@ You can get a list of restricted markets by using the following snippet:
 ``` python
 import ccxt
 ct = ccxt.bittrex()
-_ = ct.load_markets()
-res = [ f"{x['MarketCurrency']}/{x['BaseCurrency']}" for x in ct.publicGetMarkets()['result'] if x['IsRestricted']]
+lm = ct.load_markets()
+
+res = [p for p, x in lm.items() if 'US' in x['info']['prohibitedIn']]
 print(res)
 ```
 
@@ -75,8 +167,7 @@ print(res)
 
 !!! Tip "Stoploss on Exchange"
     FTX 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.
-
+    You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type of stoploss shall be used.
 
 ### Using subaccounts
 
@@ -92,17 +183,74 @@ To use subaccounts with FTX, you need to edit the configuration and add the foll
 }
 ```
 
-!!! Note
-    Older versions of freqtrade may require this key to be added to `"ccxt_async_config"` as well.
+## 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:
+
+```json
+"exchange": {
+    "name": "kucoin",
+    "key": "your_exchange_key",
+    "secret": "your_exchange_secret",
+    "password": "your_exchange_api_key_password",
+    // ...
+}
+```
+
+Kucoin supports [time_in_force](configuration.md#understand-order_time_in_force).
+
+!!! Tip "Stoploss on Exchange"
+    Kucoin 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 of stoploss shall be used.
+
+### Kucoin Blacklists
+
+For Kucoin, please add `"KCS/<STAKE>"` to your blacklist to avoid issues.
+Accounts having KCS accounts use this to pay for fees - if your first trade happens to be on `KCS`, further trades will consume this position and make the initial KCS trade unsellable as the expected amount is not there anymore.
+
+## 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.
+
+## OKX (former OKEX)
+
+OKX 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:
+
+```json
+"exchange": {
+    "name": "okx",
+    "key": "your_exchange_key",
+    "secret": "your_exchange_secret",
+    "password": "your_exchange_api_key_password",
+    // ...
+}
+```
+
+!!! Warning
+    OKX only provides 100 candles per api call. Therefore, the strategy will only have a pretty low amount of data available in backtesting mode.
+
+!!! Warning "Futures"
+    OKX Futures has the concept of "position mode" - which can be Net or long/short (hedge mode).
+    Freqtrade supports both modes - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
+    OKX also only provides MARK candles for the past ~3 months. Backtesting futures prior to that date will therefore lead to slight deviations, as funding-fees cannot be calculated correctly without this data.
+
+## Gate.io
+
+!!! Tip "Stoploss on Exchange"
+    Gate.io 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..
+
+Gate.io allows the use of `POINT` to pay for fees. As this is not a tradable currency (no regular market available), automatic fee calculations will fail (and default to a fee of 0).
+The configuration parameter `exchange.unknown_fee_rate` can be used to specify the exchange rate between Point and the stake currency. Obviously, changing the stake-currency will also require changes to this value.
 
 ## All exchanges
 
 Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
 
-
 ## Random notes for other exchanges
 
 * The Ocean (exchange id: `theocean`) exchange uses Web3 functionality and requires `web3` python package to be installed:
+
 ```shell
 $ pip3 install web3
 ```
@@ -117,3 +265,25 @@ Whether your exchange returns incomplete candles or not can be checked using [th
 Due to the danger of repainting, Freqtrade does not allow you to use this incomplete candle.
 
 However, if it is based on the need for the latest price for your strategy - then this requirement can be acquired using the [data provider](strategy-customization.md#possible-options-for-dataprovider) from within the strategy.
+
+### Advanced Freqtrade Exchange configuration
+
+Advanced options can be configured using the `_ft_has_params` setting, which will override Defaults and exchange-specific behavior.
+
+Available options are listed in the exchange-class as `_ft_has_default`.
+
+For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
+
+```json
+"exchange": {
+    "name": "kraken",
+    "_ft_has_params": {
+        "order_time_in_force": ["gtc", "fok"],
+        "ohlcv_candle_limit": 200
+        }
+    //...
+}
+```
+
+!!! Warning
+    Please make sure to fully understand the impacts of these settings before modifying them.

docs/faq.md

@@ -1,28 +1,50 @@
 # Freqtrade FAQ
 
+## Supported Markets
+
+Freqtrade supports spot trading only.
+
+### Can I open short positions?
+
+Freqtrade can open short positions in futures markets.
+This requires the strategy to be made for this - and `"trading_mode": "futures"` in the configuration.
+Please make sure to read the [relevant documentation page](leverage.md) first.
+
+In spot markets, you can in some cases use leveraged spot tokens, which reflect an inverted pair (eg. BTCUP/USD, BTCDOWN/USD, ETHBULL/USD, ETHBEAR/USD,...) which can be traded with Freqtrade.
+
+### Can I trade options or futures?
+
+Futures trading is supported for selected exchanges.
+
+## Beginner Tips & Tricks
+
+* When you work with your strategy & hyperopt file you should use a proper code editor like VSCode or PyCharm. A good code editor will provide syntax highlighting as well as line numbers, making it easy to find syntax errors (most likely pointed out by Freqtrade during startup).
+
 ## Freqtrade common issues
 
 ### The bot does not start
 
-Running the bot with `freqtrade trade --config config.json` does show the output `freqtrade: command not found`.
+Running the bot with `freqtrade trade --config config.json` shows the output `freqtrade: command not found`.
 
-This could have the following reasons:
+This could be caused by the following reasons:
 
-* The virtual environment is not active
-  * run `source .env/bin/activate` to activate the virtual environment
+* The virtual environment is not active.
+  * Run `source .env/bin/activate` to activate the virtual environment.
 * The installation did not work correctly.
   * Please check the [Installation documentation](installation.md).
 
-### I have waited 5 minutes, why hasn't the bot made any trades yet?!
+### I have waited 5 minutes, why hasn't the bot made any trades yet?
 
-Depending on the buy strategy, the amount of whitelisted coins, the
-situation of the market etc, it can take up to hours to find good entry
+* Depending on the buy strategy, the amount of whitelisted coins, the
+situation of the market etc, it can take up to hours to find a good entry
 position for a trade. Be patient!
 
-### I have made 12 trades already, why is my total profit negative?!
+* It may be because of a configuration error. It's best to check the logs, they usually tell you if the bot is simply not getting buy signals (only heartbeat messages), or if there is something wrong (errors / exceptions in the log).
+
+### I have made 12 trades already, why is my total profit negative?
 
 I understand your disappointment but unfortunately 12 trades is just
-not enough to say anything. If you run backtesting, you can see that our
+not enough to say anything. If you run backtesting, you can see that the
 current algorithm does leave you on the plus side, but that is after
 thousands of trades and even there, you will be left with losses on
 specific coins that you have traded tens if not hundreds of times. We
@@ -30,20 +52,34 @@ of course constantly aim to improve the bot but it will _always_ be a
 gamble, which should leave you with modest wins on monthly basis but
 you can't say much from few trades.
 
-### I’d like to change the stake amount. Can I just stop the bot with /stop and then change the config.json and run it again?
+### I’d like to make changes to the config. Can I do that without having to kill the bot?
 
-Not quite. Trades are persisted to a database but the configuration is
-currently only read when the bot is killed and restarted. `/stop` more
-like pauses. You can stop your bot, adjust settings and start it again.
+Yes. You can edit your config and use the `/reload_config` command to reload the configuration. The bot will stop, reload the configuration and strategy and will restart with the new configuration and strategy.
 
-### I want to improve the bot with a new strategy
+### Why does my bot not sell everything it bought?
 
-That's great. We have a nice backtesting and hyperoptimization setup. See
-the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-commands).
+This is called "coin dust" and can happen on all exchanges.
+It happens because many exchanges subtract fees from the "receiving currency" - so you buy 100 COIN - but you only get 99.9 COIN.
+As COIN is trading in full lot sizes (1COIN steps), you cannot sell 0.9 COIN (or 99.9 COIN) - but you need to round down to 99 COIN.
+
+This is not a bot-problem, but will also happen while manual trading.
+
+While freqtrade can handle this (it'll sell 99 COIN), fees are often below the minimum tradable lot-size (you can only trade full COIN, not 0.9 COIN).
+Leaving the dust (0.9 COIN) on the exchange makes usually sense, as the next time freqtrade buys COIN, it'll eat into the remaining small balance, this time selling everything it bought, and therefore slowly declining the dust balance (although it most likely will never reach exactly 0).
+
+Where possible (e.g. on binance), the use of the exchange's dedicated fee currency will fix this.  
+On binance, it's sufficient to have BNB in your account, and have "Pay fees in BNB" enabled in your profile. Your BNB balance will slowly decline (as it's used to pay fees) - but you'll no longer encounter dust (Freqtrade will include the fees in the profit calculations).
+Other exchanges don't offer such possibilities, where it's simply something you'll have to accept or move to a different exchange.
+
+### I want to use incomplete candles
+
+Freqtrade will not provide incomplete candles to strategies. Using incomplete candles will lead to repainting and consequently to strategies with "ghost" buys, which are impossible to both backtest, and verify after they happened.
+
+You can use "current" market data by using the [dataprovider](strategy-customization.md#orderbookpair-maximum)'s orderbook or ticker methods - which however cannot be used during backtesting.
 
 ### Is there a setting to only SELL the coins being held and not perform anymore BUYS?
 
-You can use the `/forcesell all` command from Telegram.
+You can use the `/stopbuy` command in Telegram to prevent future buys, followed by `/forceexit all` (sell all open trades).
 
 ### I want to run multiple bots on the same machine
 
@@ -53,37 +89,49 @@ Please look at the [advanced setup documentation Page](advanced-setup.md#running
 
 This message is just a warning that the latest candles had missing candles in them.
 Depending on the exchange, this can indicate that the pair didn't have a trade for the timeframe you are using - and the exchange does only return candles with volume.
-On low volume pairs, this is a rather common occurance.
+On low volume pairs, this is a rather common occurrence.
 
 If this happens for all pairs in the pairlist, this might indicate a recent exchange downtime. Please check your exchange's public channels for details.
 
 Irrespectively of the reason, Freqtrade will fill up these candles with "empty" candles, where open, high, low and close are set to the previous candle close - and volume is empty. In a chart, this will look like a `_` - and is aligned with how exchanges usually represent 0 volume candles.
 
+### I'm getting "Outdated history for pair xxx" in the log
+
+The bot is trying to tell you that it got an outdated last candle (not the last complete candle).
+As a consequence, Freqtrade will not enter a trade for this pair - as trading on old information is usually not what is desired.
+
+This warning can point to one of the below problems:
+
+* Exchange downtime -> Check your exchange status page / blog / twitter feed for details.
+* Wrong system time -> Ensure your system-time is correct.
+* 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 Bittrex does not support market orders." message and cannot run my strategy
+### I'm getting the "Exchange XXX does not support market orders." message and cannot run my strategy
 
-As the message says, Bittrex does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Probably your strategy was written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Bittrex).
+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).
 
-To fix it for Bittrex, redefine order types in the strategy to use "limit" instead of "market":
+To fix this, redefine order types in the strategy to use "limit" instead of "market":
 
-```
+``` python
     order_types = {
         ...
-        'stoploss': 'limit',
+        "stoploss": "limit",
         ...
     }
 ```
 
-Same fix should be done in the configuration file, if order types are defined in your custom config rather than in the strategy.
+The same fix should be applied in the configuration file, if order types are defined in your custom config rather than in the strategy.
 
 ### How do I search the bot logs for something?
 
-By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility subcommands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.
+By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility sub-commands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.
 
 * In unix shells, this normally can be done as simple as:
 ```shell
@@ -108,7 +156,7 @@ and then grep it as:
 ```shell
 $ cat /path/to/mylogfile.log | grep 'something'
 ```
-or even on the fly, as the bot works and the logfile grows:
+or even on the fly, as the bot works and the log file grows:
 ```shell
 $ tail -f /path/to/mylogfile.log | grep 'something'
 ```
@@ -121,43 +169,62 @@ On Windows, the `--logfile` option is also supported by Freqtrade and you can us
 
 ## Hyperopt module
 
-### How many epoch do I need to get a good Hyperopt result?
+### Why does freqtrade not have GPU support?
+
+First of all, most indicator libraries don't have GPU support - as such, there would be little benefit for indicator calculations.
+The GPU improvements would only apply to pandas-native calculations - or ones written by yourself.
+
+For hyperopt, freqtrade is using scikit-optimize, which is built on top of scikit-learn.
+Their statement about GPU support is [pretty clear](https://scikit-learn.org/stable/faq.html#will-you-add-gpu-support).
+
+GPU's also are only good at crunching numbers (floating point operations). 
+For hyperopt, we need both number-crunching (find next parameters) and running python code (running backtesting). 
+As such, GPU's are not too well suited for most parts of hyperopt.
+
+The benefit of using GPU would therefore be pretty slim - and will not justify the complexity introduced by trying to add GPU support.
+
+There is however nothing preventing you from using GPU-enabled indicators within your strategy if you think you must have this - you will however probably be disappointed by the slim gain that will give you (compared to the complexity).
+
+### How many epochs do I need to get a good Hyperopt result?
 
 Per default Hyperopt called without the `-e`/`--epochs` command line option will only
-run 100 epochs, means 100 evals of your triggers, guards, ... Too few
+run 100 epochs, means 100 evaluations of your triggers, guards, ... Too few
 to find a great result (unless if you are very lucky), so you probably
-have to run it for 10.000 or more. But it will take an eternity to
+have to run it for 10000 or more. But it will take an eternity to
 compute.
 
-We recommend you to run it at least 10.000 epochs:
+Since hyperopt uses Bayesian search, running for too many epochs may not produce greater results.
+
+It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10000 epochs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
 
 ```bash
-freqtrade hyperopt -e 10000
+freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
 ```
 
-or if you want intermediate result to see
+### Why does it take a long time to run hyperopt?
 
-```bash
-for i in {1..100}; do freqtrade hyperopt -e 100; done
-```
+* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [discord community](https://discord.gg/p7nuUNVfP7). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
 
-### Why it is so long to run hyperopt?
+* If you wonder why it can take from 20 minutes to days to do 1000 epochs here are some answers:
 
-Finding a great Hyperopt results takes time.
+This answer was written during the release 0.15.1, when we had:
 
-If you wonder why it takes a while to find great hyperopt results
-
-This answer was written during the under the release 0.15.1, when we had:
-
-- 8 triggers
-- 9 guards: let's say we evaluate even 10 values from each
-- 1 stoploss calculation: let's say we want 10 values from that too to be evaluated
+* 8 triggers
+* 9 guards: let's say we evaluate even 10 values from each
+* 1 stoploss calculation: let's say we want 10 values from that too to be evaluated
 
 The following calculation is still very rough and not very precise
 but it will give the idea. With only these triggers and guards there is
-already 8\*10^9\*10 evaluations. A roughly total of 80 billion evals.
-Did you run 100 000 evals? Congrats, you've done roughly 1 / 100 000 th
-of the search space.
+already 8\*10^9\*10 evaluations. A roughly total of 80 billion evaluations.
+Did you run 100 000 evaluations? Congrats, you've done roughly 1 / 100 000 th
+of the search space, assuming that the bot never tests the same parameters more than once.
+
+* The time it takes to run 1000 hyperopt epochs depends on things like: The available cpu, hard-disk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 100000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades. 
+
+Example: 4% profit 650 times vs 0,3% profit a trade 10000 times in a year. If we assume you set the --timerange to 365 days. 
+
+Example:
+`freqtrade --config config.json --strategy SampleStrategy --hyperopt SampleHyperopt -e 1000 --timerange 20190601-20200601`
 
 ## Edge module
 
@@ -165,7 +232,7 @@ of the search space.
 
 The Edge module is mostly a result of brainstorming of [@mishaker](https://github.com/mishaker) and [@creslinux](https://github.com/creslinux) freqtrade team members.
 
-You can find further info on expectancy, winrate, risk management and position size in the following sources:
+You can find further info on expectancy, win rate, risk management and position size in the following sources:
 
 - https://www.tradeciety.com/ultimate-math-guide-for-traders/
 - http://www.vantharp.com/tharp-concepts/expectancy.asp

docs/freqai.md

@@ -0,0 +1,769 @@
+![freqai-logo](assets/freqai_doc_logo.svg)
+
+# FreqAI
+
+FreqAI is a module designed to automate a variety of tasks associated with training a predictive model to generate market forecasts given a set of input features.
+
+Among the the features included:
+
+* **Self-adaptive retraining**: retrain models during live deployments to self-adapt to the market in an unsupervised manner.
+* **Rapid feature engineering**: create large rich feature sets (10k+ features) based on simple user created strategies.
+* **High performance**: adaptive retraining occurs on separate thread (or on GPU if available) from inferencing and bot trade operations. Keep newest models and data in memory for rapid inferencing.
+* **Realistic backtesting**: emulate self-adaptive retraining with backtesting module that automates past retraining.
+* **Modifiable**: use the generalized and robust architecture for incorporating any machine learning library/method available in Python. Seven examples available.
+* **Smart outlier removal**: remove outliers from training and prediction sets using a variety of outlier detection techniques.
+* **Crash resilience**: model storage to disk to make reloading from a crash fast and easy (and purge obsolete files for sustained dry/live runs).
+* **Automated data normalization**: normalize the data in a smart and statistically safe way.
+* **Automatic data download**: compute the data download timerange and update historic data (in live deployments).
+* **Clean incoming data** safe NaN handling before training and prediction.
+* **Dimensionality reduction**: reduce the size of the training data via Principal Component Analysis.
+* **Deploy bot fleets**: set one bot to train models while a fleet of other bots inference into the models and handle trades.
+
+## Quick start
+
+The easiest way to quickly test FreqAI is to run it in dry run with the following command
+
+```bash
+freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates
+```
+
+where the user will see the boot-up process of auto-data downloading, followed by simultaneous training and trading.
+
+The example strategy, example prediction model, and example config can all be found in
+`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`,
+`config_examples/config_freqai.example.json`, respectively.
+
+## General approach
+
+The user provides FreqAI with a set of custom *base* indicators (created inside the strategy the same way
+a typical Freqtrade strategy is created) as well as target values which look into the future.
+FreqAI trains a model to predict the target value based on the input of custom indicators for each pair in the whitelist. These models are consistently retrained to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining) and deploy dry/live. In dry/live conditions, FreqAI can be set to constant retraining in a background thread in an effort to keep models as young as possible.
+
+An overview of the algorithm is shown here to help users understand the data processing pipeline and the model usage. 
+
+![freqai-algo](assets/freqai_algo.png)
+
+## Background and vocabulary
+
+**Features** are the quantities with which a model is trained. $X_i$ represents the
+vector of all features for a single candle. In FreqAI, the user
+builds the features from anything they can construct in the strategy.
+
+**Labels** are the target values with which the weights inside a model are trained
+toward. Each set of features is associated with a single label, which is also
+defined within the strategy by the user. These labels intentionally look into the
+future, and are not available to the model during dryrun/live/backtesting.
+
+**Training** refers to the process of feeding individual feature sets into the
+model with associated labels with the goal of matching input feature sets to associated labels.
+
+**Train data** is a subset of the historic data which is fed to the model during
+training to adjust weights. This data directly influences weight connections in the model.
+
+**Test data** is a subset of the historic data which is used to evaluate the
+intermediate performance of the model during training. This data does not
+directly influence nodal weights within the model.
+
+## Install prerequisites
+
+The normal Freqtrade install process will ask the user if they wish to install FreqAI dependencies. The user should reply "yes" to this question if they wish to use FreqAI. If the user did not reply yes, they can manually install these dependencies after the install with:
+
+``` bash
+pip install -r requirements-freqai.txt
+```
+
+!!! Note
+    Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since Catboost does not provide wheels for this platform.
+
+### Usage with docker
+
+For docker users, a dedicated tag with freqAI dependencies is available as `:freqai`.
+As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`.
+This image contains the regular freqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
+
+## Configuring FreqAI
+
+### Parameter table
+
+The table below will list all configuration parameters available for FreqAI.
+
+Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
+
+|  Parameter | Description |
+|------------|-------------|
+| `freqai` | **Required.** The parent dictionary containing all the parameters below for controlling FreqAI. <br> **Datatype:** dictionary.
+| `identifier` | **Required.** A unique name for the current model. This can be reused to reload pre-trained models/data. <br> **Datatype:** string.
+| `train_period_days` | **Required.** Number of days to use for the training data (width of the sliding window). <br> **Datatype:** positive integer.
+| `backtest_period_days` | **Required.** Number of days to inference into the trained model before sliding the window and retraining. This can be fractional days, but beware that the user provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
+| `live_retrain_hours` | Frequency of retraining during dry/live runs. Default set to 0, which means it will retrain as often as possible. <br> **Datatype:** Float > 0.
+| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. `False` by default. <br> **Datatype:** boolean.
+| `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period. <br> **Datatype:** positive integer.
+| `fit_live_predictions_candles` | Computes target (label) statistics from prediction data, instead of from the training data set. Number of candles is the number of historical candles it uses to generate the statistics. <br> **Datatype:** positive integer.
+| `purge_old_models` | Tell FreqAI to delete obsolete models. Otherwise, all historic models will remain on disk. Defaults to `False`. <br> **Datatype:** boolean.
+| `expiration_hours` | Ask FreqAI to avoid making predictions if a model is more than `expiration_hours` old. Defaults to 0 which means models never expire. <br> **Datatype:** positive integer.
+|  |  **Feature Parameters**
+| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples shown [here](#feature-engineering) <br> **Datatype:** dictionary.
+| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` will be created for each coin in this list, and that set of features is added to the base asset feature set. <br> **Datatype:** list of assets (strings).
+| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for and added as features to the base asset feature set. <br> **Datatype:** list of timeframes (strings).
+| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators`, refer to `templates/FreqaiExampleStrategy.py` for detailed usage. The user can create custom labels, making use of this parameter not. <br> **Datatype:** positive integer.
+| `include_shifted_candles` | Parameter used to add a sense of temporal recency to flattened regression type input data. `include_shifted_candles` takes all features, duplicates and shifts them by the number indicated by user. <br> **Datatype:** positive integer.
+| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when above 0, explained in detail [here](#removing-outliers-with-the-dissimilarity-index). <br> **Datatype:** positive float (typically below 1).
+| `weight_factor` | Used to set weights for training data points according to their recency, see details and a figure of how it works [here](#controlling-the-model-learning-process). <br> **Datatype:** positive float (typically below 1).
+| `principal_component_analysis` | Ask FreqAI to automatically reduce the dimensionality of the data set using PCA. <br> **Datatype:** boolean.
+| `use_SVM_to_remove_outliers` | Ask FreqAI to train a support vector machine to detect and remove outliers from the training data set as well as from incoming data points. <br> **Datatype:** boolean.
+| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. E.g. `nu` *Very* broadly, is the percentage of data points that should be considered outliers. `shuffle` is by default false to maintain reproducibility. But these and all others can be added/changed in this dictionary. <br> **Datatype:** dictionary.
+| `stratify_training_data` | This value is used to indicate the stratification of the data. e.g. 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. <br> **Datatype:** positive integer.
+| `indicator_max_period_candles` | The maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN <br> **Datatype:** positive integer.
+| `indicator_periods_candles` | A list of integers used to duplicate all indicators according to a set of periods and add them to the feature set. <br> **Datatype:** list of positive integers.
+| `use_DBSCAN_to_remove_outliers` | Inactive by default. If true, FreqAI clusters data using DBSCAN to identify and remove outliers from training and prediction data. <br> **Datatype:** float (fraction of 1).
+|  |  **Data split parameters**
+| `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) <br> **Datatype:** dictionary.
+| `test_size` | Fraction of data that should be used for testing instead of training. <br> **Datatype:** positive float below 1.
+| `shuffle` | Shuffle the training data points during training. Typically for time-series forecasting, this is set to False. <br> **Datatype:** boolean.
+|  |  **Model training parameters**
+| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected library. For example, if the user uses `LightGBMRegressor`, then this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html). If the user selects a different model, then this dictionary can contain any parameter from that different model.  <br> **Datatype:** dictionary.
+| `n_estimators` | A common parameter among regressors which sets the number of boosted trees to fit <br> **Datatype:** integer.
+| `learning_rate` | A common parameter among regressors which sets the boosting learning rate. <br> **Datatype:** float.
+| `n_jobs`, `thread_count`, `task_type` | Different libraries use different parameter names to control the number of threads used for parallel processing or whether or not it is a `task_type` of `gpu` or `cpu`. <br> **Datatype:** float.
+|  |  **Extraneous parameters**
+| `keras` | If your model makes use of keras (typical of Tensorflow based prediction models), activate this flag so that the model save/loading follows keras standards. Default value `false`  <br> **Datatype:** boolean.
+| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for `shift` by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. Default value, 2 <br> **Datatype:** integer.
+
+### Important FreqAI dataframe key patterns
+
+Here are the values the user can expect to include/use inside the typical strategy dataframe (`df[]`):
+
+|  DataFrame Key | Description |
+|------------|-------------|
+| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target inside FreqAI (typically following the naming convention `&-s*`). These same dataframe columns names are fed back to the user as the predictions. For example, the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['&-s_close']`. FreqAI makes the predictions and gives them back to the user under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** depends on the output of the model.
+| `df['&*_std/mean']` | The standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand rarity of prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training (or historically with `fit_live_predictions_candles`)<br> **Datatype:** float.
+| `df['do_predict']` | An indication of an outlier, this return value is integer between -1 and 2 which lets the user understand if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the [Dissimilarity Index](#removing-outliers-with-the-dissimilarity-index) is above the user defined threshold, it will subtract 1 from `do_predict`. If `use_SVM_to_remove_outliers()` is active, then the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract one from `do_predict`.  A particular case is when `do_predict == 2`, it means that the model has expired due to `expired_hours`. <br> **Datatype:** integer between -1 and 2.
+| `df['DI_values']` | The raw Dissimilarity Index values to give the user a sense of confidence in the prediction. Lower DI means the data point is closer to the trained parameter space. <br> **Datatype:** float.
+| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature inside FreqAI. For example, the user can include the rsi in the training feature set (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#building-the-feature-set). <br>**Note**: since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table.) these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, you can prepend it with `%%`. <br> **Datatype:** depends on the output of the model.
+
+### Example config file
+
+The user interface is isolated to the typical config file. A typical FreqAI config setup could include:
+
+```json
+    "freqai": {
+        "startup_candles": 10000,
+        "purge_old_models": true,
+        "train_period_days": 30,
+        "backtest_period_days": 7,
+        "identifier" : "unique-id",
+        "feature_parameters" : {
+            "include_timeframes": ["5m","15m","4h"],
+            "include_corr_pairlist": [
+                "ETH/USD",
+                "LINK/USD",
+                "BNB/USD"
+            ],
+            "label_period_candles": 24,
+            "include_shifted_candles": 2,
+            "weight_factor":  0,
+            "indicator_max_period_candles": 20,
+            "indicator_periods_candles": [10, 20]
+        },
+        "data_split_parameters" : {
+            "test_size": 0.25,
+            "random_state": 42
+        },
+        "model_training_parameters" : {
+            "n_estimators": 100,
+            "random_state": 42,
+            "learning_rate": 0.02,
+            "task_type": "CPU",
+        },
+    }
+```
+
+### Feature engineering
+
+Features are added by the user inside the `populate_any_indicators()` method of the strategy
+by prepending indicators with `%` and labels are added by prepending `&`.  
+There are some important components/structures that the user *must* include when building their feature set.
+Another structure to consider is the  location of the labels at the bottom of the example function (below `if set_generalized_indicators:`).
+This is where the user will add single features and labels to their feature set to avoid duplication from
+various configuration parameters which multiply the feature set such as `include_timeframes`.
+
+```python
+    def populate_any_indicators(
+        self, pair, df, tf, informative=None, set_generalized_indicators=False
+    ):
+        """
+        Function designed to automatically generate, name and merge features
+        from user indicated timeframes in the configuration file. User controls the indicators
+        passed to the training/prediction by prepending indicators with `'%-' + coin `
+        (see convention below). I.e. user should not prepend any supporting metrics
+        (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
+        model.
+        :param pair: pair to be used as informative
+        :param df: strategy dataframe which will receive merges from informatives
+        :param tf: timeframe of the dataframe which will modify the feature names
+        :param informative: the dataframe associated with the informative pair
+        :param coin: the name of the coin which will modify the feature names.
+        """
+
+        coint = pair.split('/')[0]
+
+        if informative is None:
+            informative = self.dp.get_pair_dataframe(pair, tf)
+
+        # first loop is automatically duplicating indicators for time periods
+        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+            t = int(t)
+            informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
+            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
+            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
+
+            bollinger = qtpylib.bollinger_bands(
+                qtpylib.typical_price(informative), window=t, stds=2.2
+            )
+            informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
+            informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
+            informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
+
+            informative[f"%-{coin}bb_width-period_{t}"] = (
+                informative[f"{coin}bb_upperband-period_{t}"]
+                - informative[f"{coin}bb_lowerband-period_{t}"]
+            ) / informative[f"{coin}bb_middleband-period_{t}"]
+            informative[f"%-{coin}close-bb_lower-period_{t}"] = (
+                informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
+            )
+
+            informative[f"%-{coin}relative_volume-period_{t}"] = (
+                informative["volume"] / informative["volume"].rolling(t).mean()
+            )
+
+        indicators = [col for col in informative if col.startswith("%")]
+        # This loop duplicates and shifts all indicators to add a sense of recency to data
+        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
+            if n == 0:
+                continue
+            informative_shift = informative[indicators].shift(n)
+            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
+            informative = pd.concat((informative, informative_shift), axis=1)
+
+        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
+        skip_columns = [
+            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
+        ]
+        df = df.drop(columns=skip_columns)
+
+        # Add generalized indicators here (because in live, it will call this
+        # function to populate indicators during training). Notice how we ensure not to
+        # add them multiple times
+        if set_generalized_indicators:
+            df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
+            df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
+
+            # user adds targets here by prepending them with &- (see convention below)
+            # If user wishes to use multiple targets, a multioutput prediction model
+            # needs to be used such as templates/CatboostPredictionMultiModel.py
+            df["&-s_close"] = (
+                df["close"]
+                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+                .mean()
+                / df["close"]
+                - 1
+            )
+
+        return df
+```
+
+The user of the present example does not wish to pass the `bb_lowerband` as a feature to the model,
+and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the
+model for training/prediction and has therefore prepended it with `%`.
+
+The `include_timeframes` from the example config above are the timeframes (`tf`) of each call to `populate_any_indicators()`
+included metric for inclusion in the feature set. In the present case, the user is asking for the
+`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
+
+In addition, the user can ask for each of these features to be included from
+informative pairs using the `include_corr_pairlist`. This means that the present feature
+set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of
+`ETH/USD`, `LINK/USD`, and `BNB/USD`.
+
+`include_shifted_candles` is another user controlled parameter which indicates the number of previous
+candles to include in the present feature set. In other words, `include_shifted_candles: 2`, tells
+FreqAI to include the the past 2 candles for each of the features included in the dataset.
+
+In total, the number of features the present user has created is:
+
+length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`  
+$3 * 3 * 3 * 2 * 2 = 108$.
+
+!!! Note
+    Features **must** be defined in `populate_any_indicators()`. Making features in `populate_indicators()`
+    will fail in live/dry mode. If the user wishes to add generalized features that are not associated with 
+    a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()`
+    (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`:
+
+    ```python
+        def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False):
+
+            ...
+
+            # Add generalized indicators here (because in live, it will call only this function to populate 
+            # indicators for retraining). Notice how we ensure not to add them multiple times by associating
+            # these generalized indicators to the basepair/timeframe
+            if set_generalized_indicators:
+                df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
+                df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
+
+                # user adds targets here by prepending them with &- (see convention below)
+                # If user wishes to use multiple targets, a multioutput prediction model
+                # needs to be used such as templates/CatboostPredictionMultiModel.py
+                df["&-s_close"] = (
+                    df["close"]
+                    .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+                    .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+                    .mean()
+                    / df["close"]
+                    - 1
+                    )
+    ```
+
+    (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`)
+
+### Deciding the sliding training window and backtesting duration
+
+Users define the backtesting timerange with the typical `--timerange` parameter in the user
+configuration file. `train_period_days` is the duration of the sliding training window, while
+`backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be
+a float to indicate sub daily retraining in live/dry mode). In the present example,
+the user is asking FreqAI to use a training period of 30 days and backtest the subsequent 7 days.
+This means that if the user sets `--timerange 20210501-20210701`,
+FreqAI will train 8 separate models (because the full range comprises 8 weeks),
+and then backtest the subsequent week associated with each of the 8 training
+data set timerange months. Users can think of this as a "sliding window" which
+emulates FreqAI retraining itself once per week in live using the previous
+month of data.
+
+In live, the required training data is automatically computed and downloaded. However, in backtesting
+the user must manually enter the required number of `startup_candles` in the config. This value
+is used to increase the available data to FreqAI and should be sufficient to enable all indicators
+to be NaN free at the beginning of the first training timerange. This boils down to identifying the
+highest timeframe (`4h` in present example)  and the longest indicator period (25 in present example)
+and adding this to the `train_period_days`. The units need to be in the base candle time frame:
+
+`startup_candles` = ( 4 hours * 25 max period * 60 minutes/hour + 30 day train_period_days * 1440 minutes per day ) / 5 min (base time frame) = 1488.
+
+!!! Note
+    In dry/live, this is all precomputed and handled automatically. Thus, `startup_candle` has no influence on dry/live.
+
+!!! Note
+    Although fractional `backtest_period_days` is allowed, the user should be ware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. This is why it is physically impossible to truly backtest FreqAI adaptive training. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run.
+
+## Running FreqAI
+
+### Backtesting
+
+The FreqAI backtesting module can be executed with the following command:
+
+```bash
+freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
+```
+
+Backtesting mode requires the user to have the data pre-downloaded (unlike dry/live, where FreqAI automatically downloads the necessary data). The user should be careful to consider that the range of the downloaded data is more than the backtesting range. This is because FreqAI needs data prior to the desired backtesting range in order to train a model to be ready to make predictions on the first candle of the user set backtesting range. More details on how to calculate the data download timerange can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration).
+
+If this command has never been executed with the existing config file, then it will train a new model
+for each pair, for each backtesting window within the bigger `--timerange`.
+
+!!! Note "Model reuse"
+    Once the training is completed, the user can execute this again with the same config file and
+    FreqAI will find the trained models and load them instead of spending time training. This is useful
+    if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. IF the user
+    *wants* to retrain a new model with the same config file, then he/she should simply change the `identifier`.
+    This way, the user can return to using any model they wish by simply changing the `identifier`.
+
+---
+
+### Building a freqai strategy
+
+The FreqAI strategy requires the user to include the following lines of code in the strategy:
+
+```python
+
+    def informative_pairs(self):
+        whitelist_pairs = self.dp.current_whitelist()
+        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
+        informative_pairs = []
+        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
+            for pair in whitelist_pairs:
+                informative_pairs.append((pair, tf))
+            for pair in corr_pairs:
+                if pair in whitelist_pairs:
+                    continue  # avoid duplication
+                informative_pairs.append((pair, tf))
+        return informative_pairs
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        # All indicators must be populated by populate_any_indicators() for live functionality
+        # to work correctly.
+
+        # the model will return all labels created by user in `populate_any_indicators` 
+        # (& appended targets), an indication of whether or not the prediction should be accepted, 
+        # the target mean/std values for each of the labels created by user in 
+        # `populate_any_indicators()` for each training period.
+
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        return dataframe
+
+    def populate_any_indicators(
+        self, pair, df, tf, informative=None, set_generalized_indicators=False
+    ):
+        """
+        Function designed to automatically generate, name and merge features
+        from user indicated timeframes in the configuration file. User controls the indicators
+        passed to the training/prediction by prepending indicators with `'%-' + coin `
+        (see convention below). I.e. user should not prepend any supporting metrics
+        (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
+        model.
+        :param pair: pair to be used as informative
+        :param df: strategy dataframe which will receive merges from informatives
+        :param tf: timeframe of the dataframe which will modify the feature names
+        :param informative: the dataframe associated with the informative pair
+        :param coin: the name of the coin which will modify the feature names.
+        """
+
+        coin = pair.split('/')[0]
+
+        if informative is None:
+            informative = self.dp.get_pair_dataframe(pair, tf)
+
+        # first loop is automatically duplicating indicators for time periods
+        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+            t = int(t)
+            informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
+            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
+            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
+
+        indicators = [col for col in informative if col.startswith("%")]
+        # This loop duplicates and shifts all indicators to add a sense of recency to data
+        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
+            if n == 0:
+                continue
+            informative_shift = informative[indicators].shift(n)
+            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
+            informative = pd.concat((informative, informative_shift), axis=1)
+
+        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
+        skip_columns = [
+            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
+        ]
+        df = df.drop(columns=skip_columns)
+
+        # Add generalized indicators here (because in live, it will call this
+        # function to populate indicators during training). Notice how we ensure not to
+        # add them multiple times
+        if set_generalized_indicators:
+
+            # user adds targets here by prepending them with &- (see convention below)
+            # If user wishes to use multiple targets, a multioutput prediction model
+            # needs to be used such as templates/CatboostPredictionMultiModel.py
+            df["&-s_close"] = (
+                df["close"]
+                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+                .mean()
+                / df["close"]
+                - 1
+            )
+
+        return df
+
+
+```
+
+Notice how the `populate_any_indicators()` is where the user adds their own features and labels ([more information](#feature-engineering)). See a full example at `templates/FreqaiExampleStrategy.py`.
+
+### Setting classifier targets
+
+FreqAI includes a the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. Typically, the user would set the targets using strings:
+
+```python
+df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
+```
+
+### Running the model live
+
+FreqAI can be run dry/live using the following command
+
+```bash
+freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor
+```
+
+By default, FreqAI will not find any existing models and will start by training a new one
+given the user configuration settings. Following training, it will use that model to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the pairs to try and keep all models equally "young." FreqAI will always use the newest trained model to make predictions on incoming live data. If users do not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before retraining a new model. Additionally, users can set `expired_hours` to tell FreqAI to avoid making predictions on models aged over this number of hours.
+
+If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse
+the same `identifier` parameter
+
+```json
+    "freqai": {
+        "identifier": "example",
+        "live_retrain_hours": 1
+    }
+```
+
+In this case, although FreqAI will initiate with a
+pre-trained model, it will still check to see how much time has elapsed since the model was trained,
+and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will self retrain.
+
+## Data analysis techniques
+
+### Controlling the model learning process
+
+Model training parameters are unique to the ML library used by the user. FreqAI allows users to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration files show some of the example parameters associated with `Catboost` and `LightGBM`, but users can add any parameters available in those libraries.
+
+Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function. FreqAI includes some additional parameters such `weight_factor` which allows the user to weight more recent data more strongly
+than past data via an exponential function:
+
+$$ W_i = \exp(\frac{-i}{\alpha*n}) $$
+
+where $W_i$ is the weight of data point $i$ in a total set of $n$ data points.
+
+![weight-factor](assets/weights_factor.png)
+
+`train_test_split()` has a parameters called `shuffle`, which users also have access to in FreqAI, that allows them to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data.
+
+Finally, `label_period_candles` defines the offset used for the `labels`. In the present example,
+the user is asking for `labels` that are 24 candles in the future.
+
+### Removing outliers with the Dissimilarity Index
+
+The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each
+prediction by the model. To do so, FreqAI measures the distance between each training
+data point and all other training data points:
+
+$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$
+
+where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$
+is the number of features i.e. the length of the vector $X$. 
+The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean
+of the average distances:
+
+$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$
+
+$\overline{d}$ quantifies the spread of the training data, which is compared to
+the distance between the new prediction feature vectors, $X_k$ and all the training
+data:
+
+$$ d_k = \arg \min d_{k,i} $$
+
+which enables the estimation of a Dissimilarity Index:
+
+$$ DI_k = d_k/\overline{d} $$
+
+Equity and crypto markets suffer from a high level of non-patterned noise in the
+form of outlier data points. The dissimilarity index allows predictions which
+are outliers and not existent in the model feature space, to be thrown out due
+to low levels of certainty. Activating the Dissimilarity Index can be achieved with:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "DI_threshold": 1
+        }
+    }
+```
+
+The user can tweak the DI with `DI_threshold` to increase or decrease the extrapolation of the trained model.
+
+### Reducing data dimensionality with Principal Component Analysis
+
+Users can reduce the dimensionality of their features by activating the `principal_component_analysis`:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "principal_component_analysis": true
+        }
+    }
+```
+
+Which will perform PCA on the features and reduce the dimensionality of the data so that the explained
+variance of the data set is >= 0.999.
+
+### Removing outliers using a Support Vector Machine (SVM)
+
+The user can tell FreqAI to remove outlier data points from the training/test data sets by setting:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "use_SVM_to_remove_outliers": true
+        }
+    }
+```
+
+FreqAI will train an SVM on the training data (or components if the user activated
+`principal_component_analysis`) and remove any data point that it deems to be sitting beyond the feature space.
+
+### Clustering the training data and removing outliers with DBSCAN
+
+The user can configure FreqAI to use DBSCAN to cluster training data and remove outliers from the training data set. The user activates `use_DBSCAN_to_remove_outliers` to cluster training data for identification of outliers. Also used to detect incoming outliers for prediction data points.
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "use_DBSCAN_to_remove_outliers": true
+        }
+    }
+```
+
+### Stratifying the data
+
+The user can stratify the training/testing data using:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "stratify_training_data": 3
+        }
+    }
+```
+
+which will split the data chronologically so that every Xth data points is a testing data point. In the
+present example, the user is asking for every third data point in the dataframe to be used for
+testing, the other points are used for training.
+
+## Setting up a follower
+
+The user can define:
+
+```json
+    "freqai": {
+        "follow_mode": true,
+        "identifier": "example"
+    }
+```
+
+to indicate to the bot that it should not train models, but instead should look for models trained
+by a leader with the same `identifier`. In this example, the user has a leader bot with the
+`identifier: "example"` already running or launching simultaneously as the present follower.
+The follower will load models created by the leader and inference them to obtain predictions.
+
+## Purging old model data
+
+FreqAI stores new model files each time it retrains. These files become obsolete as new models
+are trained and FreqAI adapts to the new market conditions. Users planning to leave FreqAI running
+for extended periods of time with high frequency retraining should set `purge_old_models` in their
+config:
+
+```json
+    "freqai": {
+        "purge_old_models": true,
+    }
+```
+
+which will automatically purge all models older than the two most recently trained ones.
+
+## Defining model expirations
+
+During dry/live, FreqAI trains each pair sequentially (on separate threads/GPU from the main
+Freqtrade bot). This means there is always an age discrepancy between models. If a user is training
+on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. 
+This may be undesirable if the characteristic time scale (read trade duration target) for a strategy 
+is much less than 4 hours. The user can decide to only make trade entries if the model is less than 
+a certain number of hours in age by setting the `expiration_hours` in the config file:
+
+```json
+    "freqai": {
+        "expiration_hours": 0.5,
+    }
+```
+
+In the present example, the user will only allow predictions on models that are less than 1/2 hours
+old.
+
+## Choosing the calculation of the `target_roi`
+
+As shown in `templates/FreqaiExampleStrategy.py`, the `target_roi` is based on two metrics computed
+by FreqAI: `label_mean` and `label_std`. These are the statistics associated with the labels used
+*during the most recent training*.  
+This allows the model to know what magnitude of a target to be expecting since it is directly stemming from the training data.
+By default, FreqAI computes this based on training data and it assumes the labels are Gaussian distributed.
+These are big assumptions that the user should consider when creating their labels. If the user wants to consider the population
+of *historical predictions* for creating the dynamic target instead of the trained labels, the user
+can do so by setting `fit_live_prediction_candles` to the number of historical prediction candles
+the user wishes to use to generate target statistics.
+
+```json
+    "freqai": {
+        "fit_live_prediction_candles": 300,
+    }
+```
+
+If the user sets this value, FreqAI will initially use the predictions from the training data set
+and then subsequently begin introducing real prediction data as it is generated. FreqAI will save
+this historical data to be reloaded if the user stops and restarts with the same `identifier`.
+
+## Extra returns per train
+
+Users may find that there are some important metrics that they'd like to return to the strategy at the end of each retrain.
+Users can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction
+model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy.
+The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is
+the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of that particular label during the most recent training.
+Another example is shown below if the user wants to use live metrics from the trade database.
+
+The user needs to set the standard dictionary in the config so FreqAI can return proper dataframe shapes:
+
+```json
+    "freqai": {
+        "extra_returns_per_train": {"total_profit": 4}
+    }
+```
+
+These values will likely be overridden by the user prediction model, but in the case where the user model has yet to set them, or needs
+a default initial value - this is the value that will be returned.
+
+## Building an IFreqaiModel
+
+FreqAI has multiple example prediction model based libraries such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression.
+However, users can customize and create their own prediction models using the `IFreqaiModel` class.
+Users are encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures.
+
+## Additional information
+
+### Common pitfalls
+
+FreqAI cannot be combined with `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
+This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively,
+it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends
+new candles automatically for future retrains. But this means that if new pairs arrive later in the dry run due
+to a volume pairlist, it will not have the data ready. FreqAI does work, however, with the `ShufflePairlist`.
+
+### Feature normalization
+
+The feature set created by the user is automatically normalized to the training data only.
+This includes all test data and unseen prediction data (dry/live/backtest).
+
+### File structure
+
+`user_data_dir/models/` contains all the data associated with the trainings and backtests.
+This file structure is heavily controlled and read by the `FreqaiDataKitchen()`
+and should therefore not be modified.
+
+## Credits
+
+FreqAI was developed by a group of individuals who all contributed specific skillsets to the project.
+
+Conception and software development:
+Robert Caulk @robcaulk
+
+Theoretical brainstorming:
+Elin Törnquist @thorntwig
+
+Code review, software architecture brainstorming:
+@xmatthias
+
+Beta testing and bug reporting:
+@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
+Juha Nykänen @suikula, Wagner Costa @wagnercosta

docs/includes/pairlists.md

@@ -0,0 +1,353 @@
+## Pairlists and Pairlist Handlers
+
+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).
+
+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.
+
+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.
+
+### Pair blacklist
+
+The pair blacklist (configured via `exchange.pair_blacklist` in the configuration) disallows certain pairs from trading.
+This can be as simple as excluding `DOGE/BTC` - which will remove exactly this pair.
+
+The pair-blacklist does also support wildcards (in regex-style) - so `BNB/.*` will exclude ALL pairs that start with BNB.
+You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged tokens (check Pair naming conventions for your exchange!)
+
+### Available Pairlist Handlers
+
+* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
+* [`VolumePairList`](#volume-pair-list)
+* [`AgeFilter`](#agefilter)
+* [`OffsetFilter`](#offsetfilter)
+* [`PerformanceFilter`](#performancefilter)
+* [`PrecisionFilter`](#precisionfilter)
+* [`PriceFilter`](#pricefilter)
+* [`ShuffleFilter`](#shufflefilter)
+* [`SpreadFilter`](#spreadfilter)
+* [`RangeStabilityFilter`](#rangestabilityfilter)
+* [`VolatilityFilter`](#volatilityfilter)
+
+!!! Tip "Testing pairlists"
+    Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) utility sub-command to test your configuration quickly.
+
+#### Static Pair List
+
+By default, the `StaticPairList` method is used, which uses a statically defined pair whitelist from the configuration. The pairlist also supports wildcards (in regex-style) - so `.*/BTC` will include all pairs with BTC as a stake.
+
+It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.
+
+```json
+"pairlists": [
+    {"method": "StaticPairList"}
+],
+```
+
+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.
+
+#### Volume Pair List
+
+`VolumePairList` employs sorting/filtering of pairs by their trading volume. It selects `number_assets` top pairs with sorting based on the `sort_key` (which can only be `quoteVolume`).
+
+When used in the chain of Pairlist Handlers in a non-leading position (after StaticPairList and other Pairlist Filters), `VolumePairList` considers outputs of previous Pairlist Handlers, adding its sorting/selection of the pairs by the trading volume.
+
+When used in the leading position of the chain of Pairlist Handlers, the `pair_whitelist` configuration setting is ignored. Instead, `VolumePairList` selects the top assets from all available markets with matching stake-currency on the exchange.
+
+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.
+
+`VolumePairList` is per default based on the ticker data from exchange, as reported by the ccxt library:
+
+* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
+
+```json
+"pairlists": [
+    {
+        "method": "VolumePairList",
+        "number_assets": 20,
+        "sort_key": "quoteVolume",
+        "min_value": 0,
+        "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.
+
+### VolumePairList Advanced mode
+
+`VolumePairList` can also operate in an advanced mode to build volume over a given timerange of specified candle size. It utilizes exchange historical candle data, builds a typical price (calculated by (open+high+low)/3) and multiplies the typical price with every candle's volume. The sum is the `quoteVolume` over the given range. This allows different scenarios, for a  more smoothened volume, when using longer ranges with larger candle sizes, or the opposite when using a short range with small candles.
+
+For convenience `lookback_days` can be specified, which will imply that 1d candles will be used for the lookback. In the example below the pairlist would be created based on the last 7 days:
+
+```json
+"pairlists": [
+    {
+        "method": "VolumePairList",
+        "number_assets": 20,
+        "sort_key": "quoteVolume",
+        "min_value": 0,
+        "refresh_period": 86400,
+        "lookback_days": 7
+    }
+],
+```
+
+!!! 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 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.
+    To roughly simulate 24h volume, you can use the following configuration.
+    Please note that These pairlists will only refresh once per day.
+
+    ```json
+    "pairlists": [
+        {
+            "method": "VolumePairList",
+            "number_assets": 20,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 86400,
+            "lookback_days": 1
+        }
+    ],
+    ```
+
+More sophisticated approach can be used, by using `lookback_timeframe` for candle size and `lookback_period` which specifies the amount of candles. This example will build the volume pairs based on a rolling period of 3 days of 1h candles:
+
+```json
+"pairlists": [
+    {
+        "method": "VolumePairList",
+        "number_assets": 20,
+        "sort_key": "quoteVolume",
+        "min_value": 0,
+        "refresh_period": 3600,
+        "lookback_timeframe": "1h",
+        "lookback_period": 72
+    }
+],
+```
+
+!!! Note
+    `VolumePairList` does not support backtesting mode.
+
+#### 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).
+
+When pairs are first listed on an exchange they can suffer huge price drops and volatility
+in the first few days while the pair goes through its price-discovery period. Bots can often
+be caught out buying before the pair has finished dropping in price.
+
+This filter allows freqtrade to ignore pairs until they have been listed for at least `min_days_listed` days and listed before `max_days_listed`.
+
+#### OffsetFilter
+
+Offsets an incoming pairlist by a given `offset` value.
+
+As an example it can be used in conjunction with `VolumeFilter` to remove the top X volume pairs. Or to split a larger pairlist on two bot instances.
+
+Example to remove the first 10 pairs from the pairlist, and takes the next 20 (taking items 10-30 of the initial list):
+
+```json
+"pairlists": [
+    // ...
+    {
+        "method": "OffsetFilter",
+        "offset": 10,
+        "number_assets": 20
+    }
+],
+```
+
+!!! Warning
+    When `OffsetFilter` is used to split a larger pairlist among multiple bots in combination with `VolumeFilter` 
+    it can not be guaranteed that pairs won't overlap due to slightly different refresh intervals for the
+    `VolumeFilter`.
+
+!!! Note
+    An offset larger than the total length of the incoming pairlist will result in an empty pairlist.
+
+#### PerformanceFilter
+
+Sorts pairs by past trade performance, as follows:
+
+1. Positive performance.
+2. No closed trades yet.
+3. Negative performance.
+
+Trade count is used as a tie breaker.
+
+You can use the `minutes` parameter to only consider performance of the past X minutes (rolling window).
+Not defining this parameter (or setting it to 0) will use all-time performance.
+
+The optional `min_profit` (as ratio -> a setting of `0.01` corresponds to 1%) parameter defines the minimum profit a pair must have to be considered.
+Pairs below this level will be filtered out.
+Using this parameter without `minutes` is highly discouraged, as it can lead to an empty pairlist without a way to recover.
+
+```json
+"pairlists": [
+    // ...
+    {
+        "method": "PerformanceFilter",
+        "minutes": 1440,  // rolling 24h
+        "min_profit": 0.01  // minimal profit 1%
+    }
+],
+```
+
+As this Filter uses past performance of the bot, it'll have some startup-period - and should only be used after the bot has a few 100 trades in the database.
+
+!!! Warning "Backtesting"
+    `PerformanceFilter` does not support backtesting mode.
+
+#### PrecisionFilter
+
+Filters low-value coins which would not allow setting stoplosses.
+
+!!! Warning "Backtesting"
+    `PrecisionFilter` does not support backtesting mode using multiple strategies.
+
+#### PriceFilter
+
+The `PriceFilter` allows filtering of pairs by price. Currently the following price filters are supported:
+
+* `min_price`
+* `max_price`
+* `max_value`
+* `low_price_ratio`
+
+The `min_price` setting removes pairs where the price is below the specified price. This is useful if you wish to avoid trading very low-priced pairs.
+This option is disabled by default, and will only apply if set to > 0.
+
+The `max_price` setting removes pairs where the price is above the specified price. This is useful if you wish to trade only low-priced pairs.
+This option is disabled by default, and will only apply if set to > 0.
+
+The `max_value` setting removes pairs where the minimum value change is above a specified value.
+This is useful when an exchange has unbalanced limits. For example, if step-size = 1 (so you can only buy 1, or 2, or 3, but not 1.1 Coins) - and the price is pretty high (like 20\$) as the coin has risen sharply since the last limit adaption.
+As a result of the above, you can only buy for 20\$, or 40\$ - but not for 25\$.
+On exchanges that deduct fees from the receiving currency (e.g. FTX) - this can result in high value coins / amounts that are unsellable as the amount is slightly below the limit.
+
+The `low_price_ratio` setting removes pairs where a raise of 1 price unit (pip) is above the `low_price_ratio` ratio.
+This option is disabled by default, and will only apply if set to > 0.
+
+For `PriceFilter` at least one of its `min_price`, `max_price` or `low_price_ratio` settings must be applied.
+
+Calculation example:
+
+Min price precision for SHITCOIN/BTC is 8 decimals. If its price is 0.00000011 - one price step above would be 0.00000012, which is ~9% higher than the previous price value. You may filter out this pair by using PriceFilter with `low_price_ratio` set to 0.09 (9%) or with `min_price` set to 0.00000011, correspondingly.
+
+!!! Warning "Low priced pairs"
+    Low priced pairs with high "1 pip movements" are dangerous since they are often illiquid and it may also be impossible to place the desired stoploss, which can often result in high losses since price needs to be rounded to the next tradable price - so instead of having a stoploss of -5%, you could end up with a stoploss of -9% simply due to price rounding.
+
+#### ShuffleFilter
+
+Shuffles (randomizes) pairs in the pairlist. It can be used for preventing the bot from trading some of the pairs more frequently then others when you want all pairs be treated with the same priority.
+
+!!! Tip
+    You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order. ShuffleFilter will automatically detect runmodes and apply the `seed` only for backtesting modes - if a `seed` value is set.
+
+#### SpreadFilter
+
+Removes pairs that have a difference between asks and bids above the specified ratio, `max_spread_ratio` (defaults to `0.005`).
+
+Example:
+
+If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027, the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005` and this pair will be filtered out.
+
+#### RangeStabilityFilter
+
+Removes pairs where the difference between lowest low and highest high over `lookback_days` days is below `min_rate_of_change` or above `max_rate_of_change`. Since this is a filter that requires additional data, the results are cached for `refresh_period`.
+
+In the below example:
+If the trading range over the last 10 days is <1% or >99%, remove the pair from the whitelist.
+
+```json
+"pairlists": [
+    {
+        "method": "RangeStabilityFilter",
+        "lookback_days": 10,
+        "min_rate_of_change": 0.01,
+        "max_rate_of_change": 0.99,
+        "refresh_period": 1440
+    }
+]
+```
+
+!!! 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.
+
+#### VolatilityFilter
+
+Volatility is the degree of historical variation of a pairs over time, it is measured by the standard deviation of logarithmic daily returns. Returns are assumed to be normally distributed, although actual distribution might be different. In a normal distribution, 68% of observations fall within one standard deviation and 95% of observations fall within two standard deviations. Assuming a volatility of 0.05 means that the expected returns for 20 out of 30 days is expected to be less than 5% (one standard deviation). Volatility is a positive ratio of the expected deviation of return and can be greater than 1.00. Please refer to the wikipedia definition of [`volatility`](https://en.wikipedia.org/wiki/Volatility_(finance)).
+
+This filter removes pairs if the average volatility over a `lookback_days` days is below `min_volatility` or above `max_volatility`. Since this is a filter that requires additional data, the results are cached for `refresh_period`.
+
+This filter can be used to narrow down your pairs to a certain volatility or avoid very volatile pairs. 
+
+In the below example:
+If the volatility over the last 10 days is not in the range of 0.05-0.50, remove the pair from the whitelist. The filter is applied every 24h.
+
+```json
+"pairlists": [
+    {
+        "method": "VolatilityFilter",
+        "lookback_days": 10,
+        "min_volatility": 0.05,
+        "max_volatility": 0.50,
+        "refresh_period": 86400
+    }
+]
+```
+
+### 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.
+
+```json
+"exchange": {
+    "pair_whitelist": [],
+    "pair_blacklist": ["BNB/BTC"]
+},
+"pairlists": [
+    {
+        "method": "VolumePairList",
+        "number_assets": 20,
+        "sort_key": "quoteVolume"
+    },
+    {"method": "AgeFilter", "min_days_listed": 10},
+    {"method": "PrecisionFilter"},
+    {"method": "PriceFilter", "low_price_ratio": 0.01},
+    {"method": "SpreadFilter", "max_spread_ratio": 0.005},
+    {
+        "method": "RangeStabilityFilter",
+        "lookback_days": 10,
+        "min_rate_of_change": 0.01,
+        "refresh_period": 1440
+    },
+    {
+        "method": "VolatilityFilter",
+        "lookback_days": 10,
+        "min_volatility": 0.05,
+        "max_volatility": 0.50,
+        "refresh_period": 86400
+    },
+    {"method": "ShuffleFilter", "seed": 42}
+],
+```

docs/includes/pricing.md

@@ -0,0 +1,146 @@
+## Prices used for orders
+
+Prices for regular orders can be controlled via the parameter structures `entry_pricing` for trade entries and `exit_pricing` for trade exits.
+Prices are always retrieved right before an order is placed, either by querying the exchange tickers or by using the orderbook data.
+
+!!! Note
+    Orderbook data used by Freqtrade are the data retrieved from exchange by the ccxt's function `fetch_order_book()`, i.e. are usually data from the L2-aggregated orderbook, while the ticker data are the structures returned by the ccxt's `fetch_ticker()`/`fetch_tickers()` functions. Refer to the ccxt library [documentation](https://github.com/ccxt/ccxt/wiki/Manual#market-data) for more details.
+
+!!! Warning "Using market orders"
+    Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
+
+### Entry price
+
+#### Enter price side
+
+The configuration setting `entry_pricing.price_side` defines the side of the orderbook the bot looks for when buying.
+
+The following displays an orderbook.
+
+``` explanation
+...
+103
+102
+101  # ask
+-------------Current spread
+99   # bid
+98
+97
+...
+```
+
+If `entry_pricing.price_side` is set to `"bid"`, then the bot will use 99 as entry price.  
+In line with that, if `entry_pricing.price_side` is set to `"ask"`, then the bot will use 101 as entry price.
+
+Depending on the order direction (_long_/_short_), this will lead to different results. Therefore we recommend to use `"same"` or `"other"` for this configuration instead.
+This would result in the following pricing matrix:
+
+| direction | Order | setting | price | crosses spread |
+|------ |--------|-----|-----|-----|
+| long  | buy  | ask   | 101 | yes |
+| long  | buy  | bid   | 99  | no  |
+| long  | buy  | same  | 99  | no  |
+| long  | buy  | other | 101 | yes |
+| short | sell | ask   | 101 | no  |
+| short | sell | bid   | 99  | yes |
+| short | sell | same  | 101 | no  |
+| short | sell | other | 99  | yes |
+
+Using the other side of the orderbook often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
+Taker fees instead of maker fees will most likely apply even when using limit buy orders.
+Also, prices at the "other" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
+
+#### Entry price with Orderbook enabled
+
+When entering a trade with the orderbook enabled (`entry_pricing.use_order_book=True`), Freqtrade fetches the `entry_pricing.order_book_top` entries from the orderbook and uses the entry specified as `entry_pricing.order_book_top` on the configured side (`entry_pricing.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
+
+#### Entry price without Orderbook enabled
+
+The following section uses `side` as the configured `entry_pricing.price_side` (defaults to `"same"`).
+
+When not using orderbook (`entry_pricing.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price based on `entry_pricing.price_last_balance`.
+
+The `entry_pricing.price_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
+
+#### Check depth of market
+
+When check depth of market is enabled (`entry_pricing.check_depth_of_market.enabled=True`), the entry signals are filtered based on the orderbook depth (sum of all amounts) for each orderbook side.
+
+Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) side depth and the resulting delta is compared to the value of the `entry_pricing.check_depth_of_market.bids_to_ask_delta` parameter. The entry order is only executed if the orderbook delta is greater than or equal to the configured delta value.
+
+!!! Note
+    A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
+
+### Exit price
+
+#### Exit price side
+
+The configuration setting `exit_pricing.price_side` defines the side of the spread the bot looks for when exiting a trade.
+
+The following displays an orderbook:
+
+``` explanation
+...
+103
+102
+101  # ask
+-------------Current spread
+99   # bid
+98
+97
+...
+```
+
+If `exit_pricing.price_side` is set to `"ask"`, then the bot will use 101 as exiting price.  
+In line with that, if `exit_pricing.price_side` is set to `"bid"`, then the bot will use 99 as exiting price.
+
+Depending on the order direction (_long_/_short_), this will lead to different results. Therefore we recommend to use `"same"` or `"other"` for this configuration instead.
+This would result in the following pricing matrix:
+
+| Direction | Order | setting | price | crosses spread |
+|------ |--------|-----|-----|-----|
+| long  | sell | ask   | 101 | no  |
+| long  | sell | bid   | 99  | yes |
+| long  | sell | same  | 101 | no  |
+| long  | sell | other | 99  | yes |
+| short | buy  | ask   | 101 | yes |
+| short | buy  | bid   | 99  | no  |
+| short | buy  | same  | 99  | no  |
+| short | buy  | other | 101 | yes |
+
+#### Exit price with Orderbook enabled
+
+When exiting with the orderbook enabled (`exit_pricing.use_order_book=True`), Freqtrade fetches the `exit_pricing.order_book_top` entries in the orderbook and uses the entry specified as `exit_pricing.order_book_top` from the configured side (`exit_pricing.price_side`) as trade exit price.
+
+1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
+
+#### Exit price without Orderbook enabled
+
+The following section uses `side` as the configured `exit_pricing.price_side` (defaults to `"ask"`).
+
+When not using orderbook (`exit_pricing.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's above the `last` traded price from the ticker. Otherwise (when the `side` price is below the `last` price), it calculates a rate between `side` and `last` price based on `exit_pricing.price_last_balance`.
+
+The `exit_pricing.price_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the last price and values between those interpolate between `side` and last price.
+
+### Market order pricing
+
+When using market orders, prices should be configured to use the "correct" side of the orderbook to allow realistic pricing detection.
+Assuming both entry and exits are using market orders, a configuration similar to the following must be used
+
+``` jsonc
+  "order_types": {
+    "entry": "market",
+    "exit": "market"
+    // ...
+  },
+  "entry_pricing": {
+    "price_side": "other",
+    // ...
+  },
+  "exit_pricing":{
+    "price_side": "other",
+    // ...
+  },
+```
+
+Obviously, if only one side is using limit orders, different pricing combinations can be used.

docs/includes/protections.md

@@ -0,0 +1,198 @@
+## 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
+    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.
+* [`MaxDrawdown`](#maxdrawdown) Stop trading if max-drawdown is reached.
+* [`LowProfitPairs`](#low-profit-pairs) Lock pairs with low profits
+* [`CooldownPeriod`](#cooldown-period) Don't enter a trade right after selling a trade.
+
+### Common settings to all Protections
+
+|  Parameter| Description |
+|------------|-------------|
+| `method` | Protection name to use. <br> **Datatype:** String, selected from [available Protections](#available-protections)
+| `stop_duration_candles` | For how many candles should the lock be set? <br> **Datatype:** Positive integer (in candles)
+| `stop_duration` | how many minutes should protections be locked. <br>Cannot be used together with `stop_duration_candles`. <br> **Datatype:** Float (in minutes)
+| `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
+
+!!! Note "Durations"
+    Durations (`stop_duration*` and `lookback_period*` can be defined in either minutes or candles).
+    For more flexibility when testing different timeframes, all below examples will use the "candle" definition.
+
+#### 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`).
+
+This applies across all pairs, unless `only_per_pair` is set to true, which will then only look at one pair at a time.
+
+Similarly, this protection will by default look at all trades (long and short). 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 stoplosses.
+
+`required_profit` will determine the required relative profit (or loss) for stoplosses to consider. This should normally not be set and defaults to 0.0 - which means all losing stoplosses will be triggering a block.
+
+The below example stops trading for all pairs for 4 candles after the last trade if the bot hit stoploss 4 times within the last 24 candles.
+
+``` python
+@property
+def protections(self):
+    return [
+        {
+            "method": "StoplossGuard",
+            "lookback_period_candles": 24,
+            "trade_limit": 4,
+            "stop_duration_candles": 4,
+            "required_profit": 0.0,
+            "only_per_pair": False,
+            "only_per_side": False
+        }
+    ]
+```
+
+!!! Note
+    `StoplossGuard` considers all trades with the results `"stop_loss"`, `"stoploss_on_exchange"` and `"trailing_stop_loss"` if the resulting profit was negative.
+    `trade_limit` and `lookback_period` will need to be tuned for your strategy.
+
+#### MaxDrawdown
+
+`MaxDrawdown` uses all trades within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the maximum drawdown. If the drawdown is below `max_allowed_drawdown`, trading will stop for `stop_duration` in minutes (or in candles when using `stop_duration_candles`) after the last trade - assuming that the bot needs some time to let markets recover.
+
+The below sample stops trading for 12 candles if max-drawdown is > 20% considering all pairs - with a minimum of `trade_limit` trades - within the last 48 candles. If desired, `lookback_period` and/or `stop_duration` can be used.
+
+``` python
+@property
+def protections(self):
+    return  [
+        {
+            "method": "MaxDrawdown",
+            "lookback_period_candles": 48,
+            "trade_limit": 20,
+            "stop_duration_candles": 12,
+            "max_allowed_drawdown": 0.2
+        },
+    ]
+```
+
+#### 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`).
+
+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.
+
+The below example will stop trading a pair for 60 minutes if the pair does not have a required profit of 2% (and a minimum of 2 trades) within the last 6 candles.
+
+``` python
+@property
+def protections(self):
+    return [
+        {
+            "method": "LowProfitPairs",
+            "lookback_period_candles": 6,
+            "trade_limit": 2,
+            "stop_duration": 60,
+            "required_profit": 0.02,
+            "only_per_pair": False,
+        }
+    ]
+```
+
+#### 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.
+
+The below example will stop trading a pair for 2 candles after closing a trade, allowing this pair to "cool down".
+
+``` python
+@property
+def protections(self):
+    return  [
+        {
+            "method": "CooldownPeriod",
+            "stop_duration_candles": 2
+        }
+    ]
+```
+
+!!! Note
+    This Protection applies only at pair-level, and will never lock all pairs globally.
+    This Protection does not consider `lookback_period` as it only looks at the latest trade.
+
+### Full example of Protections
+
+All protections can be combined at will, also with different parameters, creating a increasing wall for under-performing pairs.
+All protections are evaluated in the sequence they are defined.
+
+The below example assumes a timeframe of 1 hour:
+
+* Locks each pair after selling for an additional 5 candles (`CooldownPeriod`), giving other pairs a chance to get filled.
+* Stops trading for 4 hours (`4 * 1h candles`) if the last 2 days (`48 * 1h candles`) had 20 trades, which caused a max-drawdown of more than 20%. (`MaxDrawdown`).
+* Stops trading if more than 4 stoploss occur for all pairs within a 1 day (`24 * 1h candles`) limit (`StoplossGuard`).
+* Locks all pairs that had 4 Trades within the last 6 hours (`6 * 1h candles`) with a combined profit ratio of below 0.02 (<2%) (`LowProfitPairs`).
+* Locks all pairs for 2 candles that had a profit of below 0.01 (<1%) within the last 24h (`24 * 1h candles`), a minimum of 4 trades.
+
+``` python
+from freqtrade.strategy import IStrategy
+
+class AwesomeStrategy(IStrategy)
+    timeframe = '1h'
+    
+    @property
+    def protections(self):
+        return [
+            {
+                "method": "CooldownPeriod",
+                "stop_duration_candles": 5
+            },
+            {
+                "method": "MaxDrawdown",
+                "lookback_period_candles": 48,
+                "trade_limit": 20,
+                "stop_duration_candles": 4,
+                "max_allowed_drawdown": 0.2
+            },
+            {
+                "method": "StoplossGuard",
+                "lookback_period_candles": 24,
+                "trade_limit": 4,
+                "stop_duration_candles": 2,
+                "only_per_pair": False
+            },
+            {
+                "method": "LowProfitPairs",
+                "lookback_period_candles": 6,
+                "trade_limit": 2,
+                "stop_duration_candles": 60,
+                "required_profit": 0.02
+            },
+            {
+                "method": "LowProfitPairs",
+                "lookback_period_candles": 24,
+                "trade_limit": 4,
+                "stop_duration_candles": 2,
+                "required_profit": 0.01
+            }
+        ]
+    # ...
+```

docs/index.md

@@ -1,20 +1,17 @@
-# Freqtrade
+![freqtrade](assets/freqtrade_poweredby.svg)
+
 [![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
 [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
 [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
 
 <!-- Place this tag where you want the button to render. -->
 <a class="github-button" href="https://github.com/freqtrade/freqtrade" data-icon="octicon-star" data-size="large" aria-label="Star freqtrade/freqtrade on GitHub">Star</a>
-<!-- Place this tag where you want the button to render. -->
 <a class="github-button" href="https://github.com/freqtrade/freqtrade/fork" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork freqtrade/freqtrade on GitHub">Fork</a>
-<!-- Place this tag where you want the button to render. -->
-<a class="github-button" href="https://github.com/freqtrade/freqtrade/archive/master.zip" data-icon="octicon-cloud-download" data-size="large" aria-label="Download freqtrade/freqtrade on GitHub">Download</a>
-<!-- Place this tag where you want the button to render. -->
-<a class="github-button" href="https://github.com/freqtrade" data-size="large" aria-label="Follow @freqtrade on GitHub">Follow @freqtrade</a>
+<a class="github-button" href="https://github.com/freqtrade/freqtrade/archive/stable.zip" data-icon="octicon-cloud-download" data-size="large" aria-label="Download freqtrade/freqtrade on GitHub">Download</a>
 
 ## Introduction
 
-Freqtrade is a crypto-currency algorithmic trading software developed in python (3.6+) and supported on Windows, macOS and Linux.
+Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or webUI. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
 
 !!! Danger "DISCLAIMER"
     This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
@@ -23,6 +20,8 @@ Freqtrade is a crypto-currency algorithmic trading software developed in python
 
     We strongly recommend you to have basic coding skills and Python knowledge. Do not hesitate to read the source code and understand the mechanisms of this bot, algorithms and techniques implemented in it.
 
+![freqtrade screenshot](assets/freqtrade-screenshot.png)
+
 ## Features
 
 - Develop your Strategy: Write your strategy in python, using [pandas](https://pandas.pydata.org/). Example strategies to inspire you are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
@@ -32,18 +31,42 @@ Freqtrade is a crypto-currency algorithmic trading software developed in python
 - Select markets: Create your static list or use an automatic one based on top traded volumes and/or prices (not available during backtesting). You can also explicitly blacklist markets you don't want to trade.
 - Run: Test your strategy with simulated money (Dry-Run mode) or deploy it with real money (Live-Trade mode).
 - Run using Edge (optional module): The concept is to find the best historical [trade expectancy](edge.md#expectancy) by markets based on variation of the stop-loss and then allow/reject markets to trade. The sizing of the trade is based on a risk of a percentage of your capital.
-- Control/Monitor: Use Telegram or a REST API (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
+- Control/Monitor: Use Telegram or a WebUI (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
 - Analyse: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
 
+## Supported exchange marketplaces
+
+Please read the [exchange specific notes](exchanges.md) to learn about eventual, special configurations needed for each exchange.
+
+- [X] [Binance](https://www.binance.com/)
+- [X] [Bittrex](https://bittrex.com/)
+- [X] [FTX](https://ftx.com/#a=2258149)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [Huobi](http://huobi.com/)
+- [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)_
+
+### Supported Futures Exchanges (experimental)
+
+- [X] [Binance](https://www.binance.com/)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [OKX](https://okx.com/).
+
+Please make sure to read the [exchange specific notes](exchanges.md), as well as the [trading with leverage](leverage.md) documentation before diving in.
+
+### Community tested
+
+Exchanges confirmed working by the community:
+
+- [X] [Bitvavo](https://bitvavo.com/)
+- [X] [Kucoin](https://www.kucoin.com/)
+
 ## Requirements
 
-### Up to date clock
-
-The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
-
 ### Hardware requirements
 
-To run this bot we recommend you a cloud instance with a minimum of:
+To run this bot we recommend you a linux cloud instance with a minimum of:
 
 - 2GB RAM
 - 1GB disk space
@@ -55,7 +78,7 @@ To run this bot we recommend you a cloud instance with a minimum of:
 
 Alternatively
 
-- Python 3.6.x
+- Python 3.8+
 - pip (pip3)
 - git
 - TA-Lib
@@ -63,11 +86,10 @@ Alternatively
 
 ## Support
 
-### Help / Slack
-For any questions not covered by the documentation or for further information about the bot, we encourage you to join our passionate  Slack community.
+### Help / Discord
 
-Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) to join the Freqtrade Slack channel.
+For any questions not covered by the documentation or for further information about the bot, or to simply engage with like-minded individuals, we encourage you to join the Freqtrade [discord server](https://discord.gg/p7nuUNVfP7).
 
 ## Ready to try?
 
-Begin by reading our installation guide [for docker](docker.md), or for [installation without docker](installation.md).
+Begin by reading the installation guide [for docker](docker_quickstart.md) (recommended), or for [installation without docker](installation.md).

docs/installation.md

@@ -2,70 +2,179 @@
 
 This page explains how to prepare your environment for running the bot.
 
-Please consider using the prebuilt [docker images](docker.md) to get started quickly while trying out freqtrade evaluating how it operates.
+The freqtrade documentation describes various ways to install freqtrade
 
-## Prerequisite
+* [Docker images](docker_quickstart.md) (separate page)
+* [Script Installation](#script-installation)
+* [Manual Installation](#manual-installation)
+* [Installation with Conda](#installation-with-conda)
 
-### Requirements
+Please consider using the prebuilt [docker images](docker_quickstart.md) to get started quickly while evaluating how freqtrade works.
 
-Click each one for install guide:
+------
 
-* [Python >= 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/)
+## Information
+
+For Windows installation, please use the [windows installation guide](windows_installation.md).
+
+The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the `./setup.sh` script, if it's available for your platform.
+
+!!! Note "Version considerations"
+    When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests).
+    The `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
+    Python3.8 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"
+    The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
+
+------
+
+## Requirements
+
+These requirements apply to both [Script Installation](#script-installation) and [Manual Installation](#manual-installation).
+
+!!! Note "ARM64 systems"
+    If you are running an ARM64 system (like a MacOS M1 or an Oracle VM), please use [docker](docker_quickstart.md) to run freqtrade.
+    While native installation is possible with some manual effort, this is not supported at the moment.
+
+### Install guide
+
+* [Python >= 3.8.x](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)
-* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
+* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions [below](#install-ta-lib))
 
- We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
+### Install code
 
-## Quick start
-
-Freqtrade provides the Linux/MacOS Easy Installation script to install all dependencies and help you configure the bot.
+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.
 
 !!! Note
-    Windows installation is explained [here](#windows).
+    Python3.8 or higher and the corresponding pip are assumed to be available.
 
-The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the Easy Installation script, if it's available for your platform.
+=== "Debian/Ubuntu"
+    #### Install necessary dependencies
 
-!!! Note "Version considerations"
-    When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `master` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
+    ```bash
+    # update repository
+    sudo apt-get update
 
-!!! Note
-    Python3.6 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.
+    # install packages
+    sudo apt install -y python3-pip python3-venv python3-dev python3-pandas git curl
+    ```
 
-This can be achieved with the following commands:
+=== "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.
+
+    Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
+
+
+    ```bash
+    sudo apt-get install python3-venv libatlas-base-dev cmake curl
+    # Use pywheels.org to speed up installation
+    sudo echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > tee /etc/pip.conf
+
+    git clone https://github.com/freqtrade/freqtrade.git
+    cd freqtrade
+
+    bash setup.sh -i
+    ```
+
+    !!! Note "Installation duration"
+        Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
+        Due to this, we recommend to use the pre-build docker-image for Raspberry, by following the [Docker quickstart documentation](docker_quickstart.md)
+
+    !!! Note
+        The above does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
+        We do not advise to run hyperopt on a Raspberry Pi, since this is a very resource-heavy operation, which should be done on powerful machine.
+
+------
+
+## Freqtrade repository
+
+Freqtrade is an open source crypto-currency trading bot, whose code is hosted on `github.com`
 
 ```bash
+# Download `develop` branch of freqtrade repository
 git clone https://github.com/freqtrade/freqtrade.git
+
+# Enter downloaded directory
 cd freqtrade
-git checkout master  # Optional, see (1)
-./setup.sh --install
+
+# your choice (1): novice user
+git checkout stable
+
+# your choice (2): advanced user
+git checkout develop
 ```
 
-(1) This command switches the cloned repository to the use of the `master` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout master`/`git checkout develop` commands.
+(1) This command switches the cloned repository to the use of the `stable` branch. It's not needed, if you wish to stay on the (2) `develop` branch.
 
-## Easy Installation Script (Linux/MacOS)
+You may later switch between branches at any time with the `git checkout stable`/`git checkout develop` commands.
 
-If you are on Debian, Ubuntu or MacOS Freqtrade provides the script to install, update, configure and reset the codebase of your bot.
+??? Note "Install from pypi"
+    An alternative way to install Freqtrade is from [pypi](https://pypi.org/project/freqtrade/). The downside is that this method requires ta-lib to be correctly installed beforehand, and is therefore currently not the recommended way to install Freqtrade.
+
+    ``` bash
+    pip install freqtrade
+    ```
+
+------
+
+## Script Installation
+
+First of the ways to install Freqtrade, is to use provided the Linux/MacOS `./setup.sh` script, which install all dependencies and help you configure the bot.
+
+Make sure you fulfill the [Requirements](#requirements) and have downloaded the [Freqtrade repository](#freqtrade-repository).
+
+### Use /setup.sh -install (Linux/MacOS)
+
+If you are on Debian, Ubuntu or MacOS, freqtrade provides the script to install freqtrade.
 
 ```bash
-$ ./setup.sh
-usage:
-	-i,--install    Install freqtrade from scratch
-	-u,--update     Command git pull to update.
-	-r,--reset      Hard reset your develop/master branch.
-	-c,--config     Easy config generator (Will override your existing file).
+# --install, Install freqtrade from scratch
+./setup.sh -i
 ```
 
+### Activate your virtual environment
+
+Each time you open a new terminal, you must run `source .env/bin/activate` to activate your virtual environment.
+
+```bash
+# then activate your .env
+source ./.env/bin/activate
+```
+
+### Congratulations
+
+[You are ready](#you-are-ready), and run the bot
+
+### Other options of /setup.sh script
+
+You can as well update, configure and reset the codebase of your bot with `./script.sh`
+
+```bash
+# --update, Command git pull to update.
+./setup.sh -u
+# --reset, Hard reset your develop/stable branch.
+./setup.sh -r
+```
+
+```
 ** --install **
 
 With this option, the script will install the bot and most dependencies:
-You will need to have git and python3.6+ installed beforehand for this to work.
+You will need to have git and python3.8+ installed beforehand for this to work.
 
 * Mandatory software as: `ta-lib`
 * Setup your virtualenv under `.env/`
 
-This option is a combination of installation tasks, `--reset` and `--config`.
+This option is a combination of installation tasks and `--reset`
 
 ** --update **
 
@@ -73,58 +182,18 @@ This option will pull the last version of your current branch and update your vi
 
 ** --reset **
 
-This option will hard reset your branch (only if you are on either `master` or `develop`) and recreate your virtualenv.
-
-** --config **
-
-DEPRECATED - use `freqtrade new-config -c config.json` instead.
-
-------
-
-## Custom Installation
-
-We've included/collected install instructions for Ubuntu 16.04, 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.
-
-!!! Note
-    Python3.6 or higher and the corresponding pip are assumed to be available.
-
-### Linux - Ubuntu 16.04
-
-#### Install necessary dependencies
-
-```bash
-sudo apt-get update
-sudo apt-get install build-essential git
+This option will hard reset your branch (only if you are on either `stable` or `develop`) and recreate your virtualenv.
 ```
 
-### Raspberry Pi / Raspbian
+-----
 
-The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/) from at least September 2019.
-This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
+## Manual Installation
 
-Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
+Make sure you fulfill the [Requirements](#requirements) and have downloaded the [Freqtrade repository](#freqtrade-repository).
 
-``` bash
-sudo apt-get install python3-venv libatlas-base-dev
-git clone https://github.com/freqtrade/freqtrade.git
-cd freqtrade
+### Install TA-Lib
 
-bash setup.sh -i
-```
-
-!!! Note "Installation duration"
-    Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
-
-!!! Note
-    The above does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
-    We do not advise to run hyperopt on a Raspberry Pi, since this is a very resource-heavy operation, which should be done on powerful machine.
-
-### Common
-
-#### 1. Install TA-Lib
-
-Use the provided ta-lib installation script
+#### TA-Lib script installation
 
 ```bash
 sudo ./build_helpers/install_ta-lib.sh
@@ -145,141 +214,210 @@ sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h
 ./configure --prefix=/usr/local
 make
 sudo make install
+# On debian based systems (debian, ubuntu, ...) - updating ldconfig might be necessary.
+sudo ldconfig  
 cd ..
 rm -rf ./ta-lib*
 ```
 
-!!! Note
-    An already downloaded version of ta-lib is included in the repository, as the sourceforge.net source seems to have problems frequently.
+#### Setup Python virtual environment (virtualenv)
 
-#### 2. Setup your Python virtual environment (virtualenv)
-
-!!! Note
-    This step is optional but strongly recommended to keep your system organized
+You will run freqtrade in separated `virtual environment`
 
 ```bash
+# create virtualenv in directory /freqtrade/.env
 python3 -m venv .env
+
+# run virtualenv
 source .env/bin/activate
 ```
 
-#### 3. Install Freqtrade
-
-Clone the git repository:
+#### Install python dependencies
 
 ```bash
-git clone https://github.com/freqtrade/freqtrade.git
-cd freqtrade
-```
-
-Optionally checkout the master branch to get the latest stable release:
-
-```bash
-git checkout master
-```
-
-#### 4. Install python dependencies
-
-``` bash
 python3 -m pip install --upgrade pip
 python3 -m pip install -e .
 ```
 
-#### 5. Initialize the configuration
+### Congratulations
 
-```bash
-# Initialize the user_directory
-freqtrade create-userdir --userdir user_data/
+[You are ready](#you-are-ready), and run the bot
 
-# Create a new configuration file
-freqtrade new-config --config config.json
-```
+#### (Optional) Post-installation Tasks
 
-> *To edit the config please refer to [Bot Configuration](configuration.md).*
+!!! Note 
+    If you run the bot on a server, you should consider using [Docker](docker_quickstart.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
 
-#### 6. Run the Bot
-
-If this is the first time you run the bot, ensure you are running it in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
-
-```bash
-freqtrade trade -c config.json
-```
-
-*Note*: If you run the bot on a server, you should consider using [Docker](docker.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
-
-#### 7. (Optional) Post-installation Tasks
-
-On Linux, as an optional post-installation task, you may wish to setup the bot to run as a `systemd` service or configure it to send the log messages to the `syslog`/`rsyslog` or `journald` daemons. See [Advanced Logging](advanced-setup.md#advanced-logging) for details.
+On Linux with software suite `systemd`, as an optional post-installation task, you may wish to setup the bot to run as a `systemd service` or configure it to send the log messages to the `syslog`/`rsyslog` or `journald` daemons. See [Advanced Logging](advanced-setup.md#advanced-logging) for details.
 
 ------
 
-## Using Conda
+## Installation with Conda
 
-Freqtrade can also be installed using Anaconda (or Miniconda).
+Freqtrade can also be installed with Miniconda or Anaconda. We recommend using Miniconda as it's installation footprint is smaller. Conda will automatically prepare and manage the extensive library-dependencies of the Freqtrade program.
 
-``` bash
-conda env create -f environment.yml
-```
+### What is Conda?
 
-!!! Note
-    This requires the [ta-lib](#1-install-ta-lib) C-library to be installed first.
+Conda is a package, dependency and environment manager for multiple programming languages: [conda docs](https://docs.conda.io/projects/conda/en/latest/index.html)
 
-## Windows
+### Installation with conda
 
-We recommend that Windows users use [Docker](docker.md) as this will work much easier and smoother (also more secure).
+#### Install Conda
 
-If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
-If that is not available on your system, feel free to try the instructions below, which led to success for some.
+[Installing on linux](https://conda.io/projects/conda/en/latest/user-guide/install/linux.html#install-linux-silent)
 
-### Install freqtrade manually
+[Installing on windows](https://conda.io/projects/conda/en/latest/user-guide/install/windows.html)
 
-!!! Note
-    Make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
+Answer all questions. After installation, it is mandatory to turn your terminal OFF and ON again.
 
-!!! Hint
-    Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Conda section](#using-conda) in this document for more information.
+#### Freqtrade download
 
-#### Clone the git repository
+Download and install freqtrade.
 
 ```bash
+# download freqtrade
 git clone https://github.com/freqtrade/freqtrade.git
+
+# enter downloaded directory 'freqtrade'
+cd freqtrade      
 ```
 
-#### Install ta-lib
+#### Freqtrade install: Conda Environment
 
-Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
+Prepare conda-freqtrade environment, using file `environment.yml`, which exist in main freqtrade directory
 
-As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version)
-
-```cmd
->cd \path\freqtrade-develop
->python -m venv .env
->.env\Scripts\activate.bat
-REM optionally install ta-lib from wheel
-REM >pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl
->pip install -r requirements.txt
->pip install -e .
->freqtrade
+```bash
+conda env create -n freqtrade-conda -f environment.yml
 ```
 
-> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/freqtrade/freqtrade/issues/222)
+!!! Note "Creating Conda Environment"
+    The conda command `create -n` automatically installs all nested dependencies for the selected libraries, general structure of installation command is:
 
-#### Error during installation under Windows
+    ```bash
+    # choose your own packages
+    conda env create -n [name of the environment] [python version] [packages]
 
-``` bash
-error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
+    # point to file with packages
+    conda env create -n [name of the environment] -f [file]
+    ```
+
+#### Enter/exit freqtrade-conda environment
+
+To check available environments, type
+
+```bash
+conda env list
 ```
 
-Unfortunately, many packages requiring compilation don't provide a pre-build wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
+Enter installed environment
 
-The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building c code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or [docker](docker.md) first.
+```bash
+# enter conda environment
+conda activate freqtrade-conda
 
----
+# exit conda environment - don't do it now
+conda deactivate
+```
 
-Now you have an environment ready, the next step is
-[Bot Configuration](configuration.md).
+Install last python dependencies with pip
+
+```bash
+python3 -m pip install --upgrade pip
+python3 -m pip install -e .
+```
+
+Patch conda libta-lib (Linux only)
+
+```bash
+# Ensure that the environment is active!
+conda activate freqtrade-conda
+
+cd build_helpers
+bash install_ta-lib.sh ${CONDA_PREFIX} nosudo
+```
+
+### Congratulations
+
+[You are ready](#you-are-ready), and run the bot
+
+### Important shortcuts
+
+```bash
+# list installed conda environments
+conda env list
+
+# activate base environment
+conda activate
+
+# activate freqtrade-conda environment
+conda activate freqtrade-conda
+
+#deactivate any conda environments
+conda deactivate                              
+```
+
+### Further info on anaconda
+
+!!! Info "New heavy packages"
+    It may happen that creating a new Conda environment, populated with selected packages at the moment of creation takes less time than installing a large, heavy library or application, into previously set environment.
+
+!!! Warning "pip install within conda"
+    The documentation of conda says that pip should NOT be used within conda, because internal problems can occur.
+    However, they are rare. [Anaconda Blogpost](https://www.anaconda.com/blog/using-pip-in-a-conda-environment)
+
+    Nevertheless, that is why, the `conda-forge` channel is preferred:
+
+    * more libraries are available (less need for `pip`)
+    * `conda-forge` works better with `pip`
+    * the libraries are newer
+
+Happy trading!
+
+-----
+
+## You are ready
+
+You've made it this far, so you have successfully installed freqtrade.
+
+### Initialize the configuration
+
+```bash
+# Step 1 - Initialize user folder
+freqtrade create-userdir --userdir user_data
+
+# Step 2 - Create a new configuration file
+freqtrade new-config --config config.json
+```
+
+You are ready to run, read [Bot Configuration](configuration.md), remember to start with `dry_run: True` and verify that everything is working.
+
+To learn how to setup your configuration, please refer to the [Bot Configuration](configuration.md) documentation page.
+
+### Start the Bot
+
+```bash
+freqtrade trade --config config.json --strategy SampleStrategy
+```
+
+!!! Warning
+    You should read through the rest of the documentation, backtest the strategy you're going to use, and use dry-run before enabling trading with real money.
+
+-----
 
 ## Troubleshooting
 
+### Common problem: "command not found"
+
+If you used (1)`Script` or (2)`Manual` installation, you need to run the bot in virtual environment. If you get error as below, make sure venv is active.
+
+```bash
+# if:
+bash: freqtrade: command not found
+
+# then activate your .env
+source ./.env/bin/activate
+```
+
 ### MacOS installation error
 
 Newer versions of MacOS may have installation failed with errors like `error: command 'g++' failed with exit status 1`.
@@ -287,8 +425,8 @@ Newer versions of MacOS may have installation failed with errors like `error: co
 This error will require explicit installation of the SDK Headers, which are not installed by default in this version of MacOS.
 For MacOS 10.14, this can be accomplished with the below command.
 
-``` bash
+```bash
 open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg
 ```
 
-If this file is inexistant, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
+If this file is inexistent, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.

docs/javascripts/config.js

@@ -0,0 +1,12 @@
+window.MathJax = {
+  tex: {
+    inlineMath: [["\\(", "\\)"]],
+    displayMath: [["\\[", "\\]"]],
+    processEscapes: true,
+    processEnvironments: true
+  },
+  options: {
+    ignoreHtmlClass: ".*|",
+    processHtmlClass: "arithmatex"
+  }
+};

docs/leverage.md

@@ -0,0 +1,136 @@
+# Trading with Leverage
+
+!!! 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.
+
+!!! Note "Multiple bots on one account"
+    You can't run 2 bots on the same account with leverage. For leveraged / margin trading, freqtrade assumes it's the only user of the account, and all liquidation levels are calculated based on this assumption.
+
+!!! Danger "Trading with leverage is very risky"
+    Do not trade with a leverage > 1 using a strategy that hasn't shown positive results in a live run using the spot market. Check the stoploss of your strategy. With a leverage of 2, a stoploss of 0.5 (50%) would be too low, and these trades would be liquidated before reaching that stoploss.
+    We do not assume any responsibility for eventual losses that occur from using this software or this mode.
+
+    Please only use advanced trading modes when you know how freqtrade (and your strategy) works.
+    Also, never risk more than what you can afford to lose.
+
+Please read the [strategy migration guide](strategy_migration.md#strategy-migration-between-v2-and-v3) to migrate your strategy from a freqtrade v2 strategy, to v3 strategy that can short and trade futures.
+
+## 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)
+
+For a strategy to short, the strategy class must set the class variable `can_short = True`
+
+Please read [strategy customization](strategy-customization.md#entry-signal-rules) for instructions on how to set signals to enter and exit short trades.
+
+## Understand `trading_mode`
+
+The possible values are: `spot` (default), `margin`(*Currently unavailable*) or `futures`.
+
+### Spot
+
+Regular trading mode (low risk)
+
+- Long trades only (No short trades).
+- No leverage.
+- No Liquidation.
+- Profits gained/lost are equal to the change in value of the assets (minus trading fees).
+
+### Leverage trading modes
+
+With leverage, a trader borrows capital from the exchange. The capital must be re-payed fully to the exchange (potentially with interest), and the trader keeps any profits, or pays any losses, from any trades made using the borrowed capital.
+
+Because the capital must always be re-payed, exchanges will **liquidate** (forcefully sell the traders assets) a trade made using borrowed capital when the total value of assets in the leverage account drops to a certain point (a point where the total value of losses is less than the value of the collateral that the trader actually owns in the leverage account), in order to ensure that the trader has enough capital to pay the borrowed assets back to the exchange. The exchange will also charge a **liquidation fee**, adding to the traders losses.
+
+For this reason, **DO NOT TRADE WITH LEVERAGE IF YOU DON'T KNOW EXACTLY WHAT YOUR DOING. LEVERAGE TRADING IS HIGH RISK, AND CAN RESULT IN THE VALUE OF YOUR ASSETS DROPPING TO 0 VERY QUICKLY, WITH NO CHANCE OF INCREASING IN VALUE AGAIN.**
+
+#### Margin (currently unavailable)
+
+Trading occurs on the spot market, but the exchange lends currency to you in an amount equal to the chosen leverage. You pay the amount lent to you back to the exchange with interest, and your profits/losses are multiplied by the leverage specified.
+
+#### Futures
+
+Perpetual swaps (also known as Perpetual Futures) are contracts traded at a price that is closely tied to the underlying asset they are based off of (ex.). You are not trading the actual asset but instead are trading a derivative contract. Perpetual swap contracts can last indefinitely, in contrast to futures or option contracts.
+
+In addition to the gains/losses from the change in price of the futures contract, traders also exchange _funding fees_, which are gains/losses worth an amount that is derived from the difference in price between the futures contract and the underlying asset. The difference in price between a futures contract and the underlying asset varies between exchanges.
+
+To trade in futures markets, you'll have to set `trading_mode` to "futures".
+You will also have to pick a "margin mode" (explanation below) - with freqtrade currently only supporting isolated margin.
+
+``` json
+"trading_mode": "futures",
+"margin_mode": "isolated"
+```
+
+### Margin mode
+
+On top of `trading_mode` - you will also have to configure your `margin_mode`.
+While freqtrade currently only supports one margin mode, this will change, and by configuring it now you're all set for future updates.
+
+The possible values are: `isolated`, or `cross`(*currently unavailable*).
+
+#### Isolated margin mode
+
+Each market(trading pair), keeps collateral in a separate account
+
+``` json
+"margin_mode": "isolated"
+```
+
+#### Cross margin mode (currently unavailable)
+
+One account is used to share collateral between markets (trading pairs). Margin is taken from total account balance to avoid liquidation when needed.
+
+``` json
+"margin_mode": "cross"
+```
+
+## Set leverage to use
+
+Different strategies and risk profiles will require different levels of leverage.
+While you could configure one static leverage value - freqtrade offers you the flexibility to adjust this via [strategy leverage callback](strategy-callbacks.md#leverage-callback) - which allows you to use different leverages by pair, or based on some other factor benefitting your strategy result.
+
+If not implemented, leverage defaults to 1x (no leverage).
+
+!!! Warning
+    Higher leverage also equals higher risk - be sure you fully understand the implications of using leverage!
+
+## Understand `liquidation_buffer`
+
+*Defaults to `0.05`*
+
+A ratio specifying how large of a safety net to place between the liquidation price and the stoploss to prevent a position from reaching the liquidation price.
+This artificial liquidation price is calculated as:
+
+`freqtrade_liquidation_price = liquidation_price ± (abs(open_rate - liquidation_price) * liquidation_buffer)`
+
+- `±` = `+` for long trades
+- `±` = `-` for short trades
+
+Possible values are any floats between 0.0 and 0.99
+
+**ex:** If a trade is entered at a price of 10 coin/USDT, and the liquidation price of this trade is 8 coin/USDT, then with `liquidation_buffer` set to `0.05` the minimum stoploss for this trade would be $8 + ((10 - 8) * 0.05) = 8 + 0.1 = 8.1$
+
+!!! Danger "A `liquidation_buffer` of 0.0, or a low `liquidation_buffer` is likely to result in liquidations, and liquidation fees"
+    Currently Freqtrade is able to calculate liquidation prices, but does not calculate liquidation fees. Setting your `liquidation_buffer` to 0.0, or using a low `liquidation_buffer` could result in your positions being liquidated. Freqtrade does not track liquidation fees, so liquidations will result in inaccurate profit/loss results for your bot. If you use a low `liquidation_buffer`, it is recommended to use `stoploss_on_exchange` if your exchange supports this.
+
+## Unavailable funding rates
+
+For futures data, exchanges commonly provide the futures candles, the marks, and the funding rates. However, it is common that whilst candles and marks might be available, the funding rates are not. This can affect backtesting timeranges, i.e. you may only be able to test recent timeranges and not earlier, experiencing the `No data found. Terminating.` error. To get around this, add the `futures_funding_rate` config option as listed in [configuration.md](configuration.md), and it is recommended that you set this to `0`, unless you know a given specific funding rate for your pair, exchange and timerange. Setting this to anything other than `0` can have drastic effects on your profit calculations within strategy, e.g. within the `custom_exit`, `custom_stoploss`, etc functions.
+
+!!! Warning "This will mean your backtests are inaccurate."
+    This will not overwrite funding rates that are available from the exchange, but bear in mind that setting a false funding rate will mean backtesting results will be inaccurate for historical timeranges where funding rates are not available.
+
+### Developer
+
+#### Margin mode
+
+For shorts, the currency which pays the interest fee for the `borrowed` currency is purchased at the same time of the closing trade (This means that the amount purchased in short closing trades is greater than the amount sold in short opening trades).
+
+For longs, the currency which pays the interest fee for the `borrowed` will already be owned by the user and does not need to be purchased. The interest is subtracted from the `close_value` of the trade.
+
+All Fees are included in `current_profit` calculations during the trade.
+
+#### Futures mode
+
+Funding fees are either added or subtracted from the total amount of a trade

docs/overrides/main.html

@@ -0,0 +1,68 @@
+{% extends "base.html" %}
+
+
+<!-- Navigation -->
+{% block site_nav %}
+
+  <!-- Main navigation -->
+  {% if nav %}
+  {% if page and page.meta and page.meta.hide %}
+  {% set hidden = "hidden" if "navigation" in page.meta.hide %}
+  {% endif %}
+  <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" {{ hidden }}>
+    <div class="md-sidebar__scrollwrap">
+      <div id="widget-wrapper">
+
+      </div>
+      <div class="md-sidebar__inner">
+        {% include "partials/nav.html" %}
+      </div>
+    </div>
+  </div>
+  {% endif %}
+
+  <!-- Table of contents -->
+  {% if page.toc and not "toc.integrate" in features %}
+  {% if page and page.meta and page.meta.hide %}
+  {% set hidden = "hidden" if "toc" in page.meta.hide %}
+  {% endif %}
+  <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" {{ hidden }}>
+    <div class="md-sidebar__scrollwrap">
+      <div class="md-sidebar__inner">
+        {% include "partials/toc.html" %}
+      </div>
+    </div>
+  </div>
+  {% endif %}
+{% endblock %}
+
+{% block footer %}
+    {{ super() }}
+
+    <!-- Place this tag in your head or just before your close body tag. -->
+    <script async defer src="https://buttons.github.io/buttons.js"></script>
+    <script src="https://code.jquery.com/jquery-3.4.1.min.js"
+      integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
+
+    <!-- Load binance SDK -->
+    <script async defer src="https://public.bnbstatic.com/static/js/broker-sdk/broker-sdk@1.0.0.min.js"></script>
+
+    <script>
+      window.onload = function () {
+        var sidebar = document.getElementById('widget-wrapper')
+        var newDiv = document.createElement("div");
+        newDiv.id = "widget";
+        try {
+          sidebar.prepend(newDiv);
+
+          window.binanceBrokerPortalSdk.initBrokerSDK('#widget', {
+            apiHost: 'https://www.binance.com',
+            brokerId: 'R4BD3S82',
+            slideTime: 4e4,
+          });
+        } catch(err) {
+          console.log(err)
+        }
+      }
+    </script>
+{% endblock %}

docs/partials/header.html

@@ -1,54 +0,0 @@
-<header class="md-header" data-md-component="header">
-    <nav class="md-header-nav md-grid">
-        <div class="md-flex">
-            <div class="md-flex__cell md-flex__cell--shrink">
-                <a href="{{ config.site_url | default(nav.homepage.url, true) | url }}" title="{{ config.site_name }}"
-                    class="md-header-nav__button md-logo">
-                    {% if config.theme.logo.icon %}
-                    <i class="md-icon">{{ config.theme.logo.icon }}</i>
-                    {% else %}
-                    <img src="{{ config.theme.logo | url }}" width="24" height="24">
-                    {% endif %}
-                </a>
-            </div>
-            <div class="md-flex__cell md-flex__cell--shrink">
-                <label class="md-icon md-icon--menu md-header-nav__button" for="__drawer"></label>
-            </div>
-            <div class="md-flex__cell md-flex__cell--stretch">
-                <div class="md-flex__ellipsis md-header-nav__title" data-md-component="title">
-                    {% block site_name %}
-                    {% if config.site_name == page.title %}
-                    {{ config.site_name }}
-                    {% else %}
-                    <span class="md-header-nav__topic">
-                        {{ config.site_name }}
-                    </span>
-                    <span class="md-header-nav__topic">
-                        {{ page.title }}
-                    </span>
-                    {% endif %}
-                    {% endblock %}
-                </div>
-            </div>
-            <div class="md-flex__cell md-flex__cell--shrink">
-                {% block search_box %}
-                {% if "search" in config["plugins"] %}
-                <label class="md-icon md-icon--search md-header-nav__button" for="__search"></label>
-                {% include "partials/search.html" %}
-                {% endif %}
-                {% endblock %}
-            </div>
-            {% if config.repo_url %}
-            <div class="md-flex__cell md-flex__cell--shrink">
-                <div class="md-header-nav__source">
-                    {% include "partials/source.html" %}
-                </div>
-            </div>
-            {% endif %}
-        </div>
-    </nav>
-    <!-- Place this tag in your head or just before your close body tag. -->
-    <script async defer src="https://buttons.github.io/buttons.js"></script>
-    <script src="https://code.jquery.com/jquery-3.4.1.min.js"
-        integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
-</header>

docs/plotting.md

@@ -14,7 +14,7 @@ pip install -U -r requirements-plot.txt
 
 The `freqtrade plot-dataframe` subcommand shows an interactive graph with three subplots:
 
-* Main plot with candlestics and indicators following price (sma/ema)
+* Main plot with candlesticks and indicators following price (sma/ema)
 * Volume bars
 * Additional indicators as specified by `--indicators2`
 
@@ -37,7 +37,7 @@ usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH]
 optional arguments:
   -h, --help            show this help message and exit
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
-                        Show profits for only these pairs. Pairs are space-
+                        Limit command to these pairs. Pairs are space-
                         separated.
   --indicators1 INDICATORS1 [INDICATORS1 ...]
                         Set indicators from your strategy you want in the
@@ -65,9 +65,8 @@ optional arguments:
                         _today.json`
   --timerange TIMERANGE
                         Specify what timerange of data to use.
-  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
-                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
-                        `1d`).
+  -i TIMEFRAME, --timeframe TIMEFRAME
+                        Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
   --no-trades           Skip using trades from backtesting file and DB.
 
 Common arguments:
@@ -91,12 +90,13 @@ Strategy arguments:
                         Specify strategy class name which will be used by the
                         bot.
   --strategy-path PATH  Specify additional strategy lookup path.
+
 ```
 
 Example:
 
 ``` bash
-freqtrade plot-dataframe -p BTC/ETH
+freqtrade plot-dataframe -p BTC/ETH --strategy AwesomeStrategy
 ```
 
 The `-p/--pairs` argument can be used to specify pairs you would like to plot.
@@ -107,9 +107,6 @@ The `-p/--pairs` argument can be used to specify pairs you would like to plot.
 Specify custom indicators.
 Use `--indicators1` for the main plot and `--indicators2` for the subplot below (if values are in a different range than prices).
 
-!!! Tip
-    You will almost certainly want to specify a custom strategy! This can be done by adding `-s Classname` / `--strategy ClassName` to the command.
-
 ``` bash
 freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --indicators1 sma ema --indicators2 macd
 ```
@@ -164,42 +161,117 @@ The resulting plot will have the following elements:
 
 An advanced plot configuration can be specified in the strategy in the `plot_config` parameter.
 
-Additional features when using plot_config include:
+Additional features when using `plot_config` include:
 
 * Specify colors per indicator
 * Specify additional subplots
+* Specify indicator pairs to fill area in between
 
-The sample plot configuration below specifies fixed colors for the indicators. Otherwise consecutive plots may produce different colorschemes each time, making comparisons difficult.
+The sample plot configuration below specifies fixed colors for the indicators. Otherwise, consecutive plots may produce different color schemes each time, making comparisons difficult.
 It also allows multiple subplots to display both MACD and RSI at the same time.
 
+Plot type can be configured using `type` key. Possible types are:
+
+* `scatter` corresponding to `plotly.graph_objects.Scatter` class (default).
+* `bar` corresponding to `plotly.graph_objects.Bar` class.
+
+Extra parameters to `plotly.graph_objects.*` constructor can be specified in `plotly` dict.
+
 Sample configuration with inline comments explaining the process:
 
 ``` python
-    plot_config = {
-        'main_plot': {
-            # Configuration for main plot indicators.
-            # Specifies `ema10` to be red, and `ema50` to be a shade of gray
-            'ema10': {'color': 'red'},
-            'ema50': {'color': '#CCCCCC'},
-            # By omitting color, a random color is selected.
-            'sar': {},
+@property
+def plot_config(self):
+    """
+        There are a lot of solutions how to build the return dictionary.
+        The only important point is the return value.
+        Example:
+            plot_config = {'main_plot': {}, 'subplots': {}}
+
+    """
+    plot_config = {}
+    plot_config['main_plot'] = {
+        # Configuration for main plot indicators.
+        # Assumes 2 parameters, emashort and emalong to be specified.
+        f'ema_{self.emashort.value}': {'color': 'red'},
+        f'ema_{self.emalong.value}': {'color': '#CCCCCC'},
+        # By omitting color, a random color is selected.
+        'sar': {},
+        # fill area between senkou_a and senkou_b
+        'senkou_a': {
+            'color': 'green', #optional
+            'fill_to': 'senkou_b',
+            'fill_label': 'Ichimoku Cloud', #optional
+            'fill_color': 'rgba(255,76,46,0.2)', #optional
         },
-        'subplots': {
-            # Create subplot MACD
-            "MACD": {
-                'macd': {'color': 'blue'},
-                'macdsignal': {'color': 'orange'},
-            },
-            # Additional subplot RSI
-            "RSI": {
-                'rsi': {'color': 'red'},
-            }
+        # plot senkou_b, too. Not only the area to it.
+        'senkou_b': {}
+    }
+    plot_config['subplots'] = {
+         # Create subplot MACD
+        "MACD": {
+            'macd': {'color': 'blue', 'fill_to': 'macdhist'},
+            'macdsignal': {'color': 'orange'},
+            'macdhist': {'type': 'bar', 'plotly': {'opacity': 0.9}}
+        },
+        # Additional subplot RSI
+        "RSI": {
+            'rsi': {'color': 'red'}
         }
     }
+
+    return plot_config
 ```
 
+??? Note "As attribute (former method)"
+    Assigning plot_config is also possible as Attribute (this used to be the default way).
+    This has the disadvantage that strategy parameters are not available, preventing certain configurations from working.
+
+    ``` python
+        plot_config = {
+            'main_plot': {
+                # Configuration for main plot indicators.
+                # Specifies `ema10` to be red, and `ema50` to be a shade of gray
+                'ema10': {'color': 'red'},
+                'ema50': {'color': '#CCCCCC'},
+                # By omitting color, a random color is selected.
+                'sar': {},
+            # fill area between senkou_a and senkou_b
+            'senkou_a': {
+                'color': 'green', #optional
+                'fill_to': 'senkou_b',
+                'fill_label': 'Ichimoku Cloud', #optional
+                'fill_color': 'rgba(255,76,46,0.2)', #optional
+            },
+            # plot senkou_b, too. Not only the area to it.
+            'senkou_b': {}
+            },
+            'subplots': {
+                # Create subplot MACD
+                "MACD": {
+                    'macd': {'color': 'blue', 'fill_to': 'macdhist'},
+                    'macdsignal': {'color': 'orange'},
+                    'macdhist': {'type': 'bar', 'plotly': {'opacity': 0.9}}
+                },
+                # Additional subplot RSI
+                "RSI": {
+                    'rsi': {'color': 'red'}
+                }
+            }
+        }
+
+    ```
+
+
 !!! Note
-    The above configuration assumes that `ema10`, `ema50`, `macd`, `macdsignal` and `rsi` are columns in the DataFrame created by the strategy.
+    The above configuration assumes that `ema10`, `ema50`, `senkou_a`, `senkou_b`,
+    `macd`, `macdsignal`, `macdhist` and `rsi` are columns in the DataFrame created by the strategy.
+
+!!! Warning
+    `plotly` arguments are only supported with plotly library and will not work with freq-ui.
+
+!!! Note "Trade position adjustments"
+    If `position_adjustment_enable` / `adjust_trade_position()` is used, the trade initial buy price is averaged over multiple orders and the trade start price will most likely appear outside the candle range.
 
 ## Plot profit
 
@@ -211,6 +283,8 @@ The `plot-profit` subcommand shows an interactive graph with three plots:
 * The summarized profit made by backtesting.
 Note that this is not the real-world profit, but more of an estimate.
 * Profit for each individual pair.
+* Parallelism of trades.
+* Underwater (Periods of drawdown).
 
 The first graph is good to get a grip of how the overall market progresses.
 
@@ -220,11 +294,14 @@ This graph will also highlight the start (and end) of the Max drawdown period.
 
 The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
 
+The forth graph can help you analyze trade parallelism, showing how often max_open_trades have been maxed out.
+
 Possible options for the `freqtrade plot-profit` subcommand:
 
 ```
 usage: freqtrade plot-profit [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                             [-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
+                             [-d PATH] [--userdir PATH] [-s NAME]
+                             [--strategy-path PATH] [-p PAIRS [PAIRS ...]]
                              [--timerange TIMERANGE] [--export EXPORT]
                              [--export-filename PATH] [--db-url PATH]
                              [--trade-source {DB,file}] [-i TIMEFRAME]
@@ -232,14 +309,14 @@ usage: freqtrade plot-profit [-h] [-v] [--logfile FILE] [-V] [-c PATH]
 optional arguments:
   -h, --help            show this help message and exit
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
-                        Show profits for only these pairs. Pairs are space-
+                        Limit command to these pairs. Pairs are space-
                         separated.
   --timerange TIMERANGE
                         Specify what timerange of data to use.
   --export EXPORT       Export backtest results, argument are: trades.
                         Example: `--export=trades`
-  --export-filename PATH
-                        Save backtest results to the file with this filename.
+  --export-filename PATH, --backtest-filename PATH
+                        Use backtest results from this filename.
                         Requires `--export` to be set as well. Example:
                         `--export-filename=user_data/backtest_results/backtest
                         _today.json`
@@ -250,9 +327,9 @@ optional arguments:
   --trade-source {DB,file}
                         Specify the source for trades (Can be DB or file
                         (backtest file)) Default: file
-  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
-                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
-                        `1d`).
+  -i TIMEFRAME, --timeframe TIMEFRAME
+                        Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
+  --auto-open           Automatically open generated plot.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -270,6 +347,12 @@ Common arguments:
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
 
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+
 ```
 
 The `-p/--pairs`  argument, can be used to limit the pairs that are considered for this calculation.
@@ -279,7 +362,7 @@ Examples:
 Use custom backtest-export file
 
 ``` bash
-freqtrade plot-profit  -p LTC/BTC --export-filename user_data/backtest_results/backtest-result-Strategy005.json
+freqtrade plot-profit  -p LTC/BTC --export-filename user_data/backtest_results/backtest-result.json
 ```
 
 Use custom database

docs/plugins.md

@@ -0,0 +1,3 @@
+# Plugins
+--8<-- "includes/pairlists.md"
+--8<-- "includes/protections.md"

docs/requirements-docs.txt

@@ -1,2 +1,6 @@
-mkdocs-material==5.3.3
-mdx_truly_sane_lists==1.2
+markdown==3.3.7
+mkdocs==1.3.1
+mkdocs-material==8.4.0
+mdx_truly_sane_lists==1.3
+pymdown-extensions==9.5
+jinja2==3.1.2

docs/rest-api.md

@@ -1,4 +1,19 @@
-# REST API Usage
+# REST API & FreqUI
+
+## 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`).
+
+!!! info "Alpha release"
+    FreqUI is still considered an alpha release - if you encounter bugs or inconsistencies please open a [FreqUI issue](https://github.com/freqtrade/frequi/issues/new/choose).
+
+!!! 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.
 
 ## Configuration
 
@@ -11,7 +26,8 @@ Sample configuration:
         "enabled": true,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
-        "verbosity": "info",
+        "verbosity": "error",
+        "enable_openapi": false,
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
         "username": "Freqtrader",
@@ -22,8 +38,10 @@ Sample configuration:
 !!! Danger "Security warning"
     By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
 
-!!! Danger "Password selection"
-    Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
+??? Note "API/UI Access on a remote servers"
+    If you're running on a VPS, you should consider using either a ssh tunnel, or setup a VPN (openVPN, wireguard) to connect to your bot.
+    This will ensure that freqUI is not directly exposed to the internet, which is not recommended for security reasons (freqUI does not support https out of the box).
+    Setup of these tools is not part of this tutorial, however many good tutorials can be found on the internet.
 
 You can then access the API by going to `http://127.0.0.1:8080/api/v1/ping` in a browser to check if the API is running correctly.
 This should return the response:
@@ -34,50 +52,51 @@ This should return the response:
 
 All other endpoints return sensitive info and require authentication and are therefore not available through a web browser.
 
-To generate a secure password, either use a password manager, or use the below code snipped.
+### Security
+
+To generate a secure password, best use a password manager, or use the below code.
 
 ``` python
 import secrets
 secrets.token_hex()
 ```
 
-!!! Hint
+!!! Hint "JWT token"
     Use the same method to also generate a JWT secret key (`jwt_secret_key`).
 
+!!! Danger "Password selection"
+    Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
+    Also change `jwt_secret_key` to something random (no need to remember this, but it'll be used to encrypt your session, so it better be something unique!). 
+
 ### Configuration with docker
 
-If you run your bot using docker, you'll need to have the bot listen to incomming connections. The security is then handled by docker.
+If you run your bot using docker, you'll need to have the bot listen to incoming connections. The security is then handled by docker.
 
 ``` json
     "api_server": {
         "enabled": true,
         "listen_ip_address": "0.0.0.0",
-        "listen_port": 8080
+        "listen_port": 8080,
+        "username": "Freqtrader",
+        "password": "SuperSecret1!",
+        //...
     },
 ```
 
-Add the following to your docker command:
+Make sure that the following 2 lines are available in your docker-compose file:
 
-``` bash
-  -p 127.0.0.1:8080:8080
-```
-
-A complete sample-command may then look as follows:
-
-```bash
-docker run -d \
-  --name freqtrade \
-  -v ~/.freqtrade/config.json:/freqtrade/config.json \
-  -v ~/.freqtrade/user_data/:/freqtrade/user_data \
-  -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-  -p 127.0.0.1:8080:8080 \
-  freqtrade trade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
+```yml
+    ports:
+      - "127.0.0.1:8080:8080"
 ```
 
 !!! Danger "Security warning"
-    By using `-p 8080:8080` the API is available to everyone connecting to the server under the correct port, so others may be able to control your bot.
+    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.
 
-## Consuming the API
+
+## 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.
@@ -88,14 +107,17 @@ python3 scripts/rest_client.py <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.
 
-### Minimalistic client config
+#### Minimalistic client config
 
 ``` json
 {
     "api_server": {
         "enabled": true,
         "listen_ip_address": "0.0.0.0",
-        "listen_port": 8080
+        "listen_port": 8080,
+        "username": "Freqtrader",
+        "password": "SuperSecret1!",
+        //...
     }
 }
 ```
@@ -104,28 +126,46 @@ By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be use
 python3 scripts/rest_client.py --config rest_config.json <command> [optional parameters]
 ```
 
-## Available commands
+### Available endpoints
 
-|  Command | Default | Description |
-|----------|---------|-------------|
-| `start` | | Starts the trader
-| `stop` | | Stops the trader
-| `stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
-| `reload_config` | | Reloads the configuration file
-| `show_config` | | Shows part of the current configuration with relevant settings to operation
-| `status` | | Lists all open trades
-| `count` | | Displays number of trades used and available
-| `profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
-| `forcesell <trade_id>` | | Instantly sells the given trade  (Ignoring `minimum_roi`).
-| `forcesell all` | | Instantly sells all open trades (Ignoring `minimum_roi`).
-| `forcebuy <pair> [rate]` | | Instantly buys the given pair. Rate is optional. (`forcebuy_enable` must be set to True)
-| `performance` | | Show performance of each finished trade grouped by pair
-| `balance` | | Show account balance per currency
-| `daily <n>` | 7 | Shows profit or loss per day, over the last n days
-| `whitelist` | | Show the current whitelist
-| `blacklist [pair]` | | Show the current blacklist, or adds a pair to the blacklist.
-| `edge` | | Show validated pairs by Edge if it is enabled.
-| `version` | | Show version
+|  Command | Description |
+|----------|-------------|
+| `ping` | Simple command testing the API Readiness - requires no authentication.
+| `start` | Starts the trader.
+| `stop` | Stops the trader.
+| `stopbuy` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
+| `reload_config` | Reloads the configuration file.
+| `trades` | List last trades. Limited to 500 trades per call.
+| `trade/<tradeid>` | Get specific trade.
+| `delete_trade <trade_id>` | Remove trade from the database. Tries to close open orders. Requires manual handling of this trade on the exchange.
+| `show_config` | Shows part of the current configuration with relevant settings to operation.
+| `logs` | Shows last log messages.
+| `status` | Lists all open trades.
+| `count` | Displays number of trades used and available.
+| `locks` | Displays currently locked pairs.
+| `delete_lock <lock_id>` | Deletes (disables) the lock by id.
+| `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 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)
+| `performance` | Show performance of each finished trade grouped by pair.
+| `balance` | Show account balance per currency.
+| `daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7).
+| `stats` | Display a summary of profit / loss reasons as well as average holding times.
+| `whitelist` | Show the current whitelist.
+| `blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
+| `edge` | Show validated pairs by Edge if it is enabled.
+| `pair_candles` | Returns dataframe for a pair / timeframe combination while the bot is running. **Alpha**
+| `pair_history` | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy. **Alpha**
+| `plot_config` | Get plot config from the strategy (or nothing if not configured). **Alpha**
+| `strategies` | List strategies in strategy directory. **Alpha**
+| `strategy <strategy>` | Get specific Strategy content. **Alpha**
+| `available_pairs` | List available backtest data. **Alpha**
+| `version` | Show version.
+
+!!! Warning "Alpha status"
+    Endpoints labeled with *Alpha status* above may change at any time without notice.
 
 Possible commands can be listed from the rest-client script using the `help` command.
 
@@ -135,81 +175,151 @@ python3 scripts/rest_client.py help
 
 ``` output
 Possible commands:
+
+available_pairs
+	Return available pair (backtest data) based on timeframe / stake_currency selection
+
+        :param timeframe: Only pairs with this timeframe available.
+        :param stake_currency: Only pairs that include this timeframe
+
 balance
-        Get the account balance
-        :returns: json object
+	Get the account balance.
 
 blacklist
-        Show the current blacklist
+	Show the current blacklist.
+
         :param add: List of coins to add (example: "BNB/BTC")
-        :returns: json object
 
 count
-        Returns the amount of open trades
-        :returns: json object
+	Return the amount of open trades.
 
 daily
-        Returns the amount of open trades
-        :returns: json object
+	Return the profits for each day, and amount of trades.
+
+delete_lock
+	Delete (disable) lock from the database.
+
+        :param lock_id: ID for the lock to delete
+
+delete_trade
+	Delete trade from the database.
+        Tries to close open orders. Requires manual handling of this asset on the exchange.
+
+        :param trade_id: Deletes the trade with this ID from the database.
 
 edge
-        Returns information about edge
-        :returns: json object
+	Return information about edge.
 
 forcebuy
-        Buy an asset
+	Buy an asset.
+
         :param pair: Pair to buy (ETH/BTC)
         :param price: Optional - price to buy
-        :returns: json object of the trade
 
-forcesell
-        Force-sell a trade
+forceenter
+	Force entering a trade
+
+        :param pair: Pair to buy (ETH/BTC)
+        :param side: 'long' or 'short'
+        :param price: Optional - price to buy
+
+forceexit
+	Force-exit a trade.
+
         :param tradeid: Id of the trade (can be received via status command)
-        :returns: json object
+
+locks
+	Return current locks
+
+logs
+	Show latest logs.
+
+        :param limit: Limits log messages to the last <limit> logs. No limit to get the entire log.
+
+pair_candles
+	Return live dataframe for <pair><timeframe>.
+
+        :param pair: Pair to get data for
+        :param timeframe: Only pairs with this timeframe available.
+        :param limit: Limit result to the last n candles.
+
+pair_history
+	Return historic, analyzed dataframe
+
+        :param pair: Pair to get data for
+        :param timeframe: Only pairs with this timeframe available.
+        :param strategy: Strategy to analyze and get values for
+        :param timerange: Timerange to get data for (same format than --timerange endpoints)
 
 performance
-        Returns the performance of the different coins
-        :returns: json object
+	Return the performance of the different coins.
+
+ping
+	simple ping
+
+plot_config
+	Return plot configuration if the strategy defines one.
 
 profit
-        Returns the profit summary
-        :returns: json object
+	Return the profit summary.
 
 reload_config
-        Reload configuration
-        :returns: json object
+	Reload configuration.
 
 show_config
+	
         Returns part of the configuration, relevant for trading operations.
-        :return: json object containing the version
 
 start
-        Start the bot if it's in stopped state.
-        :returns: json object
+	Start the bot if it's in the stopped state.
+
+stats
+	Return the stats report (durations, sell-reasons).
 
 status
-        Get the status of open trades
-        :returns: json object
+	Get the status of open trades.
 
 stop
-        Stop the bot. Use start to restart
-        :returns: json object
+	Stop the bot. Use `start` to restart.
 
 stopbuy
-        Stop buying (but handle sells gracefully).
-        use reload_config to reset
-        :returns: json object
+	Stop buying (but handle sells gracefully). Use `reload_config` to reset.
+
+strategies
+	Lists available strategies
+
+strategy
+	Get strategy details
+
+        :param strategy: Strategy class name
+
+sysinfo
+	Provides system information (CPU, RAM usage)
+
+trade
+	Return specific trade
+
+        :param trade_id: Specify which trade to get.
+
+trades
+	Return trades history, sorted by id
+
+        :param limit: Limits trades to the X last trades. Max 500 trades.
+        :param offset: Offset by this amount of trades.
 
 version
-        Returns the version of the bot
-        :returns: json object containing the version
+	Return the version of the bot.
 
 whitelist
-        Show the current whitelist
-        :returns: json object
+	Show the current whitelist.
 ```
 
-## Advanced API usage using JWT tokens
+### OpenAPI interface
+
+To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration.
+This will enable the Swagger UI at the `/docs` endpoint. By default, that's running at http://localhost:8080/docs/ - but it'll depend on your settings.
+
+### Advanced API usage using JWT tokens
 
 !!! Note
     The below should be done in an application (a Freqtrade REST API client, which fetches info via API), and is not intended to be used on a regular basis.
@@ -234,14 +344,17 @@ Since the access token has a short timeout (15 min) - the `token/refresh` reques
 {"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk5NzQsIm5iZiI6MTU4OTExOTk3NCwianRpIjoiMDBjNTlhMWUtMjBmYS00ZTk0LTliZjAtNWQwNTg2MTdiZDIyIiwiZXhwIjoxNTg5MTIwODc0LCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.1seHlII3WprjjclY6DpRhen0rqdF4j6jbvxIhUFaSbs"}
 ```
 
-## CORS
+### CORS
 
-All web-based frontends 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.
+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.
 
-Users can configure this themselves via the `CORS_origins` configuration setting.
-It consists of a list of allowed sites that are allowed to consume resources from the bot's API.
+??? 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:
 
@@ -254,5 +367,19 @@ Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - t
 }
 ```
 
+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.

docs/sandbox-testing.md

@@ -1,104 +1,63 @@
 # Sandbox API testing
 
-Where an exchange provides a sandbox for risk-free integration, or end-to-end, testing CCXT provides access to these.
+Some exchanges provide sandboxes or testbeds for risk-free testing, while running the bot against a real exchange.
+With some configuration, freqtrade (in combination with ccxt) provides access to these.
 
-This document is a *light overview of configuring Freqtrade and GDAX sandbox.
-This can be useful to developers and trader alike as Freqtrade is quite customisable.
+This document is an overview to configure Freqtrade to be used with sandboxes.
+This can be useful to developers and trader alike.
 
-When testing your API connectivity, make sure to use the following URLs.
-***Website**
-https://public.sandbox.gdax.com
-***REST API**
-https://api-public.sandbox.gdax.com
+!!! Warning
+    Sandboxes usually have very low volume, and either a very wide spread, or no orders available at all.
+    Therefore, sandboxes will usually not do a good job of showing you how a strategy would work in real trading.
+
+## Exchanges known to have a sandbox / testnet
+
+* [binance](https://testnet.binance.vision/)
+* [coinbasepro](https://public.sandbox.pro.coinbase.com)
+* [gemini](https://exchange.sandbox.gemini.com/)
+* [huobipro](https://www.testnet.huobi.pro/)
+* [kucoin](https://sandbox.kucoin.com/)
+* [phemex](https://testnet.phemex.com/) 
+
+!!! Note
+    We did not test correct functioning of all of the above testnets. Please report your experiences with each sandbox.
 
 ---
 
-# Configure a Sandbox account on Gdax
+## Configure a Sandbox account
 
-Aim of this document section
+When testing your API connectivity, make sure to use the appropriate sandbox / testnet URL.
 
-- An sanbox account
-- create 2FA (needed to create an API)
-- Add test 50BTC to account 
-- Create :
-- - API-KEY
-- - API-Secret
-- - API Password
+In general, you should follow these steps to enable an exchange's sandbox:
 
-## Acccount
+* Figure out if an exchange has a sandbox (most likely by using google or the exchange's support documents)
+* Create a sandbox account (often the sandbox-account requires separate registration)
+* [Add some test assets to account](#add-test-funds)
+* Create API keys
 
-This link will redirect to the sandbox main page to login / create account dialogues:
-https://public.sandbox.pro.coinbase.com/orders/
+### Add test funds
 
-After registration and Email confimation you wil be redirected into your sanbox account.  It is easy to verify you're in sandbox by checking the URL bar.
-> https://public.sandbox.pro.coinbase.com/
+Usually, sandbox exchanges allow depositing funds directly via web-interface.
+You should make sure to have a realistic amount of funds available to your test-account, so results are representable of your real account funds.
 
-## Enable 2Fa (a prerequisite to creating sandbox API Keys)
+!!! Warning
+    Test exchanges will **NEVER** require your real credit card or banking details!
 
-From within sand box site select your profile, top right.
->Or as a direct link: https://public.sandbox.pro.coinbase.com/profile
+## Configure freqtrade to use a exchange's sandbox
 
-From the menu panel to the left of the screen select
-
-> Security: "*View or Update*"
-
-In the new site select "enable authenticator" as typical google Authenticator.
-
-- open Google Authenticator on your phone
-- scan barcode
-- enter your generated 2fa
-
-## Enable API Access
-
-From within sandbox select profile>api>create api-keys
->or as a direct link: https://public.sandbox.pro.coinbase.com/profile/api
-
-Click on "create one" and ensure  **view** and **trade**  are "checked" and sumbit your 2FA
-
-- **Copy and paste the Passphase** into a notepade this will be needed later
-- **Copy and paste the API Secret** popup into a notepad this will needed later
-- **Copy and paste the API Key** into a notepad this will needed later
-
-## Add 50 BTC test funds
-
-To add funds, use the web interface deposit and withdraw buttons.
-
-To begin select 'Wallets' from the top menu.
-> Or as a direct link: https://public.sandbox.pro.coinbase.com/wallets
-
-- Deposits (bottom left of screen)
-- - Deposit Funds Bitcoin
-- - - Coinbase BTC Wallet
-- - - - Max (50 BTC)
-- - - - - Deposit
-
-*This process may be repeated for other currencies, ETH as example*
-
----
-
-# Configure Freqtrade to use Gax Sandbox 
-
-The aim of this document section
-
-- Enable sandbox URLs in Freqtrade
-- Configure API
-- - secret
-- - key
-- - passphrase
- 
-## Sandbox URLs
+### Sandbox URLs
 
 Freqtrade makes use of CCXT which in turn provides a list of URLs to Freqtrade.
 These include `['test']` and `['api']`.
 
-- `[Test]` if available will point to an Exchanges sandbox. 
-- `[Api]` normally used, and resolves to live API target on the exchange 
+* `[Test]` if available will point to an Exchanges sandbox.
+* `[Api]` normally used, and resolves to live API target on the exchange.
 
 To make use of sandbox / test add "sandbox": true, to your config.json
 
 ```json
   "exchange": {
-        "name": "gdax",
+        "name": "coinbasepro",
         "sandbox": true,
         "key": "5wowfxemogxeowo;heiohgmd",
         "secret": "/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==",
@@ -106,36 +65,57 @@ To make use of sandbox / test add "sandbox": true, to your config.json
         "outdated_offset": 5
         "pair_whitelist": [
             "BTC/USD"
+        ]
+  },
+  "datadir": "user_data/data/coinbasepro_sandbox"
 ```
 
-Also insert your 
+Also the following information:
 
-- api-key  (noted earlier)
-- api-secret (noted earlier)
-- password (the passphrase - noted earlier)
+* api-key (created for the sandbox webpage)
+* api-secret (noted earlier)
+* password (the passphrase - noted earlier)
+
+!!! Tip "Different data directory"
+    We also recommend to set `datadir` to something identifying downloaded data as sandbox data, to avoid having sandbox data mixed with data from the real exchange.
+    This can be done by adding the `"datadir"` key to the configuration.
+    Now, whenever you use this configuration, your data directory will be set to this directory.
 
 ---
 
 ## You should now be ready to test your sandbox
 
-Ensure Freqtrade logs show the sandbox URL, and trades made are shown in sandbox.
-** Typically the  BTC/USD has the most activity in sandbox to test against.
+Ensure Freqtrade logs show the sandbox URL, and trades made are shown in sandbox. Also make sure to select a pair which shows at least some decent value (which very often is BTC/<somestablecoin>).
 
-## GDAX - Old Candles problem
+## Common problems with sandbox exchanges
 
-It is my experience that GDAX sandbox candles may be 20+- minutes out of date. This can cause trades to fail as one of Freqtrades safety checks.
+Sandbox exchange instances often have very low volume, which can cause some problems which usually are not seen on a real exchange instance.
 
-To disable this check, add / change the `"outdated_offset"` parameter in the exchange section of your configuration to adjust for this delay.
-Example based on the above configuration:
+### Old Candles problem
 
-```json
-  "exchange": {
-        "name": "gdax",
-        "sandbox": true,
-        "key": "5wowfxemogxeowo;heiohgmd",
-        "secret": "/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==",
-        "password": "1bkjfkhfhfu6sr",
-        "outdated_offset": 30
-        "pair_whitelist": [
-            "BTC/USD"
-```
+Since Sandboxes often have low volume, candles can be quite old and show no volume.
+To disable the error "Outdated history for pair ...", best increase the parameter `"outdated_offset"` to a number that seems realistic for the sandbox you're using.
+
+### Unfilled orders
+
+Sandboxes often have very low volumes - which means that many trades can go unfilled, or can go unfilled for a very long time.
+
+To mitigate this, you can try to match the first order on the opposite orderbook side using the following configuration:
+
+``` jsonc
+  "order_types": {
+    "entry": "limit",
+    "exit": "limit"
+    // ...
+  },
+  "entry_pricing": {
+    "price_side": "other",
+    // ...
+  },
+  "exit_pricing":{
+    "price_side": "other",
+    // ...
+  },
+  ```
+
+  The configuration is similar to the suggested configuration for market orders - however by using limit-orders you can avoid moving the price too much, and you can set the worst price you might get.

docs/sql_cheatsheet.md

@@ -13,6 +13,15 @@ Feel free to use a visual Database editor like SqliteBrowser if you feel more co
 sudo apt-get install sqlite3
 ```
 
+### Using sqlite3 via docker-compose
+
+The freqtrade docker image does contain sqlite3, so you can edit the database without having to install anything on the host system.
+
+``` bash
+docker-compose exec freqtrade /bin/bash
+sqlite3 <database-file>.sqlite
+```
+
 ## Open the DB
 
 ```bash
@@ -34,75 +43,29 @@ sqlite3
 .schema <table_name>
 ```
 
-### Trade table structure
-
-```sql
-CREATE TABLE trades
-    id INTEGER NOT NULL,
-    exchange VARCHAR NOT NULL,
-    pair VARCHAR NOT NULL,
-    is_open BOOLEAN NOT NULL,
-    fee_open FLOAT NOT NULL,
-    fee_open_cost FLOAT,
-    fee_open_currency VARCHAR,
-    fee_close FLOAT NOT NULL,
-    fee_close_cost FLOAT,
-    fee_close_currency VARCHAR,
-    open_rate FLOAT,
-    open_rate_requested FLOAT,
-    open_trade_price FLOAT,
-    close_rate FLOAT,
-    close_rate_requested FLOAT,
-    close_profit FLOAT,
-    close_profit_abs FLOAT,
-    stake_amount FLOAT NOT NULL,
-    amount FLOAT,
-    open_date DATETIME NOT NULL,
-    close_date DATETIME,
-    open_order_id VARCHAR,
-    stop_loss FLOAT,
-    stop_loss_pct FLOAT,
-    initial_stop_loss FLOAT,
-    initial_stop_loss_pct FLOAT,
-    stoploss_order_id VARCHAR,
-    stoploss_last_update DATETIME,
-    max_rate FLOAT,
-    min_rate FLOAT,
-    sell_reason VARCHAR,
-    strategy VARCHAR,
-    timeframe INTEGER,
-    PRIMARY KEY (id),
-    CHECK (is_open IN (0, 1))
-);
-CREATE INDEX ix_trades_stoploss_order_id ON trades (stoploss_order_id);
-CREATE INDEX ix_trades_pair ON trades (pair);
-CREATE INDEX ix_trades_is_open ON trades (is_open);
-
-```
-
 ## Get all trades in the table
 
 ```sql
 SELECT * FROM trades;
 ```
 
-## Fix trade still open after a manual sell on the exchange
+## 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, forcesell <tradeid> should be used to accomplish the same thing.  
-	It is strongly advised to backup your database file before making any manual changes.
+    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.  
+    It is strongly advised to backup your database file before making any manual changes.
 
 !!! Note
-  	This should not be necessary after /forcesell, as forcesell orders are closed automatically by the bot on the next iteration.
+    This should not be necessary after /forceexit, as force_exit orders are closed automatically by the bot on the next iteration.
 
 ```sql
 UPDATE trades
 SET is_open=0,
   close_date=<close_date>,
   close_rate=<close_rate>,
-  close_profit=close_rate/open_rate-1,
-  close_profit_abs = (amount * <close_rate> * (1 - fee_close) - (amount * open_rate * 1 - fee_open)),
-  sell_reason=<sell_reason>
+  close_profit = close_rate / open_rate - 1,
+  close_profit_abs = (amount * <close_rate> * (1 - fee_close) - (amount * (open_rate * (1 - fee_open)))),
+  exit_reason=<exit_reason>
 WHERE id=<trade_ID_to_update>;
 ```
 
@@ -111,24 +74,65 @@ WHERE id=<trade_ID_to_update>;
 ```sql
 UPDATE trades
 SET is_open=0,
-  close_date='2017-12-20 03:08:45.103418',
+  close_date='2020-06-20 03:08:45.103418',
   close_rate=0.19638016,
   close_profit=0.0496,
-  close_profit_abs = (amount * 0.19638016 * (1 - fee_close) - (amount * open_rate * 1 - fee_open))
-  sell_reason='force_sell'  
+  close_profit_abs = (amount * 0.19638016 * (1 - fee_close) - (amount * (open_rate * (1 - fee_open)))),
+  exit_reason='force_exit'  
 WHERE id=31;
 ```
 
-## Insert manually a new trade
+## 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.
+
+If you'd still like to remove a trade from the database directly, you can use the below query.
+
+!!! Danger
+    Some systems (Ubuntu) disable foreign keys in their sqlite3 packaging. When using sqlite - please ensure that foreign keys are on by running `PRAGMA foreign_keys = ON` before the above query.
 
 ```sql
-INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date)
-VALUES ('bittrex', 'ETH/BTC', 1, 0.0025, 0.0025, <open_rate>, <stake_amount>, <amount>, '<datetime>')
+DELETE FROM trades WHERE id = <tradeid>;
+
+DELETE FROM trades WHERE id = 31;
 ```
 
-##### Example:
+!!! 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.
 
-```sql
-INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date)
-VALUES ('bittrex', 'ETH/BTC', 1, 0.0025, 0.0025, 0.00258580, 0.002, 0.7715262081, '2017-11-28 12:44:24.000000')
-```
+## 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

@@ -2,11 +2,82 @@
 
 The `stoploss` configuration parameter is loss as ratio that should trigger a sale.
 For example, value `-0.10` will cause immediate sell if the profit dips below -10% for a given trade. This parameter is optional.
+Stoploss calculations do include fees, so a stoploss of -10% is placed exactly 10% below the entry point.
 
 Most of the strategy files already include the optimal `stoploss` value.
 
 !!! Info
-    All stoploss properties mentioned in this file can be set in the Strategy, or in the configuration. Configuration values will override the strategy values.
+    All stoploss properties mentioned in this file can be set in the Strategy, or in the configuration.  
+    <ins>Configuration values will override the strategy values.</ins>
+
+## Stop Loss On-Exchange/Freqtrade
+
+Those stoploss modes can be *on exchange* or *off exchange*.
+
+These modes can be configured with these values:
+
+``` python
+    'emergency_exit': 'market',
+    'stoploss_on_exchange': False
+    'stoploss_on_exchange_interval': 60,
+    'stoploss_on_exchange_limit_ratio': 0.99
+```
+
+!!! Note
+    Stoploss on exchange is only supported for Binance (stop-loss-limit), Huobi (stop-limit), Kraken (stop-loss-market, stop-loss-limit), FTX (stop limit and stop-market) Gateio (stop-limit), and Kucoin (stop-limit and stop-market) as of now.  
+    <ins>Do not set too low/tight stoploss value if using stop loss on exchange!</ins>  
+    If set to low/tight then you have greater risk of missing fill on the order and stoploss will not work.
+
+### stoploss_on_exchange and stoploss_on_exchange_limit_ratio
+
+Enable or Disable stop loss on exchange.
+If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order fills. This will protect you against sudden crashes in market, as the order execution happens purely within the exchange, and has no potential network overhead.
+
+If `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.  
+`stoploss` defines the stop-price where the limit order is placed - and limit should be slightly below this.  
+If an exchange supports both limit and market stoploss orders, then the value of `stoploss` will be used to determine the stoploss type.  
+
+Calculation example: we bought the asset at 100\$.  
+Stop-price is 95\$, then limit would be `95 * 0.99 = 94.05$` - so the limit order fill can happen between 95$ and 94.05$.  
+
+For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
+
+!!! Note
+    If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new stoploss order.
+
+### stoploss_on_exchange_interval
+
+In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.  
+The bot cannot do these every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
+So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
+This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
+
+### force_exit
+
+`force_exit` is an optional value, which defaults to the same value as `exit` and is used when sending a `/forceexit` command from Telegram or from the Rest API.
+
+### force_entry
+
+`force_entry` is an optional value, which defaults to the same value as `entry` and is used when sending a `/forceentry` command from Telegram or from the Rest API.
+
+### emergency_exit
+
+`emergency_exit` is an optional value, which defaults to `market` and is used when creating stop loss on exchange orders fails.
+The below is the default which is used if not changed in strategy or configuration file.
+
+Example from strategy file:
+
+``` python
+order_types = {
+    "entry": "limit",
+    "exit": "limit",
+    "emergency_exit": "market",
+    "stoploss": "market",
+    "stoploss_on_exchange": True,
+    "stoploss_on_exchange_interval": 60,
+    "stoploss_on_exchange_limit_ratio": 0.99
+}
+```
 
 ## Stop Loss Types
 
@@ -16,30 +87,32 @@ At this stage the bot contains the following stoploss support modes:
 2. Trailing stop loss.
 3. Trailing stop loss, custom positive loss.
 4. Trailing stop loss only once the trade has reached a certain offset.
+5. [Custom stoploss function](strategy-advanced.md#custom-stoploss)
 
-Those stoploss modes can be *on exchange* or *off exchange*. If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfully. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
-
-In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.
-
-For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
-The bot cannot do this every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
-So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
-This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
-
-!!! Note
-    Stoploss on exchange is only supported for Binance (stop-loss-limit), Kraken (stop-loss-market) and FTX (stop limit and stop-market) as of now.
-
-## Static Stop Loss
+### Static Stop Loss
 
 This is very simple, you define a stop loss of x (as a ratio of price, i.e. x * 100% of price). This will try to sell the asset once the loss exceeds the defined loss.
 
-## Trailing Stop Loss
+Example of stop loss:
+
+``` python
+    stoploss = -0.10
+```
+
+For example, simplified math:
+
+* the bot buys an asset at a price of 100$
+* the stop loss is defined at -10%
+* the stop loss would get triggered once the asset drops below 90$
+
+### Trailing Stop Loss
 
 The initial value for this is `stoploss`, just as you would define your static Stop loss.
 To enable trailing stoploss:
 
 ``` python
-trailing_stop = True
+    stoploss = -0.10
+    trailing_stop = True
 ```
 
 This will now activate an algorithm, which automatically moves the stop loss up every time the price of your asset increases.
@@ -47,57 +120,110 @@ This will now activate an algorithm, which automatically moves the stop loss up
 For example, simplified math:
 
 * the bot buys an asset at a price of 100$
-* the stop loss is defined at 2%
-* the stop loss would get triggered once the asset dropps below 98$
+* the stop loss is defined at -10%
+* the stop loss would get triggered once the asset drops below 90$
 * assuming the asset now increases to 102$
-* the stop loss will now be 2% of 102$ or 99.96$
-* now the asset drops in value to 101$, the stop loss will still be 99.96$ and would trigger at 99.96$.
+* the stop loss will now be -10% of 102$ = 91.8$
+* now the asset drops in value to 101\$, the stop loss will still be 91.8$ and would trigger at 91.8$.
 
-In summary: The stoploss will be adjusted to be always be 2% of the highest observed price.
+In summary: The stoploss will be adjusted to be always be -10% of the highest observed price.
 
-### Custom positive stoploss
+### Trailing stop loss, custom positive loss
 
-It is also possible to have a default stop loss, when you are in the red with your buy, but once your profit surpasses a certain percentage, the system will utilize a new stop loss, which can have a different value.
-For example your default stop loss is 5%, but once you have 1.1% profit, it will be changed to be only a 1% stop loss, which trails the green candles until it goes below them.
+You could also have a default stop loss when you are in the red with your buy (buy - fee), but once you hit a positive result (or an offset you define) the system will utilize a new stop loss, which can have a different value.
+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.
 
-Both values require `trailing_stop` to be set to true.
+!!! 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).
+
+Both values require `trailing_stop` to be set to true and `trailing_stop_positive` with a value.
 
 ``` python
-    trailing_stop_positive = 0.01
-    trailing_stop_positive_offset = 0.011
+    stoploss = -0.10
+    trailing_stop = True
+    trailing_stop_positive = 0.02
+    trailing_stop_positive_offset = 0.0
+    trailing_only_offset_is_reached = False  # Default - not necessary for this example
 ```
 
-The 0.01 would translate to a 1% stop loss, once you hit 1.1% profit.
+For example, simplified math:
+
+* the bot buys an asset at a price of 100$
+* the stop loss is defined at -10%
+* the stop loss would get triggered once the asset drops below 90$
+* assuming the asset now increases to 102$
+* the stop loss will now be -2% of 102$ = 99.96$ (99.96$ stop loss will be locked in and will follow asset price increments with -2%)
+* now the asset drops in value to 101\$, the stop loss will still be 99.96$ and would trigger at 99.96$
+
+The 0.02 would translate to a -2% stop loss.
 Before this, `stoploss` is used for the trailing stoploss.
 
-Read the [next section](#trailing-only-once-offset-is-reached) to keep stoploss at 5% of the entry point.
+!!! Tip "Use an offset to change your stoploss"
+    Use `trailing_stop_positive_offset` to ensure that your new trailing stoploss will be in profit by setting `trailing_stop_positive_offset` higher than `trailing_stop_positive`. Your first new stoploss value will then already have locked in profits.
 
-!!! Tip
-    Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.
+    Example with simplified math:
 
-### Trailing only once offset is reached
+    ``` python
+        stoploss = -0.10
+        trailing_stop = True
+        trailing_stop_positive = 0.02
+        trailing_stop_positive_offset = 0.03
+    ```
 
-It is also possible to use a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
+    * the bot buys an asset at a price of 100$
+    * the stop loss is defined at -10%, so the stop loss would get triggered once the asset drops below 90$
+    * assuming the asset now increases to 102$
+    * the stoploss will now be at 91.8$ - 10% below the highest observed rate
+    * assuming the asset now increases to 103.5$ (above the offset configured)
+    * the stop loss will now be -2% of 103.5$ = 101.43$
+    * now the asset drops in value to 102\$, the stop loss will still be 101.43$ and would trigger once price breaks below 101.43$
 
-If `"trailing_only_offset_is_reached": true` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`.
+### Trailing stop loss only once the trade has reached a certain offset
+
+You can also keep a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
+
+If `trailing_only_offset_is_reached = True` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`.
 This option can be used with or without `trailing_stop_positive`, but uses `trailing_stop_positive_offset` as offset.
 
 ``` python
     trailing_stop_positive_offset = 0.011
-    trailing_only_offset_is_reached = true
+    trailing_only_offset_is_reached = True
 ```
 
-Simplified example:
+Configuration (offset is buy-price + 3%):
 
 ``` python
-    stoploss = 0.05
+    stoploss = -0.10
+    trailing_stop = True
+    trailing_stop_positive = 0.02
     trailing_stop_positive_offset = 0.03
     trailing_only_offset_is_reached = True
 ```
 
+For example, simplified math:
+
 * the bot buys an asset at a price of 100$
-* the stop loss is defined at 5%
-* the stop loss will remain at 95% until profit reaches +3%
+* the stop loss is defined at -10%
+* the stop loss would get triggered once the asset drops below 90$
+* stoploss will remain at 90$ unless asset increases to or above the configured offset
+* assuming the asset now increases to 103$ (where we have the offset configured)
+* the stop loss will now be -2% of 103$ = 100.94$
+* now the asset drops in value to 101\$, the stop loss will still be 100.94$ and would trigger at 100.94$
+
+!!! Tip
+    Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.
+
+## Stoploss and Leverage
+
+Stoploss should be thought of as "risk on this trade" - so a stoploss of 10% on a 100$ trade means you are willing to lose 10$ (10%) on this trade - which would trigger if the price moves 10% to the downside.
+
+When using leverage, the same principle is applied - with stoploss defining the risk on the trade (the amount you are willing to lose).
+
+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.
+
+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).
 
 ## Changing stoploss on open trades
 

docs/strategy-advanced.md

@@ -1,91 +1,228 @@
 # Advanced Strategies
 
 This page explains some advanced concepts available for strategies.
-If you're just getting started, please be familiar with the methods described in the [Strategy Customization](strategy-customization.md) documentation first.
+If you're just getting started, please be familiar with the methods described in the [Strategy Customization](strategy-customization.md) documentation and with the [Freqtrade basics](bot-basics.md) first.
 
-## Custom order timeout rules
-
-Simple, timebased order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
-
-However, freqtrade also offers a custom callback for both ordertypes, which allows you to decide based on custom criteria if a order did time out or not.
+[Freqtrade basics](bot-basics.md) describes in which sequence each method described below is called, which can be helpful to understand which method to use for your custom needs.
 
 !!! Note
-    Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
+    All callback methods described below should only be implemented in a strategy if they are actually used.
 
-### Custom order timeout example
+!!! Tip
+    You can get a strategy template containing all below methods by running `freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced`
 
-A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below.
-It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
+## Storing information
 
-The function must return either `True` (cancel order) or `False` (keep order alive).
+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 `cust_` to avoid naming collisions with predefined strategy variables.
+
+```python
+class AwesomeStrategy(IStrategy):
+    # Create custom dictionary
+    custom_info = {}
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # Check if the entry already exists
+        if not metadata["pair"] in self.custom_info:
+            # Create empty entry for this pair
+            self.custom_info[metadata["pair"]] = {}
+
+        if "crosstime" in self.custom_info[metadata["pair"]]:
+            self.custom_info[metadata["pair"]]["crosstime"] += 1
+        else:
+            self.custom_info[metadata["pair"]]["crosstime"] = 1
+```
+
+!!! 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
+    If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
+
+## Dataframe access
+
+You may access dataframe in various strategy functions by querying it from dataprovider.
 
 ``` python
-from datetime import datetime, timedelta
-from freqtrade.persistence import Trade
+from freqtrade.exchange import timeframe_to_prev_date
 
-class Awesomestrategy(IStrategy):
+class AwesomeStrategy(IStrategy):
+    def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
+                           rate: float, time_in_force: str, exit_reason: str,
+                           current_time: 'datetime', **kwargs) -> bool:
+        # Obtain pair dataframe.
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
 
-    # ... populate_* methods
+        # Obtain last available candle. Do not use current_time to look up latest candle, because 
+        # current_time points to current incomplete candle whose data is not available.
+        last_candle = dataframe.iloc[-1].squeeze()
+        # <...>
 
-    # Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
-    unfilledtimeout = {
-        'buy': 60 * 25,
-        'sell': 60 * 25
-    }
+        # In dry/live runs trade open date will not match candle open date therefore it must be 
+        # rounded.
+        trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
+        # Look up trade candle.
+        trade_candle = dataframe.loc[dataframe['date'] == trade_date]
+        # trade_candle may be empty for trades that just opened as it is still incomplete.
+        if not trade_candle.empty:
+            trade_candle = trade_candle.squeeze()
+            # <...>
+```
 
-    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
-        if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
-            return True
-        elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
-            return True
-        elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
-           return True
-        return False
+!!! Warning "Using .iloc[-1]"
+    You can use `.iloc[-1]` here because `get_analyzed_dataframe()` only returns candles that backtesting is allowed to see.
+    This will not work in `populate_*` methods, so make sure to not use `.iloc[]` in that area.
+    Also, this will only work starting with version 2021.5.
 
+***
+
+## Enter Tag
+
+When your strategy has multiple buy signals, you can name the signal that triggered.
+Then you can access you buy signal on `custom_exit`
+
+```python
+def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (dataframe['rsi'] < 35) &
+            (dataframe['volume'] > 0)
+        ),
+        ['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')
+
+    return dataframe
+
+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()
+    if trade.enter_tag == 'buy_signal_rsi' and last_candle['rsi'] > 80:
+        return 'sell_signal_rsi'
+    return None
 
-    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
-        if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
-            return True
-        elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
-            return True
-        elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
-           return True
-        return False
 ```
 
 !!! Note
-    For the above example, `unfilledtimeout` must be set to something bigger than 24h, otherwise that type of timeout will apply first.
+    `enter_tag` is limited to 100 characters, remaining data will be truncated.
 
-### Custom order timeout example (using additional data)
+## Exit tag
+
+Similar to [Buy Tagging](#buy-tag), you can also specify a sell tag.
 
 ``` python
-from datetime import datetime
-from freqtrade.persistence import Trade
+def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (dataframe['rsi'] > 70) &
+            (dataframe['volume'] > 0)
+        ),
+        ['exit_long', 'exit_tag']] = (1, 'exit_rsi')
 
-class Awesomestrategy(IStrategy):
-
-    # ... populate_* methods
-
-    # Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
-    unfilledtimeout = {
-        'buy': 60 * 25,
-        'sell': 60 * 25
-    }
-
-    def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
-        ob = self.dp.orderbook(pair, 1)
-        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_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
-        ob = self.dp.orderbook(pair, 1)
-        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
-        return False
+    return dataframe
 ```
+
+The provided exit-tag is then used as sell-reason - and shown as such in backtest results.
+
+!!! Note
+    `exit_reason` is limited to 100 characters, remaining data will be truncated.
+
+## Strategy version
+
+You can implement custom strategy versioning by using the "version" method, and returning the version you would like this strategy to have.
+
+``` python
+def version(self) -> str:
+    """
+    Returns version of the strategy.
+    """
+    return "1.1"
+```
+
+!!! Note
+    You should make sure to implement proper version control (like a git repository) alongside this, as freqtrade will not keep historic versions of your strategy, so it's up to the user to be able to eventually roll back to a prior version of the strategy.
+
+## Derived strategies
+
+The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
+
+``` python title="user_data/strategies/myawesomestrategy.py"
+class MyAwesomeStrategy(IStrategy):
+    ...
+    stoploss = 0.13
+    trailing_stop = False
+    # All other attributes and methods are here as they
+    # should be in any custom strategy...
+    ...
+
+```
+
+``` python title="user_data/strategies/MyAwesomeStrategy2.py"
+from myawesomestrategy import MyAwesomeStrategy
+class MyAwesomeStrategy2(MyAwesomeStrategy):
+    # Override something
+    stoploss = 0.08
+    trailing_stop = True
+```
+
+Both attributes and methods may be overridden, altering behavior of the original strategy in a way you need.
+
+While keeping the subclass in the same file is technically possible, it can lead to some problems with hyperopt parameter files, we therefore recommend to use separate strategy files, and import the parent strategy as shown above.
+
+## Embedding Strategies
+
+Freqtrade provides you with an easy way to embed the strategy into your configuration file.
+This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
+in your chosen config file.
+
+### Encoding a string as BASE64
+
+This is a quick example, how to generate the BASE64 string in python
+
+```python
+from base64 import urlsafe_b64encode
+
+with open(file, 'r') as f:
+    content = f.read()
+content = urlsafe_b64encode(content.encode('utf-8'))
+```
+
+The variable 'content', will contain the strategy file in a BASE64 encoded form. Which can now be set in your configurations file as following
+
+```json
+"strategy": "NameOfStrategy:BASE64String"
+```
+
+Please ensure that 'NameOfStrategy' is identical to the strategy name!
+
+## Performance warning
+
+When executing a strategy, one can sometimes be greeted by the following in the logs
+
+> PerformanceWarning: DataFrame is highly fragmented.
+
+This is a warning from [`pandas`](https://github.com/pandas-dev/pandas) and as the warning continues to say:
+use `pd.concat(axis=1)`.
+This can have slight performance implications, which are usually only visible during hyperopt (when optimizing an indicator).
+
+For example:
+
+```python
+for val in self.buy_ema_short.range:
+    dataframe[f'ema_short_{val}'] = ta.EMA(dataframe, timeperiod=val)
+```
+
+should be rewritten to
+
+```python
+frames = [dataframe]
+for val in self.buy_ema_short.range:
+    frames.append(DataFrame({
+        f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
+    }))
+
+# Append columns to existing dataframe
+merged_frame = 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.