fix footer

improve freqai tests

.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/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

@@ -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

@@ -1,9 +1,9 @@
-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: #___
 
@@ -14,4 +14,4 @@ Solve the issue: #___
 
 ## 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

@@ -5,9 +5,17 @@ updates:
   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

@@ -3,9 +3,9 @@ name: Freqtrade CI
 on:
   push:
     branches:
-      - master
       - stable
       - develop
+      - ci/*
     tags:
   release:
     types: [published]
@@ -13,33 +13,37 @@ on:
   schedule:
     - cron:  '0 5 * * 4'
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   build_linux:
 
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ ubuntu-18.04, ubuntu-20.04 ]
-        python-version: [3.7, 3.8, 3.9]
+        os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
+        python-version: ["3.8", "3.9", "3.10.6"]
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
 
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python-version }}
 
     - name: Cache_dependencies
-      uses: actions/cache@v2
+      uses: actions/cache@v3
       id: cache
       with:
         path: ~/dependencies/
         key: ${{ runner.os }}-dependencies
 
     - name: pip cache (linux)
-      uses: actions/cache@v2
-      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
@@ -50,8 +54,9 @@ jobs:
         cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
 
     - name: Installation - *nix
+      if: runner.os == 'Linux'
       run: |
-        python -m pip install --upgrade pip
+        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
@@ -61,15 +66,15 @@ jobs:
     - name: Tests
       run: |
         pytest --random-order --cov=freqtrade --cov-config=.coveragerc
-      if: matrix.python-version != '3.9'
+      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'
+      if: matrix.python-version == '3.9' && matrix.os == 'ubuntu-22.04'
 
     - name: Coveralls
-      if: (startsWith(matrix.os, 'ubuntu-20') && 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
@@ -77,11 +82,13 @@ jobs:
         # Allow failure for coveralls
         coveralls || true
 
-    - name: Backtesting
+    - name: Backtesting (multi)
       run: |
         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: |
@@ -99,44 +106,41 @@ jobs:
 
     - name: Mypy
       run: |
-        mypy freqtrade scripts
+        mypy freqtrade scripts tests
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - 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: 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: [3.7, 3.8, 3.9]
+        python-version: ["3.8", "3.9", "3.10.6"]
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
 
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python-version }}
 
     - name: Cache_dependencies
-      uses: actions/cache@v2
+      uses: actions/cache@v3
       id: cache
       with:
         path: ~/dependencies/
         key: ${{ runner.os }}-dependencies
 
     - name: pip cache (macOS)
-      uses: actions/cache@v2
-      if: startsWith(matrix.os, 'macOS')
+      uses: actions/cache@v3
+      if: runner.os == 'macOS'
       with:
         path: ~/Library/Caches/pip
         key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -147,10 +151,11 @@ jobs:
         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
+        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
@@ -159,22 +164,14 @@ jobs:
 
     - name: Tests
       run: |
-        pytest --random-order --cov=freqtrade --cov-config=.coveragerc
-
-    - name: Coveralls
-      if: (startsWith(matrix.os, 'ubuntu-20') && matrix.python-version == '3.8')
-      env:
-        # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
-        COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
-      run: |
-        # Allow failure for coveralls
-        coveralls -v  || true
+        pytest --random-order
 
     - name: Backtesting
       run: |
         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 AwesomeStrategyAdv --template advanced
+        freqtrade backtesting --datadir tests/testdata --strategy AwesomeStrategyAdv
 
     - name: Hyperopt
       run: |
@@ -194,17 +191,13 @@ jobs:
       run: |
         mypy freqtrade scripts
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - 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:
 
@@ -212,19 +205,18 @@ jobs:
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: [3.7, 3.8]
+        python-version: ["3.8", "3.9", "3.10.6"]
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
 
     - name: Set up Python
-      uses: actions/setup-python@v2
+      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
@@ -235,7 +227,7 @@ jobs:
 
     - name: Tests
       run: |
-        pytest --random-order --cov=freqtrade --cov-config=.coveragerc
+        pytest --random-order
 
     - name: Backtesting
       run: |
@@ -255,32 +247,54 @@ jobs:
 
     - name: Mypy
       run: |
-        mypy freqtrade scripts
+        mypy freqtrade scripts tests
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - 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
+
+  pre-commit:
+    runs-on: ubuntu-22.04
+    steps:
+    - uses: actions/checkout@v3
+
+    - uses: actions/setup-python@v4
+      with:
+        python-version: "3.10"
+    - uses: pre-commit/action@v3.0.0
 
   docs_check:
     runs-on: ubuntu-20.04
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
 
     - name: Documentation syntax
       run: |
         ./tests/test_docs.sh
 
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
-        python-version: 3.8
+        python-version: "3.10"
 
     - name: Documentation build
       run: |
@@ -288,28 +302,22 @@ jobs:
         pip install mkdocs
         mkdocs build
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - 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 Docs*'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+          severity: error
+          details: Freqtrade doc test failed!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
-  cleanup-prior-runs:
-    runs-on: ubuntu-20.04
-    steps:
-    - name: Cleanup previous runs on this branch
-      uses: rokroskar/workflow-run-cleanup-action@v0.3.3
-      if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/stable' && 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_linux, build_macos, build_windows, docs_check ]
+    needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
     runs-on: ubuntu-20.04
+    # Discord notification can't handle schedule events
+    if: (github.event_name != 'schedule')
+    permissions:
+      repository-projects: read
     steps:
 
     - name: Check user permission
@@ -320,28 +328,27 @@ jobs:
       env:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - 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:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade CI*'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+          severity: info
+          details: Test Completed!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   deploy:
-    needs: [ build_linux, build_macos, build_windows, docs_check ]
+    needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
     runs-on: ubuntu-20.04
 
     if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
 
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
-        python-version: 3.8
+        python-version: "3.9"
 
     - name: Extract branch name
       shell: bash
@@ -354,7 +361,7 @@ jobs:
         python setup.py sdist bdist_wheel
 
     - name: Publish to PyPI (Test)
-      uses: pypa/gh-action-pypi-publish@master
+      uses: pypa/gh-action-pypi-publish@v1.5.1
       if: (github.event_name == 'release')
       with:
         user: __token__
@@ -362,7 +369,7 @@ jobs:
         repository_url: https://test.pypi.org/legacy/
 
     - name: Publish to PyPI
-      uses: pypa/gh-action-pypi-publish@master
+      uses: pypa/gh-action-pypi-publish@v1.5.1
       if: (github.event_name == 'release')
       with:
         user: __token__
@@ -385,7 +392,7 @@ 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
@@ -400,19 +407,6 @@ jobs:
       run: |
         build_helpers/publish_docker_multi.sh
 
-
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
-      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
-      with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade CI Deploy*'
-        mention: 'here'
-        mention_if: 'failure'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
-
-
   deploy_arm:
     needs: [ deploy ]
     # Only run on 64bit machines
@@ -420,7 +414,7 @@ jobs:
     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: Extract branch name
       shell: bash
@@ -440,3 +434,11 @@ jobs:
         BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
       run: |
         build_helpers/publish_docker_arm64.sh
+
+    - 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:
+          severity: info
+          details: Deploy Succeeded!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}

.github/workflows/docker_update_readme.yml

@@ -8,11 +8,10 @@ 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
-

.github/workflows/draft-pdf.yml

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

.gitignore

@@ -1,14 +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__/
@@ -75,6 +85,8 @@ instance/
 
 # Sphinx documentation
 docs/_build/
+# Mkdocs documentation
+site/
 
 # PyBuilder
 target/
@@ -100,3 +112,5 @@ target/
 !config_examples/config_ftx.example.json
 !config_examples/config_full.example.json
 !config_examples/config_kraken.example.json
+!config_examples/config_freqai.example.json
+!config_examples/config_freqai-rl.example.json

.pre-commit-config.yaml

@@ -0,0 +1,48 @@
+# 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==2.28.11
+          - 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|
+                .*\.yml|
+                .*\.json
+            )$
+      - 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
-

.travis.yml

@@ -1,55 +0,0 @@
-os:
-- linux
-dist: bionic
-language: python
-python:
-- 3.8
-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_examples/config_bittrex.example.json config.json
-      - freqtrade create-userdir --userdir user_data
-      - freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
-      name: backtest
-    - script:
-      - 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
-      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
-
-notifications:
-  slack:
-    secure: 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
-cache:
-  pip: True
-  directories:
-    - $HOME/dependencies

CONTRIBUTING.md

@@ -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

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9.7-slim-buster as base
+FROM python:3.10.7-slim-bullseye as base
 
 # Setup env
 ENV LANG C.UTF-8
@@ -11,7 +11,7 @@ ENV FT_APP_ENV="docker"
 # Prepare environment
 RUN mkdir /freqtrade \
   && apt-get update \
-  && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev  \
+  && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev libgomp1 \
   && apt-get clean \
   && useradd -u 1000 -G sudo -U -m -s /bin/bash ftuser \
   && chown ftuser:ftuser /freqtrade \

MANIFEST.in

@@ -2,5 +2,6 @@ include LICENSE
 include README.md
 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

@@ -5,7 +5,7 @@
 [![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)
 
@@ -26,13 +26,23 @@ hesitate to read the source code and understand the mechanism of this bot.
 
 Please read the [exchange specific notes](docs/exchanges.md) to learn about eventual, special configurations needed for each exchange.
 
-- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#binance-blacklist))
+- [X] [Binance](https://www.binance.com/)
 - [X] [Bittrex](https://bittrex.com/)
-- [X] [Kraken](https://kraken.com/)
-- [X] [FTX](https://ftx.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](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:
@@ -44,34 +54,29 @@ Exchanges confirmed working by the community:
 
 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 3.7+**: For botting on any operating system - Windows, macOS and Linux.
+- [x] **Based on Python 3.8+**: For botting on any operating system - Windows, macOS and Linux.
 - [x] **Persistence**: Persistence is achieved through sqlite.
 - [x] **Dry-run**: Run the bot without paying money.
 - [x] **Backtesting**: Run a simulation of your buy/sell strategy.
 - [x] **Strategy Optimization by machine learning**: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
+- [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 -b develop https://github.com/freqtrade/freqtrade.git
-cd freqtrade
-./setup.sh --install
-```
-
-For any other type of installation please refer to [Installation doc](https://www.freqtrade.io/en/stable/installation/).
+For further (native) installation methods, please refer to the [Installation documentation page](https://www.freqtrade.io/en/stable/installation/).
 
 ## Basic Usage
 
@@ -121,14 +126,15 @@ 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.
-- `/stopbuy`: Stop entering new trades.
+- `/stopentry`: 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.
-- `/forcesell <trade_id>|all`: Instantly sells the given trade (Ignoring `minimum_roi`).
+- `/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.
@@ -152,10 +158,10 @@ For any questions not covered by the documentation or for further information ab
 ### [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/choose) and
-ensure you follow the template guide so that our team can assist you as
+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)
@@ -169,13 +175,13 @@ 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 necessity 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 [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.
@@ -188,7 +194,7 @@ Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/
 
 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:
 
@@ -196,7 +202,7 @@ To run this bot we recommend you a cloud instance with a minimum of:
 
 ### Software requirements
 
-- [Python 3.7.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)

build_helpers/install_ta-lib.sh

@@ -4,7 +4,7 @@ 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 \
@@ -17,11 +17,17 @@ if [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
     cd .. && rm -rf ./ta-lib/
     exit 1
   fi
-  which sudo && sudo make install || make install
-  if [ -x "$(command -v apt-get)" ]; then
-    echo "Updating library path using ldconfig"
-    sudo ldconfig
+  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"

build_helpers/install_windows.ps1

@@ -1,19 +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
 
-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 ($pyv -eq '3.7') {
-    pip install build_helpers\TA_Lib-0.4.21-cp37-cp37m-win_amd64.whl
-}
 if ($pyv -eq '3.8') {
-    pip install build_helpers\TA_Lib-0.4.21-cp38-cp38-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.25-cp38-cp38-win_amd64.whl
 }
 if ($pyv -eq '3.9') {
-    pip install build_helpers\TA_Lib-0.4.21-cp39-cp39-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.25-cp39-cp39-win_amd64.whl
+}
+if ($pyv -eq '3.10') {
+    pip install build_helpers\TA_Lib-0.4.25-cp310-cp310-win_amd64.whl
 }
-
 pip install -r requirements-dev.txt
 pip install -e .

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_arm64.sh

@@ -6,10 +6,12 @@ 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 ${TAG}"
@@ -38,11 +40,13 @@ fi
 docker tag freqtrade:$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 StrategyTestV2
+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"
@@ -53,6 +57,7 @@ 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
@@ -66,6 +71,9 @@ 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}

build_helpers/publish_docker_multi.sh

@@ -5,6 +5,7 @@
 # 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"
@@ -49,11 +50,13 @@ fi
 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 StrategyTestV2
+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"
@@ -64,6 +67,7 @@ docker images
 
 docker push ${CACHE_IMAGE}
 docker push ${CACHE_IMAGE}:$TAG_PLOT
+docker push ${CACHE_IMAGE}:$TAG_FREQAI
 docker push ${CACHE_IMAGE}:$TAG
 
 

config_examples/config_binance.example.json

@@ -8,19 +8,23 @@
     "dry_run": true,
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
-        "buy": 10,
-        "sell": 30
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
-    "bid_strategy": {
-        "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": {
+    "exit_pricing": {
+        "price_side": "same",
         "use_order_book": true,
         "order_book_top": 1
     },
@@ -86,7 +90,7 @@
     },
     "bot_name": "freqtrade",
     "initial_state": "running",
-    "forcebuy_enable": false,
+    "force_entry_enable": false,
     "internals": {
         "process_throttle_secs": 5
     }

config_examples/config_bittrex.example.json

@@ -8,19 +8,23 @@
     "dry_run": true,
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
-        "buy": 10,
-        "sell": 30
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
-    "bid_strategy": {
+    "entry_pricing": {
+        "price_side": "same",
         "use_order_book": true,
-        "ask_last_balance": 0.0,
         "order_book_top": 1,
+        "price_last_balance": 0.0,
         "check_depth_of_market": {
             "enabled": false,
             "bids_to_ask_delta": 1
         }
     },
-    "ask_strategy":{
+    "exit_pricing":{
+        "price_side": "same",
         "use_order_book": true,
         "order_book_top": 1
     },
@@ -83,7 +87,7 @@
     },
     "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 +1,97 @@
+{
+    "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,
+        "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,
+            "indicator_periods_candles": [
+                10,
+                20
+            ],
+            "plot_feature_importances": 0
+        },
+        "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

@@ -8,19 +8,23 @@
     "dry_run": true,
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
-        "buy": 10,
-        "sell": 30
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
-    "bid_strategy": {
-        "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": {
+    "exit_pricing": {
+        "price_side": "same",
         "use_order_book": true,
         "order_book_top": 1
     },
@@ -85,7 +89,7 @@
     },
     "bot_name": "freqtrade",
     "initial_state": "running",
-    "forcebuy_enable": false,
+    "force_entry_enable": false,
     "internals": {
         "process_throttle_secs": 5
     }

config_examples/config_full.example.json

@@ -5,19 +5,24 @@
     "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_sell_signal": true,
-    "sell_profit_only": false,
-    "sell_profit_offset": 0.0,
-    "ignore_roi_if_buy_signal": 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,
@@ -26,38 +31,41 @@
     },
     "stoploss": -0.10,
     "unfilledtimeout": {
-        "buy": 10,
-        "sell": 30,
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
         "unit": "minutes"
     },
-    "bid_strategy": {
-        "price_side": "bid",
+    "entry_pricing": {
+        "price_side": "same",
         "use_order_book": true,
-        "ask_last_balance": 0.0,
         "order_book_top": 1,
+        "price_last_balance": 0.0,
         "check_depth_of_market": {
             "enabled": false,
             "bids_to_ask_delta": 1
         }
     },
-    "ask_strategy":{
-        "price_side": "ask",
+    "exit_pricing":{
+        "price_side": "same",
         "use_order_book": true,
-        "order_book_top": 1
+        "order_book_top": 1,
+        "price_last_balance": 0.0
     },
     "order_types": {
-        "buy": "limit",
-        "sell": "limit",
-        "emergencysell": "market",
-        "forcesell": "market",
-        "forcebuy": "market",
+        "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_interval": 60,
+        "stoploss_on_exchange_limit_ratio": 0.99
     },
     "order_time_in_force": {
-        "buy": "gtc",
-        "sell": "gtc"
+        "entry": "GTC",
+        "exit": "GTC"
     },
     "pairlists": [
         {"method": "StaticPairList"},
@@ -84,6 +92,8 @@
         "key": "your_exchange_key",
         "secret": "your_exchange_secret",
         "password": "",
+        "log_responses": false,
+        // "unknown_fee_rate": 1,
         "ccxt_config": {},
         "ccxt_async_config": {},
         "pair_whitelist": [
@@ -131,23 +141,24 @@
             "status": "on",
             "warning": "on",
             "startup": "on",
-            "buy": "on",
-            "buy_fill": "on",
-            "sell": {
+            "entry": "on",
+            "entry_fill": "on",
+            "exit": {
                 "roi": "off",
-                "emergency_sell": "off",
-                "force_sell": "off",
-                "sell_signal": "off",
+                "emergency_exit": "off",
+                "force_exit": "off",
+                "exit_signal": "off",
                 "trailing_stop_loss": "off",
                 "stop_loss": "off",
                 "stoploss_on_exchange": "off",
-                "custom_sell": "off"
+                "custom_exit": "off"
             },
-            "sell_fill": "on",
-            "buy_cancel": "on",
-            "sell_cancel": "on",
+            "exit_fill": "on",
+            "entry_cancel": "on",
+            "exit_cancel": "on",
             "protection_trigger": "off",
-            "protection_trigger_global": "on"
+            "protection_trigger_global": "on",
+            "show_candle": "off"
         },
         "reload": true,
         "balance_dust_level": 0.01
@@ -161,12 +172,29 @@
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
         "username": "freqtrader",
-        "password": "SuperSecurePassword"
+        "password": "SuperSecurePassword",
+        "ws_token": "secret_ws_t0ken."
+    },
+    "external_message_consumer": {
+        "enabled": false,
+        "producers": [
+          {
+            "name": "default",
+            "host": "127.0.0.2",
+            "port": 8080,
+            "ws_token": "secret_ws_t0ken."
+          }
+        ],
+        "wait_timeout": 300,
+        "ping_timeout": 10,
+        "sleep_time": 10,
+        "remove_entry_exit_signals": false,
+        "message_size_limit": 8
     },
     "bot_name": "freqtrade",
     "db_url": "sqlite:///tradesv3.sqlite",
     "initial_state": "running",
-    "forcebuy_enable": false,
+    "force_entry_enable": false,
     "internals": {
         "process_throttle_secs": 5,
         "heartbeat_interval": 60
@@ -174,6 +202,8 @@
     "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

@@ -8,19 +8,23 @@
     "dry_run": true,
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
-        "buy": 10,
-        "sell": 30
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
-    "bid_strategy": {
+    "entry_pricing": {
+        "price_side": "same",
         "use_order_book": true,
-        "ask_last_balance": 0.0,
         "order_book_top": 1,
+        "price_last_balance": 0.0,
         "check_depth_of_market": {
             "enabled": false,
             "bids_to_ask_delta": 1
         }
     },
-    "ask_strategy":{
+    "exit_pricing":{
+        "price_side": "same",
         "use_order_book": true,
         "order_book_top": 1
     },
@@ -91,7 +95,7 @@
     },
     "bot_name": "freqtrade",
     "initial_state": "running",
-    "forcebuy_enable": false,
+    "force_entry_enable": false,
     "internals": {
         "process_throttle_secs": 5
     },

docker/Dockerfile.armhf

@@ -1,4 +1,4 @@
-FROM python:3.7.10-slim-buster as base
+FROM python:3.9.12-slim-bullseye as base
 
 # Setup env
 ENV LANG C.UTF-8

docker/Dockerfile.custom

@@ -7,4 +7,5 @@ FROM freqtradeorg/freqtrade:develop
 # 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.freqai

@@ -0,0 +1,8 @@
+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

@@ -1,7 +1,8 @@
 FROM freqtradeorg/freqtrade:develop_plot
 
 
-RUN pip install jupyterlab --user --no-cache-dir
+# Pin jupyter-client to avoid tornado version conflict
+RUN pip install jupyterlab jupyter-client==7.3.4 --user --no-cache-dir
 
 # Empty the ENTRYPOINT to allow all commands
 ENTRYPOINT []

docker/docker-compose-jupyter.yml

@@ -10,7 +10,7 @@ services:
     ports:
       - "127.0.0.1:8888:8888"
     volumes:
-      - "./user_data:/freqtrade/user_data"
+      - "../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/JOSS_paper/note_to_editors.txt

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

docs/JOSS_paper/paper.bib

@@ -0,0 +1,207 @@
+@article{scikit-learn,
+ title={Scikit-learn: Machine Learning in {P}ython},
+ author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
+         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
+         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
+         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
+ journal={Journal of Machine Learning Research},
+ volume={12},
+ pages={2825--2830},
+ year={2011}
+}
+
+@inproceedings{catboost,
+author = {Prokhorenkova, Liudmila and Gusev, Gleb and Vorobev, Aleksandr and Dorogush, Anna Veronika and Gulin, Andrey},
+title = {CatBoost: Unbiased Boosting with Categorical Features},
+year = {2018},
+publisher = {Curran Associates Inc.},
+address = {Red Hook, NY, USA},
+abstract = {This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets. Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms. In this paper, we provide a detailed analysis of this problem and demonstrate that proposed algorithms solve it effectively, leading to excellent empirical results.},
+booktitle = {Proceedings of the 32nd International Conference on Neural Information Processing Systems},
+pages = {6639–6649},
+numpages = {11},
+location = {Montr\'{e}al, Canada},
+series = {NIPS'18}
+}
+
+
+@article{lightgbm,
+  title={Lightgbm: A highly efficient gradient boosting decision tree},
+  author={Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
+  journal={Advances in neural information processing systems},
+  volume={30},
+  pages={3146--3154},
+  year={2017}
+} 
+
+@inproceedings{xgboost,
+ author = {Chen, Tianqi and Guestrin, Carlos},
+ title = {{XGBoost}: A Scalable Tree Boosting System},
+ booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
+ series = {KDD '16},
+ year = {2016},
+ isbn = {978-1-4503-4232-2},
+ location = {San Francisco, California, USA},
+ pages = {785--794},
+ numpages = {10},
+ url = {http://doi.acm.org/10.1145/2939672.2939785},
+ doi = {10.1145/2939672.2939785},
+ acmid = {2939785},
+ publisher = {ACM},
+ address = {New York, NY, USA},
+ keywords = {large-scale machine learning},
+}
+
+@article{stable-baselines3,
+  author  = {Antonin Raffin and Ashley Hill and Adam Gleave and Anssi Kanervisto and Maximilian Ernestus and Noah Dormann},
+  title   = {Stable-Baselines3: Reliable Reinforcement Learning Implementations},
+  journal = {Journal of Machine Learning Research},
+  year    = {2021},
+  volume  = {22},
+  number  = {268},
+  pages   = {1-8},
+  url     = {http://jmlr.org/papers/v22/20-1364.html}
+}
+
+@misc{openai,
+      title={OpenAI Gym}, 
+      author={Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba},
+      year={2016},
+      eprint={1606.01540},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+
+@misc{tensorflow,
+title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
+url={https://www.tensorflow.org/},
+note={Software available from tensorflow.org},
+author={
+    Mart\'{i}n~Abadi and
+    Ashish~Agarwal and
+    Paul~Barham and
+    Eugene~Brevdo and
+    Zhifeng~Chen and
+    Craig~Citro and
+    Greg~S.~Corrado and
+    Andy~Davis and
+    Jeffrey~Dean and
+    Matthieu~Devin and
+    Sanjay~Ghemawat and
+    Ian~Goodfellow and
+    Andrew~Harp and
+    Geoffrey~Irving and
+    Michael~Isard and
+    Yangqing Jia and
+    Rafal~Jozefowicz and
+    Lukasz~Kaiser and
+    Manjunath~Kudlur and
+    Josh~Levenberg and
+    Dandelion~Man\'{e} and
+    Rajat~Monga and
+    Sherry~Moore and
+    Derek~Murray and
+    Chris~Olah and
+    Mike~Schuster and
+    Jonathon~Shlens and
+    Benoit~Steiner and
+    Ilya~Sutskever and
+    Kunal~Talwar and
+    Paul~Tucker and
+    Vincent~Vanhoucke and
+    Vijay~Vasudevan and
+    Fernanda~Vi\'{e}gas and
+    Oriol~Vinyals and
+    Pete~Warden and
+    Martin~Wattenberg and
+    Martin~Wicke and
+    Yuan~Yu and
+    Xiaoqiang~Zheng},
+  year={2015},
+}
+
+@incollection{pytorch,
+title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
+author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
+booktitle = {Advances in Neural Information Processing Systems 32},
+editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
+pages = {8024--8035},
+year = {2019},
+publisher = {Curran Associates, Inc.},
+url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
+}
+
+@ARTICLE{scipy,
+  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
+            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
+            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
+            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
+            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
+            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
+            Kern, Robert and Larson, Eric and Carey, C J and
+            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
+            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
+            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
+            Harris, Charles R. and Archibald, Anne M. and
+            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
+            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
+  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
+            Computing in Python}},
+  journal = {Nature Methods},
+  year    = {2020},
+  volume  = {17},
+  pages   = {261--272},
+  adsurl  = {https://rdcu.be/b08Wh},
+  doi     = {10.1038/s41592-019-0686-2},
+}
+
+@Article{numpy,
+ title         = {Array programming with {NumPy}},
+ author        = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J.
+                 van der Walt and Ralf Gommers and Pauli Virtanen and David
+                 Cournapeau and Eric Wieser and Julian Taylor and Sebastian
+                 Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
+                 and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
+                 Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del
+                 R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre
+                 G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and
+                 Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
+                 Travis E. Oliphant},
+ year          = {2020},
+ month         = sep,
+ journal       = {Nature},
+ volume        = {585},
+ number        = {7825},
+ pages         = {357--362},
+ doi           = {10.1038/s41586-020-2649-2},
+ publisher     = {Springer Science and Business Media {LLC}},
+ url           = {https://doi.org/10.1038/s41586-020-2649-2}
+}
+
+ @inproceedings{pandas,
+  title={Data structures for statistical computing in python},
+  author={McKinney, Wes and others},
+  booktitle={Proceedings of the 9th Python in Science Conference},
+  volume={445},
+  pages={51--56},
+  year={2010},
+  organization={Austin, TX},
+  doi={10.25080/Majora-92bf1922-00a}
+}
+
+
+
+@online{finrl,
+  title = {AI4Finance-Foundation},
+  year = 2022,
+  url = {https://github.com/AI4Finance-Foundation/FinRL},
+  urldate = {2022-09-30}
+}
+
+
+@online{tensortrade,
+  title = {tensortrade},
+  year = 2022,
+  url = {https://tensortradex.readthedocs.io/en/latest/L},
+  urldate = {2022-09-30}
+}

docs/JOSS_paper/paper.jats

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

@@ -0,0 +1,212 @@
+---
+title: '`FreqAI`: generalizing adaptive modeling for chaotic time-series market forecasts'
+tags:
+  - Python
+  - Machine Learning
+  - adaptive modeling
+  - chaotic systems
+  - time-series forecasting
+authors:
+  - name: Robert A. Caulk
+    orcid: 0000-0001-5618-8629
+    affiliation: 1, 2 
+  - name: Elin Törnquist
+    orcid: 0000-0003-3289-8604
+    affiliation: 1, 2
+  - name: Matthias Voppichler 
+    orcid: 
+    affiliation: 2
+  - name: Andrew R. Lawless
+    orcid: 
+    affiliation: 2
+  - name: Ryan McMullan
+    orcid: 
+    affiliation: 2
+  - name: Wagner Costa Santos
+    orcid: 
+    affiliation: 1, 2
+  - name: Timothy C. Pogue
+    orcid: 
+    affiliation: 1, 2
+  - name: Johan van der Vlugt
+    orcid: 
+    affiliation: 2
+  - name: Stefan P. Gehring
+    orcid: 
+    affiliation: 2
+  - name: Pascal Schmidt
+    orcid: 0000-0001-9328-4345
+    affiliation: 2
+
+<!-- affiliation: "1, 2" # (Multiple affiliations must be quoted) -->
+affiliations:
+ - name: Emergent Methods LLC, Arvada Colorado, 80005, USA
+   index: 1
+ - name: Freqtrade open source project
+   index: 2
+date: October 2022
+bibliography: paper.bib
+
+
+---
+
+
+# Statement of need
+
+Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`), has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citizen scientists" to use their basic Python skills for data-exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data-collection, storage, and handling presents a disparate challenge. [`FreqAI`](https://www.freqtrade.io/en/latest/freqai/) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data. 
+
+
+# Summary
+
+[`FreqAI`](https://www.freqtrade.io/en/latest/freqai/) evolved from a desire to test and compare a range of adaptive time-series forecasting methods on chaotic data. Cryptocurrency markets provide a unique data source since they are operational 24/7 and the data is freely available via a variety of open-sourced [exchange APIs](https://docs.ccxt.com/en/latest/manual.html#exchange-structure). Luckily, an existing open-source software, [`Freqtrade`](https://www.freqtrade.io/en/stable/), had already matured under a range of talented developers to support robust data collection/storage, as well as robust live environmental interactions for standard algorithmic trading. `Freqtrade` also provides a set of data analysis/visualization tools for the evaluation of historical performance as well as live environmental feedback. `FreqAI` builds on top of `Freqtrade` to include a user-friendly well tested interface for integrating external machine learning libraries for adaptive time-series forecasting. Beyond enabling the integration of existing libraries, `FreqAI` hosts a range of custom algorithms and methodologies aimed at improving computational and predictive performances. Thus, `FreqAI` contains a range of unique features which can be easily tested in combination with all the existing Python-accessible machine learning libraries to generate novel research on live and historical data. 
+
+The high-level overview of the software is depicted in Figure 1.
+
+![freqai-algo](assets/freqai_algo.jpg)
+*Abstracted overview of FreqAI algorithm*
+
+## Connecting machine learning libraries
+
+Although the `FreqAI`  framework is designed to accommodate any Python library in the "Model training" and "Feature set engineering" portions of the software (Figure 1), it already boasts a wide range of well documented examples based on various combinations of:
+
+* scikit-learn [@scikit-learn], Catboost [@catboost], LightGBM [@lightgbm], XGBoost [@xgboost], stable_baselines3 [@stable-baselines3], openai gym [@openai], tensorflow [@tensorflow], pytorch [@pytorch], Scipy [@scipy], Numpy [@numpy], and pandas [@pandas]. 
+
+These mature projects contain a wide range of peer-reviewed and industry standard methods, including:
+
+* Regression, Classification, Neural Networks, Reinforcement Learning, Support Vector Machines, Principal Component Analysis, point clustering, and much more. 
+
+which are all leveraged in `FreqAI` for users to use as templates or extend with their own methods.
+  
+## Furnishing novel methods and features
+
+Beyond the industry standard methods available through external libraries - `FreqAI` includes novel methods which are not available anywhere else in the open-source (or scientific) world. For example, `FreqAI` provides :
+
+* a custom algorithm/methodology for adaptive modeling details [here](https://www.freqtrade.io/en/stable/freqai/#general-approach) and [here](https://www.freqtrade.io/en/stable/freqai-developers/#project-architecture)
+* rapid and self-monitored feature engineering tools, details [here](https://www.freqtrade.io/en/stable/freqai-feature-engineering/#feature-engineering)
+* unique model features/indicators, such as the [inlier metric](https://www.freqtrade.io/en/stable/freqai-feature-engineering/#inlier-metric)
+* optimized data collection/storage algorithms, all code shown [here](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/freqai/data_drawer.py)
+* safely integrated outlier detection methods, details [here](https://www.freqtrade.io/en/stable/freqai-feature-engineering/#outlier-detection)
+* websocket communicated forecasts, details [here](https://www.freqtrade.io/en/stable/producer-consumer/)
+
+Of particular interest for researchers, `FreqAI` provides the option of large scale experimentation via an optimized [websocket communications interface](https://www.freqtrade.io/en/stable/producer-consumer/).
+
+## Optimizing the back-end
+
+`FreqAI` aims to make it simple for users to combine all the above tools to run studies based in two distinct modules:
+
+* backtesting studies
+* live-deployments 
+
+Both of these modules and their respective data management systems are built on top of [`Freqtrade`](https://www.freqtrade.io/en/latest/), a mature and actively developed cryptocurrency trading software. This means that `FreqAI` benefits from a wide range of tangential/disparate feature developments such as:
+
+* FreqUI, a graphical interface for backtesting and live monitoring
+* telegram control
+* robust database handling
+* futures/leverage trading
+* dollar cost averaging
+* trading strategy handling
+* a variety of free data sources via [CCXT](https://docs.ccxt.com/en/latest/manual.html#exchange-structure) (FTX, Binance, Kucoin etc.)
+
+These features derive from a strong external developer community that shares in the benefit and stability of a communal CI (Continuous Integration) system. Beyond the developer community, `FreqAI` benefits strongly from the userbase of `Freqtrade`, where most `FreqAI` beta-testers/developers originated. This symbiotic relationship between `Freqtrade` and `FreqAI` ignited a thoroughly tested [`beta`](https://github.com/freqtrade/freqtrade/pull/6832), which demanded a four month beta and [comprehensive documentation](https://www.freqtrade.io/en/latest/freqai/) containing:
+
+* numerous example scripts
+* a full parameter table
+* methodological descriptions
+* high-resolution diagrams/figures
+* detailed parameter setting recommendations
+
+## Providing a reproducible foundation for researchers
+
+`FreqAI` provides an extensible, robust, framework for researchers and citizen data scientists. The `FreqAI` sandbox enables rapid conception and testing of exotic hypotheses. From a research perspective, `FreqAI` handles the multitude of logistics associated with live deployments, historical backtesting, and feature engineering. With `FreqAI`, researchers can focus on their primary interests of feature engineering and hypothesis testing rather than figuring out how to collect and handle data. Further - the well maintained and easily installed open-source framework of `FreqAI` enables reproducible scientific studies. This reproducibility component is essential to general scientific advancement in time-series forecasting for chaotic systems. 
+
+# Technical details
+
+Typical users configure `FreqAI` via two files:
+
+1. A `configuration` file (`--config`) which provides access to the full parameter list available [here](https://www.freqtrade.io/en/latest/freqai/):
+  * control high-level feature engineering
+  * customize adaptive modeling techniques
+  * set any model training parameters available in third-party libraries
+  * manage adaptive modeling parameters (retrain frequency, training window size, continual learning, etc.)
+
+2. A strategy file (`--strategy`) where users:
+  * list of the base training features
+  * set standard technical-analysis strategies
+  * control trade entry/exit criteria
+
+With these two files, most users can exploit a wide range of pre-existing integrations in `Catboost` and 7 other libraries with a simple command:
+
+```
+freqtrade trade --config config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel CatboostRegressor
+```
+
+Advanced users will edit one of the existing `--freqaimodel` files, which are simply an children of the `IFreqaiModel` (details below). Within these files, advanced users can customize training procedures, prediction procedures, outlier detection methods, data preparation, data saving methods, etc. This is all configured in a way where they can customize as little or as much as they want. This flexible customization is owed to the foundational architecture in `FreqAI`, which is comprised of three distinct Python objects:
+
+* `IFreqaiModel`
+  * A singular long-lived object containing all the necessary logic to collect data, store data, process data, engineer features, run training, and inference models. 
+* `FreqaiDataKitchen`
+  * A short-lived object which is uniquely created for each asset/model. Beyond metadata, it also contains a variety of data processing tools. 
+* `FreqaiDataDrawer`
+  * Singular long-lived object containing all the historical predictions, models, and save/load methods. 
+
+These objects interact with one another with one goal in mind - to provide a clean data set to machine learning experts/enthusiasts at the user endpoint. These power-users interact with an inherited `IFreqaiModel` that allows them to dig as deep or as shallow as they wish into the inheritence tree. Typical power-users focus their efforts on customizing training procedures and testing exotic functionalities available in third-party libraries. Thus, power-users are freed from the algorithmic weight associated with data management, and can instead focus their energy on testing creative hypotheses. Meanwhile, some users choose to override deeper functionalities within `IFreqaiModel` to help them craft unique data structures and training procedures.
+
+The class structure and algorithmic details are depicted in the following diagram: 
+
+![image](assets/freqai_algorithm-diagram.jpg)
+*Class diagram summarizing object interactions in FreqAI*
+
+# Online documentation
+
+The documentation for [`FreqAI`](https://www.freqtrade.io/en/latest/freqai/) is available online at [https://www.freqtrade.io/en/latest/freqai/](https://www.freqtrade.io/en/latest/freqai/) and covers a wide range of materials:
+
+* Quick-start with a single command and example files - (beginners)
+* Introduction to the feature engineering interface and basic configurations - (intermediate users)
+* Parameter table with indepth descriptions and default parameter setting recommendations - (intermediate users)
+* Data analysis and post-processing - (advanced users)
+* Methodological considerations complemented by high resolution figures - (advanced users)
+* Instructions for integrating third party machine learning libraries into custom prediction models - (advanced users)
+* Software architectural description with class diagram - (developers) 
+* File structure descriptions - (developers)
+
+The docs direct users to a variety of pre-made examples which integrate `Catboost`, `LightGBM`, `XGBoost`, `Sklearn`, `stable_baselines3`, `torch`, `tensorflow`. Meanwhile, developers will also find thorough docstrings and type hinting throughout the source code to aid in code readability and customization. 
+
+`FreqAI` also benefits from a strong support network of users and developers on the [`Freqtrade` discord](https://discord.gg/w6nDM6cM4y) as well as on the [`FreqAI` discord](https://discord.gg/xE4RMg4QYw). Within the `FreqAI` discord, users will find a deep and easily searched knowledge base containing common errors. But more importantly, users in the `FreqAI` discord share anectdotal and quantitative observations which compare performance between various third-party libraries and methods.
+
+# State of the field
+
+There are two other open-source tools which are geared toward helping users build models for time-series forecasts on market based data. However, each of these tools suffer from a non-generalized frameworks that do not permit comparison of methods and libraries. Additionally, they do not permit easy live-deployments or adaptive-modeling methods. For example, two open-sourced projects called [`tensortrade`](https://tensortradex.readthedocs.io/en/latest/) [@tensortrade] and [`FinRL`](https://github.com/AI4Finance-Foundation/FinRL) [@finrl] limit users to the exploration of reinforcement learning on historical data. These softwares also do not provide robust live deployments, they do not furnish novel feature engineering algorithms, and they do not provide custom data analysis tools. `FreqAI` fills the gap. 
+
+# On-going research
+
+Emergent Methods, based in Arvada CO, is actively using `FreqAI` to perform large scale experiments aimed at comparing machine learning libraries in live and historical environments. Past projects include backtesting parametric sweeps, while active projects include a 3 week live deployment comparison between `CatboostRegressor`, `LightGBMRegressor`, and `XGBoostRegressor`. Results from these studies are planned for submission to scientific journals as well as more general data science blogs (e.g. Medium).
+
+# Installing and running `FreqAI`
+
+`FreqAI` is automatically installed with `Freqtrade` using the following commands on linux systems:
+
+```
+git clone git@github.com:freqtrade/freqtrade.git
+cd freqtrade
+./setup.sh -i
+```
+
+However, `FreqAI` also benefits from `Freqtrade` docker distributions, and can be run with docker by pulling the stable or develop images from `Freqtrade` distributions.
+
+# Funding sources
+
+[`FreqAI`](https://www.freqtrade.io/en/latest/freqai/) has had no official sponsors, and is entirely grass roots. All donations into the project (e.g. the GitHub sponsor system) are kept inside the project to help support development of open-sourced and communally beneficial features. 
+
+# Acknowledgements
+
+We would like to acknowledge various beta testers of `FreqAI`:
+
+- Longlong Yu (lolongcovas)
+- Richárd Józsa (richardjozsa)
+- Juha Nykänen (suikula)
+- Emre Suzen (aemr3)
+- Salah Lamkadem (ikonx)
+
+As well as various `Freqtrade` [developers](https://github.com/freqtrade/freqtrade/graphs/contributors) maintaining tangential, yet essential, modules.
+
+# References

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

@@ -13,10 +13,11 @@ A sample of this can be found below, which is identical to the Default Hyperopt
 
 ``` python
 from datetime import datetime
-from typing import Dict
+from typing import Any, Dict
 
 from pandas import DataFrame
 
+from freqtrade.constants import Config
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 TARGET_TRADES = 600
@@ -31,7 +32,7 @@ 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],
+                               config: Config, processed: Dict[str, DataFrame],
                                backtest_stats: Dict[str, Any],
                                *args, **kwargs) -> float:
         """
@@ -56,7 +57,7 @@ Currently, the arguments are:
 
 * `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_ratio, profit_abs, open_date, open_rate, fee_open, close_date, close_rate, fee_close, amount, trade_duration, is_open, sell_reason, stake_amount, min_rate, max_rate, stop_loss_ratio, stop_loss_abs`
+    `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 timerange used
 * `min_date`: End date of the timerange used
@@ -98,6 +99,23 @@ class MyAwesomeStrategy(IStrategy):
 !!! Note
     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.
@@ -105,7 +123,7 @@ You can define your own estimator for Hyperopt by implementing `generate_estimat
 ```python
 class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
-        def generate_estimator():
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
             return "RF"
 
 ```
@@ -119,13 +137,34 @@ Example for `ExtraTreesRegressor` ("ET") with additional parameters:
 ```python
 class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
-        def generate_estimator():
+        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.

docs/advanced-setup.md

@@ -176,12 +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/backtesting.md

@@ -20,12 +20,14 @@ usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                              [--dry-run-wallet DRY_RUN_WALLET]
                              [--timeframe-detail TIMEFRAME_DETAIL]
                              [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
-                             [--export {none,trades}] [--export-filename PATH]
+                             [--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, --ticker-interval TIMEFRAME
+  -i TIMEFRAME, --timeframe TIMEFRAME
                         Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
   --timerange TIMERANGE
                         Specify what timerange of data to use.
@@ -62,20 +64,22 @@ optional arguments:
                         `30m`, `1h`, `1d`).
   --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-SampleStrategy.json`
-  --export {none,trades}
+                        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
-                        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`
+  --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).
@@ -103,7 +107,7 @@ Strategy arguments:
 
 ## Test your strategy with Backtesting
 
-Now you have good Buy and Sell strategies and some historic data, you want to test it against
+Now you have good Entry and exit 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).
 
 Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHLCV) data from `user_data/data/<exchange>` by default.
@@ -115,7 +119,7 @@ The result of backtesting will confirm if your bot has better odds of making a p
 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.
+    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.
 
@@ -211,7 +215,7 @@ Sometimes your account has certain fee rebates (fee reductions starting with a c
 To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
 This fee must be a ratio, and will be applied twice (once for trade entry, and once for trade exit).
 
-For example, if the buying and selling commission fee is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
+For example, if the commission fee per order is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
 
 ```bash
 freqtrade backtesting --fee 0.001
@@ -248,78 +252,90 @@ 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 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 |
-========================================================= SELL REASON STATS ==========================================================
-| Sell Reason        |   Sells |  Wins |  Draws |  Losses |
+========================================================= BACKTESTING REPORT =========================================================
+| Pair     | Entries |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration |  Wins Draws Loss   Win%  |
+|:---------|--------:|---------------:|---------------:|-----------------:|---------------:|:-------------|-------------------------:|
+| ADA/BTC  |      35 |          -0.11 |          -3.88 |      -0.00019428 |          -1.94 | 4:35:00      |    14     0    21   40.0 |
+| ARK/BTC  |      11 |          -0.41 |          -4.52 |      -0.00022647 |          -2.26 | 2:03:00      |     3     0     8   27.3 |
+| BTS/BTC  |      32 |           0.31 |           9.78 |       0.00048938 |           4.89 | 5:05:00      |    18     0    14   56.2 |
+| DASH/BTC |      13 |          -0.08 |          -1.07 |      -0.00005343 |          -0.53 | 4:39:00      |     6     0     7   46.2 |
+| ENG/BTC  |      18 |           1.36 |          24.54 |       0.00122807 |          12.27 | 2:50:00      |     8     0    10   44.4 |
+| EOS/BTC  |      36 |           0.08 |           3.06 |       0.00015304 |           1.53 | 3:34:00      |    16     0    20   44.4 |
+| ETC/BTC  |      26 |           0.37 |           9.51 |       0.00047576 |           4.75 | 6:14:00      |    11     0    15   42.3 |
+| ETH/BTC  |      33 |           0.30 |           9.96 |       0.00049856 |           4.98 | 7:31:00      |    16     0    17   48.5 |
+| IOTA/BTC |      32 |           0.03 |           1.09 |       0.00005444 |           0.54 | 3:12:00      |    14     0    18   43.8 |
+| LSK/BTC  |      15 |           1.75 |          26.26 |       0.00131413 |          13.13 | 2:58:00      |     6     0     9   40.0 |
+| LTC/BTC  |      32 |          -0.04 |          -1.38 |      -0.00006886 |          -0.69 | 4:49:00      |    11     0    21   34.4 |
+| NANO/BTC |      17 |           1.26 |          21.39 |       0.00107058 |          10.70 | 1:55:00      |    10     0     7   58.5 |
+| NEO/BTC  |      23 |           0.82 |          18.97 |       0.00094936 |           9.48 | 2:59:00      |    10     0    13   43.5 |
+| REQ/BTC  |       9 |           1.17 |          10.54 |       0.00052734 |           5.27 | 3:47:00      |     4     0     5   44.4 |
+| XLM/BTC  |      16 |           1.22 |          19.54 |       0.00097800 |           9.77 | 3:15:00      |     7     0     9   43.8 |
+| XMR/BTC  |      23 |          -0.18 |          -4.13 |      -0.00020696 |          -2.07 | 5:30:00      |    12     0    11   52.2 |
+| XRP/BTC  |      35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00      |    12     0    23   34.3 |
+| ZEC/BTC  |      22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00      |     7     0    15   31.8 |
+| TOTAL    |     429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00      |   186     0   243   43.4 |
+========================================================= EXIT REASON STATS ==========================================================
+| Exit Reason        |   Exits |  Wins |  Draws |  Losses |
 |:-------------------|--------:|------:|-------:|--------:|
 | trailing_stop_loss |     205 |   150 |      0 |      55 |
 | stop_loss          |     166 |     0 |      0 |     166 |
-| 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   |  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%               |
-| Trades per day        | 3.575               |
-| Avg. stake amount     | 0.001      BTC      |
-| Total trade volume    | 0.429      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 Buy signals  | 3089                |
-|                       |                     |
-| Min balance           | 0.00945123 BTC      |
-| Max balance           | 0.01846651 BTC      |
-| Drawdown              | 50.63%              |
-| 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%              |
-===============================================
+| Pair     |  Entries |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Win Draw Loss Win% |
+|:---------|---------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
+| ADA/BTC  |        1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |    1    0    0  100 |
+| LTC/BTC  |        1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |    1    0    0  100 |
+| TOTAL    |        2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |    2    0    0  100 |
+================== SUMMARY METRICS ==================
+| Metric                      | Value               |
+|-----------------------------+---------------------|
+| Backtesting from            | 2019-01-01 00:00:00 |
+| Backtesting to              | 2019-05-01 00:00:00 |
+| 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
@@ -340,9 +356,9 @@ The column `Avg Profit %` shows the average profit for all trades made while the
 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 entry 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": {
@@ -354,14 +370,14 @@ 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.
 
-### Sell reasons table
+### Exit reasons table
 
-The 2nd table contains a recap of sell reasons.
-This table can tell you which area needs some additional work (e.g. all or many of the `sell_signal` trades are losses, so you should work on improving the sell signal, or consider disabling it).
+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 `forcesell` at the end of the backtesting period to present you the full picture.
+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.
 
@@ -371,42 +387,55 @@ 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%               |
-| Avg. stake amount     | 0.001      BTC      |
-| Total trade volume    | 0.429      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 Buy signals  | 3089                |
-|                       |                     |
-| Min balance           | 0.00945123 BTC      |
-| Max balance           | 0.01846651 BTC      |
-| Drawdown              | 50.63%              |
-| 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%              |
-===============================================
+================== 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%              |
+=====================================================
 
 ```
 
@@ -417,6 +446,8 @@ It contains some useful key metrics about performance of your strategy on backte
 - `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 %`.
@@ -424,12 +455,22 @@ It contains some useful key metrics about performance of your strategy on backte
 - `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 Buy signals`: Buy signals that could not be acted upon due to max_open_trades being reached.
+- `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.
-- `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
+- `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
 
@@ -438,7 +479,7 @@ You can get an overview over daily / weekly or monthly results by using the `--b
 To visualize daily and weekly breakdowns, you can use the following:
 
 ``` bash
-freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day month
+freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day week
 ```
 
 ``` output
@@ -454,45 +495,81 @@ freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day month
 
 ```
 
-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.
+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 our [data analysis](data-analysis.md#backtesting) backtesting section.
+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:
 
-- Buys happen at open-price
+- Exchange [trading limits](#trading-limits-in-backtesting) are respected
+- Entries happen at open-price
 - All orders are filled at the requested price (no slippage, no unfilled orders)
-- Sell-signal sells happen at open-price of the consecutive candle
-- Sell-signal is favored over Stoploss, because sell-signals are assumed to trigger on candle's open
+- 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, 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` sell reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes
+  - 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)
+  - 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
-- 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)
+- 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)
-  - ROI (if not stoploss)
-  - Sell-signal
+  - 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.
 
-### Improved backtest accuracy
+### Trading limits in backtesting
+
+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.
+
+#### Trading precision limits
+
+Most exchanges pose precision limits on both price and amounts, so you cannot buy 1.0020401 of a pair, or at a price of 1.24567123123.  
+Instead, these prices and amounts will be rounded or truncated (based on the exchange definition) to the defined trading precision.
+The above values may for example be rounded to an amount of 1.002, and a price of 1.24567.
+
+These precision values are based on current exchange limits (as described in the [above section](#trading-limits-in-backtesting)), as historic precision limits are not available.
+
+## 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).
@@ -507,7 +584,7 @@ freqtrade backtesting --strategy AwesomeStrategy --timeframe 1h --timeframe-deta
 ```
 
 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_sell()`, `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).
+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.
 
@@ -535,11 +612,11 @@ There will be an additional table comparing win/losses of the different strategi
 Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
 
 ```
-=========================================================== STRATEGY SUMMARY =========================================================================
-| Strategy    |   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 |
+=========================================================== STRATEGY SUMMARY ===========================================================================
+| Strategy    |  Entries |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws | Losses | Drawdown % |
+|:------------|---------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|-----------:|
+| Strategy1   |      429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |    243 |       45.2 |
+| Strategy2   |     1487 |          -0.13 |        -197.58 |      -0.00988917 |         -98.79 | 4:43:00        |   662 |      0 |    825 |     241.68 |
 ```
 
 ## Next step

docs/bot-basics.md

@@ -20,29 +20,34 @@ All profit calculations of Freqtrade include fees. For Backtesting / Hyperopt /
 ## Bot execution logic
 
 Starting freqtrade in dry-run or live mode (using `freqtrade trade`) will start the bot and start the bot iteration loop.
-By default, loop runs every few seconds (`internals.process_throttle_secs`) and does roughly the following in the following sequence:
+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)  
+* 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_buy_trend()`
-  * Call `populate_sell_trend()`
+  * Call `populate_entry_trend()`
+  * Call `populate_exit_trend()`
 * Check timeouts for open orders.
-  * Calls `check_buy_timeout()` strategy callback for open buy orders.
-  * Calls `check_sell_timeout()` strategy callback for open sell orders.
-* Verifies existing positions and eventually places sell orders.
-  * Considers stoploss, ROI and sell-signal, `custom_sell()` and `custom_stoploss()`.
-  * Determine sell-price based on `ask_strategy` configuration setting or by using the `custom_exit_price()` callback.
-  * Before a sell order is placed, `confirm_trade_exit()` strategy callback is called.
+  * 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 buy signal trying to enter new positions.
-  * Determine buy-price based on `bid_strategy` configuration setting, or by using the `custom_entry_price()` callback.
+* 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 a buy order is placed, `confirm_trade_entry()` strategy callback is called.
+  * 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.
 
@@ -51,12 +56,21 @@ This loop will be repeated again and again until the bot is stopped.
 [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 buy / sell signals (calls `populate_buy_trend()` and `populate_sell_trend()` 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.
-  * Confirm trade buy / sell (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy).
-  * Call `custom_stoploss()` and `custom_sell()` to find custom 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, custom-exit and partial exits: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle).
 * Generate backtest report output
 
 !!! Note

docs/configuration.md

@@ -11,7 +11,7 @@ Per default, the bot loads the configuration from the `config.json` file, locate
 
 You can specify a different configuration file used by the bot with the `-c/--config` command-line option.
 
-If you used the [Quick start](installation.md/#quick-start) method for installing 
+If you used the [Quick start](installation.md/#quick-start) method for installing
 the bot, the installation script should have already created the default configuration file (`config.json`) for you.
 
 If the default configuration file is not created we recommend to use `freqtrade new-config --config config.json` to generate a basic configuration file.
@@ -37,6 +37,15 @@ Using this scheme, all configuration settings will also be available as environm
 
 Please note that Environment variables will overwrite corresponding settings in your configuration, but command line Arguments will always win.
 
+Common example:
+
+```
+FREQTRADE__TELEGRAM__CHAT_ID=<telegramchatid>
+FREQTRADE__TELEGRAM__TOKEN=<telegramToken>
+FREQTRADE__EXCHANGE__KEY=<yourExchangeKey>
+FREQTRADE__EXCHANGE__SECRET=<yourExchangeSecret>
+```
+
 !!! Note
     Environment variables detected are logged at startup - so if you can't find why a value is not what you think it should be based on the configuration, make sure it's not loaded from an environment variable.
 
@@ -44,20 +53,74 @@ Please note that Environment variables will overwrite corresponding settings in
 
 Multiple configuration files can be specified and used by the bot or the bot can read its configuration parameters from the process standard input stream.
 
+You can specify additional configuration files in `add_config_files`. Files specified in this parameter will be loaded and merged with the initial config file. The files are resolved relative to the initial configuration file.
+This is similar to using multiple `--config` parameters, but simpler in usage as you don't have to specify all files for all commands.
+
 !!! Tip "Use multiple configuration files to keep secrets secret"
     You can use a 2nd configuration file containing your secrets. That way you can share your "primary" configuration file, while still keeping your API keys for yourself.
-
-    ``` bash
-    freqtrade trade --config user_data/config.json --config user_data/config-private.json <...>
-    ```
     The 2nd file should only specify what you intend to override.
     If a key is in more than one of the configurations, then the "last specified configuration" wins (in the above example, `config-private.json`).
 
+    For one-off commands, you can also use the below syntax by specifying multiple "--config" parameters.
+
+    ``` bash
+    freqtrade trade --config user_data/config1.json --config user_data/config-private.json <...>
+    ```
+
+    The below is equivalent to the example above - but having 2 configuration files in the configuration, for easier reuse.
+
+    ``` json title="user_data/config.json"
+    "add_config_files": [
+        "config1.json",
+        "config-private.json"
+    ]
+    ```
+
+    ``` bash
+    freqtrade trade --config user_data/config.json <...>
+    ```
+
+??? Note "config collision handling"
+    If the same configuration setting takes place in both `config.json` and `config-import.json`, then the parent configuration wins.
+    In the below case, `max_open_trades` would be 3 after the merging - as the reusable "import" configuration has this key overwritten.
+
+    ``` json title="user_data/config.json"
+    {
+        "max_open_trades": 3,
+        "stake_currency": "USDT",
+        "add_config_files": [
+            "config-import.json"
+        ]
+    }
+    ```
+
+    ``` json title="user_data/config-import.json"
+    {
+        "max_open_trades": 10,
+        "stake_amount": "unlimited",
+    }
+    ```
+
+    Resulting combined configuration:
+
+    ``` json title="Result"
+    {
+        "max_open_trades": 3,
+        "stake_currency": "USDT",
+        "stake_amount": "unlimited"
+    }
+    ```
+
+    If multiple files are in the `add_config_files` section, then they will be assumed to be at identical levels, having the last occurrence override the earlier config (unless a parent already defined such a key).
+
 ## Configuration parameters
 
 The table below will list all configuration parameters available.
 
 Freqtrade can also load many options via command line (CLI) arguments (check out the commands `--help` output for details).
+
+### Configuration option prevalence
+
 The prevalence for all Options is as follows:
 
 - CLI arguments override any other option
@@ -65,6 +128,8 @@ The prevalence for all Options is as follows:
 - Configuration files are used in sequence (the last file wins) and override Strategy configurations.
 - Strategy configurations are only used if they are not set via configuration or command-line arguments. These options are marked with [Strategy Override](#parameters-in-the-strategy) in the below table.
 
+### Parameters table
+
 Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
 
 |  Parameter | Description |
@@ -77,87 +142,113 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
 | `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
 | `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> **Datatype:** Positive Float as ratio.
-| `timeframe` | The timeframe (former ticker interval) to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
+| `timeframe` | The timeframe to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). Usually missing in configuration, and specified in the strategy. [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
 | `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
 | `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
 | `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
 | `cancel_open_orders_on_exit` | Cancel open orders when the `/stop` RPC command is issued, `Ctrl+C` is pressed or the bot dies unexpectedly. When set to `true`, this allows you to use `/stop` to cancel unfilled and partially filled orders in the event of a market crash. It does not impact open positions. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
-| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> **Datatype:** Boolean
-| `minimal_roi` | **Required.** Set the threshold as ratio the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
+| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.*  <br> **Datatype:** Boolean
+| `minimal_roi` | **Required.** Set the threshold as ratio the bot will use to exit a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
 | `stoploss` |  **Required.** Value as ratio of the stoploss used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).  <br> **Datatype:** Float (as ratio)
 | `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md#trailing-stop-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Boolean
 | `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-custom-positive-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
 | `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
 | `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> **Datatype:** Boolean
 | `fee` | Fee used during backtesting / dry-runs. Should normally not be configured, which has freqtrade fall back to the exchange default fee. Set as ratio (e.g. 0.001 = 0.1%). Fee is applied twice for each trade, once when buying, once when selling. <br> **Datatype:** Float (as ratio)
-| `unfilledtimeout.buy` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
-| `unfilledtimeout.sell` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
+| `futures_funding_rate` | User-specified funding rate to be used when historical funding rates are not available from the exchange. This does not overwrite real historical rates. It is recommended that this be set to 0 unless you are testing a specific coin and you understand how the funding rate will affect freqtrade's profit calculations. [More information here](leverage.md#unavailable-funding-rates) <br>*Defaults to None.*<br> **Datatype:** Float
+| `trading_mode` | Specifies if you want to trade regularly, trade with leverage, or trade contracts whose prices are derived from matching cryptocurrency prices. [leverage documentation](leverage.md). <br>*Defaults to `"spot"`.*  <br> **Datatype:** String
+| `margin_mode` | When trading with leverage, this determines if the collateral owned by the trader will be shared or isolated to each trading pair [leverage documentation](leverage.md). <br> **Datatype:** String
+| `liquidation_buffer` | 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 [leverage documentation](leverage.md). <br>*Defaults to `0.05`.*  <br> **Datatype:** Float
+| | **Unfilled timeout**
+| `unfilledtimeout.entry` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled entry order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
+| `unfilledtimeout.exit` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled exit order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
 | `unfilledtimeout.unit` | Unit to use in unfilledtimeout setting. Note: If you set unfilledtimeout.unit to "seconds", "internals.process_throttle_secs" must be inferior or equal to timeout [Strategy Override](#parameters-in-the-strategy). <br> *Defaults to `minutes`.* <br> **Datatype:** String
-| `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
-| `bid_strategy.ask_last_balance` | **Required.** Interpolate the bidding price. More information [below](#buy-price-without-orderbook-enabled).
-| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
-| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book "price_side" to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.*  <br> **Datatype:** Positive Integer
-| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
-| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.*  <br> **Datatype:** Float (as ratio)
-| `ask_strategy.price_side` | Select the side of the spread the bot should look at to get the sell rate. [More information below](#sell-price-side).<br> *Defaults to `ask`.* <br> **Datatype:** String (either `ask` or `bid`).
-| `ask_strategy.bid_last_balance` | Interpolate the selling price. More information [below](#sell-price-without-orderbook-enabled).
-| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> **Datatype:** Boolean
-| `ask_strategy.order_book_top` | Bot will use the top N rate in Order Book "price_side" to sell. I.e. a value of 2 will allow the bot to pick the 2nd ask rate in [Order Book Asks](#sell-price-with-orderbook-enabled)<br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
-| `use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
-| `sell_profit_only` | Wait until the bot reaches `sell_profit_offset` before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
-| `sell_profit_offset` | Sell-signal is only active above this value. Only active in combination with `sell_profit_only=True`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0`.* <br> **Datatype:** Float (as ratio)
-| `ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
-| `ignore_buying_expired_candle_after` | Specifies the number of seconds until a buy signal is no longer used. <br> **Datatype:** Integer
-| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
-| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
+| `unfilledtimeout.exit_timeout_count` | How many times can exit orders time out. Once this number of timeouts is reached, an emergency exit is triggered. 0 to disable and allow unlimited order cancels. [Strategy Override](#parameters-in-the-strategy).<br>*Defaults to `0`.* <br> **Datatype:** Integer
+| | **Pricing**
+| `entry_pricing.price_side` | Select the side of the spread the bot should look at to get the entry rate. [More information below](#buy-price-side).<br> *Defaults to `same`.* <br> **Datatype:** String (either `ask`, `bid`, `same` or `other`).
+| `entry_pricing.price_last_balance` | **Required.** Interpolate the bidding price. More information [below](#entry-price-without-orderbook-enabled).
+| `entry_pricing.use_order_book` | Enable entering using the rates in [Order Book Entry](#entry-price-with-orderbook-enabled). <br> *Defaults to `True`.*<br> **Datatype:** Boolean
+| `entry_pricing.order_book_top` | Bot will use the top N rate in Order Book "price_side" to enter a trade. I.e. a value of 2 will allow the bot to pick the 2nd entry in [Order Book Entry](#entry-price-with-orderbook-enabled). <br>*Defaults to `1`.*  <br> **Datatype:** Positive Integer
+| `entry_pricing. check_depth_of_market.enabled` | Do not enter if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `entry_pricing. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.*  <br> **Datatype:** Float (as ratio)
+| `exit_pricing.price_side` | Select the side of the spread the bot should look at to get the exit rate. [More information below](#exit-price-side).<br> *Defaults to `same`.* <br> **Datatype:** String (either `ask`, `bid`, `same` or `other`).
+| `exit_pricing.price_last_balance` | Interpolate the exiting price. More information [below](#exit-price-without-orderbook-enabled).
+| `exit_pricing.use_order_book` | Enable exiting of open trades using [Order Book Exit](#exit-price-with-orderbook-enabled). <br> *Defaults to `True`.*<br> **Datatype:** Boolean
+| `exit_pricing.order_book_top` | Bot will use the top N rate in Order Book "price_side" to exit. I.e. a value of 2 will allow the bot to pick the 2nd ask rate in [Order Book Exit](#exit-price-with-orderbook-enabled)<br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
 | `custom_price_max_distance_ratio` | Configure maximum distance ratio between current and custom entry or exit price. <br>*Defaults to `0.02` 2%).*<br> **Datatype:** Positive float
+| | **TODO**
+| `use_exit_signal` | Use exit signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
+| `exit_profit_only` | Wait until the bot reaches `exit_profit_offset` before taking an exit decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `exit_profit_offset` | Exit-signal is only active above this value. Only active in combination with `exit_profit_only=True`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0`.* <br> **Datatype:** Float (as ratio)
+| `ignore_roi_if_entry_signal` | Do not exit if the entry signal is still active. This setting takes preference over `minimal_roi` and `use_exit_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `ignore_buying_expired_candle_after` | Specifies the number of seconds until a buy signal is no longer used. <br> **Datatype:** Integer
+| `order_types` | Configure order-types depending on the action (`"entry"`, `"exit"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
+| `order_time_in_force` | Configure time in force for entry and exit orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
+| `position_adjustment_enable` | Enables the strategy to use position adjustments (additional buys or sells). [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*<br> **Datatype:** Boolean
+| `max_entry_position_adjustment` | Maximum additional order(s) for each open trade on top of the first entry Order. Set it to `-1` for unlimited additional orders. [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `-1`.*<br> **Datatype:** Positive Integer or -1
+| | **Exchange**
 | `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
 | `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> **Datatype:** Boolean
 | `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.uid` | API uid to use for the exchange. Only required when you are in production mode and for exchanges that use uid for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Supports regex pairs as `.*/BTC`. Not used by VolumePairList. [More information](plugins.md#pairlists-and-pairlist-handlers). <br> **Datatype:** List
 | `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting. [More information](plugins.md#pairlists-and-pairlist-handlers). <br> **Datatype:** List
-| `exchange.ccxt_config` | Additional CCXT parameters passed to both ccxt instances (sync and async). This is usually the correct place for ccxt configurations. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
+| `exchange.ccxt_config` | Additional CCXT parameters passed to both ccxt instances (sync and async). This is usually the correct place for additional ccxt configurations. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation). Please avoid adding exchange secrets here (use the dedicated fields instead), as they may be contained in logs. <br> **Datatype:** Dict
 | `exchange.ccxt_sync_config` | Additional CCXT parameters passed to the regular (sync) ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
 | `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
 | `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
 | `exchange.skip_pair_validation` | Skip pairlist validation on startup.<br>*Defaults to `false`<br> **Datatype:** Boolean
 | `exchange.skip_open_order_update` | Skips open order updates on startup should the exchange cause problems. Only relevant in live conditions.<br>*Defaults to `false`<br> **Datatype:** Boolean
+| `exchange.unknown_fee_rate` | Fallback value to use when calculating trading fees. This can be useful for exchanges which have fees in non-tradable currencies. The value provided here will be multiplied with the "fee cost".<br>*Defaults to `None`<br> **Datatype:** float
 | `exchange.log_responses` | Log relevant exchange responses. For debug mode only - use with care.<br>*Defaults to `false`<br> **Datatype:** Boolean
-| `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
 | `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
+| | **Plugins**
+| `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation of all possible configuration options.
 | `pairlists` | Define one or more pairlists to be used. [More information](plugins.md#pairlists-and-pairlist-handlers). <br>*Defaults to `StaticPairList`.*  <br> **Datatype:** List of Dicts
 | `protections` | Define one or more protections to be used. [More information](plugins.md#protections). <br> **Datatype:** List of Dicts
+| | **Telegram**
 | `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
 | `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `telegram.balance_dust_level` | Dust-level (in stake currency) - currencies with a balance below this will not be shown by `/balance`. <br> **Datatype:** float
+| `telegram.reload` | Allow "reload" buttons on telegram messages. <br>*Defaults to `True`.<br> **Datatype:** boolean
+| `telegram.notification_settings.*` | Detailed notification settings. Refer to the [telegram documentation](telegram-usage.md) for details.<br> **Datatype:** dictionary
+| | **Webhook**
 | `webhook.enabled` | Enable usage of Webhook notifications <br> **Datatype:** Boolean
 | `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookbuycancel` | Payload to send on buy order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhooksell` | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhooksellcancel` | Payload to send on sell order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookentry` | Payload to send on entry. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookentrycancel` | Payload to send on entry order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookentryfill` | Payload to send on entry order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookexit` | Payload to send on exit. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookexitcancel` | Payload to send on exit order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookexitfill` | Payload to send on exit order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
 | `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| | **Rest API / FreqUI / Producer-Consumer**
 | `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
 | `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4
 | `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>**Datatype:** Integer between 1024 and 65535
 | `api_server.verbosity` | Logging verbosity. `info` will print all RPC Calls, while "error" will only display errors. <br>**Datatype:** Enum, either `info` or `error`. Defaults to `info`.
 | `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
 | `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
+| `api_server.ws_token` | API token for the Message WebSocket. See the [API Server documentation](rest-api.md) for more details.  <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `bot_name` | Name of the bot. Passed via API to a client - can be shown to distinguish / name bots.<br> *Defaults to `freqtrade`*<br> **Datatype:** String
-| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
+| `external_message_consumer` | Enable [Producer/Consumer mode](producer-consumer.md) for more details. <br> **Datatype:** Dict
+| | **Other**
 | `initial_state` | Defines the initial application state. If set to stopped, then the bot has to be explicitly started via `/start` RPC command. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
-| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> **Datatype:** Boolean
+| `force_entry_enable` | Enables the RPC Commands to force a Trade entry. More information below. <br> **Datatype:** Boolean
 | `disable_dataframe_checks` | Disable checking the OHLCV dataframe returned from the strategy methods for correctness. Only use when intentionally changing the dataframe and understand what you are doing. [Strategy Override](#parameters-in-the-strategy).<br> *Defaults to `False`*. <br> **Datatype:** Boolean
-| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
-| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
 | `internals.process_throttle_secs` | Set the process throttle, or minimum loop duration for one bot iteration loop. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
 | `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
 | `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
-| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
+| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
+| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
+| `recursive_strategy_search` | Set to `true` to recursively search sub-directories inside `user_data/strategies` for a strategy. <br> **Datatype:** Boolean
 | `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
+| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
+| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
+| `add_config_files` | Additional config files. These files will be loaded and merged with the current config file. The files are resolved relative to the initial file.<br> *Defaults to `[]`*. <br> **Datatype:** List of strings
 | `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
 | `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
 
@@ -179,11 +270,13 @@ Values set in the configuration file always overwrite values set in the strategy
 * `order_time_in_force`
 * `unfilledtimeout`
 * `disable_dataframe_checks`
-* `use_sell_signal`
-* `sell_profit_only`
-* `sell_profit_offset`
-* `ignore_roi_if_buy_signal`
+- `use_exit_signal`
+* `exit_profit_only`
+- `exit_profit_offset`
+- `ignore_roi_if_entry_signal`
 * `ignore_buying_expired_candle_after`
+* `position_adjustment_enable`
+* `max_entry_position_adjustment`
 
 ### Configuring amount per trade
 
@@ -192,9 +285,8 @@ There are several methods to configure how much of the stake currency the bot wi
 #### Minimum trade stake
 
 The minimum stake amount will depend on exchange and pair and is usually listed in the exchange support pages.
-Assuming the minimum tradable amount for XRP/USD is 20 XRP (given by the exchange), and the price is 0.6$.
 
-The minimum stake amount to buy this pair is, therefore, `20 * 0.6 ~= 12`.
+Assuming the minimum tradable amount for XRP/USD is 20 XRP (given by the exchange), and the price is 0.6$, the minimum stake amount to buy this pair is `20 * 0.6 ~= 12`.
 This exchange has also a limit on USD - where all orders must be > 10$ - which however does not apply in this case.
 
 To guarantee safe execution, freqtrade will not allow buying with a stake-amount of 10.1$, instead, it'll make sure that there's enough space to place a stoploss below the pair (+ an offset, defined by `amount_reserve_percent`, which defaults to 5%).
@@ -204,7 +296,7 @@ With a reserve of 5%, the minimum stake amount would be ~12.6$ (`12 * (1 + 0.05)
 To limit this calculation in case of large stoploss values, the calculated minimum stake-limit will never be more than 50% above the real limit.
 
 !!! Warning
-    Since the limits on exchanges are usually stable and are not updated often, some pairs can show pretty high minimum limits, simply because the price increased a lot since the last limit adjustment by the exchange.
+    Since the limits on exchanges are usually stable and are not updated often, some pairs can show pretty high minimum limits, simply because the price increased a lot since the last limit adjustment by the exchange. Freqtrade adjusts the stake-amount to this value, unless it's > 30% more than the calculated/desired stake-amount - in which case the trade is rejected.
 
 #### Tradable balance
 
@@ -291,6 +383,15 @@ To allow the bot to trade all the available `stake_currency` in your account (mi
     When using `"stake_amount" : "unlimited",` in combination with Dry-Run, Backtesting or Hyperopt, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve.
     It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise, it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
 
+#### Dynamic stake amount with position adjustment
+
+When you want to use position adjustment with unlimited stakes, you must also implement `custom_stake_amount` to a return a value depending on your strategy.
+Typical value would be in the range of 25% - 50% of the proposed stakes, but depends highly on your strategy and how much you wish to leave into the wallet as position adjustment buffer.
+
+For example if your position adjustment assumes it can do 2 additional buys with the same stake amounts then your buffer should be 66.6667% of the initially proposed unlimited stake amount.
+
+Or another example if your position adjustment assumes it can do 1 additional buy with 3x the original stake amount then `custom_stake_amount` should return 25% of proposed stake amount and leave 75% for possible later position adjustments.
+
 --8<-- "includes/pricing.md"
 
 ### Understand minimal_roi
@@ -301,10 +402,10 @@ See the example below:
 
 ```json
 "minimal_roi": {
-    "40": 0.0,    # Sell after 40 minutes if the profit is not negative
-    "30": 0.01,   # Sell after 30 minutes if there is at least 1% profit
-    "20": 0.02,   # Sell after 20 minutes if there is at least 2% profit
-    "0":  0.04    # Sell immediately if there is at least 4% profit
+    "40": 0.0,    # Exit after 40 minutes if the profit is not negative
+    "30": 0.01,   # Exit after 30 minutes if there is at least 1% profit
+    "20": 0.02,   # Exit after 20 minutes if there is at least 2% profit
+    "0":  0.04    # Exit immediately if there is at least 4% profit
 },
 ```
 
@@ -313,14 +414,14 @@ This parameter can be set in either Strategy or Configuration file. If you use i
 `minimal_roi` value from the strategy file.
 If it is not set in either Strategy or Configuration, a default of 1000% `{"0": 10}` is used, and minimal ROI is disabled unless your trade generates 1000% profit.
 
-!!! Note "Special case to forcesell after a specific time"
-    A special case presents using `"<N>": -1` as ROI. This forces the bot to sell a trade after N Minutes, no matter if it's positive or negative, so represents a time-limited force-sell.
+!!! Note "Special case to forceexit after a specific time"
+    A special case presents using `"<N>": -1` as ROI. This forces the bot to exit a trade after N Minutes, no matter if it's positive or negative, so represents a time-limited force-exit.
 
-### Understand forcebuy_enable
+### Understand force_entry_enable
 
-The `forcebuy_enable` configuration parameter enables the usage of forcebuy commands via Telegram and REST API.
+The `force_entry_enable` configuration parameter enables the usage of force-enter (`/forcelong`, `/forceshort`) commands via Telegram and REST API.
 For security reasons, it's disabled by default, and freqtrade will show a warning message on startup if enabled.
-For example, you can send `/forcebuy ETH/BTC` to the bot, which will result in freqtrade buying the pair and holds it until a regular sell-signal (ROI, stoploss, /forcesell) appears.
+For example, you can send `/forceenter ETH/BTC` to the bot, which will result in freqtrade buying the pair and holds it until a regular exit-signal (ROI, stoploss, /forceexit) appears.
 
 This can be dangerous with some strategies, so use with care.
 
@@ -347,29 +448,27 @@ For example, if your strategy is using a 1h timeframe, and you only want to buy
 
 ### Understand order_types
 
-The `order_types` configuration parameter maps actions (`buy`, `sell`, `stoploss`, `emergencysell`, `forcesell`, `forcebuy`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
+The `order_types` configuration parameter maps actions (`entry`, `exit`, `stoploss`, `emergency_exit`, `force_exit`, `force_entry`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
+
+This allows to enter using limit orders, exit using limit-orders, and create stoplosses using market orders.
+It also allows to set the
+stoploss "on exchange" which means stoploss order would be placed immediately once the buy order is fulfilled.
 
-This allows to buy using limit orders, sell using
-limit-orders, and create stoplosses using market orders. It also allows to set the
-stoploss "on exchange" which means stoploss order would be placed immediately once
-the buy order is fulfilled.
-    
 `order_types` set in the configuration file overwrites values set in the strategy as a whole, so you need to configure the whole `order_types` dictionary in one place.
 
-If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
-`stoploss_on_exchange`) need to be present, otherwise, the bot will fail to start.
+If this is configured, the following 4 values (`entry`, `exit`, `stoploss` and `stoploss_on_exchange`) need to be present, otherwise, the bot will fail to start.
 
-For information on (`emergencysell`,`forcesell`, `forcebuy`, `stoploss_on_exchange`,`stoploss_on_exchange_interval`,`stoploss_on_exchange_limit_ratio`) please see stop loss documentation [stop loss on exchange](stoploss.md)
+For information on (`emergency_exit`,`force_exit`, `force_entry`, `stoploss_on_exchange`,`stoploss_on_exchange_interval`,`stoploss_on_exchange_limit_ratio`) please see stop loss documentation [stop loss on exchange](stoploss.md)
 
 Syntax for Strategy:
 
 ```python
 order_types = {
-    "buy": "limit",
-    "sell": "limit",
-    "emergencysell": "market",
-    "forcebuy": "market",
-    "forcesell": "market",
+    "entry": "limit",
+    "exit": "limit",
+    "emergency_exit": "market",
+    "force_entry": "market",
+    "force_exit": "market",
     "stoploss": "market",
     "stoploss_on_exchange": False,
     "stoploss_on_exchange_interval": 60,
@@ -381,11 +480,11 @@ Configuration:
 
 ```json
 "order_types": {
-    "buy": "limit",
-    "sell": "limit",
-    "emergencysell": "market",
-    "forcebuy": "market",
-    "forcesell": "market",
+    "entry": "limit",
+    "exit": "limit",
+    "emergency_exit": "market",
+    "force_entry": "market",
+    "force_exit": "market",
     "stoploss": "market",
     "stoploss_on_exchange": false,
     "stoploss_on_exchange_interval": 60
@@ -408,7 +507,7 @@ Configuration:
     If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new stoploss order.
 
 !!! Warning "Warning: stoploss_on_exchange failures"
-    If stoploss on exchange creation fails for some reason, then an "emergency sell" is initiated. By default, this will sell the asset using a market order. The order-type for the emergency-sell can be changed by setting the `emergencysell` value in the `order_types` dictionary - however, this is not advised.
+    If stoploss on exchange creation fails for some reason, then an "emergency exit" is initiated. By default, this will exit the trade using a market order. The order-type for the emergency-exit can be changed by setting the `emergency_exit` value in the `order_types` dictionary - however, this is not advised.
 
 ### Understand order_time_in_force
 
@@ -430,21 +529,28 @@ It means if the order is not executed immediately AND fully then it is cancelled
 It is the same as FOK (above) except it can be partially fulfilled. The remaining part
 is automatically cancelled by the exchange.
 
-The `order_time_in_force` parameter contains a dict with buy and sell time in force policy values.
+**PO (Post only):**
+
+Post only order. The order is either placed as a maker order, or it is canceled.
+This means the order must be placed on orderbook for at at least time in an unfilled state.
+
+#### time_in_force config
+
+The `order_time_in_force` parameter contains a dict with entry and exit time in force policy values.
 This can be set in the configuration file or in the strategy.
 Values set in the configuration file overwrites values set in the strategy.
 
-The possible values are: `gtc` (default), `fok` or `ioc`.
+The possible values are: `GTC` (default), `FOK` or `IOC`.
 
 ``` python
 "order_time_in_force": {
-    "buy": "gtc",
-    "sell": "gtc"
+    "entry": "GTC",
+    "exit": "GTC"
 },
 ```
 
 !!! Warning
-    This is ongoing work. For now, it is supported only for binance and kucoin.
+    This is ongoing work. For now, it is supported only for binance, gate, ftx and kucoin.
     Please don't change the default value unless you know what you are doing and have researched the impact of using different values for your particular exchange.
 
 ### What values can be used for fiat_display_currency?
@@ -485,10 +591,10 @@ creating trades on the exchange.
 
 ```json
 "exchange": {
-        "name": "bittrex",
-        "key": "key",
-        "secret": "secret",
-        ...
+    "name": "bittrex",
+    "key": "key",
+    "secret": "secret",
+    ...
 }
 ```
 
@@ -505,7 +611,7 @@ Once you will be happy with your bot performance running in the Dry-run mode, yo
 * Market orders fill based on orderbook volume the moment the order is placed.
 * Limit orders fill once the price reaches the defined level - or time out based on `unfilledtimeout` settings.
 * In combination with `stoploss_on_exchange`, the stop_loss price is assumed to be filled.
-* Open orders (not trades, which are stored in the database) are reset on bot restart.
+* Open orders (not trades, which are stored in the database) are kept open after bot restarts, with the assumption that they were not filled while being offline.
 
 ## Switch to production mode
 
@@ -555,17 +661,7 @@ You should also make sure to read the [Exchanges](exchanges.md) section of the d
 
 ### Using proxy with Freqtrade
 
-To use a proxy with freqtrade, add the kwarg `"aiohttp_trust_env"=true` to the `"ccxt_async_kwargs"` dict in the exchange section of the configuration.
-
-An example for this can be found in `config_examples/config_full.example.json`
-
-``` json
-"ccxt_async_config": {
-    "aiohttp_trust_env": true
-}
-```
-
-Then, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values
+To use a proxy with freqtrade, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values.
 
 ``` bash
 export HTTP_PROXY="http://addr:port"
@@ -573,6 +669,20 @@ export HTTPS_PROXY="http://addr:port"
 freqtrade
 ```
 
+#### Proxy just exchange requests
+
+To use a proxy just for exchange connections (skips/ignores telegram and coingecko) - you can also define the proxies as part of the ccxt configuration.
+
+``` json
+"ccxt_config": {
+    "aiohttp_proxy": "http://addr:port",
+    "proxies": {
+        "http": "http://addr:port",
+        "https": "http://addr:port"
+    },
+}
+```
+
 ## Next step
 
 Now you have configured your config.json, the next step is to [start your bot](bot-usage.md).

docs/data-analysis.md

@@ -1,6 +1,6 @@
-# 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/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.  
+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`.
 
 ## Quick start with docker
 
@@ -41,32 +41,35 @@ ipython kernel install --user --name=freqtrade
 !!! 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.
 
-## 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.
 
@@ -119,5 +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

@@ -25,17 +25,19 @@ usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                [--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}]
+                               [-t TIMEFRAMES [TIMEFRAMES ...]] [--erase]
+                               [--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
                                [--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 ...]
                         Limit command to these pairs. Pairs are space-
                         separated.
-  --pairs-file FILE     File containing a list of pairs to download.
+  --pairs-file FILE     File containing a list of pairs. Takes precedence over
+                        --pairs or pairs configured in the configuration.
   --days INT            Download data for given number of days.
   --new-pairs-days INT  Download data of new pairs for given number of days.
                         Default: `None`.
@@ -48,17 +50,20 @@ optional arguments:
                         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} ...]
+  -t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
                         Specify which tickers to download. Space-separated
                         list. Default: `1m 5m`.
   --erase               Clean all existing data for the selected
                         exchange/pairs/timeframes.
-  --data-format-ohlcv {json,jsongz,hdf5}
+  --data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
                         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}, --tradingmode {spot,margin,futures}
+                        Select Trading mode
+  --prepend             Allow data prepending. (Data-appending is disabled)
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -71,7 +76,7 @@ Common arguments:
                         `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
+  -d PATH, --datadir PATH, --data-dir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
@@ -154,23 +159,36 @@ freqtrade download-data --exchange binance --pairs .*/USDT
 - 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. Eventually set end dates are ignored.
+- 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)
+* `json` -  plain "text" json files
+* `jsongz` - a gzip-zipped version of json files
+* `hdf5` - a high performance datastore
+* `feather` - a dataformat based on Apache Arrow
+* `parquet` - columnar 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:
+To persist this change, you should also add the following snippet to your configuration, so you don't have to insert the above arguments each time:
 
 ``` jsonc
     // ...
@@ -184,30 +202,75 @@ If the default data-format has been changed during download, then the keys `data
 !!! 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.
 
+#### Dataformat comparison
+
+The following comparisons have been made with the following data, and by using the linux `time` command.
+
+```
+Found 6 pair / timeframe combinations.
++----------+-------------+--------+---------------------+---------------------+
+|     Pair |   Timeframe |   Type |                From |                  To |
+|----------+-------------+--------+---------------------+---------------------|
+| BTC/USDT |          5m |   spot | 2017-08-17 04:00:00 | 2022-09-13 19:25:00 |
+| ETH/USDT |          1m |   spot | 2017-08-17 04:00:00 | 2022-09-13 19:26:00 |
+| BTC/USDT |          1m |   spot | 2017-08-17 04:00:00 | 2022-09-13 19:30:00 |
+| XRP/USDT |          5m |   spot | 2018-05-04 08:10:00 | 2022-09-13 19:15:00 |
+| XRP/USDT |          1m |   spot | 2018-05-04 08:11:00 | 2022-09-13 19:22:00 |
+| ETH/USDT |          5m |   spot | 2017-08-17 04:00:00 | 2022-09-13 19:20:00 |
++----------+-------------+--------+---------------------+---------------------+
+```
+
+Timings have been taken in a not very scientific way with the following command, which forces reading the data into memory.
+
+``` bash
+time freqtrade list-data --show-timerange --data-format-ohlcv <dataformat>
+```
+
+|  Format | Size | timing |
+|------------|-------------|-------------|
+| `json` | 149Mb | 25.6s |
+| `jsongz` | 39Mb | 27s |
+| `hdf5` | 145Mb | 3.9s |
+| `feather` | 72Mb | 3.5s |
+| `parquet` | 83Mb | 3.8s |
+
+Size has been taken from the BTC/USDT 1m spot combination for the timerange specified above.
+
+To have a best performance/size mix, we recommend the use of either feather or parquet.
+
 #### 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} ...]]
+                              {json,jsongz,hdf5,feather,parquet} --format-to
+                              {json,jsongz,hdf5,feather,parquet} [--erase]
+                              [--exchange EXCHANGE]
+                              [-t TIMEFRAMES [TIMEFRAMES ...]]
+                              [--trading-mode {spot,margin,futures}]
+                              [--candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]]
 
 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.
-  --format-from {json,jsongz,hdf5}
+  --format-from {json,jsongz,hdf5,feather,parquet}
                         Source format for data conversion.
-  --format-to {json,jsongz,hdf5}
+  --format-to {json,jsongz,hdf5,feather,parquet}
                         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} ...]
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
+                        config is provided.
+  -t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
                         Specify which tickers to download. Space-separated
                         list. Default: `1m 5m`.
+  --trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
+                        Select Trading mode
+  --candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]
+                        Select candle type to use
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -220,10 +283,11 @@ Common arguments:
                         `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
+  -d PATH, --datadir PATH, --data-dir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
+
 ```
 
 ##### Example converting data
@@ -241,20 +305,24 @@ freqtrade convert-data --format-from json --format-to jsongz --datadir ~/.freqtr
 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]
+                                    {json,jsongz,hdf5,feather,parquet}
+                                    --format-to
+                                    {json,jsongz,hdf5,feather,parquet}
+                                    [--erase] [--exchange EXCHANGE]
 
 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.
-  --format-from {json,jsongz,hdf5}
+  --format-from {json,jsongz,hdf5,feather,parquet}
                         Source format for data conversion.
-  --format-to {json,jsongz,hdf5}
+  --format-to {json,jsongz,hdf5,feather,parquet}
                         Destination format for data conversion.
   --erase               Clean all existing data for the selected
                         exchange/pairs/timeframes.
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
+                        config is provided.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -267,7 +335,7 @@ Common arguments:
                         `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
+  -d PATH, --datadir PATH, --data-dir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
@@ -292,9 +360,9 @@ This command will allow you to repeat this last step for additional timeframes w
 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} ...]]
+                                 [-t TIMEFRAMES [TIMEFRAMES ...]]
                                  [--exchange EXCHANGE]
-                                 [--data-format-ohlcv {json,jsongz,hdf5}]
+                                 [--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
                                  [--data-format-trades {json,jsongz,hdf5}]
 
 optional arguments:
@@ -302,12 +370,12 @@ optional arguments:
   -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} ...]
+  -t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
                         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}
+  --data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
                         Storage format for downloaded candle (OHLCV) data.
                         (default: `json`).
   --data-format-trades {json,jsongz,hdf5}
@@ -325,7 +393,7 @@ Common arguments:
                         `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
+  -d PATH, --datadir PATH, --data-dir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
@@ -345,19 +413,25 @@ 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}]
+                           [--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
                            [-p PAIRS [PAIRS ...]]
+                           [--trading-mode {spot,margin,futures}]
+                           [--show-timerange]
 
 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}
+  --data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
                         Storage format for downloaded candle (OHLCV) data.
                         (default: `json`).
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
-                        Show profits for only these pairs. Pairs are space-
+                        Limit command to these pairs. Pairs are space-
                         separated.
+  --trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
+                        Select Trading mode
+  --show-timerange      Show timerange available for available data. (May take
+                        a while to calculate).
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -370,7 +444,7 @@ Common arguments:
                         `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
+  -d PATH, --datadir PATH, --data-dir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.

docs/deprecated.md

@@ -15,8 +15,8 @@ This command line option was deprecated in 2019.7-dev (develop branch) and remov
 
 ### 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.
+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
 
@@ -24,6 +24,10 @@ and in freqtrade 2019.7.
 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.
 
+### `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
 
 The former `"pairlist"` section in the configuration has been removed, and is replaced by `"pairlists"` - being a list to specify a sequence of pairlists.
@@ -34,7 +38,7 @@ The old section of configuration parameters (`"pairlist"`) has been deprecated i
 
 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 sell price
+### 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.
@@ -43,3 +47,30 @@ As this does however increase risk and provides no benefit, it's been removed fo
 
 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

@@ -26,6 +26,11 @@ Alternatively (e.g. if your system is not supported by the setup.sh script), fol
 
 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.
@@ -63,6 +68,36 @@ def test_method_to_test(caplog):
 
 ```
 
+### Debug configuration
+
+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.
+
+``` json
+{
+    "name": "freqtrade trade",
+    "type": "python",
+    "request": "launch",
+    "module": "freqtrade",
+    "console": "integratedTerminal",
+    "args": [
+        "trade",
+        // Optional:
+        // "--userdir", "user_data",
+        "--strategy", 
+        "MyAwesomeStrategy",
+    ]
+},
+```
+
+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.
+
+A similar setup can also be taken for Pycharm - using `freqtrade` as module name, and setting the command line arguments as "parameters".
+
+!!! 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`.
@@ -195,11 +230,12 @@ For that reason, they must implement the following methods:
 * `global_stop()`
 * `stop_per_pair()`.
 
-`global_stop()` and `stop_per_pair()` must return a ProtectionReturn tuple, which consists of:
+`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.
 
@@ -218,13 +254,13 @@ Protections can have 2 different ways to stop trading for a limited :
 ##### 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 (sell order completed).
+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 (sell order completed).
+The method `global_stop()` will be called whenever a trade closed (exit order completed).
 
 ##### Protections - calculating lock end time
 
@@ -250,7 +286,23 @@ 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 and verify that the data downloaded correctly (no holes, the specified timerange was actually downloaded).
+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
 
@@ -292,6 +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.
@@ -306,9 +384,8 @@ 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 `stable` and `develop`.
+* 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`.
-* Raspberry PI Docker images are postfixed with `_pi` - so tags will be `:stable_pi` and `develop_pi`.
 * Docker images contain a file, `/freqtrade/freqtrade_commit` containing the commit this image is based of.
 * Full docker image rebuilds are run once a week via schedule.
 * Deployments run on ubuntu.
@@ -332,8 +409,9 @@ Determine if crucial bugfixes have been made between this commit and the current
 
 * Merge the release branch (stable) into this branch.
 * Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
-* Commit this part
-* push that branch to the remote and create a PR against the stable branch
+* Commit this part.
+* push that branch to the remote and create a PR against the stable branch.
+* Update develop version to next version following the pattern `2019.8-dev`.
 
 ### Create changelog from git commits
 

docs/docker_quickstart.md

@@ -126,6 +126,12 @@ All freqtrade arguments will be available by running `docker-compose run --rm fr
 !!! 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.

docs/edge.md

@@ -222,7 +222,7 @@ usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
 
 optional arguments:
   -h, --help            show this help message and exit
-  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
+  -i TIMEFRAME, --timeframe TIMEFRAME
                         Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
   --timerange TIMERANGE
                         Specify what timerange of data to use.

docs/exchanges.md

@@ -1,6 +1,6 @@
 # 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
 
@@ -46,7 +46,7 @@ In case of problems related to rate-limits (usually DDOS Exceptions in your logs
 ```
 
 This configuration enables kraken, as well as rate-limiting to avoid bans from the exchange.
-`"rateLimit": 3100` defines a wait-event of 0.2s between each call. This can also be completely disabled by setting `"enableRateLimit"` to false.
+`"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.
@@ -57,12 +57,13 @@ This configuration enables kraken, as well as rate-limiting to avoid bans from t
 Binance supports [time_in_force](configuration.md#understand-order_time_in_force).
 
 !!! 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 `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
+    On futures, Binance supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
 
-### Binance Blacklist
+### Binance Blacklist recommendation
 
-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 trade unsellable as the expected amount is not there anymore.
+For Binance, it is suggested to add `"BNB/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `BNB` on the account or unless you're willing to disable using `BNB` for fees.
+Binance accounts may use `BNB` for fees, and if a trade happens to be on `BNB`, further trades may consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
 
 ### Binance sites
 
@@ -71,6 +72,37 @@ Binance has been split into 2, and users must use the correct ccxt exchange ID f
 * [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 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 futures settings
+
+Users will also have to have the futures-setting "Position Mode" set to "One-way Mode", and "Asset Mode" set to "Single-Asset Mode".
+These settings will be checked on startup, and freqtrade will show an error if this setting is wrong.
+
+![Binance futures settings](assets/binance_futures_settings.png)
+
+Freqtrade will not attempt to change these settings.
+
 ## Kraken
 
 !!! Tip "Stoploss on Exchange"
@@ -177,10 +209,49 @@ Kucoin requires a passphrase for each api key, you will therefore need to add th
 
 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.
+For Kucoin, it is suggested to add `"KCS/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `KCS` on the account or unless you're willing to disable using `KCS` for fees. 
+Kucoin accounts may use `KCS` for fees, and if a trade happens to be on `KCS`, further trades may consume this position and make the initial `KCS` trade unsellable as the expected amount is not there anymore.
+
+## Huobi
+
+!!! 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 (we recommend to use net mode) - 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
 
@@ -217,7 +288,7 @@ For example, to test the order type `FOK` with Kraken, and modify candle limit t
 "exchange": {
     "name": "kraken",
     "_ft_has_params": {
-        "order_time_in_force": ["gtc", "fok"],
+        "order_time_in_force": ["GTC", "FOK"],
         "ohlcv_candle_limit": 200
         }
     //...

docs/faq.md

@@ -4,15 +4,17 @@
 
 Freqtrade supports spot trading only.
 
-### Can I open short positions?
+### Can my bot open short positions?
 
-No, Freqtrade does not support trading with margin / leverage, and cannot 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 some cases, your exchange may provide leveraged spot tokens which can be traded with Freqtrade eg. BTCUP/USD, BTCDOWN/USD, ETHBULL/USD, ETHBEAR/USD, etc...
+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?
+### Can my bot trade options or futures?
 
-No, options and futures trading are not supported.
+Futures trading is supported for selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an uptodate list of supported exchanges.
 
 ## Beginner Tips & Tricks
 
@@ -20,6 +22,13 @@ No, options and futures trading are not supported.
 
 ## Freqtrade common issues
 
+### Can freqtrade open multiple positions on the same pair in parallel?
+
+No. Freqtrade will only open one position per pair at a time.
+You can however use the [`adjust_trade_position()` callback](strategy-callbacks.md#adjust-trade-position) to adjust an open position.
+
+Backtesting provides an option for this in `--eps` - however this is only there to highlight "hidden" signals, and will not work in live.
+
 ### The bot does not start
 
 Running the bot with `freqtrade trade --config config.json` shows the output `freqtrade: command not found`.
@@ -28,7 +37,7 @@ 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 installation did not work correctly.
+* The installation did not complete successfully.
   * Please check the [Installation documentation](installation.md).
 
 ### I have waited 5 minutes, why hasn't the bot made any trades yet?
@@ -42,7 +51,7 @@ position for a trade. Be patient!
 ### 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
@@ -54,15 +63,30 @@ you can't say much from few trades.
 
 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.
 
+### Why does my bot not sell everything it bought?
+
+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?
+### Is there a setting to only Exit the trades being held and not perform any new Entries?
 
-You can use the `/stopbuy` command in Telegram to prevent future buys, followed by `/forcesell all` (sell all open trades).
+You can use the `/stopentry` command in Telegram to prevent future trade entry, followed by `/forceexit all` (sell all open trades).
 
 ### I want to run multiple bots on the same machine
 
@@ -78,9 +102,21 @@ If this happens for all pairs in the pairlist, this might indicate a recent exch
 
 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.  
+Currently known to happen for US Bittrex users.
 
 Read [the Bittrex section about restricted markets](exchanges.md#restricted-markets) for more information.
 
@@ -90,10 +126,10 @@ As the message says, your exchange does not support market orders and you have o
 
 To fix this, redefine order types in the strategy to use "limit" instead of "market":
 
-```
+``` python
     order_types = {
         ...
-        'stoploss': 'limit',
+        "stoploss": "limit",
         ...
     }
 ```
@@ -148,8 +184,8 @@ The GPU improvements would only apply to pandas-native calculations - or ones wr
 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). 
+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.
@@ -161,12 +197,12 @@ There is however nothing preventing you from using GPU-enabled indicators within
 Per default Hyperopt called without the `-e`/`--epochs` command line option will only
 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.
 
 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 10.000 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.
+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 --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
@@ -190,9 +226,9 @@ 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 10.0000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades. 
+* 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 10.000 times in a year. If we assume you set the --timerange to 365 days. 
+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`

docs/freqai-configuration.md

@@ -0,0 +1,217 @@
+# Configuration
+
+FreqAI is configured through the typical [Freqtrade config file](configuration.md) and the standard [Freqtrade strategy](strategy-customization.md). Examples of FreqAI config and strategy files can be found in `config_examples/config_freqai.example.json` and `freqtrade/templates/FreqaiExampleStrategy.py`, respectively.
+
+## Setting up the configuration file
+
+ Although there are plenty of additional parameters to choose from, as highlighted in the [parameter table](freqai-parameter-table.md#parameter-table), a FreqAI config must at minimum include the following parameters (the parameter values are only examples):
+
+```json
+    "freqai": {
+        "enabled": true,
+        "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,
+            "indicator_periods_candles": [10, 20]
+        },
+        "data_split_parameters" : {
+            "test_size": 0.25
+        },
+        "model_training_parameters" : {
+            "n_estimators": 100
+        },
+    }
+```
+
+A full example config is available in `config_examples/config_freqai.example.json`.
+
+## Building a FreqAI strategy
+
+The FreqAI strategy requires including the following lines of code in the standard [Freqtrade strategy](strategy-customization.md):
+
+```python
+    # user should define the maximum startup candle count (the largest number of candles
+    # passed to any single indicator)
+    startup_candle_count: int = 20
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        # 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 [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`. 
+
+Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`.
+
+!!! Note
+    The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
+
+!!! Note
+    Features **must** be defined in `populate_any_indicators()`. Defining FreqAI features in `populate_indicators()`
+    will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, the following structure inside `populate_any_indicators()` should be used
+    (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()`.
+
+## Important dataframe key patterns
+
+Below are the values you can expect to include/use inside a typical strategy dataframe (`df[]`):
+
+|  DataFrame Key | Description |
+|------------|-------------|
+| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back 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']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
+| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
+| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
+| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
+
+## Setting the `startup_candle_count`
+
+The `startup_candle_count` in the FreqAI strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., Ta-Lib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`.
+
+!!! Note
+    There are instances where the Ta-Lib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean:
+
+    ```
+    2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319.
+    ```
+
+## Creating a dynamic target threshold
+
+Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and  `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out. 
+
+```python
+dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
+dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
+```
+
+To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_prediction_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
+
+```json
+    "freqai": {
+        "fit_live_prediction_candles": 300,
+    }
+```
+
+If this value is set, FreqAI will initially use the predictions from the training data and subsequently begin introducing real prediction data as it is generated. FreqAI will save this historical data to be reloaded if you stop and restart a model with the same `identifier`.
+
+## Using different prediction models
+
+FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `Catboost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`. However, it is possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of the training procedures.
+
+### Setting classifier targets
+
+FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example:
+
+```python
+df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
+```
+
+Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column.

docs/freqai-developers.md

@@ -0,0 +1,78 @@
+# Development
+
+## Project architecture
+
+The architecture and functions of FreqAI are generalized to encourages development of unique features, functions, models, etc.
+
+The class structure and a detailed algorithmic overview is depicted in the following diagram:
+
+![image](assets/freqai_algorithm-diagram.jpg)
+
+As shown, there are three distinct objects comprising FreqAI:
+
+* **IFreqaiModel** - A singular persistent object containing all the necessary logic to collect, store, and process data, engineer features, run training, and inference models.
+* **FreqaiDataKitchen** - A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools.
+* **FreqaiDataDrawer** - A singular persistent object containing all the historical predictions, models, and save/load methods.
+
+There are a variety of built-in [prediction models](freqai-configuration.md#using-different-prediction-models) which inherit directly from `IFreqaiModel`. Each of these models have full access to all methods in `IFreqaiModel` and can therefore override any of those functions at will. However, advanced users will likely stick to overriding `fit()`, `train()`, `predict()`, and `data_cleaning_train/predict()`.
+
+## Data handling
+
+FreqAI aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash resilience by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified.
+
+### File structure
+
+The file structure is automatically generated based on the model `identifier` set in the [config](freqai-configuration.md#setting-up-the-configuration-file). The following structure shows where the data is stored for post processing:
+
+| Structure | Description |
+|-----------|-------------|
+| `config_*.json` | A copy of the model specific configuration file. |
+| `historic_predictions.pkl` | A file containing all historic predictions generated during the lifetime of the `identifier` model during live deployment. `historic_predictions.pkl` is used to reload the model after a crash or a config change. A backup file is always held in case of corruption on the main file. FreqAI **automatically** detects corruption and replaces the corrupted file with the backup. |
+| `pair_dictionary.json` | A file containing the training queue as well as the on disk location of the most recently trained model. |
+| `sub-train-*_TIMESTAMP` | A folder containing all the files associated with a single model, such as: <br>
+|| `*_metadata.json` - Metadata for the model, such as normalization max/min, expected training feature list, etc. <br>
+|| `*_model.*` - The model file saved to disk for reloading from a crash. Can be `joblib` (typical boosting libs), `zip` (stable_baselines), `hd5` (keras type), etc. <br>
+|| `*_pca_object.pkl` - The [Principal component analysis (PCA)](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis) transform (if `principal_component_analysis: True` is set in the config) which will be used to transform unseen prediction features. <br>
+|| `*_svm_model.pkl` - The [Support Vector Machine (SVM)](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm) model (if `use_SVM_to_remove_outliers: True` is set in the config) which is used to detect outliers in unseen prediction features. <br>
+|| `*_trained_df.pkl` - The dataframe containing all the training features used to train the `identifier` model. This is used for computing the [Dissimilarity Index (DI)](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) and can also be used for post-processing. <br>
+|| `*_trained_dates.df.pkl` - The dates associated with the `trained_df.pkl`, which is useful for post-processing. |
+
+The example file structure would look like this:
+
+```
+├── models
+│   └── unique-id
+│       ├── config_freqai.example.json
+│       ├── historic_predictions.backup.pkl
+│       ├── historic_predictions.pkl
+│       ├── pair_dictionary.json
+│       ├── sub-train-1INCH_1662821319
+│       │   ├── cb_1inch_1662821319_metadata.json
+│       │   ├── cb_1inch_1662821319_model.joblib
+│       │   ├── cb_1inch_1662821319_pca_object.pkl
+│       │   ├── cb_1inch_1662821319_svm_model.joblib
+│       │   ├── cb_1inch_1662821319_trained_dates_df.pkl
+│       │   └── cb_1inch_1662821319_trained_df.pkl
+│       ├── sub-train-1INCH_1662821371
+│       │   ├── cb_1inch_1662821371_metadata.json
+│       │   ├── cb_1inch_1662821371_model.joblib
+│       │   ├── cb_1inch_1662821371_pca_object.pkl
+│       │   ├── cb_1inch_1662821371_svm_model.joblib
+│       │   ├── cb_1inch_1662821371_trained_dates_df.pkl
+│       │   └── cb_1inch_1662821371_trained_df.pkl
+│       ├── sub-train-ADA_1662821344
+│       │   ├── cb_ada_1662821344_metadata.json
+│       │   ├── cb_ada_1662821344_model.joblib
+│       │   ├── cb_ada_1662821344_pca_object.pkl
+│       │   ├── cb_ada_1662821344_svm_model.joblib
+│       │   ├── cb_ada_1662821344_trained_dates_df.pkl
+│       │   └── cb_ada_1662821344_trained_df.pkl
+│       └── sub-train-ADA_1662821399
+│           ├── cb_ada_1662821399_metadata.json
+│           ├── cb_ada_1662821399_model.joblib
+│           ├── cb_ada_1662821399_pca_object.pkl
+│           ├── cb_ada_1662821399_svm_model.joblib
+│           ├── cb_ada_1662821399_trained_dates_df.pkl
+│           └── cb_ada_1662821399_trained_df.pkl
+
+```

docs/freqai-feature-engineering.md

@@ -0,0 +1,268 @@
+# Feature engineering
+
+## Defining the features
+
+Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%`, while labels/targets are prepended with `&`.
+
+Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the FreqAI config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
+
+It is advisable to start from the template `populate_any_indicators()` in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
+
+```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. The user controls the indicators
+        passed to the training/prediction by prepending indicators with `'%-' + coin `
+        (see convention below). I.e., the 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)
+
+            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
+```
+
+In the presented example, the user 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 `%`.
+
+After having defined the `base features`, the next step is to expand upon them using the powerful `feature_parameters` in the configuration file:
+
+```json
+    "freqai": {
+        //...
+        "feature_parameters" : {
+            "include_timeframes": ["5m","15m","4h"],
+            "include_corr_pairlist": [
+                "ETH/USD",
+                "LINK/USD",
+                "BNB/USD"
+            ],
+            "label_period_candles": 24,
+            "include_shifted_candles": 2,
+            "indicator_periods_candles": [10, 20]
+        },
+        //...
+    }
+```
+
+The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the presented 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.
+
+You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
+
+`include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells FreqAI to include the past 2 candles for each of the features in the feature set.
+
+In total, the number of features the user of the presented example strat 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`
+ $= 3 * 3 * 3 * 2 * 2 = 108$.
+
+### Returning additional info from training
+
+Important metrics can be returned to the strategy at the end of each model training by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the custom prediction model class. 
+
+FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the dataframe that is returned to the strategy. You can then use the returned metrics in your strategy through `dataframe['my_new_value']`. An example of how return values can be used in FreqAI are the `&*_mean` and `&*_std` values that are used to [created a dynamic target threshold](freqai-configuration.md#creating-a-dynamic-target-threshold).
+
+Another example, where the user wants to use live metrics from the trade database, is shown below:
+
+```json
+    "freqai": {
+        "extra_returns_per_train": {"total_profit": 4}
+    }
+```
+
+You need to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, the pre-set values are what will be returned.
+
+## Feature normalization
+
+FreqAI is strict when it comes to data normalization. The train features, $X^{train}$, are always normalized to [-1, 1] using a shifted min-max normalization:
+
+$$X^{train}_{norm} = 2 * \frac{X^{train} - X^{train}.min()}{X^{train}.max() - X^{train}.min()} - 1$$
+
+All other data (test data and unseen prediction data in dry/live/backtest) is always automatically normalized to the training feature space according to industry standards. FreqAI stores all the metadata required to ensure that test and prediction features will be properly normalized and that predictions are properly denormalized. For this reason, it is not recommended to eschew industry standards and modify FreqAI internals - however - advanced users can do so by inheriting `train()` in their custom `IFreqaiModel` and using their own normalization functions.
+
+## Data dimensionality reduction with Principal Component Analysis
+
+You can reduce the dimensionality of your features by activating the `principal_component_analysis` in the config:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "principal_component_analysis": true
+        }
+    }
+```
+
+This will perform PCA on the features and reduce their dimensionality so that the explained variance of the data set is >= 0.999. Reducing data dimensionality makes training the model faster and hence allows for more up-to-date models. 
+
+## Inlier metric
+
+The `inlier_metric` is a metric aimed at quantifying how similar a the features of a data point are to the most recent historic data points. 
+
+You define the lookback window by setting `inlier_metric_window` and FreqAI computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function (CDF) is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. The figure below explains the concept for an `inlier_metric_window` of 5.
+
+![inlier-metric](assets/freqai_inlier-metric.jpg)
+
+FreqAI adds the `inlier_metric` to the training features and hence gives the model access to a novel type of temporal information. 
+
+This function does **not** remove outliers from the data set.
+
+## Weighting features for temporal importance
+
+FreqAI allows you to set a `weight_factor` to weight 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. Below is a figure showing the effect of different weight factors on the data points in a feature set.
+
+![weight-factor](assets/freqai_weight-factor.jpg)
+
+## Outlier detection
+
+Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. FreqAI implements a variety of methods to identify such outliers and hence mitigate risk.
+
+### Identifying outliers with the Dissimilarity Index (DI)
+
+ The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. 
+
+You can tell FreqAI to remove outlier data points from the training/test data sets using the DI by including the following statement in the config:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "DI_threshold": 1
+        }
+    }
+```
+
+ The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, 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 $b$, and $p$ 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 a new prediction feature vectors, $X_k$ and all the training data:
+
+$$ d_k = \arg \min d_{k,i} $$
+
+This enables the estimation of the Dissimilarity Index as:
+
+$$ DI_k = d_k/\overline{d} $$
+
+You can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model. A higher `DI_threshold` means that the DI is more lenient and allows predictions further away from the training data to be used whilst a lower `DI_threshold` has the opposite effect and hence discards more predictions.
+
+Below is a figure that describes the DI for a 3D data set.
+
+![DI](assets/freqai_DI.jpg)
+
+### Identifying outliers using a Support Vector Machine (SVM)
+
+You can tell FreqAI to remove outlier data points from the training/test data sets using a Support Vector Machine (SVM) by including the following statement in the config:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "use_SVM_to_remove_outliers": true
+        }
+    }
+```
+
+The SVM will be trained on the training data and any data point that the SVM deems to be beyond the feature space will be removed.
+
+FreqAI uses `sklearn.linear_model.SGDOneClassSVM` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html) (external website)) and you can elect to provide additional parameters for the SVM, such as `shuffle`, and `nu`.
+
+The parameter `shuffle` is by default set to `False` to ensure consistent results. If it is set to `True`, running the SVM multiple times on the same data set might result in different outcomes due to `max_iter` being to low for the algorithm to reach the demanded `tol`. Increasing `max_iter` solves this issue but causes the procedure to take longer time.
+
+The parameter `nu`, *very* broadly, is the amount of data points that should be considered outliers and should be between 0 and 1.
+
+### Identifying outliers with DBSCAN
+
+You can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "use_DBSCAN_to_remove_outliers": true
+        }
+    }
+```
+
+DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be.
+
+Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$.
+
+![dbscan](assets/freqai_dbscan.jpg)
+
+FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (external website)) with `min_samples` ($N$) taken as 1/4 of the no. of time points (candles) in the feature set. `eps` ($\varepsilon$) is computed automatically as the elbow point in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set.

docs/freqai-parameter-table.md

@@ -0,0 +1,61 @@
+# Parameter table
+
+The table below will list all configuration parameters available for FreqAI. Some of the parameters are exemplified in `config_examples/config_freqai.example.json`.
+
+Mandatory parameters are marked as **Required** and have to be set in one of the suggested ways.
+
+|  Parameter | Description |
+|------------|-------------|
+|  |  **General configuration parameters**
+| `freqai` | **Required.** <br> The parent dictionary containing all the parameters for controlling FreqAI. <br> **Datatype:** Dictionary.
+| `train_period_days` | **Required.** <br> Number of days to use for the training data (width of the sliding window). <br> **Datatype:** Positive integer.
+| `backtest_period_days` | **Required.** <br> Number of days to inference from the trained model before sliding the `train_period_days` window defined above, and retraining the model during backtesting (more info [here](freqai-running.md#backtesting)). This can be fractional days, but beware that the provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
+| `identifier` | **Required.** <br> A unique ID for the current model. If models are saved to disk, the `identifier` allows for reloading specific pre-trained models/data. <br> **Datatype:** String.
+| `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> **Datatype:** Float > 0. <br> Default: `0` (models retrain as often as possible).
+| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> **Datatype:** Positive integer. <br> Default: `0` (models never expire).
+| `purge_old_models` | Delete obsolete models. <br> **Datatype:** Boolean. <br> Default: `False` (all historic models remain on disk).
+| `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
+| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
+| `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.
+| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
+|  |  **Feature parameters**
+| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
+| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
+| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
+| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
+| `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
+| `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)). <br> **Datatype:** Positive float (typically < 1).
+| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
+| `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset. <br> **Datatype:** List of positive integers.
+| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. <br> Default: `False`.
+| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features. <br> **Datatype:** Integer. <br> Default: `0`.
+| `DI_threshold` | Activates the use of the Dissimilarity Index for outlier detection when set to > 0. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Positive float (typically < 1).
+| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
+| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
+| `use_DBSCAN_to_remove_outliers` | Cluster data using the DBSCAN algorithm to identify and remove outliers from training and prediction data. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan). <br> **Datatype:** Boolean. 
+| `inlier_metric_window` | If set, FreqAI adds an `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`, i.e., the number of previous time points to compare the current candle to. Details of how the `inlier_metric` is computed can be found [here](freqai-feature-engineering.md#inlier-metric). <br> **Datatype:** Integer. <br> Default: `0`.
+| `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`.
+| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
+| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
+|  |  **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) (external website). <br> **Datatype:** Dictionary.
+| `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
+| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Defaut: `False`.
+|  |  **Model training parameters**
+| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model.  <br> **Datatype:** Dictionary.
+| `n_estimators` | The number of boosted trees to fit in the training of the model. <br> **Datatype:** Integer.
+| `learning_rate` | Boosting learning rate during training of the model. <br> **Datatype:** Float.
+| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.
+|  |  *Reinforcement Learning Parameters**
+| `rl_config` | A dictionary containing the control parameters for a Reinforcement Learning model. <br> **Datatype:** Dictionary.
+| `train_cycles` | Training time steps will be set based on the `train_cycles * number of training data points. <br> **Datatype:** Integer.
+| `cpu_count` | Number of processors to dedicate to the Reinforcement Learning training process. <br> **Datatype:** int.
+| `max_trade_duration_candles`| Guides the agent training to keep trades below desired length. Example usage shown in `prediction_models/ReinforcementLearner.py` within the user customizable `calculate_reward()` <br> **Datatype:** int.
+| `model_type` | Model string from stable_baselines3 or SBcontrib. Available strings include: `'TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO', 'PPO', 'A2C', 'DQN'`. User should ensure that `model_training_parameters` match those available to the corresponding stable_baselines3 model by visiting their documentaiton. [PPO doc](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) (external website) <br> **Datatype:** string.
+| `policy_type` | One of the available policy types from stable_baselines3 <br> **Datatype:** string.
+| `max_training_drawdown_pct` | The maximum drawdown that the agent is allowed to experience during training. <br> **Datatype:** float. <br> Default: 0.8
+| `cpu_count` | Number of threads/cpus to dedicate to the Reinforcement Learning training process (depending on if `ReinforcementLearning_multiproc` is selected or not). <br> **Datatype:** int. 
+| `model_reward_parameters` | Parameters used inside the user customizable `calculate_reward()` function in `ReinforcementLearner.py` <br> **Datatype:** int.
+|  |  **Extraneous parameters**
+| `keras` | If the selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
+| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) 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. <br> **Datatype:** Integer. <br> Default: `2`.

docs/freqai-reinforcement-learning.md

@@ -0,0 +1,202 @@
+# Reinforcement Learning
+
+!!! Note
+    Reinforcement learning dependencies include large packages such as `torch`, which should be explicitly requested during `./setup.sh -i` by answering "y" to the question "Do you also want dependencies for freqai-rl (~700mb additional space required) [y/N]?" Users who prefer docker should ensure they use the docker image appended with `_freqaiRL`. 
+
+Setting up and running a Reinforcement Learning model is the same as running a Regressor or Classifier. The same two flags, `--freqaimodel` and `--strategy`, must be defined on the command line:
+
+```bash
+freqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --config config.json
+```
+
+where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner`. The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `populate_any_indicators` as a typical Regressor:
+
+```python
+    def populate_any_indicators(
+        self, pair, df, tf, informative=None, set_generalized_indicators=False
+    ):
+
+        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)
+
+        # The following features are necessary for RL models
+        informative[f"%-{coin}raw_close"] = informative["close"]
+        informative[f"%-{coin}raw_open"] = informative["open"]
+        informative[f"%-{coin}raw_high"] = informative["high"]
+        informative[f"%-{coin}raw_low"] = informative["low"]
+
+        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:
+
+            # For RL, there are no direct targets to set. This is filler (neutral)
+            # until the agent sends an action.
+            df["&-action"] = 0
+
+        return df
+```
+
+Most of the function remains the same as for typical Regressors, however, the function above shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environent:
+
+```python
+        # The following features are necessary for RL models
+        informative[f"%-{coin}raw_close"] = informative["close"]
+        informative[f"%-{coin}raw_open"] = informative["open"]
+        informative[f"%-{coin}raw_high"] = informative["high"]
+        informative[f"%-{coin}raw_low"] = informative["low"]
+```
+
+Finally, there is no explicit "label" to make - instead the you need to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the user set the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.
+
+After users realize there are no labels to set, they will soon understand that the agent is making its "own" entry and exit decisions. This makes strategy construction rather simple. The entry and exit signals come from the agent in the form of an integer - which are used directly to decide entries and exits in the strategy:
+
+```python
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+
+        enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]
+
+        if enter_long_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
+            ] = (1, "long")
+
+        enter_short_conditions = [df["do_predict"] == 1, df["&-action"] == 3]
+
+        if enter_short_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
+            ] = (1, "short")
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        exit_long_conditions = [df["do_predict"] == 1, df["&-action"] == 2]
+        if exit_long_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
+
+        exit_short_conditions = [df["do_predict"] == 1, df["&-action"] == 4]
+        if exit_short_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
+
+        return df
+```
+
+It is important to consider that `&-action` depends on which environment they choose to use. The example above shows 5 actions, where 0 is neutral, 1 is enter long, 2 is exit long, 3 is enter short and 4 is exit short. 
+
+## Configuring the Reinforcement Learner
+
+In order to configure the `Reinforcement Learner` the following dictionary to their `freqai` config:
+
+```json
+        "rl_config": {
+            "train_cycles": 25,
+            "max_trade_duration_candles": 300,
+            "max_training_drawdown_pct": 0.02,
+            "cpu_count": 8,
+            "model_type": "PPO",
+            "policy_type": "MlpPolicy",
+            "model_reward_parameters": {
+                "rr": 1,
+                "profit_aim": 0.025
+            }
+        }
+```
+
+Parameter details can be found [here](freqai-parameter-table.md), but in general the `train_cycles` decides how many times the agent should cycle through the candle data in its artificial environemtn to train weights in the model. `model_type` is a string which selects one of the available models in [stable_baselines](https://stable-baselines3.readthedocs.io/en/master/)(external link). 
+
+## Creating the reward
+
+As users begin to modify the strategy and the prediction model, they will quickly realize some important differences between the Reinforcement Learner and the Regressors/Classifiers. Firstly, the strategy does not set a target value (no labels!). Instead, the user sets a `calculate_reward()` function inside their custom `ReinforcementLearner.py` file. A default `calculate_reward()` is provided inside `prediction_models/ReinforcementLearner.py` to give users the necessary building blocks to start their own models. It is inside the `calculate_reward()` where users express their creative theories about the market. For example, the user wants to reward their agent when it makes a winning trade, and penalize the agent when it makes a losing trade. Or perhaps, the user wishes to reward the agnet for entering trades, and penalize the agent for sitting in trades too long. Below we show examples of how these rewards are all calculated:
+
+```python
+    class MyRLEnv(Base5ActionRLEnv):
+        """
+        User made custom environment. This class inherits from BaseEnvironment and gym.env.
+        Users can override any functions from those parent classes. Here is an example
+        of a user customized `calculate_reward()` function.
+        """
+        def calculate_reward(self, action):
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                return -2
+            pnl = self.get_unrealized_profit()
+
+            factor = 100
+            # reward agent for entering trades
+            if action in (Actions.Long_enter.value, Actions.Short_enter.value) \
+                    and self._position == Positions.Neutral:
+                return 25
+            # discourage agent from not entering trades
+            if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                return -1
+            max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
+            trade_duration = self._current_tick - self._last_trade_tick
+            if trade_duration <= max_trade_duration:
+                factor *= 1.5
+            elif trade_duration > max_trade_duration:
+                factor *= 0.5
+            # discourage sitting in position
+            if self._position in (Positions.Short, Positions.Long) and \
+               action == Actions.Neutral.value:
+                return -1 * trade_duration / max_trade_duration
+            # close long
+            if action == Actions.Long_exit.value and self._position == Positions.Long:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                return float(pnl * factor)
+            # close short
+            if action == Actions.Short_exit.value and self._position == Positions.Short:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                return float(pnl * factor)
+            return 0.
+```
+
+### Creating a custom agent
+
+Users can inherit from `stable_baselines3` and customize anything they wish about their agent. Doing this is for advanced users only, an example is presented in `freqai/RL/ReinforcementLearnerCustomAgent.py`
+
+### Using Tensorboard
+
+Reinforcement Learning models benefit from tracking training metrics. FreqAI has integrated Tensorboard to allow users to track training and evaluation performance across all coins and across all retrainings. To start, the user should ensure Tensorboard is installed on their computer:
+
+```bash
+pip3 install tensorboard
+```
+
+Next, the user can activate Tensorboard with the following command:
+
+```bash
+cd freqtrade
+tensorboard --logdir user_data/models/unique-id
+```
+
+where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell if the user wishes to view the output in their browser at 127.0.0.1:6060 (6060 is the default port used by Tensorboard).
+
+![tensorboard](assets/tensorboard.png)

docs/freqai-running.md

@@ -0,0 +1,156 @@
+# Running FreqAI
+
+There are two ways to train and deploy an adaptive machine learning model - live deployment and historical backtesting. In both cases, FreqAI runs/simulates periodic retraining of models as shown in the following figure:
+
+![freqai-window](assets/freqai_moving-window.jpg)
+
+## Live deployments
+
+FreqAI can be run dry/live using the following command:
+
+```bash
+freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor
+```
+
+When launched, FreqAI will start training a new model, with a new `identifier`, based on the config settings. Following training, the model will be used 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 coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If you do not want FreqAI to retrain new models as often as possible, you can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, you can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours.
+
+Trained models are by default saved to disk to allow for reuse during backtesting or after a crash. You can opt to [purge old models](#purging-old-model-data) to save disk space by setting `"purge_old_models": true` in the config.
+
+To start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), you only need to specify the `identifier` of the specific model:
+
+```json
+    "freqai": {
+        "identifier": "example",
+        "live_retrain_hours": 0.5
+    }
+```
+
+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. If a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will start training a new model.
+
+### Automatic data download
+
+FreqAI automatically downloads the proper amount of data needed to ensure training of a model through the defined `train_period_days` and `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters). 
+
+### Saving prediction data
+
+All predictions made during the lifetime of a specific `identifier` model are stored in `historic_predictions.pkl` to allow for reloading after a crash or changes made to the config.
+
+### Purging old model data
+
+FreqAI stores new model files after each successful training. These files become obsolete as new models are generated to adapt to new market conditions. If you are planning to leave FreqAI running for extended periods of time with high frequency retraining, you should enable `purge_old_models` in the config:
+
+```json
+    "freqai": {
+        "purge_old_models": true,
+    }
+```
+
+This will automatically purge all models older than the two most recently trained ones to save disk space.
+
+## Backtesting
+
+The FreqAI backtesting module can be executed with the following command:
+
+```bash
+freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
+```
+
+If this command has never been executed with the existing config file, FreqAI will train a new model
+for each pair, for each backtesting window within the expanded `--timerange`.
+
+Backtesting mode requires [downloading the necessary data](#downloading-data-to-cover-the-full-backtest-period) before deployment (unlike in dry/live mode where FreqAI handles the data downloading automatically). You should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-size-of-the-sliding-training-window-and-backtesting-duration).
+
+!!! Note "Model reuse"
+    Once the training is completed, you can execute the backtesting 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 you want to tweak (or even hyperopt) buy and sell criteria inside the strategy. If you
+    *want* to retrain a new model with the same config file, you should simply change the `identifier`.
+    This way, you can return to using any model you wish by simply specifying the `identifier`.
+
+---
+
+### Saving prediction data
+
+To allow for tweaking your strategy (**not** the features!), FreqAI will automatically save the predictions during backtesting so that they can be reused for future backtests and live runs using the same `identifier` model. This provides a performance enhancement geared towards enabling **high-level hyperopting** of entry/exit criteria.
+
+An additional directory called `predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder.
+
+To change your **features**, you **must** set a new `identifier` in the config to signal to FreqAI to train new models.
+
+To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config.
+
+### Downloading data to cover the full backtest period
+
+For live/dry deployments, FreqAI will download the necessary data automatically. However, to use backtesting functionality, you need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). You need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that there is a sufficient amount of training data *before* the start of the backtesting time range. The amount of additional data can be roughly estimated by moving the start date of the time range backwards by `train_period_days` and the `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters) from the beginning of the desired backtesting time range. 
+
+As an example, to backtest the `--timerange 20210501-20210701` using the [example config](freqai-configuration.md#setting-up-the-configuration-file) which sets `train_period_days` to 30, together with `startup_candle_count: 40` on a maximum `include_timeframes` of 1h, the start date for the downloaded data needs to be `20210501` - 30 days - 40 * 1h / 24 hours = 20210330 (31.7 days earlier than the start of the desired training time range).
+
+### Deciding the size of the sliding training window and backtesting duration
+
+The backtesting time range is defined with the typical `--timerange` parameter in the configuration file. The duration of the sliding training window is set by `train_period_days`, whilst `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 presented [example config](freqai-configuration.md#setting-up-the-configuration-file) (found in `config_examples/config_freqai.example.json`), the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. After the training of the model, FreqAI will backtest the subsequent 7 days. The "sliding window" then moves one week forward (emulating FreqAI retraining once per week in live mode) and the new model uses the previous 30 days (including the 7 days used for backtesting by the previous model) to train. This is repeated until the end of `--timerange`.  This means that if you set `--timerange 20210501-20210701`, FreqAI will have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks).
+
+!!! Note
+    Although fractional `backtest_period_days` is allowed, you should be aware 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, by setting a `--timerange` of 10 days, and a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. Because of this, a true backtest of FreqAI adaptive training would take a *very* long time. The best way to fully test a model is to run it dry and let it train constantly. In this case, backtesting would take the exact same amount of time as a dry run.
+
+## Defining model expirations
+
+During dry/live mode, FreqAI trains each coin pair sequentially (on separate threads/GPU from the main Freqtrade bot). This means that there is always an age discrepancy between models. If you are 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 (the trade duration target) for a strategy is less than 4 hours. You can decide to only make trade entries if the model is less than a certain number of hours old by setting the `expiration_hours` in the config file:
+
+```json
+    "freqai": {
+        "expiration_hours": 0.5,
+    }
+```
+
+In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old.
+
+## Controlling the model learning process
+
+Model training parameters are unique to the selected machine learning library. FreqAI allows you to set any parameter for any library using the `model_training_parameters` dictionary in the config. The example config (found in `config_examples/config_freqai.example.json`) shows some of the example parameters associated with `Catboost` and `LightGBM`, but you can add any parameters available in those libraries or any other machine learning library you choose to implement.
+
+Data split parameters are defined in `data_split_parameters` which can be any parameters associated with Scikit-learn's `train_test_split()` function. `train_test_split()` has a parameters called `shuffle` which allows to shuffle the data or keep it unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. More details about these parameters can be found the [Scikit-learn website](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
+
+The FreqAI specific parameter `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file), the user is asking for `labels` that are 24 candles in the future.
+
+## Continual learning
+
+You can choose to adopt a continual learning scheme by setting `"continual_learning": true` in the config. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `False` which means that all new models are trained from scratch, without input from previous models.
+
+## Hyperopt
+
+You can hyperopt using the same command as for [typical Freqtrade hyperopt](hyperopt.md):
+
+```bash
+freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --timerange 20220428-20220507
+```
+
+`hyperopt` requires you to have the data pre-downloaded in the same fashion as if you were doing [backtesting](#backtesting). In addition, you must consider some restrictions when trying to hyperopt FreqAI strategies:
+
+- The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI.
+- It's not possible to hyperopt indicators in the `populate_any_indicators()` function. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
+- The backtesting instructions also apply to hyperopt.
+
+The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. You need to focus on hyperopting parameters that are not used in your features. For example, you should not try to hyperopt rolling window lengths in the feature creation, or any part of the FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
+
+A good example of a hyperoptable parameter in FreqAI is a threshold for the [Dissimilarity Index (DI)](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) `DI_values` beyond which we consider data points as outliers:
+
+```python
+di_max = IntParameter(low=1, high=20, default=10, space='buy', optimize=True, load=True)
+dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1, 0)
+```
+
+This specific hyperopt would help you understand the appropriate `DI_values` for your particular parameter space.
+
+## Setting up a follower
+
+You can indicate to the bot that it should not train models, but instead should look for models trained by a leader with a specific `identifier` by defining:
+
+```json
+    "freqai": {
+        "follow_mode": true,
+        "identifier": "example"
+    }
+```
+
+In this example, the user has a leader bot with the `"identifier": "example"`. The leader bot is already running or is launched simultaneously with the follower. The follower will load models created by the leader and inference them to obtain predictions instead of training its own models.

docs/freqai.md

@@ -0,0 +1,99 @@
+![freqai-logo](assets/freqai_doc_logo.svg)
+
+# FreqAI
+
+## Introduction
+
+FreqAI is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input features.
+
+Features include:
+
+* **Self-adaptive retraining** - Retrain models during [live deployments](freqai-running.md#live-deployments) to self-adapt to the market in a supervised manner
+* **Rapid feature engineering** - Create large rich [feature sets](freqai-feature-engineering.md#feature-engineering) (10k+ features) based on simple user-created strategies
+* **High performance** - Threading allows for adaptive model retraining on a separate thread (or on GPU if available) from model inferencing (prediction) and bot trade operations. Newest models and data are kept in RAM for rapid inferencing
+* **Realistic backtesting** - Emulate self-adaptive training on historic data with a [backtesting module](freqai-running.md#backtesting) that automates retraining
+* **Extensibility** - The generalized and robust architecture allows for incorporating any [machine learning library/method](freqai-configuration.md#using-different-prediction-models) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network
+* **Smart outlier removal** - Remove outliers from training and prediction data sets using a variety of [outlier detection techniques](freqai-feature-engineering.md#outlier-detection)
+* **Crash resilience** - Store trained models to disk to make reloading from a crash fast and easy, and [purge obsolete files](freqai-running.md#purging-old-model-data) for sustained dry/live runs
+* **Automatic data normalization** - [Normalize the data](freqai-feature-engineering.md#feature-normalization) in a smart and statistically safe way
+* **Automatic data download** - Compute timeranges for data downloads and update historic data (in live deployments)
+* **Cleaning of incoming data** - Handle NaNs safely before training and model inferencing
+* **Dimensionality reduction** - Reduce the size of the training data via [Principal Component Analysis](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis)
+* **Deploying bot fleets** - Set one bot to train models while a fleet of [follower bots](freqai-running.md#setting-up-a-follower) inference the models and handle trades
+
+## Quick start
+
+The easiest way to quickly test FreqAI is to run it in dry mode with the following command:
+
+```bash
+freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates
+```
+
+You will see the boot-up process of automatic data downloading, followed by simultaneous training and trading.
+
+An example strategy, prediction model, and config to use as a starting points can be found in
+`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, and
+`config_examples/config_freqai.example.json`, respectively.
+
+## General approach
+
+You provide FreqAI with a set of custom *base indicators* (the same way as in a [typical Freqtrade strategy](strategy-customization.md)) as well as target values (*labels*). For each pair in the whitelist, FreqAI trains a model to predict the target values based on the input of custom indicators. The models are then consistently retrained, with a predetermined frequency, to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining on historic data) and deploy dry/live runs. In dry/live conditions, FreqAI can be set to constant retraining in a background thread to keep models as up to date as possible.
+
+An overview of the algorithm, explaining the data processing pipeline and model usage, is shown below.
+
+![freqai-algo](assets/freqai_algo.jpg)
+
+### Important machine learning vocabulary
+
+**Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle are stored as a vector. In FreqAI, you build a feature data set from anything you can construct in the strategy.
+
+**Labels** - the target values that the model is trained toward. Each feature vector is associated with a single label that is defined by you within the strategy. These labels intentionally look into the future and are what you are training the model to be able to predict.
+
+**Training** - the process of "teaching" the model to match the feature sets to the associated labels. Different types of models "learn" in different ways which means that one might be better than another for a specific application. More information about the different models that are already implemented in FreqAI can be found [here](freqai-configuration.md#using-different-prediction-models).
+
+**Train data** - a subset of the feature data set that is fed to the model during training to "teach" the model how to predict the targets. This data directly influences weight connections in the model.
+
+**Test data** - a subset of the feature data set that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model.
+
+**Inferencing** - the process of feeding a trained model new unseen data on which it will make a prediction. 
+
+## Install prerequisites
+
+The normal Freqtrade install process will ask if you wish to install FreqAI dependencies. You should reply "yes" to this question if you wish to use FreqAI. If you did not reply yes, you 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 it does not provide wheels for this platform.
+
+### Usage with docker
+
+If you are using docker, 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.
+
+## Common pitfalls
+
+FreqAI cannot be combined with dynamic `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. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume).
+
+## Credits
+
+FreqAI is developed by a group of individuals who all contribute specific skillsets to the project.
+
+Conception and software development:
+Robert Caulk @robcaulk
+
+Theoretical brainstorming and data analysis:
+Elin Törnquist @th0rntwig
+
+Code review and software architecture brainstorming:
+@xmatthias
+
+Software development:
+Wagner Costa @wagnercosta
+
+Beta testing and bug reporting:
+Stefan Gehring @bloodhunter4rc, @longyu, Andrew Lawless @paranoidandy, Pascal Schmidt @smidelis, Ryan McMullan @smarmau, Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza, Timothy Pogue @wizrds

docs/hyperopt.md

@@ -40,22 +40,25 @@ pip install -r requirements-hyperopt.txt
 ```
 usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                           [--userdir PATH] [-s NAME] [--strategy-path PATH]
-                          [-i TIMEFRAME] [--timerange TIMERANGE]
+                          [--recursive-strategy-search] [--freqaimodel NAME]
+                          [--freqaimodel-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 ...]] [--hyperopt-path PATH]
                           [--eps] [--dmmp] [--enable-protections]
-                          [--dry-run-wallet DRY_RUN_WALLET] [-e INT]
+                          [--dry-run-wallet DRY_RUN_WALLET]
+                          [--timeframe-detail TIMEFRAME_DETAIL] [-e INT]
                           [--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]]
                           [--print-all] [--no-color] [--print-json] [-j JOBS]
                           [--random-state INT] [--min-trades INT]
                           [--hyperopt-loss NAME] [--disable-param-export]
-                          [--ignore-missing-spaces]
+                          [--ignore-missing-spaces] [--analyze-per-epoch]
 
 optional arguments:
   -h, --help            show this help message and exit
-  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
+  -i TIMEFRAME, --timeframe TIMEFRAME
                         Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
   --timerange TIMERANGE
                         Specify what timerange of data to use.
@@ -89,6 +92,9 @@ optional arguments:
   --dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
                         Starting balance, used for backtesting / hyperopt and
                         dry-runs.
+  --timeframe-detail TIMEFRAME_DETAIL
+                        Specify detail timeframe for backtesting (`1m`, `5m`,
+                        `30m`, `1h`, `1d`).
   -e INT, --epochs INT  Specify number of epochs (default: 100).
   --spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]
                         Specify which parameters to hyperopt. Space-separated
@@ -116,12 +122,15 @@ optional arguments:
                         ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
                         SharpeHyperOptLoss, SharpeHyperOptLossDaily,
                         SortinoHyperOptLoss, SortinoHyperOptLossDaily,
-                        MaxDrawDownHyperOptLoss
+                        CalmarHyperOptLoss, MaxDrawDownHyperOptLoss,
+                        MaxDrawDownRelativeHyperOptLoss,
+                        ProfitDrawDownHyperOptLoss
   --disable-param-export
                         Disable automatic hyperopt parameter export.
   --ignore-missing-spaces, --ignore-unparameterized-spaces
                         Suppress errors for any requested Hyperopt spaces that
                         do not contain any parameters.
+  --analyze-per-epoch   Run populate_indicators once per epoch.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -144,6 +153,12 @@ Strategy arguments:
                         Specify strategy class name which will be used by the
                         bot.
   --strategy-path PATH  Specify additional strategy lookup path.
+  --recursive-strategy-search
+                        Recursively search for a strategy in the strategies
+                        folder.
+  --freqaimodel NAME    Specify a custom freqaimodels.
+  --freqaimodel-path PATH
+                        Specify additional lookup path for freqaimodels.
 
 ```
 
@@ -153,8 +168,8 @@ Checklist on all tasks / possibilities in hyperopt
 
 Depending on the space you want to optimize, only some of the below are required:
 
-* define parameters with `space='buy'` - for buy signal optimization
-* define parameters with `space='sell'` - for sell signal optimization
+* define parameters with `space='buy'` - for entry signal optimization
+* define parameters with `space='sell'` - for exit signal optimization
 
 !!! Note
     `populate_indicators` needs to create all indicators any of the spaces may use, otherwise hyperopt will not work.
@@ -176,11 +191,11 @@ Rarely you may also need to create a [nested class](advanced-hyperopt.md#overrid
 
 ### Hyperopt execution logic
 
-Hyperopt will first load your data into memory and will then run `populate_indicators()` once per Pair to generate all indicators.
+Hyperopt will first load your data into memory and will then run `populate_indicators()` once per Pair to generate all indicators, unless `--analyze-per-epoch` is specified.
 
 Hyperopt will then spawn into different processes (number of processors, or `-j <n>`), and run backtesting over and over again, changing the parameters that are part of the `--spaces` defined.
 
-For every new set of parameters, freqtrade will run first `populate_buy_trend()` followed by `populate_sell_trend()`, and then run the regular backtesting process to simulate trades.
+For every new set of parameters, freqtrade will run first `populate_entry_trend()` followed by `populate_exit_trend()`, and then run the regular backtesting process to simulate trades.
 
 After backtesting, the results are passed into the [loss function](#loss-functions), which will evaluate if this result was better or worse than previous results.  
 Based on the loss function result, hyperopt will determine the next set of parameters to try in the next round of backtesting.
@@ -190,7 +205,7 @@ Based on the loss function result, hyperopt will determine the next set of param
 There are two places you need to change in your strategy file to add a new buy hyperopt for testing:
 
 * Define the parameters at the class level hyperopt shall be optimizing.
-* Within `populate_buy_trend()` - use defined parameter values instead of raw constants.
+* Within `populate_entry_trend()` - use defined parameter values instead of raw constants.
 
 There you have two different types of indicators: 1. `guards` and 2. `triggers`.
 
@@ -200,25 +215,25 @@ There you have two different types of indicators: 1. `guards` and 2. `triggers`.
 !!! Hint "Guards and Triggers"
     Technically, there is no difference between Guards and Triggers.  
     However, this guide will make this distinction to make it clear that signals should not be "sticking".
-    Sticking signals are signals that are active for multiple candles. This can lead into buying a signal late (right before the signal disappears - which means that the chance of success is a lot lower than right at the beginning).
+    Sticking signals are signals that are active for multiple candles. This can lead into entering a signal late (right before the signal disappears - which means that the chance of success is a lot lower than right at the beginning).
 
 Hyper-optimization will, for each epoch round, pick one trigger and possibly multiple guards.
 
-#### Sell optimization
+#### Exit signal optimization
 
-Similar to the buy-signal above, sell-signals can also be optimized.
+Similar to the entry-signal above, exit-signals can also be optimized.
 Place the corresponding settings into the following methods
 
 * Define the parameters at the class level hyperopt shall be optimizing, either naming them `sell_*`, or by explicitly defining `space='sell'`.
-* Within `populate_sell_trend()` - use defined parameter values instead of raw constants.
+* Within `populate_exit_trend()` - use defined parameter values instead of raw constants.
 
 The configuration and rules are the same than for buy signals.
 
 ## Solving a Mystery
 
-Let's say you are curious: should you use MACD crossings or lower Bollinger Bands to trigger your buys. 
-And you also wonder should you use RSI or ADX to help with those buy decisions. 
-If you decide to use RSI or ADX, which values should I use for them? 
+Let's say you are curious: should you use MACD crossings or lower Bollinger Bands to trigger your long entries.
+And you also wonder should you use RSI or ADX to help with those decisions.
+If you decide to use RSI or ADX, which values should I use for them?
 
 So let's use hyperparameter optimization to solve this mystery.
 
@@ -269,12 +284,13 @@ The last one we call `trigger` and use it to decide which buy trigger we want to
 
 !!! Note "Parameter space assignment"
     Parameters must either be assigned to a variable named `buy_*` or `sell_*` - or contain `space='buy'` | `space='sell'` to be assigned to a space correctly.
-    If no parameter is available for a space, you'll receive the error that no space was found when running hyperopt.
+    If no parameter is available for a space, you'll receive the error that no space was found when running hyperopt.  
+    Parameters with unclear space (e.g. `adx_period = IntParameter(4, 24, default=14)` - no explicit nor implicit space) will not be detected and will therefore be ignored.
 
 So let's write the buy strategy using these values:
 
 ```python
-    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         conditions = []
         # GUARDS AND TRENDS
         if self.buy_adx_enabled.value:
@@ -296,12 +312,12 @@ So let's write the buy strategy using these values:
         if conditions:
             dataframe.loc[
                 reduce(lambda x, y: x & y, conditions),
-                'buy'] = 1
+                'enter_long'] = 1
 
         return dataframe
 ```
 
-Hyperopt will now call `populate_buy_trend()` many times (`epochs`) with different value combinations.  
+Hyperopt will now call `populate_entry_trend()` many times (`epochs`) with different value combinations.  
 It will use the given historical data and simulate buys based on the buy signals generated with the above function.  
 Based on the results, hyperopt will tell you which parameter combination produced the best results (based on the configured [loss function](#loss-functions)).
 
@@ -332,6 +348,7 @@ There are four parameter types each suited for different purposes.
 ## Optimizing an indicator parameter
 
 Assuming you have a simple strategy in mind - a EMA cross strategy (2 Moving averages crossing) - and you'd like to find the ideal parameters for this strategy.
+By default, we assume a stoploss of 5% - and a take-profit (`minimal_roi`) of 10% - which means freqtrade will sell the trade once 10% profit has been reached.
 
 ``` python
 from pandas import DataFrame
@@ -346,6 +363,9 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
 class MyAwesomeStrategy(IStrategy):
     stoploss = -0.05
     timeframe = '15m'
+    minimal_roi = {
+        "0":  0.10
+    },
     # Define the parameter spaces
     buy_ema_short = IntParameter(3, 50, default=5)
     buy_ema_long = IntParameter(15, 200, default=50)
@@ -364,7 +384,7 @@ class MyAwesomeStrategy(IStrategy):
         
         return dataframe
 
-    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         conditions = []
         conditions.append(qtpylib.crossed_above(
                 dataframe[f'ema_short_{self.buy_ema_short.value}'], dataframe[f'ema_long_{self.buy_ema_long.value}']
@@ -376,11 +396,11 @@ class MyAwesomeStrategy(IStrategy):
         if conditions:
             dataframe.loc[
                 reduce(lambda x, y: x & y, conditions),
-                'buy'] = 1
+                'enter_long'] = 1
         return dataframe
 
-    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        conditions = []
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+          conditions = []
         conditions.append(qtpylib.crossed_above(
                 dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}']
             ))
@@ -391,7 +411,7 @@ class MyAwesomeStrategy(IStrategy):
         if conditions:
             dataframe.loc[
                 reduce(lambda x, y: x & y, conditions),
-                'sell'] = 1
+                'exit_long'] = 1
         return dataframe
 ```
 
@@ -401,7 +421,7 @@ Using `self.buy_ema_short.range` will return a range object containing all entri
 In this case (`IntParameter(3, 50, default=5)`), the loop would run for all numbers between 3 and 50 (`[3, 4, 5, ... 49, 50]`).
 By using this in a loop, hyperopt will generate 48 new columns (`['buy_ema_3', 'buy_ema_4', ... , 'buy_ema_50']`).
 
-Hyperopt itself will then use the selected value to create the buy and sell signals
+Hyperopt itself will then use the selected value to create the buy and sell signals.
 
 While this strategy is most likely too simple to provide consistent profit, it should serve as an example how optimize indicator parameters.
 
@@ -412,9 +432,10 @@ While this strategy is most likely too simple to provide consistent profit, it s
     `range` property may also be used with `DecimalParameter` and `CategoricalParameter`. `RealParameter` does not provide this property due to infinite search space.
 
 ??? Hint "Performance tip"
-    By doing the calculation of all possible indicators in `populate_indicators()`, the calculation of the indicator happens only once for every parameter.  
-    While this may slow down the hyperopt startup speed, the overall performance will increase as the Hyperopt execution itself may pick the same value for multiple epochs (changing other values).
-    You should however try to use space ranges as small as possible. Every new column will require more memory, and every possibility hyperopt can try will increase the search space.
+    During normal hyperopting, indicators are calculated once and supplied to each epoch, linearly increasing RAM usage as a factor of increasing cores. As this also has performance implications, hyperopt provides `--analyze-per-epoch` which will move the execution of `populate_indicators()` to the epoch process, calculating a single value per parameter per epoch instead of using the `.range` functionality. In this case, `.range` functionality will only return the actually used value. This will reduce RAM usage, but increase CPU usage. However, your hyperopting run will be less likely to fail due to Out Of Memory (OOM) issues.
+
+    In either case, you should try to use space ranges as small as possible this will improve CPU/RAM usage in both scenarios.
+
 
 ## Optimizing protections
 
@@ -508,6 +529,46 @@ class MyAwesomeStrategy(IStrategy):
 
 You will then obviously also change potential interesting entries to parameters to allow hyper-optimization.
 
+### Optimizing `max_entry_position_adjustment`
+
+While `max_entry_position_adjustment` is not a separate space, it can still be used in hyperopt by using the property approach shown above.
+
+``` python
+from pandas import DataFrame
+from functools import reduce
+
+import talib.abstract as ta
+
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, 
+                                IStrategy, IntParameter)
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+class MyAwesomeStrategy(IStrategy):
+    stoploss = -0.05
+    timeframe = '15m'
+
+    # Define the parameter spaces
+    max_epa = CategoricalParameter([-1, 0, 1, 3, 5, 10], default=1, space="buy", optimize=True)
+
+    @property
+    def max_entry_position_adjustment(self):
+        return self.max_epa.value
+        
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # ...
+```
+
+??? Tip "Using `IntParameter`"
+    You can also use the `IntParameter` for this optimization, but you must explicitly return an integer:
+    ``` python
+    max_epa = IntParameter(-1, 10, default=1, space="buy", optimize=True)
+
+    @property
+    def max_entry_position_adjustment(self):
+        return int(self.max_epa.value)
+    ```
+
 ## Loss-functions
 
 Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
@@ -523,7 +584,10 @@ Currently, the following loss functions are builtin:
 * `SharpeHyperOptLossDaily` - optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation.
 * `SortinoHyperOptLoss` - optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation.
 * `SortinoHyperOptLossDaily` - optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation.
-* `MaxDrawDownHyperOptLoss` - Optimizes Maximum drawdown.
+* `MaxDrawDownHyperOptLoss` - Optimizes Maximum absolute drawdown.
+* `MaxDrawDownRelativeHyperOptLoss` -  Optimizes both maximum absolute drawdown while also adjusting for maximum relative drawdown.
+* `CalmarHyperOptLoss` - Optimizes Calmar Ratio calculated on trade returns relative to max drawdown.
+* `ProfitDrawDownHyperOptLoss` - Optimizes by max Profit & min Drawdown objective. `DRAWDOWN_MULT` variable within the hyperoptloss file can be adjusted to be stricter or more flexible on drawdown purposes.
 
 Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
 
@@ -635,7 +699,7 @@ class MyAwesomeStrategy(IStrategy):
 
 !!! Note
     Values in the configuration file will overwrite Parameter-file level parameters - and both will overwrite parameters within the strategy.
-    The prevalence is therefore: config > parameter file > strategy
+    The prevalence is therefore: config > parameter file > strategy `*_params` > parameter default
 
 ### Understand Hyperopt ROI results
 
@@ -817,10 +881,29 @@ You can also enable position stacking in the configuration file by explicitly se
 As hyperopt consumes a lot of memory (the complete data needs to be in memory once per parallel backtesting process), it's likely that you run into "out of memory" errors.
 To combat these, you have multiple options:
 
-* reduce the amount of pairs
-* reduce the timerange used (`--timerange <timerange>`)
-* reduce the number of parallel processes (`-j <n>`)
-* Increase the memory of your machine
+* Reduce the amount of pairs.
+* Reduce the timerange used (`--timerange <timerange>`).
+* Avoid using `--timeframe-detail` (this loads a lot of additional data into memory).
+* Reduce the number of parallel processes (`-j <n>`).
+* Increase the memory of your machine.
+* Use `--analyze-per-epoch` if you're using a lot of parameters with `.range` functionality.
+
+
+## The objective has been evaluated at this point before.
+
+If you see `The objective has been evaluated at this point before.` - then this is a sign that your space has been exhausted, or is close to that.
+Basically all points in your space have been hit (or a local minima has been hit) - and hyperopt does no longer find points in the multi-dimensional space it did not try yet.
+Freqtrade tries to counter the "local minima" problem by using new, randomized points in this case.
+
+Example:
+
+``` python
+buy_ema_short = IntParameter(5, 20, default=10, space="buy", optimize=True)
+# This is the only parameter in the buy space
+```
+
+The `buy_ema_short` space has 15 possible values (`5, 6, ... 19, 20`). If you now run hyperopt for the buy space, hyperopt will only have 15 values to try before running out of options.
+Your epochs should therefore be aligned to the possible values - or you should be ready to interrupt a run if you norice a lot of `The objective has been evaluated at this point before.` warnings.
 
 ## Show details of Hyperopt results
 

docs/includes/pairlists.md

@@ -22,6 +22,7 @@ You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged
 
 * [`StaticPairList`](#static-pair-list) (default, if not configured differently)
 * [`VolumePairList`](#volume-pair-list)
+* [`ProducerPairList`](#producerpairlist)
 * [`AgeFilter`](#agefilter)
 * [`OffsetFilter`](#offsetfilter)
 * [`PerformanceFilter`](#performancefilter)
@@ -44,7 +45,7 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
 ```json
 "pairlists": [
     {"method": "StaticPairList"}
-    ],
+],
 ```
 
 By default, only currently enabled pairs are allowed.
@@ -84,7 +85,7 @@ Filtering instances (not the first position in the list) will not apply any cach
 
 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 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.
 
@@ -146,6 +147,32 @@ More sophisticated approach can be used, by using `lookback_timeframe` for candl
 !!! Note
     `VolumePairList` does not support backtesting mode.
 
+#### ProducerPairList
+
+With `ProducerPairList`, you can reuse the pairlist from a [Producer](producer-consumer.md) without explicitly defining the pairlist on each consumer.
+
+[Consumer mode](producer-consumer.md) is required for this pairlist to work.
+
+The pairlist will perform a check on active pairs against the current exchange configuration to avoid attempting to trade on invalid markets.
+
+You can limit the length of the pairlist with the optional parameter `number_assets`. Using `"number_assets"=0` or omitting this key will result in the reuse of all producer pairs valid for the current setup.
+
+```json
+"pairlists": [
+    {
+        "method": "ProducerPairList",
+        "number_assets": 5,
+        "producer_name": "default",
+    }
+],
+```
+
+
+!!! Tip "Combining pairlists"
+    This pairlist can be combined with all other pairlists and filters for further pairlist reduction, and can also act as an "additional" pairlist, on top of already defined pairs.
+    `ProducerPairList` can also be used multiple times in sequence, combining the pairs from multiple producers.
+    Obviously in complex such configurations, the Producer may not provide data for all pairs, so the strategy must be fit for this.
+
 #### 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).
@@ -160,17 +187,17 @@ This filter allows freqtrade to ignore pairs until they have been listed for at
 
 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.
+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:
+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
+        "offset": 10,
+        "number_assets": 20
     }
 ],
 ```
@@ -181,7 +208,7 @@ Example to remove the first 10 pairs from the pairlist:
     `VolumeFilter`.
 
 !!! Note
-    An offset larger then the total length of the incoming pairlist will result in an empty pairlist.
+    An offset larger than the total length of the incoming pairlist will result in an empty pairlist.
 
 #### PerformanceFilter
 
@@ -196,9 +223,9 @@ 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` parameter defines the minimum profit a pair must have to be considered.
+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 without a way to recover.
+Using this parameter without `minutes` is highly discouraged, as it can lead to an empty pairlist without a way to recover.
 
 ```json
 "pairlists": [
@@ -206,11 +233,13 @@ Using this parameter without `minutes` is highly discouraged, as it can lead to
     {
         "method": "PerformanceFilter",
         "minutes": 1440,  // rolling 24h
-        "min_profit": 0.01
+        "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.
 
@@ -218,6 +247,9 @@ Using this parameter without `minutes` is highly discouraged, as it can lead to
 
 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:
@@ -241,7 +273,7 @@ On exchanges that deduct fees from the receiving currency (e.g. FTX) - this can
 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 `PriceFiler` at least one of its `min_price`, `max_price` or `low_price_ratio` settings must be applied.
+For `PriceFilter` at least one of its `min_price`, `max_price` or `low_price_ratio` settings must be applied.
 
 Calculation example:
 
@@ -255,7 +287,7 @@ Min price precision for SHITCOIN/BTC is 8 decimals. If its price is 0.00000011 -
 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.
+    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
 
@@ -290,7 +322,7 @@ If the trading range over the last 10 days is <1% or >99%, remove the pair from
 
 #### VolatilityFilter
 
-Volatility is the degree of historical variation of a pairs over time, is 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)).
+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`.
 
@@ -344,5 +376,5 @@ The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets,
         "refresh_period": 86400
     },
     {"method": "ShuffleFilter", "seed": 42}
-    ],
+],
 ```

docs/includes/pricing.md

@@ -1,6 +1,6 @@
 ## Prices used for orders
 
-Prices for regular orders can be controlled via the parameter structures `bid_strategy` for buying and `ask_strategy` for selling.
+Prices 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
@@ -9,20 +9,11 @@ Prices are always retrieved right before an order is placed, either by querying
 !!! Warning "Using market orders"
     Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
 
-### Buy price
+### Entry price
 
-#### Check depth of market
+#### Enter price side
 
-When check depth of market is enabled (`bid_strategy.check_depth_of_market.enabled=True`), the buy signals are filtered based on the orderbook depth (sum of all amounts) for each orderbook side.
-
-Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) side depth and the resulting delta is compared to the value of the `bid_strategy.check_depth_of_market.bids_to_ask_delta` parameter. The buy order is only executed if the orderbook delta is greater than or equal to the configured delta value.
-
-!!! Note
-    A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
-
-#### Buy price side
-
-The configuration setting `bid_strategy.price_side` defines the side of the spread the bot looks for when buying.
+The configuration setting `entry_pricing.price_side` defines the side of the orderbook the bot looks for when buying.
 
 The following displays an orderbook.
 
@@ -38,30 +29,53 @@ The following displays an orderbook.
 ...
 ```
 
-If `bid_strategy.price_side` is set to `"bid"`, then the bot will use 99 as buying price.  
-In line with that, if `bid_strategy.price_side` is set to `"ask"`, then the bot will use 101 as buying price.
+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.
 
-Using `ask` price often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
+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 "ask" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
+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).
 
-#### Buy price with Orderbook enabled
+#### Entry price with Orderbook enabled
 
-When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and uses the entry specified as `bid_strategy.order_book_top` on the configured side (`bid_strategy.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
+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.
 
-#### Buy price without Orderbook enabled
+#### Entry price without Orderbook enabled
 
-The following section uses `side` as the configured `bid_strategy.price_side`.
+The following section uses `side` as the configured `entry_pricing.price_side` (defaults to `"same"`).
 
-When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
+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 `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
+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.
 
-### Sell price
+#### Check depth of market
 
-#### Sell price side
+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.
 
-The configuration setting `ask_strategy.price_side` defines the side of the spread the bot looks for when selling.
+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:
 
@@ -77,40 +91,54 @@ The following displays an orderbook:
 ...
 ```
 
-If `ask_strategy.price_side` is set to `"ask"`, then the bot will use 101 as selling price.  
-In line with that, if `ask_strategy.price_side` is set to `"bid"`, then the bot will use 99 as selling price.
+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.
 
-#### Sell price with Orderbook enabled
+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:
 
-When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_top` entries in the orderbook and uses the entry specified as `ask_strategy.order_book_top` from the configured side (`ask_strategy.price_side`) as selling price.
+| 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.
 
-#### Sell price without Orderbook enabled
+#### Exit price without Orderbook enabled
 
-When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
+The following section uses `side` as the configured `exit_pricing.price_side` (defaults to `"ask"`).
 
-When not using orderbook (`ask_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
+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 `ask_strategy.bid_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.
+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 buy and sell are using market orders, a configuration similar to the following might be used
+Assuming both entry and exits are using market orders, a configuration similar to the following must be used
 
 ``` jsonc
   "order_types": {
-    "buy": "market",
-    "sell": "market"
+    "entry": "market",
+    "exit": "market"
     // ...
   },
-  "bid_strategy": {
-    "price_side": "ask",
+  "entry_pricing": {
+    "price_side": "other",
     // ...
   },
-  "ask_strategy":{
-    "price_side": "bid",
+  "exit_pricing":{
+    "price_side": "other",
     // ...
   },
 ```

docs/includes/protections.md

@@ -48,6 +48,10 @@ If `trade_limit` or more trades resulted in stoploss, trading will stop for `sto
 
 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
@@ -59,7 +63,9 @@ def protections(self):
             "lookback_period_candles": 24,
             "trade_limit": 4,
             "stop_duration_candles": 4,
-            "only_per_pair": False
+            "required_profit": 0.0,
+            "only_per_pair": False,
+            "only_per_side": False
         }
     ]
 ```
@@ -93,6 +99,8 @@ def protections(self):
 `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
@@ -104,7 +112,8 @@ def protections(self):
             "lookback_period_candles": 6,
             "trade_limit": 2,
             "stop_duration": 60,
-            "required_profit": 0.02
+            "required_profit": 0.02,
+            "only_per_pair": False,
         }
     ]
 ```

docs/index.md

@@ -11,7 +11,7 @@
 
 ## Introduction
 
-Freqtrade is a crypto-currency algorithmic trading software developed in python (3.7+) 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.
@@ -20,6 +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).
@@ -29,20 +31,30 @@ 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/) ([*Note for binance users](docs/exchanges.md#binance-blacklist))
+- [X] [Binance](https://www.binance.com/)
 - [X] [Bittrex](https://bittrex.com/)
-- [X] [FTX](https://ftx.com)
-- [X] [Kraken](https://kraken.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:
@@ -66,7 +78,7 @@ To run this bot we recommend you a linux cloud instance with a minimum of:
 
 Alternatively
 
-- Python 3.7+
+- Python 3.8+
 - pip (pip3)
 - git
 - TA-Lib
@@ -80,4 +92,4 @@ For any questions not covered by the documentation or for further information ab
 
 ## Ready to try?
 
-Begin by reading our installation guide [for docker](docker_quickstart.md) (recommended), 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

@@ -24,7 +24,7 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
     The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
 
 !!! Note
-    Python3.7 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.  
+    Python3.8 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"
@@ -36,9 +36,13 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
 
 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 >= 3.7.x](http://docs.python-guide.org/en/latest/starting/installation/)
+* [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)
@@ -50,7 +54,7 @@ We've included/collected install instructions for Ubuntu, MacOS, and Windows. Th
 OS Specific steps are listed first, the [Common](#common) section below is necessary for all systems.
 
 !!! Note
-    Python3.7 or higher and the corresponding pip are assumed to be available.
+    Python3.8 or higher and the corresponding pip are assumed to be available.
 
 === "Debian/Ubuntu"
     #### Install necessary dependencies
@@ -60,18 +64,18 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
     sudo apt-get update
 
     # install packages
-    sudo apt install -y python3-pip python3-venv python3-dev python3-pandas git
+    sudo apt install -y python3-pip python3-venv python3-dev python3-pandas git curl
     ```
 
 === "RaspberryPi/Raspbian"
     The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/).
-    This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
+    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
+    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
 
@@ -165,7 +169,7 @@ You can as well update, configure and reset the codebase of your bot with `./scr
 ** --install **
 
 With this option, the script will install the bot and most dependencies:
-You will need to have git and python3.7+ 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/`
@@ -322,6 +326,16 @@ 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
@@ -416,16 +430,3 @@ open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10
 ```
 
 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.
-
-### MacOS installation error with python 3.9
-
-When using python 3.9 on macOS, it's currently necessary to install some os-level modules to allow dependencies to compile.
-The errors you'll see happen during installation and are related to the installation of `tables` or `blosc`.
-
-You can install the necessary libraries with the following command:
-
-```bash
-brew install hdf5 c-blosc
-```
-
-After this, please run the installation (script) again.

docs/javascripts/config.js

@@ -9,4 +9,4 @@ window.MathJax = {
     ignoreHtmlClass: ".*|",
     processHtmlClass: "arithmatex"
   }
-};
+};

docs/leverage.md

@@ -0,0 +1,143 @@
+# 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.
+
+If you already have an existing strategy, 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 strategy of version 3 which 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"
+```
+
+##### Pair namings
+
+Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/en/latest/manual.html?#perpetual-swap-perpetual-future).
+A futures pair will therefore have the naming of `base/quote:settle` (e.g. `ETH/USDT:USDT`).
+
+Binance is currently still an exception to this naming scheme, where pairs are named `ETH/USDT` also for futures markets, but will be aligned as soon as CCXT is ready.
+
+### 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/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`
 
@@ -65,7 +65,7 @@ optional arguments:
                         _today.json`
   --timerange TIMERANGE
                         Specify what timerange of data to use.
-  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
+  -i TIMEFRAME, --timeframe TIMEFRAME
                         Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
   --no-trades           Skip using trades from backtesting file and DB.
 
@@ -96,7 +96,7 @@ Strategy arguments:
 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
 ```
@@ -164,16 +161,17 @@ 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 
+* 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 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.
 
@@ -182,40 +180,89 @@ Extra parameters to `plotly.graph_objects.*` constructor can be specified in `pl
 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': {},
-	    # 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': {}
+@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', 'fill_to': 'macdhist'},
-                'macdsignal': {'color': 'orange'},
-                'macdhist': {'type': 'bar', 'plotly': {'opacity': 0.9}}
-            },
-            # 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`, `senkou_a`, `senkou_b`,
     `macd`, `macdsignal`, `macdhist` and `rsi` are columns in the DataFrame created by the strategy.
@@ -223,6 +270,9 @@ Sample configuration with inline comments explaining the process:
 !!! 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
 
 ![plot-profit](assets/plot-profit.png)
@@ -233,6 +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.
 
@@ -242,6 +294,8 @@ 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:
 
 ```
@@ -261,8 +315,8 @@ optional arguments:
                         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`
@@ -273,7 +327,7 @@ 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
+  -i TIMEFRAME, --timeframe TIMEFRAME
                         Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
   --auto-open           Automatically open generated plot.
 

docs/producer-consumer.md

@@ -0,0 +1,163 @@
+# Producer / Consumer mode
+
+freqtrade provides a mechanism whereby an instance (also called `consumer`) may listen to messages from an upstream freqtrade instance (also called `producer`) using the message websocket. Mainly, `analyzed_df` and `whitelist` messages. This allows the reuse of computed indicators (and signals) for pairs in multiple bots without needing to compute them multiple times.
+
+See [Message Websocket](rest-api.md#message-websocket) in the Rest API docs for setting up the `api_server` configuration for your message websocket (this will be your producer).
+
+!!! Note
+    We strongly recommend to set `ws_token` to something random and known only to yourself to avoid unauthorized access to your bot.
+
+## Configuration
+
+Enable subscribing to an instance by adding the `external_message_consumer` section to the consumer's config file.
+
+```json
+{
+    //...
+   "external_message_consumer": {
+        "enabled": true,
+        "producers": [
+            {
+                "name": "default", // This can be any name you'd like, default is "default"
+                "host": "127.0.0.1", // The host from your producer's api_server config
+                "port": 8080, // The port from your producer's api_server config
+                "ws_token": "sercet_Ws_t0ken" // The ws_token from your producer's api_server config
+            }
+        ],
+        // The following configurations are optional, and usually not required
+        // "wait_timeout": 300,
+        // "ping_timeout": 10,
+        // "sleep_time": 10,
+        // "remove_entry_exit_signals": false,
+        // "message_size_limit": 8
+    }
+    //...
+}
+```
+
+|  Parameter | Description |
+|------------|-------------|
+| `enabled` | **Required.** Enable consumer mode. If set to false, all other settings in this section are ignored.<br>*Defaults to `false`.*<br> **Datatype:** boolean .
+| `producers` | **Required.** List of producers <br> **Datatype:** Array.
+| `producers.name` | **Required.** Name of this producer. This name must be used in calls to `get_producer_pairs()` and `get_producer_df()` if more than one producer is used.<br> **Datatype:** string
+| `producers.host` | **Required.** The hostname or IP address from your producer.<br> **Datatype:** string
+| `producers.port` | **Required.** The port matching the above host.<br> **Datatype:** string
+| `producers.ws_token` | **Required.**  `ws_token` as configured on the producer.<br> **Datatype:** string
+| | **Optional settings**
+| `wait_timeout` | Timeout until we ping again if no message is received. <br>*Defaults to `300`.*<br> **Datatype:** Integer - in seconds.
+| `wait_timeout` | Ping timeout <br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
+| `sleep_time` | Sleep time before retrying to connect.<br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
+| `remove_entry_exit_signals` | Remove signal columns from the dataframe (set them to 0) on dataframe receipt.<br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
+| `message_size_limit` | Size limit per message<br>*Defaults to `8`.*<br> **Datatype:** Integer - Megabytes.
+
+Instead of (or as well as) calculating indicators in `populate_indicators()` the follower instance listens on the connection to a producer instance's messages (or multiple producer instances in advanced configurations) and requests the producer's most recently analyzed dataframes for each pair in the active whitelist.
+
+A consumer instance will then have a full copy of the analyzed dataframes without the need to calculate them itself.
+
+## Examples
+
+### Example - Producer Strategy
+
+A simple strategy with multiple indicators. No special considerations are required in the strategy itself.
+
+```py
+class ProducerStrategy(IStrategy):
+    #...
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Calculate indicators in the standard freqtrade way which can then be broadcast to other instances
+        """
+        dataframe['rsi'] = ta.RSI(dataframe)
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Populates the entry signal for the given dataframe
+        """
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &
+                (dataframe['tema'] <= dataframe['bb_middleband']) &
+                (dataframe['tema'] > dataframe['tema'].shift(1)) &
+                (dataframe['volume'] > 0)
+            ),
+            'enter_long'] = 1
+
+        return dataframe
+```
+
+!!! Tip "FreqAI"
+    You can use this to setup [FreqAI](freqai.md) on a powerful machine, while you run consumers on simple machines like raspberries, which can interpret the signals generated from the producer in different ways.
+
+
+### Example - Consumer Strategy
+
+A logically equivalent strategy which calculates no indicators itself, but will have the same analyzed dataframes available to make trading decisions based on the indicators calculated in the producer. In this example the consumer has the same entry criteria, however this is not necessary. The consumer may use different logic to enter/exit trades, and only use the indicators as specified.
+
+```py
+class ConsumerStrategy(IStrategy):
+    #...
+    process_only_new_candles = False # required for consumers
+
+    _columns_to_expect = ['rsi_default', 'tema_default', 'bb_middleband_default']
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Use the websocket api to get pre-populated indicators from another freqtrade instance.
+        Use `self.dp.get_producer_df(pair)` to get the dataframe
+        """
+        pair = metadata['pair']
+        timeframe = self.timeframe
+
+        producer_pairs = self.dp.get_producer_pairs()
+        # You can specify which producer to get pairs from via:
+        # self.dp.get_producer_pairs("my_other_producer")
+
+        # This func returns the analyzed dataframe, and when it was analyzed
+        producer_dataframe, _ = self.dp.get_producer_df(pair)
+        # You can get other data if the producer makes it available:
+        # self.dp.get_producer_df(
+        #   pair,
+        #   timeframe="1h",
+        #   candle_type=CandleType.SPOT,
+        #   producer_name="my_other_producer"
+        # )
+
+        if not producer_dataframe.empty:
+            # If you plan on passing the producer's entry/exit signal directly,
+            # specify ffill=False or it will have unintended results
+            merged_dataframe = merge_informative_pair(dataframe, producer_dataframe,
+                                                      timeframe, timeframe,
+                                                      append_timeframe=False,
+                                                      suffix="default")
+            return merged_dataframe
+        else:
+            dataframe[self._columns_to_expect] = 0
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Populates the entry signal for the given dataframe
+        """
+        # Use the dataframe columns as if we calculated them ourselves
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi_default'], self.buy_rsi.value)) &
+                (dataframe['tema_default'] <= dataframe['bb_middleband_default']) &
+                (dataframe['tema_default'] > dataframe['tema_default'].shift(1)) &
+                (dataframe['volume'] > 0)
+            ),
+            'enter_long'] = 1
+
+        return dataframe
+```
+
+!!! Tip "Using upstream signals"
+    By setting `remove_entry_exit_signals=false`, you can also use the producer's signals directly. They should be available as `enter_long_default` (assuming `suffix="default"` was used) - and can be used as either signal directly, or as additional indicator.

docs/requirements-docs.txt

@@ -1,4 +1,6 @@
-mkdocs==1.2.3
-mkdocs-material==7.3.4
-mdx_truly_sane_lists==1.2
-pymdown-extensions==9.0
+markdown==3.3.7
+mkdocs==1.4.0
+mkdocs-material==8.5.6
+mdx_truly_sane_lists==1.3
+pymdown-extensions==9.6
+jinja2==3.1.2

docs/rest-api.md

@@ -31,13 +31,19 @@ Sample configuration:
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
         "username": "Freqtrader",
-        "password": "SuperSecret1!"
+        "password": "SuperSecret1!",
+        "ws_token": "sercet_Ws_t0ken"
     },
 ```
 
 !!! 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.
 
+??? 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:
 
@@ -61,7 +67,7 @@ secrets.token_hex()
 
 !!! 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!). 
+    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
 
@@ -88,7 +94,6 @@ Make sure that the following 2 lines are available in your docker-compose file:
 !!! Danger "Security warning"
     By using `8080:8080` in the docker port mapping, the API will be available to everyone connecting to the server under the correct port, so others may be able to control your bot.
 
-
 ## Rest API
 
 ### Consuming the API
@@ -140,9 +145,10 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 | `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.
-| `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)
+| `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).
@@ -157,6 +163,8 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 | `strategy <strategy>` | Get specific Strategy content. **Alpha**
 | `available_pairs` | List available backtest data. **Alpha**
 | `version` | Show version.
+| `sysinfo` | Show informations about the system load.
+| `health` | Show bot health (last bot loop).
 
 !!! Warning "Alpha status"
     Endpoints labeled with *Alpha status* above may change at any time without notice.
@@ -210,10 +218,22 @@ forcebuy
         :param pair: Pair to buy (ETH/BTC)
         :param price: Optional - price to buy
 
-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)
+        :param ordertype: Order type to use (must be market or limit)
+        :param amount: Amount to sell. Full sell if not given
+
+health
+	Provides a quick health check of the running bot.
 
 locks
 	Return current locks
@@ -254,7 +274,7 @@ reload_config
 	Reload configuration.
 
 show_config
-	
+
         Returns part of the configuration, relevant for trading operations.
 
 start
@@ -280,6 +300,9 @@ strategy
 
         :param strategy: Strategy class name
 
+sysinfo
+	Provides system information (CPU, RAM usage)
+
 trade
 	Return specific trade
 
@@ -296,12 +319,80 @@ version
 
 whitelist
 	Show the current whitelist.
+
+```
+
+### Message WebSocket
+
+The API Server includes a websocket endpoint for subscribing to RPC messages from the freqtrade Bot.
+This can be used to consume real-time data from your bot, such as entry/exit fill messages, whitelist changes, populated indicators for pairs, and more.
+
+This is also used to setup [Producer/Consumer mode](producer-consumer.md) in Freqtrade.
+
+Assuming your rest API is set to `127.0.0.1` on port `8080`, the endpoint is available at `http://localhost:8080/api/v1/message/ws`.
+
+To access the websocket endpoint, the `ws_token` is required as a query parameter in the endpoint URL.
+
+To generate a safe `ws_token` you can run the following code:
+
+``` python
+>>> import secrets
+>>> secrets.token_urlsafe(25)
+'hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q'
+```
+
+You would then add that token under `ws_token` in your `api_server` config. Like so:
+
+``` json
+"api_server": {
+    "enabled": true,
+    "listen_ip_address": "127.0.0.1",
+    "listen_port": 8080,
+    "verbosity": "error",
+    "enable_openapi": false,
+    "jwt_secret_key": "somethingrandom",
+    "CORS_origins": [],
+    "username": "Freqtrader",
+    "password": "SuperSecret1!",
+    "ws_token": "hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q" // <-----
+},
+```
+
+You can now connect to the endpoint at `http://localhost:8080/api/v1/message/ws?token=hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q`.
+
+!!! Danger "Reuse of example tokens"
+    Please do not use the above example token. To make sure you are secure, generate a completely new token.
+
+#### Using the WebSocket
+
+Once connected to the WebSocket, the bot will broadcast RPC messages to anyone who is subscribed to them. To subscribe to a list of messages, you must send a JSON request through the WebSocket like the one below. The `data` key must be a list of message type strings.
+
+``` json
+{
+  "type": "subscribe",
+  "data": ["whitelist", "analyzed_df"] // A list of string message types
+}
+```
+
+For a list of message types, please refer to the RPCMessageType enum in `freqtrade/enums/rpcmessagetype.py`
+
+Now anytime those types of RPC messages are sent in the bot, you will receive them through the WebSocket as long as the connection is active. They typically take the same form as the request:
+
+``` json
+{
+  "type": "analyzed_df",
+  "data": {
+      "key": ["NEO/BTC", "5m", "spot"],
+      "df": {}, // The dataframe
+      "la": "2022-09-08 22:14:41.457786+00:00"
+  }
+}
 ```
 
 ### 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.
+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
 
@@ -330,12 +421,15 @@ Since the access token has a short timeout (15 min) - the `token/refresh` reques
 
 ### CORS
 
-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.
+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:
 
@@ -348,5 +442,19 @@ 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

@@ -104,16 +104,16 @@ To mitigate this, you can try to match the first order on the opposite orderbook
 
 ``` jsonc
   "order_types": {
-    "buy": "limit",
-    "sell": "limit"
+    "entry": "limit",
+    "exit": "limit"
     // ...
   },
-  "bid_strategy": {
-    "price_side": "ask",
+  "entry_pricing": {
+    "price_side": "other",
     // ...
   },
-  "ask_strategy":{
-    "price_side": "bid",
+  "exit_pricing":{
+    "price_side": "other",
     // ...
   },
   ```

docs/sql_cheatsheet.md

@@ -49,14 +49,14 @@ sqlite3
 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.  
+    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
@@ -65,7 +65,7 @@ SET is_open=0,
   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>
+  exit_reason=<exit_reason>
 WHERE id=<trade_ID_to_update>;
 ```
 
@@ -78,7 +78,7 @@ SET is_open=0,
   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'  
+  exit_reason='force_exit'  
 WHERE id=31;
 ```
 
@@ -89,11 +89,12 @@ WHERE id=31;
 
 If you'd still like to remove a trade from the database directly, you can use the below query.
 
-```sql
-DELETE FROM trades WHERE id = <tradeid>;
-```
+!!! 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
+DELETE FROM trades WHERE id = <tradeid>;
+
 DELETE FROM trades WHERE id = 31;
 ```
 
@@ -102,13 +103,20 @@ DELETE FROM trades WHERE id = 31;
 
 ## 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. Freqtrade will not provide any support with setup or maintenance (or backups) of the below database systems.
+    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
 
-Freqtrade supports PostgreSQL by using SQLAlchemy, which supports multiple different database systems.
-
 Installation:
 `pip install psycopg2-binary`
 

docs/stoploss.md

@@ -2,6 +2,7 @@
 
 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.
 
@@ -16,21 +17,21 @@ Those stoploss modes can be *on exchange* or *off exchange*.
 These modes can be configured with these values:
 
 ``` python
-    'emergencysell': 'market',
+    '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), Kraken (stop-loss-market, stop-loss-limit) and FTX (stop limit and stop-market) as of now.  
+    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 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.
+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.  
@@ -51,30 +52,30 @@ The bot cannot do these every 5 seconds (at each iteration), otherwise it would
 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.
 
-### forcesell
+### force_exit
 
-`forcesell` is an optional value, which defaults to the same value as `sell` and is used when sending a `/forcesell` command from Telegram or from the Rest API.
+`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.
 
-### forcebuy
+### force_entry
 
-`forcebuy` is an optional value, which defaults to the same value as `buy` and is used when sending a `/forcebuy` command from Telegram or from the Rest API.
+`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.
 
-### emergencysell
+### emergency_exit
 
-`emergencysell` is an optional value, which defaults to `market` and is used when creating stop loss on exchange orders fails.
+`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 = {
-    'buy': 'limit',
-    'sell': 'limit',
-    'emergencysell': 'market',
-    'stoploss': 'market',
-    'stoploss_on_exchange': True,
-    'stoploss_on_exchange_interval': 60,
-    'stoploss_on_exchange_limit_ratio': 0.99
+    "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
 }
 ```
 
@@ -129,7 +130,7 @@ In summary: The stoploss will be adjusted to be always be -10% of the highest ob
 
 ### 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 (buy - fee), but once you hit positive result the system will utilize a new stop loss, which can have a different value.
+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.
 
 !!! Note
@@ -141,6 +142,8 @@ Both values require `trailing_stop` to be set to true and `trailing_stop_positiv
     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
 ```
 
 For example, simplified math:
@@ -155,11 +158,31 @@ For example, simplified math:
 The 0.02 would translate to a -2% stop loss.
 Before this, `stoploss` is used for the trailing stoploss.
 
+!!! 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.
+
+    Example with simplified math:
+
+    ``` python
+        stoploss = -0.10
+        trailing_stop = True
+        trailing_stop_positive = 0.02
+        trailing_stop_positive_offset = 0.03
+    ```
+
+    * 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$
+
 ### Trailing stop loss only once the trade has reached a certain offset
 
-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.
+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`.
+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
@@ -182,7 +205,7 @@ 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$
-* stoploss will remain at 90$ unless asset increases to or above our configured offset
+* 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$
@@ -190,6 +213,18 @@ For example, simplified math:
 !!! 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
 
 A stoploss on an open trade can be changed by changing the value in the configuration or strategy and use the `/reload_config` command (alternatively, completely stopping and restarting the bot also works).

docs/strategy-advanced.md

@@ -49,7 +49,7 @@ from freqtrade.exchange import timeframe_to_prev_date
 
 class AwesomeStrategy(IStrategy):
     def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
-                           rate: float, time_in_force: str, sell_reason: str,
+                           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)
@@ -77,578 +77,82 @@ class AwesomeStrategy(IStrategy):
 
 ***
 
-## Custom sell signal
-
-It is possible to define custom sell signals, indicating that specified position should be sold. This is very useful when we need to customize sell conditions for each individual trade, or if you need the trade profit to take the sell decision. 
-
-For example you could implement a 1:2 risk-reward ROI with `custom_sell()`.
-
-Using custom_sell() signals in place of stoploss though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
-
-!!! Note
-    Returning a `string` or `True` from this method is equal to setting sell signal on a candle at specified time. This method is not called when sell signal is set already, or if sell signals are disabled (`use_sell_signal=False` or `sell_profit_only=True` while profit is below `sell_profit_offset`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
-
-An example of how we can use different indicators depending on the current profit and also sell trades that were open longer than one day:
-
-``` python
-class AwesomeStrategy(IStrategy):
-    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
-                    current_profit: float, **kwargs):
-        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
-        last_candle = dataframe.iloc[-1].squeeze()
-
-        # Above 20% profit, sell when rsi < 80
-        if current_profit > 0.2:
-            if last_candle['rsi'] < 80:
-                return 'rsi_below_80'
-
-        # Between 2% and 10%, sell if EMA-long above EMA-short
-        if 0.02 < current_profit < 0.1:
-            if last_candle['emalong'] > last_candle['emashort']:
-                return 'ema_long_below_80'
-
-        # Sell any positions at a loss if they are held for more than one day.
-        if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
-            return 'unclog'
-```
-
-See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
-
-## Buy Tag
+## 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_sell`
+Then you can access you buy signal on `custom_exit`
 
 ```python
-def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     dataframe.loc[
         (
             (dataframe['rsi'] < 35) &
             (dataframe['volume'] > 0)
         ),
-        ['buy', 'buy_tag']] = (1, 'buy_signal_rsi')
+        ['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')
 
     return dataframe
 
-def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
-                    current_profit: float, **kwargs):
+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.buy_tag == 'buy_signal_rsi' and last_candle['rsi'] > 80:
+    if trade.enter_tag == 'buy_signal_rsi' and last_candle['rsi'] > 80:
         return 'sell_signal_rsi'
     return None
 
 ```
 
 !!! Note
-    `buy_tag` is limited to 100 characters, remaining data will be truncated.
-
-
-## Custom stoploss
-
-The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss.
-
-The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
-The method must return a stoploss value (float / number) as a percentage of the current price.
-E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
-
-The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
-
-To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
-
-``` python
-# additional imports required
-from datetime import datetime
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-        """
-        Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
-        e.g. returning -0.05 would create a stoploss 5% below current_rate.
-        The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
-
-        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
-
-        When not implemented by a strategy, returns the initial stoploss value
-        Only called when use_custom_stoploss is set to True.
-
-        :param pair: Pair that's currently analyzed
-        :param trade: trade object.
-        :param current_time: datetime object, containing the current datetime
-        :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
-        :param current_profit: Current profit (as ratio), calculated based on current_rate.
-        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        :return float: New stoploss value, relative to the current rate
-        """
-        return -0.04
-```
-
-Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchange-freqtrade)).
-
-!!! Note "Use of dates"
-    All time-based calculations should be done based on `current_time` - using `datetime.now()` or `datetime.utcnow()` is discouraged, as this will break backtesting support.
-
-!!! Tip "Trailing stoploss"
-    It's recommended to disable `trailing_stop` when using custom stoploss values. Both can work in tandem, but you might encounter the trailing stop to move the price higher while your custom function would not want this, causing conflicting behavior.
-
-### Custom stoploss examples
-
-The next section will show some examples on what's possible with the custom stoploss function.
-Of course, many more things are possible, and all examples can be combined at will.
-
-#### Time based trailing stop
-
-Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
-
-``` python
-from datetime import datetime, timedelta
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        # Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
-        if current_time - timedelta(minutes=120) > trade.open_date_utc:
-            return -0.05
-        elif current_time - timedelta(minutes=60) > trade.open_date_utc:
-            return -0.10
-        return 1
-```
-
-#### Different stoploss per pair
-
-Use a different stoploss depending on the pair.
-In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
-
-``` python
-from datetime import datetime
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        if pair in ('ETH/BTC', 'XRP/BTC'):
-            return -0.10
-        elif pair in ('LTC/BTC'):
-            return -0.05
-        return -0.15
-```
-
-#### Trailing stoploss with positive offset
-
-Use the initial stoploss until the profit is above 4%, then use a trailing stoploss of 50% of the current profit with a minimum of 2.5% and a maximum of 5%.
-
-Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
-
-``` python
-from datetime import datetime, timedelta
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        if current_profit < 0.04:
-            return -1 # return a value bigger than the initial stoploss to keep using the initial stoploss
-
-        # After reaching the desired offset, allow the stoploss to trail by half the profit
-        desired_stoploss = current_profit / 2
-
-        # Use a minimum of 2.5% and a maximum of 5%
-        return max(min(desired_stoploss, 0.05), 0.025)
-```
-
-#### Calculating stoploss relative to open price
-
-Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss relative to the *open* price, we need to use `current_profit` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
-
-The helper function [`stoploss_from_open()`](strategy-customization.md#stoploss_from_open) can be used to convert from an open price relative stop, to a current price relative stop which can be returned from `custom_stoploss()`.
-
-### Calculating stoploss percentage from absolute price
-
-Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss at specified absolute price level, we need to use `stop_rate` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
-
-The helper function [`stoploss_from_absolute()`](strategy-customization.md#stoploss_from_absolute) can be used to convert from an absolute price, to a current price relative stop which can be returned from `custom_stoploss()`.
-
-#### Stepped stoploss
-
-Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
-
-* Use the regular stoploss until 20% profit is reached
-* Once profit is > 20% - set stoploss to 7% above open price.
-* Once profit is > 25% - set stoploss to 15% above open price.
-* Once profit is > 40% - set stoploss to 25% above open price.
-
-``` python
-from datetime import datetime
-from freqtrade.persistence import Trade
-from freqtrade.strategy import stoploss_from_open
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        # evaluate highest to lowest, so that highest possible stop is used
-        if current_profit > 0.40:
-            return stoploss_from_open(0.25, current_profit)
-        elif current_profit > 0.25:
-            return stoploss_from_open(0.15, current_profit)
-        elif current_profit > 0.20:
-            return stoploss_from_open(0.07, current_profit)
-
-        # return maximum stoploss value, keeping current stoploss price unchanged
-        return 1
-```
-
-#### Custom stoploss using an indicator from dataframe example
-
-Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
-
-``` python
-class AwesomeStrategy(IStrategy):
-
-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        # <...>
-        dataframe['sar'] = ta.SAR(dataframe)
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
-        last_candle = dataframe.iloc[-1].squeeze()
-
-        # Use parabolic sar as absolute stoploss price
-        stoploss_price = last_candle['sar']
-
-        # Convert absolute price to percentage relative to current_rate
-        if stoploss_price < current_rate:
-            return (stoploss_price / current_rate) - 1
-
-        # return maximum stoploss value, keeping current stoploss price unchanged
-        return 1
-```
-
-See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
-
----
-
-## Custom order price rules
-
-By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
-
-You can use this feature by creating a `custom_entry_price()` function in your strategy file to customize entry prices and `custom_exit_price()` for exits.
-
-!!! Note
-    If your custom pricing function return None or an invalid value, price will fall back to `proposed_rate`, which is based on the regular pricing configuration.
-
-### Custom order entry and exit price example
-
-``` python
-from datetime import datetime, timedelta, timezone
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def custom_entry_price(self, pair: str, current_time: datetime,
-                           proposed_rate, **kwargs) -> float:
-
-        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
-                                                                timeframe=self.timeframe)
-        new_entryprice = dataframe['bollinger_10_lowerband'].iat[-1]
-        
-        return new_entryprice
-
-    def custom_exit_price(self, pair: str, trade: Trade,
-                          current_time: datetime, proposed_rate: float,
-                          current_profit: float, **kwargs) -> float:
-
-        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
-                                                                timeframe=self.timeframe)
-        new_exitprice = dataframe['bollinger_10_upperband'].iat[-1]
-        
-        return new_exitprice
-
-```
+    `enter_tag` is limited to 100 characters, remaining data will be truncated.
 
 !!! Warning
-    Modifying entry and exit prices will only work for limit orders. Depending on the price chosen, this can result in a lot of unfilled orders. By default the maximum allowed distance between the current price and the custom price is 2%, this value can be changed in config with the `custom_price_max_distance_ratio` parameter.
+    There is only one `enter_tag` column, which is used for both long and short trades.
+    As a consequence, this column must be treated as "last write wins" (it's just a dataframe column after all).
+    In fancy situations, where multiple signals collide (or if signals are deactivated again based on different conditions), this can lead to odd results with the wrong tag applied to an entry signal.
+    These results are a consequence of the strategy overwriting prior tags - where the last tag will "stick" and will be the one freqtrade will use.
 
-!!! Example
-    If the new_entryprice is 97, the proposed_rate is 100 and the `custom_price_max_distance_ratio` is set to 2%, The retained valid custom entry price will be 98.
+## Exit tag
 
-!!! Warning "No backtesting support"
-    Custom entry-prices are currently not supported during backtesting.
-
-## Custom order timeout rules
-
-Simple, time-based 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 order types, which allows you to decide based on custom criteria if an order did time out or not.
-
-!!! 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.
-
-### Custom order timeout example
-
-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.
-
-The function must return either `True` (cancel order) or `False` (keep order alive).
+Similar to [Buy Tagging](#buy-tag), you can also specify a sell tag.
 
 ``` python
-from datetime import datetime, timedelta, timezone
-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):
+    return dataframe
+```
 
-    # ... populate_* methods
+The provided exit-tag is then used as sell-reason - and shown as such in backtest results.
 
-    # Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
-    unfilledtimeout = {
-        'buy': 60 * 25,
-        'sell': 60 * 25
-    }
+!!! Note
+    `exit_reason` is limited to 100 characters, remaining data will be truncated.
 
-    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
-        if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
-            return True
-        elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
-            return True
-        elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
-           return True
-        return False
+## Strategy version
 
+You can implement custom strategy versioning by using the "version" method, and returning the version you would like this strategy to have.
 
-    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
-        if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
-            return True
-        elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
-            return True
-        elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
-           return True
-        return False
+``` python
+def version(self) -> str:
+    """
+    Returns version of the strategy.
+    """
+    return "1.1"
 ```
 
 !!! Note
-    For the above example, `unfilledtimeout` must be set to something bigger than 24h, otherwise that type of timeout will apply first.
-
-### Custom order timeout example (using additional data)
-
-``` python
-from datetime import datetime
-from freqtrade.persistence import Trade
-
-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
-```
-
----
-
-## Bot loop start callback
-
-A simple callback which is called once at the start of every bot throttling iteration.
-This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
-
-``` python
-import requests
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def bot_loop_start(self, **kwargs) -> None:
-        """
-        Called at the start of the bot iteration (one loop).
-        Might be used to perform pair-independent tasks
-        (e.g. gather some remote resource for comparison)
-        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        """
-        if self.config['runmode'].value in ('live', 'dry_run'):
-            # Assign this to the class by using self.*
-            # can then be used by populate_* methods
-            self.remote_data = requests.get('https://some_remote_source.example.com')
-
-```
-
-## Bot order confirmation
-
-### Trade entry (buy order) confirmation
-
-`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
-
-``` python
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
-                            time_in_force: str, current_time: datetime, **kwargs) -> bool:
-        """
-        Called right before placing a buy order.
-        Timing for this function is critical, so avoid doing heavy computations or
-        network requests in this method.
-
-        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
-
-        When not implemented by a strategy, returns True (always confirming).
-
-        :param pair: Pair that's about to be bought.
-        :param order_type: Order type (as configured in order_types). usually limit or market.
-        :param amount: Amount in target (quote) currency that's going to be traded.
-        :param rate: Rate that's going to be used when using limit orders
-        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
-        :param current_time: datetime object, containing the current datetime
-        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        :return bool: When True is returned, then the buy-order is placed on the exchange.
-            False aborts the process
-        """
-        return True
-
-```
-
-### Trade exit (sell order) confirmation
-
-`confirm_trade_exit()` can be used to abort a trade exit (sell) at the latest second (maybe because the price is not what we expect).
-
-``` python
-from freqtrade.persistence import Trade
-
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
-                           rate: float, time_in_force: str, sell_reason: str,
-                           current_time: datetime, **kwargs) -> bool:
-        """
-        Called right before placing a regular sell order.
-        Timing for this function is critical, so avoid doing heavy computations or
-        network requests in this method.
-
-        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
-
-        When not implemented by a strategy, returns True (always confirming).
-
-        :param pair: Pair that's about to be sold.
-        :param order_type: Order type (as configured in order_types). usually limit or market.
-        :param amount: Amount in quote currency.
-        :param rate: Rate that's going to be used when using limit orders
-        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
-        :param sell_reason: Sell reason.
-            Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
-                           'sell_signal', 'force_sell', 'emergency_sell']
-        :param current_time: datetime object, containing the current datetime
-        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        :return bool: When True is returned, then the sell-order is placed on the exchange.
-            False aborts the process
-        """
-        if sell_reason == 'force_sell' and trade.calc_profit_ratio(rate) < 0:
-            # Reject force-sells with negative profit
-            # This is just a sample, please adjust to your needs
-            # (this does not necessarily make sense, assuming you know when you're force-selling)
-            return False
-        return True
-
-```
-
-### Stake size management
-
-It is possible to manage your risk by reducing or increasing stake amount when placing a new trade.
-
-```python
-class AwesomeStrategy(IStrategy):
-    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
-                            proposed_stake: float, min_stake: float, max_stake: float,
-                            **kwargs) -> float:
-
-        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
-        current_candle = dataframe.iloc[-1].squeeze()
-
-        if current_candle['fastk_rsi_1h'] > current_candle['fastd_rsi_1h']:
-            if self.config['stake_amount'] == 'unlimited':
-                # Use entire available wallet during favorable conditions when in compounding mode.
-                return max_stake
-            else:
-                # Compound profits during favorable conditions instead of using a static stake.
-                return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
-
-        # Use default stake amount.
-        return proposed_stake
-```
-
-Freqtrade will fall back to the `proposed_stake` value should your code raise an exception. The exception itself will be logged.
-
-!!! Tip
-    You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this acton will be logged.
-
-!!! Tip
-    Returning `0` or `None` will prevent trades from being placed.
-
----
+    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
+``` python title="user_data/strategies/myawesomestrategy.py"
 class MyAwesomeStrategy(IStrategy):
     ...
     stoploss = 0.13
@@ -657,6 +161,10 @@ class MyAwesomeStrategy(IStrategy):
     # 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
@@ -665,16 +173,7 @@ class MyAwesomeStrategy2(MyAwesomeStrategy):
 
 Both attributes and methods may be overridden, altering behavior of the original strategy in a way you need.
 
-!!! Note "Parent-strategy in different files"
-    If you have the parent-strategy in a different file, you'll need to add the following to the top of your "child"-file to ensure proper loading, otherwise freqtrade may not be able to load the parent strategy correctly.
-
-    ``` python
-    import sys
-    from pathlib import Path
-    sys.path.append(str(Path(__file__).parent))
-
-    from myawesomestrategy import MyAwesomeStrategy
-    ```
+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
 
@@ -724,10 +223,12 @@ should be rewritten to
 ```python
 frames = [dataframe]
 for val in self.buy_ema_short.range:
-    frames.append({
+    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.

docs/strategy-callbacks.md

@@ -0,0 +1,872 @@
+# Strategy Callbacks
+
+While the main strategy functions (`populate_indicators()`, `populate_entry_trend()`, `populate_exit_trend()`) should be used in a vectorized way, and are only called [once during backtesting](bot-basics.md#backtesting-hyperopt-execution-logic), callbacks are called "whenever needed".
+
+As such, you should avoid doing heavy calculations in callbacks to avoid delays during operations.
+Depending on the callback used, they may be called when entering / exiting a trade, or throughout the duration of a trade.
+
+Currently available callbacks:
+
+* [`bot_start()`](#bot-start)
+* [`bot_loop_start()`](#bot-loop-start)
+* [`custom_stake_amount()`](#stake-size-management)
+* [`custom_exit()`](#custom-exit-signal)
+* [`custom_stoploss()`](#custom-stoploss)
+* [`custom_entry_price()` and `custom_exit_price()`](#custom-order-price-rules)
+* [`check_entry_timeout()` and `check_exit_timeout()`](#custom-order-timeout-rules)
+* [`confirm_trade_entry()`](#trade-entry-buy-order-confirmation)
+* [`confirm_trade_exit()`](#trade-exit-sell-order-confirmation)
+* [`adjust_trade_position()`](#adjust-trade-position)
+* [`adjust_entry_price()`](#adjust-entry-price)
+* [`leverage()`](#leverage-callback)
+
+!!! Tip "Callback calling sequence"
+    You can find the callback calling sequence in [bot-basics](bot-basics.md#bot-execution-logic)
+
+## Bot start
+
+A simple callback which is called once when the strategy is loaded.
+This can be used to perform actions that must only be performed once and runs after dataprovider and wallet are set
+
+``` python
+import requests
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def bot_start(self, **kwargs) -> None:
+        """
+        Called only once after bot instantiation.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        if self.config['runmode'].value in ('live', 'dry_run'):
+            # Assign this to the class by using self.*
+            # can then be used by populate_* methods
+            self.cust_remote_data = requests.get('https://some_remote_source.example.com')
+
+```
+
+During hyperopt, this runs only once at startup.
+
+## Bot loop start
+
+A simple callback which is called once at the start of every bot throttling iteration (roughly every 5 seconds, unless configured differently).
+This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
+
+``` python
+import requests
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def bot_loop_start(self, **kwargs) -> None:
+        """
+        Called at the start of the bot iteration (one loop).
+        Might be used to perform pair-independent tasks
+        (e.g. gather some remote resource for comparison)
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        if self.config['runmode'].value in ('live', 'dry_run'):
+            # Assign this to the class by using self.*
+            # can then be used by populate_* methods
+            self.remote_data = requests.get('https://some_remote_source.example.com')
+
+```
+
+## Stake size management
+
+Called before entering a trade, makes it possible to manage your position size when placing a new trade.
+
+```python
+class AwesomeStrategy(IStrategy):
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: Optional[float], max_stake: float,
+                            leverage: float, entry_tag: Optional[str], side: str,
+                            **kwargs) -> float:
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+        current_candle = dataframe.iloc[-1].squeeze()
+
+        if current_candle['fastk_rsi_1h'] > current_candle['fastd_rsi_1h']:
+            if self.config['stake_amount'] == 'unlimited':
+                # Use entire available wallet during favorable conditions when in compounding mode.
+                return max_stake
+            else:
+                # Compound profits during favorable conditions instead of using a static stake.
+                return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
+
+        # Use default stake amount.
+        return proposed_stake
+```
+
+Freqtrade will fall back to the `proposed_stake` value should your code raise an exception. The exception itself will be logged.
+
+!!! Tip
+    You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this action will be logged.
+
+!!! Tip
+    Returning `0` or `None` will prevent trades from being placed.
+
+## Custom exit signal
+
+Called for open trade every throttling iteration (roughly every 5 seconds) until a trade is closed.
+
+Allows to define custom exit signals, indicating that specified position should be sold. This is very useful when we need to customize exit conditions for each individual trade, or if you need trade data to make an exit decision.
+
+For example you could implement a 1:2 risk-reward ROI with `custom_exit()`.
+
+Using `custom_exit()` signals in place of stoploss though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
+
+!!! Note
+    Returning a (none-empty) `string` or `True` from this method is equal to setting exit signal on a candle at specified time. This method is not called when exit signal is set already, or if exit signals are disabled (`use_exit_signal=False`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
+    `custom_exit()` will ignore `exit_profit_only`, and will always be called unless `use_exit_signal=False`, even if there is a new enter signal.
+
+An example of how we can use different indicators depending on the current profit and also exit trades that were open longer than one day:
+
+``` python
+class AwesomeStrategy(IStrategy):
+    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
+                    current_profit: float, **kwargs):
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+
+        # Above 20% profit, sell when rsi < 80
+        if current_profit > 0.2:
+            if last_candle['rsi'] < 80:
+                return 'rsi_below_80'
+
+        # Between 2% and 10%, sell if EMA-long above EMA-short
+        if 0.02 < current_profit < 0.1:
+            if last_candle['emalong'] > last_candle['emashort']:
+                return 'ema_long_below_80'
+
+        # Sell any positions at a loss if they are held for more than one day.
+        if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
+            return 'unclog'
+```
+
+See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
+
+## Custom stoploss
+
+Called for open trade every iteration (roughly every 5 seconds) until a trade is closed.
+
+The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
+
+The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss (before this method is called for the first time for a trade), and is still mandatory.
+
+The method must return a stoploss value (float / number) as a percentage of the current price.
+E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
+
+The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
+
+To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
+
+``` python
+# additional imports required
+from datetime import datetime
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+        """
+        Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
+        e.g. returning -0.05 would create a stoploss 5% below current_rate.
+        The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns the initial stoploss value
+        Only called when use_custom_stoploss is set to True.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: New stoploss value, relative to the current rate
+        """
+        return -0.04
+```
+
+Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchange-freqtrade)).
+
+!!! Note "Use of dates"
+    All time-based calculations should be done based on `current_time` - using `datetime.now()` or `datetime.utcnow()` is discouraged, as this will break backtesting support.
+
+!!! Tip "Trailing stoploss"
+    It's recommended to disable `trailing_stop` when using custom stoploss values. Both can work in tandem, but you might encounter the trailing stop to move the price higher while your custom function would not want this, causing conflicting behavior.
+
+### Custom stoploss examples
+
+The next section will show some examples on what's possible with the custom stoploss function.
+Of course, many more things are possible, and all examples can be combined at will.
+
+#### Time based trailing stop
+
+Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
+
+``` python
+from datetime import datetime, timedelta
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        # Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
+        if current_time - timedelta(minutes=120) > trade.open_date_utc:
+            return -0.05
+        elif current_time - timedelta(minutes=60) > trade.open_date_utc:
+            return -0.10
+        return 1
+```
+
+#### Different stoploss per pair
+
+Use a different stoploss depending on the pair.
+In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
+
+``` python
+from datetime import datetime
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        if pair in ('ETH/BTC', 'XRP/BTC'):
+            return -0.10
+        elif pair in ('LTC/BTC'):
+            return -0.05
+        return -0.15
+```
+
+#### Trailing stoploss with positive offset
+
+Use the initial stoploss until the profit is above 4%, then use a trailing stoploss of 50% of the current profit with a minimum of 2.5% and a maximum of 5%.
+
+Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
+
+``` python
+from datetime import datetime, timedelta
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        if current_profit < 0.04:
+            return -1 # return a value bigger than the initial stoploss to keep using the initial stoploss
+
+        # After reaching the desired offset, allow the stoploss to trail by half the profit
+        desired_stoploss = current_profit / 2
+
+        # Use a minimum of 2.5% and a maximum of 5%
+        return max(min(desired_stoploss, 0.05), 0.025)
+```
+
+#### Stepped stoploss
+
+Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
+
+* Use the regular stoploss until 20% profit is reached
+* Once profit is > 20% - set stoploss to 7% above open price.
+* Once profit is > 25% - set stoploss to 15% above open price.
+* Once profit is > 40% - set stoploss to 25% above open price.
+
+``` python
+from datetime import datetime
+from freqtrade.persistence import Trade
+from freqtrade.strategy import stoploss_from_open
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        # evaluate highest to lowest, so that highest possible stop is used
+        if current_profit > 0.40:
+            return stoploss_from_open(0.25, current_profit, is_short=trade.is_short)
+        elif current_profit > 0.25:
+            return stoploss_from_open(0.15, current_profit, is_short=trade.is_short)
+        elif current_profit > 0.20:
+            return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
+
+        # return maximum stoploss value, keeping current stoploss price unchanged
+        return 1
+```
+
+#### Custom stoploss using an indicator from dataframe example
+
+Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
+
+``` python
+class AwesomeStrategy(IStrategy):
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # <...>
+        dataframe['sar'] = ta.SAR(dataframe)
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+
+        # Use parabolic sar as absolute stoploss price
+        stoploss_price = last_candle['sar']
+
+        # Convert absolute price to percentage relative to current_rate
+        if stoploss_price < current_rate:
+            return (stoploss_price / current_rate) - 1
+
+        # return maximum stoploss value, keeping current stoploss price unchanged
+        return 1
+```
+
+See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
+
+### Common helpers for stoploss calculations
+
+#### Stoploss relative to open price
+
+Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss relative to the *open* price, we need to use `current_profit` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
+
+The helper function [`stoploss_from_open()`](strategy-customization.md#stoploss_from_open) can be used to convert from an open price relative stop, to a current price relative stop which can be returned from `custom_stoploss()`.
+
+#### Stoploss percentage from absolute price
+
+Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss at specified absolute price level, we need to use `stop_rate` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
+
+The helper function [`stoploss_from_absolute()`](strategy-customization.md#stoploss_from_absolute) can be used to convert from an absolute price, to a current price relative stop which can be returned from `custom_stoploss()`.
+
+---
+
+## Custom order price rules
+
+By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
+
+You can use this feature by creating a `custom_entry_price()` function in your strategy file to customize entry prices and `custom_exit_price()` for exits.
+
+Each of these methods are called right before placing an order on the exchange.
+
+!!! Note
+    If your custom pricing function return None or an invalid value, price will fall back to `proposed_rate`, which is based on the regular pricing configuration.
+
+### Custom order entry and exit price example
+
+``` python
+from datetime import datetime, timedelta, timezone
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
+                           entry_tag: Optional[str], side: str, **kwargs) -> float:
+
+        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
+                                                                timeframe=self.timeframe)
+        new_entryprice = dataframe['bollinger_10_lowerband'].iat[-1]
+
+        return new_entryprice
+
+    def custom_exit_price(self, pair: str, trade: Trade,
+                          current_time: datetime, proposed_rate: float,
+                          current_profit: float, exit_tag: Optional[str], **kwargs) -> float:
+
+        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
+                                                                timeframe=self.timeframe)
+        new_exitprice = dataframe['bollinger_10_upperband'].iat[-1]
+
+        return new_exitprice
+
+```
+
+!!! Warning
+    Modifying entry and exit prices will only work for limit orders. Depending on the price chosen, this can result in a lot of unfilled orders. By default the maximum allowed distance between the current price and the custom price is 2%, this value can be changed in config with the `custom_price_max_distance_ratio` parameter.
+    **Example**:
+    If the new_entryprice is 97, the proposed_rate is 100 and the `custom_price_max_distance_ratio` is set to 2%, The retained valid custom entry price will be 98, which is 2% below the current (proposed) rate.
+
+!!! Warning "Backtesting"
+    Custom prices are supported in backtesting (starting with 2021.12), and orders will fill if the price falls within the candle's low/high range.
+    Orders that don't fill immediately are subject to regular timeout handling, which happens once per (detail) candle.
+    `custom_exit_price()` is only called for sells of type exit_signal, Custom exit and partial exits. All other exit-types will use regular backtesting prices.
+
+## Custom order timeout rules
+
+Simple, time-based 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 order types, which allows you to decide based on custom criteria if an order did time out or not.
+
+!!! Note
+    Backtesting fills orders if their price falls within the candle's low/high range.
+    The below callbacks will be called once per (detail) candle for orders that don't fill immediately (which use custom pricing).
+
+### Custom order timeout example
+
+Called for every open order until that order is either filled or cancelled.
+`check_entry_timeout()` is called for trade entries, while `check_exit_timeout()` is called for trade exit orders.
+
+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.
+
+The function must return either `True` (cancel order) or `False` (keep order alive).
+
+``` python
+from datetime import datetime, timedelta
+from freqtrade.persistence import Trade, Order
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
+    unfilledtimeout = {
+        'entry': 60 * 25,
+        'exit': 60 * 25
+    }
+
+    def check_entry_timeout(self, pair: str, trade: 'Trade', order: 'Order',
+                            current_time: datetime, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date_utc < current_time - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date_utc < current_time - timedelta(hours=24):
+           return True
+        return False
+
+
+    def check_exit_timeout(self, pair: str, trade: Trade, order: 'Order',
+                           current_time: datetime, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date_utc < current_time - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date_utc < current_time - 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.
+
+### Custom order timeout example (using additional data)
+
+``` python
+from datetime import datetime
+from freqtrade.persistence import Trade, Order
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
+    unfilledtimeout = {
+        'entry': 60 * 25,
+        'exit': 60 * 25
+    }
+
+    def check_entry_timeout(self, pair: str, trade: 'Trade', order: 'Order',
+                            current_time: datetime, **kwargs) -> bool:
+        ob = self.dp.orderbook(pair, 1)
+        current_price = ob['bids'][0][0]
+        # Cancel buy order if price is more than 2% above the order.
+        if current_price > order.price * 1.02:
+            return True
+        return False
+
+
+    def check_exit_timeout(self, pair: str, trade: 'Trade', order: 'Order',
+                           current_time: datetime, **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
+```
+
+---
+
+## Bot order confirmation
+
+Confirm trade entry / exits.
+This are the last methods that will be called before an order is placed.
+
+### Trade entry (buy order) confirmation
+
+`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
+
+``` python
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str],
+                            side: str, **kwargs) -> bool:
+        """
+        Called right before placing a entry order.
+        Timing for this function is critical, so avoid doing heavy computations or
+        network requests in this method.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns True (always confirming).
+
+        :param pair: Pair that's about to be bought/shorted.
+        :param order_type: Order type (as configured in order_types). usually limit or market.
+        :param amount: Amount in target (base) currency that's going to be traded.
+        :param rate: Rate that's going to be used when using limit orders 
+                     or current rate for market orders.
+        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param current_time: datetime object, containing the current datetime
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the buy-order is placed on the exchange.
+            False aborts the process
+        """
+        return True
+
+```
+
+### Trade exit (sell order) confirmation
+
+`confirm_trade_exit()` can be used to abort a trade exit (sell) at the latest second (maybe because the price is not what we expect).
+
+`confirm_trade_exit()` may be called multiple times within one iteration for the same trade if different exit-reasons apply.
+The exit-reasons (if applicable) will be in the following sequence:
+
+* `exit_signal` / `custom_exit`
+* `stop_loss`
+* `roi`
+* `trailing_stop_loss`
+
+``` python
+from freqtrade.persistence import Trade
+
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    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:
+        """
+        Called right before placing a regular exit order.
+        Timing for this function is critical, so avoid doing heavy computations or
+        network requests in this method.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns True (always confirming).
+
+        :param pair: Pair for trade that's about to be exited.
+        :param trade: trade object.
+        :param order_type: Order type (as configured in order_types). usually limit or market.
+        :param amount: Amount in base currency.
+        :param rate: Rate that's going to be used when using limit orders
+                     or current rate for market orders.
+        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param exit_reason: Exit reason.
+            Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
+                           'exit_signal', 'force_exit', 'emergency_exit']
+        :param current_time: datetime object, containing the current datetime
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True, then the exit-order is placed on the exchange.
+            False aborts the process
+        """
+        if exit_reason == 'force_exit' and trade.calc_profit_ratio(rate) < 0:
+            # Reject force-sells with negative profit
+            # This is just a sample, please adjust to your needs
+            # (this does not necessarily make sense, assuming you know when you're force-selling)
+            return False
+        return True
+
+```
+
+!!! Warning
+    `confirm_trade_exit()` can prevent stoploss exits, causing significant losses as this would ignore stoploss exits.
+    `confirm_trade_exit()` will not be called for Liquidations - as liquidations are forced by the exchange, and therefore cannot be rejected.
+
+## Adjust trade position
+
+The `position_adjustment_enable` strategy property enables the usage of `adjust_trade_position()` callback in the strategy.
+For performance reasons, it's disabled by default and freqtrade will show a warning message on startup if enabled.
+`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging) or to increase or decrease positions.
+
+`max_entry_position_adjustment` property is used to limit the number of additional buys per trade (on top of the first buy) that the bot can execute. By default, the value is -1 which means the bot have no limit on number of adjustment buys.
+
+The strategy is expected to return a stake_amount (in stake currency) between `min_stake` and `max_stake` if and when an additional buy order should be made (position is increased).
+If there are not enough funds in the wallet (the return value is above `max_stake`) then the signal will be ignored.
+Additional orders also result in additional fees and those orders don't count towards `max_open_trades`.
+
+This callback is **not** called when there is an open order (either buy or sell) waiting for execution.
+
+`adjust_trade_position()` is called very frequently for the duration of a trade, so you must keep your implementation as performant as possible.
+
+Additional Buys are ignored once you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`, but the callback is called anyway looking for partial exits.
+
+Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position (negative values will decrease your position), no matter if it's a long or short trade. Modifications to leverage are not possible, and the stake-amount is assumed to be before applying leverage.
+
+!!! Note "About stake size"
+    Using fixed stake size means it will be the amount used for the first order, just like without position adjustment.
+    If you wish to buy additional orders with DCA, then make sure to leave enough funds in the wallet for that.
+    Using 'unlimited' stake amount with DCA orders requires you to also implement the `custom_stake_amount()` callback to avoid allocating all funds to the initial order.
+
+!!! Warning
+    Stoploss is still calculated from the initial opening price, not averaged price.
+    Regular stoploss rules still apply (cannot move down).
+
+    While `/stopentry` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades.
+
+!!! Warning "Backtesting"
+    During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
+
+``` python
+from freqtrade.persistence import Trade
+
+
+class DigDeeperStrategy(IStrategy):
+
+    position_adjustment_enable = True
+
+    # Attempts to handle large drops with DCA. High stoploss is required.
+    stoploss = -0.30
+
+    # ... populate_* methods
+
+    # Example specific variables
+    max_entry_position_adjustment = 3
+    # This number is explained a bit further down
+    max_dca_multiplier = 5.5
+
+    # This is called when placing the initial order (opening trade)
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: Optional[float], max_stake: float,
+                            leverage: float, entry_tag: Optional[str], side: str,
+                            **kwargs) -> float:
+
+        # We need to leave most of the funds for possible further DCA orders
+        # This also applies to fixed stakes
+        return proposed_stake / self.max_dca_multiplier
+
+    def adjust_trade_position(self, trade: Trade, current_time: datetime,
+                              current_rate: float, current_profit: float,
+                              min_stake: Optional[float], max_stake: float,
+                              current_entry_rate: float, current_exit_rate: float,
+                              current_entry_profit: float, current_exit_profit: float,
+                              **kwargs) -> Optional[float]:
+        """
+        Custom trade adjustment logic, returning the stake amount that a trade should be
+        increased or decreased.
+        This means extra buy or sell orders with additional fees.
+        Only called when `position_adjustment_enable` is set to True.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns None
+
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Current buy rate.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
+        :param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
+        :param current_entry_rate: Current rate using entry pricing.
+        :param current_exit_rate: Current rate using exit pricing.
+        :param current_entry_profit: Current profit using entry pricing.
+        :param current_exit_profit: Current profit using exit pricing.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: Stake amount to adjust your trade,
+                       Positive values to increase position, Negative values to decrease position.
+                       Return None for no action.
+        """
+
+        if current_profit > 0.05 and trade.nr_of_successful_exits == 0:
+            # Take half of the profit at +5%
+            return -(trade.stake_amount / 2)
+
+        if current_profit > -0.05:
+            return None
+
+        # Obtain pair dataframe (just to show how to access it)
+        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+        # Only buy when not actively falling price.
+        last_candle = dataframe.iloc[-1].squeeze()
+        previous_candle = dataframe.iloc[-2].squeeze()
+        if last_candle['close'] < previous_candle['close']:
+            return None
+
+        filled_entries = trade.select_filled_orders(trade.entry_side)
+        count_of_entries = trade.nr_of_successful_entries
+        # Allow up to 3 additional increasingly larger buys (4 in total)
+        # Initial buy is 1x
+        # If that falls to -5% profit, we buy 1.25x more, average profit should increase to roughly -2.2%
+        # If that falls down to -5% again, we buy 1.5x more
+        # If that falls once again down to -5%, we buy 1.75x more
+        # Total stake for this trade would be 1 + 1.25 + 1.5 + 1.75 = 5.5x of the initial allowed stake.
+        # That is why max_dca_multiplier is 5.5
+        # Hope you have a deep wallet!
+        try:
+            # This returns first order stake size
+            stake_amount = filled_entries[0].cost
+            # This then calculates current safety order size
+            stake_amount = stake_amount * (1 + (count_of_entries * 0.25))
+            return stake_amount
+        except Exception as exception:
+            return None
+
+        return None
+
+```
+
+### Position adjust calculations
+
+* Entry rates are calculated using weighted averages.
+* Exits will not influence the average entry rate.
+* Partial exit relative profit is relative to the average entry price at this point.
+* Final exit relative profit is calculated based on the total invested capital. (See example below)
+
+??? example "Calculation example"
+    *This example assumes 0 fees for simplicity, and a long position on an imaginary coin.*  
+    
+    * Buy 100@8\$ 
+    * Buy 100@9\$ -> Avg price: 8.5\$
+    * Sell 100@10\$ -> Avg price: 8.5\$, realized profit 150\$, 17.65%
+    * Buy 150@11\$ -> Avg price: 10\$, realized profit 150\$, 17.65%
+    * Sell 100@12\$ -> Avg price: 10\$, total realized profit 350\$, 20%
+    * Sell 150@14\$ -> Avg price: 10\$, total realized profit 950\$, 40%
+
+    The total profit for this trade was 950$ on a 3350$ investment (`100@8$ + 100@9$ + 150@11$`). As such - the final relative profit is 28.35% (`950 / 3350`).
+
+## Adjust Entry Price
+
+The `adjust_entry_price()` callback may be used by strategy developer to refresh/replace limit orders upon arrival of new candles.
+Be aware that `custom_entry_price()` is still the one dictating initial entry limit order price target at the time of entry trigger.
+
+Orders can be cancelled out of this callback by returning `None`.
+
+Returning `current_order_rate` will keep the order on the exchange "as is".
+Returning any other price will cancel the existing order, and replace it with a new order.
+
+The trade open-date (`trade.open_date_utc`) will remain at the time of the very first order placed.
+Please make sure to be aware of this - and eventually adjust your logic in other callbacks to account for this, and use the date of the first filled order instead.
+
+!!! Warning "Regular timeout"
+    Entry `unfilledtimeout` mechanism (as well as `check_entry_timeout()`) takes precedence over this.
+    Entry Orders that are cancelled via the above methods will not have this callback called. Be sure to update timeout values to match your expectations.
+
+```python
+from freqtrade.persistence import Trade
+from datetime import timedelta
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def adjust_entry_price(self, trade: Trade, order: Optional[Order], pair: str,
+                           current_time: datetime, proposed_rate: float, current_order_rate: float,
+                           entry_tag: Optional[str], side: str, **kwargs) -> float:
+        """
+        Entry price re-adjustment logic, returning the user desired limit price.
+        This only executes when a order was already placed, still open (unfilled fully or partially)
+        and not timed out on subsequent candles after entry trigger.
+
+        When not implemented by a strategy, returns current_order_rate as default.
+        If current_order_rate is returned then the existing order is maintained.
+        If None is returned then order gets canceled but not replaced by a new one.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: Trade object.
+        :param order: Order object
+        :param current_time: datetime object, containing the current datetime
+        :param proposed_rate: Rate, calculated based on pricing settings in entry_pricing.
+        :param current_order_rate: Rate of the existing order in place.
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: New entry price value if provided
+
+        """
+        # Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
+        if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10) > trade.open_date_utc:
+            # just cancel the order if it has been filled more than half of the amount
+            if order.filled > order.remaining:
+                return None
+            else:
+                dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+                current_candle = dataframe.iloc[-1].squeeze()
+                # desired price
+                return current_candle['sma_200']
+        # default: maintain existing order
+        return current_order_rate
+```
+
+## Leverage Callback
+
+When trading in markets that allow leverage, this method must return the desired Leverage (Defaults to 1 -> No leverage).
+
+Assuming a capital of 500USDT, a trade with leverage=3 would result in a position with 500 x 3 = 1500 USDT.
+
+Values that are above `max_leverage` will be adjusted to `max_leverage`.
+For markets / exchanges that don't support leverage, this method is ignored.
+
+``` python
+class AwesomeStrategy(IStrategy):
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                 proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
+                 **kwargs) -> float:
+        """
+        Customize leverage for each new trade. This method is only called in futures mode.
+
+        :param pair: Pair that's currently analyzed
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+        :param proposed_leverage: A leverage proposed by the bot.
+        :param max_leverage: Max leverage allowed on this pair
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :return: A leverage amount, which is between 1.0 and max_leverage.
+        """
+        return 1.0
+```
+
+All profit calculations include leverage. Stoploss / ROI also include leverage in their calculation.
+Defining a stoploss of 10% at 10x leverage would trigger the stoploss with a 1% move to the downside.

docs/strategy-customization.md

@@ -4,40 +4,30 @@ This page explains how to customize your strategies, add new indicators and set
 
 Please familiarize yourself with [Freqtrade basics](bot-basics.md) first, which provides overall info on how the bot operates.
 
-## Install a custom strategy file
-
-This is very simple. Copy paste your strategy file into the directory `user_data/strategies`.
-
-Let assume you have a class called `AwesomeStrategy` in the file `AwesomeStrategy.py`:
-
-1. Move your file into `user_data/strategies` (you should have `user_data/strategies/AwesomeStrategy.py`
-2. Start the bot with the param `--strategy AwesomeStrategy` (the parameter is the class name)
-
-```bash
-freqtrade trade --strategy AwesomeStrategy
-```
-
 ## Develop your own strategy
 
 The bot includes a default strategy file.
 Also, several other strategies are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
 
 You will however most likely have your own idea for a strategy.
-This document intends to help you develop one for yourself.
+This document intends to help you convert your strategy idea into your own strategy.
 
-To get started, use `freqtrade new-strategy --strategy AwesomeStrategy`.
+To get started, use `freqtrade new-strategy --strategy AwesomeStrategy` (you can obviously use your own naming for your strategy).
 This will create a new strategy file from a template, which will be located under `user_data/strategies/AwesomeStrategy.py`.
 
 !!! Note
     This is just a template file, which will most likely not be profitable out of the box.
 
+??? Hint "Different template levels"
+    `freqtrade new-strategy` has an additional parameter, `--template`, which controls the amount of pre-build information you get in the created strategy. Use `--template minimal` to get an empty strategy without any indicator examples, or `--template advanced` to get a template with most callbacks defined.
+
 ### Anatomy of a strategy
 
 A strategy file contains all the information needed to build a good strategy:
 
 - Indicators
-- Buy strategy rules
-- Sell strategy rules
+- Entry strategy rules
+- Exit strategy rules
 - Minimal ROI recommended
 - Stoploss strongly recommended
 
@@ -45,7 +35,7 @@ The bot also include a sample strategy called `SampleStrategy` you can update: `
 You can test it with the parameter: `--strategy SampleStrategy`
 
 Additionally, there is an attribute called `INTERFACE_VERSION`, which defines the version of the strategy interface the bot should use.
-The current version is 2 - which is also the default when it's not set explicitly in the strategy.
+The current version is 3 - which is also the default when it's not set explicitly in the strategy.
 
 Future versions will require this to be set.
 
@@ -67,11 +57,51 @@ file as reference.**
     needs to take care to avoid having the strategy utilize data from the future.
     Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
 
+### Dataframe
+
+Freqtrade uses [pandas](https://pandas.pydata.org/) to store/provide the candlestick (OHLCV) data.
+Pandas is a great library developed for processing large amounts of data.
+
+Each row in a dataframe corresponds to one candle on a chart, with the latest candle always being the last in the dataframe (sorted by date).
+
+``` output
+> dataframe.head()
+                       date      open      high       low     close     volume
+0 2021-11-09 23:25:00+00:00  67279.67  67321.84  67255.01  67300.97   44.62253
+1 2021-11-09 23:30:00+00:00  67300.97  67301.34  67183.03  67187.01   61.38076
+2 2021-11-09 23:35:00+00:00  67187.02  67187.02  67031.93  67123.81  113.42728
+3 2021-11-09 23:40:00+00:00  67123.80  67222.40  67080.33  67160.48   78.96008
+4 2021-11-09 23:45:00+00:00  67160.48  67160.48  66901.26  66943.37  111.39292
+```
+
+Pandas provides fast ways to calculate metrics. To benefit from this speed, it's advised to not use loops, but use vectorized methods instead.
+
+Vectorized operations perform calculations across the whole range of data and are therefore, compared to looping through each row, a lot faster when calculating indicators.
+
+As a dataframe is a table, simple python comparisons like the following will not work
+
+``` python
+    if dataframe['rsi'] > 30:
+        dataframe['enter_long'] = 1
+```
+
+The above section will fail with `The truth value of a Series is ambiguous. [...]`.
+
+This must instead be written in a pandas-compatible way, so the operation is performed across the whole dataframe.
+
+``` python
+    dataframe.loc[
+        (dataframe['rsi'] > 30)
+    , 'enter_long'] = 1
+```
+
+With this section, you have a new column in your dataframe, which has `1` assigned whenever RSI is above 30.
+
 ### Customize Indicators
 
-Buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
+Buy and sell signals need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
 
-You should only add the indicators used in either `populate_buy_trend()`, `populate_sell_trend()`, or to populate another indicator, otherwise performance may suffer.
+You should only add the indicators used in either `populate_entry_trend()`, `populate_exit_trend()`, or to populate another indicator, otherwise performance may suffer.
 
 It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
 
@@ -134,9 +164,9 @@ Additional technical libraries can be installed as necessary, or custom indicato
 
 ### Strategy startup period
 
-Most indicators have an instable startup period, in which they are either not available, or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
+Most indicators have an instable startup period, in which they are either not available (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
 To account for this, the strategy can be assigned the `startup_candle_count` attribute.
-This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
+This should be set to the maximum number of candles that the strategy requires to calculate stable indicators. In the case where a user includes higher timeframes with informative pairs, the `startup_candle_count` does not necessarily change. The value is the maximum period (in candles) that any of the informatives timeframes need to compute stable indicators.
 
 In this example strategy, this should be set to 100 (`startup_candle_count = 100`), since the longest needed history is 100 candles.
 
@@ -146,8 +176,14 @@ In this example strategy, this should be set to 100 (`startup_candle_count = 100
 
 By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.
 
+!!! Warning "Using x calls to get OHLCV"
+    If you receive a warning like `WARNING - Using 3 calls to get OHLCV. This can result in slower operations for the bot. Please check if you really need 1500 candles for your strategy` - you should consider if you really need this much historic data for your signals.
+    Having this will cause Freqtrade to make multiple calls for the same pair, which will obviously be slower than one network request.
+    As a consequence, Freqtrade will take longer to refresh candles - and should therefore be avoided if possible.
+    This is capped to 5 total calls to avoid overloading the exchange, or make freqtrade too slow.
+
 !!! Warning
-    `startup_candle_count` should be below `ohlcv_candle_limit` (which is 500 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
+    `startup_candle_count` should be below `ohlcv_candle_limit * 5` (which is 500 * 5 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
 
 #### Example
 
@@ -163,18 +199,18 @@ If this data is available, indicators will be calculated with this extended time
 !!! Note
     If data for the startup period is not available, then the timerange will be adjusted to account for this startup period - so Backtesting would start at 2019-01-01 08:30:00.
 
-### Buy signal rules
+### Entry signal rules
 
-Edit the method `populate_buy_trend()` in your strategy file to update your buy strategy.
+Edit the method `populate_entry_trend()` in your strategy file to update your entry strategy.
 
 It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
 
-This method will also define a new column, `"buy"`, which needs to contain 1 for buys, and 0 for "no action".
+This method will also define a new column, `"enter_long"` (`"enter_short"` for shorts), which needs to contain 1 for entries, and 0 for "no action". `enter_long` is a mandatory column that must be set even if the strategy is shorting only.
 
 Sample from `user_data/strategies/sample_strategy.py`:
 
 ```python
-def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     """
     Based on TA indicators, populates the buy signal for the given dataframe
     :param dataframe: DataFrame populated with indicators
@@ -188,29 +224,59 @@ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
             (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
             (dataframe['volume'] > 0)  # Make sure Volume is not 0
         ),
-        'buy'] = 1
+        ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
 
     return dataframe
 ```
 
+??? Note "Enter short trades"
+    Short-entries can be created by setting `enter_short` (corresponds to `enter_long` for long trades).
+    The `enter_tag` column remains identical.
+    Short-trades need to be supported by your exchange and market configuration!
+    Please make sure to set [`can_short`]() appropriately on your strategy if you intend to short.
+
+    ```python
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+                (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
+                (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
+
+        dataframe.loc[
+            (
+                (qtpylib.crossed_below(dataframe['rsi'], 70)) &  # Signal: RSI crosses below 70
+                (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
+                (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            ['enter_short', 'enter_tag']] = (1, 'rsi_cross')
+
+        return dataframe
+    ```
+
 !!! Note
     Buying requires sellers to buy from - therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods.
 
-### Sell signal rules
+### Exit signal rules
 
-Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
-Please note that the sell-signal is only used if `use_sell_signal` is set to true in the configuration.
+Edit the method `populate_exit_trend()` into your strategy file to update your exit strategy.
+The exit-signal is only used for exits if `use_exit_signal` is set to true in the configuration.
+`use_exit_signal` will not influence [signal collision rules](#colliding-signals) - which will still apply and can prevent entries.
 
 It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
 
-This method will also define a new column, `"sell"`, which needs to contain 1 for sells, and 0 for "no action".
+This method will also define a new column, `"exit_long"` (`"exit_short"` for shorts), which needs to contain 1 for exits, and 0 for "no action".
 
 Sample from `user_data/strategies/sample_strategy.py`:
 
 ```python
-def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     """
-    Based on TA indicators, populates the sell signal for the given dataframe
+    Based on TA indicators, populates the exit signal for the given dataframe
     :param dataframe: DataFrame populated with indicators
     :param metadata: Additional information, like the currently traded pair
     :return: DataFrame with buy column
@@ -222,13 +288,39 @@ def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
             (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
             (dataframe['volume'] > 0)  # Make sure Volume is not 0
         ),
-        'sell'] = 1
+        ['exit_long', 'exit_tag']] = (1, 'rsi_too_high')
     return dataframe
 ```
 
+??? Note "Exit short trades"
+    Short-exits can be created by setting `exit_short` (corresponds to `exit_long`).
+    The `exit_tag` column remains identical.
+    Short-trades need to be supported by your exchange and market configuration!
+
+    ```python
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
+                (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
+                (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            ['exit_long', 'exit_tag']] = (1, 'rsi_too_high')
+        dataframe.loc[
+            (
+                (qtpylib.crossed_below(dataframe['rsi'], 30)) &  # Signal: RSI crosses below 30
+                (dataframe['tema'] < dataframe['bb_middleband']) &  # Guard
+                (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            ['exit_short', 'exit_tag']] = (1, 'rsi_too_low')
+        return dataframe
+    ```
+
 ### Minimal ROI
 
-This dict defines the minimal Return On Investment (ROI) a trade should reach before selling, independent from the sell signal.
+This dict defines the minimal Return On Investment (ROI) a trade should reach before exiting, independent from the exit signal.
 
 It is of the following format, with the dict key (left side of the colon) being the minutes passed since the trade opened, and the value (right side of the colon) being the percentage.
 
@@ -243,10 +335,10 @@ minimal_roi = {
 
 The above configuration would therefore mean:
 
-- Sell whenever 4% profit was reached
-- Sell when 2% profit was reached (in effect after 20 minutes)
-- Sell when 1% profit was reached (in effect after 30 minutes)
-- Sell when trade is non-loosing (in effect after 40 minutes)
+- Exit whenever 4% profit was reached
+- Exit when 2% profit was reached (in effect after 20 minutes)
+- Exit when 1% profit was reached (in effect after 30 minutes)
+- Exit when trade is non-loosing (in effect after 40 minutes)
 
 The calculation does include fees.
 
@@ -258,7 +350,7 @@ minimal_roi = {
 }
 ```
 
-While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
+While technically not completely disabled, this would exit once the trade reaches 10000% Profit.
 
 To use times based on candle duration (timeframe), the following snippet can be handy.
 This will allow you to change the timeframe for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
@@ -281,36 +373,49 @@ class AwesomeStrategy(IStrategy):
 
 Setting a stoploss is highly recommended to protect your capital from strong moves against you.
 
-Sample:
+Sample of setting a 10% stoploss:
 
 ``` python
 stoploss = -0.10
 ```
 
-This would signify a stoploss of -10%.
-
 For the full documentation on stoploss features, look at the dedicated [stoploss page](stoploss.md).
 
-If your exchange supports it, it's recommended to also set `"stoploss_on_exchange"` in the order_types dictionary, so your stoploss is on the exchange and cannot be missed due to network problems, high load or other reasons.
-
-For more information on order_types please look [here](configuration.md#understand-order_types).
-
-### Timeframe (formerly ticker interval)
+### Timeframe
 
 This is the set of candles the bot should download and use for the analysis.
 Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
 
-Please note that the same buy/sell signals may work well with one timeframe, but not with the others.
+Please note that the same entry/exit signals may work well with one timeframe, but not with the others.
 
 This setting is accessible within the strategy methods as the `self.timeframe` attribute.
 
+### Can short
+
+To use short signals in futures markets, you will have to let us know to do so by setting `can_short=True`.
+Strategies which enable this will fail to load on spot markets.
+Disabling of this will have short signals ignored (also in futures markets).
+
 ### Metadata dict
 
-The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `populate_indicators`) contains additional information.
+The metadata-dict (available for `populate_entry_trend`, `populate_exit_trend`, `populate_indicators`) contains additional information.
 Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
 
 The Metadata-dict should not be modified and does not persist information across multiple calls.
-Instead, have a look at the section [Storing information](strategy-advanced.md#Storing-information)
+Instead, have a look at the [Storing information](strategy-advanced.md#Storing-information) section.
+
+## Strategy file loading
+
+By default, freqtrade will attempt to load strategies from all `.py` files within `user_data/strategies`.
+
+Assuming your strategy is called `AwesomeStrategy`, stored in the file `user_data/strategies/AwesomeStrategy.py`, then you can start freqtrade with `freqtrade trade --strategy AwesomeStrategy`.
+Note that we're using the class-name, not the file name.
+
+You can use `freqtrade list-strategies` to see a list of all strategies Freqtrade is able to load (all strategies in the correct folder).
+It will also include a "status" field, highlighting potential problems.
+
+??? Hint "Customize strategy directory"
+    You can use a different directory by using `--strategy-path user_data/otherPath`. This parameter is available to all commands that require a strategy.
 
 ## Informative Pairs
 
@@ -339,6 +444,19 @@ A full sample can be found [in the DataProvider section](#complete-data-provider
     It is however better to use resampling to longer timeframes whenever possible
     to avoid hammering the exchange with too many requests and risk being blocked.
 
+??? Note "Alternative candle types"
+    Informative_pairs can also provide a 3rd tuple element defining the candle type explicitly.
+    Availability of alternative candle-types will depend on the trading-mode and the exchange. Details about this can be found in the exchange documentation.
+
+    ``` python
+    def informative_pairs(self):
+        return [
+            ("ETH/USDT", "5m", ""),   # Uses default candletype, depends on trading_mode 
+            ("ETH/USDT", "5m", "spot"),   # Forces usage of spot candles
+            ("BTC/TUSD", "15m", "futures"),  # Uses futures candles
+            ("BTC/TUSD", "15m", "mark"),  # Uses mark candles
+        ]
+    ```
 ***
 
 ### Informative pairs decorator (`@informative()`)
@@ -352,6 +470,8 @@ for more information.
     ``` python
     def informative(timeframe: str, asset: str = '',
                     fmt: Optional[Union[str, Callable[[KwArg(str)], str]]] = None,
+                    *,
+                    candle_type: Optional[CandleType] = None,
                     ffill: bool = True) -> Callable[[PopulateIndicators], PopulateIndicators]:
         """
         A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to
@@ -380,6 +500,7 @@ for more information.
         * {column} - name of dataframe column.
         * {timeframe} - timeframe of informative dataframe.
         :param ffill: ffill dataframe after merging informative pair.
+        :param candle_type: '', mark, index, premiumIndex, or funding_rate
         """
     ```
 
@@ -408,7 +529,7 @@ for more information.
 
         # Define BTC/STAKE informative pair. Available in populate_indicators and other methods as
         # 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable 
-        # instead of hardcoding actual stake currency. Available in populate_indicators and other 
+        # instead of hard-coding actual stake currency. Available in populate_indicators and other 
         # methods as 'btc_usdt_rsi_1h' (when stake currency is USDT).
         @informative('1h', 'BTC/{stake}')
         def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -447,7 +568,7 @@ for more information.
     Use string formatting when accessing informative dataframes of other pairs. This will allow easily changing stake currency in config without having to adjust strategy code.
 
     ``` python
-    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         stake = self.config['stake_currency']
         dataframe.loc[
             (
@@ -455,7 +576,7 @@ for more information.
                 &
                 (dataframe['volume'] > 0)
             ),
-            ['buy', 'buy_tag']] = (1, 'buy_signal_rsi')
+            ['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')
     
         return dataframe
     ```
@@ -467,7 +588,6 @@ for more information.
     will overwrite previously defined method and not produce any errors due to limitations of Python programming language. In such cases you will find that indicators
     created in earlier-defined methods are not available in the dataframe. Carefully review method names and make sure they are unique!
 
-
 ## Additional data (DataProvider)
 
 The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
@@ -498,9 +618,8 @@ Please always check the mode of operation to select the correct method to get da
 ### *available_pairs*
 
 ``` python
-if self.dp:
-    for pair, timeframe in self.dp.available_pairs:
-        print(f"available {pair}, {timeframe}")
+for pair, timeframe in self.dp.available_pairs:
+    print(f"available {pair}, {timeframe}")
 ```
 
 ### *current_whitelist()*
@@ -511,9 +630,9 @@ The strategy might look something like this:
 
 *Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to buy and sell.*
 
-Due to the limited available data, it's very difficult to resample our `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
+Due to the limited available data, it's very difficult to resample `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500-1000 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
 
-Since we can't resample our data we will have to use an informative pair; and since our whitelist will be dynamic we don't know which pair(s) to use.
+Since we can't resample the data we will have to use an informative pair; and since the whitelist will be dynamic we don't know which pair(s) to use.
 
 This is where calling `self.dp.current_whitelist()` comes in handy.
 
@@ -527,14 +646,16 @@ This is where calling `self.dp.current_whitelist()` comes in handy.
         return informative_pairs
 ```
 
+??? Note "Plotting with current_whitelist"
+    Current whitelist is not supported for `plot-dataframe`, as this command is usually used by providing an explicit pairlist - and would therefore make the return values of this method misleading.
+
 ### *get_pair_dataframe(pair, timeframe)*
 
 ``` python
 # fetch live / historical candle (OHLCV) data for the first informative pair
-if self.dp:
-    inf_pair, inf_timeframe = self.informative_pairs()[0]
-    informative = self.dp.get_pair_dataframe(pair=inf_pair,
-                                             timeframe=inf_timeframe)
+inf_pair, inf_timeframe = self.informative_pairs()[0]
+informative = self.dp.get_pair_dataframe(pair=inf_pair,
+                                            timeframe=inf_timeframe)
 ```
 
 !!! Warning "Warning about backtesting"
@@ -549,10 +670,9 @@ It can also be used in specific callbacks to get the signal that caused the acti
 
 ``` python
 # fetch current dataframe
-if self.dp:
-    if self.dp.runmode.value in ('live', 'dry_run'):
-        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
-                                                                 timeframe=self.timeframe)
+if self.dp.runmode.value in ('live', 'dry_run'):
+    dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
+                                                                timeframe=self.timeframe)
 ```
 
 !!! Note "No data available"
@@ -562,11 +682,10 @@ if self.dp:
 ### *orderbook(pair, maximum)*
 
 ``` python
-if self.dp:
-    if self.dp.runmode.value in ('live', 'dry_run'):
-        ob = self.dp.orderbook(metadata['pair'], 1)
-        dataframe['best_bid'] = ob['bids'][0][0]
-        dataframe['best_ask'] = ob['asks'][0][0]
+if self.dp.runmode.value in ('live', 'dry_run'):
+    ob = self.dp.orderbook(metadata['pair'], 1)
+    dataframe['best_bid'] = ob['bids'][0][0]
+    dataframe['best_ask'] = ob['asks'][0][0]
 ```
 
 The orderbook structure is aligned with the order structure from [ccxt](https://github.com/ccxt/ccxt/wiki/Manual#order-book-structure), so the result will look as follows:
@@ -595,12 +714,11 @@ Therefore, using `ob['bids'][0][0]` as demonstrated above will result in using t
 ### *ticker(pair)*
 
 ``` python
-if self.dp:
-    if self.dp.runmode.value in ('live', 'dry_run'):
-        ticker = self.dp.ticker(metadata['pair'])
-        dataframe['last_price'] = ticker['last']
-        dataframe['volume24h'] = ticker['quoteVolume']
-        dataframe['vwap'] = ticker['vwap']
+if self.dp.runmode.value in ('live', 'dry_run'):
+    ticker = self.dp.ticker(metadata['pair'])
+    dataframe['last_price'] = ticker['last']
+    dataframe['volume24h'] = ticker['quoteVolume']
+    dataframe['vwap'] = ticker['vwap']
 ```
 
 !!! Warning
@@ -610,7 +728,24 @@ if self.dp:
     data returned from the exchange and add appropriate error handling / defaults.
 
 !!! Warning "Warning about backtesting"
-    This method will always return up-to-date values - so usage during backtesting / hyperopt will lead to wrong results.
+    This method will always return up-to-date values - so usage during backtesting / hyperopt without runmode checks will lead to wrong results.
+
+### Send Notification
+
+The dataprovider `.send_msg()` function allows you to send custom notifications from your strategy.
+Identical notifications will only be sent once per candle, unless the 2nd argument (`always_send`) is set to True.
+
+``` python
+    self.dp.send_msg(f"{metadata['pair']} just got hot!")
+
+    # Force send this notification, avoid caching (Please read warning below!)
+    self.dp.send_msg(f"{metadata['pair']} just got hot!", always_send=True)
+```
+
+Notifications will only be sent in trading modes (Live/Dry-run) - so this method can be called without conditions for backtesting.
+
+!!! Warning "Spamming"
+    You can spam yourself pretty good by setting `always_send=True` in this method. Use this with great care and only in conditions you know will not happen throughout a candle to avoid a message every 5 seconds.
 
 ### Complete Data-provider sample
 
@@ -663,7 +798,7 @@ class SampleStrategy(IStrategy):
 
         return dataframe
 
-    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
         dataframe.loc[
             (
@@ -671,7 +806,7 @@ class SampleStrategy(IStrategy):
                 (dataframe['rsi_1d'] < 30) &                     # Ensure daily RSI is < 30
                 (dataframe['volume'] > 0)                        # Ensure this candle had volume (important for backtesting)
             ),
-            'buy'] = 1
+            ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
 
 ```
 
@@ -690,6 +825,8 @@ Options:
 - Merge the dataframe without lookahead bias
 - Forward-fill (optional)
 
+For a full sample, please refer to the [complete data provider example](#complete-data-provider-sample) below.
+
 All columns of the informative dataframe will be available on the returning dataframe in a renamed fashion:
 
 !!! Example "Column renaming"
@@ -748,7 +885,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
 
     Say the open price was $100, and `current_price` is $121 (`current_profit` will be `0.21`).  
 
-    If we want a stop price at 7% above the open price we can call `stoploss_from_open(0.07, current_profit)` which will return `0.1157024793`.  11.57% below $121 is $107, which is the same as 7% above $100.
+    If we want a stop price at 7% above the open price we can call `stoploss_from_open(0.07, current_profit, False)` which will return `0.1157024793`.  11.57% below $121 is $107, which is the same as 7% above $100.
 
 
     ``` python
@@ -768,7 +905,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
 
             # once the profit has risen above 10%, keep the stoploss at 7% above the open price
             if current_profit > 0.10:
-                return stoploss_from_open(0.07, current_profit)
+                return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
 
             return 1
 
@@ -779,7 +916,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
 !!! Note
     Providing invalid input to `stoploss_from_open()` may produce "CustomStoploss function did not return valid stoploss" warnings.
     This may happen if `current_profit` parameter is below specified `open_relative_stop`. Such situations may arise when closing trade
-    is blocked by `confirm_trade_exit()` method. Warnings can be solved by never blocking stop loss sells by checking `sell_reason` in
+    is blocked by `confirm_trade_exit()` method. Warnings can be solved by never blocking stop loss sells by checking `exit_reason` in
     `confirm_trade_exit()`, or by using `return stoploss_from_open(...) or 1` idiom, which will request to not change stop loss when
     `current_profit < open_relative_stop`.
 
@@ -789,13 +926,13 @@ In some situations it may be confusing to deal with stops relative to current ra
 
 ??? Example "Returning a stoploss using absolute price from the custom stoploss function"
 
-    If we want to trail a stop price at 2xATR below current proce we can call `stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)`.
+    If we want to trail a stop price at 2xATR below current price we can call `stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short)`.
 
     ``` python
 
     from datetime import datetime
     from freqtrade.persistence import Trade
-    from freqtrade.strategy import IStrategy, stoploss_from_open
+    from freqtrade.strategy import IStrategy, stoploss_from_absolute
 
     class AwesomeStrategy(IStrategy):
 
@@ -809,7 +946,7 @@ In some situations it may be confusing to deal with stops relative to current ra
                             current_rate: float, current_profit: float, **kwargs) -> float:
             dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
             candle = dataframe.iloc[-1].squeeze()
-            return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)
+            return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short)
 
     ```
 
@@ -877,7 +1014,7 @@ if self.config['runmode'].value in ('live', 'dry_run'):
 Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015).
 
 ``` json
-{'pair': "ETH/BTC", 'profit': 0.015, 'count': 5}
+{"pair": "ETH/BTC", "profit": 0.015, "count": 5}
 ```
 
 !!! Warning
@@ -896,7 +1033,8 @@ Sometimes it may be desired to lock a pair after certain events happen (e.g. mul
 Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until, [reason])`.
 `until` must be a datetime object in the future, after which trading will be re-enabled for that pair, while `reason` is an optional string detailing why the pair was locked.
 
-Locks can also be lifted manually, by calling `self.unlock_pair(pair)`.
+Locks can also be lifted manually, by calling `self.unlock_pair(pair)` or `self.unlock_reason(<reason>)` - providing reason the pair was locked with.
+`self.unlock_reason(<reason>)` will unlock all pairs currently locked with the provided reason.
 
 To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
 
@@ -930,16 +1068,16 @@ if self.config['runmode'].value in ('live', 'dry_run'):
 
 ## Print created dataframe
 
-To inspect the created dataframe, you can issue a print-statement in either `populate_buy_trend()` or `populate_sell_trend()`.
+To inspect the created dataframe, you can issue a print-statement in either `populate_entry_trend()` or `populate_exit_trend()`.
 You may also want to print the pair so it's clear what data is currently shown.
 
 ``` python
-def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     dataframe.loc[
         (
             #>> whatever condition<<<
         ),
-        'buy'] = 1
+        ['enter_long', 'enter_tag']] = (1, 'somestring')
 
     # Print the Analyzed pair
     print(f"result for {metadata['pair']}")
@@ -966,9 +1104,18 @@ The following lists some common patterns which should be avoided to prevent frus
 - don't use `dataframe['volume'].mean()`. This uses the full DataFrame for backtesting, including data from the future. Use `dataframe['volume'].rolling(<window>).mean()` instead
 - don't use `.resample('1h')`. This uses the left border of the interval, so moves data from an hour to the start of the hour. Use `.resample('1h', label='right')` instead.
 
+### Colliding signals
+
+When conflicting signals collide (e.g. both `'enter_long'` and `'exit_long'` are 1), freqtrade will do nothing and ignore the entry signal. This will avoid trades that enter, and exit immediately. Obviously, this can potentially lead to missed entries.
+
+The following rules apply, and entry signals will be ignored if more than one of the 3 signals is set:
+
+- `enter_long` -> `exit_long`, `enter_short`
+- `enter_short` -> `exit_short`, `enter_long`
+
 ## Further strategy ideas
 
-To get additional Ideas for strategies, head over to our [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
+To get additional Ideas for strategies, head over to the [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
 Feel free to use any of them as inspiration for your own strategies.
 We're happy to accept Pull Requests containing new Strategies to that repo.