Merge pull request #4434 from freqtrade/new_release

New release 2020.2

.devcontainer/Dockerfile

@@ -3,13 +3,15 @@ FROM freqtradeorg/freqtrade:develop
 # Install dependencies
 COPY requirements-dev.txt /freqtrade/
 RUN apt-get update \
-    && apt-get -y install git sudo vim \
+    && apt-get -y install git mercurial sudo vim \
     && apt-get clean \
     && pip install autopep8 -r docs/requirements-docs.txt -r requirements-dev.txt --no-cache-dir \
     && useradd -u 1000 -U -m ftuser \
     && mkdir -p /home/ftuser/.vscode-server /home/ftuser/.vscode-server-insiders /home/ftuser/commandhistory \
     && echo "export PROMPT_COMMAND='history -a'" >> /home/ftuser/.bashrc \
     && echo "export HISTFILE=~/commandhistory/.bash_history" >> /home/ftuser/.bashrc \
+    && mv /root/.local /home/ftuser/.local/ \
+    && chown ftuser:ftuser -R /home/ftuser/.local/ \
     && chown ftuser: -R /home/ftuser/
 
 USER ftuser

.devcontainer/docker-compose.yml

@@ -7,8 +7,8 @@ services:
       dockerfile: ".devcontainer/Dockerfile"
     volumes:
       # Allow git usage within container
-      - "/home/${USER}/.ssh:/home/ftuser/.ssh:ro"
-      - "/home/${USER}/.gitconfig:/home/ftuser/.gitconfig:ro"
+      - "${HOME}/.ssh:/home/ftuser/.ssh:ro"
+      - "${HOME}/.gitconfig:/home/ftuser/.gitconfig:ro"
       - ..:/freqtrade:cached
       # Persist bash-history
       - freqtrade-vscode-server:/home/ftuser/.vscode-server

.github/ISSUE_TEMPLATE/question.md

@@ -1,5 +1,5 @@
 ---
-name: BQuestion
+name: Question
 about: Ask a question you could not find an answer in the docs
 title: ''
 labels: "Question"

.github/workflows/ci.yml

@@ -14,13 +14,13 @@ on:
     - cron:  '0 5 * * 4'
 
 jobs:
-  build:
+  build_linux:
 
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ ubuntu-18.04, ubuntu-20.04, macos-latest ]
-        python-version: [3.7, 3.8]
+        os: [ ubuntu-18.04, ubuntu-20.04 ]
+        python-version: [3.7, 3.8, 3.9]
 
     steps:
     - uses: actions/checkout@v2
@@ -31,26 +31,19 @@ jobs:
         python-version: ${{ matrix.python-version }}
 
     - name: Cache_dependencies
-      uses: actions/cache@v1
+      uses: actions/cache@v2
       id: cache
       with:
         path: ~/dependencies/
         key: ${{ runner.os }}-dependencies
 
     - name: pip cache (linux)
-      uses: actions/cache@preview
+      uses: actions/cache@v2
       if: startsWith(matrix.os, 'ubuntu')
       with:
         path: ~/.cache/pip
         key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
 
-    - name: pip cache (macOS)
-      uses: actions/cache@preview
-      if: startsWith(matrix.os, 'macOS')
-      with:
-        path: ~/Library/Caches/pip
-        key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
-
     - name: TA binary *nix
       if: steps.cache.outputs.cache-hit != 'true'
       run: |
@@ -68,6 +61,12 @@ jobs:
     - name: Tests
       run: |
         pytest --random-order --cov=freqtrade --cov-config=.coveragerc
+      if: matrix.python-version != '3.9'
+
+    - name: Tests incl. ccxt compatibility tests
+      run: |
+        pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
+      if: matrix.python-version == '3.9'
 
     - name: Coveralls
       if: (startsWith(matrix.os, 'ubuntu-20') && matrix.python-version == '3.8')
@@ -80,13 +79,13 @@ jobs:
 
     - name: Backtesting
       run: |
-        cp config.json.example config.json
+        cp config_bittrex.json.example config.json
         freqtrade create-userdir --userdir user_data
         freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
 
     - name: Hyperopt
       run: |
-        cp config.json.example config.json
+        cp config_bittrex.json.example config.json
         freqtrade create-userdir --userdir user_data
         freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
 
@@ -113,6 +112,99 @@ jobs:
         channel: '#notifications'
         url: ${{ secrets.SLACK_WEBHOOK }}
 
+  build_macos:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ macos-latest ]
+        python-version: [3.7, 3.8, 3.9]
+
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Cache_dependencies
+      uses: actions/cache@v2
+      id: cache
+      with:
+        path: ~/dependencies/
+        key: ${{ runner.os }}-dependencies
+
+    - name: pip cache (macOS)
+      uses: actions/cache@v2
+      if: startsWith(matrix.os, 'macOS')
+      with:
+        path: ~/Library/Caches/pip
+        key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
+
+    - name: TA binary *nix
+      if: steps.cache.outputs.cache-hit != 'true'
+      run: |
+        cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
+
+    - name: Installation - macOS
+      run: |
+        brew install hdf5 c-blosc
+        python -m pip install --upgrade pip
+        export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
+        export TA_LIBRARY_PATH=${HOME}/dependencies/lib
+        export TA_INCLUDE_PATH=${HOME}/dependencies/include
+        pip install -r requirements-dev.txt
+        pip install -e .
+
+    - name: Tests
+      run: |
+        pytest --random-order --cov=freqtrade --cov-config=.coveragerc
+
+    - name: Coveralls
+      if: (startsWith(matrix.os, 'ubuntu-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
+
+    - name: Backtesting
+      run: |
+        cp config_bittrex.json.example config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
+
+    - name: Hyperopt
+      run: |
+        cp config_bittrex.json.example config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
+
+    - name: Flake8
+      run: |
+        flake8
+
+    - name: Sort imports (isort)
+      run: |
+        isort --check .
+
+    - name: Mypy
+      run: |
+        mypy freqtrade scripts
+
+    - name: Slack Notification
+      uses: homoluctus/slatify@v1.8.0
+      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 }}
+
+
   build_windows:
 
     runs-on: ${{ matrix.os }}
@@ -146,13 +238,13 @@ jobs:
 
     - name: Backtesting
       run: |
-        cp config.json.example config.json
+        cp config_bittrex.json.example config.json
         freqtrade create-userdir --userdir user_data
         freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
 
     - name: Hyperopt
       run: |
-        cp config.json.example config.json
+        cp config_bittrex.json.example config.json
         freqtrade create-userdir --userdir user_data
         freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
 
@@ -215,7 +307,7 @@ jobs:
 
   # Notify on slack only once - when CI completes (and after deploy) in case it's successfull
   notify-complete:
-    needs: [ build, build_windows, docs_check ]
+    needs: [ build_linux, build_macos, build_windows, docs_check ]
     runs-on: ubuntu-20.04
     steps:
     - name: Slack Notification
@@ -228,8 +320,9 @@ jobs:
         url: ${{ secrets.SLACK_WEBHOOK }}
 
   deploy:
-    needs: [ build, build_windows, docs_check ]
+    needs: [ build_linux, build_macos, build_windows, docs_check ]
     runs-on: ubuntu-20.04
+
     if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
     steps:
     - uses: actions/checkout@v2

.gitignore

@@ -8,6 +8,7 @@ user_data/*
 user_data/notebooks/*
 freqtrade-plot.html
 freqtrade-profit-plot.html
+freqtrade/rpc/api_server/ui/*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

.readthedocs.yml

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

.travis.yml

@@ -1,9 +1,9 @@
 os:
 - linux
-dist: xenial
+dist: bionic
 language: python
 python:
-- 3.6
+- 3.8
 services:
   - docker
 env:
@@ -26,12 +26,12 @@ jobs:
       # - coveralls || true
       name: pytest
     - script:
-      - cp config.json.example config.json
+      - cp config_bittrex.json.example config.json
       - freqtrade create-userdir --userdir user_data
       - freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
       name: backtest
     - script:
-      - cp config.json.example config.json
+      - cp config_bittrex.json.example config.json
       - freqtrade create-userdir --userdir user_data
       - freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily
       name: hyperopt

CONTRIBUTING.md

@@ -12,8 +12,7 @@ Few pointers for contributions:
 - New features need to contain unit tests, must conform to PEP8 (max-line-length = 100) and should be documented with the introduction PR.
 - PR's can be declared as `[WIP]` - which signify Work in Progress Pull Requests (which are not finished).
 
-If you are unsure, discuss the feature on our [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE)
-or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
+If you are unsure, discuss the feature on our [discord server](https://discord.gg/MA9v74M), on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw) or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
 
 ## Getting started
 

Dockerfile

@@ -1,29 +1,48 @@
-FROM python:3.8.6-slim-buster
+FROM python:3.9.2-slim-buster as base
 
-RUN apt-get update \
-    && apt-get -y install curl build-essential libssl-dev sqlite3 \
-    && apt-get clean \
-    && pip install --upgrade pip
+# Setup env
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONFAULTHANDLER 1
+ENV PATH=/root/.local/bin:$PATH
 
 # Prepare environment
 RUN mkdir /freqtrade
 WORKDIR /freqtrade
 
+# Install dependencies
+FROM base as python-deps
+RUN apt-get update \
+    && apt-get -y install curl build-essential libssl-dev git \
+    && apt-get clean \
+    && pip install --upgrade pip
+
 # Install TA-lib
 COPY build_helpers/* /tmp/
 RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
-
 ENV LD_LIBRARY_PATH /usr/local/lib
 
 # Install dependencies
 COPY requirements.txt requirements-hyperopt.txt /freqtrade/
-RUN pip install numpy --no-cache-dir \
-  && pip install -r requirements-hyperopt.txt --no-cache-dir
+RUN  pip install --user --no-cache-dir numpy \
+  && pip install --user --no-cache-dir -r requirements-hyperopt.txt
+
+# Copy dependencies to runtime-image
+FROM base as runtime-image
+COPY --from=python-deps /usr/local/lib /usr/local/lib
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+COPY --from=python-deps /root/.local /root/.local
+
+
 
 # Install and execute
 COPY . /freqtrade/
 RUN pip install -e . --no-cache-dir \
-  && mkdir /freqtrade/user_data/
+  && mkdir /freqtrade/user_data/ \
+  && freqtrade install-ui
+
 ENTRYPOINT ["freqtrade"]
 # Default to trade mode
 CMD [ "trade" ]

Dockerfile.armhf

@@ -1,29 +1,49 @@
-FROM --platform=linux/arm/v7 python:3.7.7-slim-buster
+FROM --platform=linux/arm/v7 python:3.7.9-slim-buster as base
 
-RUN apt-get update \
-  && apt-get -y install curl build-essential libssl-dev libffi-dev libatlas3-base libgfortran5 sqlite3 \
-  && apt-get clean \
-  && pip install --upgrade pip \
-  && echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > /etc/pip.conf
+# Setup env
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONFAULTHANDLER 1
+ENV PATH=/root/.local/bin:$PATH
 
 # Prepare environment
 RUN mkdir /freqtrade
 WORKDIR /freqtrade
 
+RUN  apt-get update \
+  && apt-get -y install libatlas3-base curl sqlite3 \
+  && apt-get clean
+
+# Install dependencies
+FROM base as python-deps
+RUN  apt-get -y install build-essential libssl-dev libffi-dev libgfortran5 \
+  && apt-get clean \
+  && pip install --upgrade pip \
+  && echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > /etc/pip.conf
+
 # Install TA-lib
 COPY build_helpers/* /tmp/
 RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
-
 ENV LD_LIBRARY_PATH /usr/local/lib
 
 # Install dependencies
 COPY requirements.txt /freqtrade/
-RUN pip install numpy --no-cache-dir \
-  && pip install -r requirements.txt --no-cache-dir
+RUN  pip install --user --no-cache-dir numpy \
+  && pip install --user --no-cache-dir -r requirements.txt
+
+# Copy dependencies to runtime-image
+FROM base as runtime-image
+COPY --from=python-deps /usr/local/lib /usr/local/lib
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+COPY --from=python-deps /root/.local /root/.local
 
 # Install and execute
 COPY . /freqtrade/
-RUN pip install -e . --no-cache-dir
+RUN pip install -e . --no-cache-dir \
+  && freqtrade install-ui
+
 ENTRYPOINT ["freqtrade"]
 # Default to trade mode
 CMD [ "trade" ]

MANIFEST.in

@@ -1,5 +1,6 @@
 include LICENSE
 include README.md
-include config.json.example
 recursive-include freqtrade *.py
 recursive-include freqtrade/templates/ *.j2 *.ipynb
+include freqtrade/rpc/api_server/ui/fallback_file.html
+include freqtrade/rpc/api_server/ui/favicon.ico

README.md

@@ -22,12 +22,21 @@ expect.
 We strongly recommend you to have coding and Python knowledge. Do not
 hesitate to read the source code and understand the mechanism of this bot.
 
-## Exchange marketplaces supported
+## Supported Exchange marketplaces
+
+Please read the [exchange specific notes](docs/exchanges.md) to learn about eventual, special configurations needed for each exchange.
 
 - [X] [Bittrex](https://bittrex.com/)
 - [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#blacklists))
 - [X] [Kraken](https://kraken.com/)
-- [ ] [113 others to tests](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
+- [X] [FTX](https://ftx.com)
+- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
+
+### Community tested
+
+Exchanges confirmed working by the community:
+
+- [X] [Bitvavo](https://bitvavo.com/)
 
 ## Documentation
 
@@ -37,9 +46,9 @@ Please find the complete documentation on our [website](https://www.freqtrade.io
 
 ## Features
 
-- [x] **Based on Python 3.6+**: For botting on any operating system - Windows, macOS and Linux.
+- [x] **Based on Python 3.7+**: For botting on any operating system - Windows, macOS and Linux.
 - [x] **Persistence**: Persistence is achieved through sqlite.
-- [x] **Dry-run**: Run the bot without playing money.
+- [x] **Dry-run**: Run the bot without paying money.
 - [x] **Backtesting**: Run a simulation of your buy/sell strategy.
 - [x] **Strategy Optimization by machine learning**: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
 - [x] **Edge position sizing** Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market. [Learn more](https://www.freqtrade.io/en/latest/edge/).
@@ -113,7 +122,7 @@ Telegram is not mandatory. However, this is a great way to control your bot. Mor
 - `/start`: Starts the trader.
 - `/stop`: Stops the trader.
 - `/stopbuy`: Stop entering new trades.
-- `/status [table]`: Lists all open trades.
+- `/status <trade_id>|[table]`: Lists all or specific open trades.
 - `/profit`: Lists cumulative profit from all finished trades
 - `/forcesell <trade_id>|all`: Instantly sells the given trade (Ignoring `minimum_roi`).
 - `/performance`: Show performance of each finished trade grouped by pair
@@ -132,15 +141,13 @@ The project is currently setup in two main branches:
 
 ## Support
 
-### Help / Slack / Discord
+### Help / Discord / Slack
 
-For any questions not covered by the documentation or for further information about the bot, we encourage you to join our slack channel.
+For any questions not covered by the documentation or for further information about the bot, or to simply engage with like-minded individuals, we encourage you to join our slack channel.
 
-- [Click here to join Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE).
+Please check out our [discord server](https://discord.gg/MA9v74M).
 
-Alternatively, check out the newly created [discord server](https://discord.gg/MA9v74M).
-
-*Note*: Since the discord server is relatively new, answers to questions might be slightly delayed as currently the user base quite small.
+You can also join our [Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw).
 
 ### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
 
@@ -171,7 +178,7 @@ to understand the requirements before sending your pull-requests.
 Coding is not a necessity to contribute - maybe start with improving our documentation?
 Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.
 
-**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
+**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [discord](https://discord.gg/MA9v74M) or [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
 
 **Important:** Always create your PR against the `develop` branch, not `stable`.
 
@@ -189,9 +196,9 @@ To run this bot we recommend you a cloud instance with a minimum of:
 
 ### Software requirements
 
-- [Python 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/)
+- [Python 3.7.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)
 - [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)
-- [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
+- [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
 - [Docker](https://www.docker.com/products/docker) (Recommended)

build_helpers/publish_docker.sh

@@ -30,7 +30,7 @@ if [ $? -ne 0 ]; then
 fi
 
 # Run backtest
-docker run --rm -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy DefaultStrategy
+docker run --rm -v $(pwd)/config_bittrex.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy DefaultStrategy
 
 if [ $? -ne 0 ]; then
     echo "failed running backtest"
@@ -51,6 +51,8 @@ fi
 docker images
 
 docker push ${IMAGE_NAME}
+docker push ${IMAGE_NAME}:$TAG_PLOT
+docker push ${IMAGE_NAME}:$TAG
 if [ $? -ne 0 ]; then
     echo "failed pushing repo"
     return 1

config_binance.json.example

@@ -12,15 +12,15 @@
         "sell": 30
     },
     "bid_strategy": {
-        "use_order_book": false,
         "ask_last_balance": 0.0,
+        "use_order_book": false,
         "order_book_top": 1,
         "check_depth_of_market": {
             "enabled": false,
             "bids_to_ask_delta": 1
         }
     },
-    "ask_strategy":{
+    "ask_strategy": {
         "use_order_book": false,
         "order_book_min": 1,
         "order_book_max": 1,
@@ -84,12 +84,13 @@
         "enabled": false,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
-        "verbosity": "info",
+        "verbosity": "error",
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
-        "username": "",
-        "password": ""
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
     },
+    "bot_name": "freqtrade",
     "initial_state": "running",
     "forcebuy_enable": false,
     "internals": {

config_bittrex.json.example

@@ -79,12 +79,13 @@
         "enabled": false,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
-        "verbosity": "info",
+        "verbosity": "error",
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
-        "username": "",
-        "password": ""
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
     },
+    "bot_name": "freqtrade",
     "initial_state": "running",
     "forcebuy_enable": false,
     "internals": {

config_full.json.example

@@ -42,6 +42,7 @@
         "order_book_max": 1,
         "use_sell_signal": true,
         "sell_profit_only": false,
+        "sell_profit_offset": 0.0,
         "ignore_roi_if_buy_signal": false
     },
     "order_types": {
@@ -67,10 +68,43 @@
         {"method": "AgeFilter", "min_days_listed": 10},
         {"method": "PrecisionFilter"},
         {"method": "PriceFilter", "low_price_ratio": 0.01, "min_price": 0.00000010},
-        {"method": "SpreadFilter", "max_spread_ratio": 0.005}
+        {"method": "SpreadFilter", "max_spread_ratio": 0.005},
+        {
+            "method": "RangeStabilityFilter",
+            "lookback_days": 10,
+            "min_rate_of_change": 0.01,
+            "refresh_period": 1440
+        }
+    ],
+    "protections": [
+        {
+            "method": "StoplossGuard",
+            "lookback_period_candles": 60,
+            "trade_limit": 4,
+            "stop_duration_candles": 60,
+            "only_per_pair": false
+        },
+        {
+            "method": "CooldownPeriod",
+            "stop_duration_candles": 20
+        },
+        {
+            "method": "MaxDrawdown",
+            "lookback_period_candles": 200,
+            "trade_limit": 20,
+            "stop_duration_candles": 10,
+            "max_allowed_drawdown": 0.2
+        },
+        {
+            "method": "LowProfitPairs",
+            "lookback_period_candles": 360,
+            "trade_limit": 1,
+            "stop_duration_candles": 2,
+            "required_profit": 0.02
+        }
     ],
     "exchange": {
-        "name": "bittrex",
+        "name": "binance",
         "sandbox": false,
         "key": "your_exchange_key",
         "secret": "your_exchange_secret",
@@ -82,16 +116,21 @@
             "aiohttp_trust_env": false
         },
         "pair_whitelist": [
+            "ALGO/BTC",
+            "ATOM/BTC",
+            "BAT/BTC",
+            "BCH/BTC",
+            "BRD/BTC",
+            "EOS/BTC",
             "ETH/BTC",
+            "IOTA/BTC",
+            "LINK/BTC",
             "LTC/BTC",
-            "ETC/BTC",
-            "DASH/BTC",
-            "ZEC/BTC",
-            "XLM/BTC",
-            "NXT/BTC",
-            "TRX/BTC",
-            "ADA/BTC",
-            "XMR/BTC"
+            "NEO/BTC",
+            "NXS/BTC",
+            "XMR/BTC",
+            "XRP/BTC",
+            "XTZ/BTC"
         ],
         "pair_blacklist": [
             "DOGE/BTC"
@@ -114,7 +153,7 @@
         "remove_pumps": false
     },
     "telegram": {
-        "enabled": true,
+        "enabled": false,
         "token": "your_telegram_token",
         "chat_id": "your_telegram_chat_id",
         "notification_settings": {
@@ -131,12 +170,14 @@
         "enabled": false,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
-        "verbosity": "info",
+        "verbosity": "error",
+        "enable_openapi": false,
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
         "username": "freqtrader",
         "password": "SuperSecurePassword"
     },
+    "bot_name": "freqtrade",
     "db_url": "sqlite:///tradesv3.sqlite",
     "initial_state": "running",
     "forcebuy_enable": false,

config_kraken.json.example

@@ -89,12 +89,13 @@
         "enabled": false,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
-        "verbosity": "info",
+        "verbosity": "error",
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
-        "username": "",
-        "password": ""
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
     },
+    "bot_name": "freqtrade",
     "initial_state": "running",
     "forcebuy_enable": false,
     "internals": {

docker-compose.yml

@@ -9,11 +9,16 @@ services:
     # Build step - only needed when additional dependencies are needed
     # build:
     #   context: .
-    #   dockerfile: "./Dockerfile.technical"
+    #   dockerfile: "./docker/Dockerfile.technical"
     restart: unless-stopped
     container_name: freqtrade
     volumes:
       - "./user_data:/freqtrade/user_data"
+    # Expose api on port 8080 (localhost only)
+    # Please read the https://www.freqtrade.io/en/latest/rest-api/ documentation
+    # before enabling this.
+    # ports:
+    #   - "127.0.0.1:8080:8080"
     # Default command used when running `docker compose up`
     command: >
       trade

docs/advanced-hyperopt.md

@@ -40,6 +40,11 @@ For the sample below, you then need to add the command line parameter `--hyperop
 A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in [userdata/hyperopts](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py).
 
 ``` python
+from datetime import datetime
+from typing import Dict
+
+from pandas import DataFrame
+
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 TARGET_TRADES = 600
@@ -54,6 +59,7 @@ 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],
                                *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for better results
@@ -63,7 +69,7 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
         * 0.25: Avoiding trade loss
         * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
         """
-        total_profit = results['profit_percent'].sum()
+        total_profit = results['profit_ratio'].sum()
         trade_duration = results['trade_duration'].mean()
 
         trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
@@ -77,10 +83,12 @@ Currently, the arguments are:
 
 * `results`: DataFrame containing the result  
     The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):  
-    `pair, profit_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reason`
+    `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`
 * `trade_count`: Amount of trades (identical to `len(results)`)
-* `min_date`: Start date of the hyperopting TimeFrame
-* `min_date`: End date of the hyperopting TimeFrame
+* `min_date`: Start date of the timerange used
+* `min_date`: End date of the timerange used
+* `config`: Config object used (Note: Not all strategy-related parameters will be updated here if they are part of a hyperopt space).
+* `processed`: Dict of Dataframes with the pair as keys containing the data used for backtesting.
 
 This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
 

docs/backtesting.md

@@ -5,6 +5,89 @@ This page explains how to validate your strategy performance by using Backtestin
 Backtesting requires historic data to be available.
 To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
 
+## Backtesting command reference
+
+```
+usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                             [-d PATH] [--userdir PATH] [-s NAME]
+                             [--strategy-path PATH] [-i TIMEFRAME]
+                             [--timerange TIMERANGE]
+                             [--data-format-ohlcv {json,jsongz,hdf5}]
+                             [--max-open-trades INT]
+                             [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
+                             [--eps] [--dmmp] [--enable-protections]
+                             [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
+                             [--export EXPORT] [--export-filename PATH]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
+                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
+                        `1d`).
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --data-format-ohlcv {json,jsongz,hdf5}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `None`).
+  --max-open-trades INT
+                        Override the value of the `max_open_trades`
+                        configuration setting.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
+  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
+                        entry and exit).
+  --eps, --enable-position-stacking
+                        Allow buying the same pair multiple times (position
+                        stacking).
+  --dmmp, --disable-max-market-positions
+                        Disable applying `max_open_trades` during backtest
+                        (same as setting `max_open_trades` to a very high
+                        number).
+  --enable-protections, --enableprotections
+                        Enable protections for backtesting.Will slow
+                        backtesting down by a considerable amount, but will
+                        include configured protections
+  --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
+                        Provide a space-separated list of strategies to
+                        backtest. Please note that ticker-interval needs to be
+                        set either in config or via command line. When using
+                        this together with `--export trades`, the strategy-
+                        name is injected into the filename (so `backtest-
+                        data.json` becomes `backtest-data-
+                        DefaultStrategy.json`
+  --export EXPORT       Export backtest results, argument are: trades.
+                        Example: `--export=trades`
+  --export-filename PATH
+                        Save backtest results to the file with this filename.
+                        Requires `--export` to be set as well. Example:
+                        `--export-filename=user_data/backtest_results/backtest
+                        _today.json`
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+
+```
+
 ## Test your strategy with Backtesting
 
 Now you have good Buy and Sell strategies and some historic data, you want to test it against
@@ -20,7 +103,7 @@ The result of backtesting will confirm if your bot has better odds of making a p
 !!! Warning "Using dynamic pairlists for backtesting"
     Using dynamic pairlists is possible, however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
     Also, when using pairlists other than StaticPairlist, reproducability of backtesting-results cannot be guaranteed.
-    Please read the [pairlists documentation](configuration.md#pairlists) for more information.
+    Please read the [pairlists documentation](plugins.md#pairlists) for more information.
 
     To achieve reproducible results, best generate a pairlist via the [`test-pairlist`](utils.md#test-pairlist) command and use that as static pairlist.
 
@@ -162,11 +245,16 @@ A backtesting result will look like that:
 |-----------------------+---------------------|
 | Backtesting from      | 2019-01-01 00:00:00 |
 | Backtesting to        | 2019-05-01 00:00:00 |
+| Max open trades       | 3                   |
+|                       |                     |
 | Total trades          | 429                 |
-| First trade           | 2019-01-01 18:30:00 |
-| First trade Pair      | EOS/USDT            |
 | Total Profit %        | 152.41%             |
 | Trades per day        | 3.575               |
+|                       |                     |
+| 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              | 25.27%              |
 | Worst day             | -30.67%             |
 | Avg. Duration Winners | 4:23:00             |
@@ -233,11 +321,16 @@ It contains some useful key metrics about performance of your strategy on backte
 |-----------------------+---------------------|
 | Backtesting from      | 2019-01-01 00:00:00 |
 | Backtesting to        | 2019-05-01 00:00:00 |
+| Max open trades       | 3                   |
+|                       |                     |
 | Total trades          | 429                 |
-| First trade           | 2019-01-01 18:30:00 |
-| First trade Pair      | EOS/USDT            |
 | Total Profit %        | 152.41%             |
 | Trades per day        | 3.575               |
+|                       |                     |
+| 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              | 25.27%              |
 | Worst day             | -30.67%             |
 | Avg. Duration Winners | 4:23:00             |
@@ -251,16 +344,17 @@ It contains some useful key metrics about performance of your strategy on backte
 
 ```
 
-- `Total trades`: Identical to the total trades of the backtest output table.
-- `First trade`: First trade entered.
-- `First trade pair`: Which pair was part of the first trade.
 - `Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
-- `Total Profit %`: Total profit per stake amount. Aligned to the TOTAL column of the first table.
+- `Max open trades`: Setting of `max_open_trades` (or `--max-open-trades`) - or number of pairs in the pairlist (whatever is lower).
+- `Total trades`: Identical to the total trades of the backtest output table.
+- `Total Profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table.
 - `Trades per day`: Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
+- `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Cum Profit %`.
+- `Best Trade` / `Worst Trade`: Biggest winning trade and biggest losing trade
 - `Best day` / `Worst day`: Best and worst day based on daily profit.
 - `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
 - `Max Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
-- `Drawdown Start` / `Drawdown End`: Start and end datetimes for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
+- `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.
 
 ### Assumptions made by backtesting
@@ -268,18 +362,24 @@ It contains some useful key metrics about performance of your strategy on backte
 Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
 
 - Buys happen at open-price
-- Sell signal sells happen at open-price of the following candle
-- Low happens before high for stoploss, protecting capital first
+- 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
 - 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
+- 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
+- Low happens before high for stoploss, protecting capital first
 - Trailing stoploss
   - High happens first - adjusting stoploss
   - Low uses the adjusted stoploss (so sells 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)
-- Stoploss (and trailing stoploss) is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` and/or `trailing_stop` sell reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes.
+- Evaluation sequence (if multiple signals happen on the same candle)
+  - ROI (if not stoploss)
+  - Sell-signal
+  - 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.

docs/bot-basics.md

@@ -4,6 +4,7 @@ This page provides you some basic concepts on how Freqtrade works and operates.
 
 ## Freqtrade terminology
 
+* Strategy: Your trading strategy, telling the bot what to do.
 * Trade: Open position.
 * Open Order: Order which is currently placed on the exchange, and is not yet complete.
 * Pair: Tradable pair, usually in the format of Quote/Base (e.g. XRP/USDT).
@@ -49,8 +50,9 @@ 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.
-* Calculate indicators (calls `populate_indicators()`).
-* Calls `populate_buy_trend()` and `populate_sell_trend()`
+* 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)
 * Loops per candle simulating entry and exit points.
 * Generate backtest report output
 

docs/bot-usage.md

@@ -205,241 +205,6 @@ in production mode. Example command:
 freqtrade trade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
 ```
 
-## Backtesting commands
-
-Backtesting also uses the config specified via `-c/--config`.
-
-```
-usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                             [-d PATH] [--userdir PATH] [-s NAME]
-                             [--strategy-path PATH] [-i TIMEFRAME]
-                             [--timerange TIMERANGE] [--max-open-trades INT]
-                             [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
-                             [--eps] [--dmmp]
-                             [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
-                             [--export EXPORT] [--export-filename PATH]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
-                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
-                        `1d`).
-  --timerange TIMERANGE
-                        Specify what timerange of data to use.
-  --max-open-trades INT
-                        Override the value of the `max_open_trades`
-                        configuration setting.
-  --stake-amount STAKE_AMOUNT
-                        Override the value of the `stake_amount` configuration
-                        setting.
-  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
-                        entry and exit).
-  --eps, --enable-position-stacking
-                        Allow buying the same pair multiple times (position
-                        stacking).
-  --dmmp, --disable-max-market-positions
-                        Disable applying `max_open_trades` during backtest
-                        (same as setting `max_open_trades` to a very high
-                        number).
-  --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
-                        Provide a space-separated list of strategies to
-                        backtest. Please note that ticker-interval needs to be
-                        set either in config or via command line. When using
-                        this together with `--export trades`, the strategy-
-                        name is injected into the filename (so `backtest-
-                        data.json` becomes `backtest-data-
-                        DefaultStrategy.json`
-  --export EXPORT       Export backtest results, argument are: trades.
-                        Example: `--export=trades`
-  --export-filename PATH
-                        Save backtest results to the file with this filename.
-                        Requires `--export` to be set as well. Example:
-                        `--export-filename=user_data/backtest_results/backtest
-                        _today.json`
-
-Common arguments:
-  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are:
-                        'syslog', 'journald'. See the documentation for more
-                        details.
-  -V, --version         show program's version number and exit
-  -c PATH, --config PATH
-                        Specify configuration file (default:
-                        `userdir/config.json` or `config.json` whichever
-                        exists). Multiple --config options may be used. Can be
-                        set to `-` to read config from stdin.
-  -d PATH, --datadir PATH
-                        Path to directory with historical backtesting data.
-  --userdir PATH, --user-data-dir PATH
-                        Path to userdata directory.
-
-Strategy arguments:
-  -s NAME, --strategy NAME
-                        Specify strategy class name which will be used by the
-                        bot.
-  --strategy-path PATH  Specify additional strategy lookup path.
-
-```
-
-### Getting historic data for backtesting
-
-The first time your run Backtesting, you will need to download some historic data first.
-This can be accomplished by using `freqtrade download-data`.  
-Check the corresponding [Data Downloading](data-download.md) section for more details
-
-## Hyperopt commands
-
-To optimize your strategy, you can use hyperopt parameter hyperoptimization
-to find optimal parameter values for your strategy.
-
-```
-usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
-                          [--userdir PATH] [-s NAME] [--strategy-path PATH]
-                          [-i TIMEFRAME] [--timerange TIMERANGE]
-                          [--max-open-trades INT]
-                          [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
-                          [--hyperopt NAME] [--hyperopt-path PATH] [--eps]
-                          [-e INT]
-                          [--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]]
-                          [--dmmp] [--print-all] [--no-color] [--print-json]
-                          [-j JOBS] [--random-state INT] [--min-trades INT]
-                          [--hyperopt-loss NAME]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
-                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
-                        `1d`).
-  --timerange TIMERANGE
-                        Specify what timerange of data to use.
-  --max-open-trades INT
-                        Override the value of the `max_open_trades`
-                        configuration setting.
-  --stake-amount STAKE_AMOUNT
-                        Override the value of the `stake_amount` configuration
-                        setting.
-  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
-                        entry and exit).
-  --hyperopt NAME       Specify hyperopt class name which will be used by the
-                        bot.
-  --hyperopt-path PATH  Specify additional lookup path for Hyperopt and
-                        Hyperopt Loss functions.
-  --eps, --enable-position-stacking
-                        Allow buying the same pair multiple times (position
-                        stacking).
-  -e INT, --epochs INT  Specify number of epochs (default: 100).
-  --spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]
-                        Specify which parameters to hyperopt. Space-separated
-                        list.
-  --dmmp, --disable-max-market-positions
-                        Disable applying `max_open_trades` during backtest
-                        (same as setting `max_open_trades` to a very high
-                        number).
-  --print-all           Print all results, not only the best ones.
-  --no-color            Disable colorization of hyperopt results. May be
-                        useful if you are redirecting output to a file.
-  --print-json          Print output in JSON format.
-  -j JOBS, --job-workers JOBS
-                        The number of concurrently running jobs for
-                        hyperoptimization (hyperopt worker processes). If -1
-                        (default), all CPUs are used, for -2, all CPUs but one
-                        are used, etc. If 1 is given, no parallel computing
-                        code is used at all.
-  --random-state INT    Set random state to some positive integer for
-                        reproducible hyperopt results.
-  --min-trades INT      Set minimal desired number of trades for evaluations
-                        in the hyperopt optimization path (default: 1).
-  --hyperopt-loss NAME  Specify the class name of the hyperopt loss function
-                        class (IHyperOptLoss). Different functions can
-                        generate completely different results, since the
-                        target for optimization is different. Built-in
-                        Hyperopt-loss-functions are: ShortTradeDurHyperOptLoss,
-                        OnlyProfitHyperOptLoss, SharpeHyperOptLoss,
-                        SharpeHyperOptLossDaily, SortinoHyperOptLoss,
-                        SortinoHyperOptLossDaily.
-
-Common arguments:
-  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are:
-                        'syslog', 'journald'. See the documentation for more
-                        details.
-  -V, --version         show program's version number and exit
-  -c PATH, --config PATH
-                        Specify configuration file (default:
-                        `userdir/config.json` or `config.json` whichever
-                        exists). Multiple --config options may be used. Can be
-                        set to `-` to read config from stdin.
-  -d PATH, --datadir PATH
-                        Path to directory with historical backtesting data.
-  --userdir PATH, --user-data-dir PATH
-                        Path to userdata directory.
-
-Strategy arguments:
-  -s NAME, --strategy NAME
-                        Specify strategy class name which will be used by the
-                        bot.
-  --strategy-path PATH  Specify additional strategy lookup path.
-
-```
-
-## Edge commands
-
-To know your trade expectancy and winrate against historical data, you can use Edge.
-
-```
-usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
-                      [--userdir PATH] [-s NAME] [--strategy-path PATH]
-                      [-i TIMEFRAME] [--timerange TIMERANGE]
-                      [--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
-                      [--fee FLOAT] [--stoplosses STOPLOSS_RANGE]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
-                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
-                        `1d`).
-  --timerange TIMERANGE
-                        Specify what timerange of data to use.
-  --max-open-trades INT
-                        Override the value of the `max_open_trades`
-                        configuration setting.
-  --stake-amount STAKE_AMOUNT
-                        Override the value of the `stake_amount` configuration
-                        setting.
-  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
-                        entry and exit).
-  --stoplosses STOPLOSS_RANGE
-                        Defines a range of stoploss values against which edge
-                        will assess the strategy. The format is "min,max,step"
-                        (without any space). Example:
-                        `--stoplosses=-0.01,-0.1,-0.001`
-
-Common arguments:
-  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are:
-                        'syslog', 'journald'. See the documentation for more
-                        details.
-  -V, --version         show program's version number and exit
-  -c PATH, --config PATH
-                        Specify configuration file (default:
-                        `userdir/config.json` or `config.json` whichever
-                        exists). Multiple --config options may be used. Can be
-                        set to `-` to read config from stdin.
-  -d PATH, --datadir PATH
-                        Path to directory with historical backtesting data.
-  --userdir PATH, --user-data-dir PATH
-                        Path to userdata directory.
-
-Strategy arguments:
-  -s NAME, --strategy NAME
-                        Specify strategy class name which will be used by the
-                        bot.
-  --strategy-path PATH  Specify additional strategy lookup path.
-
-```
-
-To understand edge and how to read the results, please read the [edge documentation](edge.md).
-
 ## Next step
 
 The optimal strategy of the bot will change with time depending of the market trends. The next step is to

docs/configuration.md

@@ -16,8 +16,7 @@ In some advanced use cases, multiple configuration files can be specified and us
 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 default configuration file is not created we recommend you to copy and use the `config.json.example` as a template
-for your bot configuration.
+If default configuration file is not created we recommend you to use `freqtrade new-config --config config.json` to generate a basic configuration file.
 
 The Freqtrade configuration file is to be written in the JSON format.
 
@@ -72,8 +71,10 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
 | `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
 | `ask_strategy.use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
-| `ask_strategy.sell_profit_only` | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `ask_strategy.sell_profit_only` | Wait until the bot reaches `ask_strategy.sell_profit_offset` before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `ask_strategy.sell_profit_offset` | Sell-signal is only active above this value. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0`.* <br> **Datatype:** Float (as ratio)
 | `ask_strategy.ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `ask_strategy.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
 | `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
@@ -81,15 +82,18 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `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.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#pairlists-and-pairlist-handlers)). <br> **Datatype:** List
-| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#pairlists-and-pairlist-handlers)). <br> **Datatype:** List
+| `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_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
 | `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
-| `pairlists` | Define one or more pairlists to be used. [More information below](#pairlists-and-pairlist-handlers). <br>*Defaults to `StaticPairList`.*  <br> **Datatype:** List of Dicts
+| `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). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** List of Dicts
 | `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
@@ -106,13 +110,14 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `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
+| `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
-| `initial_state` | Defines the initial application state. More information below. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
+| `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
 | `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. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
+| `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
@@ -132,6 +137,7 @@ Values set in the configuration file always overwrite values set in the strategy
 * `trailing_stop_positive`
 * `trailing_stop_positive_offset`
 * `trailing_only_offset_is_reached`
+* `use_custom_stoploss`
 * `process_only_new_candles`
 * `order_types`
 * `order_time_in_force`
@@ -139,9 +145,12 @@ Values set in the configuration file always overwrite values set in the strategy
 * `stake_amount`
 * `unfilledtimeout`
 * `disable_dataframe_checks`
+* `protections`
 * `use_sell_signal` (ask_strategy)
 * `sell_profit_only` (ask_strategy)
+* `sell_profit_offset` (ask_strategy)
 * `ignore_roi_if_buy_signal` (ask_strategy)
+* `ignore_buying_expired_candle_after` (ask_strategy)
 
 ### Configuring amount per trade
 
@@ -176,7 +185,7 @@ In the example above this would mean:
     This option only applies with [Static stake amount](#static-stake-amount) - since [Dynamic stake amount](#dynamic-stake-amount) divides the balances evenly.
 
 !!! Note
-    The minimum last stake amount can be configured using `amend_last_stake_amount` - which defaults to 0.5 (50%). This means that the minimum stake amount that's ever used is `stake_amount * 0.5`. This avoids very low stake amounts, that are close to the minimum tradable amount for the pair and can be refused by the exchange.
+    The minimum last stake amount can be configured using `last_stake_amount_min_ratio` - which defaults to 0.5 (50%). This means that the minimum stake amount that's ever used is `stake_amount * 0.5`. This avoids very low stake amounts, that are close to the minimum tradable amount for the pair and can be refused by the exchange.
 
 #### Static stake amount
 
@@ -238,37 +247,31 @@ If it is not set in either Strategy or Configuration, a default of 1000% `{"0":
 !!! Note "Special case to forcesell after a specific time"
     A special case presents using `"<N>": -1` as ROI. This forces the bot to sell a trade after N Minutes, no matter if it's positive or negative, so represents a time-limited force-sell.
 
-### Understand stoploss
-
-Go to the [stoploss documentation](stoploss.md) for more details.
-
-### Understand trailing stoploss
-
-Go to the [trailing stoploss Documentation](stoploss.md#trailing-stop-loss) for details on trailing stoploss.
-
-### Understand initial_state
-
-The `initial_state` configuration parameter is an optional field that defines the initial application state.
-Possible values are `running` or `stopped`. (default=`running`)
-If the value is `stopped` the bot has to be started with `/start` first.
-
 ### Understand forcebuy_enable
 
-The `forcebuy_enable` configuration parameter enables the usage of forcebuy commands via Telegram.
-This is disabled for security reasons by default, and will show a warning message on startup if enabled.
-For example, you can send `/forcebuy ETH/BTC` Telegram command when this feature if enabled to the bot,
-who then buys the pair and holds it until a regular sell-signal (ROI, stoploss, /forcesell) appears.
+The `forcebuy_enable` configuration parameter enables the usage of forcebuy 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.
 
 This can be dangerous with some strategies, so use with care.
 
 See [the telegram documentation](telegram-usage.md) for details on usage.
 
-### Understand process_throttle_secs
+### Ignoring expired candles
 
-The `process_throttle_secs` configuration parameter is an optional field that defines in seconds how long the bot should wait
-before asking the strategy if we should buy or a sell an asset. After each wait period, the strategy is asked again for
-every opened trade wether or not we should sell, and for all the remaining pairs (either the dynamic list of pairs or
-the static list of pairs) if we should buy.
+When working with larger timeframes (for example 1h or more) and using a low `max_open_trades` value, the last candle can be processed as soon as a trade slot becomes available. When processing the last candle, this can lead to a situation where it may not be desirable to use the buy signal on that candle. For example, when using a condition in your strategy where you use a cross-over, that point may have passed too long ago for you to start a trade on it.
+
+In these situations, you can enable the functionality to ignore candles that are beyond a specified period by setting `ask_strategy.ignore_buying_expired_candle_after` to a positive number, indicating the number of seconds after which the buy signal becomes expired.
+
+For example, if your strategy is using a 1h timeframe, and you only want to buy within the first 5 minutes when a new candle comes in, you can add the following configuration to your strategy:
+
+``` json
+  "ask_strategy":{
+    "ignore_buying_expired_candle_after": 300,
+    "price_side": "bid",
+    // ...
+  },
+```
 
 ### Understand order_types
 
@@ -313,22 +316,21 @@ Configuration:
 }
 ```
 
-!!! Note
+!!! Note "Market order support"
     Not all exchanges support "market" orders.
     The following message will be shown if your exchange does not support market orders:
-    `"Exchange <yourexchange>  does not support market orders."`
+    `"Exchange <yourexchange> does not support market orders."` and the bot will refuse to start.
 
-!!! Note
-    Stoploss on exchange interval is not mandatory. Do not change its value if you are
+!!! Warning "Using market orders"
+    Please carefully read the section [Market order pricing](#market-order-pricing) section when using market orders.
+
+!!! Note "Stoploss on exchange"
+    `stoploss_on_exchange_interval` is not mandatory. Do not change its value if you are
     unsure of what you are doing. For more information about how stoploss works please
     refer to [the stoploss documentation](stoploss.md).
 
-!!! Note
     If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new stoploss order.
 
-!!! Warning "Using market orders"
-    Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
-
 !!! 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.
 
@@ -447,136 +449,9 @@ The valid values are:
 "BTC", "ETH", "XRP", "LTC", "BCH", "USDT"
 ```
 
-## Prices used for orders
+--8<-- "includes/pricing.md"
 
-Prices for regular orders can be controlled via the parameter structures `bid_strategy` for buying and `ask_strategy` for selling.
-Prices are always retrieved right before an order is placed, either by querying the exchange tickers or by using the orderbook data.
-
-!!! Note
-    Orderbook data used by Freqtrade are the data retrieved from exchange by the ccxt's function `fetch_order_book()`, i.e. are usually data from the L2-aggregated orderbook, while the ticker data are the structures returned by the ccxt's `fetch_ticker()`/`fetch_tickers()` functions. Refer to the ccxt library [documentation](https://github.com/ccxt/ccxt/wiki/Manual#market-data) for more details.
-
-!!! Warning "Using market orders"
-    Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
-
-### Buy price
-
-#### Check depth of market
-
-When check depth of market is enabled (`bid_strategy.check_depth_of_market.enabled=True`), the buy signals are filtered based on the orderbook depth (sum of all amounts) for each orderbook side.
-
-Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) side depth and the resulting delta is compared to the value of the `bid_strategy.check_depth_of_market.bids_to_ask_delta` parameter. The buy order is only executed if the orderbook delta is greater than or equal to the configured delta value.
-
-!!! Note
-    A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
-
-#### Buy price side
-
-The configuration setting `bid_strategy.price_side` defines the side of the spread the bot looks for when buying.
-
-The following displays an orderbook.
-
-``` explanation
-...
-103
-102
-101  # ask
--------------Current spread
-99   # bid
-98
-97
-...
-```
-
-If `bid_strategy.price_side` is set to `"bid"`, then the bot will use 99 as buying price.  
-In line with that, if `bid_strategy.price_side` is set to `"ask"`, then the bot will use 101 as buying price.
-
-Using `ask` price often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
-Taker fees instead of maker fees will most likely apply even when using limit buy orders.
-Also, prices at the "ask" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
-
-#### Buy price with Orderbook enabled
-
-When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the configured side (`bid_strategy.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
-
-#### Buy price without Orderbook enabled
-
-The following section uses `side` as the configured `bid_strategy.price_side`.
-
-When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
-
-The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
-
-### Sell price
-
-#### Sell price side
-
-The configuration setting `ask_strategy.price_side` defines the side of the spread the bot looks for when selling.
-
-The following displays an orderbook:
-
-``` explanation
-...
-103
-102
-101  # ask
--------------Current spread
-99   # bid
-98
-97
-...
-```
-
-If `ask_strategy.price_side` is set to `"ask"`, then the bot will use 101 as selling price.  
-In line with that, if `ask_strategy.price_side` is set to `"bid"`, then the bot will use 99 as selling price.
-
-#### Sell price with Orderbook enabled
-
-When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the configured orderbook side are validated for a profitable sell-possibility based on the strategy configuration (`minimal_roi` conditions) and the sell order is placed at the first profitable spot.
-
-!!! Note
-    Using `order_book_max` higher than `order_book_min` only makes sense when ask_strategy.price_side is set to `"ask"`.
-
-The idea here is to place the sell order early, to be ahead in the queue.
-
-A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting `ask_strategy.order_book_min` and `ask_strategy.order_book_max` to the same number.
-
-!!! Warning "Order_book_max > 1 - increased risks for stoplosses!"
-    Using `ask_strategy.order_book_max` higher than 1 will increase the risk the stoploss on exchange is cancelled too early, since an eventual [stoploss on exchange](#understand-order_types) will be cancelled as soon as the order is placed.
-    Also, the sell order will remain on the exchange for `unfilledtimeout.sell` (or until it's filled) - which can lead to missed stoplosses (with or without using stoploss on exchange).
-
-!!! Warning "Order_book_max > 1 in dry-run"
-    Using `ask_strategy.order_book_max` higher than 1 will result in improper dry-run results (significantly better than real orders executed on exchange), since dry-run assumes orders to be filled almost instantly.
-    It is therefore advised to not use this setting for dry-runs.
-
-#### Sell price without Orderbook enabled
-
-When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
-
-### 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
-
-``` jsonc
-  "order_types": {
-    "buy": "market",
-    "sell": "market"
-    // ...
-  },
-  "bid_strategy": {
-    "price_side": "ask",
-    // ...
-  },
-  "ask_strategy":{
-    "price_side": "bid",
-    // ...
-  },
-```
-
-Obviously, if only one side is using limit orders, different pricing combinations can be used.
---8<-- "includes/pairlists.md"
-
-## Switch to Dry-run mode
+## Using Dry-run mode
 
 We recommend starting the bot in the Dry-run mode to see how your bot will
 behave and what is the performance of your strategy. In the Dry-run mode the
@@ -609,9 +484,10 @@ Once you will be happy with your bot performance running in the Dry-run mode, yo
 
 ### Considerations for dry-run
 
-* API-keys may or may not be provided. Only Read-Only operations (i.e. operations that do not alter account state) on the exchange are performed in the dry-run mode.
-* Wallets (`/balance`) are simulated.
+* API-keys may or may not be provided. Only Read-Only operations (i.e. operations that do not alter account state) on the exchange are performed in dry-run mode.
+* Wallets (`/balance`) are simulated based on `dry_run_wallet`.
 * Orders are simulated, and will not be posted to the exchange.
+* Orders are assumed to fill immediately, and will never time out.
 * 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.
 
@@ -669,32 +545,6 @@ export HTTPS_PROXY="http://addr:port"
 freqtrade
 ```
 
-## Embedding Strategies
-
-Freqtrade provides you with with an easy way to embed the strategy into your configuration file.
-This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
-in your chosen config file.
-
-### Encoding a string as BASE64
-
-This is a quick example, how to generate the BASE64 string in python
-
-```python
-from base64 import urlsafe_b64encode
-
-with open(file, 'r') as f:
-    content = f.read()
-content = urlsafe_b64encode(content.encode('utf-8'))
-```
-
-The variable 'content', will contain the strategy file in a BASE64 encoded form. Which can now be set in your configurations file as following
-
-```json
-"strategy": "NameOfStrategy:BASE64String"
-```
-
-Please ensure that 'NameOfStrategy' is identical to the strategy name!
-
 ## Next step
 
 Now you have configured your config.json, the next step is to [start your bot](bot-usage.md).

docs/data-download.md

@@ -8,7 +8,7 @@ If no additional parameter is specified, freqtrade will download data for `"1m"`
 Exchange and pairs will come from `config.json` (if specified using `-c/--config`).
 Otherwise `--exchange` becomes mandatory.
 
-You can use a relative timerange (`--days 20`) or an absolute starting point (`--timerange 20200101`). For incremental downloads, the relative approach should be used.
+You can use a relative timerange (`--days 20`) or an absolute starting point (`--timerange 20200101-`). For incremental downloads, the relative approach should be used.
 
 !!! Tip "Tip: Updating existing data"
     If you already have backtesting data available in your data-directory and would like to refresh this data up to today, use `--days xx` with a number slightly higher than the missing number of days. Freqtrade will keep the available data and only download the missing data.
@@ -264,7 +264,19 @@ If you are using Binance for example:
 
 ```bash
 mkdir -p user_data/data/binance
-cp freqtrade/tests/testdata/pairs.json user_data/data/binance
+cp tests/testdata/pairs.json user_data/data/binance
+```
+
+If you your configuration directory `user_data` was made by docker, you may get the following error:
+
+```
+cp: cannot create regular file 'user_data/data/binance/pairs.json': Permission denied
+```
+
+You can fix the permissions of your user-data directory as follows:
+
+```
+sudo chown -R $UID:$GID user_data
 ```
 
 The format of the `pairs.json` file is a simple json list.
@@ -308,10 +320,13 @@ Since this data is large by default, the files use gzip by default. They are sto
 
 To use this mode, simply add `--dl-trades` to your call. This will swap the download method to download trades, and resamples the data locally.
 
+!!! Warning "do not use"
+    You should not use this unless you're a kraken user. Most other exchanges provide OHLCV data with sufficient history.
+
 Example call:
 
 ```bash
-freqtrade download-data --exchange binance --pairs XRP/ETH ETH/BTC --days 20 --dl-trades
+freqtrade download-data --exchange kraken --pairs XRP/EUR ETH/EUR --days 20 --dl-trades
 ```
 
 !!! Note

docs/developer.md

@@ -2,7 +2,7 @@
 
 This page is intended for developers of Freqtrade, people who want to contribute to the Freqtrade codebase or documentation, or people who want to understand the source code of the application they're running.
 
-All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel in [slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) where you can ask questions.
+All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel on [discord](https://discord.gg/MA9v74M) or [slack](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw) where you can ask questions.
 
 ## Documentation
 
@@ -94,7 +94,9 @@ Below is an outline of exception inheritance hierarchy:
 +---+ StrategyError
 ```
 
-## Modules
+---
+
+## Plugins
 
 ### Pairlists
 
@@ -119,6 +121,9 @@ The base-class provides an instance of the exchange (`self._exchange`) the pairl
         self._pairlist_pos = pairlist_pos
 ```
 
+!!! Tip
+    Don't forget to register your pairlist in `constants.py` under the variable `AVAILABLE_PAIRLISTS` - otherwise it will not be selectable.
+
 Now, let's step through the methods which require actions:
 
 #### Pairlist configuration
@@ -170,6 +175,66 @@ In `VolumePairList`, this implements different methods of sorting, does early va
         return pairs
 ```
 
+### Protections
+
+Best read the [Protection documentation](plugins.md#protections) to understand protections.
+This Guide is directed towards Developers who want to develop a new protection.
+
+No protection should use datetime directly, but use the provided `date_now` variable for date calculations. This preserves the ability to backtest protections.
+
+!!! Tip "Writing a new Protection"
+    Best copy one of the existing Protections to have a good example.
+    Don't forget to register your protection in `constants.py` under the variable `AVAILABLE_PROTECTIONS` - otherwise it will not be selectable.
+
+#### Implementation of a new protection
+
+All Protection implementations must have `IProtection` as parent class.
+For that reason, they must implement the following methods:
+
+* `short_desc()`
+* `global_stop()`
+* `stop_per_pair()`.
+
+`global_stop()` and `stop_per_pair()` must return a ProtectionReturn tuple, 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
+
+The `until` portion should be calculated using the provided `calculate_lock_end()` method.
+
+All Protections should use `"stop_duration"` / `"stop_duration_candles"` to define how long a a pair (or all pairs) should be locked.
+The content of this is made available as `self._stop_duration` to the each Protection.
+
+If your protection requires a look-back period, please use `"lookback_period"` / `"lockback_period_candles"` to keep all protections aligned.
+
+#### Global vs. local stops
+
+Protections can have 2 different ways to stop trading for a limited :
+
+* Per pair (local)
+* For all Pairs (globally)
+
+##### Protections - per pair
+
+Protections that implement the per pair approach must set `has_local_stop=True`.
+The method `stop_per_pair()` will be called whenever a trade closed (sell 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).
+
+##### Protections - calculating lock end time
+
+Protections should calculate the lock end time based on the last trade it considers.
+This avoids re-locking should the lookback-period be longer than the actual lock period.
+
+The `IProtection` parent class provides a helper method for this in `calculate_lock_end()`.
+
+---
+
 ## Implement a new Exchange (WIP)
 
 !!! Note
@@ -177,6 +242,9 @@ In `VolumePairList`, this implements different methods of sorting, does early va
 
 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).
+
 ### Stoploss On Exchange
 
 Check if the new exchange supports Stoploss on Exchange orders through their API.

docs/docker.md

@@ -1,201 +0,0 @@
-## Freqtrade with docker without docker-compose
-
-!!! Warning
-    The below documentation is provided for completeness and assumes that you are familiar with running docker containers. If you're just starting out with Docker, we recommend to follow the [Quickstart](docker.md) instructions.
-
-### Download the official Freqtrade docker image
-
-Pull the image from docker hub.
-
-Branches / tags available can be checked out on [Dockerhub tags page](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
-
-```bash
-docker pull freqtradeorg/freqtrade:stable
-# Optionally tag the repository so the run-commands remain shorter
-docker tag freqtradeorg/freqtrade:stable freqtrade
-```
-
-To update the image, simply run the above commands again and restart your running container.
-
-Should you require additional libraries, please [build the image yourself](#build-your-own-docker-image).
-
-!!! Note "Docker image update frequency"
-    The official docker images with tags `stable`, `develop` and `latest` are automatically rebuild once a week to keep the base image up-to-date.
-    In addition to that, every merge to `develop` will trigger a rebuild for `develop` and `latest`.
-
-### Prepare the configuration files
-
-Even though you will use docker, you'll still need some files from the github repository.
-
-#### Clone the git repository
-
-Linux/Mac/Windows with WSL
-
-```bash
-git clone https://github.com/freqtrade/freqtrade.git
-```
-
-Windows with docker
-
-```bash
-git clone --config core.autocrlf=input https://github.com/freqtrade/freqtrade.git
-```
-
-#### Copy `config.json.example` to `config.json`
-
-```bash
-cd freqtrade
-cp -n config.json.example config.json
-```
-
-> To understand the configuration options, please refer to the [Bot Configuration](configuration.md) page.
-
-#### Create your database file
-
-=== "Dry-Run"
-    ``` bash
-    touch tradesv3.dryrun.sqlite
-    ```
-
-=== "Production"
-    ``` bash
-    touch tradesv3.sqlite
-    ```
-
-
-!!! Warning "Database File Path"
-    Make sure to use the path to the correct database file when starting the bot in Docker.
-
-### Build your own Docker image
-
-Best start by pulling the official docker image from dockerhub as explained [here](#download-the-official-docker-image) to speed up building.
-
-To add additional libraries to your docker image, best check out [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.technical) which adds the [technical](https://github.com/freqtrade/technical) module to the image.
-
-```bash
-docker build -t freqtrade -f docker/Dockerfile.technical .
-```
-
-If you are developing using Docker, use `docker/Dockerfile.develop` to build a dev Docker image, which will also set up develop dependencies:
-
-```bash
-docker build -f docker/Dockerfile.develop -t freqtrade-dev .
-```
-
-!!! Warning "Include your config file manually"
-    For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see [5. Run a restartable docker image](#run-a-restartable-docker-image)") to keep it between  updates.
-
-#### Verify the Docker image
-
-After the build process you can verify that the image was created with:
-
-```bash
-docker images
-```
-
-The output should contain the freqtrade image.
-
-### Run the Docker image
-
-You can run a one-off container that is immediately deleted upon exiting with the following command (`config.json` must be in the current working directory):
-
-```bash
-docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
-```
-
-!!! Warning
-    In this example, the database will be created inside the docker instance and will be lost when you refresh your image.
-
-#### Adjust timezone
-
-By default, the container will use UTC timezone.
-If you would like to change the timezone use the following commands:
-
-=== "Linux"
-    ``` bash
-    -v /etc/timezone:/etc/timezone:ro
-
-    # Complete command:
-    docker run --rm -v /etc/timezone:/etc/timezone:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
-    ```
-
-=== "MacOS"
-    ```bash
-    docker run --rm -e TZ=`ls -la /etc/localtime | cut -d/ -f8-9` -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
-    ```
-
-!!! Note "MacOS Issues"
-    The OSX Docker versions after 17.09.1 have a known issue whereby `/etc/localtime` cannot be shared causing Docker to not start.<br>
-    A work-around for this is to start with the MacOS command above
-    More information on this docker issue and work-around can be read [here](https://github.com/docker/for-mac/issues/2396).
-
-### Run a restartable docker image
-
-To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem).
-
-#### 1. Move your config file and database
-
-The following will assume that you place your configuration / database files to `~/.freqtrade`, which is a hidden directory in your home directory. Feel free to use a different directory and replace the directory in the upcomming commands.
-
-```bash
-mkdir ~/.freqtrade
-mv config.json ~/.freqtrade
-mv tradesv3.sqlite ~/.freqtrade
-```
-
-#### 2. Run the docker image
-
-```bash
-docker run -d \
-  --name freqtrade \
-  -v ~/.freqtrade/config.json:/freqtrade/config.json \
-  -v ~/.freqtrade/user_data/:/freqtrade/user_data \
-  -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-  freqtrade trade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
-```
-
-!!! Note
-    When using docker, it's best to specify `--db-url` explicitly to ensure that the database URL and the mounted database file match.
-
-!!! Note
-    All available bot command line parameters can be added to the end of the `docker run` command.
-
-!!! Note
-    You can define a [restart policy](https://docs.docker.com/config/containers/start-containers-automatically/) in docker. It can be useful in some cases to use the `--restart unless-stopped` flag (crash of freqtrade or reboot of your system).
-
-### Monitor your Docker instance
-
-You can use the following commands to monitor and manage your container:
-
-```bash
-docker logs freqtrade
-docker logs -f freqtrade
-docker restart freqtrade
-docker stop freqtrade
-docker start freqtrade
-```
-
-For more information on how to operate Docker, please refer to the [official Docker documentation](https://docs.docker.com/).
-
-!!! Note
-    You do not need to rebuild the image for configuration changes, it will suffice to edit `config.json` and restart the container.
-
-### Backtest with docker
-
-The following assumes that the download/setup of the docker image have been completed successfully.
-Also, backtest-data should be available at `~/.freqtrade/user_data/`.
-
-```bash
-docker run -d \
-  --name freqtrade \
-  -v /etc/localtime:/etc/localtime:ro \
-  -v ~/.freqtrade/config.json:/freqtrade/config.json \
-  -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-  -v ~/.freqtrade/user_data/:/freqtrade/user_data/ \
-  freqtrade backtesting --strategy AwsomelyProfitableStrategy
-```
-
-Head over to the [Backtesting Documentation](backtesting.md) for more details.
-
-!!! Note
-    Additional bot command line parameters can be appended after the image name (`freqtrade` in the above example).

docs/docker_quickstart.md

@@ -8,9 +8,7 @@ Start by downloading and installing Docker CE for your platform:
 * [Windows](https://docs.docker.com/docker-for-windows/install/)
 * [Linux](https://docs.docker.com/install/)
 
-Optionally, [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
-
-Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
+To simplify running freqtrade, please install [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the below [docker quick start guide](#docker-quick-start).
 
 ## Freqtrade with docker-compose
 
@@ -71,7 +69,7 @@ The last 2 steps in the snippet create the directory with `user_data`, as well a
 !!! Question "How to edit the bot configuration?"
     You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
 
-    You can also change the both Strategy and commands by editing the `docker-compose.yml` file.
+    You can also change the both Strategy and commands by editing the command section of your `docker-compose.yml` file.
 
 #### Adding a custom strategy
 
@@ -83,7 +81,8 @@ The `SampleStrategy` is run by default.
 
 !!! Warning "`SampleStrategy` is just a demo!"
     The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
-    Please always backtest the strategy and use dry-run for some time before risking real money!
+    Please always backtest your strategy and use dry-run for some time before risking real money!
+    You will find more information about Strategy development in the [Strategy documentation](strategy-customization.md).
 
 Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
 
@@ -91,18 +90,23 @@ Once this is done, you're ready to launch the bot in trading mode (Dry-run or Li
 docker-compose up -d
 ```
 
+#### Monitoring the bot
+
+You can check for running instances with `docker-compose ps`.
+This should list the service `freqtrade` as `running`. If that's not the case, best check the logs (see next point).
+
 #### Docker-compose logs
 
-Logs will be located at: `user_data/logs/freqtrade.log`. 
-You can check the latest log with the command `docker-compose logs -f`.
+Logs will be written to: `user_data/logs/freqtrade.log`.  
+You can also check the latest log with the command `docker-compose logs -f`.
 
 #### Database
 
-The database will be at: `user_data/tradesv3.sqlite`
+The database will be located at: `user_data/tradesv3.sqlite`
 
 #### Updating freqtrade with docker-compose
 
-To update freqtrade when using `docker-compose` is as simple as running the following 2 commands:
+Updating freqtrade when using `docker-compose` is as simple as running the following 2 commands:
 
 ``` bash
 # Download the latest image
@@ -120,10 +124,10 @@ This will first pull the latest image, and will then restart the container with
 
 Advanced users may edit the docker-compose file further to include all possible options or arguments.
 
-All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
+All freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
 
 !!! Note "`docker-compose run --rm`"
-    Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
+    Including `--rm` will remove the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
 
 #### Example: Download data with docker-compose
 
@@ -172,19 +176,19 @@ docker-compose run --rm freqtrade plot-dataframe --strategy AwesomeStrategy -p B
 
 The output will be stored in the `user_data/plot` directory, and can be opened with any modern browser.
 
-## Data analayis using docker compose
+## Data analysis using docker compose
 
 Freqtrade provides a docker-compose file which starts up a jupyter lab server.
 You can run this server using the following command:
 
 ``` bash
-docker-compose --rm -f docker/docker-compose-jupyter.yml up
+docker-compose -f docker/docker-compose-jupyter.yml up
 ```
 
-This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
+This will create a docker-container running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
 Please use the link that's printed in the console after startup for simplified login.
 
-Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) uptodate.
+Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) up-to-date.
 
 ``` bash
 docker-compose -f docker/docker-compose-jupyter.yml build --no-cache

docs/edge.md

@@ -1,6 +1,6 @@
 # Edge positioning
 
-The `Edge Positioning` module uses probability to calculate your win rate and risk reward ration. It will use these statistics to control your strategy trade entry points, position side and, stoploss. 
+The `Edge Positioning` module uses probability to calculate your win rate and risk reward ratio. It will use these statistics to control your strategy trade entry points, position size and, stoploss. 
 
 !!! Warning
     `Edge positioning` is not compatible with dynamic (volume-based) whitelist.
@@ -9,6 +9,7 @@ The `Edge Positioning` module uses probability to calculate your win rate and ri
     `Edge Positioning` only considers *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
     `Edge Positioning` improves the performance of some trading strategies and *decreases* the performance of others.
 
+
 ## Introduction
 
 Trading strategies are not perfect. They are frameworks that are susceptible to the market and its indicators. Because the market is not at all predictable, sometimes a strategy will win and sometimes the same strategy will lose.
@@ -23,8 +24,8 @@ The Edge Positioning module seeks to improve a strategy's winning probability an
 We raise the following question[^1]:
 
 !!! Question "Which trade is a better option?"
-    a) A trade with 80% of chance of losing $100 and 20% chance of winning $200<br/>
-    b) A trade with 100% of chance of losing $30
+    a) A trade with 80% of chance of losing 100\$ and 20% chance of winning 200\$<br/>
+    b) A trade with 100% of chance of losing 30\$
 
 ???+ Info "Answer"
     The expected value of *a)* is smaller than the expected value of *b)*.<br/>
@@ -34,8 +35,8 @@ We raise the following question[^1]:
 Another way to look at it is to ask a similar question:
 
 !!! Question "Which trade is a better option?"
-    a) A trade with 80% of chance of winning 100 and 20% chance of losing $200<br/>
-    b) A trade with 100% of chance of winning $30
+    a) A trade with 80% of chance of winning 100\$ and 20% chance of losing 200\$<br/>
+    b) A trade with 100% of chance of winning 30\$
 
 Edge positioning tries to answer the hard questions about risk/reward and position size automatically, seeking to minimizes the chances of losing of a given strategy.
 
@@ -55,7 +56,7 @@ Similarly, we can discover the set of losing trades $T_{lose}$ as follows:
 $$ T_{lose} = \{o \in O | o \leq 0\} $$
 
 !!! Example
-    In a section where a strategy made three transactions $O = \{3.5, -1, 15, 0\}$:<br>
+    In a section where a strategy made four transactions $O = \{3.5, -1, 15, 0\}$:<br>
     $T_{win} = \{3.5, 15\}$<br>
     $T_{lose} = \{-1, 0\}$<br>
 
@@ -82,7 +83,7 @@ Risk Reward Ratio ($R$) is a formula used to measure the expected gains of a giv
 $$ R = \frac{\text{potential_profit}}{\text{potential_loss}} $$
 
 ???+ Example "Worked example of $R$ calculation"
-    Let's say that you think that the price of *stonecoin* today is $10.0. You believe that, because they will start mining stonecoin, it will go up to $15.0 tomorrow. There is the risk that the stone is too hard, and the GPUs can't mine it, so the price might go to $0 tomorrow. You are planning to invest $100, which will give you 10 shares (100 / 10).
+    Let's say that you think that the price of *stonecoin* today is 10.0\$. You believe that, because they will start mining stonecoin, it will go up to 15.0\$ tomorrow. There is the risk that the stone is too hard, and the GPUs can't mine it, so the price might go to 0\$ tomorrow. You are planning to invest 100\$, which will give you 10 shares (100 / 10).
 
     Your potential profit is calculated as:
 
@@ -92,9 +93,9 @@ $$ R = \frac{\text{potential_profit}}{\text{potential_loss}} $$
                                 &= 50
     \end{aligned}$
 
-    Since the price might go to $0, the $100 dollars invested could turn into 0.
+    Since the price might go to 0\$, the 100\$ dollars invested could turn into 0.
 
-    We do however use a stoploss of 15% - so in the worst case, we'll sell 15% below entry price (or at 8.5$).
+    We do however use a stoploss of 15% - so in the worst case, we'll sell 15% below entry price (or at 8.5$\).
 
     $\begin{aligned}
         \text{potential_loss} &= (\text{entry_price} - \text{stoploss}) * \frac{\text{investment}}{\text{entry_price}} \\
@@ -109,7 +110,7 @@ $$ R = \frac{\text{potential_profit}}{\text{potential_loss}} $$
             &= \frac{50}{15}\\
             &= 3.33
     \end{aligned}$<br>
-    What it effectively means is that the strategy have the potential to make 3.33$ for each $1 invested.
+    What it effectively means is that the strategy have the potential to make 3.33\$ for each 1\$ invested.
 
 On a long horizon, that is, on many trades, we can calculate the risk reward by dividing the strategy' average profit on winning trades by the strategy' average loss on losing trades. We can calculate the average profit, $\mu_{win}$, as follows:
 
@@ -141,7 +142,7 @@ $$E = R * W - L$$
     $E = R * W - L = 5 * 0.28 - 0.72 = 0.68$
     <br>
 
-The expectancy worked out in the example above means that, on average, this strategy' trades will return 1.68 times the size of its losses. Said another way, the strategy makes $1.68 for every $1 it loses, on average. 
+The expectancy worked out in the example above means that, on average, this strategy' trades will return 1.68 times the size of its losses. Said another way, the strategy makes 1.68\$ for every 1\$ it loses, on average. 
 
 This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
 
@@ -206,7 +207,61 @@ Let's say the stake currency is **ETH** and there is $10$ **ETH** on the wallet.
 
 -   The strategy detects a sell signal in the **XLM/ETH** market. The bot exits **Trade 1** for a profit of $1$ **ETH**. The total capital in the wallet becomes $11$ **ETH** and the available capital for trading becomes $5.5$ **ETH**.
 
--   **Trade 4** The strategy detects a new buy signal int the **XLM/ETH** market. `Edge Positioning` calculates the stoploss of $2%$, and the position size of $0.055 / 0.02 = 2.75$ **ETH**.
+-   **Trade 4** The strategy detects a new buy signal int the **XLM/ETH** market. `Edge Positioning` calculates the stoploss of $2\%$, and the position size of $0.055 / 0.02 = 2.75$ **ETH**.
+
+## Edge command reference
+
+```
+usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
+                      [--userdir PATH] [-s NAME] [--strategy-path PATH]
+                      [-i TIMEFRAME] [--timerange TIMERANGE]
+                      [--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
+                      [--fee FLOAT] [--stoplosses STOPLOSS_RANGE]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
+                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
+                        `1d`).
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --max-open-trades INT
+                        Override the value of the `max_open_trades`
+                        configuration setting.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
+  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
+                        entry and exit).
+  --stoplosses STOPLOSS_RANGE
+                        Defines a range of stoploss values against which edge
+                        will assess the strategy. The format is "min,max,step"
+                        (without any space). Example:
+                        `--stoplosses=-0.01,-0.1,-0.001`
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+
+```
 
 ## Configurations
 
@@ -222,7 +277,7 @@ Edge module has following configuration options:
 | `stoploss_range_max` | Maximum stoploss. <br>*Defaults to `-0.10`.* <br> **Datatype:** Float
 | `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br> **Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br> **Datatype:** Float
 | `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
-| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
+| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10\$ on a trade you expect a 12\$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
 | `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
 | `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
 | `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> **Datatype:** Boolean

docs/exchanges.md

@@ -23,7 +23,8 @@ Binance has been split into 3, and users must use the correct ccxt exchange ID f
 ## Kraken
 
 !!! Tip "Stoploss on Exchange"
-    Kraken supports `stoploss_on_exchange` and uses stop-loss-market orders. It provides great advantages, so we recommend to benefit from it, however since the resulting order is a stoploss-market order, sell-rates are not guaranteed, which makes this feature less secure than on other exchanges. This limitation is based on kraken's policy [source](https://blog.kraken.com/post/1234/announcement-delisting-pairs-and-temporary-suspension-of-advanced-order-types/) and [source2](https://blog.kraken.com/post/1494/kraken-enables-advanced-orders-and-adds-10-currency-pairs/) - which has stoploss-limit orders disabled.
+    Kraken supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
+    You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
 
 ### Historic Kraken data
 
@@ -75,8 +76,7 @@ print(res)
 
 !!! Tip "Stoploss on Exchange"
     FTX supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
-    You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide.
-
+    You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type of stoploss shall be used.
 
 ### Using subaccounts
 
@@ -99,10 +99,10 @@ To use subaccounts with FTX, you need to edit the configuration and add the foll
 
 Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
 
-
 ## Random notes for other exchanges
 
 * The Ocean (exchange id: `theocean`) exchange uses Web3 functionality and requires `web3` python package to be installed:
+
 ```shell
 $ pip3 install web3
 ```

docs/faq.md

@@ -2,30 +2,30 @@
 
 ## Beginner Tips & Tricks
 
-* When you work with your strategy & hyperopt file you should use a proper code editor like vscode or Pycharm. A good code editor will provide syntax highlighting as well as line numbers, making it easy to find syntax errors (most likely, pointed out by Freqtrade during startup).
+* When you work with your strategy & hyperopt file you should use a proper code editor like VSCode or PyCharm. A good code editor will provide syntax highlighting as well as line numbers, making it easy to find syntax errors (most likely pointed out by Freqtrade during startup).
 
 ## Freqtrade common issues
 
 ### The bot does not start
 
-Running the bot with `freqtrade trade --config config.json` does show the output `freqtrade: command not found`.
+Running the bot with `freqtrade trade --config config.json` shows the output `freqtrade: command not found`.
 
-This could have the following reasons:
+This could be caused by the following reasons:
 
-* The virtual environment is not active
-  * run `source .env/bin/activate` to activate the virtual environment
+* The virtual environment is not active.
+  * Run `source .env/bin/activate` to activate the virtual environment.
 * The installation did not work correctly.
   * Please check the [Installation documentation](installation.md).
 
-### I have waited 5 minutes, why hasn't the bot made any trades yet?!
+### I have waited 5 minutes, why hasn't the bot made any trades yet?
 
 * Depending on the buy strategy, the amount of whitelisted coins, the
-situation of the market etc, it can take up to hours to find good entry
+situation of the market etc, it can take up to hours to find a good entry
 position for a trade. Be patient!
 
-* Or it may because of a configuration error? Best check the logs, it's usually telling you if the bot is simply not getting buy signals (only heartbeat messages), or if there is something wrong (errors / exceptions in the log).
+* It may be because of a configuration error. It's best to check the logs, they usually tell you if the bot is simply not getting buy signals (only heartbeat messages), or if there is something wrong (errors / exceptions in the log).
 
-### I have made 12 trades already, why is my total profit negative?!
+### I 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
@@ -36,20 +36,17 @@ of course constantly aim to improve the bot but it will _always_ be a
 gamble, which should leave you with modest wins on monthly basis but
 you can't say much from few trades.
 
-### I’d like to change the stake amount. Can I just stop the bot with /stop and then change the config.json and run it again?
+### I’d like to make changes to the config. Can I do that without having to kill the bot?
 
-Not quite. Trades are persisted to a database but the configuration is
-currently only read when the bot is killed and restarted. `/stop` more
-like pauses. You can stop your bot, adjust settings and start it again.
+Yes. You can edit your config and use the `/reload_config` command to reload the configuration. The bot will stop, reload the configuration and strategy and will restart with the new configuration and strategy.
 
 ### I want to improve the bot with a new strategy
 
-That's great. We have a nice backtesting and hyperoptimization setup. See
-the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-commands).
+That's great. We have a nice backtesting and hyperoptimization setup. See the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-commands).
 
 ### Is there a setting to only SELL the coins being held and not perform anymore BUYS?
 
-You can use the `/forcesell all` command from Telegram.
+You can use the `/stopbuy` command in Telegram to prevent future buys, followed by `/forcesell all` (sell all open trades).
 
 ### I want to run multiple bots on the same machine
 
@@ -59,7 +56,7 @@ Please look at the [advanced setup documentation Page](advanced-setup.md#running
 
 This message is just a warning that the latest candles had missing candles in them.
 Depending on the exchange, this can indicate that the pair didn't have a trade for the timeframe you are using - and the exchange does only return candles with volume.
-On low volume pairs, this is a rather common occurance.
+On low volume pairs, this is a rather common occurrence.
 
 If this happens for all pairs in the pairlist, this might indicate a recent exchange downtime. Please check your exchange's public channels for details.
 
@@ -73,7 +70,7 @@ Read [the Bittrex section about restricted markets](exchanges.md#restricted-mark
 
 ### I'm getting the "Exchange Bittrex does not support market orders." message and cannot run my strategy
 
-As the message says, Bittrex does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Probably your strategy was written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Bittrex).
+As the message says, Bittrex does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Your strategy was probably written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Bittrex).
 
 To fix it for Bittrex, redefine order types in the strategy to use "limit" instead of "market":
 
@@ -85,7 +82,7 @@ To fix it for Bittrex, redefine order types in the strategy to use "limit" inste
     }
 ```
 
-Same fix should be done in the configuration file, if order types are defined in your custom config rather than in the strategy.
+The same fix should be applied in the configuration file, if order types are defined in your custom config rather than in the strategy.
 
 ### How do I search the bot logs for something?
 
@@ -127,10 +124,10 @@ On Windows, the `--logfile` option is also supported by Freqtrade and you can us
 
 ## Hyperopt module
 
-### How many epoch do I need to get a good Hyperopt result?
+### How many epochs do I need to get a good Hyperopt result?
 
 Per default Hyperopt called without the `-e`/`--epochs` command line option will only
-run 100 epochs, means 100 evals of your triggers, guards, ... Too few
+run 100 epochs, means 100 evaluations of your triggers, guards, ... Too few
 to find a great result (unless if you are very lucky), so you probably
 have to run it for 10.000 or more. But it will take an eternity to
 compute.
@@ -140,32 +137,32 @@ Since hyperopt uses Bayesian search, running for too many epochs may not produce
 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.
 
 ```bash
-freqtrade hyperopt --hyperop SampleHyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
+freqtrade hyperopt --hyperopt SampleHyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
 ```
 
 ### Why does it take a long time to run hyperopt?
 
-* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) - or the Freqtrade [discord community](https://discord.gg/X89cVG). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
+* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw) - or the Freqtrade [discord community](https://discord.gg/X89cVG). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
 
 * If you wonder why it can take from 20 minutes to days to do 1000 epochs here are some answers:
 
 This answer was written during the release 0.15.1, when we had:
 
-- 8 triggers
-- 9 guards: let's say we evaluate even 10 values from each
-- 1 stoploss calculation: let's say we want 10 values from that too to be evaluated
+* 8 triggers
+* 9 guards: let's say we evaluate even 10 values from each
+* 1 stoploss calculation: let's say we want 10 values from that too to be evaluated
 
 The following calculation is still very rough and not very precise
 but it will give the idea. With only these triggers and guards there is
-already 8\*10^9\*10 evaluations. A roughly total of 80 billion evals.
-Did you run 100 000 evals? Congrats, you've done roughly 1 / 100 000 th
+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. 
 
 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: 
+Example:
 `freqtrade --config config.json --strategy SampleStrategy --hyperopt SampleHyperopt -e 1000 --timerange 20190601-20200601`
 
 ## Edge module

docs/hyperopt.md

@@ -32,6 +32,107 @@ source .env/bin/activate
 pip install -r requirements-hyperopt.txt
 ```
 
+## Hyperopt command reference
+
+
+```
+usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
+                          [--userdir PATH] [-s NAME] [--strategy-path PATH]
+                          [-i TIMEFRAME] [--timerange TIMERANGE]
+                          [--data-format-ohlcv {json,jsongz,hdf5}]
+                          [--max-open-trades INT]
+                          [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
+                          [--hyperopt NAME] [--hyperopt-path PATH] [--eps]
+                          [--dmmp] [--enable-protections] [-e INT]
+                          [--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]]
+                          [--print-all] [--no-color] [--print-json] [-j JOBS]
+                          [--random-state INT] [--min-trades INT]
+                          [--hyperopt-loss NAME]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
+                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
+                        `1d`).
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --data-format-ohlcv {json,jsongz,hdf5}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `None`).
+  --max-open-trades INT
+                        Override the value of the `max_open_trades`
+                        configuration setting.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
+  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
+                        entry and exit).
+  --hyperopt NAME       Specify hyperopt class name which will be used by the
+                        bot.
+  --hyperopt-path PATH  Specify additional lookup path for Hyperopt and
+                        Hyperopt Loss functions.
+  --eps, --enable-position-stacking
+                        Allow buying the same pair multiple times (position
+                        stacking).
+  --dmmp, --disable-max-market-positions
+                        Disable applying `max_open_trades` during backtest
+                        (same as setting `max_open_trades` to a very high
+                        number).
+  --enable-protections, --enableprotections
+                        Enable protections for backtesting.Will slow
+                        backtesting down by a considerable amount, but will
+                        include configured protections
+  -e INT, --epochs INT  Specify number of epochs (default: 100).
+  --spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]
+                        Specify which parameters to hyperopt. Space-separated
+                        list.
+  --print-all           Print all results, not only the best ones.
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --print-json          Print output in JSON format.
+  -j JOBS, --job-workers JOBS
+                        The number of concurrently running jobs for
+                        hyperoptimization (hyperopt worker processes). If -1
+                        (default), all CPUs are used, for -2, all CPUs but one
+                        are used, etc. If 1 is given, no parallel computing
+                        code is used at all.
+  --random-state INT    Set random state to some positive integer for
+                        reproducible hyperopt results.
+  --min-trades INT      Set minimal desired number of trades for evaluations
+                        in the hyperopt optimization path (default: 1).
+  --hyperopt-loss NAME  Specify the class name of the hyperopt loss function
+                        class (IHyperOptLoss). Different functions can
+                        generate completely different results, since the
+                        target for optimization is different. Built-in
+                        Hyperopt-loss-functions are:
+                        ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
+                        SharpeHyperOptLoss, SharpeHyperOptLossDaily,
+                        SortinoHyperOptLoss, SortinoHyperOptLossDaily
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+
+```
+
 ## Prepare Hyperopting
 
 Before we start digging into Hyperopt, we recommend you to take a look at
@@ -64,9 +165,9 @@ Depending on the space you want to optimize, only some of the below are required
 
 Optional in hyperopt - can also be loaded from a strategy (recommended):
 
-* copy `populate_indicators` from your strategy - otherwise default-strategy will be used
-* copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
-* copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
+* `populate_indicators` - fallback to create indicators
+* `populate_buy_trend` - fallback if not optimizing for buy space. should come from strategy
+* `populate_sell_trend` - fallback if not optimizing for sell space. should come from strategy
 
 !!! Note
     You always have to provide a strategy to Hyperopt, even if your custom Hyperopt class contains all methods.
@@ -104,7 +205,7 @@ This command will create a new hyperopt file from a template, allowing you to ge
 There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing:
 
 * Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
-* Inside `populate_buy_trend()` - applying the parameters.
+* Within `buy_strategy_generator()` - populate the nested `populate_buy_trend()` to apply the parameters.
 
 There you have two different types of indicators: 1. `guards` and 2. `triggers`.
 
@@ -128,7 +229,7 @@ Similar to the buy-signal above, sell-signals can also be optimized.
 Place the corresponding settings into the following methods
 
 * Inside `sell_indicator_space()` - the parameters hyperopt shall be optimizing.
-* Inside `populate_sell_trend()` - applying the parameters.
+* Within `sell_strategy_generator()` - populate the nested method `populate_sell_trend()` to apply the parameters.
 
 The configuration and rules are the same than for buy signals.
 To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
@@ -173,6 +274,11 @@ one we call `trigger` and use it to decide which buy trigger we want to use.
 So let's write the buy strategy using these values:
 
 ```python
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by Hyperopt.
+        """
         def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
             conditions = []
             # GUARDS AND TRENDS

docs/includes/pairlists.md

@@ -10,22 +10,32 @@ If multiple Pairlist Handlers are used, they are chained and a combination of al
 
 Inactive markets are always removed from the resulting pairlist. Explicitly blacklisted pairs (those in the `pair_blacklist` configuration setting) are also always removed from the resulting pairlist.
 
+### Pair blacklist
+
+The pair blacklist (configured via `exchange.pair_blacklist` in the configuration) disallows certain pairs from trading.
+This can be as simple as excluding `DOGE/BTC` - which will remove exactly this pair.
+
+The pair-blacklist does also support wildcards (in regex-style) - so `BNB/.*` will exclude ALL pairs that start with BNB.
+You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged tokens (check Pair naming conventions for your exchange!)
+
 ### Available Pairlist Handlers
 
 * [`StaticPairList`](#static-pair-list) (default, if not configured differently)
 * [`VolumePairList`](#volume-pair-list)
 * [`AgeFilter`](#agefilter)
+* [`PerformanceFilter`](#performancefilter)
 * [`PrecisionFilter`](#precisionfilter)
 * [`PriceFilter`](#pricefilter)
 * [`ShuffleFilter`](#shufflefilter)
 * [`SpreadFilter`](#spreadfilter)
+* [`RangeStabilityFilter`](#rangestabilityfilter)
 
 !!! Tip "Testing pairlists"
     Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) utility sub-command to test your configuration quickly.
 
 #### Static Pair List
 
-By default, the `StaticPairList` method is used, which uses a statically defined pair whitelist from the configuration.
+By default, the `StaticPairList` method is used, which uses a statically defined pair whitelist from the configuration. The pairlist also supports wildcards (in regex-style) - so `.*/BTC` will include all pairs with BTC as a stake.
 
 It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.
 
@@ -35,6 +45,11 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
     ],
 ```
 
+By default, only currently enabled pairs are allowed.
+To skip pair validation against active markets, set `"allow_inactive": true` within the `StaticPairList` configuration.
+This can be useful for backtesting expired pairs (like quarterly spot-markets).
+This option must be configured along with `exchange.skip_pair_validation` in the exchange configuration.
+
 #### Volume Pair List
 
 `VolumePairList` employs sorting/filtering of pairs by their trading volume. It selects `number_assets` top pairs with sorting based on the `sort_key` (which can only be `quoteVolume`).
@@ -54,10 +69,13 @@ The `refresh_period` setting allows to define the period (in seconds), at which
         "method": "VolumePairList",
         "number_assets": 20,
         "sort_key": "quoteVolume",
-        "refresh_period": 1800,
+        "refresh_period": 1800
 }],
 ```
 
+!!! Note
+    `VolumePairList` does not support backtesting mode.
+
 #### AgeFilter
 
 Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`).
@@ -68,6 +86,18 @@ be caught out buying before the pair has finished dropping in price.
 
 This filter allows freqtrade to ignore pairs until they have been listed for at least `min_days_listed` days.
 
+#### PerformanceFilter
+
+Sorts pairs by past trade performance, as follows:
+1. Positive performance.
+2. No closed trades yet.
+3. Negative performance.
+
+Trade count is used as a tie breaker.
+
+!!! Note
+    `PerformanceFilter` does not support backtesting mode.
+
 #### PrecisionFilter
 
 Filters low-value coins which would not allow setting stoplosses.
@@ -113,6 +143,27 @@ Example:
 
 If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027, the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005` and this pair will be filtered out.
 
+#### RangeStabilityFilter
+
+Removes pairs where the difference between lowest low and highest high over `lookback_days` days is below `min_rate_of_change`. Since this is a filter that requires additional data, the results are cached for `refresh_period`.
+
+In the below example:
+If the trading range over the last 10 days is <1%, remove the pair from the whitelist.
+
+```json
+"pairlists": [
+    {
+        "method": "RangeStabilityFilter",
+        "lookback_days": 10,
+        "min_rate_of_change": 0.01,
+        "refresh_period": 1440
+    }
+]
+```
+
+!!! Tip
+    This Filter can be used to automatically remove stable coin pairs, which have a very low trading range, and are therefore extremely difficult to trade with profit.
+
 ### Full example of Pairlist Handlers
 
 The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies both [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#price-filter), filtering all assets where 1 price unit is > 1%. Then the `SpreadFilter` is applied and pairs are finally shuffled with the random seed set to some predefined value.
@@ -132,6 +183,12 @@ The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets,
     {"method": "PrecisionFilter"},
     {"method": "PriceFilter", "low_price_ratio": 0.01},
     {"method": "SpreadFilter", "max_spread_ratio": 0.005},
+    {
+        "method": "RangeStabilityFilter",
+        "lookback_days": 10,
+        "min_rate_of_change": 0.01,
+        "refresh_period": 1440
+    },
     {"method": "ShuffleFilter", "seed": 42}
     ],
 ```

docs/includes/pricing.md

@@ -0,0 +1,127 @@
+## Prices used for orders
+
+Prices for regular orders can be controlled via the parameter structures `bid_strategy` for buying and `ask_strategy` for selling.
+Prices are always retrieved right before an order is placed, either by querying the exchange tickers or by using the orderbook data.
+
+!!! Note
+    Orderbook data used by Freqtrade are the data retrieved from exchange by the ccxt's function `fetch_order_book()`, i.e. are usually data from the L2-aggregated orderbook, while the ticker data are the structures returned by the ccxt's `fetch_ticker()`/`fetch_tickers()` functions. Refer to the ccxt library [documentation](https://github.com/ccxt/ccxt/wiki/Manual#market-data) for more details.
+
+!!! Warning "Using market orders"
+    Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
+
+### Buy price
+
+#### Check depth of market
+
+When check depth of market is enabled (`bid_strategy.check_depth_of_market.enabled=True`), the buy signals are filtered based on the orderbook depth (sum of all amounts) for each orderbook side.
+
+Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) side depth and the resulting delta is compared to the value of the `bid_strategy.check_depth_of_market.bids_to_ask_delta` parameter. The buy order is only executed if the orderbook delta is greater than or equal to the configured delta value.
+
+!!! Note
+    A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
+
+#### Buy price side
+
+The configuration setting `bid_strategy.price_side` defines the side of the spread the bot looks for when buying.
+
+The following displays an orderbook.
+
+``` explanation
+...
+103
+102
+101  # ask
+-------------Current spread
+99   # bid
+98
+97
+...
+```
+
+If `bid_strategy.price_side` is set to `"bid"`, then the bot will use 99 as buying price.  
+In line with that, if `bid_strategy.price_side` is set to `"ask"`, then the bot will use 101 as buying price.
+
+Using `ask` price often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
+Taker fees instead of maker fees will most likely apply even when using limit buy orders.
+Also, prices at the "ask" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
+
+#### Buy price with Orderbook enabled
+
+When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the configured side (`bid_strategy.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
+
+#### Buy price without Orderbook enabled
+
+The following section uses `side` as the configured `bid_strategy.price_side`.
+
+When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
+
+The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
+
+### Sell price
+
+#### Sell price side
+
+The configuration setting `ask_strategy.price_side` defines the side of the spread the bot looks for when selling.
+
+The following displays an orderbook:
+
+``` explanation
+...
+103
+102
+101  # ask
+-------------Current spread
+99   # bid
+98
+97
+...
+```
+
+If `ask_strategy.price_side` is set to `"ask"`, then the bot will use 101 as selling price.  
+In line with that, if `ask_strategy.price_side` is set to `"bid"`, then the bot will use 99 as selling price.
+
+#### Sell price with Orderbook enabled
+
+When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the configured orderbook side are validated for a profitable sell-possibility based on the strategy configuration (`minimal_roi` conditions) and the sell order is placed at the first profitable spot.
+
+!!! Note
+    Using `order_book_max` higher than `order_book_min` only makes sense when ask_strategy.price_side is set to `"ask"`.
+
+The idea here is to place the sell order early, to be ahead in the queue.
+
+A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting `ask_strategy.order_book_min` and `ask_strategy.order_book_max` to the same number.
+
+!!! Warning "Order_book_max > 1 - increased risks for stoplosses!"
+    Using `ask_strategy.order_book_max` higher than 1 will increase the risk the stoploss on exchange is cancelled too early, since an eventual [stoploss on exchange](#understand-order_types) will be cancelled as soon as the order is placed.
+    Also, the sell order will remain on the exchange for `unfilledtimeout.sell` (or until it's filled) - which can lead to missed stoplosses (with or without using stoploss on exchange).
+
+!!! Warning "Order_book_max > 1 in dry-run"
+    Using `ask_strategy.order_book_max` higher than 1 will result in improper dry-run results (significantly better than real orders executed on exchange), since dry-run assumes orders to be filled almost instantly.
+    It is therefore advised to not use this setting for dry-runs.
+
+#### Sell price without Orderbook enabled
+
+When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
+
+### 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
+
+``` jsonc
+  "order_types": {
+    "buy": "market",
+    "sell": "market"
+    // ...
+  },
+  "bid_strategy": {
+    "price_side": "ask",
+    // ...
+  },
+  "ask_strategy":{
+    "price_side": "bid",
+    // ...
+  },
+```
+
+Obviously, if only one side is using limit orders, different pricing combinations can be used.

docs/includes/protections.md

@@ -0,0 +1,215 @@
+## Protections
+
+!!! Warning "Beta feature"
+    This feature is still in it's testing phase. Should you notice something you think is wrong please let us know via Discord, Slack or via Github Issue.
+
+Protections will protect your strategy from unexpected events and market conditions by temporarily stop trading for either one pair, or for all pairs.
+All protection end times are rounded up to the next candle to avoid sudden, unexpected intra-candle buys.
+
+!!! Note
+    Not all Protections will work for all strategies, and parameters will need to be tuned for your strategy to improve performance.  
+    To align your protection with your strategy, you can define protections in the strategy.
+
+!!! Tip
+    Each Protection can be configured multiple times with different parameters, to allow different levels of protection (short-term / long-term).
+
+!!! Note "Backtesting"
+    Protections are supported by backtesting and hyperopt, but must be explicitly enabled by using the `--enable-protections` flag.
+
+### Available Protections
+
+* [`StoplossGuard`](#stoploss-guard) Stop trading if a certain amount of stoploss occurred within a certain time window.
+* [`MaxDrawdown`](#maxdrawdown) Stop trading if max-drawdown is reached.
+* [`LowProfitPairs`](#low-profit-pairs) Lock pairs with low profits
+* [`CooldownPeriod`](#cooldown-period) Don't enter a trade right after selling a trade.
+
+### Common settings to all Protections
+
+|  Parameter| Description |
+|------------|-------------|
+| `method` | Protection name to use. <br> **Datatype:** String, selected from [available Protections](#available-protections)
+| `stop_duration_candles` | For how many candles should the lock be set? <br> **Datatype:** Positive integer (in candles)
+| `stop_duration` | how many minutes should protections be locked. <br>Cannot be used together with `stop_duration_candles`. <br> **Datatype:** Float (in minutes)
+| `lookback_period_candles` | Only trades that completed within the last `lookback_period_candles` candles will be considered. This setting may be ignored by some Protections. <br> **Datatype:** Positive integer (in candles).
+| `lookback_period` | Only trades that completed after `current_time - lookback_period` will be considered. <br>Cannot be used together with `lookback_period_candles`. <br>This setting may be ignored by some Protections. <br> **Datatype:**  Float (in minutes)
+| `trade_limit` | Number of trades required at minimum (not used by all Protections). <br> **Datatype:** Positive integer
+
+!!! Note "Durations"
+    Durations (`stop_duration*` and `lookback_period*` can be defined in either minutes or candles).
+    For more flexibility when testing different timeframes, all below examples will use the "candle" definition.
+
+#### Stoploss Guard
+
+`StoplossGuard` selects all trades within `lookback_period` in minutes (or in candles when using `lookback_period_candles`).
+If `trade_limit` or more trades resulted in stoploss, trading will stop for `stop_duration` in minutes (or in candles when using `stop_duration_candles`).
+
+This applies across all pairs, unless `only_per_pair` is set to true, which will then only look at one pair at a time.
+
+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.
+
+```json
+"protections": [
+    {
+        "method": "StoplossGuard",
+        "lookback_period_candles": 24,
+        "trade_limit": 4,
+        "stop_duration_candles": 4,
+        "only_per_pair": false
+    }
+],
+```
+
+!!! Note
+    `StoplossGuard` considers all trades with the results `"stop_loss"`, `"stoploss_on_exchange"` and `"trailing_stop_loss"` if the resulting profit was negative.
+    `trade_limit` and `lookback_period` will need to be tuned for your strategy.
+
+#### MaxDrawdown
+
+`MaxDrawdown` uses all trades within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the maximum drawdown. If the drawdown is below `max_allowed_drawdown`, trading will stop for `stop_duration` in minutes (or in candles when using `stop_duration_candles`) after the last trade - assuming that the bot needs some time to let markets recover.
+
+The below sample stops trading for 12 candles if max-drawdown is > 20% considering all pairs - with a minimum of `trade_limit` trades - within the last 48 candles. If desired, `lookback_period` and/or `stop_duration` can be used.
+
+```json
+"protections": [
+      {
+        "method": "MaxDrawdown",
+        "lookback_period_candles": 48,
+        "trade_limit": 20,
+        "stop_duration_candles": 12,
+        "max_allowed_drawdown": 0.2
+      },
+],
+```
+
+#### Low Profit Pairs
+
+`LowProfitPairs` uses all trades for a pair within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the overall profit ratio.
+If that ratio is below `required_profit`, that pair will be locked for `stop_duration` in minutes (or in candles when using `stop_duration_candles`).
+
+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.
+
+```json
+"protections": [
+    {
+        "method": "LowProfitPairs",
+        "lookback_period_candles": 6,
+        "trade_limit": 2,
+        "stop_duration": 60,
+        "required_profit": 0.02
+    }
+],
+```
+
+#### Cooldown Period
+
+`CooldownPeriod` locks a pair for `stop_duration` in minutes (or in candles when using `stop_duration_candles`) after selling, avoiding a re-entry for this pair for `stop_duration` minutes.
+
+The below example will stop trading a pair for 2 candles after closing a trade, allowing this pair to "cool down".
+
+```json
+"protections": [
+    {
+        "method": "CooldownPeriod",
+        "stop_duration_candles": 2
+    }
+],
+```
+
+!!! Note
+    This Protection applies only at pair-level, and will never lock all pairs globally.
+    This Protection does not consider `lookback_period` as it only looks at the latest trade.
+
+### Full example of Protections
+
+All protections can be combined at will, also with different parameters, creating a increasing wall for under-performing pairs.
+All protections are evaluated in the sequence they are defined.
+
+The below example assumes a timeframe of 1 hour:
+
+* Locks each pair after selling for an additional 5 candles (`CooldownPeriod`), giving other pairs a chance to get filled.
+* Stops trading for 4 hours (`4 * 1h candles`) if the last 2 days (`48 * 1h candles`) had 20 trades, which caused a max-drawdown of more than 20%. (`MaxDrawdown`).
+* Stops trading if more than 4 stoploss occur for all pairs within a 1 day (`24 * 1h candles`) limit (`StoplossGuard`).
+* Locks all pairs that had 4 Trades within the last 6 hours (`6 * 1h candles`) with a combined profit ratio of below 0.02 (<2%) (`LowProfitPairs`).
+* Locks all pairs for 2 candles that had a profit of below 0.01 (<1%) within the last 24h (`24 * 1h candles`), a minimum of 4 trades.
+
+```json
+"timeframe": "1h",
+"protections": [
+    {
+        "method": "CooldownPeriod",
+        "stop_duration_candles": 5
+    },
+    {
+        "method": "MaxDrawdown",
+        "lookback_period_candles": 48,
+        "trade_limit": 20,
+        "stop_duration_candles": 4,
+        "max_allowed_drawdown": 0.2
+    },
+    {
+        "method": "StoplossGuard",
+        "lookback_period_candles": 24,
+        "trade_limit": 4,
+        "stop_duration_candles": 2,
+        "only_per_pair": false
+    },
+    {
+        "method": "LowProfitPairs",
+        "lookback_period_candles": 6,
+        "trade_limit": 2,
+        "stop_duration_candles": 60,
+        "required_profit": 0.02
+    },
+    {
+        "method": "LowProfitPairs",
+        "lookback_period_candles": 24,
+        "trade_limit": 4,
+        "stop_duration_candles": 2,
+        "required_profit": 0.01
+    }
+    ],
+```
+
+You can use the same in your strategy, the syntax is only slightly different:
+
+``` python
+from freqtrade.strategy import IStrategy
+
+class AwesomeStrategy(IStrategy)
+    timeframe = '1h'
+    protections = [
+        {
+            "method": "CooldownPeriod",
+            "stop_duration_candles": 5
+        },
+        {
+            "method": "MaxDrawdown",
+            "lookback_period_candles": 48,
+            "trade_limit": 20,
+            "stop_duration_candles": 4,
+            "max_allowed_drawdown": 0.2
+        },
+        {
+            "method": "StoplossGuard",
+            "lookback_period_candles": 24,
+            "trade_limit": 4,
+            "stop_duration_candles": 2,
+            "only_per_pair": False
+        },
+        {
+            "method": "LowProfitPairs",
+            "lookback_period_candles": 6,
+            "trade_limit": 2,
+            "stop_duration_candles": 60,
+            "required_profit": 0.02
+        },
+        {
+            "method": "LowProfitPairs",
+            "lookback_period_candles": 24,
+            "trade_limit": 4,
+            "stop_duration_candles": 2,
+            "required_profit": 0.01
+        }
+    ]
+    # ...
+```

docs/index.md

@@ -14,7 +14,7 @@
 
 ## Introduction
 
-Freqtrade is a crypto-currency algorithmic trading software developed in python (3.6+) and supported on Windows, macOS and Linux.
+Freqtrade is a crypto-currency algorithmic trading software developed in python (3.7+) and supported on Windows, macOS and Linux.
 
 !!! Danger "DISCLAIMER"
     This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
@@ -35,6 +35,22 @@ Freqtrade is a crypto-currency algorithmic trading software developed in python
 - Control/Monitor: Use Telegram or a REST API (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](exchanges.md#blacklists))
+- [X] [Bittrex](https://bittrex.com/)
+- [X] [FTX](https://ftx.com)
+- [X] [Kraken](https://kraken.com/)
+- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
+
+### Community tested
+
+Exchanges confirmed working by the community:
+
+- [X] [Bitvavo](https://bitvavo.com/)
+
 ## Requirements
 
 ### Hardware requirements
@@ -51,7 +67,7 @@ To run this bot we recommend you a linux cloud instance with a minimum of:
 
 Alternatively
 
-- Python 3.6.x
+- Python 3.7+
 - pip (pip3)
 - git
 - TA-Lib
@@ -59,17 +75,14 @@ Alternatively
 
 ## Support
 
-### Help / Slack / Discord
+### Help / Discord / Slack
 
-For any questions not covered by the documentation or for further information about the bot, we encourage you to join our passionate  Slack community.
+For any questions not covered by the documentation or for further information about the bot, or to simply engage with like-minded individuals, we encourage you to join our slack channel.
 
-Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) to join the Freqtrade Slack channel.
+Please check out our [discord server](https://discord.gg/MA9v74M).
 
-Alternatively, check out the newly created [discord server](https://discord.gg/MA9v74M).
-
-!!! Note
-    Since the discord server is relatively new, answers to questions might be slightly delayed as currently the user base quite small.
+You can also join our [Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw).
 
 ## Ready to try?
 
-Begin by reading our installation guide [for docker](docker.md) (recommended), or for [installation without docker](installation.md).
+Begin by reading our installation guide [for docker](docker_quickstart.md) (recommended), or for [installation without docker](installation.md).

docs/installation.md

@@ -2,116 +2,79 @@
 
 This page explains how to prepare your environment for running the bot.
 
-Please consider using the prebuilt [docker images](docker.md) to get started quickly while trying out freqtrade evaluating how it operates.
+The freqtrade documentation describes various ways to install freqtrade
 
-## Prerequisite
+* [Docker images](docker_quickstart.md) (separate page)
+* [Script Installation](#script-installation)
+* [Manual Installation](#manual-installation)
+* [Installation with Conda](#installation-with-conda)
 
-### Requirements
+Please consider using the prebuilt [docker images](docker_quickstart.md) to get started quickly while evaluating how freqtrade works.
 
-Click each one for install guide:
+------
 
-* [Python >= 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/)
-* [pip](https://pip.pypa.io/en/stable/installing/)
-* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
-* [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
-* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
+## Information
 
- We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
+For Windows installation, please use the [windows installation guide](windows_installation.md).
+
+The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the `./setup.sh` script, if it's available for your platform.
+
+!!! Note "Version considerations"
+    When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests).
+    The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
+
+!!! Note
+    Python3.7 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.  
+    Also, python headers (`python<yourversion>-dev` / `python<yourversion>-devel`) must be available for the installation to complete successfully.
 
 !!! Warning "Up-to-date clock"
     The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
 
-## Quick start
-
-Freqtrade provides the Linux/MacOS Easy Installation script to install all dependencies and help you configure the bot.
-
-!!! Note
-    Windows installation is explained [here](#windows).
-
-The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the Easy Installation script, if it's available for your platform.
-
-!!! Note "Version considerations"
-    When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
-
-!!! Note
-    Python3.6 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.
-
-This can be achieved with the following commands:
-
-```bash
-git clone https://github.com/freqtrade/freqtrade.git
-cd freqtrade
-# git checkout stable  # Optional, see (1)
-./setup.sh --install
-```
-
-(1) This command switches the cloned repository to the use of the `stable` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout stable`/`git checkout develop` commands.
-
-## Easy Installation Script (Linux/MacOS)
-
-If you are on Debian, Ubuntu or MacOS Freqtrade provides the script to install, update, configure and reset the codebase of your bot.
-
-```bash
-$ ./setup.sh
-usage:
-	-i,--install    Install freqtrade from scratch
-	-u,--update     Command git pull to update.
-	-r,--reset      Hard reset your develop/stable branch.
-	-c,--config     Easy config generator (Will override your existing file).
-```
-
-** --install **
-
-With this option, the script will install the bot and most dependencies:
-You will need to have git and python3.6+ installed beforehand for this to work.
-
-* Mandatory software as: `ta-lib`
-* Setup your virtualenv under `.env/`
-
-This option is a combination of installation tasks, `--reset` and `--config`.
-
-** --update **
-
-This option will pull the last version of your current branch and update your virtualenv. Run the script with this option periodically to update your bot.
-
-** --reset **
-
-This option will hard reset your branch (only if you are on either `stable` or `develop`) and recreate your virtualenv.
-
-** --config **
-
-DEPRECATED - use `freqtrade new-config -c config.json` instead.
-
-### Activate your virtual environment
-
-Each time you open a new terminal, you must run `source .env/bin/activate`.
-
 ------
 
-## Custom Installation
+## Requirements
 
-We've included/collected install instructions for Ubuntu 16.04, MacOS, and Windows. These are guidelines and your success may vary with other distros.
+These requirements apply to both [Script Installation](#script-installation) and [Manual Installation](#manual-installation).
+
+### Install guide
+
+* [Python >= 3.7.x](http://docs.python-guide.org/en/latest/starting/installation/)
+* [pip](https://pip.pypa.io/en/stable/installing/)
+* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
+* [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
+* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions [below](#install-ta-lib))
+
+### Install code
+
+We've included/collected install instructions for Ubuntu, MacOS, and Windows. These are guidelines and your success may vary with other distros.
 OS Specific steps are listed first, the [Common](#common) section below is necessary for all systems.
 
 !!! Note
-    Python3.6 or higher and the corresponding pip are assumed to be available.
+    Python3.7 or higher and the corresponding pip are assumed to be available.
 
-=== "Ubuntu 16.04"
+=== "Debian/Ubuntu"
     #### Install necessary dependencies
 
     ```bash
+    # update repository
     sudo apt-get update
-    sudo apt-get install build-essential git
+
+    # install packages
+    sudo apt install -y python3-pip python3-venv python3-pandas python3-pip git
     ```
 
 === "RaspberryPi/Raspbian"
-    The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/) from at least September 2019.
+    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.
 
     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
+
+    ```bash
+    sudo apt-get install python3-venv libatlas-base-dev cmake
+    # Use pywheels.org to speed up installation
+    sudo echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > tee /etc/pip.conf
+
     git clone https://github.com/freqtrade/freqtrade.git
     cd freqtrade
 
@@ -120,16 +83,106 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
 
     !!! Note "Installation duration"
         Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
+        Due to this, we recommend to use the pre-build docker-image for Raspberry, by following the [Docker quickstart documentation](docker_quickstart.md)
 
     !!! Note
         The above does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
         We do not advise to run hyperopt on a Raspberry Pi, since this is a very resource-heavy operation, which should be done on powerful machine.
 
-### Common
+------
 
-#### 1. Install TA-Lib
+## Freqtrade repository
 
-Use the provided ta-lib installation script
+Freqtrade is an open source crypto-currency trading bot, whose code is hosted on `github.com`
+
+```bash
+# Download `develop` branch of freqtrade repository
+git clone https://github.com/freqtrade/freqtrade.git
+
+# Enter downloaded directory
+cd freqtrade
+
+# your choice (1): novice user
+git checkout stable
+
+# your choice (2): advanced user
+git checkout develop
+```
+
+(1) This command switches the cloned repository to the use of the `stable` branch. It's not needed, if you wish to stay on the (2) `develop` branch.
+
+You may later switch between branches at any time with the `git checkout stable`/`git checkout develop` commands.
+
+------
+
+## Script Installation
+
+First of the ways to install Freqtrade, is to use provided the Linux/MacOS `./setup.sh` script, which install all dependencies and help you configure the bot.
+
+Make sure you fulfill the [Requirements](#requirements) and have downloaded the [Freqtrade repository](#freqtrade-repository).
+
+### Use /setup.sh -install (Linux/MacOS)
+
+If you are on Debian, Ubuntu or MacOS, freqtrade provides the script to install freqtrade.
+
+```bash
+# --install, Install freqtrade from scratch
+./setup.sh -i
+```
+
+### Activate your virtual environment
+
+Each time you open a new terminal, you must run `source .env/bin/activate` to activate your virtual environment.
+
+```bash
+# then activate your .env
+source ./.env/bin/activate
+```
+
+### Congratulations
+
+[You are ready](#you-are-ready), and run the bot
+
+### Other options of /setup.sh script
+
+You can as well update, configure and reset the codebase of your bot with `./script.sh`
+
+```bash
+# --update, Command git pull to update.
+./setup.sh -u
+# --reset, Hard reset your develop/stable branch.
+./setup.sh -r
+```
+
+```
+** --install **
+
+With this option, the script will install the bot and most dependencies:
+You will need to have git and python3.7+ installed beforehand for this to work.
+
+* Mandatory software as: `ta-lib`
+* Setup your virtualenv under `.env/`
+
+This option is a combination of installation tasks and `--reset`
+
+** --update **
+
+This option will pull the last version of your current branch and update your virtualenv. Run the script with this option periodically to update your bot.
+
+** --reset **
+
+This option will hard reset your branch (only if you are on either `stable` or `develop`) and recreate your virtualenv.
+```
+
+-----
+
+## Manual Installation
+
+Make sure you fulfill the [Requirements](#requirements) and have downloaded the [Freqtrade repository](#freqtrade-repository).
+
+### Install TA-Lib
+
+#### TA-Lib script installation
 
 ```bash
 sudo ./build_helpers/install_ta-lib.sh
@@ -154,77 +207,193 @@ cd ..
 rm -rf ./ta-lib*
 ```
 
-!!! Note
-    An already downloaded version of ta-lib is included in the repository, as the sourceforge.net source seems to have problems frequently.
+#### Setup Python virtual environment (virtualenv)
 
-#### 2. Setup your Python virtual environment (virtualenv)
-
-!!! Note
-    This step is optional but strongly recommended to keep your system organized
+You will run freqtrade in separated `virtual environment`
 
 ```bash
+# create virtualenv in directory /freqtrade/.env
 python3 -m venv .env
+
+# run virtualenv
 source .env/bin/activate
 ```
 
-#### 3. Install Freqtrade
-
-Clone the git repository:
+#### Install python dependencies
 
 ```bash
-git clone https://github.com/freqtrade/freqtrade.git
-cd freqtrade
-git checkout stable
-```
-
-#### 4. Install python dependencies
-
-``` bash
 python3 -m pip install --upgrade pip
 python3 -m pip install -e .
 ```
 
-#### 5. Initialize the configuration
+### Congratulations
 
-```bash
-# Initialize the user_directory
-freqtrade create-userdir --userdir user_data/
+[You are ready](#you-are-ready), and run the bot
 
-# Create a new configuration file
-freqtrade new-config --config config.json
-```
+#### (Optional) Post-installation Tasks
 
-> *To edit the config please refer to [Bot Configuration](configuration.md).*
+!!! Note 
+    If you run the bot on a server, you should consider using [Docker](docker_quickstart.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
 
-#### 6. Run the Bot
-
-If this is the first time you run the bot, ensure you are running it in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
-
-```bash
-freqtrade trade -c config.json
-```
-
-*Note*: If you run the bot on a server, you should consider using [Docker](docker.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
-
-#### 7. (Optional) Post-installation Tasks
-
-On Linux, as an optional post-installation task, you may wish to setup the bot to run as a `systemd` service or configure it to send the log messages to the `syslog`/`rsyslog` or `journald` daemons. See [Advanced Logging](advanced-setup.md#advanced-logging) for details.
+On Linux with software suite `systemd`, as an optional post-installation task, you may wish to setup the bot to run as a `systemd service` or configure it to send the log messages to the `syslog`/`rsyslog` or `journald` daemons. See [Advanced Logging](advanced-setup.md#advanced-logging) for details.
 
 ------
 
-### Anaconda
+## Installation with Conda
 
-Freqtrade can also be installed using Anaconda (or Miniconda).
+Freqtrade can also be installed with Miniconda or Anaconda. We recommend using Miniconda as it's installation footprint is smaller. Conda will automatically prepare and manage the extensive library-dependencies of the Freqtrade program.
 
-!!! Note
-    This requires the [ta-lib](#1-install-ta-lib) C-library to be installed first. See below.
+### What is Conda?
 
-``` bash
-conda env create -f environment.yml
+Conda is a package, dependency and environment manager for multiple programming languages: [conda docs](https://docs.conda.io/projects/conda/en/latest/index.html)
+
+### Installation with conda
+
+#### Install Conda
+
+[Installing on linux](https://conda.io/projects/conda/en/latest/user-guide/install/linux.html#install-linux-silent)
+
+[Installing on windows](https://conda.io/projects/conda/en/latest/user-guide/install/windows.html)
+
+Answer all questions. After installation, it is mandatory to turn your terminal OFF and ON again.
+
+#### Freqtrade download
+
+Download and install freqtrade.
+
+```bash
+# download freqtrade
+git clone https://github.com/freqtrade/freqtrade.git
+
+# enter downloaded directory 'freqtrade'
+cd freqtrade      
 ```
 
+#### Freqtrade instal: Conda Environment
+
+Prepare conda-freqtrade environment, using file `environment.yml`, which exist in main freqtrade directory
+
+```bash
+conda env create -n freqtrade-conda -f environment.yml
+```
+
+!!! Note "Creating Conda Environment"
+    The conda command `create -n` automatically installs all nested dependencies for the selected libraries, general structure of installation command is:
+
+    ```bash
+    # choose your own packages
+    conda env create -n [name of the environment] [python version] [packages]
+
+    # point to file with packages
+    conda env create -n [name of the environment] -f [file]
+    ```
+
+#### Enter/exit freqtrade-conda environment
+
+To check available environments, type
+
+```bash
+conda env list
+```
+
+Enter installed environment
+
+```bash
+# enter conda environment
+conda activate freqtrade-conda
+
+# exit conda environment - don't do it now
+conda deactivate
+```
+
+Install last python dependencies with pip
+
+```bash
+python3 -m pip install --upgrade pip
+python3 -m pip install -e .
+```
+
+### Congratulations
+
+[You are ready](#you-are-ready), and run the bot
+
+### Important shortcuts
+
+```bash
+# list installed conda environments
+conda env list
+
+# activate base environment
+conda activate
+
+# activate freqtrade-conda environment
+conda activate freqtrade-conda
+
+#deactivate any conda environments
+conda deactivate                              
+```
+
+### Further info on anaconda
+
+!!! Info "New heavy packages"
+    It may happen that creating a new Conda environment, populated with selected packages at the moment of creation takes less time than installing a large, heavy library or application, into previously set environment.
+
+!!! Warning "pip install within conda"
+    The documentation of conda says that pip should NOT be used within conda, because internal problems can occur.
+    However, they are rare. [Anaconda Blogpost](https://www.anaconda.com/blog/using-pip-in-a-conda-environment)
+
+    Nevertheless, that is why, the `conda-forge` channel is preferred:
+
+    * more libraries are available (less need for `pip`)
+    * `conda-forge` works better with `pip`
+    * the libraries are newer
+
+Happy trading!
+
 -----
-## Troubleshooting 
+
+## You are ready
+
+You've made it this far, so you have successfully installed freqtrade.
+
+### Initialize the configuration
+
+```bash
+# Step 1 - Initialize user folder
+freqtrade create-userdir --userdir user_data
+
+# Step 2 - Create a new configuration file
+freqtrade new-config --config config.json
+```
+
+You are ready to run, read [Bot Configuration](configuration.md), remember to start with `dry_run: True` and verify that everything is working.
+
+To learn how to setup your configuration, please refer to the [Bot Configuration](configuration.md) documentation page.
+
+### Start the Bot
+
+```bash
+freqtrade trade --config config.json --strategy SampleStrategy
+```
+
+!!! Warning
+    You should read through the rest of the documentation, backtest the strategy you're going to use, and use dry-run before enabling trading with real money.
+
+-----
+
+## Troubleshooting
+
+### Common problem: "command not found"
+
+If you used (1)`Script` or (2)`Manual` installation, you need to run the bot in virtual environment. If you get error as below, make sure venv is active.
+
+```bash
+# if:
+bash: freqtrade: command not found
+
+# then activate your .env
+source ./.env/bin/activate
+```
 
 ### MacOS installation error
 
@@ -233,13 +402,21 @@ Newer versions of MacOS may have installation failed with errors like `error: co
 This error will require explicit installation of the SDK Headers, which are not installed by default in this version of MacOS.
 For MacOS 10.14, this can be accomplished with the below command.
 
-``` bash
+```bash
 open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg
 ```
 
 If this file is 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
 
-Now you have an environment ready, the next step is
-[Bot Configuration](configuration.md).
+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/partials/header.html

@@ -1,54 +1,51 @@
+{#-
+This file was automatically generated - do not edit
+-#}
+{% set site_url = config.site_url | d(nav.homepage.url, true) | url %}
+{% if not config.use_directory_urls and site_url[0] == site_url[-1] == "." %}
+{% set site_url = site_url ~ "/index.html" %}
+{% endif %}
 <header class="md-header" data-md-component="header">
-    <nav class="md-header-nav md-grid">
-        <div class="md-flex">
-            <div class="md-flex__cell md-flex__cell--shrink">
-                <a href="{{ config.site_url | default(nav.homepage.url, true) | url }}" title="{{ config.site_name }}"
-                    class="md-header-nav__button md-logo">
-                    {% if config.theme.logo.icon %}
-                    <i class="md-icon">{{ config.theme.logo.icon }}</i>
-                    {% else %}
-                    <img src="{{ config.theme.logo | url }}" width="24" height="24">
-                    {% endif %}
-                </a>
-            </div>
-            <div class="md-flex__cell md-flex__cell--shrink">
-                <label class="md-icon md-icon--menu md-header-nav__button" for="__drawer"></label>
-            </div>
-            <div class="md-flex__cell md-flex__cell--stretch">
-                <div class="md-flex__ellipsis md-header-nav__title" data-md-component="title">
-                    {% block site_name %}
-                    {% if config.site_name == page.title %}
-                    {{ config.site_name }}
-                    {% else %}
-                    <span class="md-header-nav__topic">
-                        {{ config.site_name }}
-                    </span>
-                    <span class="md-header-nav__topic">
-                        {{ page.title }}
-                    </span>
-                    {% endif %}
-                    {% endblock %}
-                </div>
-            </div>
-            <div class="md-flex__cell md-flex__cell--shrink">
-                {% block search_box %}
-                {% if "search" in config["plugins"] %}
-                <label class="md-icon md-icon--search md-header-nav__button" for="__search"></label>
-                {% include "partials/search.html" %}
-                {% endif %}
-                {% endblock %}
-            </div>
-            {% if config.repo_url %}
-            <div class="md-flex__cell md-flex__cell--shrink">
-                <div class="md-header-nav__source">
-                    {% include "partials/source.html" %}
-                </div>
-            </div>
-            {% endif %}
+  <nav class="md-header-nav md-grid" aria-label="{{ lang.t('header.title') }}">
+    <a href="{{ site_url }}" title="{{ config.site_name | e }}" class="md-header-nav__button md-logo"
+      aria-label="{{ config.site_name }}">
+      {% include "partials/logo.html" %}
+    </a>
+    <label class="md-header-nav__button md-icon" for="__drawer">
+      {% include ".icons/material/menu" ~ ".svg" %}
+    </label>
+    <div class="md-header-nav__title" data-md-component="header-title">
+      <div class="md-header-nav__ellipsis">
+        <div class="md-header-nav__topic">
+          <span class="md-ellipsis">
+            {{ config.site_name }}
+          </span>
         </div>
-    </nav>
-    <!-- Place this tag in your head or just before your close body tag. -->
-    <script async defer src="https://buttons.github.io/buttons.js"></script>
-    <script src="https://code.jquery.com/jquery-3.4.1.min.js"
-        integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
+        <div class="md-header-nav__topic">
+          <span class="md-ellipsis">
+            {% if page and page.meta and page.meta.title %}
+            {{ page.meta.title }}
+            {% else %}
+            {{ page.title }}
+            {% endif %}
+          </span>
+        </div>
+      </div>
+    </div>
+    {% if "search" in config["plugins"] %}
+    <label class="md-header-nav__button md-icon" for="__search">
+      {% include ".icons/material/magnify.svg" %}
+    </label>
+    {% include "partials/search.html" %}
+    {% endif %}
+    {% if config.repo_url %}
+    <div class="md-header-nav__source">
+      {% include "partials/source.html" %}
+    </div>
+    {% endif %}
+  </nav>
+  <!-- Place this tag in your head or just before your close body tag. -->
+  <script async defer src="https://buttons.github.io/buttons.js"></script>
+  <script src="https://code.jquery.com/jquery-3.4.1.min.js"
+    integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
 </header>

docs/plotting.md

@@ -168,6 +168,7 @@ Additional features when using plot_config include:
 
 * Specify colors per indicator
 * Specify additional subplots
+* Specify indicator pairs to fill area in between 
 
 The sample plot configuration below specifies fixed colors for the indicators. Otherwise consecutive plots may produce different colorschemes each time, making comparisons difficult.
 It also allows multiple subplots to display both MACD and RSI at the same time.
@@ -183,23 +184,34 @@ Sample configuration with inline comments explaining the process:
             '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'},
-                'macdsignal': {'color': 'orange'},
+                'macd': {'color': 'blue', 'fill_to': 'macdhist'},
+                'macdsignal': {'color': 'orange'}
             },
             # Additional subplot RSI
             "RSI": {
-                'rsi': {'color': 'red'},
+                'rsi': {'color': 'red'}
             }
         }
     }
+
 ```
 
 !!! Note
-    The above configuration assumes that `ema10`, `ema50`, `macd`, `macdsignal` and `rsi` are columns in the DataFrame created by the strategy.
+    The above configuration assumes that `ema10`, `ema50`, `senkou_a`, `senkou_b`,
+    `macd`, `macdsignal`, `macdhist` and `rsi` are columns in the DataFrame created by the strategy.
 
 ## Plot profit
 

docs/plugins.md

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

docs/requirements-docs.txt

@@ -1,3 +1,3 @@
-mkdocs-material==6.1.0
+mkdocs-material==6.2.8
 mdx_truly_sane_lists==1.2
-pymdown-extensions==8.0.1
+pymdown-extensions==8.1.1

docs/rest-api.md

@@ -1,4 +1,19 @@
-# REST API Usage
+# REST API & FreqUI
+
+## FreqUI
+
+Freqtrade provides a builtin webserver, which can serve [FreqUI](https://github.com/freqtrade/frequi), the freqtrade UI.
+
+By default, the UI is not included in the installation (except for docker images), and must be installed explicitly with `freqtrade install-ui`.
+This same command can also be used to update freqUI, should there be a new release.
+
+Once the bot is started in trade / dry-run mode (with `freqtrade trade`) - the UI will be available under the configured port below (usually `http://127.0.0.1:8080`).
+
+!!! info "Alpha release"
+    FreqUI is still considered an alpha release - if you encounter bugs or inconsistencies please open a [FreqUI issue](https://github.com/freqtrade/frequi/issues/new/choose).
+
+!!! Note "developers"
+    Developers should not use this method, but instead use the method described in the [freqUI repository](https://github.com/freqtrade/frequi) to get the source-code of freqUI.
 
 ## Configuration
 
@@ -11,7 +26,8 @@ Sample configuration:
         "enabled": true,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
-        "verbosity": "info",
+        "verbosity": "error",
+        "enable_openapi": false,
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
         "username": "Freqtrader",
@@ -22,9 +38,6 @@ Sample configuration:
 !!! Danger "Security warning"
     By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
 
-!!! Danger "Password selection"
-    Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
-
 You can then access the API by going to `http://127.0.0.1:8080/api/v1/ping` in a browser to check if the API is running correctly.
 This should return the response:
 
@@ -34,16 +47,22 @@ This should return the response:
 
 All other endpoints return sensitive info and require authentication and are therefore not available through a web browser.
 
-To generate a secure password, either use a password manager, or use the below code snipped.
+### Security
+
+To generate a secure password, best use a password manager, or use the below code.
 
 ``` python
 import secrets
 secrets.token_hex()
 ```
 
-!!! Hint
+!!! Hint "JWT token"
     Use the same method to also generate a JWT secret key (`jwt_secret_key`).
 
+!!! Danger "Password selection"
+    Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
+    Also change `jwt_secret_key` to something random (no need to remember this, but it'll be used to encrypt your session, so it better be something unique!). 
+
 ### Configuration with docker
 
 If you run your bot using docker, you'll need to have the bot listen to incoming connections. The security is then handled by docker.
@@ -56,28 +75,20 @@ If you run your bot using docker, you'll need to have the bot listen to incoming
     },
 ```
 
-Add the following to your docker command:
+Uncomment the following from your docker-compose file:
 
-``` bash
-  -p 127.0.0.1:8080:8080
-```
-
-A complete sample-command may then look as follows:
-
-```bash
-docker run -d \
-  --name freqtrade \
-  -v ~/.freqtrade/config.json:/freqtrade/config.json \
-  -v ~/.freqtrade/user_data/:/freqtrade/user_data \
-  -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-  -p 127.0.0.1:8080:8080 \
-  freqtrade trade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
+```yml
+    ports:
+      - "127.0.0.1:8080:8080"
 ```
 
 !!! Danger "Security warning"
-    By using `-p 8080:8080` the API is available to everyone connecting to the server under the correct port, so others may be able to control your bot.
+    By using `8080:8080` in the docker port mapping, the API will be available to everyone connecting to the server under the correct port, so others may be able to control your bot.
 
-## Consuming the API
+
+## Rest API
+
+### Consuming the API
 
 You can consume the API by using the script `scripts/rest_client.py`.
 The client script only requires the `requests` module, so Freqtrade does not need to be installed on the system.
@@ -88,7 +99,7 @@ python3 scripts/rest_client.py <command> [optional parameters]
 
 By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be used, however you can specify a configuration file to override this behaviour.
 
-### Minimalistic client config
+#### Minimalistic client config
 
 ``` json
 {
@@ -104,7 +115,7 @@ By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be use
 python3 scripts/rest_client.py --config rest_config.json <command> [optional parameters]
 ```
 
-## Available endpoints
+### Available endpoints
 
 |  Command | Description |
 |----------|-------------|
@@ -127,6 +138,7 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 | `performance` | Show performance of each finished trade grouped by pair.
 | `balance` | Show account balance per currency.
 | `daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7).
+| `stats` | Display a summary of profit / loss reasons as well as average holding times.
 | `whitelist` | Show the current whitelist.
 | `blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
 | `edge` | Show validated pairs by Edge if it is enabled.
@@ -229,6 +241,9 @@ show_config
 start
 	Start the bot if it's in the stopped state.
 
+stats
+	Return the stats report (durations, sell-reasons).
+
 status
 	Get the status of open trades.
 
@@ -259,7 +274,12 @@ whitelist
 
 ```
 
-## Advanced API usage using JWT tokens
+### OpenAPI interface
+
+To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration.
+This will enable the Swagger UI at the `/docs` endpoint. By default, that's running at http://localhost:8080/docs/ - but it'll depend on your settings.
+
+### Advanced API usage using JWT tokens
 
 !!! Note
     The below should be done in an application (a Freqtrade REST API client, which fetches info via API), and is not intended to be used on a regular basis.
@@ -284,9 +304,9 @@ Since the access token has a short timeout (15 min) - the `token/refresh` reques
 {"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk5NzQsIm5iZiI6MTU4OTExOTk3NCwianRpIjoiMDBjNTlhMWUtMjBmYS00ZTk0LTliZjAtNWQwNTg2MTdiZDIyIiwiZXhwIjoxNTg5MTIwODc0LCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.1seHlII3WprjjclY6DpRhen0rqdF4j6jbvxIhUFaSbs"}
 ```
 
-## CORS
+### CORS
 
-All web-based frontends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
+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.
 

docs/stoploss.md

@@ -23,11 +23,12 @@ These modes can be configured with these values:
 ```
 
 !!! Note
-    Stoploss on exchange is only supported for Binance (stop-loss-limit), Kraken (stop-loss-market) and FTX (stop limit and stop-market) as of now.  
-    <ins>Do not set too low 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 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.  
+    <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.
 
@@ -35,18 +36,23 @@ If `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the st
 `stoploss` defines the stop-price where the limit order is placed - and limit should be slightly below this.  
 If an exchange supports both limit and market stoploss orders, then the value of `stoploss` will be used to determine the stoploss type.  
 
-Calculation example: we bought the asset at 100$.  
-Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the limit order fill can happen between 95$ and 94.05$.  
+Calculation example: we bought the asset at 100\$.  
+Stop-price is 95\$, then limit would be `95 * 0.99 = 94.05$` - so the limit order fill can happen between 95$ and 94.05$.  
 
 For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
 
+!!! Note
+    If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new stoploss order.
+
 ### stoploss_on_exchange_interval
+
 In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.  
 The bot cannot do these every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
 So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
 This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
 
 ### emergencysell
+
 `emergencysell` 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.
 
@@ -72,6 +78,7 @@ At this stage the bot contains the following stoploss support modes:
 2. Trailing stop loss.
 3. Trailing stop loss, custom positive loss.
 4. Trailing stop loss only once the trade has reached a certain offset.
+5. [Custom stoploss function](strategy-advanced.md#custom-stoploss)
 
 ### Static Stop Loss
 
@@ -84,6 +91,7 @@ Example of stop loss:
 ```
 
 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$
@@ -107,7 +115,7 @@ For example, simplified math:
 * the stop loss would get triggered once the asset drops below 90$
 * assuming the asset now increases to 102$
 * the stop loss will now be -10% of 102$ = 91.8$
-* now the asset drops in value to 101$, the stop loss will still be 91.8$ and would trigger at 91.8$.
+* now the asset drops in value to 101\$, the stop loss will still be 91.8$ and would trigger at 91.8$.
 
 In summary: The stoploss will be adjusted to be always be -10% of the highest observed price.
 
@@ -133,8 +141,8 @@ For example, simplified math:
 * the stop loss is defined at -10%
 * the stop loss would get triggered once the asset drops below 90$
 * assuming the asset now increases to 102$
-* the stop loss will now be -2% of 102$ = 99.96$ (99.96$ stop loss will be locked in and will follow asset price increasements with -2%)
-* now the asset drops in value to 101$, the stop loss will still be 99.96$ and would trigger at 99.96$
+* the stop loss will now be -2% of 102$ = 99.96$ (99.96$ stop loss will be locked in and will follow asset price increments with -2%)
+* now the asset drops in value to 101\$, the stop loss will still be 99.96$ and would trigger at 99.96$
 
 The 0.02 would translate to a -2% stop loss.
 Before this, `stoploss` is used for the trailing stoploss.
@@ -151,7 +159,7 @@ This option can be used with or without `trailing_stop_positive`, but uses `trai
     trailing_only_offset_is_reached = True
 ```
 
-Configuration (offset is buyprice + 3%):
+Configuration (offset is buy-price + 3%):
 
 ``` python
     stoploss = -0.10
@@ -169,7 +177,7 @@ For example, simplified math:
 * stoploss will remain at 90$ unless asset increases to or above our 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$
+* now the asset drops in value to 101\$, the stop loss will still be 100.94$ and would trigger at 100.94$
 
 !!! Tip
     Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.

docs/strategy-advanced.md

@@ -8,11 +8,185 @@ If you're just getting started, please be familiar with the methods described in
 !!! Note
     All callback methods described below should only be implemented in a strategy if they are actually used.
 
+!!! Tip
+    You can get a strategy template containing all below methods by running `freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced`
+
+## Custom stoploss
+
+A stoploss can only ever move upwards - so if you set it to an absolute profit of 2%, you can never move it below this price.
+Also, 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) with a relative ratio below the current price.
+E.g. `current_profit = 0.05` (5% profit) - stoploss returns `0.02` - then you "locked in" a profit of 3% (`0.05 - 0.02 = 0.03`).
+
+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 currentrate
+        """
+        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:
+            return -0.05
+        elif current_time - timedelta(minutes=60) > trade.open_date:
+            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 inital stoploss to keep using the inital 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)
+```
+
+#### Absolute stoploss
+
+The below example sets absolute profit levels based on the current profit.
+
+* Use the regular stoploss until 20% profit is reached
+* Once profit is > 40%, stoploss will be at 25%, locking in at least 25% of the profit.
+* Once profit is > 25% - stoploss will be 15%.
+* Once profit is > 20% - stoploss will be set to 7%.
+
+``` 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:
+
+        # Calculate as `-desired_stop_from_open + current_profit` to get the distance between current_profit and initial price
+        if current_profit > 0.40:
+            return (-0.25 + current_profit)
+        if current_profit > 0.25:
+            return (-0.15 + current_profit)
+        if current_profit > 0.20:
+            return (-0.7 + current_profit)
+        return 1
+```
+
+---
+
 ## Custom order timeout rules
 
-Simple, timebased order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
+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 ordertypes, which allows you to decide based on custom criteria if a order did time out or not.
+However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if a 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.
@@ -28,7 +202,7 @@ The function must return either `True` (cancel order) or `False` (keep order ali
 from datetime import datetime, timedelta
 from freqtrade.persistence import Trade
 
-class Awesomestrategy(IStrategy):
+class AwesomeStrategy(IStrategy):
 
     # ... populate_* methods
 
@@ -67,7 +241,7 @@ class Awesomestrategy(IStrategy):
 from datetime import datetime
 from freqtrade.persistence import Trade
 
-class Awesomestrategy(IStrategy):
+class AwesomeStrategy(IStrategy):
 
     # ... populate_* methods
 
@@ -95,6 +269,8 @@ class Awesomestrategy(IStrategy):
         return False
 ```
 
+---
+
 ## Bot loop start callback
 
 A simple callback which is called once at the start of every bot throttling iteration.
@@ -103,7 +279,7 @@ This can be used to perform calculations which are pair independent (apply to al
 ``` python
 import requests
 
-class Awesomestrategy(IStrategy):
+class AwesomeStrategy(IStrategy):
 
     # ... populate_* methods
 
@@ -128,7 +304,7 @@ class Awesomestrategy(IStrategy):
 `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):
+class AwesomeStrategy(IStrategy):
 
     # ... populate_* methods
 
@@ -164,7 +340,7 @@ class Awesomestrategy(IStrategy):
 from freqtrade.persistence import Trade
 
 
-class Awesomestrategy(IStrategy):
+class AwesomeStrategy(IStrategy):
 
     # ... populate_* methods
 
@@ -200,6 +376,8 @@ class Awesomestrategy(IStrategy):
 
 ```
 
+---
+
 ## 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:
@@ -219,4 +397,41 @@ class MyAwesomeStrategy2(MyAwesomeStrategy):
     trailing_stop = True
 ```
 
-Both attributes and methods may be overriden, altering behavior of the original strategy in a way you need.
+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
+    ```
+
+## Embedding Strategies
+
+Freqtrade provides you with with an easy way to embed the strategy into your configuration file.
+This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
+in your chosen config file.
+
+### Encoding a string as BASE64
+
+This is a quick example, how to generate the BASE64 string in python
+
+```python
+from base64 import urlsafe_b64encode
+
+with open(file, 'r') as f:
+    content = f.read()
+content = urlsafe_b64encode(content.encode('utf-8'))
+```
+
+The variable 'content', will contain the strategy file in a BASE64 encoded form. Which can now be set in your configurations file as following
+
+```json
+"strategy": "NameOfStrategy:BASE64String"
+```
+
+Please ensure that 'NameOfStrategy' is identical to the strategy name!

docs/strategy-customization.md

@@ -147,7 +147,7 @@ Let's try to backtest 1 month (January 2019) of 5m candles using an example stra
 freqtrade backtesting --timerange 20190101-20190201 --timeframe 5m
 ```
 
-Assuming `startup_candle_count` is set to 100, backtesting knows it needs 100 candles to generate valid buy signals. It will load data from `20190101 - (100 * 5m)` - which is ~2019-12-31 15:30:00.
+Assuming `startup_candle_count` is set to 100, backtesting knows it needs 100 candles to generate valid buy signals. It will load data from `20190101 - (100 * 5m)` - which is ~2018-12-31 15:30:00.
 If this data is available, indicators will be calculated with this extended timerange. The instable startup period (up to 2019-01-01 00:00:00) will then be removed before starting backtesting.
 
 !!! Note
@@ -309,17 +309,17 @@ Storing information can be accomplished by creating a new dictionary within the
 The name of the variable can be chosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
 
 ```python
-class Awesomestrategy(IStrategy):
+class AwesomeStrategy(IStrategy):
     # Create custom dictionary
     cust_info = {}
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         # Check if the entry already exists
-        if not metadata["pair"] in self._cust_info:
+        if not metadata["pair"] in self.cust_info:
             # Create empty entry for this pair
-            self._cust_info[metadata["pair"]] = {}
+            self.cust_info[metadata["pair"]] = {}
 
-        if "crosstime" in self.cust_info[metadata["pair"]:
+        if "crosstime" in self.cust_info[metadata["pair"]]:
             self.cust_info[metadata["pair"]]["crosstime"] += 1
         else:
             self.cust_info[metadata["pair"]]["crosstime"] = 1
@@ -444,14 +444,19 @@ It can also be used in specific callbacks to get the signal that caused the acti
 ``` python
 # fetch current dataframe
 if self.dp:
-    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"
     Returns an empty dataframe if the requested pair was not cached.
     This should not happen when using whitelisted pairs.
 
+
+!!! Warning "Warning about backtesting"
+    This method will return an empty dataframe during backtesting.
+
 ### *orderbook(pair, maximum)*
 
 ``` python
@@ -462,8 +467,8 @@ if self.dp:
         dataframe['best_ask'] = ob['asks'][0][0]
 ```
 
-!!! Warning
-    The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used.
+!!! Warning "Warning about backtesting"
+    The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return uptodate values.
 
 ### *ticker(pair)*
 
@@ -653,7 +658,7 @@ The following example queries for the current pair and trades from today, howeve
 if self.config['runmode'].value in ('live', 'dry_run'):
     trades = Trade.get_trades([Trade.pair == metadata['pair'],
                                Trade.open_date > datetime.utcnow() - timedelta(days=1),
-                               Trade.is_open == False,
+                               Trade.is_open.is_(False),
                 ]).order_by(Trade.close_date).all()
     # Summarize profit for this pair.
     curdayprofit = sum(trade.close_profit for trade in trades)
@@ -719,7 +724,7 @@ if self.config['runmode'].value in ('live', 'dry_run'):
    # fetch closed trades for the last 2 days
     trades = Trade.get_trades([Trade.pair == metadata['pair'],
                                Trade.open_date > datetime.utcnow() - timedelta(days=2),
-                               Trade.is_open == False,
+                               Trade.is_open.is_(False),
                 ]).all()
     # Analyze the conditions you'd like to lock the pair .... will probably be different for every strategy
     sumprofit = sum(trade.close_profit for trade in trades)
@@ -770,8 +775,6 @@ To get additional Ideas for strategies, head over to our [strategy repository](h
 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.
 
-We also got a *strategy-sharing* channel in our [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) which is a great place to get and/or share ideas.
-
 ## Next step
 
 Now you have a perfect strategy you probably want to backtest it.

docs/strategy_analysis_example.md

@@ -24,7 +24,7 @@ config["strategy"] = "SampleStrategy"
 # Location of the data
 data_location = Path(config['user_data_dir'], 'data', 'binance')
 # Pair to analyze - Only use one pair here
-pair = "BTC_USDT"
+pair = "BTC/USDT"
 ```
 
 
@@ -34,7 +34,9 @@ from freqtrade.data.history import load_pair_history
 
 candles = load_pair_history(datadir=data_location,
                             timeframe=config["timeframe"],
-                            pair=pair)
+                            pair=pair,
+                            data_format = "hdf5",
+                            )
 
 # Confirm success
 print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}")

docs/telegram-usage.md

@@ -35,12 +35,30 @@ Copy the API Token (`22222222:APITOKEN` in the above example) and keep use it fo
 
 Don't forget to start the conversation with your bot, by clicking `/START` button
 
-### 2. Get your user id
+### 2. Telegram user_id
+
+#### Get your user id
 
 Talk to the [userinfobot](https://telegram.me/userinfobot)
 
 Get your "Id", you will use it for the config parameter `chat_id`.
 
+#### Use Group id
+
+You can use bots in telegram groups by just adding them to the group. You can find the group id by first adding a [RawDataBot](https://telegram.me/rawdatabot) to your group. The Group id is shown as id in the `"chat"` section, which the RawDataBot will send to you:
+
+``` json
+"chat":{
+   "id":-1001332619709
+}
+```
+
+For the Freqtrade configuration, you can then use the the full value (including `-` if it's there) as string:
+
+```json
+   "chat_id": "-1001332619709"
+```
+
 ## Control telegram noise
 
 Freqtrade provides means to control the verbosity of your telegram bot.
@@ -69,6 +87,41 @@ Example configuration showing the different settings:
    },
 ```
 
+## Create a custom keyboard (command shortcut buttons)
+
+Telegram allows us to create a custom keyboard with buttons for commands.
+The default custom keyboard looks like this.
+
+```python
+[
+    ["/daily", "/profit", "/balance"], # row 1, 3 commands
+    ["/status", "/status table", "/performance"], # row 2, 3 commands
+    ["/count", "/start", "/stop", "/help"] # row 3, 4 commands
+]
+```
+
+### Usage
+
+You can create your own keyboard in `config.json`:
+
+``` json
+"telegram": {
+      "enabled": true,
+      "token": "your_telegram_token",
+      "chat_id": "your_telegram_chat_id",
+      "keyboard": [   
+          ["/daily", "/stats", "/balance", "/profit"],
+          ["/status table", "/performance"],
+          ["/reload_config", "/count", "/logs"]
+      ]
+   },
+```
+
+!!! Note "Supported Commands"
+    Only the following commands are allowed. Command arguments are not supported!
+
+    `/start`, `/stop`, `/status`, `/status table`, `/trades`, `/profit`, `/performance`, `/daily`, `/stats`, `/count`, `/locks`, `/balance`, `/stopbuy`, `/reload_config`, `/show_config`, `/logs`, `/whitelist`, `/blacklist`, `/edge`, `/help`, `/version`
+
 ## Telegram commands
 
 Per default, the Telegram bot shows predefined commands. Some commands
@@ -84,10 +137,12 @@ official commands. You can ask at any moment for help with `/help`.
 | `/show_config` | Shows part of the current configuration with relevant settings to operation
 | `/logs [limit]` | Show last log messages.
 | `/status` | Lists all open trades
+| `/status <trade_id>` | Lists one or more specific trade. Separate multiple <trade_id> with a blank space.
 | `/status table` | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
 | `/trades [limit]` | List all recently closed trades in a table format.
 | `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
 | `/count` | Displays number of trades used and available
+| `/locks` | Show currently locked pairs.
 | `/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`).
@@ -95,6 +150,7 @@ official commands. You can ask at any moment for help with `/help`.
 | `/performance` | Show performance of each finished trade grouped by pair
 | `/balance` | Show account balance per currency
 | `/daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
+| `/stats` | Shows Wins / losses by Sell reason as well as Avg. holding durations for buys and sells
 | `/whitelist` | Show the current whitelist
 | `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
 | `/edge` | Show validated pairs by Edge if it is enabled.
@@ -189,7 +245,7 @@ Return a summary of your profit/loss and performance.
 
 Note that for this to work, `forcebuy_enable` needs to be set to true.
 
-[More details](configuration.md/#understand-forcebuy_enable)
+[More details](configuration.md#understand-forcebuy_enable)
 
 ### /performance
 

docs/updating.md

@@ -0,0 +1,31 @@
+# How to update
+
+To update your freqtrade installation, please use one of the below methods, corresponding to your installation method.
+
+## docker-compose
+
+!!! Note "Legacy installations using the `master` image"
+    We're switching from master to stable for the release Images - please adjust your docker-file and replace `freqtradeorg/freqtrade:master` with `freqtradeorg/freqtrade:stable`
+
+``` bash
+docker-compose pull
+docker-compose up -d
+```
+
+## Installation via setup script
+
+``` bash
+./setup.sh --update
+```
+
+!!! Note
+    Make sure to run this command with your virtual environment disabled!
+
+## Plain native installation
+
+Please ensure that you're also updating dependencies - otherwise things might break without you noticing.
+
+``` bash
+git pull
+pip install -U -r requirements.txt
+```

docs/utils.md

@@ -391,7 +391,7 @@ $ freqtrade list-markets --exchange kraken --all
 
 ## Test pairlist
 
-Use the `test-pairlist` subcommand to test the configuration of [dynamic pairlists](configuration.md#pairlists).
+Use the `test-pairlist` subcommand to test the configuration of [dynamic pairlists](plugins.md#pairlists).
 
 Requires a configuration with specified `pairlists` attribute.
 Can be used to generate static pairlists to be used during backtesting / hyperopt.
@@ -415,7 +415,7 @@ optional arguments:
 
 ### Examples
 
-Show whitelist when using a [dynamic pairlist](configuration.md#pairlists).
+Show whitelist when using a [dynamic pairlist](plugins.md#pairlists).
 
 ```
 freqtrade test-pairlist --config config.json --quote USDT BTC

docs/windows_installation.md

@@ -1,4 +1,4 @@
-We **strongly** recommend that Windows users use [Docker](docker.md) as this will work much easier and smoother (also more secure).
+We **strongly** recommend that Windows users use [Docker](docker_quickstart.md) as this will work much easier and smoother (also more secure).
 
 If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
 Otherwise, try the instructions below.
@@ -32,7 +32,7 @@ python -m venv .env
 .env\Scripts\activate.ps1
 # optionally install ta-lib from wheel
 # Eventually adjust the below filename to match the downloaded wheel
-pip install build_helpes/TA_Lib‑0.4.19‑cp38‑cp38‑win_amd64.whl
+pip install build_helpers/TA_Lib-0.4.19-cp38-cp38-win_amd64.whl
 pip install -r requirements.txt
 pip install -e .
 freqtrade
@@ -50,8 +50,8 @@ freqtrade
 error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
 ```
 
-Unfortunately, many packages requiring compilation don't provide a pre-build wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
+Unfortunately, many packages requiring compilation don't provide a pre-built wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
 
-The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building c code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or [docker](docker.md) first.
+The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building C code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or [docker compose](docker_quickstart.md) first.
 
 ---

environment.yml

@@ -1,60 +1,71 @@
 name: freqtrade
 channels:
-  - defaults
   - conda-forge
+# - defaults
 dependencies:
-  # Required for app
-  - python>=3.6
-  - pip
-  - wheel
-  - numpy
-  - pandas
-  - SQLAlchemy
-  - arrow
-  - requests
-  - urllib3
-  - wrapt
-  - jsonschema
-  - tabulate
-  - python-rapidjson
-  - flask
-  - python-dotenv
-  - cachetools
-  - python-telegram-bot
-  # Optional for plotting
-  - plotly
-  # Optional for hyperopt
-  - scipy
-  - scikit-optimize
-  - scikit-learn
-  - filelock
-  - joblib
-  # Optional for development
-  - flake8
-  - pytest
-  - pytest-mock
-  - pytest-asyncio
-  - pytest-cov
-  - coveralls
-  - mypy
-  # Useful for jupyter
-  - jupyter
-  - ipykernel
-  - isort
-  - yapf
-  - pip:
-    # Required for app
-    - cython
-    - pycoingecko
-    - ccxt
+# 1/4 req main
+    - python>=3.7
+    - numpy
+    - pandas
+    - pip
+
+    - aiohttp
+    - SQLAlchemy
+    - python-telegram-bot
+    - arrow
+    - cachetools
+    - requests
+    - urllib3
+    - wrapt
+    - jsonschema
     - TA-Lib
-    - py_find_1st
+    - tabulate
+    - jinja2
+    - blosc
     - sdnotify
-    # Optional for develpment
-    - flake8-tidy-imports
-    - flake8-type-annotations
-    - pytest-random-order
-    - -e .
+    - fastapi
+    - uvicorn
+    - pyjwt
+    - colorama
+    - questionary
+    - prompt-toolkit
 
 
+    # ============================
+    # 2/4 req dev
 
+    - coveralls
+    - flake8
+    - mypy
+    - pytest
+    - pytest-asyncio
+    - pytest-cov
+    - pytest-mock
+    - isort
+    - nbconvert
+
+    # ============================
+    # 3/4 req hyperopt
+
+    - scipy
+    - scikit-learn
+    - filelock
+    - scikit-optimize
+    - joblib
+    - progressbar2
+    # ============================
+    # 4/4 req plot
+
+    - plotly
+    - jupyter
+
+    - pip:
+        - pycoingecko
+        - py_find_1st
+        - tables
+        - pytest-random-order
+        - flake8-type-annotations
+        - ccxt
+        - flake8-tidy-imports
+        - -e .
+    # - python-rapidjso

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2020.10'
+__version__ = '2021.2'
 
 if __version__ == 'develop':
 

freqtrade/__main__.py

@@ -3,7 +3,7 @@
 __main__.py for Freqtrade
 To launch Freqtrade as a module
 
-> python -m freqtrade (with Python >= 3.6)
+> python -m freqtrade (with Python >= 3.7)
 """
 
 from freqtrade import main

freqtrade/commands/__init__.py

@@ -10,8 +10,8 @@ from freqtrade.commands.arguments import Arguments
 from freqtrade.commands.build_config_commands import start_new_config
 from freqtrade.commands.data_commands import (start_convert_data, start_download_data,
                                               start_list_data)
-from freqtrade.commands.deploy_commands import (start_create_userdir, start_new_hyperopt,
-                                                start_new_strategy)
+from freqtrade.commands.deploy_commands import (start_create_userdir, start_install_ui,
+                                                start_new_hyperopt, start_new_strategy)
 from freqtrade.commands.hyperopt_commands import start_hyperopt_list, start_hyperopt_show
 from freqtrade.commands.list_commands import (start_list_exchanges, start_list_hyperopts,
                                               start_list_markets, start_list_strategies,

freqtrade/commands/arguments.py

@@ -20,11 +20,13 @@ ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
                         "max_open_trades", "stake_amount", "fee"]
 
 ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
+                                        "enable_protections",
                                         "strategy_list", "export", "exportfilename"]
 
 ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
-                                        "position_stacking", "epochs", "spaces",
-                                        "use_max_market_positions", "print_all",
+                                        "position_stacking", "use_max_market_positions",
+                                        "enable_protections",
+                                        "epochs", "spaces", "print_all",
                                         "print_colorized", "print_json", "hyperopt_jobs",
                                         "hyperopt_random_state", "hyperopt_min_trades",
                                         "hyperopt_loss"]
@@ -42,7 +44,8 @@ ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
 ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one_column",
                    "print_csv", "base_currencies", "quote_currencies", "list_pairs_all"]
 
-ARGS_TEST_PAIRLIST = ["config", "quote_currencies", "print_one_column", "list_pairs_print_json"]
+ARGS_TEST_PAIRLIST = ["verbosity", "config", "quote_currencies", "print_one_column",
+                      "list_pairs_print_json"]
 
 ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
 
@@ -67,6 +70,8 @@ ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
 ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
                     "trade_source", "timeframe"]
 
+ARGS_INSTALL_UI = ["erase_ui_only"]
+
 ARGS_SHOW_TRADES = ["db_url", "trade_ids", "print_json"]
 
 ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
@@ -164,8 +169,8 @@ class Arguments:
 
         from freqtrade.commands import (start_backtesting, start_convert_data, start_create_userdir,
                                         start_download_data, start_edge, start_hyperopt,
-                                        start_hyperopt_list, start_hyperopt_show, start_list_data,
-                                        start_list_exchanges, start_list_hyperopts,
+                                        start_hyperopt_list, start_hyperopt_show, start_install_ui,
+                                        start_list_data, start_list_exchanges, start_list_hyperopts,
                                         start_list_markets, start_list_strategies,
                                         start_list_timeframes, start_new_config, start_new_hyperopt,
                                         start_new_strategy, start_plot_dataframe, start_plot_profit,
@@ -352,6 +357,14 @@ class Arguments:
         test_pairlist_cmd.set_defaults(func=start_test_pairlist)
         self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_cmd)
 
+        # Add install-ui subcommand
+        install_ui_cmd = subparsers.add_parser(
+            'install-ui',
+            help='Install FreqUI',
+        )
+        install_ui_cmd.set_defaults(func=start_install_ui)
+        self._build_args(optionlist=ARGS_INSTALL_UI, parser=install_ui_cmd)
+
         # Add Plotting subcommand
         plot_dataframe_cmd = subparsers.add_parser(
             'plot-dataframe',

freqtrade/commands/cli_options.py

@@ -144,6 +144,14 @@ AVAILABLE_CLI_OPTIONS = {
         action='store_false',
         default=True,
     ),
+    "enable_protections": Arg(
+        '--enable-protections', '--enableprotections',
+        help='Enable protections for backtesting.'
+        'Will slow backtesting down by a considerable amount, but will include '
+        'configured protections',
+        action='store_true',
+        default=False,
+    ),
     "strategy_list": Arg(
         '--strategy-list',
         help='Provide a space-separated list of strategies to backtest. '
@@ -354,13 +362,11 @@ AVAILABLE_CLI_OPTIONS = {
         '--data-format-ohlcv',
         help='Storage format for downloaded candle (OHLCV) data. (default: `%(default)s`).',
         choices=constants.AVAILABLE_DATAHANDLERS,
-        default='json'
     ),
     "dataformat_trades": Arg(
         '--data-format-trades',
         help='Storage format for downloaded trades data. (default: `%(default)s`).',
         choices=constants.AVAILABLE_DATAHANDLERS,
-        default='jsongz'
     ),
     "exchange": Arg(
         '--exchange',
@@ -381,6 +387,12 @@ AVAILABLE_CLI_OPTIONS = {
         help='Clean all existing data for the selected exchange/pairs/timeframes.',
         action='store_true',
     ),
+    "erase_ui_only": Arg(
+        '--erase',
+        help="Clean UI folder, don't download new version.",
+        action='store_true',
+        default=False,
+    ),
     # Templating options
     "template": Arg(
         '--template',

freqtrade/commands/data_commands.py

@@ -1,16 +1,16 @@
 import logging
 import sys
 from collections import defaultdict
+from datetime import datetime, timedelta
 from typing import Any, Dict, List
 
-import arrow
-
 from freqtrade.configuration import TimeRange, setup_utils_configuration
 from freqtrade.data.converter import convert_ohlcv_format, convert_trades_format
 from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_ohlcv_data,
                                     refresh_backtest_trades_data)
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.resolvers import ExchangeResolver
 from freqtrade.state import RunMode
 
@@ -29,7 +29,7 @@ def start_download_data(args: Dict[str, Any]) -> None:
                                    "You can only specify one or the other.")
     timerange = TimeRange()
     if 'days' in config:
-        time_since = arrow.utcnow().shift(days=-config['days']).strftime("%Y%m%d")
+        time_since = (datetime.now() - timedelta(days=config['days'])).strftime("%Y%m%d")
         timerange = TimeRange.parse_timerange(f'{time_since}-')
 
     if 'timerange' in config:
@@ -43,15 +43,17 @@ def start_download_data(args: Dict[str, Any]) -> None:
             "Downloading data requires a list of pairs. "
             "Please check the documentation on how to configure this.")
 
-    logger.info(f"About to download pairs: {config['pairs']}, "
-                f"intervals: {config['timeframes']} to {config['datadir']}")
-
     pairs_not_available: List[str] = []
 
     # Init exchange
     exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
     # Manual validations of relevant settings
     exchange.validate_pairs(config['pairs'])
+    expanded_pairs = expand_pairlist(config['pairs'], list(exchange.markets))
+
+    logger.info(f"About to download pairs: {expanded_pairs}, "
+                f"intervals: {config['timeframes']} to {config['datadir']}")
+
     for timeframe in config['timeframes']:
         exchange.validate_timeframes(timeframe)
 
@@ -59,20 +61,20 @@ def start_download_data(args: Dict[str, Any]) -> None:
 
         if config.get('download_trades'):
             pairs_not_available = refresh_backtest_trades_data(
-                exchange, pairs=config['pairs'], datadir=config['datadir'],
+                exchange, pairs=expanded_pairs, datadir=config['datadir'],
                 timerange=timerange, erase=bool(config.get('erase')),
                 data_format=config['dataformat_trades'])
 
             # Convert downloaded trade data to different timeframes
             convert_trades_to_ohlcv(
-                pairs=config['pairs'], timeframes=config['timeframes'],
+                pairs=expanded_pairs, timeframes=config['timeframes'],
                 datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
                 data_format_ohlcv=config['dataformat_ohlcv'],
                 data_format_trades=config['dataformat_trades'],
-                )
+            )
         else:
             pairs_not_available = refresh_backtest_ohlcv_data(
-                exchange, pairs=config['pairs'], timeframes=config['timeframes'],
+                exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
                 datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
                 data_format=config['dataformat_ohlcv'])
 

freqtrade/commands/deploy_commands.py

@@ -1,7 +1,9 @@
 import logging
 import sys
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any, Dict, Optional, Tuple
+
+import requests
 
 from freqtrade.configuration import setup_utils_configuration
 from freqtrade.configuration.directory_operations import copy_sample_files, create_userdata_dir
@@ -137,3 +139,87 @@ def start_new_hyperopt(args: Dict[str, Any]) -> None:
         deploy_new_hyperopt(args['hyperopt'], new_path, args['template'])
     else:
         raise OperationalException("`new-hyperopt` requires --hyperopt to be set.")
+
+
+def clean_ui_subdir(directory: Path):
+    if directory.is_dir():
+        logger.info("Removing UI directory content.")
+
+        for p in reversed(list(directory.glob('**/*'))):  # iterate contents from leaves to root
+            if p.name in ('.gitkeep', 'fallback_file.html'):
+                continue
+            if p.is_file():
+                p.unlink()
+            elif p.is_dir():
+                p.rmdir()
+
+
+def read_ui_version(dest_folder: Path) -> Optional[str]:
+    file = dest_folder / '.uiversion'
+    if not file.is_file():
+        return None
+
+    with file.open('r') as f:
+        return f.read()
+
+
+def download_and_install_ui(dest_folder: Path, dl_url: str, version: str):
+    from io import BytesIO
+    from zipfile import ZipFile
+
+    logger.info(f"Downloading {dl_url}")
+    resp = requests.get(dl_url).content
+    dest_folder.mkdir(parents=True, exist_ok=True)
+    with ZipFile(BytesIO(resp)) as zf:
+        for fn in zf.filelist:
+            with zf.open(fn) as x:
+                destfile = dest_folder / fn.filename
+                if fn.is_dir():
+                    destfile.mkdir(exist_ok=True)
+                else:
+                    destfile.write_bytes(x.read())
+    with (dest_folder / '.uiversion').open('w') as f:
+        f.write(version)
+
+
+def get_ui_download_url() -> Tuple[str, str]:
+    base_url = 'https://api.github.com/repos/freqtrade/frequi/'
+    # Get base UI Repo path
+
+    resp = requests.get(f"{base_url}releases")
+    resp.raise_for_status()
+    r = resp.json()
+
+    latest_version = r[0]['name']
+    assets = r[0].get('assets', [])
+    dl_url = ''
+    if assets and len(assets) > 0:
+        dl_url = assets[0]['browser_download_url']
+
+    # URL not found - try assets url
+    if not dl_url:
+        assets = r[0]['assets_url']
+        resp = requests.get(assets)
+        r = resp.json()
+        dl_url = r[0]['browser_download_url']
+
+    return dl_url, latest_version
+
+
+def start_install_ui(args: Dict[str, Any]) -> None:
+
+    dest_folder = Path(__file__).parents[1] / 'rpc/api_server/ui/installed/'
+    # First make sure the assets are removed.
+    dl_url, latest_version = get_ui_download_url()
+
+    curr_version = read_ui_version(dest_folder)
+    if curr_version == latest_version and not args.get('erase_ui_only'):
+        logger.info(f"UI already up-to-date, FreqUI Version {curr_version}.")
+        return
+
+    clean_ui_subdir(dest_folder)
+    if args.get('erase_ui_only'):
+        logger.info("Erased UI directory content. Not downloading new version.")
+    else:
+        # Download a new version
+        download_and_install_ui(dest_folder, dl_url, latest_version)

freqtrade/commands/pairlist_commands.py

@@ -15,7 +15,7 @@ def start_test_pairlist(args: Dict[str, Any]) -> None:
     """
     Test Pairlist configuration
     """
-    from freqtrade.pairlist.pairlistmanager import PairListManager
+    from freqtrade.plugins.pairlistmanager import PairListManager
     config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
 
     exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)

freqtrade/configuration/config_validation.py

@@ -54,7 +54,7 @@ def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
         return conf
     except ValidationError as e:
         logger.critical(
-            f"Invalid configuration. See config.json.example. Reason: {e}"
+            f"Invalid configuration. Reason: {e}"
         )
         raise ValidationError(
             best_match(Draft4Validator(conf_schema).iter_errors(conf)).message
@@ -74,6 +74,7 @@ def validate_config_consistency(conf: Dict[str, Any]) -> None:
     _validate_trailing_stoploss(conf)
     _validate_edge(conf)
     _validate_whitelist(conf)
+    _validate_protections(conf)
     _validate_unlimited_amount(conf)
 
     # validate configuration before returning
@@ -137,6 +138,10 @@ def _validate_edge(conf: Dict[str, Any]) -> None:
             "Edge and VolumePairList are incompatible, "
             "Edge will override whatever pairs VolumePairlist selects."
         )
+    if not conf.get('ask_strategy', {}).get('use_sell_signal', True):
+        raise OperationalException(
+            "Edge requires `use_sell_signal` to be True, otherwise no sells will happen."
+        )
 
 
 def _validate_whitelist(conf: Dict[str, Any]) -> None:
@@ -151,3 +156,22 @@ def _validate_whitelist(conf: Dict[str, Any]) -> None:
         if (pl.get('method') == 'StaticPairList'
                 and not conf.get('exchange', {}).get('pair_whitelist')):
             raise OperationalException("StaticPairList requires pair_whitelist to be set.")
+
+
+def _validate_protections(conf: Dict[str, Any]) -> None:
+    """
+    Validate protection configuration validity
+    """
+
+    for prot in conf.get('protections', []):
+        if ('stop_duration' in prot and 'stop_duration_candles' in prot):
+            raise OperationalException(
+                "Protections must specify either `stop_duration` or `stop_duration_candles`.\n"
+                f"Please fix the protection {prot.get('method')}"
+                )
+
+        if ('lookback_period' in prot and 'lookback_period_candles' in prot):
+            raise OperationalException(
+                "Protections must specify either `lookback_period` or `lookback_period_candles`.\n"
+                f"Please fix the protection {prot.get('method')}"
+            )

freqtrade/configuration/configuration.py

@@ -211,6 +211,9 @@ class Configuration:
         self._args_to_config(config, argname='position_stacking',
                              logstring='Parameter --enable-position-stacking detected ...')
 
+        self._args_to_config(
+            config, argname='enable_protections',
+            logstring='Parameter --enable-protections detected, enabling Protections. ...')
         # Setting max_open_trades to infinite if -1
         if config.get('max_open_trades') == -1:
             config['max_open_trades'] = float('inf')

freqtrade/configuration/deprecated_settings.py

@@ -26,6 +26,24 @@ def check_conflicting_settings(config: Dict[str, Any],
         )
 
 
+def process_removed_setting(config: Dict[str, Any],
+                            section1: str, name1: str,
+                            section2: str, name2: str) -> None:
+    """
+    :param section1: Removed section
+    :param name1: Removed setting name
+    :param section2: new section for this key
+    :param name2: new setting name
+    """
+    section1_config = config.get(section1, {})
+    if name1 in section1_config:
+        raise OperationalException(
+            f"Setting `{section1}.{name1}` has been moved to `{section2}.{name2}. "
+            f"Please delete it from your configuration and use the `{section2}.{name2}` "
+            "setting instead."
+        )
+
+
 def process_deprecated_setting(config: Dict[str, Any],
                                section1: str, name1: str,
                                section2: str, name2: str) -> None:
@@ -44,19 +62,18 @@ def process_deprecated_setting(config: Dict[str, Any],
 
 def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
 
-    check_conflicting_settings(config, 'ask_strategy', 'use_sell_signal',
-                               'experimental', 'use_sell_signal')
-    check_conflicting_settings(config, 'ask_strategy', 'sell_profit_only',
-                               'experimental', 'sell_profit_only')
-    check_conflicting_settings(config, 'ask_strategy', 'ignore_roi_if_buy_signal',
-                               'experimental', 'ignore_roi_if_buy_signal')
+    # Kept for future deprecated / moved settings
+    # check_conflicting_settings(config, 'ask_strategy', 'use_sell_signal',
+    #                            'experimental', 'use_sell_signal')
+    # process_deprecated_setting(config, 'ask_strategy', 'use_sell_signal',
+    #                            'experimental', 'use_sell_signal')
 
-    process_deprecated_setting(config, 'ask_strategy', 'use_sell_signal',
-                               'experimental', 'use_sell_signal')
-    process_deprecated_setting(config, 'ask_strategy', 'sell_profit_only',
-                               'experimental', 'sell_profit_only')
-    process_deprecated_setting(config, 'ask_strategy', 'ignore_roi_if_buy_signal',
-                               'experimental', 'ignore_roi_if_buy_signal')
+    process_removed_setting(config, 'experimental', 'use_sell_signal',
+                            'ask_strategy', 'use_sell_signal')
+    process_removed_setting(config, 'experimental', 'sell_profit_only',
+                            'ask_strategy', 'sell_profit_only')
+    process_removed_setting(config, 'experimental', 'ignore_roi_if_buy_signal',
+                            'ask_strategy', 'ignore_roi_if_buy_signal')
 
     if (config.get('edge', {}).get('enabled', False)
        and 'capital_available_percentage' in config.get('edge', {})):

freqtrade/configuration/timerange.py

@@ -52,11 +52,11 @@ class TimeRange:
         :return: None (Modifies the object in place)
         """
         if (not self.starttype or (startup_candles
-                                   and min_date.timestamp >= self.startts)):
+                                   and min_date.int_timestamp >= self.startts)):
             # If no startts was defined, or backtest-data starts at the defined backtest-date
             logger.warning("Moving start-date by %s candles to account for startup time.",
                            startup_candles)
-            self.startts = (min_date.timestamp + timeframe_secs * startup_candles)
+            self.startts = (min_date.int_timestamp + timeframe_secs * startup_candles)
             self.starttype = 'date'
 
     @staticmethod
@@ -89,7 +89,7 @@ class TimeRange:
                 if stype[0]:
                     starts = rvals[index]
                     if stype[0] == 'date' and len(starts) == 8:
-                        start = arrow.get(starts, 'YYYYMMDD').timestamp
+                        start = arrow.get(starts, 'YYYYMMDD').int_timestamp
                     elif len(starts) == 13:
                         start = int(starts) // 1000
                     else:
@@ -98,7 +98,7 @@ class TimeRange:
                 if stype[1]:
                     stops = rvals[index]
                     if stype[1] == 'date' and len(stops) == 8:
-                        stop = arrow.get(stops, 'YYYYMMDD').timestamp
+                        stop = arrow.get(stops, 'YYYYMMDD').int_timestamp
                     elif len(stops) == 13:
                         stop = int(stops) // 1000
                     else:

freqtrade/constants.py

@@ -24,8 +24,10 @@ HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
                          'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
                          'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily']
 AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
-                       'AgeFilter', 'PrecisionFilter', 'PriceFilter',
-                       'ShuffleFilter', 'SpreadFilter']
+                       'AgeFilter', 'PerformanceFilter', 'PrecisionFilter',
+                       'PriceFilter', 'RangeStabilityFilter', 'ShuffleFilter',
+                       'SpreadFilter']
+AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
 AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
 DRY_RUN_WALLET = 1000
 DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
@@ -43,6 +45,16 @@ USERPATH_NOTEBOOKS = 'notebooks'
 
 TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
 
+
+# Define decimals per coin for outputs
+# Only used for outputs.
+DECIMAL_PER_COIN_FALLBACK = 3  # Should be low to avoid listing all possible FIAT's
+DECIMALS_PER_COIN = {
+    'BTC': 8,
+    'ETH': 5,
+}
+
+
 # Soure files with destination directories within user-directory
 USER_DATA_FILES = {
     'sample_strategy.py': USERPATH_STRATEGIES,
@@ -114,6 +126,7 @@ CONF_SCHEMA = {
         'trailing_stop_positive': {'type': 'number', 'minimum': 0, 'maximum': 1},
         'trailing_stop_positive_offset': {'type': 'number', 'minimum': 0, 'maximum': 1},
         'trailing_only_offset_is_reached': {'type': 'boolean'},
+        'bot_name': {'type': 'string'},
         'unfilledtimeout': {
             'type': 'object',
             'properties': {
@@ -152,6 +165,7 @@ CONF_SCHEMA = {
                 'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
                 'use_sell_signal': {'type': 'boolean'},
                 'sell_profit_only': {'type': 'boolean'},
+                'sell_profit_offset': {'type': 'number', 'minimum': 0.0},
                 'ignore_roi_if_buy_signal': {'type': 'boolean'}
             }
         },
@@ -182,9 +196,6 @@ CONF_SCHEMA = {
         'experimental': {
             'type': 'object',
             'properties': {
-                'use_sell_signal': {'type': 'boolean'},
-                'sell_profit_only': {'type': 'boolean'},
-                'ignore_roi_if_buy_signal': {'type': 'boolean'},
                 'block_bad_exchanges': {'type': 'boolean'}
             }
         },
@@ -194,7 +205,21 @@ CONF_SCHEMA = {
                 'type': 'object',
                 'properties': {
                     'method': {'type': 'string', 'enum': AVAILABLE_PAIRLISTS},
-                    'config': {'type': 'object'}
+                },
+                'required': ['method'],
+            }
+        },
+        'protections': {
+            'type': 'array',
+            'items': {
+                'type': 'object',
+                'properties': {
+                    'method': {'type': 'string', 'enum': AVAILABLE_PROTECTIONS},
+                    'stop_duration': {'type': 'number', 'minimum': 0.0},
+                    'stop_duration_candles': {'type': 'number', 'minimum': 0},
+                    'trade_limit': {'type': 'number', 'minimum': 1},
+                    'lookback_period': {'type': 'number', 'minimum': 1},
+                    'lookback_period_candles': {'type': 'number', 'minimum': 1},
                 },
                 'required': ['method'],
             }
@@ -365,3 +390,6 @@ CANCEL_REASON = {
 # List of pairs with their timeframes
 PairWithTimeframe = Tuple[str, str]
 ListPairsWithTimeframes = List[PairWithTimeframe]
+
+# Type for trades list
+TradeList = List[List]

freqtrade/data/btanalysis.py

@@ -2,9 +2,8 @@
 Helpers when analyzing backtest data
 """
 import logging
-from datetime import timezone
 from pathlib import Path
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -16,9 +15,22 @@ from freqtrade.persistence import Trade, init_db
 
 logger = logging.getLogger(__name__)
 
-# must align with columns in backtest.py
-BT_DATA_COLUMNS = ["pair", "profit_percent", "open_date", "close_date", "index", "trade_duration",
-                   "open_rate", "close_rate", "open_at_end", "sell_reason"]
+# Old format - maybe remove?
+BT_DATA_COLUMNS_OLD = ["pair", "profit_percent", "open_date", "close_date", "index",
+                       "trade_duration", "open_rate", "close_rate", "open_at_end", "sell_reason"]
+
+# Mid-term format, crated by BacktestResult Named Tuple
+BT_DATA_COLUMNS_MID = ['pair', 'profit_percent', 'open_date', 'close_date', 'trade_duration',
+                       'open_rate', 'close_rate', 'open_at_end', 'sell_reason', 'fee_open',
+                       'fee_close', 'amount', 'profit_abs', 'profit_ratio']
+
+# Newest format
+BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
+                   'open_rate', 'close_rate',
+                   'fee_open', 'fee_close', 'trade_duration',
+                   'profit_ratio', 'profit_abs', 'sell_reason',
+                   'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
+                   'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', ]
 
 
 def get_latest_optimize_filename(directory: Union[Path, str], variant: str) -> str:
@@ -154,7 +166,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
                                           )
     else:
         # old format - only with lists.
-        df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
+        df = pd.DataFrame(data, columns=BT_DATA_COLUMNS_OLD)
 
         df['open_date'] = pd.to_datetime(df['open_date'],
                                          unit='s',
@@ -166,7 +178,10 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
                                           utc=True,
                                           infer_datetime_format=True
                                           )
+        # Create compatibility with new format
         df['profit_abs'] = df['close_rate'] - df['open_rate']
+    if 'profit_ratio' not in df.columns:
+        df['profit_ratio'] = df['profit_percent']
     df = df.sort_values("open_date").reset_index(drop=True)
     return df
 
@@ -209,6 +224,20 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
     return df_final[df_final['open_trades'] > max_open_trades]
 
 
+def trade_list_to_dataframe(trades: List[Trade]) -> pd.DataFrame:
+    """
+    Convert list of Trade objects to pandas Dataframe
+    :param trades: List of trade objects
+    :return: Dataframe with BT_DATA_COLUMNS
+    """
+    df = pd.DataFrame.from_records([t.to_json() for t in trades], columns=BT_DATA_COLUMNS)
+    if len(df) > 0:
+        df.loc[:, 'close_date'] = pd.to_datetime(df['close_date'], utc=True)
+        df.loc[:, 'open_date'] = pd.to_datetime(df['open_date'], utc=True)
+        df.loc[:, 'close_rate'] = df['close_rate'].astype('float64')
+    return df
+
+
 def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataFrame:
     """
     Load trades from a DB (using dburl)
@@ -219,36 +248,10 @@ def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataF
     """
     init_db(db_url, clean_open_orders=False)
 
-    columns = ["pair", "open_date", "close_date", "profit", "profit_percent",
-               "open_rate", "close_rate", "amount", "trade_duration", "sell_reason",
-               "fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
-               "stake_amount", "max_rate", "min_rate", "id", "exchange",
-               "stop_loss", "initial_stop_loss", "strategy", "timeframe"]
-
     filters = []
     if strategy:
         filters.append(Trade.strategy == strategy)
-
-    trades = pd.DataFrame([(t.pair,
-                            t.open_date.replace(tzinfo=timezone.utc),
-                            t.close_date.replace(tzinfo=timezone.utc) if t.close_date else None,
-                            t.calc_profit(), t.calc_profit_ratio(),
-                            t.open_rate, t.close_rate, t.amount,
-                            (round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
-                             if t.close_date else None),
-                            t.sell_reason,
-                            t.fee_open, t.fee_close,
-                            t.open_rate_requested,
-                            t.close_rate_requested,
-                            t.stake_amount,
-                            t.max_rate,
-                            t.min_rate,
-                            t.id, t.exchange,
-                            t.stop_loss, t.initial_stop_loss,
-                            t.strategy, t.timeframe
-                            )
-                           for t in Trade.get_trades(filters).all()],
-                          columns=columns)
+    trades = trade_list_to_dataframe(Trade.get_trades(filters).all())
 
     return trades
 
@@ -309,7 +312,7 @@ def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close"
         end = df[column].dropna().iloc[-1]
         tmp_means.append((end - start) / start)
 
-    return np.mean(tmp_means)
+    return float(np.mean(tmp_means))
 
 
 def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
@@ -334,7 +337,7 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     """
     Adds a column `col_name` with the cumulative profit for the given trades array.
     :param df: DataFrame with date index
-    :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
+    :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
     :param col_name: Column name that will be assigned the results
     :param timeframe: Timeframe used during the operations
     :return: Returns df with one additional column, col_name, containing the cumulative profit.
@@ -346,8 +349,8 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     timeframe_minutes = timeframe_to_minutes(timeframe)
     # Resample to timeframe to make sure trades match candles
     _trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date'
-                                  )[['profit_percent']].sum()
-    df.loc[:, col_name] = _trades_sum.cumsum()
+                                  )[['profit_ratio']].sum()
+    df.loc[:, col_name] = _trades_sum['profit_ratio'].cumsum()
     # Set first value to 0
     df.loc[df.iloc[0].name, col_name] = 0
     # FFill to get continuous
@@ -356,13 +359,13 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
 
 
 def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
-                           value_col: str = 'profit_percent'
+                           value_col: str = 'profit_ratio'
                            ) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
     """
     Calculate max drawdown and the corresponding close dates
-    :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
+    :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
     :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
-    :param value_col: Column in DataFrame to use for values (defaults to 'profit_percent')
+    :param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
     :return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
     :raise: ValueError if trade-dataframe was found empty.
     """
@@ -380,3 +383,21 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
     high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
     low_date = profit_results.loc[idxmin, date_col]
     return abs(min(max_drawdown_df['drawdown'])), high_date, low_date
+
+
+def calculate_csum(trades: pd.DataFrame) -> Tuple[float, float]:
+    """
+    Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
+    :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
+    :return: Tuple (float, float) with cumsum of profit_abs
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+
+    csum_df = pd.DataFrame()
+    csum_df['sum'] = trades['profit_abs'].cumsum()
+    csum_min = csum_df['sum'].min()
+    csum_max = csum_df['sum'].max()
+
+    return csum_min, csum_max

freqtrade/data/converter.py

@@ -10,7 +10,7 @@ from typing import Any, Dict, List
 import pandas as pd
 from pandas import DataFrame, to_datetime
 
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
 
 
 logger = logging.getLogger(__name__)
@@ -168,7 +168,7 @@ def trades_remove_duplicates(trades: List[List]) -> List[List]:
     return [i for i, _ in itertools.groupby(sorted(trades, key=itemgetter(0)))]
 
 
-def trades_dict_to_list(trades: List[Dict]) -> List[List]:
+def trades_dict_to_list(trades: List[Dict]) -> TradeList:
     """
     Convert fetch_trades result into a List (to be more memory efficient).
     :param trades: List of trades, as returned by ccxt.fetch_trades.
@@ -177,16 +177,18 @@ def trades_dict_to_list(trades: List[Dict]) -> List[List]:
     return [[t[col] for col in DEFAULT_TRADES_COLUMNS] for t in trades]
 
 
-def trades_to_ohlcv(trades: List, timeframe: str) -> DataFrame:
+def trades_to_ohlcv(trades: TradeList, timeframe: str) -> DataFrame:
     """
     Converts trades list to OHLCV list
-    TODO: This should get a dedicated test
     :param trades: List of trades, as returned by ccxt.fetch_trades.
     :param timeframe: Timeframe to resample data to
     :return: OHLCV Dataframe.
+    :raises: ValueError if no trades are provided
     """
     from freqtrade.exchange import timeframe_to_minutes
     timeframe_minutes = timeframe_to_minutes(timeframe)
+    if not trades:
+        raise ValueError('Trade-list empty.')
     df = pd.DataFrame(trades, columns=DEFAULT_TRADES_COLUMNS)
     df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms',
                                      utc=True,)

freqtrade/data/dataprovider.py

@@ -8,7 +8,6 @@ import logging
 from datetime import datetime, timezone
 from typing import Any, Dict, List, Optional, Tuple
 
-from arrow import Arrow
 from pandas import DataFrame
 
 from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe
@@ -38,7 +37,7 @@ class DataProvider:
         :param timeframe: Timeframe to get data for
         :param dataframe: analyzed dataframe
         """
-        self.__cached_pairs[(pair, timeframe)] = (dataframe, Arrow.utcnow().datetime)
+        self.__cached_pairs[(pair, timeframe)] = (dataframe, datetime.now(timezone.utc))
 
     def add_pairlisthandler(self, pairlists) -> None:
         """
@@ -88,7 +87,8 @@ class DataProvider:
         """
         return load_pair_history(pair=pair,
                                  timeframe=timeframe or self._config['timeframe'],
-                                 datadir=self._config['datadir']
+                                 datadir=self._config['datadir'],
+                                 data_format=self._config.get('dataformat_ohlcv', 'json')
                                  )
 
     def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:

freqtrade/data/history/hdf5datahandler.py

@@ -3,14 +3,15 @@ import re
 from pathlib import Path
 from typing import List, Optional
 
+import numpy as np
 import pandas as pd
 
 from freqtrade import misc
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS,
-                                 ListPairsWithTimeframes)
+                                 ListPairsWithTimeframes, TradeList)
 
-from .idatahandler import IDataHandler, TradeList
+from .idatahandler import IDataHandler
 
 
 logger = logging.getLogger(__name__)
@@ -175,7 +176,8 @@ class HDF5DataHandler(IDataHandler):
             if timerange.stoptype == 'date':
                 where.append(f"timestamp < {timerange.stopts * 1e3}")
 
-        trades = pd.read_hdf(filename, key=key, mode="r", where=where)
+        trades: pd.DataFrame = pd.read_hdf(filename, key=key, mode="r", where=where)
+        trades[['id', 'type']] = trades[['id', 'type']].replace({np.nan: None})
         return trades.values.tolist()
 
     def trades_purge(self, pair: str) -> bool:

freqtrade/data/history/history_utils.py

@@ -214,10 +214,9 @@ def _download_pair_history(datadir: Path,
         data_handler.ohlcv_store(pair, timeframe, data=data)
         return True
 
-    except Exception as e:
-        logger.error(
-            f'Failed to download history data for pair: "{pair}", timeframe: {timeframe}. '
-            f'Error: {e}'
+    except Exception:
+        logger.exception(
+            f'Failed to download history data for pair: "{pair}", timeframe: {timeframe}.'
         )
         return False
 
@@ -304,10 +303,9 @@ def _download_trades_history(exchange: Exchange,
         logger.info(f"New Amount of trades: {len(trades)}")
         return True
 
-    except Exception as e:
-        logger.error(
+    except Exception:
+        logger.exception(
             f'Failed to download historic trades for pair: "{pair}". '
-            f'Error: {e}'
         )
         return False
 
@@ -356,9 +354,12 @@ def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
             if erase:
                 if data_handler_ohlcv.ohlcv_purge(pair, timeframe):
                     logger.info(f'Deleting existing data for pair {pair}, interval {timeframe}.')
-            ohlcv = trades_to_ohlcv(trades, timeframe)
-            # Store ohlcv
-            data_handler_ohlcv.ohlcv_store(pair, timeframe, data=ohlcv)
+            try:
+                ohlcv = trades_to_ohlcv(trades, timeframe)
+                # Store ohlcv
+                data_handler_ohlcv.ohlcv_store(pair, timeframe, data=ohlcv)
+            except ValueError:
+                logger.exception(f'Could not convert {pair} to OHLCV.')
 
 
 def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:

freqtrade/data/history/idatahandler.py

@@ -13,16 +13,13 @@ from typing import List, Optional, Type
 from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.constants import ListPairsWithTimeframes, TradeList
 from freqtrade.data.converter import clean_ohlcv_dataframe, trades_remove_duplicates, trim_dataframe
 from freqtrade.exchange import timeframe_to_seconds
 
 
 logger = logging.getLogger(__name__)
 
-# Type for trades list
-TradeList = List[List]
-
 
 class IDataHandler(ABC):
 

freqtrade/data/history/jsondatahandler.py

@@ -8,10 +8,10 @@ from pandas import DataFrame, read_json, to_datetime
 
 from freqtrade import misc
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, ListPairsWithTimeframes
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, ListPairsWithTimeframes, TradeList
 from freqtrade.data.converter import trades_dict_to_list
 
-from .idatahandler import IDataHandler, TradeList
+from .idatahandler import IDataHandler
 
 
 logger = logging.getLogger(__name__)
@@ -86,8 +86,12 @@ class JsonDataHandler(IDataHandler):
         filename = self._pair_data_filename(self._datadir, pair, timeframe)
         if not filename.exists():
             return DataFrame(columns=self._columns)
-        pairdata = read_json(filename, orient='values')
-        pairdata.columns = self._columns
+        try:
+            pairdata = read_json(filename, orient='values')
+            pairdata.columns = self._columns
+        except ValueError:
+            logger.error(f"Could not load data for {pair}.")
+            return DataFrame(columns=self._columns)
         pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
                                           'low': 'float', 'close': 'float', 'volume': 'float'})
         pairdata['date'] = to_datetime(pairdata['date'],

freqtrade/edge/edge_positioning.py

@@ -12,6 +12,7 @@ from freqtrade.configuration import TimeRange
 from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
 from freqtrade.data.history import get_timerange, load_data, refresh_data
 from freqtrade.exceptions import OperationalException
+from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.strategy.interface import SellType
 
 
@@ -80,14 +81,16 @@ class Edge:
         if config.get('fee'):
             self.fee = config['fee']
         else:
-            self.fee = self.exchange.get_fee(symbol=self.config['exchange']['pair_whitelist'][0])
+            self.fee = self.exchange.get_fee(symbol=expand_pairlist(
+                self.config['exchange']['pair_whitelist'], list(self.exchange.markets))[0])
 
     def calculate(self) -> bool:
-        pairs = self.config['exchange']['pair_whitelist']
+        pairs = expand_pairlist(self.config['exchange']['pair_whitelist'],
+                                list(self.exchange.markets))
         heartbeat = self.edge_config.get('process_throttle_secs')
 
         if (self._last_updated > 0) and (
-                self._last_updated + heartbeat > arrow.utcnow().timestamp):
+                self._last_updated + heartbeat > arrow.utcnow().int_timestamp):
             return False
 
         data: Dict[str, Any] = {}
@@ -101,6 +104,7 @@ class Edge:
                 exchange=self.exchange,
                 timeframe=self.strategy.timeframe,
                 timerange=self._timerange,
+                data_format=self.config.get('dataformat_ohlcv', 'json'),
             )
 
         data = load_data(
@@ -146,7 +150,7 @@ class Edge:
         # Fill missing, calculable columns, profit, duration , abs etc.
         trades_df = self._fill_calculable_fields(DataFrame(trades))
         self._cached_pairs = self._process_expectancy(trades_df)
-        self._last_updated = arrow.utcnow().timestamp
+        self._last_updated = arrow.utcnow().int_timestamp
 
         return True
 
@@ -156,7 +160,8 @@ class Edge:
         available_capital = (total_capital + capital_in_trade) * self._capital_ratio
         allowed_capital_at_risk = available_capital * self._allowed_risk
         max_position_size = abs(allowed_capital_at_risk / stoploss)
-        position_size = min(max_position_size, free_capital)
+        # Position size must be below available capital.
+        position_size = min(min(max_position_size, free_capital), available_capital)
         if pair in self._cached_pairs:
             logger.info(
                 'winrate: %s, expectancy: %s, position size: %s, pair: %s,'

freqtrade/exchange/__init__.py

@@ -6,6 +6,7 @@ from freqtrade.exchange.exchange import Exchange
 from freqtrade.exchange.bibox import Bibox
 from freqtrade.exchange.binance import Binance
 from freqtrade.exchange.bittrex import Bittrex
+from freqtrade.exchange.bybit import Bybit
 from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges,
                                          get_exchange_bad_reason, is_exchange_bad,
                                          is_exchange_known_ccxt, is_exchange_officially_supported,

freqtrade/exchange/binance.py

@@ -18,6 +18,7 @@ class Binance(Exchange):
     _ft_has: Dict = {
         "stoploss_on_exchange": True,
         "order_time_in_force": ['gtc', 'fok', 'ioc'],
+        "ohlcv_candle_limit": 1000,
         "trades_pagination": "id",
         "trades_pagination_arg": "fromId",
         "l2_limit_range": [5, 10, 20, 50, 100, 500, 1000],

freqtrade/exchange/bittrex.py

@@ -19,5 +19,11 @@ class Bittrex(Exchange):
     """
 
     _ft_has: Dict = {
+        "ohlcv_candle_limit_per_timeframe": {
+            '1m': 1440,
+            '5m': 288,
+            '1h': 744,
+            '1d': 365,
+        },
         "l2_limit_range": [1, 25, 500],
     }

freqtrade/exchange/bybit.py

@@ -0,0 +1,24 @@
+""" Bybit exchange subclass """
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+class Bybit(Exchange):
+    """
+    Bybit exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+
+    Please note that this exchange is not included in the list of exchanges
+    officially supported by the Freqtrade development team. So some features
+    may still not work as expected.
+    """
+
+    # fetchCurrencies API point requires authentication for Bybit,
+    _ft_has: Dict = {
+        "ohlcv_candle_limit": 200,
+    }

freqtrade/exchange/common.py

@@ -21,6 +21,7 @@ BAD_EXCHANGES = {
     "hitbtc": "This API cannot be used with Freqtrade. "
               "Use `hitbtc2` exchange id to access this exchange.",
     "phemex": "Does not provide history. ",
+    "poloniex": "Does not provide fetch_order endpoint to fetch both open and closed orders.",
     **dict.fromkeys([
         'adara',
         'anxpro',

freqtrade/exchange/exchange.py

@@ -3,6 +3,7 @@
 Cryptocurrency Exchanges support
 """
 import asyncio
+import http
 import inspect
 import logging
 from copy import deepcopy
@@ -17,7 +18,7 @@ from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE, TRU
                                             decimal_to_precision)
 from pandas import DataFrame
 
-from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.constants import DEFAULT_AMOUNT_RESERVE_PERCENT, ListPairsWithTimeframes
 from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
 from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
                                   InvalidOrderException, OperationalException, RetryableOrderError,
@@ -25,6 +26,7 @@ from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFun
 from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES, retrier,
                                        retrier_async)
 from freqtrade.misc import deep_merge_dicts, safe_value_fallback2
+from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 
 
 CcxtModuleType = Any
@@ -33,6 +35,12 @@ CcxtModuleType = Any
 logger = logging.getLogger(__name__)
 
 
+# Workaround for adding samesite support to pre 3.8 python
+# Only applies to python3.7, and only on certain exchanges (kraken)
+# Replicates the fix from starlette (which is actually causing this problem)
+http.cookies.Morsel._reserved["samesite"] = "SameSite"  # type: ignore
+
+
 class Exchange:
 
     _config: Dict = {}
@@ -65,6 +73,7 @@ class Exchange:
         """
         self._api: ccxt.Exchange = None
         self._api_async: ccxt_async.Exchange = None
+        self._markets: Dict = {}
 
         self._config.update(config)
 
@@ -92,7 +101,6 @@ class Exchange:
             logger.info("Overriding exchange._ft_has with config params, result: %s", self._ft_has)
 
         # Assign this directly for easy access
-        self._ohlcv_candle_limit = self._ft_has['ohlcv_candle_limit']
         self._ohlcv_partial_candle = self._ft_has['ohlcv_partial_candle']
 
         self._trades_pagination = self._ft_has['trades_pagination']
@@ -124,10 +132,12 @@ class Exchange:
 
             # Check if all pairs are available
             self.validate_stakecurrency(config['stake_currency'])
-            self.validate_pairs(config['exchange']['pair_whitelist'])
+            if not exchange_config.get('skip_pair_validation'):
+                self.validate_pairs(config['exchange']['pair_whitelist'])
             self.validate_ordertypes(config.get('order_types', {}))
             self.validate_order_time_in_force(config.get('order_time_in_force', {}))
-            self.validate_required_startup_candles(config.get('startup_candle_count', 0))
+            self.validate_required_startup_candles(config.get('startup_candle_count', 0),
+                                                   config.get('timeframe', ''))
 
         # Converts the interval provided in minutes in config to seconds
         self.markets_refresh_interval: int = exchange_config.get(
@@ -188,26 +198,32 @@ class Exchange:
     def timeframes(self) -> List[str]:
         return list((self._api.timeframes or {}).keys())
 
-    @property
-    def ohlcv_candle_limit(self) -> int:
-        """exchange ohlcv candle limit"""
-        return int(self._ohlcv_candle_limit)
-
     @property
     def markets(self) -> Dict:
         """exchange ccxt markets"""
-        if not self._api.markets:
+        if not self._markets:
             logger.info("Markets were not loaded. Loading them now..")
             self._load_markets()
-        return self._api.markets
+        return self._markets
 
     @property
     def precisionMode(self) -> str:
         """exchange ccxt precisionMode"""
         return self._api.precisionMode
 
+    def ohlcv_candle_limit(self, timeframe: str) -> int:
+        """
+        Exchange ohlcv candle limit
+        Uses ohlcv_candle_limit_per_timeframe if the exchange has different limts
+        per timeframe (e.g. bittrex), otherwise falls back to ohlcv_candle_limit
+        :param timeframe: Timeframe to check
+        :return: Candle limit as integer
+        """
+        return int(self._ft_has.get('ohlcv_candle_limit_per_timeframe', {}).get(
+            timeframe, self._ft_has.get('ohlcv_candle_limit')))
+
     def get_markets(self, base_currencies: List[str] = None, quote_currencies: List[str] = None,
-                    pairs_only: bool = False, active_only: bool = False) -> Dict:
+                    pairs_only: bool = False, active_only: bool = False) -> Dict[str, Any]:
         """
         Return exchange ccxt markets, filtered out by base currency and quote currency
         if this was requested in parameters.
@@ -282,16 +298,16 @@ class Exchange:
                 asyncio.get_event_loop().run_until_complete(
                     self._api_async.load_markets(reload=reload))
 
-        except ccxt.BaseError as e:
+        except (asyncio.TimeoutError, ccxt.BaseError) as e:
             logger.warning('Could not load async markets. Reason: %s', e)
             return
 
     def _load_markets(self) -> None:
         """ Initialize markets both sync and async """
         try:
-            self._api.load_markets()
+            self._markets = self._api.load_markets()
             self._load_async_markets()
-            self._last_markets_refresh = arrow.utcnow().timestamp
+            self._last_markets_refresh = arrow.utcnow().int_timestamp
         except ccxt.BaseError as e:
             logger.warning('Unable to initialize markets. Reason: %s', e)
 
@@ -300,14 +316,14 @@ class Exchange:
         # Check whether markets have to be reloaded
         if (self._last_markets_refresh > 0) and (
                 self._last_markets_refresh + self.markets_refresh_interval
-                > arrow.utcnow().timestamp):
+                > arrow.utcnow().int_timestamp):
             return None
         logger.debug("Performing scheduled market reload..")
         try:
-            self._api.load_markets(reload=True)
+            self._markets = self._api.load_markets(reload=True)
             # Also reload async markets to avoid issues with newly listed pairs
             self._load_async_markets(reload=True)
-            self._last_markets_refresh = arrow.utcnow().timestamp
+            self._last_markets_refresh = arrow.utcnow().int_timestamp
         except ccxt.BaseError:
             logger.exception("Could not reload markets.")
 
@@ -334,8 +350,9 @@ class Exchange:
         if not self.markets:
             logger.warning('Unable to validate pairs (assuming they are correct).')
             return
+        extended_pairs = expand_pairlist(pairs, list(self.markets), keep_invalid=True)
         invalid_pairs = []
-        for pair in pairs:
+        for pair in extended_pairs:
             # Note: ccxt has BaseCurrency/QuoteCurrency format for pairs
             # TODO: add a support for having coins in BTC/USDT format
             if self.markets and pair not in self.markets:
@@ -417,15 +434,16 @@ class Exchange:
             raise OperationalException(
                 f'Time in force policies are not supported for {self.name} yet.')
 
-    def validate_required_startup_candles(self, startup_candles: int) -> None:
+    def validate_required_startup_candles(self, startup_candles: int, timeframe: str) -> None:
         """
-        Checks if required startup_candles is more than ohlcv_candle_limit.
+        Checks if required startup_candles is more than ohlcv_candle_limit().
         Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
         """
-        if startup_candles + 5 > self._ft_has['ohlcv_candle_limit']:
+        candle_limit = self.ohlcv_candle_limit(timeframe)
+        if startup_candles + 5 > candle_limit:
             raise OperationalException(
                 f"This strategy requires {startup_candles} candles to start. "
-                f"{self.name} only provides {self._ft_has['ohlcv_candle_limit']}.")
+                f"{self.name} only provides {candle_limit} for {timeframe}.")
 
     def exchange_has(self, endpoint: str) -> bool:
         """
@@ -486,6 +504,41 @@ class Exchange:
         else:
             return 1 / pow(10, precision)
 
+    def get_min_pair_stake_amount(self, pair: str, price: float,
+                                  stoploss: float) -> Optional[float]:
+        try:
+            market = self.markets[pair]
+        except KeyError:
+            raise ValueError(f"Can't get market information for symbol {pair}")
+
+        if 'limits' not in market:
+            return None
+
+        min_stake_amounts = []
+        limits = market['limits']
+        if ('cost' in limits and 'min' in limits['cost']
+                and limits['cost']['min'] is not None):
+            min_stake_amounts.append(limits['cost']['min'])
+
+        if ('amount' in limits and 'min' in limits['amount']
+                and limits['amount']['min'] is not None):
+            min_stake_amounts.append(limits['amount']['min'] * price)
+
+        if not min_stake_amounts:
+            return None
+
+        # reserve some percent defined in config (5% default) + stoploss
+        amount_reserve_percent = 1.0 - self._config.get('amount_reserve_percent',
+                                                        DEFAULT_AMOUNT_RESERVE_PERCENT)
+        amount_reserve_percent += stoploss
+        # it should not be more than 50%
+        amount_reserve_percent = max(amount_reserve_percent, 0.5)
+
+        # The value returned should satisfy both limits: for amount (base currency) and
+        # for cost (quote, stake currency), so max() is used here.
+        # See also #2575 at github.
+        return max(min_stake_amounts) / amount_reserve_percent
+
     def dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
                       rate: float, params: Dict = {}) -> Dict[str, Any]:
         order_id = f'dry_run_{side}_{datetime.now().timestamp()}'
@@ -501,7 +554,7 @@ class Exchange:
             'side': side,
             'remaining': _amount,
             'datetime': arrow.utcnow().isoformat(),
-            'timestamp': int(arrow.utcnow().timestamp * 1000),
+            'timestamp': int(arrow.utcnow().int_timestamp * 1000),
             'status': "closed" if ordertype == "market" else "open",
             'fee': None,
             'info': {}
@@ -523,7 +576,7 @@ class Exchange:
                     'rate': self.get_fee(pair)
                 }
             })
-        if closed_order["type"] in ["stop_loss_limit"]:
+        if closed_order["type"] in ["stop_loss_limit", "stop-loss-limit"]:
             closed_order["info"].update({"stopPrice": closed_order["price"]})
         self._dry_run_open_orders[closed_order["id"]] = closed_order
 
@@ -657,7 +710,8 @@ class Exchange:
     @retrier
     def fetch_ticker(self, pair: str) -> dict:
         try:
-            if pair not in self._api.markets or not self._api.markets[pair].get('active'):
+            if (pair not in self.markets or
+                    self.markets[pair].get('active', False) is False):
                 raise ExchangeError(f"Pair {pair} not available")
             data = self._api.fetch_ticker(pair)
             return data
@@ -674,16 +728,29 @@ class Exchange:
         """
         Get candle history using asyncio and returns the list of candles.
         Handles all async work for this.
-        Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
+        Async over one pair, assuming we get `self.ohlcv_candle_limit()` candles per call.
         :param pair: Pair to download
         :param timeframe: Timeframe to get data for
         :param since_ms: Timestamp in milliseconds to get history from
-        :returns List with candle (OHLCV) data
+        :return: List with candle (OHLCV) data
         """
         return asyncio.get_event_loop().run_until_complete(
             self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
                                            since_ms=since_ms))
 
+    def get_historic_ohlcv_as_df(self, pair: str, timeframe: str,
+                                 since_ms: int) -> DataFrame:
+        """
+        Minimal wrapper around get_historic_ohlcv - converting the result into a dataframe
+        :param pair: Pair to download
+        :param timeframe: Timeframe to get data for
+        :param since_ms: Timestamp in milliseconds to get history from
+        :return: OHLCV DataFrame
+        """
+        ticks = self.get_historic_ohlcv(pair, timeframe, since_ms=since_ms)
+        return ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
+                                  drop_incomplete=self._ohlcv_partial_candle)
+
     async def _async_get_historic_ohlcv(self, pair: str,
                                         timeframe: str,
                                         since_ms: int) -> List:
@@ -691,7 +758,7 @@ class Exchange:
         Download historic ohlcv
         """
 
-        one_call = timeframe_to_msecs(timeframe) * self._ohlcv_candle_limit
+        one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(timeframe)
         logger.debug(
             "one_call: %s msecs (%s)",
             one_call,
@@ -699,7 +766,7 @@ class Exchange:
         )
         input_coroutines = [self._async_get_candle_history(
             pair, timeframe, since) for since in
-            range(since_ms, arrow.utcnow().timestamp * 1000, one_call)]
+            range(since_ms, arrow.utcnow().int_timestamp * 1000, one_call)]
 
         results = await asyncio.gather(*input_coroutines, return_exceptions=True)
 
@@ -718,13 +785,17 @@ class Exchange:
         logger.info("Downloaded data for %s with length %s.", pair, len(data))
         return data
 
-    def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes) -> List[Tuple[str, List]]:
+    def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,
+                             since_ms: Optional[int] = None, cache: bool = True
+                             ) -> Dict[Tuple[str, str], DataFrame]:
         """
         Refresh in-memory OHLCV asynchronously and set `_klines` with the result
         Loops asynchronously over pair_list and downloads all pairs async (semi-parallel).
         Only used in the dataprovider.refresh() method.
         :param pair_list: List of 2 element tuples containing pair, interval to refresh
-        :return: TODO: return value is only used in the tests, get rid of it
+        :param since_ms: time since when to download, in milliseconds
+        :param cache: Assign result to _klines. Usefull for one-off downloads like for pairlists
+        :return: Dict of [{(pair, timeframe): Dataframe}]
         """
         logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
 
@@ -734,7 +805,8 @@ class Exchange:
         for pair, timeframe in set(pair_list):
             if (not ((pair, timeframe) in self._klines)
                     or self._now_is_time_to_refresh(pair, timeframe)):
-                input_coroutines.append(self._async_get_candle_history(pair, timeframe))
+                input_coroutines.append(self._async_get_candle_history(pair, timeframe,
+                                                                       since_ms=since_ms))
             else:
                 logger.debug(
                     "Using cached candle (OHLCV) data for pair %s, timeframe %s ...",
@@ -744,6 +816,7 @@ class Exchange:
         results = asyncio.get_event_loop().run_until_complete(
             asyncio.gather(*input_coroutines, return_exceptions=True))
 
+        results_df = {}
         # handle caching
         for res in results:
             if isinstance(res, Exception):
@@ -755,18 +828,20 @@ class Exchange:
             if ticks:
                 self._pairs_last_refresh_time[(pair, timeframe)] = ticks[-1][0] // 1000
             # keeping parsed dataframe in cache
-            self._klines[(pair, timeframe)] = ohlcv_to_dataframe(
-                ticks, timeframe, pair=pair, fill_missing=True,
-                drop_incomplete=self._ohlcv_partial_candle)
-
-        return results
+            ohlcv_df = ohlcv_to_dataframe(
+                    ticks, timeframe, pair=pair, fill_missing=True,
+                    drop_incomplete=self._ohlcv_partial_candle)
+            results_df[(pair, timeframe)] = ohlcv_df
+            if cache:
+                self._klines[(pair, timeframe)] = ohlcv_df
+        return results_df
 
     def _now_is_time_to_refresh(self, pair: str, timeframe: str) -> bool:
         # Timeframe in seconds
         interval_in_sec = timeframe_to_seconds(timeframe)
 
         return not ((self._pairs_last_refresh_time.get((pair, timeframe), 0)
-                     + interval_in_sec) >= arrow.utcnow().timestamp)
+                     + interval_in_sec) >= arrow.utcnow().int_timestamp)
 
     @retrier_async
     async def _async_get_candle_history(self, pair: str, timeframe: str,
@@ -784,7 +859,8 @@ class Exchange:
             )
 
             data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
-                                                     since=since_ms)
+                                                     since=since_ms,
+                                                     limit=self.ohlcv_candle_limit(timeframe))
 
             # Some exchanges sort OHLCV in ASC order and others in DESC.
             # Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
@@ -912,7 +988,7 @@ class Exchange:
         while True:
             t = await self._async_fetch_trades(pair, since=since)
             if len(t):
-                since = t[-1][1]
+                since = t[-1][0]
                 trades.extend(t)
                 # Reached the end of the defined-download period
                 if until and t[-1][0] > until:
@@ -957,7 +1033,7 @@ class Exchange:
         """
         Get trade history data using asyncio.
         Handles all async work and returns the list of candles.
-        Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
+        Async over one pair, assuming we get `self.ohlcv_candle_limit()` candles per call.
         :param pair: Pair to download
         :param since: Timestamp in milliseconds to get history from
         :param until: Timestamp in milliseconds. Defaults to current timestamp if not defined.

freqtrade/exchange/kraken.py

@@ -18,6 +18,7 @@ class Kraken(Exchange):
     _params: Dict = {"trading_agreement": "agree"}
     _ft_has: Dict = {
         "stoploss_on_exchange": True,
+        "ohlcv_candle_limit": 720,
         "trades_pagination": "id",
         "trades_pagination_arg": "since",
     }
@@ -47,7 +48,7 @@ class Kraken(Exchange):
 
             orders = self._api.fetch_open_orders()
             order_list = [(x["symbol"].split("/")[0 if x["side"] == "sell" else 1],
-                           x["remaining"],
+                           x["remaining"] if x["side"] == "sell" else x["remaining"] * x["price"],
                            # Don't remove the below comment, this can be important for debuggung
                            # x["side"], x["amount"],
                            ) for x in orders]
@@ -69,7 +70,8 @@ class Kraken(Exchange):
         Verify stop_loss against stoploss-order value (limit or price)
         Returns True if adjustment is necessary.
         """
-        return order['type'] == 'stop-loss' and stop_loss > float(order['price'])
+        return (order['type'] in ('stop-loss', 'stop-loss-limit')
+                and stop_loss > float(order['price']))
 
     @retrier(retries=0)
     def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
@@ -77,8 +79,15 @@ class Kraken(Exchange):
         Creates a stoploss market order.
         Stoploss market orders is the only stoploss type supported by kraken.
         """
+        params = self._params.copy()
 
-        ordertype = "stop-loss"
+        if order_types.get('stoploss', 'market') == 'limit':
+            ordertype = "stop-loss-limit"
+            limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
+            limit_rate = stop_price * limit_price_pct
+            params['price2'] = self.price_to_precision(pair, limit_rate)
+        else:
+            ordertype = "stop-loss"
 
         stop_price = self.price_to_precision(pair, stop_price)
 
@@ -88,8 +97,6 @@ class Kraken(Exchange):
             return dry_order
 
         try:
-            params = self._params.copy()
-
             amount = self.amount_to_precision(pair, amount)
 
             order = self._api.create_order(symbol=pair, type=ordertype, side='sell',

freqtrade/freqtradebot.py

@@ -19,10 +19,12 @@ from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
 from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError,
                                   InvalidOrderException, PricingError)
-from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
 from freqtrade.misc import safe_value_fallback, safe_value_fallback2
-from freqtrade.pairlist.pairlistmanager import PairListManager
+from freqtrade.mixins import LoggingMixin
 from freqtrade.persistence import Order, PairLocks, Trade, cleanup_db, init_db
+from freqtrade.plugins.pairlistmanager import PairListManager
+from freqtrade.plugins.protectionmanager import ProtectionManager
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.rpc import RPCManager, RPCMessageType
 from freqtrade.state import State
@@ -34,7 +36,7 @@ from freqtrade.wallets import Wallets
 logger = logging.getLogger(__name__)
 
 
-class FreqtradeBot:
+class FreqtradeBot(LoggingMixin):
     """
     Freqtrade is the main class of the bot.
     This is from here the bot start its logic.
@@ -78,6 +80,8 @@ class FreqtradeBot:
 
         self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists)
 
+        self.protections = ProtectionManager(self.config)
+
         # Attach Dataprovider to Strategy baseclass
         IStrategy.dp = self.dataprovider
         # Attach Wallets to Strategy baseclass
@@ -101,6 +105,7 @@ class FreqtradeBot:
         self.rpc: RPCManager = RPCManager(self)
         # Protect sell-logic from forcesell and viceversa
         self._sell_lock = Lock()
+        LoggingMixin.__init__(self, logger, timeframe_to_seconds(self.strategy.timeframe))
 
     def notify_status(self, msg: str) -> None:
         """
@@ -132,7 +137,7 @@ class FreqtradeBot:
         Called on startup and after reloading the bot - triggers notifications and
         performs startup tasks
         """
-        self.rpc.startup_messages(self.config, self.pairlists)
+        self.rpc.startup_messages(self.config, self.pairlists, self.protections)
         if not self.edge:
             # Adjust stoploss if it was changed
             Trade.stoploss_reinitialization(self.strategy.stoploss)
@@ -174,6 +179,7 @@ class FreqtradeBot:
         # Without this, freqtrade my try to recreate stoploss_on_exchange orders
         # while selling is in process, since telegram messages arrive in an different thread.
         with self._sell_lock:
+            trades = Trade.get_open_trades()
             # First process current opened trades (positions)
             self.exit_positions(trades)
 
@@ -195,7 +201,7 @@ class FreqtradeBot:
         Notify the user when the bot is stopped
         and there are still open trades active.
         """
-        open_trades = Trade.get_trades([Trade.is_open == 1]).all()
+        open_trades = Trade.get_trades([Trade.is_open.is_(True)]).all()
 
         if len(open_trades) != 0:
             msg = {
@@ -228,7 +234,7 @@ class FreqtradeBot:
             _whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
         return _whitelist
 
-    def get_free_open_trades(self):
+    def get_free_open_trades(self) -> int:
         """
         Return the number of free open trades slots or 0 if
         max number of open trades reached
@@ -241,6 +247,10 @@ class FreqtradeBot:
         Updates open orders based on order list kept in the database.
         Mainly updates the state of orders - but may also close trades
         """
+        if self.config['dry_run'] or self.config['exchange'].get('skip_open_order_update', False):
+            # Updating open orders in dry-run does not make sense and will fail.
+            return
+
         orders = Order.get_open_orders()
         logger.info(f"Updating {len(orders)} open orders.")
         for order in orders:
@@ -251,6 +261,7 @@ class FreqtradeBot:
                 self.update_trade_state(order.trade, order.order_id, fo)
 
             except ExchangeError as e:
+
                 logger.warning(f"Error updating Order {order.order_id} due to {e}")
 
     def update_closed_trades_without_assigned_fees(self):
@@ -258,6 +269,10 @@ class FreqtradeBot:
         Update closed trades without close fees assigned.
         Only acts when Orders are in the database, otherwise the last orderid is unknown.
         """
+        if self.config['dry_run']:
+            # Updating open orders in dry-run does not make sense and will fail.
+            return
+
         trades: List[Trade] = Trade.get_sold_trades_without_assigned_fees()
         for trade in trades:
 
@@ -358,6 +373,15 @@ class FreqtradeBot:
             logger.info("No currency pair in active pair whitelist, "
                         "but checking to sell open trades.")
             return trades_created
+        if PairLocks.is_global_lock():
+            lock = PairLocks.get_pair_longest_lock('*')
+            if lock:
+                self.log_once(f"Global pairlock active until "
+                              f"{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)}. "
+                              "Not creating new trades.", logger.info)
+            else:
+                self.log_once("Global pairlock active. Not creating new trades.", logger.info)
+            return trades_created
         # Create entity and execute trade for each pair from whitelist
         for pair in whitelist:
             try:
@@ -366,8 +390,7 @@ class FreqtradeBot:
                 logger.warning('Unable to create trade for %s: %s', pair, exception)
 
         if not trades_created:
-            logger.debug("Found no buy signals for whitelisted currencies. "
-                         "Trying again...")
+            logger.debug("Found no buy signals for whitelisted currencies. Trying again...")
 
         return trades_created
 
@@ -417,118 +440,6 @@ class FreqtradeBot:
 
         return used_rate
 
-    def get_trade_stake_amount(self, pair: str) -> float:
-        """
-        Calculate stake amount for the trade
-        :return: float: Stake amount
-        :raise: DependencyException if the available stake amount is too low
-        """
-        stake_amount: float
-        # Ensure wallets are uptodate.
-        self.wallets.update()
-
-        if self.edge:
-            stake_amount = self.edge.stake_amount(
-                pair,
-                self.wallets.get_free(self.config['stake_currency']),
-                self.wallets.get_total(self.config['stake_currency']),
-                Trade.total_open_trades_stakes()
-            )
-        else:
-            stake_amount = self.config['stake_amount']
-            if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
-                stake_amount = self._calculate_unlimited_stake_amount()
-
-        return self._check_available_stake_amount(stake_amount)
-
-    def _get_available_stake_amount(self) -> float:
-        """
-        Return the total currently available balance in stake currency,
-        respecting tradable_balance_ratio.
-        Calculated as
-        <open_trade stakes> + free amount ) * tradable_balance_ratio - <open_trade stakes>
-        """
-        val_tied_up = Trade.total_open_trades_stakes()
-
-        # Ensure <tradable_balance_ratio>% is used from the overall balance
-        # Otherwise we'd risk lowering stakes with each open trade.
-        # (tied up + current free) * ratio) - tied up
-        available_amount = ((val_tied_up + self.wallets.get_free(self.config['stake_currency'])) *
-                            self.config['tradable_balance_ratio']) - val_tied_up
-        return available_amount
-
-    def _calculate_unlimited_stake_amount(self) -> float:
-        """
-        Calculate stake amount for "unlimited" stake amount
-        :return: 0 if max number of trades reached, else stake_amount to use.
-        """
-        free_open_trades = self.get_free_open_trades()
-        if not free_open_trades:
-            return 0
-
-        available_amount = self._get_available_stake_amount()
-
-        return available_amount / free_open_trades
-
-    def _check_available_stake_amount(self, stake_amount: float) -> float:
-        """
-        Check if stake amount can be fulfilled with the available balance
-        for the stake currency
-        :return: float: Stake amount
-        """
-        available_amount = self._get_available_stake_amount()
-
-        if self.config['amend_last_stake_amount']:
-            # Remaining amount needs to be at least stake_amount * last_stake_amount_min_ratio
-            # Otherwise the remaining amount is too low to trade.
-            if available_amount > (stake_amount * self.config['last_stake_amount_min_ratio']):
-                stake_amount = min(stake_amount, available_amount)
-            else:
-                stake_amount = 0
-
-        if available_amount < stake_amount:
-            raise DependencyException(
-                f"Available balance ({available_amount} {self.config['stake_currency']}) is "
-                f"lower than stake amount ({stake_amount} {self.config['stake_currency']})"
-            )
-
-        return stake_amount
-
-    def _get_min_pair_stake_amount(self, pair: str, price: float) -> Optional[float]:
-        try:
-            market = self.exchange.markets[pair]
-        except KeyError:
-            raise ValueError(f"Can't get market information for symbol {pair}")
-
-        if 'limits' not in market:
-            return None
-
-        min_stake_amounts = []
-        limits = market['limits']
-        if ('cost' in limits and 'min' in limits['cost']
-                and limits['cost']['min'] is not None):
-            min_stake_amounts.append(limits['cost']['min'])
-
-        if ('amount' in limits and 'min' in limits['amount']
-                and limits['amount']['min'] is not None):
-            min_stake_amounts.append(limits['amount']['min'] * price)
-
-        if not min_stake_amounts:
-            return None
-
-        # reserve some percent defined in config (5% default) + stoploss
-        amount_reserve_percent = 1.0 - self.config.get('amount_reserve_percent',
-                                                       constants.DEFAULT_AMOUNT_RESERVE_PERCENT)
-        if self.strategy.stoploss is not None:
-            amount_reserve_percent += self.strategy.stoploss
-        # it should not be more than 50%
-        amount_reserve_percent = max(amount_reserve_percent, 0.5)
-
-        # The value returned should satisfy both limits: for amount (base currency) and
-        # for cost (quote, stake currency), so max() is used here.
-        # See also #2575 at github.
-        return max(min_stake_amounts) / amount_reserve_percent
-
     def create_trade(self, pair: str) -> bool:
         """
         Check the implemented trading strategy for buy signals.
@@ -541,9 +452,15 @@ class FreqtradeBot:
         logger.debug(f"create_trade for pair {pair}")
 
         analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(pair, self.strategy.timeframe)
-        if self.strategy.is_pair_locked(
-                pair, analyzed_df.iloc[-1]['date'] if len(analyzed_df) > 0 else None):
-            logger.info(f"Pair {pair} is currently locked.")
+        nowtime = analyzed_df.iloc[-1]['date'] if len(analyzed_df) > 0 else None
+        if self.strategy.is_pair_locked(pair, nowtime):
+            lock = PairLocks.get_pair_longest_lock(pair, nowtime)
+            if lock:
+                self.log_once(f"Pair {pair} is still locked until "
+                              f"{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)}.",
+                              logger.info)
+            else:
+                self.log_once(f"Pair {pair} is still locked.", logger.info)
             return False
 
         # get_free_open_trades is checked before create_trade is called
@@ -556,7 +473,8 @@ class FreqtradeBot:
         (buy, sell) = self.strategy.get_signal(pair, self.strategy.timeframe, analyzed_df)
 
         if buy and not sell:
-            stake_amount = self.get_trade_stake_amount(pair)
+            stake_amount = self.wallets.get_trade_stake_amount(pair, self.get_free_open_trades(),
+                                                               self.edge)
             if not stake_amount:
                 logger.debug(f"Stake amount is 0, ignoring possible trade for {pair}.")
                 return False
@@ -616,7 +534,11 @@ class FreqtradeBot:
             # Calculate price
             buy_limit_requested = self.get_buy_rate(pair, True)
 
-        min_stake_amount = self._get_min_pair_stake_amount(pair, buy_limit_requested)
+        if not buy_limit_requested:
+            raise PricingError('Could not determine buy price.')
+
+        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, buy_limit_requested,
+                                                                   self.strategy.stoploss)
         if min_stake_amount is not None and min_stake_amount > stake_amount:
             logger.warning(
                 f"Can't open a new trade for {pair}: stake amount "
@@ -968,7 +890,8 @@ class FreqtradeBot:
                 logger.warning('Stoploss order was cancelled, but unable to recreate one.')
 
         # Finally we check if stoploss on exchange should be moved up because of trailing.
-        if stoploss_order and self.config.get('trailing_stop', False):
+        if stoploss_order and (self.config.get('trailing_stop', False)
+                               or self.config.get('use_custom_stoploss', False)):
             # if trailing stoploss is enabled we check if stoploss value has changed
             # in which case we cancel stoploss order and put another one with new
             # value immediately
@@ -1148,7 +1071,9 @@ class FreqtradeBot:
             if not self.exchange.check_order_canceled_empty(order):
                 try:
                     # if trade is not partially completed, just delete the order
-                    self.exchange.cancel_order(trade.open_order_id, trade.pair)
+                    co = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
+                                                                trade.amount)
+                    trade.update_order(co)
                 except InvalidOrderException:
                     logger.exception(f"Could not cancel sell order {trade.open_order_id}")
                     return 'error cancelling order'
@@ -1156,6 +1081,7 @@ class FreqtradeBot:
             else:
                 reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
                 logger.info('Sell order %s for %s.', reason, trade)
+                trade.update_order(order)
 
             trade.close_rate = None
             trade.close_rate_requested = None
@@ -1258,6 +1184,7 @@ class FreqtradeBot:
         trade.orders.append(order_obj)
 
         trade.open_order_id = order['id']
+        trade.sell_order_status = ''
         trade.close_rate_requested = limit
         trade.sell_reason = sell_reason.value
         # In case of market sell orders the order can be closed immediately
@@ -1393,7 +1320,7 @@ class FreqtradeBot:
                            abs_tol=constants.MATH_CLOSE_PREC):
                 order['amount'] = new_amount
                 order.pop('filled', None)
-                trade.recalc_open_trade_price()
+                trade.recalc_open_trade_value()
         except DependencyException as exception:
             logger.warning("Could not update trade amount: %s", exception)
 
@@ -1405,6 +1332,8 @@ class FreqtradeBot:
 
         # Updating wallets when order is closed
         if not trade.is_open:
+            self.protections.stop_per_pair(trade.pair)
+            self.protections.global_stop()
             self.wallets.update()
         return False
 
@@ -1446,13 +1375,16 @@ class FreqtradeBot:
             fee_cost, fee_currency, fee_rate = self.exchange.extract_cost_curr_rate(order)
             logger.info(f"Fee for Trade {trade} [{order.get('side')}]: "
                         f"{fee_cost:.8g} {fee_currency} - rate: {fee_rate}")
-
-            trade.update_fee(fee_cost, fee_currency, fee_rate, order.get('side', ''))
-            if trade_base_currency == fee_currency:
-                # Apply fee to amount
-                return self.apply_fee_conditional(trade, trade_base_currency,
-                                                  amount=order_amount, fee_abs=fee_cost)
-            return order_amount
+            if fee_rate is None or fee_rate < 0.02:
+                # Reject all fees that report as > 2%.
+                # These are most likely caused by a parsing bug in ccxt
+                # due to multiple trades (https://github.com/ccxt/ccxt/issues/8025)
+                trade.update_fee(fee_cost, fee_currency, fee_rate, order.get('side', ''))
+                if trade_base_currency == fee_currency:
+                    # Apply fee to amount
+                    return self.apply_fee_conditional(trade, trade_base_currency,
+                                                      amount=order_amount, fee_abs=fee_cost)
+                return order_amount
         return self.fee_detection_from_trades(trade, order, order_amount)
 
     def fee_detection_from_trades(self, trade: Trade, order: Dict, order_amount: float) -> float:

freqtrade/loggers.py

@@ -37,6 +37,13 @@ def _set_loggers(verbosity: int = 0, api_verbosity: str = 'info') -> None:
     )
 
 
+def get_existing_handlers(handlertype):
+    """
+    Returns Existing handler or None (if the handler has not yet been added to the root handlers).
+    """
+    return next((h for h in logging.root.handlers if isinstance(h, handlertype)), None)
+
+
 def setup_logging_pre() -> None:
     """
     Early setup for logging.
@@ -71,18 +78,24 @@ def setup_logging(config: Dict[str, Any]) -> None:
             # config['logfilename']), which defaults to '/dev/log', applicable for most
             # of the systems.
             address = (s[1], int(s[2])) if len(s) > 2 else s[1] if len(s) > 1 else '/dev/log'
-            handler = SysLogHandler(address=address)
+            handler_sl = get_existing_handlers(SysLogHandler)
+            if handler_sl:
+                logging.root.removeHandler(handler_sl)
+            handler_sl = SysLogHandler(address=address)
             # No datetime field for logging into syslog, to allow syslog
             # to perform reduction of repeating messages if this is set in the
             # syslog config. The messages should be equal for this.
-            handler.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
-            logging.root.addHandler(handler)
+            handler_sl.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
+            logging.root.addHandler(handler_sl)
         elif s[0] == 'journald':
             try:
                 from systemd.journal import JournaldLogHandler
             except ImportError:
                 raise OperationalException("You need the systemd python package be installed in "
                                            "order to use logging to journald.")
+            handler_jd = get_existing_handlers(JournaldLogHandler)
+            if handler_jd:
+                logging.root.removeHandler(handler_jd)
             handler_jd = JournaldLogHandler()
             # No datetime field for logging into journald, to allow syslog
             # to perform reduction of repeating messages if this is set in the
@@ -90,6 +103,9 @@ def setup_logging(config: Dict[str, Any]) -> None:
             handler_jd.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
             logging.root.addHandler(handler_jd)
         else:
+            handler_rf = get_existing_handlers(RotatingFileHandler)
+            if handler_rf:
+                logging.root.removeHandler(handler_rf)
             handler_rf = RotatingFileHandler(logfile,
                                              maxBytes=1024 * 1024 * 10,  # 10Mb
                                              backupCount=10)

freqtrade/main.py

@@ -9,8 +9,8 @@ from typing import Any, List
 
 
 # check min. python version
-if sys.version_info < (3, 6):
-    sys.exit("Freqtrade requires Python version >= 3.6")
+if sys.version_info < (3, 7):
+    sys.exit("Freqtrade requires Python version >= 3.7")
 
 from freqtrade.commands import Arguments
 from freqtrade.exceptions import FreqtradeException, OperationalException

freqtrade/misc.py

@@ -9,13 +9,37 @@ from pathlib import Path
 from typing import Any
 from typing.io import IO
 
-import numpy as np
 import rapidjson
 
+from freqtrade.constants import DECIMAL_PER_COIN_FALLBACK, DECIMALS_PER_COIN
+
 
 logger = logging.getLogger(__name__)
 
 
+def decimals_per_coin(coin: str):
+    """
+    Helper method getting decimal amount for this coin
+    example usage: f".{decimals_per_coin('USD')}f"
+    :param coin: Which coin are we printing the price / value for
+    """
+    return DECIMALS_PER_COIN.get(coin, DECIMAL_PER_COIN_FALLBACK)
+
+
+def round_coin_value(value: float, coin: str, show_coin_name=True) -> str:
+    """
+    Get price value for this coin
+    :param value: Value to be printed
+    :param coin: Which coin are we printing the price / value for
+    :param show_coin_name: Return string in format: "222.22 USDT" or "222.22"
+    :return: Formatted / rounded value (with or without coin name)
+    """
+    if show_coin_name:
+        return f"{value:.{decimals_per_coin(coin)}f} {coin}"
+    else:
+        return f"{value:.{decimals_per_coin(coin)}f}"
+
+
 def shorten_date(_date: str) -> str:
     """
     Trim the date so it fits on small screens
@@ -28,20 +52,6 @@ def shorten_date(_date: str) -> str:
     return new_date
 
 
-############################################
-# Used by scripts                          #
-# Matplotlib doesn't support ::datetime64, #
-# so we need to convert it into ::datetime #
-############################################
-def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
-    """
-    Convert an pandas-array of timestamps into
-    An numpy-array of datetimes
-    :return: numpy-array of datetime
-    """
-    return dates.dt.to_pydatetime()
-
-
 def file_dump_json(filename: Path, data: Any, is_zip: bool = False, log: bool = True) -> None:
     """
     Dump JSON data into a file

freqtrade/mixins/__init__.py

@@ -0,0 +1,2 @@
+# flake8: noqa: F401
+from freqtrade.mixins.logging_mixin import LoggingMixin

freqtrade/mixins/logging_mixin.py

@@ -0,0 +1,38 @@
+from typing import Callable
+
+from cachetools import TTLCache, cached
+
+
+class LoggingMixin():
+    """
+    Logging Mixin
+    Shows similar messages only once every `refresh_period`.
+    """
+    # Disable output completely
+    show_output = True
+
+    def __init__(self, logger, refresh_period: int = 3600):
+        """
+        :param refresh_period: in seconds - Show identical messages in this intervals
+        """
+        self.logger = logger
+        self.refresh_period = refresh_period
+        self._log_cache: TTLCache = TTLCache(maxsize=1024, ttl=self.refresh_period)
+
+    def log_once(self, message: str, logmethod: Callable) -> None:
+        """
+        Logs message - not more often than "refresh_period" to avoid log spamming
+        Logs the log-message as debug as well to simplify debugging.
+        :param message: String containing the message to be sent to the function.
+        :param logmethod: Function that'll be called. Most likely `logger.info`.
+        :return: None.
+        """
+        @cached(cache=self._log_cache)
+        def _log_once(message: str):
+            logmethod(message)
+
+        # Log as debug first
+        self.logger.debug(message)
+        # Call hidden function.
+        if self.show_output:
+            _log_once(message)

freqtrade/optimize/backtesting.py

@@ -6,24 +6,28 @@ This module contains the backtesting logic
 import logging
 from collections import defaultdict
 from copy import deepcopy
-from datetime import datetime, timedelta
-from typing import Any, Dict, List, NamedTuple, Optional, Tuple
+from datetime import datetime, timedelta, timezone
+from typing import Any, Dict, List, Optional, Tuple
 
 from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
 from freqtrade.constants import DATETIME_PRINT_FORMAT
 from freqtrade.data import history
+from freqtrade.data.btanalysis import trade_list_to_dataframe
 from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
+from freqtrade.mixins import LoggingMixin
 from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
                                                  store_backtest_stats)
-from freqtrade.pairlist.pairlistmanager import PairListManager
-from freqtrade.persistence import Trade
+from freqtrade.persistence import PairLocks, Trade
+from freqtrade.plugins.pairlistmanager import PairListManager
+from freqtrade.plugins.protectionmanager import ProtectionManager
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
+from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
 
 
 logger = logging.getLogger(__name__)
@@ -38,25 +42,6 @@ LOW_IDX = 5
 HIGH_IDX = 6
 
 
-class BacktestResult(NamedTuple):
-    """
-    NamedTuple Defining BacktestResults inputs.
-    """
-    pair: str
-    profit_percent: float
-    profit_abs: float
-    open_date: datetime
-    open_rate: float
-    open_fee: float
-    close_date: datetime
-    close_rate: float
-    close_fee: float
-    amount: float
-    trade_duration: float
-    open_at_end: bool
-    sell_reason: SellType
-
-
 class Backtesting:
     """
     Backtesting class, this class contains all the logic to run a backtest
@@ -67,11 +52,15 @@ class Backtesting:
     """
 
     def __init__(self, config: Dict[str, Any]) -> None:
+
+        LoggingMixin.show_output = False
         self.config = config
 
         # Reset keys for backtesting
         remove_credentials(self.config)
         self.strategylist: List[IStrategy] = []
+        self.all_results: Dict[str, Dict] = {}
+
         self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
 
         dataprovider = DataProvider(self.config, self.exchange)
@@ -98,6 +87,8 @@ class Backtesting:
         self.pairlists = PairListManager(self.exchange, self.config)
         if 'VolumePairList' in self.pairlists.name_list:
             raise OperationalException("VolumePairList not allowed for backtesting.")
+        if 'PerformanceFilter' in self.pairlists.name_list:
+            raise OperationalException("PerformanceFilter not allowed for backtesting.")
 
         if len(self.strategylist) > 1 and 'PrecisionFilter' in self.pairlists.name_list:
             raise OperationalException(
@@ -115,11 +106,24 @@ class Backtesting:
         else:
             self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
 
+        Trade.use_db = False
+        Trade.reset_trades()
+        PairLocks.timeframe = self.config['timeframe']
+        PairLocks.use_db = False
+        PairLocks.reset_locks()
+        if self.config.get('enable_protections', False):
+            self.protections = ProtectionManager(self.config)
+
         # Get maximum required startup period
         self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
         # Load one (first) strategy
         self._set_strategy(self.strategylist[0])
 
+    def __del__(self):
+        LoggingMixin.show_output = True
+        PairLocks.use_db = True
+        Trade.use_db = True
+
     def _set_strategy(self, strategy):
         """
         Load strategy into backtesting
@@ -131,6 +135,10 @@ class Backtesting:
         self.strategy.order_types['stoploss_on_exchange'] = False
 
     def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
+        """
+        Loads backtest data and returns the data combined with the timerange
+        as tuple.
+        """
         timerange = TimeRange.parse_timerange(None if self.config.get(
             'timerange') is None else str(self.config.get('timerange')))
 
@@ -156,6 +164,18 @@ class Backtesting:
 
         return data, timerange
 
+    def prepare_backtest(self, enable_protections):
+        """
+        Backtesting setup method - called once for every call to "backtest()".
+        """
+        PairLocks.use_db = False
+        PairLocks.timeframe = self.config['timeframe']
+        Trade.use_db = False
+        if enable_protections:
+            # Reset persisted data - used for protections only
+            PairLocks.reset_locks()
+            Trade.reset_trades()
+
     def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
         """
         Helper function to convert a processed dataframes into lists for performance reasons.
@@ -226,7 +246,7 @@ class Backtesting:
         else:
             return sell_row[OPEN_IDX]
 
-    def _get_sell_trade_entry(self, trade: Trade, sell_row: Tuple) -> Optional[BacktestResult]:
+    def _get_sell_trade_entry(self, trade: Trade, sell_row: Tuple) -> Optional[Trade]:
 
         sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], sell_row[DATE_IDX],
                                          sell_row[BUY_IDX], sell_row[SELL_IDX],
@@ -235,24 +255,15 @@ class Backtesting:
             trade_dur = int((sell_row[DATE_IDX] - trade.open_date).total_seconds() // 60)
             closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
 
-            return BacktestResult(pair=trade.pair,
-                                  profit_percent=trade.calc_profit_ratio(rate=closerate),
-                                  profit_abs=trade.calc_profit(rate=closerate),
-                                  open_date=trade.open_date,
-                                  open_rate=trade.open_rate,
-                                  open_fee=self.fee,
-                                  close_date=sell_row[DATE_IDX],
-                                  close_rate=closerate,
-                                  close_fee=self.fee,
-                                  amount=trade.amount,
-                                  trade_duration=trade_dur,
-                                  open_at_end=False,
-                                  sell_reason=sell.sell_type
-                                  )
+            trade.close_date = sell_row[DATE_IDX]
+            trade.sell_reason = sell.sell_type
+            trade.close(closerate, show_msg=False)
+            return trade
+
         return None
 
     def handle_left_open(self, open_trades: Dict[str, List[Trade]],
-                         data: Dict[str, List[Tuple]]) -> List[BacktestResult]:
+                         data: Dict[str, List[Tuple]]) -> List[Trade]:
         """
         Handling of left open trades at the end of backtesting
         """
@@ -261,29 +272,18 @@ class Backtesting:
             if len(open_trades[pair]) > 0:
                 for trade in open_trades[pair]:
                     sell_row = data[pair][-1]
-                    trade_entry = BacktestResult(pair=trade.pair,
-                                                 profit_percent=trade.calc_profit_ratio(
-                                                     rate=sell_row[OPEN_IDX]),
-                                                 profit_abs=trade.calc_profit(sell_row[OPEN_IDX]),
-                                                 open_date=trade.open_date,
-                                                 open_rate=trade.open_rate,
-                                                 open_fee=self.fee,
-                                                 close_date=sell_row[DATE_IDX],
-                                                 close_rate=sell_row[OPEN_IDX],
-                                                 close_fee=self.fee,
-                                                 amount=trade.amount,
-                                                 trade_duration=int((
-                                                     sell_row[DATE_IDX] - trade.open_date
-                                                 ).total_seconds() // 60),
-                                                 open_at_end=True,
-                                                 sell_reason=SellType.FORCE_SELL
-                                                 )
-                    trades.append(trade_entry)
+
+                    trade.close_date = sell_row[DATE_IDX]
+                    trade.sell_reason = SellType.FORCE_SELL
+                    trade.close(sell_row[OPEN_IDX], show_msg=False)
+                    trade.is_open = True
+                    trades.append(trade)
         return trades
 
     def backtest(self, processed: Dict, stake_amount: float,
                  start_date: datetime, end_date: datetime,
-                 max_open_trades: int = 0, position_stacking: bool = False) -> DataFrame:
+                 max_open_trades: int = 0, position_stacking: bool = False,
+                 enable_protections: bool = False) -> DataFrame:
         """
         Implement backtesting functionality
 
@@ -297,13 +297,15 @@ class Backtesting:
         :param end_date: backtesting timerange end datetime
         :param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
         :param position_stacking: do we allow position stacking?
+        :param enable_protections: Should protections be enabled?
         :return: DataFrame with trades (results of backtesting)
         """
         logger.debug(f"Run backtest, stake_amount: {stake_amount}, "
                      f"start_date: {start_date}, end_date: {end_date}, "
                      f"max_open_trades: {max_open_trades}, position_stacking: {position_stacking}"
                      )
-        trades = []
+        trades: List[Trade] = []
+        self.prepare_backtest(enable_protections)
 
         # Use dict of lists with data for performance
         # (looping lists is a lot faster than pandas DataFrames)
@@ -340,9 +342,10 @@ class Backtesting:
                 # max_open_trades must be respected
                 # don't open on the last row
                 if ((position_stacking or len(open_trades[pair]) == 0)
-                        and max_open_trades > 0 and open_trade_count_start < max_open_trades
+                        and (max_open_trades <= 0 or open_trade_count_start < max_open_trades)
                         and tmp != end_date
-                        and row[BUY_IDX] == 1 and row[SELL_IDX] != 1):
+                        and row[BUY_IDX] == 1 and row[SELL_IDX] != 1
+                        and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
                     # Enter trade
                     trade = Trade(
                         pair=pair,
@@ -361,6 +364,7 @@ class Backtesting:
                     open_trade_count += 1
                     # logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
                     open_trades[pair].append(trade)
+                    Trade.trades.append(trade)
 
                 for trade in open_trades[pair]:
                     # since indexes has been incremented before, we need to go one step back to
@@ -372,13 +376,63 @@ class Backtesting:
                         open_trade_count -= 1
                         open_trades[pair].remove(trade)
                         trades.append(trade_entry)
+                        if enable_protections:
+                            self.protections.stop_per_pair(pair, row[DATE_IDX])
+                            self.protections.global_stop(tmp)
 
             # Move time one configured time_interval ahead.
             tmp += timedelta(minutes=self.timeframe_min)
 
         trades += self.handle_left_open(open_trades, data=data)
 
-        return DataFrame.from_records(trades, columns=BacktestResult._fields)
+        return trade_list_to_dataframe(trades)
+
+    def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
+        logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
+        backtest_start_time = datetime.now(timezone.utc)
+        self._set_strategy(strat)
+
+        strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
+
+        # Use max_open_trades in backtesting, except --disable-max-market-positions is set
+        if self.config.get('use_max_market_positions', True):
+            # Must come from strategy config, as the strategy may modify this setting.
+            max_open_trades = self.strategy.config['max_open_trades']
+        else:
+            logger.info(
+                'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
+            max_open_trades = 0
+
+        # need to reprocess data every time to populate signals
+        preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
+
+        # Trim startup period from analyzed dataframe
+        for pair, df in preprocessed.items():
+            preprocessed[pair] = trim_dataframe(df, timerange)
+        min_date, max_date = history.get_timerange(preprocessed)
+
+        logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
+                    f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
+                    f'({(max_date - min_date).days} days)..')
+        # Execute backtest and store results
+        results = self.backtest(
+            processed=preprocessed,
+            stake_amount=self.config['stake_amount'],
+            start_date=min_date.datetime,
+            end_date=max_date.datetime,
+            max_open_trades=max_open_trades,
+            position_stacking=self.config.get('position_stacking', False),
+            enable_protections=self.config.get('enable_protections', False),
+        )
+        backtest_end_time = datetime.now(timezone.utc)
+        self.all_results[self.strategy.get_strategy_name()] = {
+            'results': results,
+            'config': self.strategy.config,
+            'locks': PairLocks.locks,
+            'backtest_start_time': int(backtest_start_time.timestamp()),
+            'backtest_end_time': int(backtest_end_time.timestamp()),
+        }
+        return min_date, max_date
 
     def start(self) -> None:
         """
@@ -387,53 +441,15 @@ class Backtesting:
         """
         data: Dict[str, Any] = {}
 
-        logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
-        logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
-
-        position_stacking = self.config.get('position_stacking', False)
-
         data, timerange = self.load_bt_data()
 
-        all_results = {}
+        min_date = None
+        max_date = None
         for strat in self.strategylist:
-            logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
-            self._set_strategy(strat)
+            min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
 
-            # Use max_open_trades in backtesting, except --disable-max-market-positions is set
-            if self.config.get('use_max_market_positions', True):
-                # Must come from strategy config, as the strategy may modify this setting.
-                max_open_trades = self.strategy.config['max_open_trades']
-            else:
-                logger.info(
-                    'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
-                max_open_trades = 0
-
-            # need to reprocess data every time to populate signals
-            preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
-
-            # Trim startup period from analyzed dataframe
-            for pair, df in preprocessed.items():
-                preprocessed[pair] = trim_dataframe(df, timerange)
-            min_date, max_date = history.get_timerange(preprocessed)
-
-            logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
-                        f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
-                        f'({(max_date - min_date).days} days)..')
-            # Execute backtest and print results
-            results = self.backtest(
-                processed=preprocessed,
-                stake_amount=self.config['stake_amount'],
-                start_date=min_date.datetime,
-                end_date=max_date.datetime,
-                max_open_trades=max_open_trades,
-                position_stacking=position_stacking,
-            )
-            all_results[self.strategy.get_strategy_name()] = {
-                'results': results,
-                'config': self.strategy.config,
-            }
-
-        stats = generate_backtest_stats(data, all_results, min_date=min_date, max_date=max_date)
+        stats = generate_backtest_stats(data, self.all_results,
+                                        min_date=min_date, max_date=max_date)
 
         if self.config.get('export', False):
             store_backtest_stats(self.config['exportfilename'], stats)

freqtrade/optimize/default_hyperopt_loss.py

@@ -42,7 +42,7 @@ class ShortTradeDurHyperOptLoss(IHyperOptLoss):
         * 0.25: Avoiding trade loss
         * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
         """
-        total_profit = results['profit_percent'].sum()
+        total_profit = results['profit_ratio'].sum()
         trade_duration = results['trade_duration'].mean()
 
         trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)

freqtrade/optimize/hyperopt.py

@@ -542,12 +542,15 @@ class Hyperopt:
             end_date=max_date.datetime,
             max_open_trades=self.max_open_trades,
             position_stacking=self.position_stacking,
+            enable_protections=self.config.get('enable_protections', False),
+
         )
         return self._get_results_dict(backtesting_results, min_date, max_date,
-                                      params_dict, params_details)
+                                      params_dict, params_details,
+                                      processed=processed)
 
     def _get_results_dict(self, backtesting_results, min_date, max_date,
-                          params_dict, params_details):
+                          params_dict, params_details, processed: Dict[str, DataFrame]):
         results_metrics = self._calculate_results_metrics(backtesting_results)
         results_explanation = self._format_results_explanation_string(results_metrics)
 
@@ -561,7 +564,8 @@ class Hyperopt:
         loss: float = MAX_LOSS
         if trade_count >= self.config['hyperopt_min_trades']:
             loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
-                                       min_date=min_date.datetime, max_date=max_date.datetime)
+                                       min_date=min_date.datetime, max_date=max_date.datetime,
+                                       config=self.config, processed=processed)
         return {
             'loss': loss,
             'params_dict': params_dict,
@@ -572,20 +576,20 @@ class Hyperopt:
         }
 
     def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
-        wins = len(backtesting_results[backtesting_results.profit_percent > 0])
-        draws = len(backtesting_results[backtesting_results.profit_percent == 0])
-        losses = len(backtesting_results[backtesting_results.profit_percent < 0])
+        wins = len(backtesting_results[backtesting_results['profit_ratio'] > 0])
+        draws = len(backtesting_results[backtesting_results['profit_ratio'] == 0])
+        losses = len(backtesting_results[backtesting_results['profit_ratio'] < 0])
         return {
             'trade_count': len(backtesting_results.index),
             'wins': wins,
             'draws': draws,
             'losses': losses,
             'winsdrawslosses': f"{wins:>4} {draws:>4} {losses:>4}",
-            'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
-            'median_profit': backtesting_results.profit_percent.median() * 100.0,
-            'total_profit': backtesting_results.profit_abs.sum(),
-            'profit': backtesting_results.profit_percent.sum() * 100.0,
-            'duration': backtesting_results.trade_duration.mean(),
+            'avg_profit': backtesting_results['profit_ratio'].mean() * 100.0,
+            'median_profit': backtesting_results['profit_ratio'].median() * 100.0,
+            'total_profit': backtesting_results['profit_abs'].sum(),
+            'profit': backtesting_results['profit_ratio'].sum() * 100.0,
+            'duration': backtesting_results['trade_duration'].mean(),
         }
 
     def _format_results_explanation_string(self, results_metrics: Dict) -> str:
@@ -648,7 +652,7 @@ class Hyperopt:
         # Trim startup period from analyzed dataframe
         for pair, df in preprocessed.items():
             preprocessed[pair] = trim_dataframe(df, timerange)
-        min_date, max_date = get_timerange(data)
+        min_date, max_date = get_timerange(preprocessed)
 
         logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
                     f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '

freqtrade/optimize/hyperopt_loss_interface.py

@@ -5,6 +5,7 @@ This module defines the interface for the loss-function for hyperopt
 
 from abc import ABC, abstractmethod
 from datetime import datetime
+from typing import Dict
 
 from pandas import DataFrame
 
@@ -19,7 +20,9 @@ class IHyperOptLoss(ABC):
     @staticmethod
     @abstractmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
-                               min_date: datetime, max_date: datetime, *args, **kwargs) -> float:
+                               min_date: datetime, max_date: datetime,
+                               config: Dict, processed: Dict[str, DataFrame],
+                               *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for better results
         """

freqtrade/optimize/hyperopt_loss_onlyprofit.py

@@ -34,5 +34,5 @@ class OnlyProfitHyperOptLoss(IHyperOptLoss):
         """
         Objective function, returns smaller number for better results.
         """
-        total_profit = results['profit_percent'].sum()
+        total_profit = results['profit_ratio'].sum()
         return 1 - total_profit / EXPECTED_MAX_PROFIT

freqtrade/optimize/hyperopt_loss_sharpe.py

@@ -28,7 +28,7 @@ class SharpeHyperOptLoss(IHyperOptLoss):
 
         Uses Sharpe Ratio calculation.
         """
-        total_profit = results["profit_percent"]
+        total_profit = results["profit_ratio"]
         days_period = (max_date - min_date).days
 
         # adding slippage of 0.1% per trade

freqtrade/optimize/hyperopt_loss_sharpe_daily.py

@@ -34,9 +34,9 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
         annual_risk_free_rate = 0.0
         risk_free_rate = annual_risk_free_rate / days_in_year
 
-        # apply slippage per trade to profit_percent
-        results.loc[:, 'profit_percent_after_slippage'] = \
-            results['profit_percent'] - slippage_per_trade_ratio
+        # apply slippage per trade to profit_ratio
+        results.loc[:, 'profit_ratio_after_slippage'] = \
+            results['profit_ratio'] - slippage_per_trade_ratio
 
         # create the index within the min_date and end max_date
         t_index = date_range(start=min_date, end=max_date, freq=resample_freq,
@@ -44,10 +44,10 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
 
         sum_daily = (
             results.resample(resample_freq, on='close_date').agg(
-                {"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
+                {"profit_ratio_after_slippage": sum}).reindex(t_index).fillna(0)
         )
 
-        total_profit = sum_daily["profit_percent_after_slippage"] - risk_free_rate
+        total_profit = sum_daily["profit_ratio_after_slippage"] - risk_free_rate
         expected_returns_mean = total_profit.mean()
         up_stdev = total_profit.std()
 

freqtrade/optimize/hyperopt_loss_sortino.py

@@ -28,7 +28,7 @@ class SortinoHyperOptLoss(IHyperOptLoss):
 
         Uses Sortino Ratio calculation.
         """
-        total_profit = results["profit_percent"]
+        total_profit = results["profit_ratio"]
         days_period = (max_date - min_date).days
 
         # adding slippage of 0.1% per trade
@@ -36,7 +36,7 @@ class SortinoHyperOptLoss(IHyperOptLoss):
         expected_returns_mean = total_profit.sum() / days_period
 
         results['downside_returns'] = 0
-        results.loc[total_profit < 0, 'downside_returns'] = results['profit_percent']
+        results.loc[total_profit < 0, 'downside_returns'] = results['profit_ratio']
         down_stdev = np.std(results['downside_returns'])
 
         if down_stdev != 0:

freqtrade/optimize/hyperopt_loss_sortino_daily.py

@@ -36,9 +36,9 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
         days_in_year = 365
         minimum_acceptable_return = 0.0
 
-        # apply slippage per trade to profit_percent
-        results.loc[:, 'profit_percent_after_slippage'] = \
-            results['profit_percent'] - slippage_per_trade_ratio
+        # apply slippage per trade to profit_ratio
+        results.loc[:, 'profit_ratio_after_slippage'] = \
+            results['profit_ratio'] - slippage_per_trade_ratio
 
         # create the index within the min_date and end max_date
         t_index = date_range(start=min_date, end=max_date, freq=resample_freq,
@@ -46,17 +46,17 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
 
         sum_daily = (
             results.resample(resample_freq, on='close_date').agg(
-                {"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
+                {"profit_ratio_after_slippage": sum}).reindex(t_index).fillna(0)
         )
 
-        total_profit = sum_daily["profit_percent_after_slippage"] - minimum_acceptable_return
+        total_profit = sum_daily["profit_ratio_after_slippage"] - minimum_acceptable_return
         expected_returns_mean = total_profit.mean()
 
         sum_daily['downside_returns'] = 0
         sum_daily.loc[total_profit < 0, 'downside_returns'] = total_profit
         total_downside = sum_daily['downside_returns']
         # Here total_downside contains min(0, P - MAR) values,
-        # where P = sum_daily["profit_percent_after_slippage"]
+        # where P = sum_daily["profit_ratio_after_slippage"]
         down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))
 
         if down_stdev != 0:

freqtrade/optimize/optimize_reports.py

@@ -9,8 +9,9 @@ from pandas import DataFrame
 from tabulate import tabulate
 
 from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
-from freqtrade.data.btanalysis import calculate_market_change, calculate_max_drawdown
-from freqtrade.misc import file_dump_json
+from freqtrade.data.btanalysis import (calculate_csum, calculate_market_change,
+                                       calculate_max_drawdown)
+from freqtrade.misc import decimals_per_coin, file_dump_json, round_coin_value
 
 
 logger = logging.getLogger(__name__)
@@ -38,11 +39,12 @@ def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> N
     file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
 
 
-def _get_line_floatfmt() -> List[str]:
+def _get_line_floatfmt(stake_currency: str) -> List[str]:
     """
     Generate floatformat (goes in line with _generate_result_line())
     """
-    return ['s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', 'd', 'd', 'd']
+    return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f',
+            '.2f', 'd', 'd', 'd', 'd']
 
 
 def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
@@ -58,16 +60,19 @@ def _generate_result_line(result: DataFrame, max_open_trades: int, first_column:
     """
     Generate one result dict, with "first_column" as key.
     """
+    profit_sum = result['profit_ratio'].sum()
+    profit_total = profit_sum / max_open_trades
+
     return {
         'key': first_column,
         'trades': len(result),
-        'profit_mean': result['profit_percent'].mean() if len(result) > 0 else 0.0,
-        'profit_mean_pct': result['profit_percent'].mean() * 100.0 if len(result) > 0 else 0.0,
-        'profit_sum': result['profit_percent'].sum(),
-        'profit_sum_pct': result['profit_percent'].sum() * 100.0,
+        'profit_mean': result['profit_ratio'].mean() if len(result) > 0 else 0.0,
+        'profit_mean_pct': result['profit_ratio'].mean() * 100.0 if len(result) > 0 else 0.0,
+        'profit_sum': profit_sum,
+        'profit_sum_pct': round(profit_sum * 100.0, 2),
         'profit_total_abs': result['profit_abs'].sum(),
-        'profit_total': result['profit_percent'].sum() / max_open_trades,
-        'profit_total_pct': result['profit_percent'].sum() * 100.0 / max_open_trades,
+        'profit_total': profit_total,
+        'profit_total_pct': round(profit_total * 100.0, 2),
         'duration_avg': str(timedelta(
                             minutes=round(result['trade_duration'].mean()))
                             ) if not result.empty else '0:00',
@@ -121,9 +126,9 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
     for reason, count in results['sell_reason'].value_counts().iteritems():
         result = results.loc[results['sell_reason'] == reason]
 
-        profit_mean = result['profit_percent'].mean()
-        profit_sum = result["profit_percent"].sum()
-        profit_percent_tot = result['profit_percent'].sum() / max_open_trades
+        profit_mean = result['profit_ratio'].mean()
+        profit_sum = result['profit_ratio'].sum()
+        profit_total = profit_sum / max_open_trades
 
         tabular_data.append(
             {
@@ -137,8 +142,8 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
                 'profit_sum': profit_sum,
                 'profit_sum_pct': round(profit_sum * 100, 2),
                 'profit_total_abs': result['profit_abs'].sum(),
-                'profit_total': profit_percent_tot,
-                'profit_total_pct': round(profit_percent_tot * 100, 2),
+                'profit_total': profit_total,
+                'profit_total_pct': round(profit_total * 100, 2),
             }
         )
     return tabular_data
@@ -147,7 +152,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
 def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
     """
     Generate summary per strategy
-    :param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
+    :param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
     :return: List of Dicts containing the metrics per Strategy
     """
 
@@ -196,15 +201,15 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
             'winner_holding_avg': timedelta(),
             'loser_holding_avg': timedelta(),
         }
-    daily_profit = results.resample('1d', on='close_date')['profit_percent'].sum()
+    daily_profit = results.resample('1d', on='close_date')['profit_ratio'].sum()
     worst = min(daily_profit)
     best = max(daily_profit)
     winning_days = sum(daily_profit > 0)
     draw_days = sum(daily_profit == 0)
     losing_days = sum(daily_profit < 0)
 
-    winning_trades = results.loc[results['profit_percent'] > 0]
-    losing_trades = results.loc[results['profit_percent'] < 0]
+    winning_trades = results.loc[results['profit_ratio'] > 0]
+    losing_trades = results.loc[results['profit_ratio'] < 0]
 
     return {
         'backtest_best_day': best,
@@ -240,7 +245,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
         if not isinstance(results, DataFrame):
             continue
         config = content['config']
-        max_open_trades = config['max_open_trades']
+        max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
         stake_currency = config['stake_currency']
 
         pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
@@ -250,46 +255,61 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
                                                        results=results)
         left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
                                                   max_open_trades=max_open_trades,
-                                                  results=results.loc[results['open_at_end']],
+                                                  results=results.loc[results['is_open']],
                                                   skip_nan=True)
         daily_stats = generate_daily_stats(results)
-
+        best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
+                        key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
+        worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
+                         key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
         results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
         results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
 
         backtest_days = (max_date - min_date).days
         strat_stats = {
             'trades': results.to_dict(orient='records'),
+            'locks': [lock.to_json() for lock in content['locks']],
+            'best_pair': best_pair,
+            'worst_pair': worst_pair,
             'results_per_pair': pair_results,
             'sell_reason_summary': sell_reason_stats,
             'left_open_trades': left_open_results,
             'total_trades': len(results),
-            'profit_mean': results['profit_percent'].mean() if len(results) > 0 else 0,
-            'profit_total': results['profit_percent'].sum(),
+            'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
+            'profit_total': results['profit_ratio'].sum() / max_open_trades,
             'profit_total_abs': results['profit_abs'].sum(),
             'backtest_start': min_date.datetime,
-            'backtest_start_ts': min_date.timestamp * 1000,
+            'backtest_start_ts': min_date.int_timestamp * 1000,
             'backtest_end': max_date.datetime,
-            'backtest_end_ts': max_date.timestamp * 1000,
+            'backtest_end_ts': max_date.int_timestamp * 1000,
             'backtest_days': backtest_days,
 
+            'backtest_run_start_ts': content['backtest_start_time'],
+            'backtest_run_end_ts': content['backtest_end_time'],
+
             'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
             'market_change': market_change,
             'pairlist': list(btdata.keys()),
             'stake_amount': config['stake_amount'],
             'stake_currency': config['stake_currency'],
-            'max_open_trades': (config['max_open_trades']
-                                if config['max_open_trades'] != float('inf') else -1),
+            'max_open_trades': max_open_trades,
+            'max_open_trades_setting': (config['max_open_trades']
+                                        if config['max_open_trades'] != float('inf') else -1),
             'timeframe': config['timeframe'],
+            'timerange': config.get('timerange', ''),
+            'enable_protections': config.get('enable_protections', False),
+            'strategy_name': strategy,
             # Parameters relevant for backtesting
             'stoploss': config['stoploss'],
             'trailing_stop': config.get('trailing_stop', False),
             'trailing_stop_positive': config.get('trailing_stop_positive'),
             'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
             'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
+            'use_custom_stoploss': config.get('use_custom_stoploss', False),
             'minimal_roi': config['minimal_roi'],
             'use_sell_signal': config['ask_strategy']['use_sell_signal'],
             'sell_profit_only': config['ask_strategy']['sell_profit_only'],
+            'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
             'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
             **daily_stats,
         }
@@ -297,7 +317,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
 
         try:
             max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
-                results, value_col='profit_percent')
+                results, value_col='profit_ratio')
             strat_stats.update({
                 'max_drawdown': max_drawdown,
                 'drawdown_start': drawdown_start,
@@ -305,6 +325,13 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
                 'drawdown_end': drawdown_end,
                 'drawdown_end_ts': drawdown_end.timestamp() * 1000,
             })
+
+            csum_min, csum_max = calculate_csum(results)
+            strat_stats.update({
+                'csum_min': csum_min,
+                'csum_max': csum_max
+            })
+
         except ValueError:
             strat_stats.update({
                 'max_drawdown': 0.0,
@@ -312,6 +339,8 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
                 'drawdown_start_ts': 0,
                 'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
                 'drawdown_end_ts': 0,
+                'csum_min': 0,
+                'csum_max': 0
             })
 
     strategy_results = generate_strategy_metrics(all_results=all_results)
@@ -334,7 +363,7 @@ def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: st
     """
 
     headers = _get_line_header('Pair', stake_currency)
-    floatfmt = _get_line_floatfmt()
+    floatfmt = _get_line_floatfmt(stake_currency)
     output = [[
         t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
         t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
@@ -365,7 +394,9 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
 
     output = [[
         t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
-        t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_total_pct'],
+        t['profit_mean_pct'], t['profit_sum_pct'],
+        round_coin_value(t['profit_total_abs'], stake_currency, False),
+        t['profit_total_pct'],
     ] for t in sell_reason_stats]
     return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
 
@@ -375,10 +406,10 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
     Generate summary table per strategy
     :param stake_currency: stake-currency - used to correctly name headers
     :param max_open_trades: Maximum allowed open trades used for backtest
-    :param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
+    :param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
     :return: pretty printed table with tabulate as string
     """
-    floatfmt = _get_line_floatfmt()
+    floatfmt = _get_line_floatfmt(stake_currency)
     headers = _get_line_header('Strategy', stake_currency)
 
     output = [[
@@ -392,15 +423,25 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
 
 def text_table_add_metrics(strat_results: Dict) -> str:
     if len(strat_results['trades']) > 0:
-        min_trade = min(strat_results['trades'], key=lambda x: x['open_date'])
+        best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
+        worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])
         metrics = [
             ('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
             ('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
+            ('Max open trades', strat_results['max_open_trades']),
+            ('', ''),  # Empty line to improve readability
             ('Total trades', strat_results['total_trades']),
-            ('First trade', min_trade['open_date'].strftime(DATETIME_PRINT_FORMAT)),
-            ('First trade Pair', min_trade['pair']),
             ('Total Profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
             ('Trades per day', strat_results['trades_per_day']),
+            ('', ''),  # Empty line to improve readability
+            ('Best Pair', f"{strat_results['best_pair']['key']} "
+                          f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
+            ('Worst Pair', f"{strat_results['worst_pair']['key']} "
+                           f"{round(strat_results['worst_pair']['profit_sum_pct'], 2)}%"),
+            ('Best trade', f"{best_trade['pair']} {round(best_trade['profit_ratio'] * 100, 2)}%"),
+            ('Worst trade', f"{worst_trade['pair']} "
+                            f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
+
             ('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
             ('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
             ('Days win/draw/lose', f"{strat_results['winning_days']} / "
@@ -408,6 +449,12 @@ def text_table_add_metrics(strat_results: Dict) -> str:
             ('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
             ('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
             ('', ''),  # Empty line to improve readability
+
+            ('Abs Profit Min', round_coin_value(strat_results['csum_min'],
+                                                strat_results['stake_currency'])),
+            ('Abs Profit Max', round_coin_value(strat_results['csum_max'],
+                                                strat_results['stake_currency'])),
+
             ('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
             ('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
             ('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),

freqtrade/persistence/migrations.py

@@ -53,11 +53,11 @@ def migrate_trades_table(decl_base, inspector, engine, table_back_name: str, col
     else:
         timeframe = get_column_def(cols, 'timeframe', 'null')
 
-    open_trade_price = get_column_def(cols, 'open_trade_price',
+    open_trade_value = get_column_def(cols, 'open_trade_value',
                                       f'amount * open_rate * (1 + {fee_open})')
     close_profit_abs = get_column_def(
         cols, 'close_profit_abs',
-        f"(amount * close_rate * (1 - {fee_close})) - {open_trade_price}")
+        f"(amount * close_rate * (1 - {fee_close})) - {open_trade_value}")
     sell_order_status = get_column_def(cols, 'sell_order_status', 'null')
     amount_requested = get_column_def(cols, 'amount_requested', 'amount')
 
@@ -79,7 +79,7 @@ def migrate_trades_table(decl_base, inspector, engine, table_back_name: str, col
             stop_loss, stop_loss_pct, initial_stop_loss, initial_stop_loss_pct,
             stoploss_order_id, stoploss_last_update,
             max_rate, min_rate, sell_reason, sell_order_status, strategy,
-            timeframe, open_trade_price, close_profit_abs
+            timeframe, open_trade_value, close_profit_abs
             )
         select id, lower(exchange),
             case
@@ -102,7 +102,7 @@ def migrate_trades_table(decl_base, inspector, engine, table_back_name: str, col
             {max_rate} max_rate, {min_rate} min_rate, {sell_reason} sell_reason,
             {sell_order_status} sell_order_status,
             {strategy} strategy, {timeframe} timeframe,
-            {open_trade_price} open_trade_price, {close_profit_abs} close_profit_abs
+            {open_trade_value} open_trade_value, {close_profit_abs} close_profit_abs
             from {table_back_name}
             """)
 
@@ -134,7 +134,7 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
     table_back_name = get_backup_name(tabs, 'trades_bak')
 
     # Check for latest column
-    if not has_column(cols, 'amount_requested'):
+    if not has_column(cols, 'open_trade_value'):
         logger.info(f'Running database migration for trades - backup: {table_back_name}')
         migrate_trades_table(decl_base, inspector, engine, table_back_name, cols)
         # Reread columns - the above recreated the table!

freqtrade/persistence/models.py

@@ -171,6 +171,10 @@ class Order(_DECL_BASE):
         """
         Get all non-closed orders - useful when trying to batch-update orders
         """
+        if not isinstance(order, dict):
+            logger.warning(f"{order} is not a valid response object.")
+            return
+
         filtered_orders = [o for o in orders if o.order_id == order.get('id')]
         if filtered_orders:
             oobj = filtered_orders[0]
@@ -202,6 +206,10 @@ class Trade(_DECL_BASE):
     """
     __tablename__ = 'trades'
 
+    use_db: bool = True
+    # Trades container for backtesting
+    trades: List['Trade'] = []
+
     id = Column(Integer, primary_key=True)
 
     orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
@@ -217,8 +225,8 @@ class Trade(_DECL_BASE):
     fee_close_currency = Column(String, nullable=True)
     open_rate = Column(Float)
     open_rate_requested = Column(Float)
-    # open_trade_price - calculated via _calc_open_trade_price
-    open_trade_price = Column(Float)
+    # open_trade_value - calculated via _calc_open_trade_value
+    open_trade_value = Column(Float)
     close_rate = Column(Float)
     close_rate_requested = Column(Float)
     close_profit = Column(Float)
@@ -252,7 +260,7 @@ class Trade(_DECL_BASE):
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
-        self.recalc_open_trade_price()
+        self.recalc_open_trade_value()
 
     def __repr__(self):
         open_since = self.open_date.strftime(DATETIME_PRINT_FORMAT) if self.is_open else 'closed'
@@ -270,7 +278,6 @@ class Trade(_DECL_BASE):
             'amount_requested': round(self.amount_requested, 8) if self.amount_requested else None,
             'stake_amount': round(self.stake_amount, 8),
             'strategy': self.strategy,
-            'ticker_interval': self.timeframe,  # DEPRECATED
             'timeframe': self.timeframe,
 
             'fee_open': self.fee_open,
@@ -285,7 +292,7 @@ class Trade(_DECL_BASE):
             'open_timestamp': int(self.open_date.replace(tzinfo=timezone.utc).timestamp() * 1000),
             'open_rate': self.open_rate,
             'open_rate_requested': self.open_rate_requested,
-            'open_trade_price': round(self.open_trade_price, 8),
+            'open_trade_value': round(self.open_trade_value, 8),
 
             'close_date_hum': (arrow.get(self.close_date).humanize()
                                if self.close_date else None),
@@ -295,12 +302,21 @@ class Trade(_DECL_BASE):
                 tzinfo=timezone.utc).timestamp() * 1000) if self.close_date else None,
             'close_rate': self.close_rate,
             'close_rate_requested': self.close_rate_requested,
-            'close_profit': self.close_profit,
-            'close_profit_abs': self.close_profit_abs,
+            'close_profit': self.close_profit,  # Deprecated
+            'close_profit_pct': round(self.close_profit * 100, 2) if self.close_profit else None,
+            'close_profit_abs': self.close_profit_abs,  # Deprecated
+
+            'trade_duration_s': (int((self.close_date - self.open_date).total_seconds())
+                                 if self.close_date else None),
+            'trade_duration': (int((self.close_date - self.open_date).total_seconds() // 60)
+                               if self.close_date else None),
+
+            'profit_ratio': self.close_profit,
+            'profit_pct': round(self.close_profit * 100, 2) if self.close_profit else None,
+            'profit_abs': self.close_profit_abs,
 
             'sell_reason': self.sell_reason,
             'sell_order_status': self.sell_order_status,
-            'stop_loss': self.stop_loss,  # Deprecated - should not be used
             'stop_loss_abs': self.stop_loss,
             'stop_loss_ratio': self.stop_loss_pct if self.stop_loss_pct else None,
             'stop_loss_pct': (self.stop_loss_pct * 100) if self.stop_loss_pct else None,
@@ -309,7 +325,6 @@ class Trade(_DECL_BASE):
                                      if self.stoploss_last_update else None),
             'stoploss_last_update_timestamp': int(self.stoploss_last_update.replace(
                 tzinfo=timezone.utc).timestamp() * 1000) if self.stoploss_last_update else None,
-            'initial_stop_loss': self.initial_stop_loss,  # Deprecated - should not be used
             'initial_stop_loss_abs': self.initial_stop_loss,
             'initial_stop_loss_ratio': (self.initial_stop_loss_pct
                                         if self.initial_stop_loss_pct else None),
@@ -321,6 +336,14 @@ class Trade(_DECL_BASE):
             'open_order_id': self.open_order_id,
         }
 
+    @staticmethod
+    def reset_trades() -> None:
+        """
+        Resets all trades. Only active for backtesting mode.
+        """
+        if not Trade.use_db:
+            Trade.trades = []
+
     def adjust_min_max_rates(self, current_price: float) -> None:
         """
         Adjust the max_rate and min_rate.
@@ -328,6 +351,12 @@ class Trade(_DECL_BASE):
         self.max_rate = max(current_price, self.max_rate or self.open_rate)
         self.min_rate = min(current_price, self.min_rate or self.open_rate)
 
+    def _set_new_stoploss(self, new_loss: float, stoploss: float):
+        """Assign new stop value"""
+        self.stop_loss = new_loss
+        self.stop_loss_pct = -1 * abs(stoploss)
+        self.stoploss_last_update = datetime.utcnow()
+
     def adjust_stop_loss(self, current_price: float, stoploss: float,
                          initial: bool = False) -> None:
         """
@@ -346,19 +375,15 @@ class Trade(_DECL_BASE):
         # no stop loss assigned yet
         if not self.stop_loss:
             logger.debug(f"{self.pair} - Assigning new stoploss...")
-            self.stop_loss = new_loss
-            self.stop_loss_pct = -1 * abs(stoploss)
+            self._set_new_stoploss(new_loss, stoploss)
             self.initial_stop_loss = new_loss
             self.initial_stop_loss_pct = -1 * abs(stoploss)
-            self.stoploss_last_update = datetime.utcnow()
 
         # evaluate if the stop loss needs to be updated
         else:
             if new_loss > self.stop_loss:  # stop losses only walk up, never down!
                 logger.debug(f"{self.pair} - Adjusting stoploss...")
-                self.stop_loss = new_loss
-                self.stop_loss_pct = -1 * abs(stoploss)
-                self.stoploss_last_update = datetime.utcnow()
+                self._set_new_stoploss(new_loss, stoploss)
             else:
                 logger.debug(f"{self.pair} - Keeping current stoploss...")
 
@@ -387,7 +412,7 @@ class Trade(_DECL_BASE):
             # Update open rate and actual amount
             self.open_rate = Decimal(safe_value_fallback(order, 'average', 'price'))
             self.amount = Decimal(safe_value_fallback(order, 'filled', 'amount'))
-            self.recalc_open_trade_price()
+            self.recalc_open_trade_value()
             if self.is_open:
                 logger.info(f'{order_type.upper()}_BUY has been fulfilled for {self}.')
             self.open_order_id = None
@@ -395,7 +420,7 @@ class Trade(_DECL_BASE):
             if self.is_open:
                 logger.info(f'{order_type.upper()}_SELL has been fulfilled for {self}.')
             self.close(safe_value_fallback(order, 'average', 'price'))
-        elif order_type in ('stop_loss_limit', 'stop-loss', 'stop'):
+        elif order_type in ('stop_loss_limit', 'stop-loss', 'stop-loss-limit', 'stop'):
             self.stoploss_order_id = None
             self.close_rate_requested = self.stop_loss
             if self.is_open:
@@ -405,7 +430,7 @@ class Trade(_DECL_BASE):
             raise ValueError(f'Unknown order type: {order_type}')
         cleanup_db()
 
-    def close(self, rate: float) -> None:
+    def close(self, rate: float, *, show_msg: bool = True) -> None:
         """
         Sets close_rate to the given rate, calculates total profit
         and marks trade as closed
@@ -417,10 +442,11 @@ class Trade(_DECL_BASE):
         self.is_open = False
         self.sell_order_status = 'closed'
         self.open_order_id = None
-        logger.info(
-            'Marking %s as closed as the trade is fulfilled and found no open orders for it.',
-            self
-        )
+        if show_msg:
+            logger.info(
+                'Marking %s as closed as the trade is fulfilled and found no open orders for it.',
+                self
+            )
 
     def update_fee(self, fee_cost: float, fee_currency: Optional[str], fee_rate: Optional[float],
                    side: str) -> None:
@@ -462,7 +488,7 @@ class Trade(_DECL_BASE):
         Trade.session.delete(self)
         Trade.session.flush()
 
-    def _calc_open_trade_price(self) -> float:
+    def _calc_open_trade_value(self) -> float:
         """
         Calculate the open_rate including open_fee.
         :return: Price in of the open trade incl. Fees
@@ -471,14 +497,14 @@ class Trade(_DECL_BASE):
         fees = buy_trade * Decimal(self.fee_open)
         return float(buy_trade + fees)
 
-    def recalc_open_trade_price(self) -> None:
+    def recalc_open_trade_value(self) -> None:
         """
-        Recalculate open_trade_price.
+        Recalculate open_trade_value.
         Must be called whenever open_rate or fee_open is changed.
         """
-        self.open_trade_price = self._calc_open_trade_price()
+        self.open_trade_value = self._calc_open_trade_value()
 
-    def calc_close_trade_price(self, rate: Optional[float] = None,
+    def calc_close_trade_value(self, rate: Optional[float] = None,
                                fee: Optional[float] = None) -> float:
         """
         Calculate the close_rate including fee
@@ -505,11 +531,11 @@ class Trade(_DECL_BASE):
             If rate is not set self.close_rate will be used
         :return:  profit in stake currency as float
         """
-        close_trade_price = self.calc_close_trade_price(
+        close_trade_value = self.calc_close_trade_value(
             rate=(rate or self.close_rate),
             fee=(fee or self.fee_close)
         )
-        profit = close_trade_price - self.open_trade_price
+        profit = close_trade_value - self.open_trade_value
         return float(f"{profit:.8f}")
 
     def calc_profit_ratio(self, rate: Optional[float] = None,
@@ -521,11 +547,11 @@ class Trade(_DECL_BASE):
         :param fee: fee to use on the close rate (optional).
         :return: profit ratio as float
         """
-        close_trade_price = self.calc_close_trade_price(
+        close_trade_value = self.calc_close_trade_value(
             rate=(rate or self.close_rate),
             fee=(fee or self.fee_close)
         )
-        profit_ratio = (close_trade_price / self.open_trade_price) - 1
+        profit_ratio = (close_trade_value / self.open_trade_value) - 1
         return float(f"{profit_ratio:.8f}")
 
     def select_order(self, order_side: str, is_open: Optional[bool]) -> Optional[Order]:
@@ -560,6 +586,43 @@ class Trade(_DECL_BASE):
         else:
             return Trade.query
 
+    @staticmethod
+    def get_trades_proxy(*, pair: str = None, is_open: bool = None,
+                         open_date: datetime = None, close_date: datetime = None,
+                         ) -> List['Trade']:
+        """
+        Helper function to query Trades.
+        Returns a List of trades, filtered on the parameters given.
+        In live mode, converts the filter to a database query and returns all rows
+        In Backtest mode, uses filters on Trade.trades to get the result.
+
+        :return: unsorted List[Trade]
+        """
+        if Trade.use_db:
+            trade_filter = []
+            if pair:
+                trade_filter.append(Trade.pair == pair)
+            if open_date:
+                trade_filter.append(Trade.open_date > open_date)
+            if close_date:
+                trade_filter.append(Trade.close_date > close_date)
+            if is_open is not None:
+                trade_filter.append(Trade.is_open.is_(is_open))
+            return Trade.get_trades(trade_filter).all()
+        else:
+            # Offline mode - without database
+            sel_trades = [trade for trade in Trade.trades]
+            if pair:
+                sel_trades = [trade for trade in sel_trades if trade.pair == pair]
+            if open_date:
+                sel_trades = [trade for trade in sel_trades if trade.open_date > open_date]
+            if close_date:
+                sel_trades = [trade for trade in sel_trades if trade.close_date
+                              and trade.close_date > close_date]
+            if is_open is not None:
+                sel_trades = [trade for trade in sel_trades if trade.is_open == is_open]
+            return sel_trades
+
     @staticmethod
     def get_open_trades() -> List[Any]:
         """
@@ -686,7 +749,7 @@ class PairLock(_DECL_BASE):
     @staticmethod
     def query_pair_locks(pair: Optional[str], now: datetime) -> Query:
         """
-        Get all locks for this pair
+        Get all currently active locks for this pair
         :param pair: Pair to check for. Returns all current locks if pair is empty
         :param now: Datetime object (generated via datetime.now(timezone.utc)).
         """

freqtrade/persistence/pairlock_middleware.py

@@ -22,10 +22,27 @@ class PairLocks():
     timeframe: str = ''
 
     @staticmethod
-    def lock_pair(pair: str, until: datetime, reason: str = None) -> None:
+    def reset_locks() -> None:
+        """
+        Resets all locks. Only active for backtesting mode.
+        """
+        if not PairLocks.use_db:
+            PairLocks.locks = []
+
+    @staticmethod
+    def lock_pair(pair: str, until: datetime, reason: str = None, *, now: datetime = None) -> None:
+        """
+        Create PairLock from now to "until".
+        Uses database by default, unless PairLocks.use_db is set to False,
+        in which case a list is maintained.
+        :param pair: pair to lock. use '*' to lock all pairs
+        :param until: End time of the lock. Will be rounded up to the next candle.
+        :param reason: Reason string that will be shown as reason for the lock
+        :param now: Current timestamp. Used to determine lock start time.
+        """
         lock = PairLock(
             pair=pair,
-            lock_time=datetime.now(timezone.utc),
+            lock_time=now or datetime.now(timezone.utc),
             lock_end_time=timeframe_to_next_date(PairLocks.timeframe, until),
             reason=reason,
             active=True
@@ -57,6 +74,15 @@ class PairLocks():
             )]
             return locks
 
+    @staticmethod
+    def get_pair_longest_lock(pair: str, now: Optional[datetime] = None) -> Optional[PairLock]:
+        """
+        Get the lock that expires the latest for the pair given.
+        """
+        locks = PairLocks.get_pair_locks(pair, now)
+        locks = sorted(locks, key=lambda l: l.lock_end_time, reverse=True)
+        return locks[0] if locks else None
+
     @staticmethod
     def unlock_pair(pair: str, now: Optional[datetime] = None) -> None:
         """

freqtrade/plot/plotting.py

@@ -9,10 +9,11 @@ from freqtrade.data.btanalysis import (calculate_max_drawdown, combine_dataframe
                                        create_cum_profit, extract_trades_of_period, load_trades)
 from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.dataprovider import DataProvider
-from freqtrade.data.history import load_data
+from freqtrade.data.history import get_timerange, load_data
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange import timeframe_to_prev_date
+from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_seconds
 from freqtrade.misc import pair_to_filename
+from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.strategy import IStrategy
 
@@ -29,16 +30,16 @@ except ImportError:
     exit(1)
 
 
-def init_plotscript(config):
+def init_plotscript(config, markets: List, startup_candles: int = 0):
     """
     Initialize objects needed for plotting
     :return: Dict with candle (OHLCV) data, trades and pairs
     """
 
     if "pairs" in config:
-        pairs = config['pairs']
+        pairs = expand_pairlist(config['pairs'], markets)
     else:
-        pairs = config['exchange']['pair_whitelist']
+        pairs = expand_pairlist(config['exchange']['pair_whitelist'], markets)
 
     # Set timerange to use
     timerange = TimeRange.parse_timerange(config.get('timerange'))
@@ -48,9 +49,16 @@ def init_plotscript(config):
         pairs=pairs,
         timeframe=config.get('timeframe', '5m'),
         timerange=timerange,
+        startup_candles=startup_candles,
         data_format=config.get('dataformat_ohlcv', 'json'),
     )
 
+    if startup_candles and data:
+        min_date, max_date = get_timerange(data)
+        logger.info(f"Loading data from {min_date} to {max_date}")
+        timerange.adjust_start_if_necessary(timeframe_to_seconds(config.get('timeframe', '5m')),
+                                            startup_candles, min_date)
+
     no_trades = False
     filename = config.get('exportfilename')
     if config.get('no_trades', False):
@@ -59,19 +67,22 @@ def init_plotscript(config):
         if not filename.is_dir() and not filename.is_file():
             logger.warning("Backtest file is missing skipping trades.")
             no_trades = True
-
-    trades = load_trades(
-        config['trade_source'],
-        db_url=config.get('db_url'),
-        exportfilename=filename,
-        no_trades=no_trades,
-        strategy=config.get('strategy'),
-    )
+    try:
+        trades = load_trades(
+            config['trade_source'],
+            db_url=config.get('db_url'),
+            exportfilename=filename,
+            no_trades=no_trades,
+            strategy=config.get('strategy'),
+        )
+    except ValueError as e:
+        raise OperationalException(e) from e
     trades = trim_dataframe(trades, timerange, 'open_date')
 
     return {"ohlcv": data,
             "trades": trades,
             "pairs": pairs,
+            "timerange": timerange,
             }
 
 
@@ -166,10 +177,10 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
     # Trades can be empty
     if trades is not None and len(trades) > 0:
         # Create description for sell summarizing the trade
-        trades['desc'] = trades.apply(lambda row: f"{round(row['profit_percent'] * 100, 1)}%, "
+        trades['desc'] = trades.apply(lambda row: f"{round(row['profit_ratio'] * 100, 1)}%, "
                                                   f"{row['sell_reason']}, "
                                                   f"{row['trade_duration']} min",
-                                                  axis=1)
+                                      axis=1)
         trade_buys = go.Scatter(
             x=trades["open_date"],
             y=trades["open_rate"],
@@ -186,9 +197,9 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
         )
 
         trade_sells = go.Scatter(
-            x=trades.loc[trades['profit_percent'] > 0, "close_date"],
-            y=trades.loc[trades['profit_percent'] > 0, "close_rate"],
-            text=trades.loc[trades['profit_percent'] > 0, "desc"],
+            x=trades.loc[trades['profit_ratio'] > 0, "close_date"],
+            y=trades.loc[trades['profit_ratio'] > 0, "close_rate"],
+            text=trades.loc[trades['profit_ratio'] > 0, "desc"],
             mode='markers',
             name='Sell - Profit',
             marker=dict(
@@ -199,9 +210,9 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
             )
         )
         trade_sells_loss = go.Scatter(
-            x=trades.loc[trades['profit_percent'] <= 0, "close_date"],
-            y=trades.loc[trades['profit_percent'] <= 0, "close_rate"],
-            text=trades.loc[trades['profit_percent'] <= 0, "desc"],
+            x=trades.loc[trades['profit_ratio'] <= 0, "close_date"],
+            y=trades.loc[trades['profit_ratio'] <= 0, "close_rate"],
+            text=trades.loc[trades['profit_ratio'] <= 0, "desc"],
             mode='markers',
             name='Sell - Loss',
             marker=dict(
@@ -255,6 +266,65 @@ def create_plotconfig(indicators1: List[str], indicators2: List[str],
     return plot_config
 
 
+def plot_area(fig, row: int, data: pd.DataFrame, indicator_a: str,
+              indicator_b: str, label: str = "",
+              fill_color: str = "rgba(0,176,246,0.2)") -> make_subplots:
+    """ Creates a plot for the area between two traces and adds it to fig.
+    :param fig: Plot figure to append to
+    :param row: row number for this plot
+    :param data: candlestick DataFrame
+    :param indicator_a: indicator name as populated in stragetie
+    :param indicator_b: indicator name as populated in stragetie
+    :param label: label for the filled area
+    :param fill_color: color to be used for the filled area
+    :return: fig with added  filled_traces plot
+    """
+    if indicator_a in data and indicator_b in data:
+        # make lines invisible to get the area plotted, only.
+        line = {'color': 'rgba(255,255,255,0)'}
+        # TODO: Figure out why scattergl causes problems plotly/plotly.js#2284
+        trace_a = go.Scatter(x=data.date, y=data[indicator_a],
+                             showlegend=False,
+                             line=line)
+        trace_b = go.Scatter(x=data.date, y=data[indicator_b], name=label,
+                             fill="tonexty", fillcolor=fill_color,
+                             line=line)
+        fig.add_trace(trace_a, row, 1)
+        fig.add_trace(trace_b, row, 1)
+    return fig
+
+
+def add_areas(fig, row: int, data: pd.DataFrame, indicators) -> make_subplots:
+    """ Adds all area plots (specified in plot_config) to fig.
+    :param fig: Plot figure to append to
+    :param row: row number for this plot
+    :param data: candlestick DataFrame
+    :param indicators: dict with indicators. ie.: plot_config['main_plot'] or
+                            plot_config['subplots'][subplot_label]
+    :return: fig with added  filled_traces plot
+    """
+    for indicator, ind_conf in indicators.items():
+        if 'fill_to' in ind_conf:
+            indicator_b = ind_conf['fill_to']
+            if indicator in data and indicator_b in data:
+                label = ind_conf.get('fill_label',
+                                     f'{indicator}<>{indicator_b}')
+                fill_color = ind_conf.get('fill_color', 'rgba(0,176,246,0.2)')
+                fig = plot_area(fig, row, data, indicator, indicator_b,
+                                label=label, fill_color=fill_color)
+            elif indicator not in data:
+                logger.info(
+                    'Indicator "%s" ignored. Reason: This indicator is not '
+                    'found in your strategy.', indicator
+                )
+            elif indicator_b not in data:
+                logger.info(
+                        'fill_to: "%s" ignored. Reason: This indicator is not '
+                        'in your strategy.', indicator_b
+                )
+    return fig
+
+
 def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFrame = None, *,
                                indicators1: List[str] = [],
                                indicators2: List[str] = [],
@@ -272,7 +342,6 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
     :return: Plotly figure
     """
     plot_config = create_plotconfig(indicators1, indicators2, plot_config)
-
     rows = 2 + len(plot_config['subplots'])
     row_widths = [1 for _ in plot_config['subplots']]
     # Define the graph
@@ -338,36 +407,20 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
             fig.add_trace(sells, 1, 1)
         else:
             logger.warning("No sell-signals found.")
-
-    # TODO: Figure out why scattergl causes problems plotly/plotly.js#2284
-    if 'bb_lowerband' in data and 'bb_upperband' in data:
-        bb_lower = go.Scatter(
-            x=data.date,
-            y=data.bb_lowerband,
-            showlegend=False,
-            line={'color': 'rgba(255,255,255,0)'},
-        )
-        bb_upper = go.Scatter(
-            x=data.date,
-            y=data.bb_upperband,
-            name='Bollinger Band',
-            fill="tonexty",
-            fillcolor="rgba(0,176,246,0.2)",
-            line={'color': 'rgba(255,255,255,0)'},
-        )
-        fig.add_trace(bb_lower, 1, 1)
-        fig.add_trace(bb_upper, 1, 1)
-        if ('bb_upperband' in plot_config['main_plot']
-           and 'bb_lowerband' in plot_config['main_plot']):
-            del plot_config['main_plot']['bb_upperband']
-            del plot_config['main_plot']['bb_lowerband']
-
-    # Add indicators to main plot
+    # Add Bollinger Bands
+    fig = plot_area(fig, 1, data, 'bb_lowerband', 'bb_upperband',
+                    label="Bollinger Band")
+    # prevent bb_lower and bb_upper from plotting
+    try:
+        del plot_config['main_plot']['bb_lowerband']
+        del plot_config['main_plot']['bb_upperband']
+    except KeyError:
+        pass
+    # main plot goes to row 1
     fig = add_indicators(fig=fig, row=1, indicators=plot_config['main_plot'], data=data)
-
+    fig = add_areas(fig, 1, data, plot_config['main_plot'])
     fig = plot_trades(fig, trades)
-
-    # Volume goes to row 2
+    # sub plot: Volume goes to row 2
     volume = go.Bar(
         x=data['date'],
         y=data['volume'],
@@ -376,13 +429,14 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
         marker_line_color='DarkSlateGrey'
     )
     fig.add_trace(volume, 2, 1)
-
-    # Add indicators to separate row
-    for i, name in enumerate(plot_config['subplots']):
-        fig = add_indicators(fig=fig, row=3 + i,
-                             indicators=plot_config['subplots'][name],
+    # add each sub plot to a separate row
+    for i, label in enumerate(plot_config['subplots']):
+        sub_config = plot_config['subplots'][label]
+        row = 3 + i
+        fig = add_indicators(fig=fig, row=row, indicators=sub_config,
                              data=data)
-
+        # fill area between indicators ( 'fill_to': 'other_indicator')
+        fig = add_areas(fig, row, data, sub_config)
     return fig
 
 
@@ -393,6 +447,8 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
 
     # Trim trades to available OHLCV data
     trades = extract_trades_of_period(df_comb, trades, date_index=True)
+    if len(trades) == 0:
+        raise OperationalException('No trades found in selected timerange.')
 
     # Add combined cumulative profit
     df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
@@ -474,7 +530,8 @@ def load_and_plot_trades(config: Dict[str, Any]):
 
     exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
     IStrategy.dp = DataProvider(config, exchange)
-    plot_elements = init_plotscript(config)
+    plot_elements = init_plotscript(config, list(exchange.markets), strategy.startup_candle_count)
+    timerange = plot_elements['timerange']
     trades = plot_elements['trades']
     pair_counter = 0
     for pair, data in plot_elements["ohlcv"].items():
@@ -482,6 +539,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
         logger.info("analyse pair %s", pair)
 
         df_analyzed = strategy.analyze_ticker(data, {'pair': pair})
+        df_analyzed = trim_dataframe(df_analyzed, timerange)
         trades_pair = trades.loc[trades['pair'] == pair]
         trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
 
@@ -507,7 +565,8 @@ def plot_profit(config: Dict[str, Any]) -> None:
     But should be somewhat proportional, and therefor useful
     in helping out to find a good algorithm.
     """
-    plot_elements = init_plotscript(config)
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
+    plot_elements = init_plotscript(config, list(exchange.markets))
     trades = plot_elements['trades']
     # Filter trades to relevant pairs
     # Remove open pairs - we don't know the profit yet so can't calculate profit for these.

freqtrade/plugins/pairlist/AgeFilter.py

@@ -2,13 +2,15 @@
 Minimum age (days listed) pair list filter
 """
 import logging
-from typing import Any, Dict
+from copy import deepcopy
+from typing import Any, Dict, List, Optional
 
 import arrow
+from pandas import DataFrame
 
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import plural
-from freqtrade.pairlist.IPairList import IPairList
+from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
 logger = logging.getLogger(__name__)
@@ -28,19 +30,19 @@ class AgeFilter(IPairList):
 
         if self._min_days_listed < 1:
             raise OperationalException("AgeFilter requires min_days_listed to be >= 1")
-        if self._min_days_listed > exchange.ohlcv_candle_limit:
+        if self._min_days_listed > exchange.ohlcv_candle_limit('1d'):
             raise OperationalException("AgeFilter requires min_days_listed to not exceed "
                                        "exchange max request size "
-                                       f"({exchange.ohlcv_candle_limit})")
+                                       f"({exchange.ohlcv_candle_limit('1d')})")
 
     @property
     def needstickers(self) -> bool:
         """
         Boolean property defining if tickers are necessary.
-        If no Pairlist requires tickers, an empty List is passed
+        If no Pairlist requires tickers, an empty Dict is passed
         as tickers argument to filter_pairlist
         """
-        return True
+        return False
 
     def short_desc(self) -> str:
         """
@@ -49,36 +51,49 @@ class AgeFilter(IPairList):
         return (f"{self.name} - Filtering pairs with age less than "
                 f"{self._min_days_listed} {plural(self._min_days_listed, 'day')}.")
 
-    def _validate_pair(self, ticker: dict) -> bool:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
-        Validate age for the ticker
-        :param ticker: ticker dict as returned from ccxt.load_markets()
-        :return: True if the pair can stay, False if it should be removed
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :return: new allowlist
         """
-
-        # Check symbol in cache
-        if ticker['symbol'] in self._symbolsChecked:
-            return True
+        needed_pairs = [(p, '1d') for p in pairlist if p not in self._symbolsChecked]
+        if not needed_pairs:
+            return pairlist
 
         since_ms = int(arrow.utcnow()
                        .floor('day')
-                       .shift(days=-self._min_days_listed)
+                       .shift(days=-self._min_days_listed - 1)
                        .float_timestamp) * 1000
+        candles = self._exchange.refresh_latest_ohlcv(needed_pairs, since_ms=since_ms, cache=False)
+        if self._enabled:
+            for p in deepcopy(pairlist):
+                daily_candles = candles[(p, '1d')] if (p, '1d') in candles else None
+                if not self._validate_pair_loc(p, daily_candles):
+                    pairlist.remove(p)
+        logger.info(f"Validated {len(pairlist)} pairs.")
+        return pairlist
 
-        daily_candles = self._exchange.get_historic_ohlcv(pair=ticker['symbol'],
-                                                          timeframe='1d',
-                                                          since_ms=since_ms)
+    def _validate_pair_loc(self, pair: str, daily_candles: Optional[DataFrame]) -> bool:
+        """
+        Validate age for the ticker
+        :param pair: Pair that's currently validated
+        :param ticker: ticker dict as returned from ccxt.load_markets()
+        :return: True if the pair can stay, false if it should be removed
+        """
+        # Check symbol in cache
+        if pair in self._symbolsChecked:
+            return True
 
         if daily_candles is not None:
             if len(daily_candles) > self._min_days_listed:
                 # We have fetched at least the minimum required number of daily candles
                 # Add to cache, store the time we last checked this symbol
-                self._symbolsChecked[ticker['symbol']] = int(arrow.utcnow().float_timestamp) * 1000
+                self._symbolsChecked[pair] = int(arrow.utcnow().float_timestamp) * 1000
                 return True
             else:
-                self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
-                                                 f"because age {len(daily_candles)} is less than "
-                                                 f"{self._min_days_listed} "
-                                                 f"{plural(self._min_days_listed, 'day')}")
+                self.log_once(f"Removed {pair} from whitelist, because age "
+                              f"{len(daily_candles)} is less than {self._min_days_listed} "
+                              f"{plural(self._min_days_listed, 'day')}", logger.info)
                 return False
         return False