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Matthias 2021-05-27 14:58:19 +02:00
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9
.devcontainer/Dockerfile
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@ -1,20 +1,21 @@
FROM freqtradeorg/freqtrade:develop FROM freqtradeorg/freqtrade:develop
USER root
# Install dependencies # Install dependencies
COPY requirements-dev.txt /freqtrade/ COPY requirements-dev.txt /freqtrade/
RUN apt-get update \ RUN apt-get update \
&& apt-get -y install git mercurial sudo vim \ && apt-get -y install git mercurial sudo vim build-essential \
&& apt-get clean \ && 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 \ && 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 PROMPT_COMMAND='history -a'" >> /home/ftuser/.bashrc \
&& echo "export HISTFILE=~/commandhistory/.bash_history" >> /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:ftuser -R /home/ftuser/.local/ \
&& chown ftuser: -R /home/ftuser/ && chown ftuser: -R /home/ftuser/
USER ftuser USER ftuser
RUN pip install --user autopep8 -r docs/requirements-docs.txt -r requirements-dev.txt --no-cache-dir
# Empty the ENTRYPOINT to allow all commands # Empty the ENTRYPOINT to allow all commands
ENTRYPOINT [] ENTRYPOINT []

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.gitattributes vendored
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@ -1,4 +1,3 @@
* encoding=UTF-8
*.py eol=lf *.py eol=lf
*.sh eol=lf *.sh eol=lf
*.ps1 eol=crlf *.ps1 eol=crlf

6
.github/ISSUE_TEMPLATE/config.yml vendored Normal file
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@ -0,0 +1,6 @@
---
blank_issues_enabled: false
contact_links:
- name: Discord Server
url: https://discord.gg/MA9v74M
about: Ask a question or get community support from our Discord server

2
.github/workflows/ci.yml vendored
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@ -301,7 +301,7 @@ jobs:
runs-on: ubuntu-20.04 runs-on: ubuntu-20.04
steps: steps:
- name: Cleanup previous runs on this branch - name: Cleanup previous runs on this branch
uses: rokroskar/workflow-run-cleanup-action@v0.3.2 uses: rokroskar/workflow-run-cleanup-action@v0.3.3
if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/stable' && github.repository == 'freqtrade/freqtrade'" if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/stable' && github.repository == 'freqtrade/freqtrade'"
env: env:
GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}" GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"

2
Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.9.4-slim-buster as base FROM python:3.9.5-slim-buster as base
# Setup env # Setup env
ENV LANG C.UTF-8 ENV LANG C.UTF-8

6
README.md
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@ -1,4 +1,4 @@
# ![freqtrade](docs/assets/freqtrade_poweredby.svg) # ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade_poweredby.svg)
[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/) [![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
[![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop) [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
@ -154,7 +154,7 @@ You can also join our [Slack channel](https://join.slack.com/t/highfrequencybot/
If you discover a bug in the bot, please If you discover a bug in the bot, please
[search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue) [search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
first. If it hasn't been reported, please first. If it hasn't been reported, please
[create a new issue](https://github.com/freqtrade/freqtrade/issues/new) and [create a new issue](https://github.com/freqtrade/freqtrade/issues/new/choose) and
ensure you follow the template guide so that our team can assist you as ensure you follow the template guide so that our team can assist you as
quickly as possible. quickly as possible.
@ -163,7 +163,7 @@ quickly as possible.
Have you a great idea to improve the bot you want to share? Please, Have you a great idea to improve the bot you want to share? Please,
first search if this feature was not [already discussed](https://github.com/freqtrade/freqtrade/labels/enhancement). first search if this feature was not [already discussed](https://github.com/freqtrade/freqtrade/labels/enhancement).
If it hasn't been requested, please If it hasn't been requested, please
[create a new request](https://github.com/freqtrade/freqtrade/issues/new) [create a new request](https://github.com/freqtrade/freqtrade/issues/new/choose)
and ensure you follow the template guide so that it does not get lost and ensure you follow the template guide so that it does not get lost
in the bug reports. in the bug reports.

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build_helpers/install_ta-lib.sh
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@ -8,10 +8,13 @@ if [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
tar zxvf ta-lib-0.4.0-src.tar.gz tar zxvf ta-lib-0.4.0-src.tar.gz
cd ta-lib \ cd ta-lib \
&& sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \ && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \
&& curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess \
&& curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub \
&& ./configure --prefix=${INSTALL_LOC}/ \ && ./configure --prefix=${INSTALL_LOC}/ \
&& make \ && make -j$(nproc) \
&& which sudo && sudo make install || make install \ && which sudo && sudo make install || make install \
&& cd .. && cd ..
else else
echo "TA-lib already installed, skipping installation" echo "TA-lib already installed, skipping installation"
fi fi
# && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \

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build_helpers/install_windows.ps1
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@ -1,16 +1,15 @@
# Downloads don't work automatically, since the URL is regenerated via javascript. # Downloads don't work automatically, since the URL is regenerated via javascript.
# Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib # Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
# Invoke-WebRequest -Uri "https://download.lfd.uci.edu/pythonlibs/xxxxxxx/TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl" -OutFile "TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl"
python -m pip install --upgrade pip python -m pip install --upgrade pip
$pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')" $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
if ($pyv -eq '3.7') { if ($pyv -eq '3.7') {
pip install build_helpers\TA_Lib-0.4.19-cp37-cp37m-win_amd64.whl pip install build_helpers\TA_Lib-0.4.20-cp37-cp37m-win_amd64.whl
} }
if ($pyv -eq '3.8') { if ($pyv -eq '3.8') {
pip install build_helpers\TA_Lib-0.4.19-cp38-cp38-win_amd64.whl pip install build_helpers\TA_Lib-0.4.20-cp38-cp38-win_amd64.whl
} }
pip install -r requirements-dev.txt pip install -r requirements-dev.txt

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config_full.json.example
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@ -23,7 +23,8 @@
"stoploss": -0.10, "stoploss": -0.10,
"unfilledtimeout": { "unfilledtimeout": {
"buy": 10, "buy": 10,
"sell": 30 "sell": 30,
"unit": "minutes"
}, },
"bid_strategy": { "bid_strategy": {
"price_side": "bid", "price_side": "bid",
@ -164,7 +165,16 @@
"startup": "on", "startup": "on",
"buy": "on", "buy": "on",
"buy_fill": "on", "buy_fill": "on",
"sell": "on", "sell": {
"roi": "off",
"emergency_sell": "off",
"force_sell": "off",
"sell_signal": "off",
"trailing_stop_loss": "off",
"stop_loss": "off",
"stoploss_on_exchange": "off",
"custom_sell": "off"
},
"sell_fill": "on", "sell_fill": "on",
"buy_cancel": "on", "buy_cancel": "on",
"sell_cancel": "on" "sell_cancel": "on"

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docker/Dockerfile.aarch64 Normal file
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FROM --platform=linux/arm64/v8 python:3.9.4-slim-buster as base
# Setup env
ENV LANG C.UTF-8
ENV LC_ALL C.UTF-8
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONFAULTHANDLER 1
ENV PATH=/home/ftuser/.local/bin:$PATH
ENV FT_APP_ENV="docker"
# Prepare environment
RUN mkdir /freqtrade \
&& apt-get update \
&& apt-get -y install libatlas3-base curl sqlite3 libhdf5-serial-dev sudo \
&& apt-get clean \
&& useradd -u 1000 -G sudo -U -m ftuser \
&& chown ftuser:ftuser /freqtrade \
# Allow sudoers
&& echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers
WORKDIR /freqtrade
# Install dependencies
FROM base as python-deps
RUN apt-get update \
&& apt-get -y install curl build-essential libssl-dev git libffi-dev libgfortran5 pkg-config cmake gcc \
&& apt-get clean \
&& pip install --upgrade pip
# Install TA-lib
COPY build_helpers/* /tmp/
RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
ENV LD_LIBRARY_PATH /usr/local/lib
# Install dependencies
COPY --chown=ftuser:ftuser requirements.txt requirements-hyperopt.txt /freqtrade/
USER ftuser
RUN pip install --user --no-cache-dir numpy \
&& pip install --user --no-cache-dir -r requirements-hyperopt.txt
# Copy dependencies to runtime-image
FROM base as runtime-image
COPY --from=python-deps /usr/local/lib /usr/local/lib
ENV LD_LIBRARY_PATH /usr/local/lib
COPY --from=python-deps --chown=ftuser:ftuser /home/ftuser/.local /home/ftuser/.local
USER ftuser
# Install and execute
COPY --chown=ftuser:ftuser . /freqtrade/
RUN pip install -e . --user --no-cache-dir \
&& mkdir /freqtrade/user_data/ \
&& freqtrade install-ui
ENTRYPOINT ["freqtrade"]
# Default to trade mode
CMD [ "trade" ]

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74
docs/backtesting.md
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@ -237,29 +237,29 @@ The most important in the backtesting is to understand the result.
A backtesting result will look like that: A backtesting result will look like that:
``` ```
========================================================= BACKTESTING REPORT ======================================================== ========================================================= BACKTESTING REPORT ==========================================================
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | | Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins Draws Loss Win% |
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:| |:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:-------------|-------------------------:|
| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 | 0 | 21 | | ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 0 21 40.0 |
| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 | 0 | 8 | | ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 0 8 27.3 |
| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 | 0 | 14 | | BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 0 14 56.2 |
| DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 | 0 | 7 | | DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 0 7 46.2 |
| ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 | 0 | 10 | | ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 0 10 44.4 |
| EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 | 0 | 20 | | EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 0 20 44.4 |
| ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 | 0 | 15 | | ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 0 15 42.3 |
| ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 | 0 | 17 | | ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 0 17 48.5 |
| IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 | 0 | 18 | | IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 0 18 43.8 |
| LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 | 0 | 9 | | LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 0 9 40.0 |
| LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 | 0 | 21 | | LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 0 21 34.4 |
| NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 | 0 | 7 | | NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 0 7 58.5 |
| NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 | 0 | 13 | | NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 0 13 43.5 |
| REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 | 0 | 5 | | REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 0 5 44.4 |
| XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 | 0 | 9 | | XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 0 9 43.8 |
| XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 | 0 | 11 | | XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 0 11 52.2 |
| XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 | 0 | 23 | | XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 0 23 34.3 |
| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 | 0 | 15 | | ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 0 15 31.8 |
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 | | TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 0 243 43.4 |
========================================================= SELL REASON STATS ========================================================= ========================================================= SELL REASON STATS ==========================================================
| Sell Reason | Sells | Wins | Draws | Losses | | Sell Reason | Sells | Wins | Draws | Losses |
|:-------------------|--------:|------:|-------:|--------:| |:-------------------|--------:|------:|-------:|--------:|
| trailing_stop_loss | 205 | 150 | 0 | 55 | | trailing_stop_loss | 205 | 150 | 0 | 55 |
@ -267,11 +267,11 @@ A backtesting result will look like that:
| sell_signal | 56 | 36 | 0 | 20 | | sell_signal | 56 | 36 | 0 | 20 |
| force_sell | 2 | 0 | 0 | 2 | | force_sell | 2 | 0 | 0 | 2 |
====================================================== LEFT OPEN TRADES REPORT ====================================================== ====================================================== LEFT OPEN TRADES REPORT ======================================================
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | | Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Win Draw Loss Win% |
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:| |:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 | 0 | 0 | | ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 0 0 100 |
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 | 0 | 0 | | LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 0 0 100 |
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 | 0 | 0 | | TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 0 0 100 |
=============== SUMMARY METRICS =============== =============== SUMMARY METRICS ===============
| Metric | Value | | Metric | Value |
|-----------------------+---------------------| |-----------------------+---------------------|
@ -297,6 +297,8 @@ A backtesting result will look like that:
| Days win/draw/lose | 12 / 82 / 25 | | Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 | | Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 | | Avg. Duration Loser | 6:55:00 |
| Zero Duration Trades | 4.6% (20) |
| Rejected Buy signals | 3089 |
| | | | | |
| Min balance | 0.00945123 BTC | | Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC | | Max balance | 0.01846651 BTC |
@ -318,7 +320,7 @@ The last line will give you the overall performance of your strategy,
here: here:
``` ```
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 | | TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 0 243 43.4 |
``` ```
The bot has made `429` trades for an average duration of `4:12:00`, with a performance of `76.20%` (profit), that means it has The bot has made `429` trades for an average duration of `4:12:00`, with a performance of `76.20%` (profit), that means it has
@ -384,6 +386,8 @@ It contains some useful key metrics about performance of your strategy on backte
| Days win/draw/lose | 12 / 82 / 25 | | Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 | | Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 | | Avg. Duration Loser | 6:55:00 |
| Zero Duration Trades | 4.6% (20) |
| Rejected Buy signals | 3089 |
| | | | | |
| Min balance | 0.00945123 BTC | | Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC | | Max balance | 0.01846651 BTC |
@ -413,6 +417,8 @@ It contains some useful key metrics about performance of your strategy on backte
- `Best day` / `Worst day`: Best and worst day based on daily profit. - `Best day` / `Worst day`: Best and worst day based on daily profit.
- `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade). - `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades. - `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
- `Zero Duration Trades`: A number of trades that completed within same candle as they opened and had `trailing_stop_loss` sell reason. A significant amount of such trades may indicate that strategy is exploiting trailing stoploss behavior in backtesting and produces unrealistic results.
- `Rejected Buy signals`: Buy signals that could not be acted upon due to max_open_trades being reached.
- `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period. - `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
- `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced). - `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
- `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost. - `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
@ -472,11 +478,11 @@ There will be an additional table comparing win/losses of the different strategi
Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy. Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
``` ```
=========================================================== STRATEGY SUMMARY =========================================================== =========================================================== STRATEGY SUMMARY =========================================================================
| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | | Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | Drawdown % |
|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:| |:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|-----------:|
| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 | | Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 | 45.2 |
| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 825 | | Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 825 | 241.68 |
``` ```
## Next step ## Next step

5
docs/configuration.md
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@ -68,8 +68,9 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float | `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean | `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `fee` | Fee used during backtesting / dry-runs. Should normally not be configured, which has freqtrade fall back to the exchange default fee. Set as ratio (e.g. 0.001 = 0.1%). Fee is applied twice for each trade, once when buying, once when selling. <br> **Datatype:** Float (as ratio) | `fee` | Fee used during backtesting / dry-runs. Should normally not be configured, which has freqtrade fall back to the exchange default fee. Set as ratio (e.g. 0.001 = 0.1%). Fee is applied twice for each trade, once when buying, once when selling. <br> **Datatype:** Float (as ratio)
| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer | `unfilledtimeout.buy` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer | `unfilledtimeout.sell` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `unfilledtimeout.unit` | Unit to use in unfilledtimeout setting. Note: If you set unfilledtimeout.unit to "seconds", "internals.process_throttle_secs" must be inferior or equal to timeout [Strategy Override](#parameters-in-the-strategy). <br> *Defaults to `minutes`.* <br> **Datatype:** String
| `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`). | `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
| `bid_strategy.ask_last_balance` | **Required.** Interpolate the bidding price. More information [below](#buy-price-without-orderbook-enabled). | `bid_strategy.ask_last_balance` | **Required.** Interpolate the bidding price. More information [below](#buy-price-without-orderbook-enabled).
| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean | `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean

36
docs/docker_quickstart.md
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@ -48,6 +48,8 @@ Create a new directory and place the [docker-compose file](https://raw.githubuse
# Download the docker-compose file from the repository # Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml
# Edit the compose file to use an image named `*_pi` (stable_pi or develop_pi)
# Pull the freqtrade image # Pull the freqtrade image
docker-compose pull docker-compose pull
@ -65,6 +67,40 @@ Create a new directory and place the [docker-compose file](https://raw.githubuse
# image: freqtradeorg/freqtrade:develop_pi # image: freqtradeorg/freqtrade:develop_pi
``` ```
=== "ARM 64 Systenms (Mac M1, Raspberry Pi 4, Jetson Nano)"
In case of a Mac M1, make sure that your docker installation is running in native mode
Arm64 images are not yet provided via Docker Hub and need to be build locally first.
Depending on the device, this may take a few minutes (Apple M1) or multiple hours (Raspberry Pi)
``` bash
# Clone Freqtrade repository
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
# Optionally switch to the stable version
git checkout stable
# Modify your docker-compose file to enable building and change the image name
# (see the Note Box below for necessary changes)
# Build image
docker-compose build
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
# Create configuration - Requires answering interactive questions
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
!!! Note "Change your docker Image"
You have to change the docker image in the docker-compose file for your arm64 build to work properly.
``` yml
image: freqtradeorg/freqtrade:custom_arm64
build:
context: .
dockerfile: "./docker/Dockerfile.aarch64"
```
The above snippet creates a new directory called `ft_userdata`, downloads the latest compose file and pulls the freqtrade image. The above snippet creates a new directory called `ft_userdata`, downloads the latest compose file and pulls the freqtrade image.
The last 2 steps in the snippet create the directory with `user_data`, as well as (interactively) the default configuration based on your selections. The last 2 steps in the snippet create the directory with `user_data`, as well as (interactively) the default configuration based on your selections.

19
docs/hyperopt.md
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@ -249,19 +249,24 @@ We continue to define hyperoptable parameters:
```python ```python
class MyAwesomeStrategy(IStrategy): class MyAwesomeStrategy(IStrategy):
buy_adx = IntParameter(20, 40, default=30) buy_adx = DecimalParameter(20, 40, decimals=1, default=30.1, space="buy")
buy_rsi = IntParameter(20, 40, default=30) buy_rsi = IntParameter(20, 40, default=30, space="buy")
buy_adx_enabled = CategoricalParameter([True, False]), buy_adx_enabled = CategoricalParameter([True, False], default=True, space="buy")
buy_rsi_enabled = CategoricalParameter([True, False]), buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy")
buy_trigger = CategoricalParameter(['bb_lower', 'macd_cross_signal']), buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy")
``` ```
Above definition says: I have five parameters I want to randomly combine to find the best combination. The above definition says: I have five parameters I want to randomly combine to find the best combination.
Two of them are integer values (`buy_adx` and `buy_rsi`) and I want you test in the range of values 20 to 40. `buy_rsi` is an integer parameter, which will be tested between 20 and 40. This space has a size of 20.
`buy_adx` is a decimal parameter, which will be evaluated between 20 and 40 with 1 decimal place (so values are 20.1, 20.2, ...). This space has a size of 200.
Then we have three category variables. First two are either `True` or `False`. Then we have three category variables. First two are either `True` or `False`.
We use these to either enable or disable the ADX and RSI guards. We use these to either enable or disable the ADX and RSI guards.
The last one we call `trigger` and use it to decide which buy trigger we want to use. The last one we call `trigger` and use it to decide which buy trigger we want to use.
!!! Note "Parameter space assignment"
Parameters must either be assigned to a variable named `buy_*` or `sell_*` - or contain `space='buy'` | `space='sell'` to be assigned to a space correctly.
If no parameter is available for a space, you'll receive the error that no space was found when running hyperopt.
So let's write the buy strategy using these values: So let's write the buy strategy using these values:
```python ```python

10
docs/includes/pairlists.md
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@ -112,6 +112,7 @@ The `PriceFilter` allows filtering of pairs by price. Currently the following pr
* `min_price` * `min_price`
* `max_price` * `max_price`
* `max_value`
* `low_price_ratio` * `low_price_ratio`
The `min_price` setting removes pairs where the price is below the specified price. This is useful if you wish to avoid trading very low-priced pairs. The `min_price` setting removes pairs where the price is below the specified price. This is useful if you wish to avoid trading very low-priced pairs.
@ -120,6 +121,11 @@ This option is disabled by default, and will only apply if set to > 0.
The `max_price` setting removes pairs where the price is above the specified price. This is useful if you wish to trade only low-priced pairs. The `max_price` setting removes pairs where the price is above the specified price. This is useful if you wish to trade only low-priced pairs.
This option is disabled by default, and will only apply if set to > 0. This option is disabled by default, and will only apply if set to > 0.
The `max_value` setting removes pairs where the minimum value change is above a specified value.
This is useful when an exchange has unbalanced limits. For example, if step-size = 1 (so you can only buy 1, or 2, or 3, but not 1.1 Coins) - and the price is pretty high (like 20$) as the coin has risen sharply since the last limit adaption.
As a result of the above, you can only buy for 20$, or 40$ - but not for 25$.
On exchanges that deduct fees from the receiving currency (e.g. FTX) - this can result in high value coins / amounts that are unsellable as the amount is slightly below the limit.
The `low_price_ratio` setting removes pairs where a raise of 1 price unit (pip) is above the `low_price_ratio` ratio. The `low_price_ratio` setting removes pairs where a raise of 1 price unit (pip) is above the `low_price_ratio` ratio.
This option is disabled by default, and will only apply if set to > 0. This option is disabled by default, and will only apply if set to > 0.
@ -193,7 +199,7 @@ If the volatility over the last 10 days is not in the range of 0.05-0.50, remove
### Full example of Pairlist Handlers ### Full example of Pairlist Handlers
The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#price-filter), filtering all assets where 1 price unit is > 1%. Then the [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) is applied and pairs are finally shuffled with the random seed set to some predefined value. The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#pricefilter), filtering all assets where 1 price unit is > 1%. Then the [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) is applied and pairs are finally shuffled with the random seed set to some predefined value.
```json ```json
"exchange": { "exchange": {
@ -204,7 +210,7 @@ The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets,
{ {
"method": "VolumePairList", "method": "VolumePairList",
"number_assets": 20, "number_assets": 20,
"sort_key": "quoteVolume", "sort_key": "quoteVolume"
}, },
{"method": "AgeFilter", "min_days_listed": 10}, {"method": "AgeFilter", "min_days_listed": 10},
{"method": "PrecisionFilter"}, {"method": "PrecisionFilter"},

4
docs/installation.md
View File

@ -60,7 +60,7 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
sudo apt-get update sudo apt-get update
# install packages # install packages
sudo apt install -y python3-pip python3-venv python3-pandas python3-pip git sudo apt install -y python3-pip python3-venv python3-pandas git
``` ```
=== "RaspberryPi/Raspbian" === "RaspberryPi/Raspbian"
@ -269,7 +269,7 @@ git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade cd freqtrade
``` ```
#### Freqtrade instal: Conda Environment #### Freqtrade install: Conda Environment
Prepare conda-freqtrade environment, using file `environment.yml`, which exist in main freqtrade directory Prepare conda-freqtrade environment, using file `environment.yml`, which exist in main freqtrade directory

4
docs/requirements-docs.txt
View File

@ -1,3 +1,3 @@
mkdocs-material==7.1.3 mkdocs-material==7.1.5
mdx_truly_sane_lists==1.2 mdx_truly_sane_lists==1.2
pymdown-extensions==8.1.1 pymdown-extensions==8.2

10
docs/rest-api.md
View File

@ -71,7 +71,10 @@ If you run your bot using docker, you'll need to have the bot listen to incoming
"api_server": { "api_server": {
"enabled": true, "enabled": true,
"listen_ip_address": "0.0.0.0", "listen_ip_address": "0.0.0.0",
"listen_port": 8080 "listen_port": 8080,
"username": "Freqtrader",
"password": "SuperSecret1!",
//...
}, },
``` ```
@ -106,7 +109,10 @@ By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be use
"api_server": { "api_server": {
"enabled": true, "enabled": true,
"listen_ip_address": "0.0.0.0", "listen_ip_address": "0.0.0.0",
"listen_port": 8080 "listen_port": 8080,
"username": "Freqtrader",
"password": "SuperSecret1!",
//...
} }
} }
``` ```

31
docs/sql_cheatsheet.md
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@ -19,7 +19,7 @@ The freqtrade docker image does contain sqlite3, so you can edit the database wi
``` bash ``` bash
docker-compose exec freqtrade /bin/bash docker-compose exec freqtrade /bin/bash
sqlite3 <databasefile>.sqlite sqlite3 <database-file>.sqlite
``` ```
## Open the DB ## Open the DB
@ -99,3 +99,32 @@ DELETE FROM trades WHERE id = 31;
!!! Warning !!! Warning
This will remove this trade from the database. Please make sure you got the correct id and **NEVER** run this query without the `where` clause. This will remove this trade from the database. Please make sure you got the correct id and **NEVER** run this query without the `where` clause.
## Use a different database system
!!! Warning
By using one of the below database systems, you acknowledge that you know how to manage such a system. Freqtrade will not provide any support with setup or maintenance (or backups) of the below database systems.
### PostgreSQL
Freqtrade supports PostgreSQL by using SQLAlchemy, which supports multiple different database systems.
Installation:
`pip install psycopg2`
Usage:
`... --db-url postgresql+psycopg2://<username>:<password>@localhost:5432/<database>`
Freqtrade will automatically create the tables necessary upon startup.
If you're running different instances of Freqtrade, you must either setup one database per Instance or use different users / schemas for your connections.
### MariaDB / MySQL
Freqtrade supports MariaDB by using SQLAlchemy, which supports multiple different database systems.
Installation:
`pip install pymysql`
Usage:
`... --db-url mysql+pymysql://<username>:<password>@localhost:3306/<database>`

129
docs/strategy-advanced.md
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@ -40,31 +40,71 @@ class AwesomeStrategy(IStrategy):
!!! Note !!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary. If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
## Dataframe access
You may access dataframe in various strategy functions by querying it from dataprovider.
``` python
from freqtrade.exchange import timeframe_to_prev_date
class AwesomeStrategy(IStrategy):
def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str,
current_time: 'datetime', **kwargs) -> bool:
# Obtain pair dataframe.
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
# Obtain last available candle. Do not use current_time to look up latest candle, because
# current_time points to curret incomplete candle whose data is not available.
last_candle = dataframe.iloc[-1].squeeze()
# <...>
# In dry/live runs trade open date will not match candle open date therefore it must be
# rounded.
trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
# Look up trade candle.
trade_candle = dataframe.loc[dataframe['date'] == trade_date]
# trade_candle may be empty for trades that just opened as it is still incomplete.
if not trade_candle.empty:
trade_candle = trade_candle.squeeze()
# <...>
```
!!! Warning "Using .iloc[-1]"
You can use `.iloc[-1]` here because `get_analyzed_dataframe()` only returns candles that backtesting is allowed to see.
This will not work in `populate_*` methods, so make sure to not use `.iloc[]` in that area.
Also, this will only work starting with version 2021.5.
*** ***
## Custom sell signal ## Custom sell signal
It is possible to define custom sell signals. This is very useful when we need to customize sell conditions for each individual trade, or if you need the trade profit to take the sell decision. It is possible to define custom sell signals, indicating that specified position should be sold. This is very useful when we need to customize sell conditions for each individual trade, or if you need the trade profit to take the sell decision.
For example you could implement a 1:2 risk-reward ROI with `custom_sell()`.
Using custom_sell() signals in place of stoplosses though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
!!! Note
Returning a `string` or `True` from this method is equal to setting sell signal on a candle at specified time. This method is not called when sell signal is set already, or if sell signals are disabled (`use_sell_signal=False` or `sell_profit_only=True` while profit is below `sell_profit_offset`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
An example of how we can use different indicators depending on the current profit and also sell trades that were open longer than one day: An example of how we can use different indicators depending on the current profit and also sell trades that were open longer than one day:
``` python ``` python
from freqtrade.strategy import IStrategy, timeframe_to_prev_date
class AwesomeStrategy(IStrategy): class AwesomeStrategy(IStrategy):
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, dataframe: DataFrame, **kwargs): current_profit: float, **kwargs):
trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
trade_row = dataframe.loc[dataframe['date'] == trade_open_date].squeeze() last_candle = dataframe.iloc[-1].squeeze()
# Above 20% profit, sell when rsi < 80 # Above 20% profit, sell when rsi < 80
if current_profit > 0.2: if current_profit > 0.2:
if trade_row['rsi'] < 80: if last_candle['rsi'] < 80:
return 'rsi_below_80' return 'rsi_below_80'
# Between 2% and 10%, sell if EMA-long above EMA-short # Between 2% and 10%, sell if EMA-long above EMA-short
if 0.02 < current_profit < 0.1: if 0.02 < current_profit < 0.1:
if trade_row['emalong'] > trade_row['emashort']: if last_candle['emalong'] > last_candle['emashort']:
return 'ema_long_below_80' return 'ema_long_below_80'
# Sell any positions at a loss if they are held for more than one day. # Sell any positions at a loss if they are held for more than one day.
@ -72,7 +112,7 @@ class AwesomeStrategy(IStrategy):
return 'unclog' return 'unclog'
``` ```
See [Custom stoploss using an indicator from dataframe example](#custom-stoploss-using-an-indicator-from-dataframe-example) for explanation on how to use `dataframe` parameter. See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
## Custom stoploss ## Custom stoploss
@ -98,8 +138,7 @@ class AwesomeStrategy(IStrategy):
use_custom_stoploss = True use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame, current_rate: float, current_profit: float, **kwargs) -> float:
**kwargs) -> float:
""" """
Custom stoploss logic, returning the new distance relative to current_rate (as ratio). 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. e.g. returning -0.05 would create a stoploss 5% below current_rate.
@ -149,8 +188,7 @@ class AwesomeStrategy(IStrategy):
use_custom_stoploss = True use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame, current_rate: float, current_profit: float, **kwargs) -> float:
**kwargs) -> float:
# Make sure you have the longest interval first - these conditions are evaluated from top to bottom. # Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
if current_time - timedelta(minutes=120) > trade.open_date_utc: if current_time - timedelta(minutes=120) > trade.open_date_utc:
@ -176,8 +214,7 @@ class AwesomeStrategy(IStrategy):
use_custom_stoploss = True use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame, current_rate: float, current_profit: float, **kwargs) -> float:
**kwargs) -> float:
if pair in ('ETH/BTC', 'XRP/BTC'): if pair in ('ETH/BTC', 'XRP/BTC'):
return -0.10 return -0.10
@ -203,8 +240,7 @@ class AwesomeStrategy(IStrategy):
use_custom_stoploss = True use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame, current_rate: float, current_profit: float, **kwargs) -> float:
**kwargs) -> float:
if current_profit < 0.04: if current_profit < 0.04:
return -1 # return a value bigger than the inital stoploss to keep using the inital stoploss return -1 # return a value bigger than the inital stoploss to keep using the inital stoploss
@ -243,8 +279,7 @@ class AwesomeStrategy(IStrategy):
use_custom_stoploss = True use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame, current_rate: float, current_profit: float, **kwargs) -> float:
**kwargs) -> float:
# evaluate highest to lowest, so that highest possible stop is used # evaluate highest to lowest, so that highest possible stop is used
if current_profit > 0.40: if current_profit > 0.40:
@ -260,59 +295,43 @@ class AwesomeStrategy(IStrategy):
#### Custom stoploss using an indicator from dataframe example #### Custom stoploss using an indicator from dataframe example
Imagine you want to use `custom_stoploss()` to use a trailing indicator like e.g. "ATR" Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
!!! Warning
Only use `dataframe` values up until and including `current_time` value. Reading past
`current_time` you will look into the future, which will produce incorrect backtesting results
and throw an exception in dry/live runs.
see [Common mistakes when developing strategies](strategy-customization.md#common-mistakes-when-developing-strategies) for more info.
!!! Note
`dataframe['date']` contains the candle's open date. During dry/live runs `current_time` and
`trade.open_date_utc` will not match the candle date precisely and using them directly will throw
an error. Use `date = timeframe_to_prev_date(self.timeframe, date)` to round a date to the candle's open date
before using it to access `dataframe`.
``` python ``` python
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.persistence import Trade
from freqtrade.state import RunMode
class AwesomeStrategy(IStrategy): class AwesomeStrategy(IStrategy):
# ... populate_* methods def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# <...>
dataframe['sar'] = ta.SAR(dataframe)
use_custom_stoploss = True use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame, current_rate: float, current_profit: float, **kwargs) -> float:
**kwargs) -> float:
# Default return value dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
result = 1 last_candle = dataframe.iloc[-1].squeeze()
if trade:
# Using current_time directly would only work in backtesting. Live/dry runs need time to
# be rounded to previous candle to be used as dataframe index. Rounding must also be
# applied to `trade.open_date(_utc)` if it is used for `dataframe` indexing.
current_time = timeframe_to_prev_date(self.timeframe, current_time)
current_row = dataframe.loc[dataframe['date'] == current_time].squeeze()
if 'atr' in current_row:
# new stoploss relative to current_rate
new_stoploss = (current_rate - current_row['atr']) / current_rate
# turn into relative negative offset required by `custom_stoploss` return implementation
result = new_stoploss - 1
return result # Use parabolic sar as absolute stoploss price
stoploss_price = last_candle['sar']
# Convert absolute price to percentage relative to current_rate
if stoploss_price < current_rate:
return (stoploss_price / current_rate) - 1
# return maximum stoploss value, keeping current stoploss price unchanged
return 1
``` ```
See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
--- ---
## Custom order timeout rules ## Custom order timeout rules
Simple, time-based 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 order types, 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 an order did time out or not.
!!! Note !!! 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. 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.
@ -538,7 +557,7 @@ Both attributes and methods may be overridden, altering behavior of the original
## Embedding Strategies ## Embedding Strategies
Freqtrade provides you with with an easy way to embed the strategy into your configuration file. Freqtrade provides you with an easy way to embed the strategy into your configuration file.
This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field, This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
in your chosen config file. in your chosen config file.

11
docs/strategy-customization.md
View File

@ -159,7 +159,7 @@ Edit the method `populate_buy_trend()` in your strategy file to update your buy
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected. It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
This will method will also define a new column, `"buy"`, which needs to contain 1 for buys, and 0 for "no action". This method will also define a new column, `"buy"`, which needs to contain 1 for buys, and 0 for "no action".
Sample from `user_data/strategies/sample_strategy.py`: Sample from `user_data/strategies/sample_strategy.py`:
@ -193,7 +193,7 @@ Please note that the sell-signal is only used if `use_sell_signal` is set to tru
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected. It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
This will method will also define a new column, `"sell"`, which needs to contain 1 for sells, and 0 for "no action". This method will also define a new column, `"sell"`, which needs to contain 1 for sells, and 0 for "no action".
Sample from `user_data/strategies/sample_strategy.py`: Sample from `user_data/strategies/sample_strategy.py`:
@ -422,10 +422,6 @@ if self.dp:
Returns an empty dataframe if the requested pair was not cached. Returns an empty dataframe if the requested pair was not cached.
This should not happen when using whitelisted pairs. This should not happen when using whitelisted pairs.
!!! Warning "Warning about backtesting"
This method will return an empty dataframe during backtesting.
### *orderbook(pair, maximum)* ### *orderbook(pair, maximum)*
``` python ``` python
@ -631,8 +627,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
use_custom_stoploss = True use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame, current_rate: float, current_profit: float, **kwargs) -> float:
**kwargs) -> float:
# once the profit has risen above 10%, keep the stoploss at 7% above the open price # once the profit has risen above 10%, keep the stoploss at 7% above the open price
if current_profit > 0.10: if current_profit > 0.10:

29
docs/telegram-usage.md
View File

@ -80,14 +80,23 @@ Example configuration showing the different settings:
"warning": "on", "warning": "on",
"startup": "off", "startup": "off",
"buy": "silent", "buy": "silent",
"sell": "on", "sell": {
"roi": "silent",
"emergency_sell": "on",
"force_sell": "on",
"sell_signal": "silent",
"trailing_stop_loss": "on",
"stop_loss": "on",
"stoploss_on_exchange": "on",
"custom_sell": "silent"
},
"buy_cancel": "silent", "buy_cancel": "silent",
"sell_cancel": "on", "sell_cancel": "on",
"buy_fill": "off", "buy_fill": "off",
"sell_fill": "off" "sell_fill": "off"
}, },
"balance_dust_level": 0.01 "balance_dust_level": 0.01
}, },
``` ```
`buy` notifications are sent when the order is placed, while `buy_fill` notifications are sent when the order is filled on the exchange. `buy` notifications are sent when the order is placed, while `buy_fill` notifications are sent when the order is filled on the exchange.
@ -250,10 +259,14 @@ Return a summary of your profit/loss and performance.
> **BITTREX:** Selling BTC/LTC with limit `0.01650000 (profit: ~-4.07%, -0.00008168)` > **BITTREX:** Selling BTC/LTC with limit `0.01650000 (profit: ~-4.07%, -0.00008168)`
### /forcebuy <pair> ### /forcebuy <pair> [rate]
> **BITTREX:** Buying ETH/BTC with limit `0.03400000` (`1.000000 ETH`, `225.290 USD`) > **BITTREX:** Buying ETH/BTC with limit `0.03400000` (`1.000000 ETH`, `225.290 USD`)
Omitting the pair will open a query asking for the pair to buy (based on the current whitelist).
![Telegram force-buy screenshot](assets/telegram_forcebuy.png)
Note that for this to work, `forcebuy_enable` needs to be set to true. 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)
@ -262,11 +275,11 @@ Note that for this to work, `forcebuy_enable` needs to be set to true.
Return the performance of each crypto-currency the bot has sold. Return the performance of each crypto-currency the bot has sold.
> Performance: > Performance:
> 1. `RCN/BTC 57.77%` > 1. `RCN/BTC 0.003 BTC (57.77%) (1)`
> 2. `PAY/BTC 56.91%` > 2. `PAY/BTC 0.0012 BTC (56.91%) (1)`
> 3. `VIB/BTC 47.07%` > 3. `VIB/BTC 0.0011 BTC (47.07%) (1)`
> 4. `SALT/BTC 30.24%` > 4. `SALT/BTC 0.0010 BTC (30.24%) (1)`
> 5. `STORJ/BTC 27.24%` > 5. `STORJ/BTC 0.0009 BTC (27.24%) (1)`
> ... > ...
### /balance ### /balance

4
docs/windows_installation.md
View File

@ -1,3 +1,5 @@
# Windows installation
We **strongly** recommend that Windows users use [Docker](docker_quickstart.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. If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
@ -21,7 +23,7 @@ git clone https://github.com/freqtrade/freqtrade.git
Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows). Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.19‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version) As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.20‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version).
Freqtrade provides these dependencies for the latest 2 Python versions (3.7 and 3.8) and for 64bit Windows. Freqtrade provides these dependencies for the latest 2 Python versions (3.7 and 3.8) and for 64bit Windows.
Other versions must be downloaded from the above link. Other versions must be downloaded from the above link.

78
freqtrade/commands/hyperopt_commands.py
View File

@ -7,6 +7,7 @@ from colorama import init as colorama_init
from freqtrade.configuration import setup_utils_configuration from freqtrade.configuration import setup_utils_configuration
from freqtrade.data.btanalysis import get_latest_hyperopt_file from freqtrade.data.btanalysis import get_latest_hyperopt_file
from freqtrade.exceptions import OperationalException from freqtrade.exceptions import OperationalException
from freqtrade.optimize.optimize_reports import show_backtest_result
from freqtrade.state import RunMode from freqtrade.state import RunMode
@ -125,6 +126,12 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
if epochs: if epochs:
val = epochs[n] val = epochs[n]
metrics = val['results_metrics']
if 'strategy_name' in metrics:
show_backtest_result(metrics['strategy_name'], metrics,
metrics['stake_currency'])
HyperoptTools.print_epoch_details(val, total_epochs, print_json, no_header, HyperoptTools.print_epoch_details(val, total_epochs, print_json, no_header,
header_str="Epoch details") header_str="Epoch details")
@ -132,11 +139,13 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List: def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
""" """
Filter our items from the list of hyperopt results Filter our items from the list of hyperopt results
TODO: after 2021.5 remove all "legacy" mode queries.
""" """
if filteroptions['only_best']: if filteroptions['only_best']:
epochs = [x for x in epochs if x['is_best']] epochs = [x for x in epochs if x['is_best']]
if filteroptions['only_profitable']: if filteroptions['only_profitable']:
epochs = [x for x in epochs if x['results_metrics']['profit'] > 0] epochs = [x for x in epochs if x['results_metrics'].get(
'profit', x['results_metrics'].get('profit_total', 0)) > 0]
epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions) epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
@ -153,34 +162,55 @@ def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
return epochs return epochs
def _hyperopt_filter_epochs_trade(epochs: List, trade_count: int):
"""
Filter epochs with trade-counts > trades
"""
return [
x for x in epochs
if x['results_metrics'].get(
'trade_count', x['results_metrics'].get('total_trades', 0)
) > trade_count
]
def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List: def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_trades'] > 0: if filteroptions['filter_min_trades'] > 0:
epochs = [ epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions['filter_min_trades'])
x for x in epochs
if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
]
if filteroptions['filter_max_trades'] > 0: if filteroptions['filter_max_trades'] > 0:
epochs = [ epochs = [
x for x in epochs x for x in epochs
if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades'] if x['results_metrics'].get(
'trade_count', x['results_metrics'].get('total_trades')
) < filteroptions['filter_max_trades']
] ]
return epochs return epochs
def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List: def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
def get_duration_value(x):
# Duration in minutes ...
if 'duration' in x['results_metrics']:
return x['results_metrics']['duration']
else:
# New mode
avg = x['results_metrics']['holding_avg']
return avg.total_seconds() // 60
if filteroptions['filter_min_avg_time'] is not None: if filteroptions['filter_min_avg_time'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [ epochs = [
x for x in epochs x for x in epochs
if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time'] if get_duration_value(x) > filteroptions['filter_min_avg_time']
] ]
if filteroptions['filter_max_avg_time'] is not None: if filteroptions['filter_max_avg_time'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [ epochs = [
x for x in epochs x for x in epochs
if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time'] if get_duration_value(x) < filteroptions['filter_max_avg_time']
] ]
return epochs return epochs
@ -189,28 +219,36 @@ def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List: def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_avg_profit'] is not None: if filteroptions['filter_min_avg_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [ epochs = [
x for x in epochs x for x in epochs
if x['results_metrics']['avg_profit'] > filteroptions['filter_min_avg_profit'] if x['results_metrics'].get(
'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
) > filteroptions['filter_min_avg_profit']
] ]
if filteroptions['filter_max_avg_profit'] is not None: if filteroptions['filter_max_avg_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [ epochs = [
x for x in epochs x for x in epochs
if x['results_metrics']['avg_profit'] < filteroptions['filter_max_avg_profit'] if x['results_metrics'].get(
'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
) < filteroptions['filter_max_avg_profit']
] ]
if filteroptions['filter_min_total_profit'] is not None: if filteroptions['filter_min_total_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [ epochs = [
x for x in epochs x for x in epochs
if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit'] if x['results_metrics'].get(
'profit', x['results_metrics'].get('profit_total_abs', 0)
) > filteroptions['filter_min_total_profit']
] ]
if filteroptions['filter_max_total_profit'] is not None: if filteroptions['filter_max_total_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [ epochs = [
x for x in epochs x for x in epochs
if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit'] if x['results_metrics'].get(
'profit', x['results_metrics'].get('profit_total_abs', 0)
) < filteroptions['filter_max_total_profit']
] ]
return epochs return epochs
@ -218,11 +256,11 @@ def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List: def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_objective'] is not None: if filteroptions['filter_min_objective'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']] epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
if filteroptions['filter_max_objective'] is not None: if filteroptions['filter_max_objective'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']] epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]

7
freqtrade/configuration/timerange.py
View File

@ -3,6 +3,7 @@ This module contains the argument manager class
""" """
import logging import logging
import re import re
from datetime import datetime
from typing import Optional from typing import Optional
import arrow import arrow
@ -43,7 +44,7 @@ class TimeRange:
self.startts = self.startts - seconds self.startts = self.startts - seconds
def adjust_start_if_necessary(self, timeframe_secs: int, startup_candles: int, def adjust_start_if_necessary(self, timeframe_secs: int, startup_candles: int,
min_date: arrow.Arrow) -> None: min_date: datetime) -> None:
""" """
Adjust startts by <startup_candles> candles. Adjust startts by <startup_candles> candles.
Applies only if no startup-candles have been available. Applies only if no startup-candles have been available.
@ -54,11 +55,11 @@ class TimeRange:
:return: None (Modifies the object in place) :return: None (Modifies the object in place)
""" """
if (not self.starttype or (startup_candles if (not self.starttype or (startup_candles
and min_date.int_timestamp >= self.startts)): and min_date.timestamp() >= self.startts)):
# If no startts was defined, or backtest-data starts at the defined backtest-date # 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.", logger.warning("Moving start-date by %s candles to account for startup time.",
startup_candles) startup_candles)
self.startts = (min_date.int_timestamp + timeframe_secs * startup_candles) self.startts = int(min_date.timestamp() + timeframe_secs * startup_candles)
self.starttype = 'date' self.starttype = 'date'
@staticmethod @staticmethod

12
freqtrade/constants.py
View File

@ -11,6 +11,7 @@ DEFAULT_EXCHANGE = 'bittrex'
PROCESS_THROTTLE_SECS = 5 # sec PROCESS_THROTTLE_SECS = 5 # sec
HYPEROPT_EPOCH = 100 # epochs HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec RETRY_TIMEOUT = 30 # sec
TIMEOUT_UNITS = ['minutes', 'seconds']
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite' DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite' DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
UNLIMITED_STAKE_AMOUNT = 'unlimited' UNLIMITED_STAKE_AMOUNT = 'unlimited'
@ -137,7 +138,8 @@ CONF_SCHEMA = {
'type': 'object', 'type': 'object',
'properties': { 'properties': {
'buy': {'type': 'number', 'minimum': 1}, 'buy': {'type': 'number', 'minimum': 1},
'sell': {'type': 'number', 'minimum': 1} 'sell': {'type': 'number', 'minimum': 1},
'unit': {'type': 'string', 'enum': TIMEOUT_UNITS, 'default': 'minutes'}
} }
}, },
'bid_strategy': { 'bid_strategy': {
@ -258,7 +260,13 @@ CONF_SCHEMA = {
'enum': TELEGRAM_SETTING_OPTIONS, 'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off' 'default': 'off'
}, },
'sell': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, 'sell': {
'type': ['string', 'object'],
'additionalProperties': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS
}
},
'sell_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, 'sell_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell_fill': { 'sell_fill': {
'type': 'string', 'type': 'string',

4
freqtrade/data/btanalysis.py
View File

@ -156,6 +156,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
data = data['strategy'][strategy]['trades'] data = data['strategy'][strategy]['trades']
df = pd.DataFrame(data) df = pd.DataFrame(data)
if not df.empty:
df['open_date'] = pd.to_datetime(df['open_date'], df['open_date'] = pd.to_datetime(df['open_date'],
utc=True, utc=True,
infer_datetime_format=True infer_datetime_format=True
@ -167,7 +168,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
else: else:
# old format - only with lists. # old format - only with lists.
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS_OLD) df = pd.DataFrame(data, columns=BT_DATA_COLUMNS_OLD)
if not df.empty:
df['open_date'] = pd.to_datetime(df['open_date'], df['open_date'] = pd.to_datetime(df['open_date'],
unit='s', unit='s',
utc=True, utc=True,
@ -180,6 +181,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
) )
# Create compatibility with new format # Create compatibility with new format
df['profit_abs'] = df['close_rate'] - df['open_rate'] df['profit_abs'] = df['close_rate'] - df['open_rate']
if not df.empty:
if 'profit_ratio' not in df.columns: if 'profit_ratio' not in df.columns:
df['profit_ratio'] = df['profit_percent'] df['profit_ratio'] = df['profit_percent']
df = df.sort_values("open_date").reset_index(drop=True) df = df.sort_values("open_date").reset_index(drop=True)

21
freqtrade/data/converter.py
View File

@ -145,6 +145,27 @@ def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
return df return df
def trim_dataframes(preprocessed: Dict[str, DataFrame], timerange,
startup_candles: int) -> Dict[str, DataFrame]:
"""
Trim startup period from analyzed dataframes
:param preprocessed: Dict of pair: dataframe
:param timerange: timerange (use start and end date if available)
:param startup_candles: Startup-candles that should be removed
:return: Dict of trimmed dataframes
"""
processed: Dict[str, DataFrame] = {}
for pair, df in preprocessed.items():
trimed_df = trim_dataframe(df, timerange, startup_candles=startup_candles)
if not trimed_df.empty:
processed[pair] = trimed_df
else:
logger.warning(f'{pair} has no data left after adjusting for startup candles, '
f'skipping.')
return processed
def order_book_to_dataframe(bids: list, asks: list) -> DataFrame: def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
""" """
TODO: This should get a dedicated test TODO: This should get a dedicated test

174
freqtrade/data/dataprovider.py
View File

@ -19,14 +19,25 @@ from freqtrade.state import RunMode
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
NO_EXCHANGE_EXCEPTION = 'Exchange is not available to DataProvider.'
MAX_DATAFRAME_CANDLES = 1000
class DataProvider: class DataProvider:
def __init__(self, config: dict, exchange: Exchange, pairlists=None) -> None: def __init__(self, config: dict, exchange: Optional[Exchange], pairlists=None) -> None:
self._config = config self._config = config
self._exchange = exchange self._exchange = exchange
self._pairlists = pairlists self._pairlists = pairlists
self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {} self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
self.__slice_index: Optional[int] = None
def _set_dataframe_max_index(self, limit_index: int):
"""
Limit analyzed dataframe to max specified index.
:param limit_index: dataframe index.
"""
self.__slice_index = limit_index
def _set_cached_df(self, pair: str, timeframe: str, dataframe: DataFrame) -> None: def _set_cached_df(self, pair: str, timeframe: str, dataframe: DataFrame) -> None:
""" """
@ -45,40 +56,6 @@ class DataProvider:
""" """
self._pairlists = pairlists self._pairlists = pairlists
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
"""
Refresh data, called with each cycle
"""
if helping_pairs:
self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
else:
self._exchange.refresh_latest_ohlcv(pairlist)
@property
def available_pairs(self) -> ListPairsWithTimeframes:
"""
Return a list of tuples containing (pair, timeframe) for which data is currently cached.
Should be whitelist + open trades.
"""
return list(self._exchange._klines.keys())
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get candle (OHLCV) data for the given pair as DataFrame
Please use the `available_pairs` method to verify which pairs are currently cached.
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param copy: copy dataframe before returning if True.
Use False only for read-only operations (where the dataframe is not modified)
"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
return self._exchange.klines((pair, timeframe or self._config['timeframe']),
copy=copy)
else:
return DataFrame()
def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame: def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
""" """
Get stored historical candle (OHLCV) data Get stored historical candle (OHLCV) data
@ -111,47 +88,27 @@ class DataProvider:
def get_analyzed_dataframe(self, pair: str, timeframe: str) -> Tuple[DataFrame, datetime]: def get_analyzed_dataframe(self, pair: str, timeframe: str) -> Tuple[DataFrame, datetime]:
""" """
Retrieve the analyzed dataframe. Returns the full dataframe in trade mode (live / dry),
and the last 1000 candles (up to the time evaluated at this moment) in all other modes.
:param pair: pair to get the data for :param pair: pair to get the data for
:param timeframe: timeframe to get data for :param timeframe: timeframe to get data for
:return: Tuple of (Analyzed Dataframe, lastrefreshed) for the requested pair / timeframe :return: Tuple of (Analyzed Dataframe, lastrefreshed) for the requested pair / timeframe
combination. combination.
Returns empty dataframe and Epoch 0 (1970-01-01) if no dataframe was cached. Returns empty dataframe and Epoch 0 (1970-01-01) if no dataframe was cached.
""" """
if (pair, timeframe) in self.__cached_pairs: pair_key = (pair, timeframe)
return self.__cached_pairs[(pair, timeframe)] if pair_key in self.__cached_pairs:
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
df, date = self.__cached_pairs[pair_key]
else:
df, date = self.__cached_pairs[pair_key]
if self.__slice_index is not None:
max_index = self.__slice_index
df = df.iloc[max(0, max_index - MAX_DATAFRAME_CANDLES):max_index]
return df, date
else: else:
return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc)) return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
def market(self, pair: str) -> Optional[Dict[str, Any]]:
"""
Return market data for the pair
:param pair: Pair to get the data for
:return: Market data dict from ccxt or None if market info is not available for the pair
"""
return self._exchange.markets.get(pair)
def ticker(self, pair: str):
"""
Return last ticker data from exchange
:param pair: Pair to get the data for
:return: Ticker dict from exchange or empty dict if ticker is not available for the pair
"""
try:
return self._exchange.fetch_ticker(pair)
except ExchangeError:
return {}
def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
"""
Fetch latest l2 orderbook data
Warning: Does a network request - so use with common sense.
:param pair: pair to get the data for
:param maximum: Maximum number of orderbook entries to query
:return: dict including bids/asks with a total of `maximum` entries.
"""
return self._exchange.fetch_l2_order_book(pair, maximum)
@property @property
def runmode(self) -> RunMode: def runmode(self) -> RunMode:
""" """
@ -173,3 +130,86 @@ class DataProvider:
return self._pairlists.whitelist.copy() return self._pairlists.whitelist.copy()
else: else:
raise OperationalException("Dataprovider was not initialized with a pairlist provider.") raise OperationalException("Dataprovider was not initialized with a pairlist provider.")
def clear_cache(self):
"""
Clear pair dataframe cache.
"""
self.__cached_pairs = {}
# Exchange functions
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
"""
Refresh data, called with each cycle
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
if helping_pairs:
self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
else:
self._exchange.refresh_latest_ohlcv(pairlist)
@property
def available_pairs(self) -> ListPairsWithTimeframes:
"""
Return a list of tuples containing (pair, timeframe) for which data is currently cached.
Should be whitelist + open trades.
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return list(self._exchange._klines.keys())
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get candle (OHLCV) data for the given pair as DataFrame
Please use the `available_pairs` method to verify which pairs are currently cached.
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param copy: copy dataframe before returning if True.
Use False only for read-only operations (where the dataframe is not modified)
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
return self._exchange.klines((pair, timeframe or self._config['timeframe']),
copy=copy)
else:
return DataFrame()
def market(self, pair: str) -> Optional[Dict[str, Any]]:
"""
Return market data for the pair
:param pair: Pair to get the data for
:return: Market data dict from ccxt or None if market info is not available for the pair
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return self._exchange.markets.get(pair)
def ticker(self, pair: str):
"""
Return last ticker data from exchange
:param pair: Pair to get the data for
:return: Ticker dict from exchange or empty dict if ticker is not available for the pair
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
try:
return self._exchange.fetch_ticker(pair)
except ExchangeError:
return {}
def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
"""
Fetch latest l2 orderbook data
Warning: Does a network request - so use with common sense.
:param pair: pair to get the data for
:param maximum: Maximum number of orderbook entries to query
:return: dict including bids/asks with a total of `maximum` entries.
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return self._exchange.fetch_l2_order_book(pair, maximum)

15
freqtrade/data/history/history_utils.py
View File

@ -265,9 +265,13 @@ def _download_trades_history(exchange: Exchange,
""" """
try: try:
since = timerange.startts * 1000 if \ until = None
(timerange and timerange.starttype == 'date') else int(arrow.utcnow().shift( if (timerange and timerange.starttype == 'date'):
days=-new_pairs_days).float_timestamp) * 1000 since = timerange.startts * 1000
if timerange.stoptype == 'date':
until = timerange.stopts * 1000
else:
since = int(arrow.utcnow().shift(days=-new_pairs_days).float_timestamp) * 1000
trades = data_handler.trades_load(pair) trades = data_handler.trades_load(pair)
@ -295,6 +299,7 @@ def _download_trades_history(exchange: Exchange,
# Default since_ms to 30 days if nothing is given # Default since_ms to 30 days if nothing is given
new_trades = exchange.get_historic_trades(pair=pair, new_trades = exchange.get_historic_trades(pair=pair,
since=since, since=since,
until=until,
from_id=from_id, from_id=from_id,
) )
trades.extend(new_trades[1]) trades.extend(new_trades[1])
@ -367,7 +372,7 @@ def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
logger.exception(f'Could not convert {pair} to OHLCV.') logger.exception(f'Could not convert {pair} to OHLCV.')
def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]: def get_timerange(data: Dict[str, DataFrame]) -> Tuple[datetime, datetime]:
""" """
Get the maximum common timerange for the given backtest data. Get the maximum common timerange for the given backtest data.
@ -375,7 +380,7 @@ def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]
:return: tuple containing min_date, max_date :return: tuple containing min_date, max_date
""" """
timeranges = [ timeranges = [
(arrow.get(frame['date'].min()), arrow.get(frame['date'].max())) (frame['date'].min().to_pydatetime(), frame['date'].max().to_pydatetime())
for frame in data.values() for frame in data.values()
] ]
return (min(timeranges, key=operator.itemgetter(0))[0], return (min(timeranges, key=operator.itemgetter(0))[0],

38
freqtrade/edge/edge_positioning.py
View File

@ -1,6 +1,8 @@
# pragma pylint: disable=W0603 # pragma pylint: disable=W0603
""" Edge positioning package """ """ Edge positioning package """
import logging import logging
from collections import defaultdict
from copy import deepcopy
from typing import Any, Dict, List, NamedTuple from typing import Any, Dict, List, NamedTuple
import arrow import arrow
@ -12,8 +14,10 @@ from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
from freqtrade.data.history import get_timerange, load_data, refresh_data from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.exceptions import OperationalException from freqtrade.exceptions import OperationalException
from freqtrade.exchange.exchange import timeframe_to_seconds
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.strategy.interface import SellType from freqtrade.state import RunMode
from freqtrade.strategy.interface import IStrategy, SellType
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -45,7 +49,7 @@ class Edge:
self.config = config self.config = config
self.exchange = exchange self.exchange = exchange
self.strategy = strategy self.strategy: IStrategy = strategy
self.edge_config = self.config.get('edge', {}) self.edge_config = self.config.get('edge', {})
self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
@ -102,12 +106,31 @@ class Edge:
logger.info('Using local backtesting data (using whitelist in given config) ...') logger.info('Using local backtesting data (using whitelist in given config) ...')
if self._refresh_pairs: if self._refresh_pairs:
timerange_startup = deepcopy(self._timerange)
timerange_startup.subtract_start(timeframe_to_seconds(
self.strategy.timeframe) * self.strategy.startup_candle_count)
refresh_data( refresh_data(
datadir=self.config['datadir'], datadir=self.config['datadir'],
pairs=pairs, pairs=pairs,
exchange=self.exchange, exchange=self.exchange,
timeframe=self.strategy.timeframe, timeframe=self.strategy.timeframe,
timerange=self._timerange, timerange=timerange_startup,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
# Download informative pairs too
res = defaultdict(list)
for p, t in self.strategy.informative_pairs():
res[t].append(p)
for timeframe, inf_pairs in res.items():
timerange_startup = deepcopy(self._timerange)
timerange_startup.subtract_start(timeframe_to_seconds(
timeframe) * self.strategy.startup_candle_count)
refresh_data(
datadir=self.config['datadir'],
pairs=inf_pairs,
exchange=self.exchange,
timeframe=timeframe,
timerange=timerange_startup,
data_format=self.config.get('dataformat_ohlcv', 'json'), data_format=self.config.get('dataformat_ohlcv', 'json'),
) )
@ -125,8 +148,11 @@ class Edge:
self._cached_pairs = {} self._cached_pairs = {}
logger.critical("No data found. Edge is stopped ...") logger.critical("No data found. Edge is stopped ...")
return False return False
# Fake run-mode to Edge
prior_rm = self.config['runmode']
self.config['runmode'] = RunMode.EDGE
preprocessed = self.strategy.ohlcvdata_to_dataframe(data) preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
self.config['runmode'] = prior_rm
# Print timeframe # Print timeframe
min_date, max_date = get_timerange(preprocessed) min_date, max_date = get_timerange(preprocessed)
@ -183,7 +209,7 @@ class Edge:
if pair in self._cached_pairs: if pair in self._cached_pairs:
return self._cached_pairs[pair].stoploss return self._cached_pairs[pair].stoploss
else: else:
logger.warning('tried to access stoploss of a non-existing pair, ' logger.warning(f'Tried to access stoploss of non-existing pair {pair}, '
'strategy stoploss is returned instead.') 'strategy stoploss is returned instead.')
return self.strategy.stoploss return self.strategy.stoploss
@ -214,7 +240,7 @@ class Edge:
return self._final_pairs return self._final_pairs
def accepted_pairs(self) -> list: def accepted_pairs(self) -> List[Dict[str, Any]]:
""" """
return a list of accepted pairs along with their winrate, expectancy and stoploss return a list of accepted pairs along with their winrate, expectancy and stoploss
""" """

2
freqtrade/exchange/__init__.py
View File

@ -7,6 +7,7 @@ from freqtrade.exchange.bibox import Bibox
from freqtrade.exchange.binance import Binance from freqtrade.exchange.binance import Binance
from freqtrade.exchange.bittrex import Bittrex from freqtrade.exchange.bittrex import Bittrex
from freqtrade.exchange.bybit import Bybit from freqtrade.exchange.bybit import Bybit
from freqtrade.exchange.coinbasepro import Coinbasepro
from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges, from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges,
is_exchange_known_ccxt, is_exchange_officially_supported, is_exchange_known_ccxt, is_exchange_officially_supported,
market_is_active, timeframe_to_minutes, timeframe_to_msecs, market_is_active, timeframe_to_minutes, timeframe_to_msecs,
@ -14,5 +15,6 @@ from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges,
timeframe_to_seconds, validate_exchange, timeframe_to_seconds, validate_exchange,
validate_exchanges) validate_exchanges)
from freqtrade.exchange.ftx import Ftx from freqtrade.exchange.ftx import Ftx
from freqtrade.exchange.hitbtc import Hitbtc
from freqtrade.exchange.kraken import Kraken from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.kucoin import Kucoin from freqtrade.exchange.kucoin import Kucoin

1
freqtrade/exchange/bybit.py
View File

@ -18,7 +18,6 @@ class Bybit(Exchange):
may still not work as expected. may still not work as expected.
""" """
# fetchCurrencies API point requires authentication for Bybit,
_ft_has: Dict = { _ft_has: Dict = {
"ohlcv_candle_limit": 200, "ohlcv_candle_limit": 200,
} }

23
freqtrade/exchange/coinbasepro.py Normal file
View File

@ -0,0 +1,23 @@
""" CoinbasePro exchange subclass """
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Coinbasepro(Exchange):
"""
CoinbasePro 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.
"""
_ft_has: Dict = {
"ohlcv_candle_limit": 300,
}

34
freqtrade/exchange/exchange.py
View File

@ -59,6 +59,7 @@ class Exchange:
_ft_has_default: Dict = { _ft_has_default: Dict = {
"stoploss_on_exchange": False, "stoploss_on_exchange": False,
"order_time_in_force": ["gtc"], "order_time_in_force": ["gtc"],
"ohlcv_params": {},
"ohlcv_candle_limit": 500, "ohlcv_candle_limit": 500,
"ohlcv_partial_candle": True, "ohlcv_partial_candle": True,
"trades_pagination": "time", # Possible are "time" or "id" "trades_pagination": "time", # Possible are "time" or "id"
@ -465,7 +466,7 @@ class Exchange:
def amount_to_precision(self, pair: str, amount: float) -> float: def amount_to_precision(self, pair: str, amount: float) -> float:
''' '''
Returns the amount to buy or sell to a precision the Exchange accepts Returns the amount to buy or sell to a precision the Exchange accepts
Reimplementation of ccxt internal methods - ensuring we can test the result is correct Re-implementation of ccxt internal methods - ensuring we can test the result is correct
based on our definitions. based on our definitions.
''' '''
if self.markets[pair]['precision']['amount']: if self.markets[pair]['precision']['amount']:
@ -479,7 +480,7 @@ class Exchange:
def price_to_precision(self, pair: str, price: float) -> float: def price_to_precision(self, pair: str, price: float) -> float:
''' '''
Returns the price rounded up to the precision the Exchange accepts. Returns the price rounded up to the precision the Exchange accepts.
Partial Reimplementation of ccxt internal method decimal_to_precision(), Partial Re-implementation of ccxt internal method decimal_to_precision(),
which does not support rounding up which does not support rounding up
TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and
align with amount_to_precision(). align with amount_to_precision().
@ -862,10 +863,11 @@ class Exchange:
"Fetching pair %s, interval %s, since %s %s...", "Fetching pair %s, interval %s, since %s %s...",
pair, timeframe, since_ms, s pair, timeframe, since_ms, s
) )
params = self._ft_has.get('ohlcv_params', {})
data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe, data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
since=since_ms, since=since_ms,
limit=self.ohlcv_candle_limit(timeframe)) limit=self.ohlcv_candle_limit(timeframe),
params=params)
# Some exchanges sort OHLCV in ASC order and others in DESC. # 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) # Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
@ -1118,6 +1120,27 @@ class Exchange:
return order return order
def cancel_stoploss_order_with_result(self, order_id: str, pair: str, amount: float) -> Dict:
"""
Cancel stoploss order returning a result.
Creates a fake result if cancel order returns a non-usable result
and fetch_order does not work (certain exchanges don't return cancelled orders)
:param order_id: stoploss-order-id to cancel
:param pair: Pair corresponding to order_id
:param amount: Amount to use for fake response
:return: Result from either cancel_order if usable, or fetch_order
"""
corder = self.cancel_stoploss_order(order_id, pair)
if self.is_cancel_order_result_suitable(corder):
return corder
try:
order = self.fetch_stoploss_order(order_id, pair)
except InvalidOrderException:
logger.warning(f"Could not fetch cancelled stoploss order {order_id}.")
order = {'fee': {}, 'status': 'canceled', 'amount': amount, 'info': {}}
return order
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT) @retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
def fetch_order(self, order_id: str, pair: str) -> Dict: def fetch_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']: if self._config['dry_run']:
@ -1237,6 +1260,9 @@ class Exchange:
except ccxt.BaseError as e: except ccxt.BaseError as e:
raise OperationalException(e) from e raise OperationalException(e) from e
def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
return order['id']
@retrier @retrier
def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1, def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
price: float = 1, taker_or_maker: str = 'maker') -> float: price: float = 1, taker_or_maker: str = 'maker') -> float:

6
freqtrade/exchange/ftx.py
View File

@ -8,6 +8,7 @@ from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, Invali
OperationalException, TemporaryError) OperationalException, TemporaryError)
from freqtrade.exchange import Exchange from freqtrade.exchange import Exchange
from freqtrade.exchange.common import API_FETCH_ORDER_RETRY_COUNT, retrier from freqtrade.exchange.common import API_FETCH_ORDER_RETRY_COUNT, retrier
from freqtrade.misc import safe_value_fallback2
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -135,3 +136,8 @@ class Ftx(Exchange):
f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e: except ccxt.BaseError as e:
raise OperationalException(e) from e raise OperationalException(e) from e
def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
if order['type'] == 'stop':
return safe_value_fallback2(order['info'], order, 'orderId', 'id')
return order['id']

23
freqtrade/exchange/hitbtc.py Normal file
View File

@ -0,0 +1,23 @@
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Hitbtc(Exchange):
"""
Hitbtc 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.
"""
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
"ohlcv_params": {"sort": "DESC"}
}

2
freqtrade/exchange/kraken.py
View File

@ -53,6 +53,8 @@ class Kraken(Exchange):
# x["side"], x["amount"], # x["side"], x["amount"],
) for x in orders] ) for x in orders]
for bal in balances: for bal in balances:
if not isinstance(balances[bal], dict):
continue
balances[bal]['used'] = sum(order[1] for order in order_list if order[0] == bal) balances[bal]['used'] = sum(order[1] for order in order_list if order[0] == bal)
balances[bal]['free'] = balances[bal]['total'] - balances[bal]['used'] balances[bal]['free'] = balances[bal]['total'] - balances[bal]['used']

68
freqtrade/freqtradebot.py
View File

@ -11,7 +11,6 @@ from typing import Any, Dict, List, Optional
import arrow import arrow
from cachetools import TTLCache from cachetools import TTLCache
from pandas import DataFrame
from freqtrade import __version__, constants from freqtrade import __version__, constants
from freqtrade.configuration import validate_config_consistency from freqtrade.configuration import validate_config_consistency
@ -268,7 +267,7 @@ class FreqtradeBot(LoggingMixin):
def update_closed_trades_without_assigned_fees(self): def update_closed_trades_without_assigned_fees(self):
""" """
Update closed trades without close fees assigned. Update closed trades without close fees assigned.
Only acts when Orders are in the database, otherwise the last orderid is unknown. Only acts when Orders are in the database, otherwise the last order-id is unknown.
""" """
if self.config['dry_run']: if self.config['dry_run']:
# Updating open orders in dry-run does not make sense and will fail. # Updating open orders in dry-run does not make sense and will fail.
@ -553,7 +552,7 @@ class FreqtradeBot(LoggingMixin):
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)( if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=amount, rate=buy_limit_requested, pair=pair, order_type=order_type, amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force): time_in_force=time_in_force, current_time=datetime.now(timezone.utc)):
logger.info(f"User requested abortion of buying {pair}") logger.info(f"User requested abortion of buying {pair}")
return False return False
amount = self.exchange.amount_to_precision(pair, amount) amount = self.exchange.amount_to_precision(pair, amount)
@ -602,6 +601,7 @@ class FreqtradeBot(LoggingMixin):
pair=pair, pair=pair,
stake_amount=stake_amount, stake_amount=stake_amount,
amount=amount, amount=amount,
is_open=True,
amount_requested=amount_requested, amount_requested=amount_requested,
fee_open=fee, fee_open=fee,
fee_close=fee, fee_close=fee,
@ -631,7 +631,7 @@ class FreqtradeBot(LoggingMixin):
def _notify_buy(self, trade: Trade, order_type: str) -> None: def _notify_buy(self, trade: Trade, order_type: str) -> None:
""" """
Sends rpc notification when a buy occured. Sends rpc notification when a buy occurred.
""" """
msg = { msg = {
'trade_id': trade.id, 'trade_id': trade.id,
@ -653,7 +653,7 @@ class FreqtradeBot(LoggingMixin):
def _notify_buy_cancel(self, trade: Trade, order_type: str, reason: str) -> None: def _notify_buy_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
""" """
Sends rpc notification when a buy cancel occured. Sends rpc notification when a buy cancel occurred.
""" """
current_rate = self.get_buy_rate(trade.pair, False) current_rate = self.get_buy_rate(trade.pair, False)
@ -714,7 +714,7 @@ class FreqtradeBot(LoggingMixin):
except DependencyException as exception: except DependencyException as exception:
logger.warning('Unable to sell trade %s: %s', trade.pair, exception) logger.warning('Unable to sell trade %s: %s', trade.pair, exception)
# Updating wallets if any trade occured # Updating wallets if any trade occurred
if trades_closed: if trades_closed:
self.wallets.update() self.wallets.update()
@ -784,10 +784,10 @@ class FreqtradeBot(LoggingMixin):
config_ask_strategy = self.config.get('ask_strategy', {}) config_ask_strategy = self.config.get('ask_strategy', {})
analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(trade.pair,
self.strategy.timeframe)
if (config_ask_strategy.get('use_sell_signal', True) or if (config_ask_strategy.get('use_sell_signal', True) or
config_ask_strategy.get('ignore_roi_if_buy_signal', False)): config_ask_strategy.get('ignore_roi_if_buy_signal', False)):
analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(trade.pair,
self.strategy.timeframe)
(buy, sell) = self.strategy.get_signal(trade.pair, self.strategy.timeframe, analyzed_df) (buy, sell) = self.strategy.get_signal(trade.pair, self.strategy.timeframe, analyzed_df)
@ -814,13 +814,13 @@ class FreqtradeBot(LoggingMixin):
# resulting in outdated RPC messages # resulting in outdated RPC messages
self._sell_rate_cache[trade.pair] = sell_rate self._sell_rate_cache[trade.pair] = sell_rate
if self._check_and_execute_sell(analyzed_df, trade, sell_rate, buy, sell): if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True return True
else: else:
logger.debug('checking sell') logger.debug('checking sell')
sell_rate = self.get_sell_rate(trade.pair, True) sell_rate = self.get_sell_rate(trade.pair, True)
if self._check_and_execute_sell(analyzed_df, trade, sell_rate, buy, sell): if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True return True
logger.debug('Found no sell signal for %s.', trade) logger.debug('Found no sell signal for %s.', trade)
@ -933,14 +933,15 @@ class FreqtradeBot(LoggingMixin):
:return: None :return: None
""" """
if self.exchange.stoploss_adjust(trade.stop_loss, order): if self.exchange.stoploss_adjust(trade.stop_loss, order):
# we check if the update is neccesary # we check if the update is necessary
update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60) update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat: if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat:
# cancelling the current stoploss on exchange first # cancelling the current stoploss on exchange first
logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} " logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} "
f"(orderid:{order['id']}) in order to add another one ...") f"(orderid:{order['id']}) in order to add another one ...")
try: try:
co = self.exchange.cancel_stoploss_order(order['id'], trade.pair) co = self.exchange.cancel_stoploss_order_with_result(order['id'], trade.pair,
trade.amount)
trade.update_order(co) trade.update_order(co)
except InvalidOrderException: except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {order['id']} " logger.exception(f"Could not cancel stoploss order {order['id']} "
@ -951,13 +952,13 @@ class FreqtradeBot(LoggingMixin):
logger.warning(f"Could not create trailing stoploss order " logger.warning(f"Could not create trailing stoploss order "
f"for pair {trade.pair}.") f"for pair {trade.pair}.")
def _check_and_execute_sell(self, dataframe: DataFrame, trade: Trade, sell_rate: float, def _check_and_execute_sell(self, trade: Trade, sell_rate: float,
buy: bool, sell: bool) -> bool: buy: bool, sell: bool) -> bool:
""" """
Check and execute sell Check and execute sell
""" """
should_sell = self.strategy.should_sell( should_sell = self.strategy.should_sell(
dataframe, trade, sell_rate, datetime.now(timezone.utc), buy, sell, trade, sell_rate, datetime.now(timezone.utc), buy, sell,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0 force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
) )
@ -974,15 +975,16 @@ class FreqtradeBot(LoggingMixin):
timeout = self.config.get('unfilledtimeout', {}).get(side) timeout = self.config.get('unfilledtimeout', {}).get(side)
ordertime = arrow.get(order['datetime']).datetime ordertime = arrow.get(order['datetime']).datetime
if timeout is not None: if timeout is not None:
timeout_threshold = arrow.utcnow().shift(minutes=-timeout).datetime timeout_unit = self.config.get('unfilledtimeout', {}).get('unit', 'minutes')
timeout_kwargs = {timeout_unit: -timeout}
timeout_threshold = arrow.utcnow().shift(**timeout_kwargs).datetime
return (order['status'] == 'open' and order['side'] == side return (order['status'] == 'open' and order['side'] == side
and ordertime < timeout_threshold) and ordertime < timeout_threshold)
return False return False
def check_handle_timedout(self) -> None: def check_handle_timedout(self) -> None:
""" """
Check if any orders are timed out and cancel if neccessary Check if any orders are timed out and cancel if necessary
:param timeoutvalue: Number of minutes until order is considered timed out :param timeoutvalue: Number of minutes until order is considered timed out
:return: None :return: None
""" """
@ -1044,6 +1046,16 @@ class FreqtradeBot(LoggingMixin):
# Cancelled orders may have the status of 'canceled' or 'closed' # Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in ('cancelled', 'canceled', 'closed'): if order['status'] not in ('cancelled', 'canceled', 'closed'):
filled_val = order.get('filled', 0.0) or 0.0
filled_stake = filled_val * trade.open_rate
minstake = self.exchange.get_min_pair_stake_amount(
trade.pair, trade.open_rate, self.strategy.stoploss)
if filled_val > 0 and filled_stake < minstake:
logger.warning(
f"Order {trade.open_order_id} for {trade.pair} not cancelled, "
f"as the filled amount of {filled_val} would result in an unsellable trade.")
return False
corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair, corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount) trade.amount)
# Avoid race condition where the order could not be cancelled coz its already filled. # Avoid race condition where the order could not be cancelled coz its already filled.
@ -1173,7 +1185,9 @@ class FreqtradeBot(LoggingMixin):
# First cancelling stoploss on exchange ... # First cancelling stoploss on exchange ...
if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id: if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
try: try:
self.exchange.cancel_stoploss_order(trade.stoploss_order_id, trade.pair) co = self.exchange.cancel_stoploss_order_with_result(trade.stoploss_order_id,
trade.pair, trade.amount)
trade.update_order(co)
except InvalidOrderException: except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}") logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
@ -1191,8 +1205,8 @@ class FreqtradeBot(LoggingMixin):
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)( if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair, trade=trade, order_type=order_type, amount=amount, rate=limit, pair=trade.pair, trade=trade, order_type=order_type, amount=amount, rate=limit,
time_in_force=time_in_force, time_in_force=time_in_force, sell_reason=sell_reason.sell_reason,
sell_reason=sell_reason.sell_reason): current_time=datetime.now(timezone.utc)):
logger.info(f"User requested abortion of selling {trade.pair}") logger.info(f"User requested abortion of selling {trade.pair}")
return False return False
@ -1221,7 +1235,7 @@ class FreqtradeBot(LoggingMixin):
self.update_trade_state(trade, trade.open_order_id, order) self.update_trade_state(trade, trade.open_order_id, order)
Trade.query.session.flush() Trade.query.session.flush()
# Lock pair for one candle to prevent immediate rebuys # Lock pair for one candle to prevent immediate re-buys
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc), self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock') reason='Auto lock')
@ -1231,7 +1245,7 @@ class FreqtradeBot(LoggingMixin):
def _notify_sell(self, trade: Trade, order_type: str, fill: bool = False) -> None: def _notify_sell(self, trade: Trade, order_type: str, fill: bool = False) -> None:
""" """
Sends rpc notification when a sell occured. Sends rpc notification when a sell occurred.
""" """
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate) profit_trade = trade.calc_profit(rate=profit_rate)
@ -1272,7 +1286,7 @@ class FreqtradeBot(LoggingMixin):
def _notify_sell_cancel(self, trade: Trade, order_type: str, reason: str) -> None: def _notify_sell_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
""" """
Sends rpc notification when a sell cancel occured. Sends rpc notification when a sell cancel occurred.
""" """
if trade.sell_order_status == reason: if trade.sell_order_status == reason:
return return
@ -1325,7 +1339,7 @@ class FreqtradeBot(LoggingMixin):
Handles closing both buy and sell orders. Handles closing both buy and sell orders.
:param trade: Trade object of the trade we're analyzing :param trade: Trade object of the trade we're analyzing
:param order_id: Order-id of the order we're analyzing :param order_id: Order-id of the order we're analyzing
:param action_order: Already aquired order object :param action_order: Already acquired order object
:return: True if order has been cancelled without being filled partially, False otherwise :return: True if order has been cancelled without being filled partially, False otherwise
""" """
if not order_id: if not order_id:
@ -1395,7 +1409,7 @@ class FreqtradeBot(LoggingMixin):
def get_real_amount(self, trade: Trade, order: Dict) -> float: def get_real_amount(self, trade: Trade, order: Dict) -> float:
""" """
Detect and update trade fee. Detect and update trade fee.
Calls trade.update_fee() uppon correct detection. Calls trade.update_fee() upon correct detection.
Returns modified amount if the fee was taken from the destination currency. Returns modified amount if the fee was taken from the destination currency.
Necessary for exchanges which charge fees in base currency (e.g. binance) Necessary for exchanges which charge fees in base currency (e.g. binance)
:return: identical (or new) amount for the trade :return: identical (or new) amount for the trade
@ -1428,8 +1442,8 @@ class FreqtradeBot(LoggingMixin):
""" """
fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee. fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee.
""" """
trades = self.exchange.get_trades_for_order(order['id'], trade.pair, trades = self.exchange.get_trades_for_order(self.exchange.get_order_id_conditional(order),
trade.open_date) trade.pair, trade.open_date)
if len(trades) == 0: if len(trades) == 0:
logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade) logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)

13
freqtrade/misc.py
View File

@ -6,7 +6,7 @@ import logging
import re import re
from datetime import datetime from datetime import datetime
from pathlib import Path from pathlib import Path
from typing import Any from typing import Any, Iterator, List
from typing.io import IO from typing.io import IO
import rapidjson import rapidjson
@ -202,3 +202,14 @@ def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
return render_template(templatefile, arguments) return render_template(templatefile, arguments)
except TemplateNotFound: except TemplateNotFound:
return render_template(templatefallbackfile, arguments) return render_template(templatefallbackfile, arguments)
def chunks(lst: List[Any], n: int) -> Iterator[List[Any]]:
"""
Split lst into chunks of the size n.
:param lst: list to split into chunks
:param n: number of max elements per chunk
:return: None
"""
for chunk in range(0, len(lst), n):
yield (lst[chunk:chunk + n])

114
freqtrade/optimize/backtesting.py
View File

@ -15,7 +15,7 @@ from freqtrade.configuration import TimeRange, remove_credentials, validate_conf
from freqtrade.constants import DATETIME_PRINT_FORMAT from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history from freqtrade.data import history
from freqtrade.data.btanalysis import trade_list_to_dataframe from freqtrade.data.btanalysis import trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe from freqtrade.data.converter import trim_dataframes
from freqtrade.data.dataprovider import DataProvider from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import DependencyException, OperationalException from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
@ -63,9 +63,7 @@ class Backtesting:
self.all_results: Dict[str, Dict] = {} self.all_results: Dict[str, Dict] = {}
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config) self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
self.dataprovider = DataProvider(self.config, None)
dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = dataprovider
if self.config.get('strategy_list', None): if self.config.get('strategy_list', None):
for strat in list(self.config['strategy_list']): for strat in list(self.config['strategy_list']):
@ -96,7 +94,7 @@ class Backtesting:
"PrecisionFilter not allowed for backtesting multiple strategies." "PrecisionFilter not allowed for backtesting multiple strategies."
) )
dataprovider.add_pairlisthandler(self.pairlists) self.dataprovider.add_pairlisthandler(self.pairlists)
self.pairlists.refresh_pairlist() self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0: if len(self.pairlists.whitelist) == 0:
@ -112,15 +110,11 @@ class Backtesting:
PairLocks.timeframe = self.config['timeframe'] PairLocks.timeframe = self.config['timeframe']
PairLocks.use_db = False PairLocks.use_db = False
PairLocks.reset_locks() PairLocks.reset_locks()
if self.config.get('enable_protections', False):
self.protections = ProtectionManager(self.config)
self.wallets = Wallets(self.config, self.exchange, log=False) self.wallets = Wallets(self.config, self.exchange, log=False)
# Get maximum required startup period # Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist]) 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): def __del__(self):
LoggingMixin.show_output = True LoggingMixin.show_output = True
@ -132,10 +126,17 @@ class Backtesting:
Load strategy into backtesting Load strategy into backtesting
""" """
self.strategy: IStrategy = strategy self.strategy: IStrategy = strategy
strategy.dp = self.dataprovider
# Set stoploss_on_exchange to false for backtesting, # Set stoploss_on_exchange to false for backtesting,
# since a "perfect" stoploss-sell is assumed anyway # since a "perfect" stoploss-sell is assumed anyway
# And the regular "stoploss" function would not apply to that case # And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False self.strategy.order_types['stoploss_on_exchange'] = False
if self.config.get('enable_protections', False):
conf = self.config
if hasattr(strategy, 'protections'):
conf = deepcopy(conf)
conf['protections'] = strategy.protections
self.protections = ProtectionManager(conf)
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]: def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
""" """
@ -159,7 +160,7 @@ class Backtesting:
logger.info(f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} ' logger.info(f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} ' f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..') f'({(max_date - min_date).days} days).')
# Adjust startts forward if not enough data is available # Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe), timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
@ -176,6 +177,8 @@ class Backtesting:
Trade.use_db = False Trade.use_db = False
PairLocks.reset_locks() PairLocks.reset_locks()
Trade.reset_trades() Trade.reset_trades()
self.rejected_trades = 0
self.dataprovider.clear_cache()
def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]: def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
""" """
@ -189,8 +192,9 @@ class Backtesting:
data: Dict = {} data: Dict = {}
# Create dict with data # Create dict with data
for pair, pair_data in processed.items(): for pair, pair_data in processed.items():
pair_data.loc[:, 'buy'] = 0 # cleanup from previous run if not pair_data.empty:
pair_data.loc[:, 'sell'] = 0 # cleanup from previous run pair_data.loc[:, 'buy'] = 0 # cleanup if buy_signal is exist
pair_data.loc[:, 'sell'] = 0 # cleanup if sell_signal is exist
df_analyzed = self.strategy.advise_sell( df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy() self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
@ -214,6 +218,12 @@ class Backtesting:
""" """
# Special handling if high or low hit STOP_LOSS or ROI # Special handling if high or low hit STOP_LOSS or ROI
if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS): if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
if trade.stop_loss > sell_row[HIGH_IDX]:
# our stoploss was already higher than candle high,
# possibly due to a cancelled trade exit.
# sell at open price.
return sell_row[OPEN_IDX]
# Set close_rate to stoploss # Set close_rate to stoploss
return trade.stop_loss return trade.stop_loss
elif sell.sell_type == (SellType.ROI): elif sell.sell_type == (SellType.ROI):
@ -247,10 +257,9 @@ class Backtesting:
else: else:
return sell_row[OPEN_IDX] return sell_row[OPEN_IDX]
def _get_sell_trade_entry(self, dataframe: DataFrame, trade: LocalTrade, def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
sell_row: Tuple) -> Optional[LocalTrade]:
sell = self.strategy.should_sell(dataframe, trade, sell_row[OPEN_IDX], # type: ignore sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
sell_row[DATE_IDX].to_pydatetime(), sell_row[BUY_IDX], sell_row[DATE_IDX].to_pydatetime(), sell_row[BUY_IDX],
sell_row[SELL_IDX], sell_row[SELL_IDX],
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX]) low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
@ -267,7 +276,8 @@ class Backtesting:
pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount, pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
rate=closerate, rate=closerate,
time_in_force=time_in_force, time_in_force=time_in_force,
sell_reason=sell.sell_reason): sell_reason=sell.sell_reason,
current_time=sell_row[DATE_IDX].to_pydatetime()):
return None return None
trade.close(closerate, show_msg=False) trade.close(closerate, show_msg=False)
@ -287,7 +297,7 @@ class Backtesting:
# Confirm trade entry: # Confirm trade entry:
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)( if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX], pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
time_in_force=time_in_force): time_in_force=time_in_force, current_time=row[DATE_IDX].to_pydatetime()):
return None return None
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount): if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
@ -327,10 +337,18 @@ class Backtesting:
trades.append(trade1) trades.append(trade1)
return trades return trades
def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
# Always allow trades when max_open_trades is enabled.
if max_open_trades <= 0 or open_trade_count < max_open_trades:
return True
# Rejected trade
self.rejected_trades += 1
return False
def backtest(self, processed: Dict, def backtest(self, processed: Dict,
start_date: datetime, end_date: datetime, start_date: datetime, end_date: datetime,
max_open_trades: int = 0, position_stacking: bool = False, max_open_trades: int = 0, position_stacking: bool = False,
enable_protections: bool = False) -> DataFrame: enable_protections: bool = False) -> Dict[str, Any]:
""" """
Implement backtesting functionality Implement backtesting functionality
@ -349,6 +367,10 @@ class Backtesting:
trades: List[LocalTrade] = [] trades: List[LocalTrade] = []
self.prepare_backtest(enable_protections) self.prepare_backtest(enable_protections)
# Update dataprovider cache
for pair, dataframe in processed.items():
self.dataprovider._set_cached_df(pair, self.timeframe, dataframe)
# Use dict of lists with data for performance # Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames) # (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed) data: Dict = self._get_ohlcv_as_lists(processed)
@ -365,8 +387,9 @@ class Backtesting:
open_trade_count_start = open_trade_count open_trade_count_start = open_trade_count
for i, pair in enumerate(data): for i, pair in enumerate(data):
row_index = indexes[pair]
try: try:
row = data[pair][indexes[pair]] row = data[pair][row_index]
except IndexError: except IndexError:
# missing Data for one pair at the end. # missing Data for one pair at the end.
# Warnings for this are shown during data loading # Warnings for this are shown during data loading
@ -375,16 +398,22 @@ class Backtesting:
# Waits until the time-counter reaches the start of the data for this pair. # Waits until the time-counter reaches the start of the data for this pair.
if row[DATE_IDX] > tmp: if row[DATE_IDX] > tmp:
continue continue
indexes[pair] += 1
row_index += 1
self.dataprovider._set_dataframe_max_index(row_index)
indexes[pair] = row_index
# without positionstacking, we can only have one open trade per pair. # without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected # max_open_trades must be respected
# don't open on the last row # don't open on the last row
if ((position_stacking or len(open_trades[pair]) == 0) if (
and (max_open_trades <= 0 or open_trade_count_start < max_open_trades) (position_stacking or len(open_trades[pair]) == 0)
and self.trade_slot_available(max_open_trades, open_trade_count_start)
and tmp != end_date and tmp != end_date
and row[BUY_IDX] == 1 and row[SELL_IDX] != 1 and row[BUY_IDX] == 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])): and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])
):
trade = self._enter_trade(pair, row) trade = self._enter_trade(pair, row)
if trade: if trade:
# TODO: hacky workaround to avoid opening > max_open_trades # TODO: hacky workaround to avoid opening > max_open_trades
@ -398,7 +427,7 @@ class Backtesting:
for trade in open_trades[pair]: for trade in open_trades[pair]:
# also check the buying candle for sell conditions. # also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(processed[pair], trade, row) trade_entry = self._get_sell_trade_entry(trade, row)
# Sell occured # Sell occured
if trade_entry: if trade_entry:
# logger.debug(f"{pair} - Backtesting sell {trade}") # logger.debug(f"{pair} - Backtesting sell {trade}")
@ -417,7 +446,14 @@ class Backtesting:
trades += self.handle_left_open(open_trades, data=data) trades += self.handle_left_open(open_trades, data=data)
self.wallets.update() self.wallets.update()
return trade_list_to_dataframe(trades) results = trade_list_to_dataframe(trades)
return {
'results': results,
'config': self.strategy.config,
'locks': PairLocks.get_all_locks(),
'rejected_signals': self.rejected_trades,
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
}
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange): def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name()) logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
@ -439,32 +475,32 @@ class Backtesting:
preprocessed = self.strategy.ohlcvdata_to_dataframe(data) preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe # Trim startup period from analyzed dataframe
for pair, df in preprocessed.items(): preprocessed = trim_dataframes(preprocessed, timerange, self.required_startup)
preprocessed[pair] = trim_dataframe(df, timerange,
startup_candles=self.required_startup)
min_date, max_date = history.get_timerange(preprocessed)
if not preprocessed:
raise OperationalException(
"No data left after adjusting for startup candles.")
min_date, max_date = history.get_timerange(preprocessed)
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} ' logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} ' f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..') f'({(max_date - min_date).days} days).')
# Execute backtest and store results # Execute backtest and store results
results = self.backtest( results = self.backtest(
processed=preprocessed, processed=preprocessed,
start_date=min_date.datetime, start_date=min_date,
end_date=max_date.datetime, end_date=max_date,
max_open_trades=max_open_trades, max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False), position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False), enable_protections=self.config.get('enable_protections', False),
) )
backtest_end_time = datetime.now(timezone.utc) backtest_end_time = datetime.now(timezone.utc)
self.all_results[self.strategy.get_strategy_name()] = { results.update({
'results': results,
'config': self.strategy.config,
'locks': PairLocks.get_all_locks(),
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
'backtest_start_time': int(backtest_start_time.timestamp()), 'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()), 'backtest_end_time': int(backtest_end_time.timestamp()),
} })
self.all_results[self.strategy.get_strategy_name()] = results
return min_date, max_date return min_date, max_date
def start(self) -> None: def start(self) -> None:

265
freqtrade/optimize/hyperopt.py
View File

@ -7,20 +7,21 @@ This module contains the hyperopt logic
import logging import logging
import random import random
import warnings import warnings
from datetime import datetime from datetime import datetime, timezone
from math import ceil from math import ceil
from operator import itemgetter
from pathlib import Path from pathlib import Path
from typing import Any, Dict, List, Optional from typing import Any, Dict, List, Optional
import numpy as np
import progressbar import progressbar
import rapidjson
from colorama import Fore, Style from colorama import Fore, Style
from colorama import init as colorama_init from colorama import init as colorama_init
from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects
from pandas import DataFrame from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.converter import trim_dataframe from freqtrade.data.converter import trim_dataframes
from freqtrade.data.history import get_timerange from freqtrade.data.history import get_timerange
from freqtrade.misc import file_dump_json, plural from freqtrade.misc import file_dump_json, plural
from freqtrade.optimize.backtesting import Backtesting from freqtrade.optimize.backtesting import Backtesting
@ -29,8 +30,8 @@ from freqtrade.optimize.hyperopt_auto import HyperOptAuto
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401 from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.optimize.hyperopt_tools import HyperoptTools from freqtrade.optimize.hyperopt_tools import HyperoptTools
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
from freqtrade.strategy import IStrategy
# Suppress scikit-learn FutureWarnings from skopt # Suppress scikit-learn FutureWarnings from skopt
@ -64,6 +65,13 @@ class Hyperopt:
custom_hyperopt: IHyperOpt custom_hyperopt: IHyperOpt
def __init__(self, config: Dict[str, Any]) -> None: def __init__(self, config: Dict[str, Any]) -> None:
self.buy_space: List[Dimension] = []
self.sell_space: List[Dimension] = []
self.roi_space: List[Dimension] = []
self.stoploss_space: List[Dimension] = []
self.trailing_space: List[Dimension] = []
self.dimensions: List[Dimension] = []
self.config = config self.config = config
self.backtesting = Backtesting(self.config) self.backtesting = Backtesting(self.config)
@ -72,15 +80,15 @@ class Hyperopt:
self.custom_hyperopt = HyperOptAuto(self.config) self.custom_hyperopt = HyperOptAuto(self.config)
else: else:
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config) self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
self.backtesting._set_strategy(self.backtesting.strategylist[0])
self.custom_hyperopt.strategy = self.backtesting.strategy self.custom_hyperopt.strategy = self.backtesting.strategy
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config) self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
strategy = str(self.config['strategy']) strategy = str(self.config['strategy'])
self.results_file = (self.config['user_data_dir'] / self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
'hyperopt_results' / f'strategy_{strategy}_{time_now}.fthypt')
f'strategy_{strategy}_hyperopt_results_{time_now}.pickle')
self.data_pickle_file = (self.config['user_data_dir'] / self.data_pickle_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_tickerdata.pkl') 'hyperopt_results' / 'hyperopt_tickerdata.pkl')
self.total_epochs = config.get('epochs', 0) self.total_epochs = config.get('epochs', 0)
@ -90,9 +98,7 @@ class Hyperopt:
self.clean_hyperopt() self.clean_hyperopt()
self.num_epochs_saved = 0 self.num_epochs_saved = 0
self.current_best_epoch: Optional[Dict[str, Any]] = None
# Previous evaluations
self.epochs: List = []
# Populate functions here (hasattr is slow so should not be run during "regular" operations) # Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'): if hasattr(self.custom_hyperopt, 'populate_indicators'):
@ -113,7 +119,7 @@ class Hyperopt:
self.max_open_trades = 0 self.max_open_trades = 0
self.position_stacking = self.config.get('position_stacking', False) self.position_stacking = self.config.get('position_stacking', False)
if self.has_space('sell'): if HyperoptTools.has_space(self.config, 'sell'):
# Make sure use_sell_signal is enabled # Make sure use_sell_signal is enabled
if 'ask_strategy' not in self.config: if 'ask_strategy' not in self.config:
self.config['ask_strategy'] = {} self.config['ask_strategy'] = {}
@ -139,9 +145,7 @@ class Hyperopt:
logger.info(f"Removing `{p}`.") logger.info(f"Removing `{p}`.")
p.unlink() p.unlink()
def _get_params_dict(self, raw_params: List[Any]) -> Dict: def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
dimensions: List[Dimension] = self.dimensions
# Ensure the number of dimensions match # Ensure the number of dimensions match
# the number of parameters in the list. # the number of parameters in the list.
@ -152,15 +156,24 @@ class Hyperopt:
# and the values are taken from the list of parameters. # and the values are taken from the list of parameters.
return {d.name: v for d, v in zip(dimensions, raw_params)} return {d.name: v for d, v in zip(dimensions, raw_params)}
def _save_results(self) -> None: def _save_result(self, epoch: Dict) -> None:
""" """
Save hyperopt results to file Save hyperopt results to file
Store one line per epoch.
While not a valid json object - this allows appending easily.
:param epoch: result dictionary for this epoch.
""" """
num_epochs = len(self.epochs) def default_parser(x):
if num_epochs > self.num_epochs_saved: if isinstance(x, np.integer):
logger.debug(f"Saving {num_epochs} {plural(num_epochs, 'epoch')}.") return int(x)
dump(self.epochs, self.results_file) return str(x)
self.num_epochs_saved = num_epochs
with self.results_file.open('a') as f:
rapidjson.dump(epoch, f, default=default_parser,
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN)
f.write("\n")
self.num_epochs_saved += 1
logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} " logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.") f"saved to '{self.results_file}'.")
# Store hyperopt filename # Store hyperopt filename
@ -174,18 +187,16 @@ class Hyperopt:
""" """
result: Dict = {} result: Dict = {}
if self.has_space('buy'): if HyperoptTools.has_space(self.config, 'buy'):
result['buy'] = {p.name: params.get(p.name) result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
for p in self.hyperopt_space('buy')} if HyperoptTools.has_space(self.config, 'sell'):
if self.has_space('sell'): result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
result['sell'] = {p.name: params.get(p.name) if HyperoptTools.has_space(self.config, 'roi'):
for p in self.hyperopt_space('sell')} result['roi'] = {str(k): v for k, v in
if self.has_space('roi'): self.custom_hyperopt.generate_roi_table(params).items()}
result['roi'] = self.custom_hyperopt.generate_roi_table(params) if HyperoptTools.has_space(self.config, 'stoploss'):
if self.has_space('stoploss'): result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
result['stoploss'] = {p.name: params.get(p.name) if HyperoptTools.has_space(self.config, 'trailing'):
for p in self.hyperopt_space('stoploss')}
if self.has_space('trailing'):
result['trailing'] = self.custom_hyperopt.generate_trailing_params(params) result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
return result return result
@ -207,71 +218,58 @@ class Hyperopt:
) )
self.hyperopt_table_header = 2 self.hyperopt_table_header = 2
def has_space(self, space: str) -> bool: def init_spaces(self):
""" """
Tell if the space value is contained in the configuration Assign the dimensions in the hyperoptimization space.
""" """
# The 'trailing' space is not included in the 'default' set of spaces
if space == 'trailing':
return any(s in self.config['spaces'] for s in [space, 'all'])
else:
return any(s in self.config['spaces'] for s in [space, 'all', 'default'])
def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]: if HyperoptTools.has_space(self.config, 'buy'):
"""
Return the dimensions in the hyperoptimization space.
:param space: Defines hyperspace to return dimensions for.
If None, then the self.has_space() will be used to return dimensions
for all hyperspaces used.
"""
spaces: List[Dimension] = []
if space == 'buy' or (space is None and self.has_space('buy')):
logger.debug("Hyperopt has 'buy' space") logger.debug("Hyperopt has 'buy' space")
spaces += self.custom_hyperopt.indicator_space() self.buy_space = self.custom_hyperopt.indicator_space()
if space == 'sell' or (space is None and self.has_space('sell')): if HyperoptTools.has_space(self.config, 'sell'):
logger.debug("Hyperopt has 'sell' space") logger.debug("Hyperopt has 'sell' space")
spaces += self.custom_hyperopt.sell_indicator_space() self.sell_space = self.custom_hyperopt.sell_indicator_space()
if space == 'roi' or (space is None and self.has_space('roi')): if HyperoptTools.has_space(self.config, 'roi'):
logger.debug("Hyperopt has 'roi' space") logger.debug("Hyperopt has 'roi' space")
spaces += self.custom_hyperopt.roi_space() self.roi_space = self.custom_hyperopt.roi_space()
if space == 'stoploss' or (space is None and self.has_space('stoploss')): if HyperoptTools.has_space(self.config, 'stoploss'):
logger.debug("Hyperopt has 'stoploss' space") logger.debug("Hyperopt has 'stoploss' space")
spaces += self.custom_hyperopt.stoploss_space() self.stoploss_space = self.custom_hyperopt.stoploss_space()
if space == 'trailing' or (space is None and self.has_space('trailing')): if HyperoptTools.has_space(self.config, 'trailing'):
logger.debug("Hyperopt has 'trailing' space") logger.debug("Hyperopt has 'trailing' space")
spaces += self.custom_hyperopt.trailing_space() self.trailing_space = self.custom_hyperopt.trailing_space()
self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
return spaces self.stoploss_space + self.trailing_space)
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict: def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
""" """
Used Optimize function. Called once per epoch to optimize whatever is configured. Used Optimize function. Called once per epoch to optimize whatever is configured.
Keep this function as optimized as possible! Keep this function as optimized as possible!
""" """
params_dict = self._get_params_dict(raw_params) backtest_start_time = datetime.now(timezone.utc)
params_details = self._get_params_details(params_dict) params_dict = self._get_params_dict(self.dimensions, raw_params)
if self.has_space('roi'): # Apply parameters
if HyperoptTools.has_space(self.config, 'roi'):
self.backtesting.strategy.minimal_roi = ( # type: ignore self.backtesting.strategy.minimal_roi = ( # type: ignore
self.custom_hyperopt.generate_roi_table(params_dict)) self.custom_hyperopt.generate_roi_table(params_dict))
if self.has_space('buy'): if HyperoptTools.has_space(self.config, 'buy'):
self.backtesting.strategy.advise_buy = ( # type: ignore self.backtesting.strategy.advise_buy = ( # type: ignore
self.custom_hyperopt.buy_strategy_generator(params_dict)) self.custom_hyperopt.buy_strategy_generator(params_dict))
if self.has_space('sell'): if HyperoptTools.has_space(self.config, 'sell'):
self.backtesting.strategy.advise_sell = ( # type: ignore self.backtesting.strategy.advise_sell = ( # type: ignore
self.custom_hyperopt.sell_strategy_generator(params_dict)) self.custom_hyperopt.sell_strategy_generator(params_dict))
if self.has_space('stoploss'): if HyperoptTools.has_space(self.config, 'stoploss'):
self.backtesting.strategy.stoploss = params_dict['stoploss'] self.backtesting.strategy.stoploss = params_dict['stoploss']
if self.has_space('trailing'): if HyperoptTools.has_space(self.config, 'trailing'):
d = self.custom_hyperopt.generate_trailing_params(params_dict) d = self.custom_hyperopt.generate_trailing_params(params_dict)
self.backtesting.strategy.trailing_stop = d['trailing_stop'] self.backtesting.strategy.trailing_stop = d['trailing_stop']
self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive'] self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
@ -280,30 +278,42 @@ class Hyperopt:
self.backtesting.strategy.trailing_only_offset_is_reached = \ self.backtesting.strategy.trailing_only_offset_is_reached = \
d['trailing_only_offset_is_reached'] d['trailing_only_offset_is_reached']
processed = load(self.data_pickle_file) with self.data_pickle_file.open('rb') as f:
processed = load(f, mmap_mode='r')
min_date, max_date = get_timerange(processed) bt_results = self.backtesting.backtest(
backtesting_results = self.backtesting.backtest(
processed=processed, processed=processed,
start_date=min_date.datetime, start_date=self.min_date,
end_date=max_date.datetime, end_date=self.max_date,
max_open_trades=self.max_open_trades, max_open_trades=self.max_open_trades,
position_stacking=self.position_stacking, position_stacking=self.position_stacking,
enable_protections=self.config.get('enable_protections', False), enable_protections=self.config.get('enable_protections', False),
) )
return self._get_results_dict(backtesting_results, min_date, max_date, backtest_end_time = datetime.now(timezone.utc)
params_dict, params_details, bt_results.update({
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
})
return self._get_results_dict(bt_results, self.min_date, self.max_date,
params_dict,
processed=processed) processed=processed)
def _get_results_dict(self, backtesting_results, min_date, max_date, def _get_results_dict(self, backtesting_results, min_date, max_date,
params_dict, params_details, processed: Dict[str, DataFrame]): params_dict, processed: Dict[str, DataFrame]
results_metrics = self._calculate_results_metrics(backtesting_results) ) -> Dict[str, Any]:
results_explanation = self._format_results_explanation_string(results_metrics) params_details = self._get_params_details(params_dict)
trade_count = results_metrics['trade_count'] strat_stats = generate_strategy_stats(
total_profit = results_metrics['total_profit'] processed, self.backtesting.strategy.get_strategy_name(),
backtesting_results, min_date, max_date, market_change=0
)
results_explanation = HyperoptTools.format_results_explanation_string(
strat_stats, self.config['stake_currency'])
not_optimized = self.backtesting.strategy.get_params_dict()
trade_count = strat_stats['total_trades']
total_profit = strat_stats['profit_total']
# If this evaluation contains too short amount of trades to be # If this evaluation contains too short amount of trades to be
# interesting -- consider it as 'bad' (assigned max. loss value) # interesting -- consider it as 'bad' (assigned max. loss value)
@ -311,50 +321,20 @@ class Hyperopt:
# path. We do not want to optimize 'hodl' strategies. # path. We do not want to optimize 'hodl' strategies.
loss: float = MAX_LOSS loss: float = MAX_LOSS
if trade_count >= self.config['hyperopt_min_trades']: if trade_count >= self.config['hyperopt_min_trades']:
loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count, loss = self.calculate_loss(results=backtesting_results['results'],
min_date=min_date.datetime, max_date=max_date.datetime, trade_count=trade_count,
min_date=min_date, max_date=max_date,
config=self.config, processed=processed) config=self.config, processed=processed)
return { return {
'loss': loss, 'loss': loss,
'params_dict': params_dict, 'params_dict': params_dict,
'params_details': params_details, 'params_details': params_details,
'results_metrics': results_metrics, 'params_not_optimized': not_optimized,
'results_metrics': strat_stats,
'results_explanation': results_explanation, 'results_explanation': results_explanation,
'total_profit': total_profit, 'total_profit': total_profit,
} }
def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
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_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:
"""
Return the formatted results explanation in a string
"""
stake_cur = self.config['stake_currency']
return (f"{results_metrics['trade_count']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
f"Median profit {results_metrics['median_profit']: 6.2f}%. "
f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
f"({results_metrics['profit']: 7.2f}%). "
f"Avg duration {results_metrics['duration']:5.1f} min."
)
def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer: def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
return Optimizer( return Optimizer(
dimensions, dimensions,
@ -373,25 +353,31 @@ class Hyperopt:
def _set_random_state(self, random_state: Optional[int]) -> int: def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1) return random_state or random.randint(1, 2**16 - 1)
def prepare_hyperopt_data(self) -> None:
data, timerange = self.backtesting.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)
self.min_date, self.max_date = get_timerange(processed)
logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(self.max_date - self.min_date).days} days)..')
dump(processed, self.data_pickle_file)
def start(self) -> None: def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None)) self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
logger.info(f"Using optimizer random state: {self.random_state}") logger.info(f"Using optimizer random state: {self.random_state}")
self.hyperopt_table_header = -1 self.hyperopt_table_header = -1
data, timerange = self.backtesting.load_bt_data() # Initialize spaces ...
logger.info("Dataload complete. Calculating indicators") self.init_spaces()
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe self.prepare_hyperopt_data()
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange,
startup_candles=self.backtesting.required_startup)
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)} '
f'({(max_date - min_date).days} days)..')
dump(preprocessed, self.data_pickle_file)
# We don't need exchange instance anymore while running hyperopt # We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange.close() self.backtesting.exchange.close()
@ -399,15 +385,12 @@ class Hyperopt:
self.backtesting.exchange._api_async = None # type: ignore self.backtesting.exchange._api_async = None # type: ignore
# self.backtesting.exchange = None # type: ignore # self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore self.backtesting.pairlists = None # type: ignore
self.backtesting.strategy.dp = None # type: ignore
IStrategy.dp = None # type: ignore
cpus = cpu_count() cpus = cpu_count()
logger.info(f"Found {cpus} CPU cores. Let's make them scream!") logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
config_jobs = self.config.get('hyperopt_jobs', -1) config_jobs = self.config.get('hyperopt_jobs', -1)
logger.info(f'Number of parallel jobs set as: {config_jobs}') logger.info(f'Number of parallel jobs set as: {config_jobs}')
self.dimensions: List[Dimension] = self.hyperopt_space()
self.opt = self.get_optimizer(self.dimensions, config_jobs) self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.print_colorized: if self.print_colorized:
@ -473,25 +456,21 @@ class Hyperopt:
if is_best: if is_best:
self.current_best_loss = val['loss'] self.current_best_loss = val['loss']
self.epochs.append(val) self.current_best_epoch = val
# Save results after each best epoch and every 100 epochs self._save_result(val)
if is_best or current % 100 == 0:
self._save_results()
pbar.update(current) pbar.update(current)
except KeyboardInterrupt: except KeyboardInterrupt:
print('User interrupted..') print('User interrupted..')
self._save_results()
logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} " logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.") f"saved to '{self.results_file}'.")
if self.epochs: if self.current_best_epoch:
sorted_epochs = sorted(self.epochs, key=itemgetter('loss')) HyperoptTools.print_epoch_details(self.current_best_epoch, self.total_epochs,
best_epoch = sorted_epochs[0] self.print_json)
HyperoptTools.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
else: else:
# This is printed when Ctrl+C is pressed quickly, before first epochs have # This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated. # a chance to be evaluated.

163
freqtrade/optimize/hyperopt_tools.py
View File

@ -3,17 +3,15 @@ import io
import logging import logging
from collections import OrderedDict from collections import OrderedDict
from pathlib import Path from pathlib import Path
from pprint import pformat from typing import Any, Dict, List
from typing import Dict, List
import rapidjson import rapidjson
import tabulate import tabulate
from colorama import Fore, Style from colorama import Fore, Style
from joblib import load
from pandas import isna, json_normalize from pandas import isna, json_normalize
from freqtrade.exceptions import OperationalException from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_dict from freqtrade.misc import round_coin_value, round_dict
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -21,13 +19,38 @@ logger = logging.getLogger(__name__)
class HyperoptTools(): class HyperoptTools():
@staticmethod
def has_space(config: Dict[str, Any], space: str) -> bool:
"""
Tell if the space value is contained in the configuration
"""
# The 'trailing' space is not included in the 'default' set of spaces
if space == 'trailing':
return any(s in config['spaces'] for s in [space, 'all'])
else:
return any(s in config['spaces'] for s in [space, 'all', 'default'])
@staticmethod
def _read_results_pickle(results_file: Path) -> List:
"""
Read hyperopt results from pickle file
LEGACY method - new files are written as json and cannot be read with this method.
"""
from joblib import load
logger.info(f"Reading pickled epochs from '{results_file}'")
data = load(results_file)
return data
@staticmethod @staticmethod
def _read_results(results_file: Path) -> List: def _read_results(results_file: Path) -> List:
""" """
Read hyperopt results from file Read hyperopt results from file
""" """
logger.info("Reading epochs from '%s'", results_file) import rapidjson
data = load(results_file) logger.info(f"Reading epochs from '{results_file}'")
with results_file.open('r') as f:
data = [rapidjson.loads(line) for line in f]
return data return data
@staticmethod @staticmethod
@ -37,6 +60,9 @@ class HyperoptTools():
""" """
epochs: List = [] epochs: List = []
if results_file.is_file() and results_file.stat().st_size > 0: if results_file.is_file() and results_file.stat().st_size > 0:
if results_file.suffix == '.pickle':
epochs = HyperoptTools._read_results_pickle(results_file)
else:
epochs = HyperoptTools._read_results(results_file) epochs = HyperoptTools._read_results(results_file)
# Detection of some old format, without 'is_best' field saved # Detection of some old format, without 'is_best' field saved
if epochs[0].get('is_best') is None: if epochs[0].get('is_best') is None:
@ -53,6 +79,7 @@ class HyperoptTools():
Display details of the hyperopt result Display details of the hyperopt result
""" """
params = results.get('params_details', {}) params = results.get('params_details', {})
non_optimized = results.get('params_not_optimized', {})
# Default header string # Default header string
if header_str is None: if header_str is None:
@ -69,8 +96,10 @@ class HyperoptTools():
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE)) print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else: else:
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:") HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:",
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:") non_optimized)
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:",
non_optimized)
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:") HyperoptTools._params_pretty_print(params, 'roi', "ROI table:")
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:") HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:")
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:") HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:")
@ -96,12 +125,12 @@ class HyperoptTools():
result_dict.update(space_params) result_dict.update(space_params)
@staticmethod @staticmethod
def _params_pretty_print(params, space: str, header: str) -> None: def _params_pretty_print(params, space: str, header: str, non_optimized={}) -> None:
if space in params: if space in params or space in non_optimized:
space_params = HyperoptTools._space_params(params, space, 5) space_params = HyperoptTools._space_params(params, space, 5)
params_result = f"\n# {header}\n" result = f"\n# {header}\n"
if space == 'stoploss': if space == 'stoploss':
params_result += f"stoploss = {space_params.get('stoploss')}" result += f"stoploss = {space_params.get('stoploss')}"
elif space == 'roi': elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be # TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature) # dropped (dicts keep the order as the language feature)
@ -110,28 +139,64 @@ class HyperoptTools():
(str(k), v) for k, v in space_params.items() (str(k), v) for k, v in space_params.items()
), ),
default=str, indent=4, number_mode=rapidjson.NM_NATIVE) default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
params_result += f"minimal_roi = {minimal_roi_result}" result += f"minimal_roi = {minimal_roi_result}"
elif space == 'trailing': elif space == 'trailing':
for k, v in space_params.items(): for k, v in space_params.items():
params_result += f'{k} = {v}\n' result += f'{k} = {v}\n'
else: else:
params_result += f"{space}_params = {pformat(space_params, indent=4)}" no_params = HyperoptTools._space_params(non_optimized, space, 5)
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
params_result = params_result.replace("\n", "\n ") result += f"{space}_params = {HyperoptTools._pprint(space_params, no_params)}"
print(params_result)
result = result.replace("\n", "\n ")
print(result)
@staticmethod @staticmethod
def _space_params(params, space: str, r: int = None) -> Dict: def _space_params(params, space: str, r: int = None) -> Dict:
d = params[space] d = params.get(space)
if d:
# Round floats to `r` digits after the decimal point if requested # Round floats to `r` digits after the decimal point if requested
return round_dict(d, r) if r else d return round_dict(d, r) if r else d
return {}
@staticmethod
def _pprint(params, non_optimized, indent: int = 4):
"""
Pretty-print hyperopt results (based on 2 dicts - with add. comment)
"""
p = params.copy()
p.update(non_optimized)
result = '{\n'
for k, param in p.items():
result += " " * indent + f'"{k}": '
result += f'"{param}",' if isinstance(param, str) else f'{param},'
if k in non_optimized:
result += " # value loaded from strategy"
result += "\n"
result += '}'
return result
@staticmethod @staticmethod
def is_best_loss(results, current_best_loss: float) -> bool: def is_best_loss(results, current_best_loss: float) -> bool:
return results['loss'] < current_best_loss return bool(results['loss'] < current_best_loss)
@staticmethod
def format_results_explanation_string(results_metrics: Dict, stake_currency: str) -> str:
"""
Return the formatted results explanation in a string
"""
return (f"{results_metrics['total_trades']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. "
f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} "
f"({results_metrics['profit_total'] * 100: 7.2f}%). "
f"Avg duration {results_metrics['holding_avg']} min."
)
@staticmethod @staticmethod
def _format_explanation_string(results, total_epochs) -> str: def _format_explanation_string(results, total_epochs) -> str:
@ -156,12 +221,27 @@ class HyperoptTools():
if 'results_metrics.winsdrawslosses' not in trials.columns: if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results # Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A' trials['results_metrics.winsdrawslosses'] = 'N/A'
legacy_mode = True
if 'results_metrics.total_trades' in trials:
legacy_mode = False
# New mode, using backtest result for metrics
trials['results_metrics.winsdrawslosses'] = trials.apply(
lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
f"{x['results_metrics.losses']:>4}", axis=1)
trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.winsdrawslosses',
'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
'results_metrics.profit_total', 'results_metrics.holding_avg',
'loss', 'is_initial_point', 'is_best']]
else:
# Legacy mode
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count', trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses', 'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit', 'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']] 'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit', trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Objective', 'Total profit', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best'] 'is_initial_point', 'is_best']
@ -171,26 +251,28 @@ class HyperoptTools():
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best' trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str) trials['Trades'] = trials['Trades'].astype(str)
perc_multi = 1 if legacy_mode else 100
trials['Epoch'] = trials['Epoch'].apply( trials['Epoch'] = trials['Epoch'].apply(
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs) lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
) )
trials['Avg profit'] = trials['Avg profit'].apply( trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ') lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
) )
trials['Avg duration'] = trials['Avg duration'].apply( trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ') lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
if not isna(x) else "--".rjust(7, ' ')
) )
trials['Objective'] = trials['Objective'].apply( trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ') lambda x: f'{x:,.5f}'.rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
) )
stake_currency = config['stake_currency']
trials['Profit'] = trials.apply( trials['Profit'] = trials.apply(
lambda x: '{:,.8f} {} {}'.format( lambda x: '{} {}'.format(
x['Total profit'], config['stake_currency'], round_coin_value(x['Total profit'], stake_currency),
'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ') '({:,.2f}%)'.format(x['Profit'] * perc_multi).rjust(10, ' ')
).rjust(25+len(config['stake_currency'])) ).rjust(25+len(stake_currency))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])), if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)),
axis=1 axis=1
) )
trials = trials.drop(columns=['Total profit']) trials = trials.drop(columns=['Total profit'])
@ -251,6 +333,16 @@ class HyperoptTools():
trials['Best'] = '' trials['Best'] = ''
trials['Stake currency'] = config['stake_currency'] trials['Stake currency'] = config['stake_currency']
if 'results_metrics.total_trades' in trials:
base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.profit_mean', 'results_metrics.profit_median',
'results_metrics.profit_total',
'Stake currency',
'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
'loss', 'is_initial_point', 'is_best']
perc_multi = 100
else:
perc_multi = 1
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count', base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.median_profit', 'results_metrics.avg_profit', 'results_metrics.median_profit',
'results_metrics.total_profit', 'results_metrics.total_profit',
@ -272,21 +364,24 @@ class HyperoptTools():
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str) trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str) trials['Trades'] = trials['Trades'].astype(str)
trials['Median profit'] = trials['Median profit'] * perc_multi
trials['Total profit'] = trials['Total profit'].apply( trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{:,.8f}'.format(x) if x != 0.0 else "" lambda x: f'{x:,.8f}' if x != 0.0 else ""
) )
trials['Profit'] = trials['Profit'].apply( trials['Profit'] = trials['Profit'].apply(
lambda x: '{:,.2f}'.format(x) if not isna(x) else "" lambda x: f'{x:,.2f}' if not isna(x) else ""
) )
trials['Avg profit'] = trials['Avg profit'].apply( trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else "" lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
) )
if perc_multi == 1:
trials['Avg duration'] = trials['Avg duration'].apply( trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x) if not isna(x) else "" lambda x: f'{x:,.1f} m' if isinstance(
x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
) )
trials['Objective'] = trials['Objective'].apply( trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x) if x != 100000 else "" lambda x: f'{x:,.5f}' if x != 100000 else ""
) )
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit']) trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])

201
freqtrade/optimize/optimize_reports.py
View File

@ -3,7 +3,6 @@ from datetime import datetime, timedelta, timezone
from pathlib import Path from pathlib import Path
from typing import Any, Dict, List, Union from typing import Any, Dict, List, Union
from arrow import Arrow
from numpy import int64 from numpy import int64
from pandas import DataFrame from pandas import DataFrame
from tabulate import tabulate from tabulate import tabulate
@ -44,7 +43,7 @@ def _get_line_floatfmt(stake_currency: str) -> List[str]:
Generate floatformat (goes in line with _generate_result_line()) Generate floatformat (goes in line with _generate_result_line())
""" """
return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f', return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f',
'.2f', 'd', 'd', 'd', 'd'] '.2f', 'd', 's', 's']
def _get_line_header(first_column: str, stake_currency: str) -> List[str]: def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
@ -53,7 +52,17 @@ def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
""" """
return [first_column, 'Buys', 'Avg Profit %', 'Cum Profit %', return [first_column, 'Buys', 'Avg Profit %', 'Cum Profit %',
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration', f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
'Wins', 'Draws', 'Losses'] 'Win Draw Loss Win%']
def _generate_wins_draws_losses(wins, draws, losses):
if wins > 0 and losses == 0:
wl_ratio = '100'
elif wins == 0:
wl_ratio = '0'
else:
wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100'
return f'{wins:>4} {draws:>4} {losses:>4} {wl_ratio:>4}'
def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict: def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
@ -153,7 +162,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
return tabular_data return tabular_data
def generate_strategy_metrics(all_results: Dict) -> List[Dict]: def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
""" """
Generate summary per strategy Generate summary per strategy
:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies :param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
@ -165,6 +174,17 @@ def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
tabular_data.append(_generate_result_line( tabular_data.append(_generate_result_line(
results['results'], results['config']['dry_run_wallet'], strategy) results['results'], results['config']['dry_run_wallet'], strategy)
) )
try:
max_drawdown_per, _, _, _, _ = calculate_max_drawdown(results['results'],
value_col='profit_ratio')
max_drawdown_abs, _, _, _, _ = calculate_max_drawdown(results['results'],
value_col='profit_abs')
except ValueError:
max_drawdown_per = 0
max_drawdown_abs = 0
tabular_data[-1]['max_drawdown_per'] = round(max_drawdown_per * 100, 2)
tabular_data[-1]['max_drawdown_abs'] = \
round_coin_value(max_drawdown_abs, results['config']['stake_currency'], False)
return tabular_data return tabular_data
@ -194,7 +214,40 @@ def generate_edge_table(results: dict) -> str:
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
""" Generate overall trade statistics """
if len(results) == 0:
return {
'wins': 0,
'losses': 0,
'draws': 0,
'holding_avg': timedelta(),
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
winning_trades = results.loc[results['profit_ratio'] > 0]
draw_trades = results.loc[results['profit_ratio'] == 0]
losing_trades = results.loc[results['profit_ratio'] < 0]
zero_duration_trades = len(results.loc[(results['trade_duration'] == 0) &
(results['sell_reason'] == 'trailing_stop_loss')])
return {
'wins': len(winning_trades),
'losses': len(losing_trades),
'draws': len(draw_trades),
'holding_avg': (timedelta(minutes=round(results['trade_duration'].mean()))
if not results.empty else timedelta()),
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta()),
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta()),
'zero_duration_trades': zero_duration_trades,
}
def generate_daily_stats(results: DataFrame) -> Dict[str, Any]: def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
""" Generate daily statistics """
if len(results) == 0: if len(results) == 0:
return { return {
'backtest_best_day': 0, 'backtest_best_day': 0,
@ -204,8 +257,6 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
'winning_days': 0, 'winning_days': 0,
'draw_days': 0, 'draw_days': 0,
'losing_days': 0, 'losing_days': 0,
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
} }
daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum() daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10) daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
@ -217,9 +268,6 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
draw_days = sum(daily_profit == 0) draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0) losing_days = sum(daily_profit < 0)
winning_trades = results.loc[results['profit_ratio'] > 0]
losing_trades = results.loc[results['profit_ratio'] < 0]
return { return {
'backtest_best_day': best_rel, 'backtest_best_day': best_rel,
'backtest_worst_day': worst_rel, 'backtest_worst_day': worst_rel,
@ -228,33 +276,28 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
'winning_days': winning_days, 'winning_days': winning_days,
'draw_days': draw_days, 'draw_days': draw_days,
'losing_days': losing_days, 'losing_days': losing_days,
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta()),
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta()),
} }
def generate_backtest_stats(btdata: Dict[str, DataFrame], def generate_strategy_stats(btdata: Dict[str, DataFrame],
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]], strategy: str,
min_date: Arrow, max_date: Arrow content: Dict[str, Any],
min_date: datetime, max_date: datetime,
market_change: float
) -> Dict[str, Any]: ) -> Dict[str, Any]:
""" """
:param btdata: Backtest data :param btdata: Backtest data
:param all_results: backtest result - dictionary in the form: :param strategy: Strategy name
{ Strategy: {'results: results, 'config: config}}. :param content: Backtest result data in the format:
{'results: results, 'config: config}}.
:param min_date: Backtest start date :param min_date: Backtest start date
:param max_date: Backtest end date :param max_date: Backtest end date
:return: :param market_change: float indicating the market change
Dictionary containing results per strategy and a stratgy summary. :return: Dictionary containing results per strategy and a stratgy summary.
""" """
result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, content in all_results.items():
results: Dict[str, DataFrame] = content['results'] results: Dict[str, DataFrame] = content['results']
if not isinstance(results, DataFrame): if not isinstance(results, DataFrame):
continue return {}
config = content['config'] config = content['config']
max_open_trades = min(config['max_open_trades'], len(btdata.keys())) max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
starting_balance = config['dry_run_wallet'] starting_balance = config['dry_run_wallet']
@ -270,6 +313,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
results=results.loc[results['is_open']], results=results.loc[results['is_open']],
skip_nan=True) skip_nan=True)
daily_stats = generate_daily_stats(results) daily_stats = generate_daily_stats(results)
trade_stats = generate_trading_stats(results)
best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'], 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 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'], worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
@ -290,12 +334,13 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
'total_volume': float(results['stake_amount'].sum()), 'total_volume': float(results['stake_amount'].sum()),
'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0, 'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0, 'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
'profit_median': results['profit_ratio'].median() if len(results) > 0 else 0,
'profit_total': results['profit_abs'].sum() / starting_balance, 'profit_total': results['profit_abs'].sum() / starting_balance,
'profit_total_abs': results['profit_abs'].sum(), 'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.datetime, 'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_start_ts': min_date.int_timestamp * 1000, 'backtest_start_ts': int(min_date.timestamp() * 1000),
'backtest_end': max_date.datetime, 'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_end_ts': max_date.int_timestamp * 1000, 'backtest_end_ts': int(max_date.timestamp() * 1000),
'backtest_days': backtest_days, 'backtest_days': backtest_days,
'backtest_run_start_ts': content['backtest_start_time'], 'backtest_run_start_ts': content['backtest_start_time'],
@ -310,6 +355,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
'starting_balance': starting_balance, 'starting_balance': starting_balance,
'dry_run_wallet': starting_balance, 'dry_run_wallet': starting_balance,
'final_balance': content['final_balance'], 'final_balance': content['final_balance'],
'rejected_signals': content['rejected_signals'],
'max_open_trades': max_open_trades, 'max_open_trades': max_open_trades,
'max_open_trades_setting': (config['max_open_trades'] 'max_open_trades_setting': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1), if config['max_open_trades'] != float('inf') else -1),
@ -330,8 +376,8 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
'sell_profit_offset': config['ask_strategy']['sell_profit_offset'], 'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'], 'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
**daily_stats, **daily_stats,
**trade_stats
} }
result['strategy'][strategy] = strat_stats
try: try:
max_drawdown, _, _, _, _ = calculate_max_drawdown( max_drawdown, _, _, _, _ = calculate_max_drawdown(
@ -341,9 +387,9 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
strat_stats.update({ strat_stats.update({
'max_drawdown': max_drawdown, 'max_drawdown': max_drawdown,
'max_drawdown_abs': drawdown_abs, 'max_drawdown_abs': drawdown_abs,
'drawdown_start': drawdown_start, 'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
'drawdown_start_ts': drawdown_start.timestamp() * 1000, 'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end, 'drawdown_end': drawdown_end.strftime(DATETIME_PRINT_FORMAT),
'drawdown_end_ts': drawdown_end.timestamp() * 1000, 'drawdown_end_ts': drawdown_end.timestamp() * 1000,
'max_drawdown_low': low_val, 'max_drawdown_low': low_val,
@ -370,7 +416,30 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
'csum_max': 0 'csum_max': 0
}) })
strategy_results = generate_strategy_metrics(all_results=all_results) return strat_stats
def generate_backtest_stats(btdata: Dict[str, DataFrame],
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
min_date: datetime, max_date: datetime
) -> Dict[str, Any]:
"""
:param btdata: Backtest data
:param all_results: backtest result - dictionary in the form:
{ Strategy: {'results: results, 'config: config}}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:return: Dictionary containing results per strategy and a stratgy summary.
"""
result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, content in all_results.items():
strat_stats = generate_strategy_stats(btdata, strategy, content,
min_date, max_date, market_change=market_change)
result['strategy'][strategy] = strat_stats
strategy_results = generate_strategy_comparison(all_results=all_results)
result['strategy_comparison'] = strategy_results result['strategy_comparison'] = strategy_results
@ -393,7 +462,8 @@ def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: st
floatfmt = _get_line_floatfmt(stake_currency) floatfmt = _get_line_floatfmt(stake_currency)
output = [[ output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], 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'] t['profit_total_pct'], t['duration_avg'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses'])
] for t in pair_results] ] for t in pair_results]
# Ignore type as floatfmt does allow tuples but mypy does not know that # Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers, return tabulate(output, headers=headers,
@ -410,9 +480,7 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
headers = [ headers = [
'Sell Reason', 'Sell Reason',
'Sells', 'Sells',
'Wins', 'Win Draws Loss Win%',
'Draws',
'Losses',
'Avg Profit %', 'Avg Profit %',
'Cum Profit %', 'Cum Profit %',
f'Tot Profit {stake_currency}', f'Tot Profit {stake_currency}',
@ -420,7 +488,8 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
] ]
output = [[ output = [[
t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'], t['sell_reason'], t['trades'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses']),
t['profit_mean_pct'], t['profit_sum_pct'], t['profit_mean_pct'], t['profit_sum_pct'],
round_coin_value(t['profit_total_abs'], stake_currency, False), round_coin_value(t['profit_total_abs'], stake_currency, False),
t['profit_total_pct'], t['profit_total_pct'],
@ -438,11 +507,22 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
""" """
floatfmt = _get_line_floatfmt(stake_currency) floatfmt = _get_line_floatfmt(stake_currency)
headers = _get_line_header('Strategy', stake_currency) headers = _get_line_header('Strategy', stake_currency)
# _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless
# therefore we slip this column in only for strategy summary here.
headers.append('Drawdown')
# Align drawdown string on the center two space separator.
drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
dd_pad_per = max([len(dd) for dd in drawdown])
drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%'
for t, dd in zip(strategy_results, drawdown)]
output = [[ output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], 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'] t['profit_total_pct'], t['duration_avg'],
] for t in strategy_results] _generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), drawdown]
for t, drawdown in zip(strategy_results, drawdown)]
# Ignore type as floatfmt does allow tuples but mypy does not know that # Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers, return tabulate(output, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
@ -452,9 +532,21 @@ def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0: if len(strat_results['trades']) > 0:
best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio']) best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio']) worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])
# Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
# command stores these results and newer version of freqtrade must be able to handle old
# results with missing new fields.
zero_duration_trades = '--'
if 'zero_duration_trades' in strat_results:
zero_duration_trades_per = \
100.0 / strat_results['total_trades'] * strat_results['zero_duration_trades']
zero_duration_trades = f'{zero_duration_trades_per:.2f}% ' \
f'({strat_results["zero_duration_trades"]})'
metrics = [ metrics = [
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)), ('Backtesting from', strat_results['backtest_start']),
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)), ('Backtesting to', strat_results['backtest_end']),
('Max open trades', strat_results['max_open_trades']), ('Max open trades', strat_results['max_open_trades']),
('', ''), # Empty line to improve readability ('', ''), # Empty line to improve readability
('Total trades', strat_results['total_trades']), ('Total trades', strat_results['total_trades']),
@ -464,13 +556,12 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])), strat_results['stake_currency'])),
('Absolute profit ', round_coin_value(strat_results['profit_total_abs'], ('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
strat_results['stake_currency'])), strat_results['stake_currency'])),
('Total profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"), ('Total profit %', f"{round(strat_results['profit_total'] * 100, 2):}%"),
('Trades per day', strat_results['trades_per_day']), ('Trades per day', strat_results['trades_per_day']),
('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'], ('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
strat_results['stake_currency'])), strat_results['stake_currency'])),
('Total trade volume', round_coin_value(strat_results['total_volume'], ('Total trade volume', round_coin_value(strat_results['total_volume'],
strat_results['stake_currency'])), strat_results['stake_currency'])),
('', ''), # Empty line to improve readability ('', ''), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} " ('Best Pair', f"{strat_results['best_pair']['key']} "
f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"), f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
@ -488,6 +579,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
f"{strat_results['draw_days']} / {strat_results['losing_days']}"), f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"), ('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"), ('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
('Zero Duration Trades', zero_duration_trades),
('Rejected Buy signals', strat_results.get('rejected_signals', 'N/A')),
('', ''), # Empty line to improve readability ('', ''), # Empty line to improve readability
('Min balance', round_coin_value(strat_results['csum_min'], ('Min balance', round_coin_value(strat_results['csum_min'],
@ -502,8 +595,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])), strat_results['stake_currency'])),
('Drawdown low', round_coin_value(strat_results['max_drawdown_low'], ('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
strat_results['stake_currency'])), strat_results['stake_currency'])),
('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)), ('Drawdown Start', strat_results['drawdown_start']),
('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)), ('Drawdown End', strat_results['drawdown_end']),
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"), ('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
] ]
@ -522,11 +615,10 @@ def text_table_add_metrics(strat_results: Dict) -> str:
return message return message
def show_backtest_results(config: Dict, backtest_stats: Dict): def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str):
stake_currency = config['stake_currency'] """
Print results for one strategy
for strategy, results in backtest_stats['strategy'].items(): """
# Print results # Print results
print(f"Result for strategy {strategy}") print(f"Result for strategy {strategy}")
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency) table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
@ -554,6 +646,13 @@ def show_backtest_results(config: Dict, backtest_stats: Dict):
print('=' * len(table.splitlines()[0])) print('=' * len(table.splitlines()[0]))
print() print()
def show_backtest_results(config: Dict, backtest_stats: Dict):
stake_currency = config['stake_currency']
for strategy, results in backtest_stats['strategy'].items():
show_backtest_result(strategy, results, stake_currency)
if len(backtest_stats['strategy']) > 1: if len(backtest_stats['strategy']) > 1:
# Print Strategy summary table # Print Strategy summary table

30
freqtrade/persistence/migrations.py
View File

@ -123,6 +123,27 @@ def migrate_open_orders_to_trades(engine):
""") """)
def migrate_orders_table(decl_base, inspector, engine, table_back_name: str, cols: List):
# Schema migration necessary
engine.execute(f"alter table orders rename to {table_back_name}")
# drop indexes on backup table
for index in inspector.get_indexes(table_back_name):
engine.execute(f"drop index {index['name']}")
# let SQLAlchemy create the schema as required
decl_base.metadata.create_all(engine)
engine.execute(f"""
insert into orders ( id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id, status,
symbol, order_type, side, price, amount, filled, average, remaining, cost, order_date,
order_filled_date, order_update_date)
select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id, status,
symbol, order_type, side, price, amount, filled, null average, remaining, cost, order_date,
order_filled_date, order_update_date
from {table_back_name}
""")
def check_migrate(engine, decl_base, previous_tables) -> None: def check_migrate(engine, decl_base, previous_tables) -> None:
""" """
Checks if migration is necessary and migrates if necessary Checks if migration is necessary and migrates if necessary
@ -145,6 +166,11 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
logger.info('Moving open orders to Orders table.') logger.info('Moving open orders to Orders table.')
migrate_open_orders_to_trades(engine) migrate_open_orders_to_trades(engine)
else: else:
pass cols_order = inspector.get_columns('orders')
if not has_column(cols_order, 'average'):
tabs = get_table_names_for_table(inspector, 'orders')
# Empty for now - as there is only one iteration of the orders table so far. # Empty for now - as there is only one iteration of the orders table so far.
# table_back_name = get_backup_name(tabs, 'orders_bak') table_back_name = get_backup_name(tabs, 'orders_bak')
migrate_orders_table(decl_base, inspector, engine, table_back_name, cols)

46
freqtrade/persistence/models.py
View File

@ -112,16 +112,17 @@ class Order(_DECL_BASE):
trade = relationship("Trade", back_populates="orders") trade = relationship("Trade", back_populates="orders")
ft_order_side = Column(String, nullable=False) ft_order_side = Column(String(25), nullable=False)
ft_pair = Column(String, nullable=False) ft_pair = Column(String(25), nullable=False)
ft_is_open = Column(Boolean, nullable=False, default=True, index=True) ft_is_open = Column(Boolean, nullable=False, default=True, index=True)
order_id = Column(String, nullable=False, index=True) order_id = Column(String(255), nullable=False, index=True)
status = Column(String, nullable=True) status = Column(String(255), nullable=True)
symbol = Column(String, nullable=True) symbol = Column(String(25), nullable=True)
order_type = Column(String, nullable=True) order_type = Column(String(50), nullable=True)
side = Column(String, nullable=True) side = Column(String(25), nullable=True)
price = Column(Float, nullable=True) price = Column(Float, nullable=True)
average = Column(Float, nullable=True)
amount = Column(Float, nullable=True) amount = Column(Float, nullable=True)
filled = Column(Float, nullable=True) filled = Column(Float, nullable=True)
remaining = Column(Float, nullable=True) remaining = Column(Float, nullable=True)
@ -150,6 +151,7 @@ class Order(_DECL_BASE):
self.price = order.get('price', self.price) self.price = order.get('price', self.price)
self.amount = order.get('amount', self.amount) self.amount = order.get('amount', self.amount)
self.filled = order.get('filled', self.filled) self.filled = order.get('filled', self.filled)
self.average = order.get('average', self.average)
self.remaining = order.get('remaining', self.remaining) self.remaining = order.get('remaining', self.remaining)
self.cost = order.get('cost', self.cost) self.cost = order.get('cost', self.cost)
if 'timestamp' in order and order['timestamp'] is not None: if 'timestamp' in order and order['timestamp'] is not None:
@ -656,15 +658,15 @@ class Trade(_DECL_BASE, LocalTrade):
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan") orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
exchange = Column(String, nullable=False) exchange = Column(String(25), nullable=False)
pair = Column(String, nullable=False, index=True) pair = Column(String(25), nullable=False, index=True)
is_open = Column(Boolean, nullable=False, default=True, index=True) is_open = Column(Boolean, nullable=False, default=True, index=True)
fee_open = Column(Float, nullable=False, default=0.0) fee_open = Column(Float, nullable=False, default=0.0)
fee_open_cost = Column(Float, nullable=True) fee_open_cost = Column(Float, nullable=True)
fee_open_currency = Column(String, nullable=True) fee_open_currency = Column(String(25), nullable=True)
fee_close = Column(Float, nullable=False, default=0.0) fee_close = Column(Float, nullable=False, default=0.0)
fee_close_cost = Column(Float, nullable=True) fee_close_cost = Column(Float, nullable=True)
fee_close_currency = Column(String, nullable=True) fee_close_currency = Column(String(25), nullable=True)
open_rate = Column(Float) open_rate = Column(Float)
open_rate_requested = Column(Float) open_rate_requested = Column(Float)
# open_trade_value - calculated via _calc_open_trade_value # open_trade_value - calculated via _calc_open_trade_value
@ -678,7 +680,7 @@ class Trade(_DECL_BASE, LocalTrade):
amount_requested = Column(Float) amount_requested = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow) open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
close_date = Column(DateTime) close_date = Column(DateTime)
open_order_id = Column(String) open_order_id = Column(String(255))
# absolute value of the stop loss # absolute value of the stop loss
stop_loss = Column(Float, nullable=True, default=0.0) stop_loss = Column(Float, nullable=True, default=0.0)
# percentage value of the stop loss # percentage value of the stop loss
@ -688,16 +690,16 @@ class Trade(_DECL_BASE, LocalTrade):
# percentage value of the initial stop loss # percentage value of the initial stop loss
initial_stop_loss_pct = Column(Float, nullable=True) initial_stop_loss_pct = Column(Float, nullable=True)
# stoploss order id which is on exchange # stoploss order id which is on exchange
stoploss_order_id = Column(String, nullable=True, index=True) stoploss_order_id = Column(String(255), nullable=True, index=True)
# last update time of the stoploss order on exchange # last update time of the stoploss order on exchange
stoploss_last_update = Column(DateTime, nullable=True) stoploss_last_update = Column(DateTime, nullable=True)
# absolute value of the highest reached price # absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0) max_rate = Column(Float, nullable=True, default=0.0)
# Lowest price reached # Lowest price reached
min_rate = Column(Float, nullable=True) min_rate = Column(Float, nullable=True)
sell_reason = Column(String, nullable=True) sell_reason = Column(String(100), nullable=True)
sell_order_status = Column(String, nullable=True) sell_order_status = Column(String(100), nullable=True)
strategy = Column(String, nullable=True) strategy = Column(String(100), nullable=True)
timeframe = Column(Integer, nullable=True) timeframe = Column(Integer, nullable=True)
def __init__(self, **kwargs): def __init__(self, **kwargs):
@ -815,18 +817,20 @@ class Trade(_DECL_BASE, LocalTrade):
pair_rates = Trade.query.with_entities( pair_rates = Trade.query.with_entities(
Trade.pair, Trade.pair,
func.sum(Trade.close_profit).label('profit_sum'), func.sum(Trade.close_profit).label('profit_sum'),
func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
func.count(Trade.pair).label('count') func.count(Trade.pair).label('count')
).filter(Trade.is_open.is_(False))\ ).filter(Trade.is_open.is_(False))\
.group_by(Trade.pair) \ .group_by(Trade.pair) \
.order_by(desc('profit_sum')) \ .order_by(desc('profit_sum_abs')) \
.all() .all()
return [ return [
{ {
'pair': pair, 'pair': pair,
'profit': rate, 'profit': profit,
'profit_abs': profit_abs,
'count': count 'count': count
} }
for pair, rate, count in pair_rates for pair, profit, profit_abs, count in pair_rates
] ]
@staticmethod @staticmethod
@ -852,8 +856,8 @@ class PairLock(_DECL_BASE):
id = Column(Integer, primary_key=True) id = Column(Integer, primary_key=True)
pair = Column(String, nullable=False, index=True) pair = Column(String(25), nullable=False, index=True)
reason = Column(String, nullable=True) reason = Column(String(255), nullable=True)
# Time the pair was locked (start time) # Time the pair was locked (start time)
lock_time = Column(DateTime, nullable=False) lock_time = Column(DateTime, nullable=False)
# Time until the pair is locked (end time) # Time until the pair is locked (end time)

4
freqtrade/plot/plotting.py
View File

@ -77,6 +77,7 @@ def init_plotscript(config, markets: List, startup_candles: int = 0):
) )
except ValueError as e: except ValueError as e:
raise OperationalException(e) from e raise OperationalException(e) from e
if not trades.empty:
trades = trim_dataframe(trades, timerange, 'open_date') trades = trim_dataframe(trades, timerange, 'open_date')
return {"ohlcv": data, return {"ohlcv": data,
@ -540,8 +541,11 @@ def load_and_plot_trades(config: Dict[str, Any]):
df_analyzed = strategy.analyze_ticker(data, {'pair': pair}) df_analyzed = strategy.analyze_ticker(data, {'pair': pair})
df_analyzed = trim_dataframe(df_analyzed, timerange) df_analyzed = trim_dataframe(df_analyzed, timerange)
if not trades.empty:
trades_pair = trades.loc[trades['pair'] == pair] trades_pair = trades.loc[trades['pair'] == pair]
trades_pair = extract_trades_of_period(df_analyzed, trades_pair) trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
else:
trades_pair = trades
fig = generate_candlestick_graph( fig = generate_candlestick_graph(
pair=pair, pair=pair,

6
freqtrade/plugins/pairlist/AgeFilter.py
View File

@ -71,14 +71,14 @@ class AgeFilter(IPairList):
daily_candles = candles[(p, '1d')] if (p, '1d') in candles else None daily_candles = candles[(p, '1d')] if (p, '1d') in candles else None
if not self._validate_pair_loc(p, daily_candles): if not self._validate_pair_loc(p, daily_candles):
pairlist.remove(p) pairlist.remove(p)
logger.info(f"Validated {len(pairlist)} pairs.") self.log_once(f"Validated {len(pairlist)} pairs.", logger.info)
return pairlist return pairlist
def _validate_pair_loc(self, pair: str, daily_candles: Optional[DataFrame]) -> bool: def _validate_pair_loc(self, pair: str, daily_candles: Optional[DataFrame]) -> bool:
""" """
Validate age for the ticker Validate age for the ticker
:param pair: Pair that's currently validated :param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets() :param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed :return: True if the pair can stay, false if it should be removed
""" """
# Check symbol in cache # Check symbol in cache
@ -86,7 +86,7 @@ class AgeFilter(IPairList):
return True return True
if daily_candles is not None: if daily_candles is not None:
if len(daily_candles) > self._min_days_listed: if len(daily_candles) >= self._min_days_listed:
# We have fetched at least the minimum required number of daily candles # We have fetched at least the minimum required number of daily candles
# Add to cache, store the time we last checked this symbol # Add to cache, store the time we last checked this symbol
self._symbolsChecked[pair] = int(arrow.utcnow().float_timestamp) * 1000 self._symbolsChecked[pair] = int(arrow.utcnow().float_timestamp) * 1000

8
freqtrade/plugins/pairlist/IPairList.py
View File

@ -7,7 +7,7 @@ from copy import deepcopy
from typing import Any, Dict, List from typing import Any, Dict, List
from freqtrade.exceptions import OperationalException from freqtrade.exceptions import OperationalException
from freqtrade.exchange import market_is_active from freqtrade.exchange import Exchange, market_is_active
from freqtrade.mixins import LoggingMixin from freqtrade.mixins import LoggingMixin
@ -16,7 +16,7 @@ logger = logging.getLogger(__name__)
class IPairList(LoggingMixin, ABC): class IPairList(LoggingMixin, ABC):
def __init__(self, exchange, pairlistmanager, def __init__(self, exchange: Exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any], config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None: pairlist_pos: int) -> None:
""" """
@ -28,7 +28,7 @@ class IPairList(LoggingMixin, ABC):
""" """
self._enabled = True self._enabled = True
self._exchange = exchange self._exchange: Exchange = exchange
self._pairlistmanager = pairlistmanager self._pairlistmanager = pairlistmanager
self._config = config self._config = config
self._pairlistconfig = pairlistconfig self._pairlistconfig = pairlistconfig
@ -68,7 +68,7 @@ class IPairList(LoggingMixin, ABC):
filter_pairlist() method. filter_pairlist() method.
:param pair: Pair that's currently validated :param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets() :param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed :return: True if the pair can stay, false if it should be removed
""" """
raise NotImplementedError() raise NotImplementedError()

5
freqtrade/plugins/pairlist/PerformanceFilter.py
View File

@ -39,7 +39,12 @@ class PerformanceFilter(IPairList):
:return: new allowlist :return: new allowlist
""" """
# Get the trading performance for pairs from database # Get the trading performance for pairs from database
try:
performance = pd.DataFrame(Trade.get_overall_performance()) performance = pd.DataFrame(Trade.get_overall_performance())
except AttributeError:
# Performancefilter does not work in backtesting.
self.log_once("PerformanceFilter is not available in this mode.", logger.warning)
return pairlist
# Skip performance-based sorting if no performance data is available # Skip performance-based sorting if no performance data is available
if len(performance) == 0: if len(performance) == 0:

2
freqtrade/plugins/pairlist/PrecisionFilter.py
View File

@ -48,7 +48,7 @@ class PrecisionFilter(IPairList):
Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
low value pairs. low value pairs.
:param pair: Pair that's currently validated :param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets() :param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed :return: True if the pair can stay, false if it should be removed
""" """
stop_price = ticker['ask'] * self._stoploss stop_price = ticker['ask'] * self._stoploss

38
freqtrade/plugins/pairlist/PriceFilter.py
View File

@ -27,9 +27,13 @@ class PriceFilter(IPairList):
self._max_price = pairlistconfig.get('max_price', 0) self._max_price = pairlistconfig.get('max_price', 0)
if self._max_price < 0: if self._max_price < 0:
raise OperationalException("PriceFilter requires max_price to be >= 0") raise OperationalException("PriceFilter requires max_price to be >= 0")
self._max_value = pairlistconfig.get('max_value', 0)
if self._max_value < 0:
raise OperationalException("PriceFilter requires max_value to be >= 0")
self._enabled = ((self._low_price_ratio > 0) or self._enabled = ((self._low_price_ratio > 0) or
(self._min_price > 0) or (self._min_price > 0) or
(self._max_price > 0)) (self._max_price > 0) or
(self._max_value > 0))
@property @property
def needstickers(self) -> bool: def needstickers(self) -> bool:
@ -51,6 +55,8 @@ class PriceFilter(IPairList):
active_price_filters.append(f"below {self._min_price:.8f}") active_price_filters.append(f"below {self._min_price:.8f}")
if self._max_price != 0: if self._max_price != 0:
active_price_filters.append(f"above {self._max_price:.8f}") active_price_filters.append(f"above {self._max_price:.8f}")
if self._max_value != 0:
active_price_filters.append(f"Value above {self._max_value:.8f}")
if len(active_price_filters): if len(active_price_filters):
return f"{self.name} - Filtering pairs priced {' or '.join(active_price_filters)}." return f"{self.name} - Filtering pairs priced {' or '.join(active_price_filters)}."
@ -61,7 +67,7 @@ class PriceFilter(IPairList):
""" """
Check if if one price-step (pip) is > than a certain barrier. Check if if one price-step (pip) is > than a certain barrier.
:param pair: Pair that's currently validated :param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets() :param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed :return: True if the pair can stay, false if it should be removed
""" """
if ticker.get('last', None) is None or ticker.get('last') == 0: if ticker.get('last', None) is None or ticker.get('last') == 0:
@ -79,6 +85,32 @@ class PriceFilter(IPairList):
f"because 1 unit is {changeperc * 100:.3f}%", logger.info) f"because 1 unit is {changeperc * 100:.3f}%", logger.info)
return False return False
# Perform low_amount check
if self._max_value != 0:
price = ticker['last']
market = self._exchange.markets[pair]
limits = market['limits']
if ('amount' in limits and 'min' in limits['amount']
and limits['amount']['min'] is not None):
min_amount = limits['amount']['min']
min_precision = market['precision']['amount']
min_value = min_amount * price
if self._exchange.precisionMode == 4:
# tick size
next_value = (min_amount + min_precision) * price
else:
# Decimal places
min_precision = pow(0.1, min_precision)
next_value = (min_amount + min_precision) * price
diff = next_value - min_value
if diff > self._max_value:
self.log_once(f"Removed {pair} from whitelist, "
f"because min value change of {diff} > {self._max_value}.",
logger.info)
return False
# Perform min_price check. # Perform min_price check.
if self._min_price != 0: if self._min_price != 0:
if ticker['last'] < self._min_price: if ticker['last'] < self._min_price:
@ -89,7 +121,7 @@ class PriceFilter(IPairList):
# Perform max_price check. # Perform max_price check.
if self._max_price != 0: if self._max_price != 0:
if ticker['last'] > self._max_price: if ticker['last'] > self._max_price:
self.log_once(f"Removed {ticker['symbol']} from whitelist, " self.log_once(f"Removed {pair} from whitelist, "
f"because last price > {self._max_price:.8f}", logger.info) f"because last price > {self._max_price:.8f}", logger.info)
return False return False

2
freqtrade/plugins/pairlist/SpreadFilter.py
View File

@ -40,7 +40,7 @@ class SpreadFilter(IPairList):
""" """
Validate spread for the ticker Validate spread for the ticker
:param pair: Pair that's currently validated :param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets() :param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed :return: True if the pair can stay, false if it should be removed
""" """
if 'bid' in ticker and 'ask' in ticker and ticker['ask']: if 'bid' in ticker and 'ask' in ticker and ticker['ask']:

2
freqtrade/plugins/pairlist/VolatilityFilter.py
View File

@ -90,7 +90,7 @@ class VolatilityFilter(IPairList):
""" """
Validate trading range Validate trading range
:param pair: Pair that's currently validated :param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets() :param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed :return: True if the pair can stay, false if it should be removed
""" """
# Check symbol in cache # Check symbol in cache

2
freqtrade/plugins/pairlist/rangestabilityfilter.py
View File

@ -83,7 +83,7 @@ class RangeStabilityFilter(IPairList):
""" """
Validate trading range Validate trading range
:param pair: Pair that's currently validated :param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets() :param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed :return: True if the pair can stay, false if it should be removed
""" """
# Check symbol in cache # Check symbol in cache

3
freqtrade/rpc/api_server/api_schemas.py
View File

@ -57,6 +57,7 @@ class Count(BaseModel):
class PerformanceEntry(BaseModel): class PerformanceEntry(BaseModel):
pair: str pair: str
profit: float profit: float
profit_abs: float
count: int count: int
@ -268,7 +269,7 @@ class DeleteTrade(BaseModel):
class PlotConfig_(BaseModel): class PlotConfig_(BaseModel):
main_plot: Dict[str, Any] main_plot: Dict[str, Any]
subplots: Optional[Dict[str, Any]] subplots: Dict[str, Any]
class PlotConfig(BaseModel): class PlotConfig(BaseModel):

27
freqtrade/rpc/fiat_convert.py
View File

@ -3,11 +3,13 @@ Module that define classes to convert Crypto-currency to FIAT
e.g BTC to USD e.g BTC to USD
""" """
import datetime
import logging import logging
from typing import Dict from typing import Dict
from cachetools.ttl import TTLCache from cachetools.ttl import TTLCache
from pycoingecko import CoinGeckoAPI from pycoingecko import CoinGeckoAPI
from requests.exceptions import RequestException
from freqtrade.constants import SUPPORTED_FIAT from freqtrade.constants import SUPPORTED_FIAT
@ -25,6 +27,7 @@ class CryptoToFiatConverter:
_coingekko: CoinGeckoAPI = None _coingekko: CoinGeckoAPI = None
_cryptomap: Dict = {} _cryptomap: Dict = {}
_backoff: float = 0.0
def __new__(cls): def __new__(cls):
""" """
@ -47,8 +50,21 @@ class CryptoToFiatConverter:
def _load_cryptomap(self) -> None: def _load_cryptomap(self) -> None:
try: try:
coinlistings = self._coingekko.get_coins_list() coinlistings = self._coingekko.get_coins_list()
# Create mapping table from synbol to coingekko_id # Create mapping table from symbol to coingekko_id
self._cryptomap = {x['symbol']: x['id'] for x in coinlistings} self._cryptomap = {x['symbol']: x['id'] for x in coinlistings}
except RequestException as request_exception:
if "429" in str(request_exception):
logger.warning(
"Too many requests for Coingecko API, backing off and trying again later.")
# Set backoff timestamp to 60 seconds in the future
self._backoff = datetime.datetime.now().timestamp() + 60
return
# If the request is not a 429 error we want to raise the normal error
logger.error(
"Could not load FIAT Cryptocurrency map for the following problem: {}".format(
request_exception
)
)
except (Exception) as exception: except (Exception) as exception:
logger.error( logger.error(
f"Could not load FIAT Cryptocurrency map for the following problem: {exception}") f"Could not load FIAT Cryptocurrency map for the following problem: {exception}")
@ -127,6 +143,15 @@ class CryptoToFiatConverter:
if crypto_symbol == fiat_symbol: if crypto_symbol == fiat_symbol:
return 1.0 return 1.0
if self._cryptomap == {}:
if self._backoff <= datetime.datetime.now().timestamp():
self._load_cryptomap()
# return 0.0 if we still dont have data to check, no reason to proceed
if self._cryptomap == {}:
return 0.0
else:
return 0.0
if crypto_symbol not in self._cryptomap: if crypto_symbol not in self._cryptomap:
# return 0 for unsupported stake currencies (fiat-convert should not break the bot) # return 0 for unsupported stake currencies (fiat-convert should not break the bot)
logger.warning("unsupported crypto-symbol %s - returning 0.0", crypto_symbol) logger.warning("unsupported crypto-symbol %s - returning 0.0", crypto_symbol)

13
freqtrade/rpc/rpc.py
View File

@ -178,7 +178,7 @@ class RPC:
current_rate = trade.close_rate current_rate = trade.close_rate
current_profit = trade.calc_profit_ratio(current_rate) current_profit = trade.calc_profit_ratio(current_rate)
current_profit_abs = trade.calc_profit(current_rate) current_profit_abs = trade.calc_profit(current_rate)
current_profit_fiat: Optional[float] = None
# Calculate fiat profit # Calculate fiat profit
if self._fiat_converter: if self._fiat_converter:
current_profit_fiat = self._fiat_converter.convert_amount( current_profit_fiat = self._fiat_converter.convert_amount(
@ -220,12 +220,13 @@ class RPC:
return results return results
def _rpc_status_table(self, stake_currency: str, def _rpc_status_table(self, stake_currency: str,
fiat_display_currency: str) -> Tuple[List, List]: fiat_display_currency: str) -> Tuple[List, List, float]:
trades = Trade.get_open_trades() trades = Trade.get_open_trades()
if not trades: if not trades:
raise RPCException('no active trade') raise RPCException('no active trade')
else: else:
trades_list = [] trades_list = []
fiat_profit_sum = NAN
for trade in trades: for trade in trades:
# calculate profit and send message to user # calculate profit and send message to user
try: try:
@ -243,6 +244,8 @@ class RPC:
) )
if fiat_profit and not isnan(fiat_profit): if fiat_profit and not isnan(fiat_profit):
profit_str += f" ({fiat_profit:.2f})" profit_str += f" ({fiat_profit:.2f})"
fiat_profit_sum = fiat_profit if isnan(fiat_profit_sum) \
else fiat_profit_sum + fiat_profit
trades_list.append([ trades_list.append([
trade.id, trade.id,
trade.pair + ('*' if (trade.open_order_id is not None trade.pair + ('*' if (trade.open_order_id is not None
@ -256,7 +259,7 @@ class RPC:
profitcol += " (" + fiat_display_currency + ")" profitcol += " (" + fiat_display_currency + ")"
columns = ['ID', 'Pair', 'Since', profitcol] columns = ['ID', 'Pair', 'Since', profitcol]
return trades_list, columns return trades_list, columns, fiat_profit_sum
def _rpc_daily_profit( def _rpc_daily_profit(
self, timescale: int, self, timescale: int,
@ -845,5 +848,7 @@ class RPC:
df_analyzed, arrow.Arrow.utcnow().datetime) df_analyzed, arrow.Arrow.utcnow().datetime)
def _rpc_plot_config(self) -> Dict[str, Any]: def _rpc_plot_config(self) -> Dict[str, Any]:
if (self._freqtrade.strategy.plot_config and
'subplots' not in self._freqtrade.strategy.plot_config):
self._freqtrade.strategy.plot_config['subplots'] = {}
return self._freqtrade.strategy.plot_config return self._freqtrade.strategy.plot_config

161
freqtrade/rpc/telegram.py
View File

@ -8,19 +8,21 @@ import logging
from datetime import timedelta from datetime import timedelta
from html import escape from html import escape
from itertools import chain from itertools import chain
from typing import Any, Callable, Dict, List, Union from math import isnan
from typing import Any, Callable, Dict, List, Optional, Union, cast
import arrow import arrow
from tabulate import tabulate from tabulate import tabulate
from telegram import KeyboardButton, ParseMode, ReplyKeyboardMarkup, Update from telegram import (InlineKeyboardButton, InlineKeyboardMarkup, KeyboardButton, ParseMode,
ReplyKeyboardMarkup, Update)
from telegram.error import NetworkError, TelegramError from telegram.error import NetworkError, TelegramError
from telegram.ext import CallbackContext, CommandHandler, Updater from telegram.ext import CallbackContext, CallbackQueryHandler, CommandHandler, Updater
from telegram.utils.helpers import escape_markdown from telegram.utils.helpers import escape_markdown
from freqtrade.__init__ import __version__ from freqtrade.__init__ import __version__
from freqtrade.constants import DUST_PER_COIN from freqtrade.constants import DUST_PER_COIN
from freqtrade.exceptions import OperationalException from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_coin_value from freqtrade.misc import chunks, round_coin_value
from freqtrade.rpc import RPC, RPCException, RPCHandler, RPCMessageType from freqtrade.rpc import RPC, RPCException, RPCHandler, RPCMessageType
@ -87,7 +89,7 @@ class Telegram(RPCHandler):
Validates the keyboard configuration from telegram config Validates the keyboard configuration from telegram config
section. section.
""" """
self._keyboard: List[List[Union[str, KeyboardButton]]] = [ self._keyboard: List[List[Union[str, KeyboardButton, InlineKeyboardButton]]] = [
['/daily', '/profit', '/balance'], ['/daily', '/profit', '/balance'],
['/status', '/status table', '/performance'], ['/status', '/status table', '/performance'],
['/count', '/start', '/stop', '/help'] ['/count', '/start', '/stop', '/help']
@ -169,6 +171,11 @@ class Telegram(RPCHandler):
[h.command for h in handles] [h.command for h in handles]
) )
self._current_callback_query_handler: Optional[CallbackQueryHandler] = None
self._callback_query_handlers = {
'forcebuy': CallbackQueryHandler(self._forcebuy_inline)
}
def cleanup(self) -> None: def cleanup(self) -> None:
""" """
Stops all running telegram threads. Stops all running telegram threads.
@ -226,44 +233,58 @@ class Telegram(RPCHandler):
def send_msg(self, msg: Dict[str, Any]) -> None: def send_msg(self, msg: Dict[str, Any]) -> None:
""" Send a message to telegram channel """ """ Send a message to telegram channel """
noti = self._config['telegram'].get('notification_settings', {} default_noti = 'on'
).get(str(msg['type']), 'on')
msg_type = msg['type']
noti = ''
if msg_type == RPCMessageType.SELL:
sell_noti = self._config['telegram'] \
.get('notification_settings', {}).get(str(msg_type), {})
# For backward compatibility sell still be string
if isinstance(noti, str):
noti = sell_noti
else:
noti = sell_noti.get(str(msg['sell_reason']), default_noti)
else:
noti = self._config['telegram'] \
.get('notification_settings', {}).get(str(msg_type), default_noti)
if noti == 'off': if noti == 'off':
logger.info(f"Notification '{msg['type']}' not sent.") logger.info(f"Notification '{msg_type}' not sent.")
# Notification disabled # Notification disabled
return return
if msg['type'] == RPCMessageType.BUY: if msg_type == RPCMessageType.BUY:
message = self._format_buy_msg(msg) message = self._format_buy_msg(msg)
elif msg['type'] in (RPCMessageType.BUY_CANCEL, RPCMessageType.SELL_CANCEL): elif msg_type in (RPCMessageType.BUY_CANCEL, RPCMessageType.SELL_CANCEL):
msg['message_side'] = 'buy' if msg['type'] == RPCMessageType.BUY_CANCEL else 'sell' msg['message_side'] = 'buy' if msg_type == RPCMessageType.BUY_CANCEL else 'sell'
message = ("\N{WARNING SIGN} *{exchange}:* " message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling open {message_side} Order for {pair} (#{trade_id}). " "Cancelling open {message_side} Order for {pair} (#{trade_id}). "
"Reason: {reason}.".format(**msg)) "Reason: {reason}.".format(**msg))
elif msg['type'] == RPCMessageType.BUY_FILL: elif msg_type == RPCMessageType.BUY_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* " message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Buy order for {pair} (#{trade_id}) filled " "Buy order for {pair} (#{trade_id}) filled "
"for {open_rate}.".format(**msg)) "for {open_rate}.".format(**msg))
elif msg['type'] == RPCMessageType.SELL_FILL: elif msg_type == RPCMessageType.SELL_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* " message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Sell order for {pair} (#{trade_id}) filled " "Sell order for {pair} (#{trade_id}) filled "
"for {close_rate}.".format(**msg)) "for {close_rate}.".format(**msg))
elif msg['type'] == RPCMessageType.SELL: elif msg_type == RPCMessageType.SELL:
message = self._format_sell_msg(msg) message = self._format_sell_msg(msg)
elif msg['type'] == RPCMessageType.STATUS: elif msg_type == RPCMessageType.STATUS:
message = '*Status:* `{status}`'.format(**msg) message = '*Status:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.WARNING: elif msg_type == RPCMessageType.WARNING:
message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg) message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.STARTUP: elif msg_type == RPCMessageType.STARTUP:
message = '{status}'.format(**msg) message = '{status}'.format(**msg)
else: else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type'])) raise NotImplementedError('Unknown message type: {}'.format(msg_type))
self._send_msg(message, disable_notification=(noti == 'silent')) self._send_msg(message, disable_notification=(noti == 'silent'))
@ -354,19 +375,31 @@ class Telegram(RPCHandler):
:return: None :return: None
""" """
try: try:
statlist, head = self._rpc._rpc_status_table( fiat_currency = self._config.get('fiat_display_currency', '')
self._config['stake_currency'], self._config.get('fiat_display_currency', '')) statlist, head, fiat_profit_sum = self._rpc._rpc_status_table(
self._config['stake_currency'], fiat_currency)
show_total = not isnan(fiat_profit_sum) and len(statlist) > 1
max_trades_per_msg = 50 max_trades_per_msg = 50
""" """
Calculate the number of messages of 50 trades per message Calculate the number of messages of 50 trades per message
0.99 is used to make sure that there are no extra (empty) messages 0.99 is used to make sure that there are no extra (empty) messages
As an example with 50 trades, there will be int(50/50 + 0.99) = 1 message As an example with 50 trades, there will be int(50/50 + 0.99) = 1 message
""" """
for i in range(0, max(int(len(statlist) / max_trades_per_msg + 0.99), 1)): messages_count = max(int(len(statlist) / max_trades_per_msg + 0.99), 1)
message = tabulate(statlist[i * max_trades_per_msg:(i + 1) * max_trades_per_msg], for i in range(0, messages_count):
trades = statlist[i * max_trades_per_msg:(i + 1) * max_trades_per_msg]
if show_total and i == messages_count - 1:
# append total line
trades.append(["Total", "", "", f"{fiat_profit_sum:.2f} {fiat_currency}"])
message = tabulate(trades,
headers=head, headers=head,
tablefmt='simple') tablefmt='simple')
if show_total and i == messages_count - 1:
# insert separators line between Total
lines = message.split("\n")
message = "\n".join(lines[:-1] + [lines[1]] + [lines[-1]])
self._send_msg(f"<pre>{message}</pre>", parse_mode=ParseMode.HTML) self._send_msg(f"<pre>{message}</pre>", parse_mode=ParseMode.HTML)
except RPCException as e: except RPCException as e:
self._send_msg(str(e)) self._send_msg(str(e))
@ -624,6 +657,25 @@ class Telegram(RPCHandler):
except RPCException as e: except RPCException as e:
self._send_msg(str(e)) self._send_msg(str(e))
def _forcebuy_action(self, pair, price=None):
try:
self._rpc._rpc_forcebuy(pair, price)
except RPCException as e:
self._send_msg(str(e))
def _forcebuy_inline(self, update: Update, _: CallbackContext) -> None:
if update.callback_query:
query = update.callback_query
pair = query.data
query.answer()
query.edit_message_text(text=f"Force Buying: {pair}")
self._forcebuy_action(pair)
@staticmethod
def _layout_inline_keyboard(buttons: List[InlineKeyboardButton],
cols=3) -> List[List[InlineKeyboardButton]]:
return [buttons[i:i + cols] for i in range(0, len(buttons), cols)]
@authorized_only @authorized_only
def _forcebuy(self, update: Update, context: CallbackContext) -> None: def _forcebuy(self, update: Update, context: CallbackContext) -> None:
""" """
@ -636,10 +688,13 @@ class Telegram(RPCHandler):
if context.args: if context.args:
pair = context.args[0] pair = context.args[0]
price = float(context.args[1]) if len(context.args) > 1 else None price = float(context.args[1]) if len(context.args) > 1 else None
try: self._forcebuy_action(pair, price)
self._rpc._rpc_forcebuy(pair, price) else:
except RPCException as e: whitelist = self._rpc._rpc_whitelist()['whitelist']
self._send_msg(str(e)) pairs = [InlineKeyboardButton(pair, callback_data=pair) for pair in whitelist]
self._send_inline_msg("Which pair?",
keyboard=self._layout_inline_keyboard(pairs),
callback_query_handler='forcebuy')
@authorized_only @authorized_only
def _trades(self, update: Update, context: CallbackContext) -> None: def _trades(self, update: Update, context: CallbackContext) -> None:
@ -711,7 +766,10 @@ class Telegram(RPCHandler):
trades = self._rpc._rpc_performance() trades = self._rpc._rpc_performance()
output = "<b>Performance:</b>\n" output = "<b>Performance:</b>\n"
for i, trade in enumerate(trades): for i, trade in enumerate(trades):
stat_line = (f"{i+1}.\t <code>{trade['pair']}\t{trade['profit']:.2f}% " stat_line = (
f"{i+1}.\t <code>{trade['pair']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit']:.2f}%) "
f"({trade['count']})</code>\n") f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH: if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
@ -750,12 +808,16 @@ class Telegram(RPCHandler):
Handler for /locks. Handler for /locks.
Returns the currently active locks Returns the currently active locks
""" """
locks = self._rpc._rpc_locks() rpc_locks = self._rpc._rpc_locks()
if not rpc_locks['locks']:
self._send_msg('No active locks.', parse_mode=ParseMode.HTML)
for locks in chunks(rpc_locks['locks'], 25):
message = tabulate([[ message = tabulate([[
lock['id'], lock['id'],
lock['pair'], lock['pair'],
lock['lock_end_time'], lock['lock_end_time'],
lock['reason']] for lock in locks['locks']], lock['reason']] for lock in locks],
headers=['ID', 'Pair', 'Until', 'Reason'], headers=['ID', 'Pair', 'Until', 'Reason'],
tablefmt='simple') tablefmt='simple')
message = f"<pre>{escape(message)}</pre>" message = f"<pre>{escape(message)}</pre>"
@ -860,9 +922,17 @@ class Telegram(RPCHandler):
""" """
try: try:
edge_pairs = self._rpc._rpc_edge() edge_pairs = self._rpc._rpc_edge()
edge_pairs_tab = tabulate(edge_pairs, headers='keys', tablefmt='simple') if not edge_pairs:
message = f'<b>Edge only validated following pairs:</b>\n<pre>{edge_pairs_tab}</pre>' message = '<b>Edge only validated following pairs:</b>'
self._send_msg(message, parse_mode=ParseMode.HTML) self._send_msg(message, parse_mode=ParseMode.HTML)
for chunk in chunks(edge_pairs, 25):
edge_pairs_tab = tabulate(chunk, headers='keys', tablefmt='simple')
message = (f'<b>Edge only validated following pairs:</b>\n'
f'<pre>{edge_pairs_tab}</pre>')
self._send_msg(message, parse_mode=ParseMode.HTML)
except RPCException as e: except RPCException as e:
self._send_msg(str(e)) self._send_msg(str(e))
@ -959,8 +1029,9 @@ class Telegram(RPCHandler):
f"*Current state:* `{val['state']}`" f"*Current state:* `{val['state']}`"
) )
def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN, def _send_inline_msg(self, msg: str, callback_query_handler,
disable_notification: bool = False) -> None: parse_mode: str = ParseMode.MARKDOWN, disable_notification: bool = False,
keyboard: List[List[InlineKeyboardButton]] = None, ) -> None:
""" """
Send given markdown message Send given markdown message
:param msg: message :param msg: message
@ -968,7 +1039,29 @@ class Telegram(RPCHandler):
:param parse_mode: telegram parse mode :param parse_mode: telegram parse mode
:return: None :return: None
""" """
reply_markup = ReplyKeyboardMarkup(self._keyboard, resize_keyboard=True) if self._current_callback_query_handler:
self._updater.dispatcher.remove_handler(self._current_callback_query_handler)
self._current_callback_query_handler = self._callback_query_handlers[callback_query_handler]
self._updater.dispatcher.add_handler(self._current_callback_query_handler)
self._send_msg(msg, parse_mode, disable_notification,
cast(List[List[Union[str, KeyboardButton, InlineKeyboardButton]]], keyboard),
reply_markup=InlineKeyboardMarkup)
def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN,
disable_notification: bool = False,
keyboard: List[List[Union[str, KeyboardButton, InlineKeyboardButton]]] = None,
reply_markup=ReplyKeyboardMarkup) -> None:
"""
Send given markdown message
:param msg: message
:param bot: alternative bot
:param parse_mode: telegram parse mode
:return: None
"""
if keyboard is None:
keyboard = self._keyboard
reply_markup = reply_markup(keyboard, resize_keyboard=True)
try: try:
try: try:
self._updater.bot.send_message( self._updater.bot.send_message(

52
freqtrade/strategy/hyper.py
View File

@ -5,7 +5,9 @@ This module defines a base class for auto-hyperoptable strategies.
import logging import logging
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
from contextlib import suppress from contextlib import suppress
from typing import Any, Dict, Iterator, Optional, Sequence, Tuple, Union from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
from freqtrade.optimize.hyperopt_tools import HyperoptTools
with suppress(ImportError): with suppress(ImportError):
@ -26,7 +28,8 @@ class BaseParameter(ABC):
category: Optional[str] category: Optional[str]
default: Any default: Any
value: Any value: Any
hyperopt: bool = False in_space: bool = False
name: str
def __init__(self, *, default: Any, space: Optional[str] = None, def __init__(self, *, default: Any, space: Optional[str] = None,
optimize: bool = True, load: bool = True, **kwargs): optimize: bool = True, load: bool = True, **kwargs):
@ -131,7 +134,7 @@ class IntParameter(NumericParameter):
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators. calculating 100ds of indicators.
""" """
if self.hyperopt: if self.in_space and self.optimize:
# Scikit-optimize ranges are "inclusive", while python's "range" is exclusive # Scikit-optimize ranges are "inclusive", while python's "range" is exclusive
return range(self.low, self.high + 1) return range(self.low, self.high + 1)
else: else:
@ -247,6 +250,10 @@ class HyperStrategyMixin(object):
""" """
Initialize hyperoptable strategy mixin. Initialize hyperoptable strategy mixin.
""" """
self.config = config
self.ft_buy_params: List[BaseParameter] = []
self.ft_sell_params: List[BaseParameter] = []
self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT) self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]: def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
@ -257,15 +264,26 @@ class HyperStrategyMixin(object):
""" """
if category not in ('buy', 'sell', None): if category not in ('buy', 'sell', None):
raise OperationalException('Category must be one of: "buy", "sell", None.') raise OperationalException('Category must be one of: "buy", "sell", None.')
if category is None:
params = self.ft_buy_params + self.ft_sell_params
else:
params = getattr(self, f"ft_{category}_params")
for par in params:
yield par.name, par
def _detect_parameters(self, category: str) -> Iterator[Tuple[str, BaseParameter]]:
""" Detect all parameters for 'category' """
for attr_name in dir(self): for attr_name in dir(self):
if not attr_name.startswith('__'): # Ignore internals, not strictly necessary. if not attr_name.startswith('__'): # Ignore internals, not strictly necessary.
attr = getattr(self, attr_name) attr = getattr(self, attr_name)
if issubclass(attr.__class__, BaseParameter): if issubclass(attr.__class__, BaseParameter):
if (category and attr_name.startswith(category + '_') if (attr_name.startswith(category + '_')
and attr.category is not None and attr.category != category): and attr.category is not None and attr.category != category):
raise OperationalException( raise OperationalException(
f'Inconclusive parameter name {attr_name}, category: {attr.category}.') f'Inconclusive parameter name {attr_name}, category: {attr.category}.')
if (category is None or category == attr.category or if (category == attr.category or
(attr_name.startswith(category + '_') and attr.category is None)): (attr_name.startswith(category + '_') and attr.category is None)):
yield attr_name, attr yield attr_name, attr
@ -283,9 +301,16 @@ class HyperStrategyMixin(object):
""" """
if not params: if not params:
logger.info(f"No params for {space} found, using default values.") logger.info(f"No params for {space} found, using default values.")
param_container: List[BaseParameter] = getattr(self, f"ft_{space}_params")
for attr_name, attr in self._detect_parameters(space):
attr.name = attr_name
attr.in_space = hyperopt and HyperoptTools.has_space(self.config, space)
if not attr.category:
attr.category = space
param_container.append(attr)
for attr_name, attr in self.enumerate_parameters():
attr.hyperopt = hyperopt
if params and attr_name in params: if params and attr_name in params:
if attr.load: if attr.load:
attr.value = params[attr_name] attr.value = params[attr_name]
@ -295,3 +320,16 @@ class HyperStrategyMixin(object):
f'Default value "{attr.value}" used.') f'Default value "{attr.value}" used.')
else: else:
logger.info(f'Strategy Parameter(default): {attr_name} = {attr.value}') logger.info(f'Strategy Parameter(default): {attr_name} = {attr.value}')
def get_params_dict(self):
"""
Returns list of Parameters that are not part of the current optimize job
"""
params = {
'buy': {},
'sell': {}
}
for name, p in self.enumerate_parameters():
if not p.optimize or not p.in_space:
params[p.category][name] = p.value
return params

36
freqtrade/strategy/interface.py
View File

@ -161,6 +161,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param metadata: Additional information, like the currently traded pair :param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies :return: a Dataframe with all mandatory indicators for the strategies
""" """
return dataframe
@abstractmethod @abstractmethod
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@ -170,6 +171,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param metadata: Additional information, like the currently traded pair :param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column :return: DataFrame with buy column
""" """
return dataframe
@abstractmethod @abstractmethod
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@ -179,6 +181,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param metadata: Additional information, like the currently traded pair :param metadata: Additional information, like the currently traded pair
:return: DataFrame with sell column :return: DataFrame with sell column
""" """
return dataframe
def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool: def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
""" """
@ -226,7 +229,7 @@ class IStrategy(ABC, HyperStrategyMixin):
pass pass
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, **kwargs) -> bool: time_in_force: str, current_time: datetime, **kwargs) -> bool:
""" """
Called right before placing a buy order. Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or Timing for this function is critical, so avoid doing heavy computations or
@ -241,6 +244,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param amount: Amount in target (quote) currency that's going to be traded. :param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders :param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled). :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange. :return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process False aborts the process
@ -248,7 +252,8 @@ class IStrategy(ABC, HyperStrategyMixin):
return True return True
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: rate: float, time_in_force: str, sell_reason: str,
current_time: datetime, **kwargs) -> bool:
""" """
Called right before placing a regular sell order. Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or Timing for this function is critical, so avoid doing heavy computations or
@ -267,6 +272,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param sell_reason: Sell reason. :param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss', Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell'] 'sell_signal', 'force_sell', 'emergency_sell']
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange. :return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process False aborts the process
@ -274,7 +280,7 @@ class IStrategy(ABC, HyperStrategyMixin):
return True return True
def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, dataframe: DataFrame, **kwargs) -> float: current_profit: float, **kwargs) -> float:
""" """
Custom stoploss logic, returning the new distance relative to current_rate (as ratio). 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. e.g. returning -0.05 would create a stoploss 5% below current_rate.
@ -290,15 +296,13 @@ class IStrategy(ABC, HyperStrategyMixin):
:param current_time: datetime object, containing the current datetime :param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in ask_strategy. :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 current_profit: Current profit (as ratio), calculated based on current_rate.
:param dataframe: Analyzed dataframe for this pair. Can contain future data in backtesting.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :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 float: New stoploss value, relative to the currentrate
""" """
return self.stoploss return self.stoploss
def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, dataframe: DataFrame, current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
**kwargs) -> Optional[Union[str, bool]]:
""" """
Custom sell signal logic indicating that specified position should be sold. Returning a Custom sell signal logic indicating that specified position should be sold. Returning a
string or True from this method is equal to setting sell signal on a candle at specified string or True from this method is equal to setting sell signal on a candle at specified
@ -536,8 +540,8 @@ class IStrategy(ABC, HyperStrategyMixin):
else: else:
return False return False
def should_sell(self, dataframe: DataFrame, trade: Trade, rate: float, date: datetime, def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
buy: bool, sell: bool, low: float = None, high: float = None, sell: bool, low: float = None, high: float = None,
force_stoploss: float = 0) -> SellCheckTuple: force_stoploss: float = 0) -> SellCheckTuple:
""" """
This function evaluates if one of the conditions required to trigger a sell This function evaluates if one of the conditions required to trigger a sell
@ -553,9 +557,8 @@ class IStrategy(ABC, HyperStrategyMixin):
trade.adjust_min_max_rates(high or current_rate) trade.adjust_min_max_rates(high or current_rate)
stoplossflag = self.stop_loss_reached(dataframe=dataframe, current_rate=current_rate, stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
trade=trade, current_time=date, current_time=date, current_profit=current_profit,
current_profit=current_profit,
force_stoploss=force_stoploss, high=high) force_stoploss=force_stoploss, high=high)
# Set current rate to high for backtesting sell # Set current rate to high for backtesting sell
@ -570,6 +573,10 @@ class IStrategy(ABC, HyperStrategyMixin):
sell_signal = SellType.NONE sell_signal = SellType.NONE
custom_reason = '' custom_reason = ''
# use provided rate in backtesting, not high/low.
current_rate = rate
current_profit = trade.calc_profit_ratio(current_rate)
if (ask_strategy.get('sell_profit_only', False) if (ask_strategy.get('sell_profit_only', False)
and current_profit <= ask_strategy.get('sell_profit_offset', 0)): and current_profit <= ask_strategy.get('sell_profit_offset', 0)):
# sell_profit_only and profit doesn't reach the offset - ignore sell signal # sell_profit_only and profit doesn't reach the offset - ignore sell signal
@ -580,7 +587,7 @@ class IStrategy(ABC, HyperStrategyMixin):
else: else:
custom_reason = strategy_safe_wrapper(self.custom_sell, default_retval=False)( custom_reason = strategy_safe_wrapper(self.custom_sell, default_retval=False)(
pair=trade.pair, trade=trade, current_time=date, current_rate=current_rate, pair=trade.pair, trade=trade, current_time=date, current_rate=current_rate,
current_profit=current_profit, dataframe=dataframe) current_profit=current_profit)
if custom_reason: if custom_reason:
sell_signal = SellType.CUSTOM_SELL sell_signal = SellType.CUSTOM_SELL
if isinstance(custom_reason, str): if isinstance(custom_reason, str):
@ -617,7 +624,7 @@ class IStrategy(ABC, HyperStrategyMixin):
# logger.debug(f"{trade.pair} - No sell signal.") # logger.debug(f"{trade.pair} - No sell signal.")
return SellCheckTuple(sell_type=SellType.NONE) return SellCheckTuple(sell_type=SellType.NONE)
def stop_loss_reached(self, dataframe: DataFrame, current_rate: float, trade: Trade, def stop_loss_reached(self, current_rate: float, trade: Trade,
current_time: datetime, current_profit: float, current_time: datetime, current_profit: float,
force_stoploss: float, high: float = None) -> SellCheckTuple: force_stoploss: float, high: float = None) -> SellCheckTuple:
""" """
@ -635,8 +642,7 @@ class IStrategy(ABC, HyperStrategyMixin):
)(pair=trade.pair, trade=trade, )(pair=trade.pair, trade=trade,
current_time=current_time, current_time=current_time,
current_rate=current_rate, current_rate=current_rate,
current_profit=current_profit, current_profit=current_profit)
dataframe=dataframe)
# Sanity check - error cases will return None # Sanity check - error cases will return None
if stop_loss_value: if stop_loss_value:
# logger.info(f"{trade.pair} {stop_loss_value=} {current_profit=}") # logger.info(f"{trade.pair} {stop_loss_value=} {current_profit=}")

3
freqtrade/templates/base_config.json.j2
View File

@ -9,7 +9,8 @@
"cancel_open_orders_on_exit": false, "cancel_open_orders_on_exit": false,
"unfilledtimeout": { "unfilledtimeout": {
"buy": 10, "buy": 10,
"sell": 30 "sell": 30,
"unit": "minutes"
}, },
"bid_strategy": { "bid_strategy": {
"price_side": "bid", "price_side": "bid",

7
freqtrade/templates/subtemplates/strategy_methods_advanced.j2
View File

@ -39,7 +39,7 @@ def custom_stoploss(self, pair: str, trade: 'Trade', current_time: 'datetime',
return self.stoploss return self.stoploss
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, **kwargs) -> bool: time_in_force: str, current_time: 'datetime', **kwargs) -> bool:
""" """
Called right before placing a buy order. Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or Timing for this function is critical, so avoid doing heavy computations or
@ -54,6 +54,7 @@ def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: f
:param amount: Amount in target (quote) currency that's going to be traded. :param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders :param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled). :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange. :return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process False aborts the process
@ -61,7 +62,8 @@ def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: f
return True return True
def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float, def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: rate: float, time_in_force: str, sell_reason: str,
current_time: 'datetime', **kwargs) -> bool:
""" """
Called right before placing a regular sell order. Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or Timing for this function is critical, so avoid doing heavy computations or
@ -80,6 +82,7 @@ def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount:
:param sell_reason: Sell reason. :param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss', Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell'] 'sell_signal', 'force_sell', 'emergency_sell']
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange. :return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process False aborts the process

10
requirements-dev.txt
View File

@ -4,14 +4,14 @@
-r requirements-hyperopt.txt -r requirements-hyperopt.txt
coveralls==3.0.1 coveralls==3.0.1
flake8==3.9.1 flake8==3.9.2
flake8-type-annotations==0.1.0 flake8-type-annotations==0.1.0
flake8-tidy-imports==4.2.1 flake8-tidy-imports==4.3.0
mypy==0.812 mypy==0.812
pytest==6.2.3 pytest==6.2.4
pytest-asyncio==0.15.1 pytest-asyncio==0.15.1
pytest-cov==2.11.1 pytest-cov==2.12.0
pytest-mock==3.6.0 pytest-mock==3.6.1
pytest-random-order==1.0.4 pytest-random-order==1.0.4
isort==5.8.0 isort==5.8.0

2
requirements-hyperopt.txt
View File

@ -3,7 +3,7 @@
# Required for hyperopt # Required for hyperopt
scipy==1.6.3 scipy==1.6.3
scikit-learn==0.24.1 scikit-learn==0.24.2
scikit-optimize==0.8.1 scikit-optimize==0.8.1
filelock==3.0.12 filelock==3.0.12
joblib==1.0.1 joblib==1.0.1

24
requirements.txt
View File

@ -1,23 +1,23 @@
numpy==1.20.2 numpy==1.20.3
pandas==1.2.4 pandas==1.2.4
ccxt==1.48.76 ccxt==1.50.48
# Pin cryptography for now due to rust build errors with piwheels # Pin cryptography for now due to rust build errors with piwheels
cryptography==3.4.7 cryptography==3.4.7
aiohttp==3.7.4.post0 aiohttp==3.7.4.post0
SQLAlchemy==1.4.11 SQLAlchemy==1.4.15
python-telegram-bot==13.4.1 python-telegram-bot==13.5
arrow==1.0.3 arrow==1.1.0
cachetools==4.2.1 cachetools==4.2.2
requests==2.25.1 requests==2.25.1
urllib3==1.26.4 urllib3==1.26.4
wrapt==1.12.1 wrapt==1.12.1
jsonschema==3.2.0 jsonschema==3.2.0
TA-Lib==0.4.19 TA-Lib==0.4.20
technical==1.2.2 technical==1.3.0
tabulate==0.8.9 tabulate==0.8.9
pycoingecko==2.0.0 pycoingecko==2.0.0
jinja2==2.11.3 jinja2==3.0.1
tables==3.6.1 tables==3.6.1
blosc==1.10.2 blosc==1.10.2
@ -31,10 +31,10 @@ python-rapidjson==1.0
sdnotify==0.3.2 sdnotify==0.3.2
# API Server # API Server
fastapi==0.63.0 fastapi==0.65.1
uvicorn==0.13.4 uvicorn==0.13.4
pyjwt==2.0.1 pyjwt==2.1.0
aiofiles==0.6.0 aiofiles==0.7.0
# Support for colorized terminal output # Support for colorized terminal output
colorama==0.4.4 colorama==0.4.4

2
scripts/rest_client.py
View File

@ -396,7 +396,7 @@ def main(args):
sys.exit() sys.exit()
config = load_config(args['config']) config = load_config(args['config'])
url = config.get('api_server', {}).get('server_url', '127.0.0.1') url = config.get('api_server', {}).get('listen_ip_address', '127.0.0.1')
port = config.get('api_server', {}).get('listen_port', '8080') port = config.get('api_server', {}).get('listen_port', '8080')
username = config.get('api_server', {}).get('username') username = config.get('api_server', {}).get('username')
password = config.get('api_server', {}).get('password') password = config.get('api_server', {}).get('password')

10
tests/commands/test_commands.py
View File

@ -918,10 +918,12 @@ def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):
captured.out) captured.out)
def test_hyperopt_list(mocker, capsys, caplog, hyperopt_results): def test_hyperopt_list(mocker, capsys, caplog, saved_hyperopt_results,
saved_hyperopt_results_legacy):
for _ in (saved_hyperopt_results, saved_hyperopt_results_legacy):
mocker.patch( mocker.patch(
'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results', 'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
MagicMock(return_value=hyperopt_results) MagicMock(return_value=saved_hyperopt_results_legacy)
) )
args = [ args = [
@ -1150,10 +1152,10 @@ def test_hyperopt_list(mocker, capsys, caplog, hyperopt_results):
f.unlink() f.unlink()
def test_hyperopt_show(mocker, capsys, hyperopt_results): def test_hyperopt_show(mocker, capsys, saved_hyperopt_results):
mocker.patch( mocker.patch(
'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results', 'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
MagicMock(return_value=hyperopt_results) MagicMock(return_value=saved_hyperopt_results)
) )
args = [ args = [

139
tests/conftest.py
View File

@ -3,7 +3,7 @@ import json
import logging import logging
import re import re
from copy import deepcopy from copy import deepcopy
from datetime import datetime from datetime import datetime, timedelta
from functools import reduce from functools import reduce
from pathlib import Path from pathlib import Path
from unittest.mock import MagicMock, Mock, PropertyMock from unittest.mock import MagicMock, Mock, PropertyMock
@ -21,6 +21,7 @@ from freqtrade.exchange import Exchange
from freqtrade.freqtradebot import FreqtradeBot from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.persistence import LocalTrade, Trade, init_db from freqtrade.persistence import LocalTrade, Trade, init_db
from freqtrade.resolvers import ExchangeResolver from freqtrade.resolvers import ExchangeResolver
from freqtrade.state import RunMode
from freqtrade.worker import Worker from freqtrade.worker import Worker
from tests.conftest_trades import (mock_trade_1, mock_trade_2, mock_trade_3, mock_trade_4, from tests.conftest_trades import (mock_trade_1, mock_trade_2, mock_trade_3, mock_trade_4,
mock_trade_5, mock_trade_6) mock_trade_5, mock_trade_6)
@ -1677,6 +1678,7 @@ def buy_order_fee():
@pytest.fixture(scope="function") @pytest.fixture(scope="function")
def edge_conf(default_conf): def edge_conf(default_conf):
conf = deepcopy(default_conf) conf = deepcopy(default_conf)
conf['runmode'] = RunMode.DRY_RUN
conf['max_open_trades'] = -1 conf['max_open_trades'] = -1
conf['tradable_balance_ratio'] = 0.5 conf['tradable_balance_ratio'] = 0.5
conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
@ -1778,7 +1780,7 @@ def open_trade():
@pytest.fixture @pytest.fixture
def hyperopt_results(): def saved_hyperopt_results_legacy():
return [ return [
{ {
'loss': 0.4366182531161519, 'loss': 0.4366182531161519,
@ -1907,3 +1909,136 @@ def hyperopt_results():
'is_best': False 'is_best': False
} }
] ]
@pytest.fixture
def saved_hyperopt_results():
return [
{
'loss': 0.4366182531161519,
'params_dict': {
'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1190, 'roi_t2': 541, 'roi_t3': 408, 'roi_p1': 0.026035863879169705, 'roi_p2': 0.12508730043628782, 'roi_p3': 0.27766427921605896, 'stoploss': -0.2562930402099556}, # noqa: E501
'params_details': {'buy': {'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4287874435315165, 408: 0.15112316431545753, 949: 0.026035863879169705, 2139: 0}, 'stoploss': {'stoploss': -0.2562930402099556}}, # noqa: E501
'results_metrics': {'total_trades': 2, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'holding_avg': timedelta(minutes=3930.0)}, # noqa: E501
'results_explanation': ' 2 trades. Avg profit -1.25%. Total profit -0.00125625 BTC ( -2.51Σ%). Avg duration 3930.0 min.', # noqa: E501
'total_profit': -0.00125625,
'current_epoch': 1,
'is_initial_point': True,
'is_best': True
}, {
'loss': 20.0,
'params_dict': {
'mfi-value': 17, 'fastd-value': 38, 'adx-value': 48, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 334, 'roi_t2': 683, 'roi_t3': 140, 'roi_p1': 0.06403981740598495, 'roi_p2': 0.055519840060645045, 'roi_p3': 0.3253712811342459, 'stoploss': -0.338070047333259}, # noqa: E501
'params_details': {
'buy': {'mfi-value': 17, 'fastd-value': 38, 'adx-value': 48, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, # noqa: E501
'sell': {'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, # noqa: E501
'roi': {0: 0.4449309386008759, 140: 0.11955965746663, 823: 0.06403981740598495, 1157: 0}, # noqa: E501
'stoploss': {'stoploss': -0.338070047333259}},
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'holding_avg': timedelta(minutes=1200.0)}, # noqa: E501
'results_explanation': ' 1 trades. Avg profit 0.12%. Total profit 0.00006185 BTC ( 0.12Σ%). Avg duration 1200.0 min.', # noqa: E501
'total_profit': 6.185e-05,
'current_epoch': 2,
'is_initial_point': True,
'is_best': False
}, {
'loss': 14.241196856510731,
'params_dict': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 889, 'roi_t2': 533, 'roi_t3': 263, 'roi_p1': 0.04759065393663096, 'roi_p2': 0.1488819964638463, 'roi_p3': 0.4102801822104605, 'stoploss': -0.05394588767607611}, # noqa: E501
'params_details': {'buy': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.6067528326109377, 263: 0.19647265040047726, 796: 0.04759065393663096, 1685: 0}, 'stoploss': {'stoploss': -0.05394588767607611}}, # noqa: E501
'results_metrics': {'total_trades': 621, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)}, # noqa: E501
'results_explanation': ' 621 trades. Avg profit -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.', # noqa: E501
'total_profit': -0.13639474,
'current_epoch': 3,
'is_initial_point': True,
'is_best': False
}, {
'loss': 100000,
'params_dict': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1402, 'roi_t2': 676, 'roi_t3': 215, 'roi_p1': 0.06264755784937427, 'roi_p2': 0.14258587851894644, 'roi_p3': 0.20671291201040828, 'stoploss': -0.11818343570194478}, # noqa: E501
'params_details': {'buy': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.411946348378729, 215: 0.2052334363683207, 891: 0.06264755784937427, 2293: 0}, 'stoploss': {'stoploss': -0.11818343570194478}}, # noqa: E501
'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit': 0.0, 'holding_avg': timedelta()}, # noqa: E501
'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_best': False
}, {
'loss': 0.22195522184191518,
'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014}, # noqa: E501
'params_details': {'buy': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3077646493708299, 444: 0.16227697603830155, 1045: 0.07280999507931168, 2314: 0}, 'stoploss': {'stoploss': -0.18181041180901014}}, # noqa: E501
'results_metrics': {'total_trades': 14, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)}, # noqa: E501
'results_explanation': ' 14 trades. Avg profit -0.35%. Total profit -0.00248014 BTC ( -4.96Σ%). Avg duration 3402.9 min.', # noqa: E501
'total_profit': -0.002480140000000001,
'current_epoch': 5,
'is_initial_point': True,
'is_best': True
}, {
'loss': 0.545315889154162,
'params_dict': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower', 'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 319, 'roi_t2': 556, 'roi_t3': 216, 'roi_p1': 0.06251955472249589, 'roi_p2': 0.11659519602202795, 'roi_p3': 0.0953744132197762, 'stoploss': -0.024551752215582423}, # noqa: E501
'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.2744891639643, 216: 0.17911475074452382, 772: 0.06251955472249589, 1091: 0}, 'stoploss': {'stoploss': -0.024551752215582423}}, # noqa: E501
'results_metrics': {'total_trades': 39, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'holding_avg': timedelta(minutes=636.9230769230769)}, # noqa: E501
'results_explanation': ' 39 trades. Avg profit -0.21%. Total profit -0.00417730 BTC ( -8.35Σ%). Avg duration 636.9 min.', # noqa: E501
'total_profit': -0.0041773,
'current_epoch': 6,
'is_initial_point': True,
'is_best': False
}, {
'loss': 4.713497421432944,
'params_dict': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 771, 'roi_t2': 620, 'roi_t3': 145, 'roi_p1': 0.0586919200378493, 'roi_p2': 0.04984118697312542, 'roi_p3': 0.37521058680247044, 'stoploss': -0.14613268022709905}, # noqa: E501
'params_details': {
'buy': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.4837436938134452, 145: 0.10853310701097472, 765: 0.0586919200378493, 1536: 0}, # noqa: E501
'stoploss': {'stoploss': -0.14613268022709905}}, # noqa: E501
'results_metrics': {'total_trades': 318, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'holding_avg': timedelta(minutes=3140.377358490566)}, # noqa: E501
'results_explanation': ' 318 trades. Avg profit -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.', # noqa: E501
'total_profit': -0.06339929,
'current_epoch': 7,
'is_initial_point': True,
'is_best': False
}, {
'loss': 20.0, # noqa: E501
'params_dict': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal', 'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 1149, 'roi_t2': 375, 'roi_t3': 289, 'roi_p1': 0.05571820757172588, 'roi_p2': 0.0606240398618907, 'roi_p3': 0.1729012220156157, 'stoploss': -0.1588514289110401}, # noqa: E501
'params_details': {'buy': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.2892434694492323, 289: 0.11634224743361658, 664: 0.05571820757172588, 1813: 0}, 'stoploss': {'stoploss': -0.1588514289110401}}, # noqa: E501
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'holding_avg': timedelta(minutes=5340.0)}, # noqa: E501
'results_explanation': ' 1 trades. Avg profit 0.00%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration 5340.0 min.', # noqa: E501
'total_profit': 0.0,
'current_epoch': 8,
'is_initial_point': True,
'is_best': False
}, {
'loss': 2.4731817780991223,
'params_dict': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1012, 'roi_t2': 584, 'roi_t3': 422, 'roi_p1': 0.036764323603472565, 'roi_p2': 0.10335480573205287, 'roi_p3': 0.10322347377503042, 'stoploss': -0.2780610808108503}, # noqa: E501
'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.2433426031105559, 422: 0.14011912933552545, 1006: 0.036764323603472565, 2018: 0}, 'stoploss': {'stoploss': -0.2780610808108503}}, # noqa: E501
'results_metrics': {'total_trades': 229, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'holding_avg': timedelta(minutes=6505.676855895196)}, # noqa: E501
'results_explanation': ' 229 trades. Avg profit -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.', # noqa: E501
'total_profit': -0.044050070000000004, # noqa: E501
'current_epoch': 9,
'is_initial_point': True,
'is_best': False
}, {
'loss': -0.2604606005845212, # noqa: E501
'params_dict': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 792, 'roi_t2': 464, 'roi_t3': 215, 'roi_p1': 0.04594053535385903, 'roi_p2': 0.09623192684243963, 'roi_p3': 0.04428219070850663, 'stoploss': -0.16992287161634415}, # noqa: E501
'params_details': {'buy': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.18645465290480528, 215: 0.14217246219629864, 679: 0.04594053535385903, 1471: 0}, 'stoploss': {'stoploss': -0.16992287161634415}}, # noqa: E501
'results_metrics': {'total_trades': 4, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'holding_avg': timedelta(minutes=2850.0)}, # noqa: E501
'results_explanation': ' 4 trades. Avg profit 0.11%. Total profit 0.00021629 BTC ( 0.43Σ%). Avg duration 2850.0 min.', # noqa: E501
'total_profit': 0.00021629,
'current_epoch': 10,
'is_initial_point': True,
'is_best': True
}, {
'loss': 4.876465945994304, # noqa: E501
'params_dict': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 579, 'roi_t2': 614, 'roi_t3': 273, 'roi_p1': 0.05307643172744114, 'roi_p2': 0.1352282078262871, 'roi_p3': 0.1913307406325751, 'stoploss': -0.25728526022513887}, # noqa: E501
'params_details': {'buy': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3796353801863034, 273: 0.18830463955372825, 887: 0.05307643172744114, 1466: 0}, 'stoploss': {'stoploss': -0.25728526022513887}}, # noqa: E501
# New Hyperopt mode!
'results_metrics': {'total_trades': 117, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'holding_avg': timedelta(minutes=4282.5641025641025)}, # noqa: E501
'results_explanation': ' 117 trades. Avg profit -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.', # noqa: E501
'total_profit': -0.07436117,
'current_epoch': 11,
'is_initial_point': True,
'is_best': False
}, {
'loss': 100000,
'params_dict': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1156, 'roi_t2': 581, 'roi_t3': 408, 'roi_p1': 0.06860454019988212, 'roi_p2': 0.12473718444931989, 'roi_p3': 0.2896360635226823, 'stoploss': -0.30889015124682806}, # noqa: E501
'params_details': {'buy': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4829777881718843, 408: 0.19334172464920202, 989: 0.06860454019988212, 2145: 0}, 'stoploss': {'stoploss': -0.30889015124682806}}, # noqa: E501
'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'holding_avg': timedelta()}, # noqa: E501
'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
'total_profit': 0,
'current_epoch': 12,
'is_initial_point': True,
'is_best': False
}
]

43
tests/data/test_dataprovider.py
View File

@ -246,3 +246,46 @@ def test_get_analyzed_dataframe(mocker, default_conf, ohlcv_history):
assert dataframe.empty assert dataframe.empty
assert isinstance(time, datetime) assert isinstance(time, datetime)
assert time == datetime(1970, 1, 1, tzinfo=timezone.utc) assert time == datetime(1970, 1, 1, tzinfo=timezone.utc)
# Test backtest mode
default_conf["runmode"] = RunMode.BACKTEST
dp._set_dataframe_max_index(1)
dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
assert len(dataframe) == 1
dp._set_dataframe_max_index(2)
dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
assert len(dataframe) == 2
dp._set_dataframe_max_index(3)
dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
assert len(dataframe) == 3
dp._set_dataframe_max_index(500)
dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
assert len(dataframe) == len(ohlcv_history)
def test_no_exchange_mode(default_conf):
dp = DataProvider(default_conf, None)
message = "Exchange is not available to DataProvider."
with pytest.raises(OperationalException, match=message):
dp.refresh([()])
with pytest.raises(OperationalException, match=message):
dp.ohlcv('XRP/USDT', '5m')
with pytest.raises(OperationalException, match=message):
dp.market('XRP/USDT')
with pytest.raises(OperationalException, match=message):
dp.ticker('XRP/USDT')
with pytest.raises(OperationalException, match=message):
dp.orderbook('XRP/USDT', 20)
with pytest.raises(OperationalException, match=message):
dp.available_pairs()

2
tests/edge/test_edge.py
View File

@ -344,6 +344,8 @@ def test_edge_process_no_trades(mocker, edge_conf, caplog):
def test_edge_process_no_pairs(mocker, edge_conf, caplog): def test_edge_process_no_pairs(mocker, edge_conf, caplog):
edge_conf['exchange']['pair_whitelist'] = [] edge_conf['exchange']['pair_whitelist'] = []
mocker.patch('freqtrade.freqtradebot.validate_config_consistency')
freqtrade = get_patched_freqtradebot(mocker, edge_conf) freqtrade = get_patched_freqtradebot(mocker, edge_conf)
fee_mock = mocker.patch('freqtrade.exchange.Exchange.get_fee', return_value=0.001) fee_mock = mocker.patch('freqtrade.exchange.Exchange.get_fee', return_value=0.001)
mocker.patch('freqtrade.edge.edge_positioning.refresh_data') mocker.patch('freqtrade.edge.edge_positioning.refresh_data')

40
tests/exchange/test_exchange.py
View File

@ -2084,6 +2084,46 @@ def test_cancel_stoploss_order(default_conf, mocker, exchange_name):
order_id='_', pair='TKN/BTC') order_id='_', pair='TKN/BTC')
@pytest.mark.parametrize("exchange_name", EXCHANGES)
def test_cancel_stoploss_order_with_result(default_conf, mocker, exchange_name):
default_conf['dry_run'] = False
mocker.patch('freqtrade.exchange.Exchange.fetch_stoploss_order', return_value={'for': 123})
mocker.patch('freqtrade.exchange.Ftx.fetch_stoploss_order', return_value={'for': 123})
exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
mocker.patch('freqtrade.exchange.Exchange.cancel_stoploss_order',
return_value={'fee': {}, 'status': 'canceled', 'amount': 1234})
mocker.patch('freqtrade.exchange.Ftx.cancel_stoploss_order',
return_value={'fee': {}, 'status': 'canceled', 'amount': 1234})
co = exchange.cancel_stoploss_order_with_result(order_id='_', pair='TKN/BTC', amount=555)
assert co == {'fee': {}, 'status': 'canceled', 'amount': 1234}
mocker.patch('freqtrade.exchange.Exchange.cancel_stoploss_order',
return_value='canceled')
mocker.patch('freqtrade.exchange.Ftx.cancel_stoploss_order',
return_value='canceled')
# Fall back to fetch_stoploss_order
co = exchange.cancel_stoploss_order_with_result(order_id='_', pair='TKN/BTC', amount=555)
assert co == {'for': 123}
mocker.patch('freqtrade.exchange.Exchange.fetch_stoploss_order',
side_effect=InvalidOrderException(""))
mocker.patch('freqtrade.exchange.Ftx.fetch_stoploss_order',
side_effect=InvalidOrderException(""))
co = exchange.cancel_stoploss_order_with_result(order_id='_', pair='TKN/BTC', amount=555)
assert co['amount'] == 555
assert co == {'fee': {}, 'status': 'canceled', 'amount': 555, 'info': {}}
with pytest.raises(InvalidOrderException):
mocker.patch('freqtrade.exchange.Exchange.cancel_stoploss_order',
side_effect=InvalidOrderException("Did not find order"))
mocker.patch('freqtrade.exchange.Ftx.cancel_stoploss_order',
side_effect=InvalidOrderException("Did not find order"))
exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
exchange.cancel_stoploss_order_with_result(order_id='_', pair='TKN/BTC', amount=123)
@pytest.mark.parametrize("exchange_name", EXCHANGES) @pytest.mark.parametrize("exchange_name", EXCHANGES)
def test_fetch_order(default_conf, mocker, exchange_name): def test_fetch_order(default_conf, mocker, exchange_name):
default_conf['dry_run'] = True default_conf['dry_run'] = True

23
tests/exchange/test_ftx.py
View File

@ -157,3 +157,26 @@ def test_fetch_stoploss_order(default_conf, mocker):
'fetch_stoploss_order', 'fetch_orders', 'fetch_stoploss_order', 'fetch_orders',
retries=API_FETCH_ORDER_RETRY_COUNT + 1, retries=API_FETCH_ORDER_RETRY_COUNT + 1,
order_id='_', pair='TKN/BTC') order_id='_', pair='TKN/BTC')
def test_get_order_id(mocker, default_conf):
exchange = get_patched_exchange(mocker, default_conf, id='ftx')
order = {
'type': STOPLOSS_ORDERTYPE,
'price': 1500,
'id': '1111',
'info': {
'orderId': '1234'
}
}
assert exchange.get_order_id_conditional(order) == '1234'
order = {
'type': 'limit',
'price': 1500,
'id': '1111',
'info': {
'orderId': '1234'
}
}
assert exchange.get_order_id_conditional(order) == '1111'

3
tests/exchange/test_kraken.py
View File

@ -90,6 +90,7 @@ def test_get_balances_prod(default_conf, mocker):
'3ST': balance_item.copy(), '3ST': balance_item.copy(),
'4ST': balance_item.copy(), '4ST': balance_item.copy(),
'EUR': balance_item.copy(), 'EUR': balance_item.copy(),
'timestamp': 123123
}) })
kraken_open_orders = [{'symbol': '1ST/EUR', kraken_open_orders = [{'symbol': '1ST/EUR',
'type': 'limit', 'type': 'limit',
@ -138,7 +139,7 @@ def test_get_balances_prod(default_conf, mocker):
default_conf['dry_run'] = False default_conf['dry_run'] = False
exchange = get_patched_exchange(mocker, default_conf, api_mock, id="kraken") exchange = get_patched_exchange(mocker, default_conf, api_mock, id="kraken")
balances = exchange.get_balances() balances = exchange.get_balances()
assert len(balances) == 5 assert len(balances) == 6
assert balances['1ST']['free'] == 9.0 assert balances['1ST']['free'] == 9.0
assert balances['1ST']['total'] == 10.0 assert balances['1ST']['total'] == 10.0

24
tests/optimize/test_backtest_detail.py
View File

@ -185,7 +185,7 @@ tc11 = BTContainer(data=[
[0, 5000, 5050, 4950, 5000, 6172, 1, 0], [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5100, 6172, 0, 0], [1, 5000, 5050, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 5100, 5100, 6172, 0, 0], [2, 5100, 5251, 5100, 5100, 6172, 0, 0],
[3, 4850, 5050, 4650, 4750, 6172, 0, 0], [3, 5000, 5150, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]], [4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
stop_loss=-0.10, roi={"0": 0.10}, profit_perc=0.019, trailing_stop=True, stop_loss=-0.10, roi={"0": 0.10}, profit_perc=0.019, trailing_stop=True,
trailing_only_offset_is_reached=True, trailing_stop_positive_offset=0.05, trailing_only_offset_is_reached=True, trailing_stop_positive_offset=0.05,
@ -440,6 +440,23 @@ tc27 = BTContainer(data=[
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=4)] trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=4)]
) )
# Test 28: trailing_stop should raise so candle 3 causes a stoploss
# Same case than tc11 - but candle 3 "gaps down" - the stoploss will be above the candle,
# therefore "open" will be used
# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
tc28 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 5100, 5100, 6172, 0, 0],
[3, 4850, 5050, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
stop_loss=-0.10, roi={"0": 0.10}, profit_perc=-0.03, trailing_stop=True,
trailing_only_offset_is_reached=True, trailing_stop_positive_offset=0.05,
trailing_stop_positive=0.03,
trades=[BTrade(sell_reason=SellType.TRAILING_STOP_LOSS, open_tick=1, close_tick=3)]
)
TESTS = [ TESTS = [
tc0, tc0,
tc1, tc1,
@ -469,6 +486,7 @@ TESTS = [
tc25, tc25,
tc26, tc26,
tc27, tc27,
tc28,
] ]
@ -493,6 +511,7 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
patch_exchange(mocker) patch_exchange(mocker)
frame = _build_backtest_dataframe(data.data) frame = _build_backtest_dataframe(data.data)
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.strategy.advise_buy = lambda a, m: frame backtesting.strategy.advise_buy = lambda a, m: frame
backtesting.strategy.advise_sell = lambda a, m: frame backtesting.strategy.advise_sell = lambda a, m: frame
caplog.set_level(logging.DEBUG) caplog.set_level(logging.DEBUG)
@ -501,13 +520,14 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
# Dummy data as we mock the analyze functions # Dummy data as we mock the analyze functions
data_processed = {pair: frame.copy()} data_processed = {pair: frame.copy()}
min_date, max_date = get_timerange({pair: frame}) min_date, max_date = get_timerange({pair: frame})
results = backtesting.backtest( result = backtesting.backtest(
processed=data_processed, processed=data_processed,
start_date=min_date, start_date=min_date,
end_date=max_date, end_date=max_date,
max_open_trades=10, max_open_trades=10,
) )
results = result['results']
assert len(results) == len(data.trades) assert len(results) == len(data.trades)
assert round(results["profit_ratio"].sum(), 3) == round(data.profit_perc, 3) assert round(results["profit_ratio"].sum(), 3) == round(data.profit_perc, 3)

105
tests/optimize/test_backtesting.py
View File

@ -83,6 +83,7 @@ def simple_backtest(config, contour, mocker, testdatadir) -> None:
patch_exchange(mocker) patch_exchange(mocker)
config['timeframe'] = '1m' config['timeframe'] = '1m'
backtesting = Backtesting(config) backtesting = Backtesting(config)
backtesting._set_strategy(backtesting.strategylist[0])
data = load_data_test(contour, testdatadir) data = load_data_test(contour, testdatadir)
processed = backtesting.strategy.ohlcvdata_to_dataframe(data) processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
@ -106,6 +107,7 @@ def _make_backtest_conf(mocker, datadir, conf=None, pair='UNITTEST/BTC'):
data = trim_dictlist(data, -201) data = trim_dictlist(data, -201)
patch_exchange(mocker) patch_exchange(mocker)
backtesting = Backtesting(conf) backtesting = Backtesting(conf)
backtesting._set_strategy(backtesting.strategylist[0])
processed = backtesting.strategy.ohlcvdata_to_dataframe(data) processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
min_date, max_date = get_timerange(processed) min_date, max_date = get_timerange(processed)
return { return {
@ -285,6 +287,7 @@ def test_backtesting_init(mocker, default_conf, order_types) -> None:
patch_exchange(mocker) patch_exchange(mocker)
get_fee = mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.5)) get_fee = mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.5))
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
assert backtesting.config == default_conf assert backtesting.config == default_conf
assert backtesting.timeframe == '5m' assert backtesting.timeframe == '5m'
assert callable(backtesting.strategy.ohlcvdata_to_dataframe) assert callable(backtesting.strategy.ohlcvdata_to_dataframe)
@ -315,11 +318,13 @@ def test_data_with_fee(default_conf, mocker, testdatadir) -> None:
fee_mock = mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.5)) fee_mock = mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.5))
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
assert backtesting.fee == 0.1234 assert backtesting.fee == 0.1234
assert fee_mock.call_count == 0 assert fee_mock.call_count == 0
default_conf['fee'] = 0.0 default_conf['fee'] = 0.0
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
assert backtesting.fee == 0.0 assert backtesting.fee == 0.0
assert fee_mock.call_count == 0 assert fee_mock.call_count == 0
@ -330,6 +335,7 @@ def test_data_to_dataframe_bt(default_conf, mocker, testdatadir) -> None:
data = history.load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange, data = history.load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
fill_up_missing=True) fill_up_missing=True)
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
processed = backtesting.strategy.ohlcvdata_to_dataframe(data) processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
assert len(processed['UNITTEST/BTC']) == 102 assert len(processed['UNITTEST/BTC']) == 102
@ -361,12 +367,13 @@ def test_backtesting_start(default_conf, mocker, testdatadir, caplog) -> None:
default_conf['timerange'] = '-1510694220' default_conf['timerange'] = '-1510694220'
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.strategy.bot_loop_start = MagicMock() backtesting.strategy.bot_loop_start = MagicMock()
backtesting.start() backtesting.start()
# check the logs, that will contain the backtest result # check the logs, that will contain the backtest result
exists = [ exists = [
'Backtesting with data from 2017-11-14 21:17:00 ' 'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14 22:59:00 (0 days)..' 'up to 2017-11-14 22:59:00 (0 days).'
] ]
for line in exists: for line in exists:
assert log_has(line, caplog) assert log_has(line, caplog)
@ -393,6 +400,7 @@ def test_backtesting_start_no_data(default_conf, mocker, caplog, testdatadir) ->
default_conf['timerange'] = '20180101-20180102' default_conf['timerange'] = '20180101-20180102'
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
with pytest.raises(OperationalException, match='No data found. Terminating.'): with pytest.raises(OperationalException, match='No data found. Terminating.'):
backtesting.start() backtesting.start()
@ -465,6 +473,7 @@ def test_backtest__enter_trade(default_conf, fee, mocker) -> None:
default_conf['stake_amount'] = 'unlimited' default_conf['stake_amount'] = 'unlimited'
default_conf['max_open_trades'] = 2 default_conf['max_open_trades'] = 2
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
pair = 'UNITTEST/BTC' pair = 'UNITTEST/BTC'
row = [ row = [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0), pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0),
@ -508,19 +517,21 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001) mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
patch_exchange(mocker) patch_exchange(mocker)
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
pair = 'UNITTEST/BTC' pair = 'UNITTEST/BTC'
timerange = TimeRange('date', None, 1517227800, 0) timerange = TimeRange('date', None, 1517227800, 0)
data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'], data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
timerange=timerange) timerange=timerange)
processed = backtesting.strategy.ohlcvdata_to_dataframe(data) processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
min_date, max_date = get_timerange(processed) min_date, max_date = get_timerange(processed)
results = backtesting.backtest( result = backtesting.backtest(
processed=processed, processed=processed,
start_date=min_date, start_date=min_date,
end_date=max_date, end_date=max_date,
max_open_trades=10, max_open_trades=10,
position_stacking=False, position_stacking=False,
) )
results = result['results']
assert not results.empty assert not results.empty
assert len(results) == 2 assert len(results) == 2
@ -569,6 +580,7 @@ def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None
mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001) mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
patch_exchange(mocker) patch_exchange(mocker)
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
# Run a backtesting for an exiting 1min timeframe # Run a backtesting for an exiting 1min timeframe
timerange = TimeRange.parse_timerange('1510688220-1510700340') timerange = TimeRange.parse_timerange('1510688220-1510700340')
@ -583,13 +595,14 @@ def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None
max_open_trades=1, max_open_trades=1,
position_stacking=False, position_stacking=False,
) )
assert not results.empty assert not results['results'].empty
assert len(results) == 1 assert len(results['results']) == 1
def test_processed(default_conf, mocker, testdatadir) -> None: def test_processed(default_conf, mocker, testdatadir) -> None:
patch_exchange(mocker) patch_exchange(mocker)
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
dict_of_tickerrows = load_data_test('raise', testdatadir) dict_of_tickerrows = load_data_test('raise', testdatadir)
dataframes = backtesting.strategy.ohlcvdata_to_dataframe(dict_of_tickerrows) dataframes = backtesting.strategy.ohlcvdata_to_dataframe(dict_of_tickerrows)
@ -623,7 +636,7 @@ def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatad
# While buy-signals are unrealistic, running backtesting # While buy-signals are unrealistic, running backtesting
# over and over again should not cause different results # over and over again should not cause different results
for [contour, numres] in tests: for [contour, numres] in tests:
assert len(simple_backtest(default_conf, contour, mocker, testdatadir)) == numres assert len(simple_backtest(default_conf, contour, mocker, testdatadir)['results']) == numres
@pytest.mark.parametrize('protections,contour,expected', [ @pytest.mark.parametrize('protections,contour,expected', [
@ -648,7 +661,7 @@ def test_backtest_pricecontours(default_conf, fee, mocker, testdatadir,
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
# While buy-signals are unrealistic, running backtesting # While buy-signals are unrealistic, running backtesting
# over and over again should not cause different results # over and over again should not cause different results
assert len(simple_backtest(default_conf, contour, mocker, testdatadir)) == expected assert len(simple_backtest(default_conf, contour, mocker, testdatadir)['results']) == expected
def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir): def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir):
@ -660,10 +673,11 @@ def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir):
backtest_conf = _make_backtest_conf(mocker, conf=default_conf, datadir=testdatadir) backtest_conf = _make_backtest_conf(mocker, conf=default_conf, datadir=testdatadir)
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.strategy.advise_buy = fun # Override backtesting.strategy.advise_buy = fun # Override
backtesting.strategy.advise_sell = fun # Override backtesting.strategy.advise_sell = fun # Override
results = backtesting.backtest(**backtest_conf) result = backtesting.backtest(**backtest_conf)
assert results.empty assert result['results'].empty
def test_backtest_only_sell(mocker, default_conf, testdatadir): def test_backtest_only_sell(mocker, default_conf, testdatadir):
@ -675,10 +689,11 @@ def test_backtest_only_sell(mocker, default_conf, testdatadir):
backtest_conf = _make_backtest_conf(mocker, conf=default_conf, datadir=testdatadir) backtest_conf = _make_backtest_conf(mocker, conf=default_conf, datadir=testdatadir)
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.strategy.advise_buy = fun # Override backtesting.strategy.advise_buy = fun # Override
backtesting.strategy.advise_sell = fun # Override backtesting.strategy.advise_sell = fun # Override
results = backtesting.backtest(**backtest_conf) result = backtesting.backtest(**backtest_conf)
assert results.empty assert result['results'].empty
def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir): def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
@ -688,12 +703,14 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
pair='UNITTEST/BTC', datadir=testdatadir) pair='UNITTEST/BTC', datadir=testdatadir)
default_conf['timeframe'] = '1m' default_conf['timeframe'] = '1m'
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.strategy.advise_buy = _trend_alternate # Override backtesting.strategy.advise_buy = _trend_alternate # Override
backtesting.strategy.advise_sell = _trend_alternate # Override backtesting.strategy.advise_sell = _trend_alternate # Override
results = backtesting.backtest(**backtest_conf) result = backtesting.backtest(**backtest_conf)
# 200 candles in backtest data # 200 candles in backtest data
# won't buy on first (shifted by 1) # won't buy on first (shifted by 1)
# 100 buys signals # 100 buys signals
results = result['results']
assert len(results) == 100 assert len(results) == 100
# One trade was force-closed at the end # One trade was force-closed at the end
assert len(results.loc[results['is_open']]) == 0 assert len(results.loc[results['is_open']]) == 0
@ -729,6 +746,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
default_conf['timeframe'] = '5m' default_conf['timeframe'] = '5m'
backtesting = Backtesting(default_conf) backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.strategy.advise_buy = _trend_alternate_hold # Override backtesting.strategy.advise_buy = _trend_alternate_hold # Override
backtesting.strategy.advise_sell = _trend_alternate_hold # Override backtesting.strategy.advise_sell = _trend_alternate_hold # Override
@ -745,9 +763,9 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
results = backtesting.backtest(**backtest_conf) results = backtesting.backtest(**backtest_conf)
# Make sure we have parallel trades # Make sure we have parallel trades
assert len(evaluate_result_multi(results, '5m', 2)) > 0 assert len(evaluate_result_multi(results['results'], '5m', 2)) > 0
# make sure we don't have trades with more than configured max_open_trades # make sure we don't have trades with more than configured max_open_trades
assert len(evaluate_result_multi(results, '5m', 3)) == 0 assert len(evaluate_result_multi(results['results'], '5m', 3)) == 0
backtest_conf = { backtest_conf = {
'processed': processed, 'processed': processed,
@ -757,7 +775,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
'position_stacking': False, 'position_stacking': False,
} }
results = backtesting.backtest(**backtest_conf) results = backtesting.backtest(**backtest_conf)
assert len(evaluate_result_multi(results, '5m', 1)) == 0 assert len(evaluate_result_multi(results['results'], '5m', 1)) == 0
def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir): def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
@ -789,9 +807,9 @@ def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
'Parameter --timerange detected: 1510694220-1510700340 ...', 'Parameter --timerange detected: 1510694220-1510700340 ...',
f'Using data directory: {testdatadir} ...', f'Using data directory: {testdatadir} ...',
'Loading data from 2017-11-14 20:57:00 ' 'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14 22:58:00 (0 days)..', 'up to 2017-11-14 22:58:00 (0 days).',
'Backtesting with data from 2017-11-14 21:17:00 ' 'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14 22:58:00 (0 days)..', 'up to 2017-11-14 22:58:00 (0 days).',
'Parameter --enable-position-stacking detected ...' 'Parameter --enable-position-stacking detected ...'
] ]
@ -802,8 +820,20 @@ def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
@pytest.mark.filterwarnings("ignore:deprecated") @pytest.mark.filterwarnings("ignore:deprecated")
def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir): def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
default_conf['ask_strategy'].update({
"use_sell_signal": True,
"sell_profit_only": False,
"sell_profit_offset": 0.0,
"ignore_roi_if_buy_signal": False,
})
patch_exchange(mocker) patch_exchange(mocker)
backtestmock = MagicMock(return_value=pd.DataFrame(columns=BT_DATA_COLUMNS)) backtestmock = MagicMock(return_value={
'results': pd.DataFrame(columns=BT_DATA_COLUMNS),
'config': default_conf,
'locks': [],
'rejected_signals': 20,
'final_balance': 1000,
})
mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist', mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
PropertyMock(return_value=['UNITTEST/BTC'])) PropertyMock(return_value=['UNITTEST/BTC']))
mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock) mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
@ -817,7 +847,7 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
text_table_strategy=strattable_mock, text_table_strategy=strattable_mock,
generate_pair_metrics=MagicMock(), generate_pair_metrics=MagicMock(),
generate_sell_reason_stats=sell_reason_mock, generate_sell_reason_stats=sell_reason_mock,
generate_strategy_metrics=strat_summary, generate_strategy_comparison=strat_summary,
generate_daily_stats=MagicMock(), generate_daily_stats=MagicMock(),
) )
patched_configuration_load_config_file(mocker, default_conf) patched_configuration_load_config_file(mocker, default_conf)
@ -851,9 +881,9 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
'Parameter --timerange detected: 1510694220-1510700340 ...', 'Parameter --timerange detected: 1510694220-1510700340 ...',
f'Using data directory: {testdatadir} ...', f'Using data directory: {testdatadir} ...',
'Loading data from 2017-11-14 20:57:00 ' 'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14 22:58:00 (0 days)..', 'up to 2017-11-14 22:58:00 (0 days).',
'Backtesting with data from 2017-11-14 21:17:00 ' 'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14 22:58:00 (0 days)..', 'up to 2017-11-14 22:58:00 (0 days).',
'Parameter --enable-position-stacking detected ...', 'Parameter --enable-position-stacking detected ...',
'Running backtesting for Strategy DefaultStrategy', 'Running backtesting for Strategy DefaultStrategy',
'Running backtesting for Strategy TestStrategyLegacy', 'Running backtesting for Strategy TestStrategyLegacy',
@ -865,10 +895,14 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
@pytest.mark.filterwarnings("ignore:deprecated") @pytest.mark.filterwarnings("ignore:deprecated")
def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdatadir, capsys): def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdatadir, capsys):
default_conf['ask_strategy'].update({
"use_sell_signal": True,
"sell_profit_only": False,
"sell_profit_offset": 0.0,
"ignore_roi_if_buy_signal": False,
})
patch_exchange(mocker) patch_exchange(mocker)
backtestmock = MagicMock(side_effect=[ result1 = pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
'profit_ratio': [0.0, 0.0], 'profit_ratio': [0.0, 0.0],
'profit_abs': [0.0, 0.0], 'profit_abs': [0.0, 0.0],
'open_date': pd.to_datetime(['2018-01-29 18:40:00', 'open_date': pd.to_datetime(['2018-01-29 18:40:00',
@ -882,8 +916,8 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
'open_rate': [0.104445, 0.10302485], 'open_rate': [0.104445, 0.10302485],
'close_rate': [0.104969, 0.103541], 'close_rate': [0.104969, 0.103541],
'sell_reason': [SellType.ROI, SellType.ROI] 'sell_reason': [SellType.ROI, SellType.ROI]
}), })
pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'], result2 = pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'],
'profit_ratio': [0.03, 0.01, 0.1], 'profit_ratio': [0.03, 0.01, 0.1],
'profit_abs': [0.01, 0.02, 0.2], 'profit_abs': [0.01, 0.02, 0.2],
'open_date': pd.to_datetime(['2018-01-29 18:40:00', 'open_date': pd.to_datetime(['2018-01-29 18:40:00',
@ -899,7 +933,22 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
'open_rate': [0.104445, 0.10302485, 0.122541], 'open_rate': [0.104445, 0.10302485, 0.122541],
'close_rate': [0.104969, 0.103541, 0.123541], 'close_rate': [0.104969, 0.103541, 0.123541],
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS] 'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
}), })
backtestmock = MagicMock(side_effect=[
{
'results': result1,
'config': default_conf,
'locks': [],
'rejected_signals': 20,
'final_balance': 1000,
},
{
'results': result2,
'config': default_conf,
'locks': [],
'rejected_signals': 20,
'final_balance': 1000,
}
]) ])
mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist', mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
PropertyMock(return_value=['UNITTEST/BTC'])) PropertyMock(return_value=['UNITTEST/BTC']))
@ -930,9 +979,9 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
'Parameter --timerange detected: 1510694220-1510700340 ...', 'Parameter --timerange detected: 1510694220-1510700340 ...',
f'Using data directory: {testdatadir} ...', f'Using data directory: {testdatadir} ...',
'Loading data from 2017-11-14 20:57:00 ' 'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14 22:58:00 (0 days)..', 'up to 2017-11-14 22:58:00 (0 days).',
'Backtesting with data from 2017-11-14 21:17:00 ' 'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14 22:58:00 (0 days)..', 'up to 2017-11-14 22:58:00 (0 days).',
'Parameter --enable-position-stacking detected ...', 'Parameter --enable-position-stacking detected ...',
'Running backtesting for Strategy DefaultStrategy', 'Running backtesting for Strategy DefaultStrategy',
'Running backtesting for Strategy TestStrategyLegacy', 'Running backtesting for Strategy TestStrategyLegacy',

318
tests/optimize/test_hyperopt.py
View File

@ -4,7 +4,7 @@ import re
from datetime import datetime from datetime import datetime
from pathlib import Path from pathlib import Path
from typing import Dict, List from typing import Dict, List
from unittest.mock import MagicMock from unittest.mock import ANY, MagicMock
import pandas as pd import pandas as pd
import pytest import pytest
@ -17,10 +17,12 @@ from freqtrade.exceptions import OperationalException
from freqtrade.optimize.hyperopt import Hyperopt from freqtrade.optimize.hyperopt import Hyperopt
from freqtrade.optimize.hyperopt_auto import HyperOptAuto from freqtrade.optimize.hyperopt_auto import HyperOptAuto
from freqtrade.optimize.hyperopt_tools import HyperoptTools from freqtrade.optimize.hyperopt_tools import HyperoptTools
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.optimize.space import SKDecimal from freqtrade.optimize.space import SKDecimal
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
from freqtrade.state import RunMode from freqtrade.state import RunMode
from freqtrade.strategy.hyper import IntParameter from freqtrade.strategy.hyper import IntParameter
from freqtrade.strategy.interface import SellType
from tests.conftest import (get_args, log_has, log_has_re, patch_exchange, from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
patched_configuration_load_config_file) patched_configuration_load_config_file)
@ -28,23 +30,7 @@ from .hyperopts.default_hyperopt import DefaultHyperOpt
# Functions for recurrent object patching # Functions for recurrent object patching
def create_results(mocker, hyperopt, testdatadir) -> List[Dict]: def create_results() -> List[Dict]:
"""
When creating results, mock the hyperopt so that *by default*
- we don't create any pickle'd files in the filesystem
- we might have a pickle'd file so make sure that we return
false when looking for it
"""
hyperopt.results_file = testdatadir / 'optimize/ut_results.pickle'
mocker.patch.object(Path, "is_file", MagicMock(return_value=False))
stat_mock = MagicMock()
stat_mock.st_size = 1
mocker.patch.object(Path, "stat", MagicMock(return_value=stat_mock))
mocker.patch.object(Path, "unlink", MagicMock(return_value=True))
mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None)
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
return [{'loss': 1, 'result': 'foo', 'params': {}, 'is_best': True}] return [{'loss': 1, 'result': 'foo', 'params': {}, 'is_best': True}]
@ -318,54 +304,49 @@ def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
assert caplog.record_tuples == [] assert caplog.record_tuples == []
def test_save_results_saves_epochs(mocker, hyperopt, testdatadir, caplog) -> None: def test_save_results_saves_epochs(mocker, hyperopt, tmpdir, caplog) -> None:
epochs = create_results(mocker, hyperopt, testdatadir) # Test writing to temp dir and reading again
mock_dump = mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None) epochs = create_results()
mock_dump_json = mocker.patch('freqtrade.optimize.hyperopt.file_dump_json', return_value=None) hyperopt.results_file = Path(tmpdir / 'ut_results.fthypt')
results_file = testdatadir / 'optimize' / 'ut_results.pickle'
caplog.set_level(logging.DEBUG) caplog.set_level(logging.DEBUG)
hyperopt.epochs = epochs for epoch in epochs:
hyperopt._save_results() hyperopt._save_result(epoch)
assert log_has(f"1 epoch saved to '{results_file}'.", caplog) assert log_has(f"1 epoch saved to '{hyperopt.results_file}'.", caplog)
mock_dump.assert_called_once()
mock_dump_json.assert_called_once()
hyperopt.epochs = epochs + epochs hyperopt._save_result(epochs[0])
hyperopt._save_results() assert log_has(f"2 epochs saved to '{hyperopt.results_file}'.", caplog)
assert log_has(f"2 epochs saved to '{results_file}'.", caplog)
hyperopt_epochs = HyperoptTools.load_previous_results(hyperopt.results_file)
assert len(hyperopt_epochs) == 2
def test_read_results_returns_epochs(mocker, hyperopt, testdatadir, caplog) -> None: def test_load_previous_results(testdatadir, caplog) -> None:
epochs = create_results(mocker, hyperopt, testdatadir)
mock_load = mocker.patch('freqtrade.optimize.hyperopt_tools.load', return_value=epochs)
results_file = testdatadir / 'optimize' / 'ut_results.pickle'
hyperopt_epochs = HyperoptTools._read_results(results_file)
assert log_has(f"Reading epochs from '{results_file}'", caplog)
assert hyperopt_epochs == epochs
mock_load.assert_called_once()
results_file = testdatadir / 'hyperopt_results_SampleStrategy.pickle'
def test_load_previous_results(mocker, hyperopt, testdatadir, caplog) -> None:
epochs = create_results(mocker, hyperopt, testdatadir)
mock_load = mocker.patch('freqtrade.optimize.hyperopt_tools.load', return_value=epochs)
mocker.patch.object(Path, 'is_file', MagicMock(return_value=True))
statmock = MagicMock()
statmock.st_size = 5
# mocker.patch.object(Path, 'stat', MagicMock(return_value=statmock))
results_file = testdatadir / 'optimize' / 'ut_results.pickle'
hyperopt_epochs = HyperoptTools.load_previous_results(results_file) hyperopt_epochs = HyperoptTools.load_previous_results(results_file)
assert hyperopt_epochs == epochs assert len(hyperopt_epochs) == 5
mock_load.assert_called_once() assert log_has_re(r"Reading pickled epochs from .*", caplog)
del epochs[0]['is_best'] caplog.clear()
mock_load = mocker.patch('freqtrade.optimize.hyperopt_tools.load', return_value=epochs)
with pytest.raises(OperationalException): # Modern version
results_file = testdatadir / 'strategy_SampleStrategy.fthypt'
hyperopt_epochs = HyperoptTools.load_previous_results(results_file)
assert len(hyperopt_epochs) == 5
assert log_has_re(r"Reading epochs from .*", caplog)
def test_load_previous_results2(mocker, testdatadir, caplog) -> None:
mocker.patch('freqtrade.optimize.hyperopt_tools.HyperoptTools._read_results_pickle',
return_value=[{'asdf': '222'}])
results_file = testdatadir / 'hyperopt_results_SampleStrategy.pickle'
with pytest.raises(OperationalException, match=r"The file .* incompatible.*"):
HyperoptTools.load_previous_results(results_file) HyperoptTools.load_previous_results(results_file)
@ -383,7 +364,8 @@ def test_roi_table_generation(hyperopt) -> None:
def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None: def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock()) dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json') mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data', mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
@ -422,9 +404,9 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
out, err = capsys.readouterr() out, err = capsys.readouterr()
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
assert dumper.called # Should be called for historical candle data
# Should be called twice, once for historical candle data, once to save evaluations assert dumper.call_count == 1
assert dumper.call_count == 2 assert dumper2.call_count == 1
assert hasattr(hyperopt.backtesting.strategy, "advise_sell") assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy") assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
assert hasattr(hyperopt, "max_open_trades") assert hasattr(hyperopt, "max_open_trades")
@ -432,18 +414,42 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
assert hasattr(hyperopt, "position_stacking") assert hasattr(hyperopt, "position_stacking")
def test_format_results(hyperopt): def test_hyperopt_format_results(hyperopt):
# Test with BTC as stake_currency
trades = [ bt_result = {
('ETH/BTC', 2, 2, 123), 'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
('LTC/BTC', 1, 1, 123), "UNITTEST/BTC", "UNITTEST/BTC"],
('XPR/BTC', -1, -2, -246) "profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
] "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
labels = ['currency', 'profit_ratio', 'profit_abs', 'trade_duration'] "open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
df = pd.DataFrame.from_records(trades, columns=labels) Arrow(2017, 11, 14, 21, 36, 00).datetime,
results_metrics = hyperopt._calculate_results_metrics(df) Arrow(2017, 11, 14, 22, 12, 00).datetime,
results_explanation = hyperopt._format_results_explanation_string(results_metrics) Arrow(2017, 11, 14, 22, 44, 00).datetime],
total_profit = results_metrics['total_profit'] "close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
"trade_duration": [123, 34, 31, 14],
"is_open": [False, False, False, True],
"stake_amount": [0.01, 0.01, 0.01, 0.01],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
}),
'config': hyperopt.config,
'locks': [],
'final_balance': 0.02,
'rejected_signals': 2,
'backtest_start_time': 1619718665,
'backtest_end_time': 1619718665,
}
results_metrics = generate_strategy_stats({'XRP/BTC': None}, '', bt_result,
Arrow(2017, 11, 14, 19, 32, 00),
Arrow(2017, 12, 14, 19, 32, 00), market_change=0)
results_explanation = HyperoptTools.format_results_explanation_string(results_metrics, 'BTC')
total_profit = results_metrics['profit_total_abs']
results = { results = {
'loss': 0.0, 'loss': 0.0,
@ -457,21 +463,9 @@ def test_format_results(hyperopt):
} }
result = HyperoptTools._format_explanation_string(results, 1) result = HyperoptTools._format_explanation_string(results, 1)
assert result.find(' 66.67%') assert ' 0.71%' in result
assert result.find('Total profit 1.00000000 BTC') assert 'Total profit 0.00003100 BTC' in result
assert result.find('2.0000Σ %') assert '0:50:00 min' in result
# Test with EUR as stake_currency
trades = [
('ETH/EUR', 2, 2, 123),
('LTC/EUR', 1, 1, 123),
('XPR/EUR', -1, -2, -246)
]
df = pd.DataFrame.from_records(trades, columns=labels)
results_metrics = hyperopt._calculate_results_metrics(df)
results['total_profit'] = results_metrics['total_profit']
result = HyperoptTools._format_explanation_string(results, 1)
assert result.find('Total profit 1.00000000 EUR')
@pytest.mark.parametrize("spaces, expected_results", [ @pytest.mark.parametrize("spaces, expected_results", [
@ -502,10 +496,10 @@ def test_format_results(hyperopt):
(['default', 'buy'], (['default', 'buy'],
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': False}), {'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': False}),
]) ])
def test_has_space(hyperopt, spaces, expected_results): def test_has_space(hyperopt_conf, spaces, expected_results):
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']: for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
hyperopt.config.update({'spaces': spaces}) hyperopt_conf.update({'spaces': spaces})
assert hyperopt.has_space(s) == expected_results[s] assert HyperoptTools.has_space(hyperopt_conf, s) == expected_results[s]
def test_populate_indicators(hyperopt, testdatadir) -> None: def test_populate_indicators(hyperopt, testdatadir) -> None:
@ -576,22 +570,39 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
'hyperopt_min_trades': 1, 'hyperopt_min_trades': 1,
}) })
trades = [ backtest_result = {
('TRX/BTC', 0.023117, 0.000233, 100) 'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
] "UNITTEST/BTC", "UNITTEST/BTC"],
labels = ['currency', 'profit_ratio', 'profit_abs', 'trade_duration'] "profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
backtest_result = pd.DataFrame.from_records(trades, columns=labels) "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
"trade_duration": [123, 34, 31, 14],
"is_open": [False, False, False, True],
"stake_amount": [0.01, 0.01, 0.01, 0.01],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
}),
'config': hyperopt_conf,
'locks': [],
'rejected_signals': 20,
'final_balance': 1000,
}
mocker.patch( mocker.patch('freqtrade.optimize.hyperopt.Backtesting.backtest', return_value=backtest_result)
'freqtrade.optimize.hyperopt.Backtesting.backtest', mocker.patch('freqtrade.optimize.hyperopt.get_timerange',
MagicMock(return_value=backtest_result) return_value=(Arrow(2017, 12, 10), Arrow(2017, 12, 13)))
)
mocker.patch(
'freqtrade.optimize.hyperopt.get_timerange',
MagicMock(return_value=(Arrow(2017, 12, 10), Arrow(2017, 12, 13)))
)
patch_exchange(mocker) patch_exchange(mocker)
mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock()) mocker.patch.object(Path, 'open')
mocker.patch('freqtrade.optimize.hyperopt.load', return_value={'XRP/BTC': None})
optimizer_param = { optimizer_param = {
'adx-value': 0, 'adx-value': 0,
@ -625,11 +636,11 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
'trailing_only_offset_is_reached': False, 'trailing_only_offset_is_reached': False,
} }
response_expected = { response_expected = {
'loss': 1.9840569076926293, 'loss': 1.9147239021396234,
'results_explanation': (' 1 trades. 1/0/0 Wins/Draws/Losses. ' 'results_explanation': (' 4 trades. 4/0/0 Wins/Draws/Losses. '
'Avg profit 2.31%. Median profit 2.31%. Total profit ' 'Avg profit 0.77%. Median profit 0.71%. Total profit '
'0.00023300 BTC ( 2.31%). ' '0.00003100 BTC ( 0.00%). '
'Avg duration 100.0 min.' 'Avg duration 0:50:00 min.'
), ),
'params_details': {'buy': {'adx-enabled': False, 'params_details': {'buy': {'adx-enabled': False,
'adx-value': 0, 'adx-value': 0,
@ -640,10 +651,10 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
'rsi-enabled': False, 'rsi-enabled': False,
'rsi-value': 0, 'rsi-value': 0,
'trigger': 'macd_cross_signal'}, 'trigger': 'macd_cross_signal'},
'roi': {0: 0.12000000000000001, 'roi': {"0": 0.12000000000000001,
20.0: 0.02, "20.0": 0.02,
50.0: 0.01, "50.0": 0.01,
110.0: 0}, "110.0": 0},
'sell': {'sell-adx-enabled': False, 'sell': {'sell-adx-enabled': False,
'sell-adx-value': 0, 'sell-adx-value': 0,
'sell-fastd-enabled': True, 'sell-fastd-enabled': True,
@ -659,21 +670,16 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
'trailing_stop_positive': 0.02, 'trailing_stop_positive': 0.02,
'trailing_stop_positive_offset': 0.07}}, 'trailing_stop_positive_offset': 0.07}},
'params_dict': optimizer_param, 'params_dict': optimizer_param,
'results_metrics': {'avg_profit': 2.3117, 'params_not_optimized': {'buy': {}, 'sell': {}},
'draws': 0, 'results_metrics': ANY,
'duration': 100.0, 'total_profit': 3.1e-08
'losses': 0,
'winsdrawslosses': ' 1 0 0',
'median_profit': 2.3117,
'profit': 2.3117,
'total_profit': 0.000233,
'trade_count': 1,
'wins': 1},
'total_profit': 0.00023300
} }
hyperopt = Hyperopt(hyperopt_conf) hyperopt = Hyperopt(hyperopt_conf)
hyperopt.dimensions = hyperopt.hyperopt_space() hyperopt.min_date = Arrow(2017, 12, 10)
hyperopt.max_date = Arrow(2017, 12, 13)
hyperopt.init_spaces()
hyperopt.dimensions = hyperopt.dimensions
generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values())) generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values()))
assert generate_optimizer_value == response_expected assert generate_optimizer_value == response_expected
@ -690,7 +696,8 @@ def test_clean_hyperopt(mocker, hyperopt_conf, caplog):
def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None: def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock()) dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json') mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data', mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
@ -741,13 +748,14 @@ def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
':{},"stoploss":null,"trailing_stop":null}' ':{},"stoploss":null,"trailing_stop":null}'
) )
assert result_str in out # noqa: E501 assert result_str in out # noqa: E501
assert dumper.called # Should be called for historical candle data
# Should be called twice, once for historical candle data, once to save evaluations assert dumper.call_count == 1
assert dumper.call_count == 2 assert dumper2.call_count == 1
def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None: def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock()) dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json') mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data', mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None))) MagicMock(return_value=(MagicMock(), None)))
@ -789,13 +797,14 @@ def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
out, err = capsys.readouterr() out, err = capsys.readouterr()
assert '{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi":{},"stoploss":null}' in out # noqa: E501 assert '{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi":{},"stoploss":null}' in out # noqa: E501
assert dumper.called # Should be called for historical candle data
# Should be called twice, once for historical candle data, once to save evaluations assert dumper.call_count == 1
assert dumper.call_count == 2 assert dumper2.call_count == 1
def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None: def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock()) dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json') mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data', mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None))) MagicMock(return_value=(MagicMock(), None)))
@ -836,13 +845,14 @@ def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
out, err = capsys.readouterr() out, err = capsys.readouterr()
assert '{"minimal_roi":{},"stoploss":null}' in out assert '{"minimal_roi":{},"stoploss":null}' in out
assert dumper.called
# Should be called twice, once for historical candle data, once to save evaluations assert dumper.call_count == 1
assert dumper.call_count == 2 assert dumper2.call_count == 1
def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> None: def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock()) dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json') mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data', mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None))) MagicMock(return_value=(MagicMock(), None)))
@ -884,9 +894,9 @@ def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> Non
out, err = capsys.readouterr() out, err = capsys.readouterr()
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
assert dumper.called assert dumper.call_count == 1
# Should be called twice, once for historical candle data, once to save evaluations assert dumper2.call_count == 1
assert dumper.call_count == 2
assert hasattr(hyperopt.backtesting.strategy, "advise_sell") assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy") assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
assert hasattr(hyperopt, "max_open_trades") assert hasattr(hyperopt, "max_open_trades")
@ -922,7 +932,8 @@ def test_simplified_interface_all_failed(mocker, hyperopt_conf) -> None:
def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None: def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock()) dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json') mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data', mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None))) MagicMock(return_value=(MagicMock(), None)))
@ -965,8 +976,8 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
out, err = capsys.readouterr() out, err = capsys.readouterr()
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
assert dumper.called assert dumper.called
# Should be called twice, once for historical candle data, once to save evaluations assert dumper.call_count == 1
assert dumper.call_count == 2 assert dumper2.call_count == 1
assert hasattr(hyperopt.backtesting.strategy, "advise_sell") assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy") assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
assert hasattr(hyperopt, "max_open_trades") assert hasattr(hyperopt, "max_open_trades")
@ -975,7 +986,8 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None: def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock()) dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json') mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data', mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None))) MagicMock(return_value=(MagicMock(), None)))
@ -1018,8 +1030,8 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
out, err = capsys.readouterr() out, err = capsys.readouterr()
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
assert dumper.called assert dumper.called
# Should be called twice, once for historical candle data, once to save evaluations assert dumper.call_count == 1
assert dumper.call_count == 2 assert dumper2.call_count == 1
assert hasattr(hyperopt.backtesting.strategy, "advise_sell") assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy") assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
assert hasattr(hyperopt, "max_open_trades") assert hasattr(hyperopt, "max_open_trades")
@ -1107,7 +1119,7 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmpdir, fee) -> None:
assert isinstance(hyperopt.custom_hyperopt, HyperOptAuto) assert isinstance(hyperopt.custom_hyperopt, HyperOptAuto)
assert isinstance(hyperopt.backtesting.strategy.buy_rsi, IntParameter) assert isinstance(hyperopt.backtesting.strategy.buy_rsi, IntParameter)
assert hyperopt.backtesting.strategy.buy_rsi.hyperopt is True assert hyperopt.backtesting.strategy.buy_rsi.in_space is True
assert hyperopt.backtesting.strategy.buy_rsi.value == 35 assert hyperopt.backtesting.strategy.buy_rsi.value == 35
buy_rsi_range = hyperopt.backtesting.strategy.buy_rsi.range buy_rsi_range = hyperopt.backtesting.strategy.buy_rsi.range
assert isinstance(buy_rsi_range, range) assert isinstance(buy_rsi_range, range)
@ -1132,3 +1144,17 @@ def test_SKDecimal():
assert space.transform([2.0]) == [200] assert space.transform([2.0]) == [200]
assert space.transform([1.0]) == [100] assert space.transform([1.0]) == [100]
assert space.transform([1.5, 1.6]) == [150, 160] assert space.transform([1.5, 1.6]) == [150, 160]
def test___pprint():
params = {'buy_std': 1.2, 'buy_rsi': 31, 'buy_enable': True, 'buy_what': 'asdf'}
non_params = {'buy_notoptimied': 55}
x = HyperoptTools._pprint(params, non_params)
assert x == """{
"buy_std": 1.2,
"buy_rsi": 31,
"buy_enable": True,
"buy_what": "asdf",
"buy_notoptimied": 55, # value loaded from strategy
}"""

97
tests/optimize/test_optimize_reports.py
View File

@ -1,5 +1,6 @@
import datetime
import re import re
from datetime import datetime, timedelta, timezone from datetime import timedelta
from pathlib import Path from pathlib import Path
import pandas as pd import pandas as pd
@ -7,14 +8,15 @@ import pytest
from arrow import Arrow from arrow import Arrow
from freqtrade.configuration import TimeRange from freqtrade.configuration import TimeRange
from freqtrade.constants import LAST_BT_RESULT_FN from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data import history from freqtrade.data import history
from freqtrade.data.btanalysis import get_latest_backtest_filename, load_backtest_data from freqtrade.data.btanalysis import get_latest_backtest_filename, load_backtest_data
from freqtrade.edge import PairInfo from freqtrade.edge import PairInfo
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_daily_stats, from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_daily_stats,
generate_edge_table, generate_pair_metrics, generate_edge_table, generate_pair_metrics,
generate_sell_reason_stats, generate_sell_reason_stats,
generate_strategy_metrics, store_backtest_stats, generate_strategy_comparison,
generate_trading_stats, store_backtest_stats,
text_table_bt_results, text_table_sell_reason, text_table_bt_results, text_table_sell_reason,
text_table_strategy) text_table_strategy)
from freqtrade.resolvers.strategy_resolver import StrategyResolver from freqtrade.resolvers.strategy_resolver import StrategyResolver
@ -26,25 +28,22 @@ def test_text_table_bt_results():
results = pd.DataFrame( results = pd.DataFrame(
{ {
'pair': ['ETH/BTC', 'ETH/BTC'], 'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
'profit_ratio': [0.1, 0.2], 'profit_ratio': [0.1, 0.2, -0.05],
'profit_abs': [0.2, 0.4], 'profit_abs': [0.2, 0.4, -0.1],
'trade_duration': [10, 30], 'trade_duration': [10, 30, 20],
'wins': [2, 0],
'draws': [0, 0],
'losses': [0, 0]
} }
) )
result_str = ( result_str = (
'| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC |' '| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % |'
' Tot Profit % | Avg Duration | Wins | Draws | Losses |\n' ' Avg Duration | Win Draw Loss Win% |\n'
'|---------+--------+----------------+----------------+------------------+' '|---------+--------+----------------+----------------+------------------+----------------+'
'----------------+----------------+--------+---------+----------|\n' '----------------+-------------------------|\n'
'| ETH/BTC | 2 | 15.00 | 30.00 | 0.60000000 |' '| ETH/BTC | 3 | 8.33 | 25.00 | 0.50000000 | 12.50 |'
' 15.00 | 0:20:00 | 2 | 0 | 0 |\n' ' 0:20:00 | 2 0 1 66.7 |\n'
'| TOTAL | 2 | 15.00 | 30.00 | 0.60000000 |' '| TOTAL | 3 | 8.33 | 25.00 | 0.50000000 | 12.50 |'
' 15.00 | 0:20:00 | 2 | 0 | 0 |' ' 0:20:00 | 2 0 1 66.7 |'
) )
pair_results = generate_pair_metrics(data={'ETH/BTC': {}}, stake_currency='BTC', pair_results = generate_pair_metrics(data={'ETH/BTC': {}}, stake_currency='BTC',
@ -80,6 +79,7 @@ def test_generate_backtest_stats(default_conf, testdatadir):
'config': default_conf, 'config': default_conf,
'locks': [], 'locks': [],
'final_balance': 1000.02, 'final_balance': 1000.02,
'rejected_signals': 20,
'backtest_start_time': Arrow.utcnow().int_timestamp, 'backtest_start_time': Arrow.utcnow().int_timestamp,
'backtest_end_time': Arrow.utcnow().int_timestamp, 'backtest_end_time': Arrow.utcnow().int_timestamp,
} }
@ -96,8 +96,8 @@ def test_generate_backtest_stats(default_conf, testdatadir):
assert 'DefStrat' in stats['strategy'] assert 'DefStrat' in stats['strategy']
assert 'strategy_comparison' in stats assert 'strategy_comparison' in stats
strat_stats = stats['strategy']['DefStrat'] strat_stats = stats['strategy']['DefStrat']
assert strat_stats['backtest_start'] == min_date.datetime assert strat_stats['backtest_start'] == min_date.strftime(DATETIME_PRINT_FORMAT)
assert strat_stats['backtest_end'] == max_date.datetime assert strat_stats['backtest_end'] == max_date.strftime(DATETIME_PRINT_FORMAT)
assert strat_stats['total_trades'] == len(results['DefStrat']['results']) assert strat_stats['total_trades'] == len(results['DefStrat']['results'])
# Above sample had no loosing trade # Above sample had no loosing trade
assert strat_stats['max_drawdown'] == 0.0 assert strat_stats['max_drawdown'] == 0.0
@ -127,6 +127,7 @@ def test_generate_backtest_stats(default_conf, testdatadir):
'config': default_conf, 'config': default_conf,
'locks': [], 'locks': [],
'final_balance': 1000.02, 'final_balance': 1000.02,
'rejected_signals': 20,
'backtest_start_time': Arrow.utcnow().int_timestamp, 'backtest_start_time': Arrow.utcnow().int_timestamp,
'backtest_end_time': Arrow.utcnow().int_timestamp, 'backtest_end_time': Arrow.utcnow().int_timestamp,
} }
@ -140,8 +141,8 @@ def test_generate_backtest_stats(default_conf, testdatadir):
strat_stats = stats['strategy']['DefStrat'] strat_stats = stats['strategy']['DefStrat']
assert strat_stats['max_drawdown'] == 0.013803 assert strat_stats['max_drawdown'] == 0.013803
assert strat_stats['drawdown_start'] == datetime(2017, 11, 14, 22, 10, tzinfo=timezone.utc) assert strat_stats['drawdown_start'] == '2017-11-14 22:10:00'
assert strat_stats['drawdown_end'] == datetime(2017, 11, 14, 22, 43, tzinfo=timezone.utc) assert strat_stats['drawdown_end'] == '2017-11-14 22:43:00'
assert strat_stats['drawdown_end_ts'] == 1510699380000 assert strat_stats['drawdown_end_ts'] == 1510699380000
assert strat_stats['drawdown_start_ts'] == 1510697400000 assert strat_stats['drawdown_start_ts'] == 1510697400000
assert strat_stats['pairlist'] == ['UNITTEST/BTC'] assert strat_stats['pairlist'] == ['UNITTEST/BTC']
@ -226,8 +227,6 @@ def test_generate_daily_stats(testdatadir):
assert res['winning_days'] == 14 assert res['winning_days'] == 14
assert res['draw_days'] == 4 assert res['draw_days'] == 4
assert res['losing_days'] == 3 assert res['losing_days'] == 3
assert res['winner_holding_avg'] == timedelta(seconds=1440)
assert res['loser_holding_avg'] == timedelta(days=1, seconds=21420)
# Select empty dataframe! # Select empty dataframe!
res = generate_daily_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :]) res = generate_daily_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :])
@ -238,6 +237,23 @@ def test_generate_daily_stats(testdatadir):
assert res['losing_days'] == 0 assert res['losing_days'] == 0
def test_generate_trading_stats(testdatadir):
filename = testdatadir / "backtest-result_new.json"
bt_data = load_backtest_data(filename)
res = generate_trading_stats(bt_data)
assert isinstance(res, dict)
assert res['winner_holding_avg'] == timedelta(seconds=1440)
assert res['loser_holding_avg'] == timedelta(days=1, seconds=21420)
assert 'wins' in res
assert 'losses' in res
assert 'draws' in res
# Select empty dataframe!
res = generate_trading_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :])
assert res['wins'] == 0
assert res['losses'] == 0
def test_text_table_sell_reason(): def test_text_table_sell_reason():
results = pd.DataFrame( results = pd.DataFrame(
@ -254,14 +270,14 @@ def test_text_table_sell_reason():
) )
result_str = ( result_str = (
'| Sell Reason | Sells | Wins | Draws | Losses |' '| Sell Reason | Sells | Win Draws Loss Win% | Avg Profit % | Cum Profit % |'
' Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % |\n' ' Tot Profit BTC | Tot Profit % |\n'
'|---------------+---------+--------+---------+----------+' '|---------------+---------+--------------------------+----------------+----------------+'
'----------------+----------------+------------------+----------------|\n' '------------------+----------------|\n'
'| roi | 2 | 2 | 0 | 0 |' '| roi | 2 | 2 0 0 100 | 15 | 30 |'
' 15 | 30 | 0.6 | 15 |\n' ' 0.6 | 15 |\n'
'| stop_loss | 1 | 0 | 0 | 1 |' '| stop_loss | 1 | 0 0 1 0 | -10 | -10 |'
' -10 | -10 | -0.2 | -5 |' ' -0.2 | -5 |'
) )
sell_reason_stats = generate_sell_reason_stats(max_open_trades=2, sell_reason_stats = generate_sell_reason_stats(max_open_trades=2,
@ -309,9 +325,12 @@ def test_text_table_strategy(default_conf):
default_conf['max_open_trades'] = 2 default_conf['max_open_trades'] = 2
default_conf['dry_run_wallet'] = 3 default_conf['dry_run_wallet'] = 3
results = {} results = {}
date = datetime.datetime(year=2020, month=1, day=1, hour=12, minute=30)
delta = datetime.timedelta(days=1)
results['TestStrategy1'] = {'results': pd.DataFrame( results['TestStrategy1'] = {'results': pd.DataFrame(
{ {
'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'], 'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
'close_date': [date, date + delta, date + delta * 2],
'profit_ratio': [0.1, 0.2, 0.3], 'profit_ratio': [0.1, 0.2, 0.3],
'profit_abs': [0.2, 0.4, 0.5], 'profit_abs': [0.2, 0.4, 0.5],
'trade_duration': [10, 30, 10], 'trade_duration': [10, 30, 10],
@ -324,6 +343,7 @@ def test_text_table_strategy(default_conf):
results['TestStrategy2'] = {'results': pd.DataFrame( results['TestStrategy2'] = {'results': pd.DataFrame(
{ {
'pair': ['LTC/BTC', 'LTC/BTC', 'LTC/BTC'], 'pair': ['LTC/BTC', 'LTC/BTC', 'LTC/BTC'],
'close_date': [date, date + delta, date + delta * 2],
'profit_ratio': [0.4, 0.2, 0.3], 'profit_ratio': [0.4, 0.2, 0.3],
'profit_abs': [0.4, 0.4, 0.5], 'profit_abs': [0.4, 0.4, 0.5],
'trade_duration': [15, 30, 15], 'trade_duration': [15, 30, 15],
@ -335,18 +355,17 @@ def test_text_table_strategy(default_conf):
), 'config': default_conf} ), 'config': default_conf}
result_str = ( result_str = (
'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot' '| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC |'
' Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |\n' ' Tot Profit % | Avg Duration | Win Draw Loss Win% | Drawdown |\n'
'|---------------+--------+----------------+----------------+------------------+' '|---------------+--------+----------------+----------------+------------------+'
'----------------+----------------+--------+---------+----------|\n' '----------------+----------------+-------------------------+-----------------------|\n'
'| TestStrategy1 | 3 | 20.00 | 60.00 | 1.10000000 |' '| TestStrategy1 | 3 | 20.00 | 60.00 | 1.10000000 |'
' 36.67 | 0:17:00 | 3 | 0 | 0 |\n' ' 36.67 | 0:17:00 | 3 0 0 100 | 0.00000000 BTC 0.00% |\n'
'| TestStrategy2 | 3 | 30.00 | 90.00 | 1.30000000 |' '| TestStrategy2 | 3 | 30.00 | 90.00 | 1.30000000 |'
' 43.33 | 0:20:00 | 3 | 0 | 0 |' ' 43.33 | 0:20:00 | 3 0 0 100 | 0.00000000 BTC 0.00% |'
) )
strategy_results = generate_strategy_metrics(all_results=results) strategy_results = generate_strategy_comparison(all_results=results)
assert text_table_strategy(strategy_results, 'BTC') == result_str assert text_table_strategy(strategy_results, 'BTC') == result_str

28
tests/plugins/test_pairlist.py
View File

@ -7,10 +7,11 @@ import pytest
from freqtrade.constants import AVAILABLE_PAIRLISTS from freqtrade.constants import AVAILABLE_PAIRLISTS
from freqtrade.exceptions import OperationalException from freqtrade.exceptions import OperationalException
from freqtrade.persistence import Trade
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.plugins.pairlistmanager import PairListManager from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.resolvers import PairListResolver from freqtrade.resolvers import PairListResolver
from tests.conftest import get_patched_freqtradebot, log_has, log_has_re from tests.conftest import get_patched_exchange, get_patched_freqtradebot, log_has, log_has_re
@pytest.fixture(scope="function") @pytest.fixture(scope="function")
@ -406,6 +407,9 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
([{"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"}, ([{"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"},
{"method": "PriceFilter", "low_price_ratio": 0.02}], {"method": "PriceFilter", "low_price_ratio": 0.02}],
"USDT", ['ETH/USDT', 'NANO/USDT']), "USDT", ['ETH/USDT', 'NANO/USDT']),
([{"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"},
{"method": "PriceFilter", "max_value": 0.000001}],
"USDT", ['NANO/USDT']),
([{"method": "StaticPairList"}, ([{"method": "StaticPairList"},
{"method": "RangeStabilityFilter", "lookback_days": 10, {"method": "RangeStabilityFilter", "lookback_days": 10,
"min_rate_of_change": 0.01, "refresh_period": 1440}], "min_rate_of_change": 0.01, "refresh_period": 1440}],
@ -488,6 +492,8 @@ def test_VolumePairList_whitelist_gen(mocker, whitelist_conf, shitcoinmarkets, t
r'because last price < .*%$', caplog) or r'because last price < .*%$', caplog) or
log_has_re(r'^Removed .* from whitelist, ' log_has_re(r'^Removed .* from whitelist, '
r'because last price > .*%$', caplog) or r'because last price > .*%$', caplog) or
log_has_re(r'^Removed .* from whitelist, '
r'because min value change of .*', caplog) or
log_has_re(r"^Removed .* from whitelist, because ticker\['last'\] " log_has_re(r"^Removed .* from whitelist, because ticker\['last'\] "
r"is empty.*", caplog)) r"is empty.*", caplog))
if pairlist['method'] == 'VolumePairList': if pairlist['method'] == 'VolumePairList':
@ -512,6 +518,18 @@ def test_PrecisionFilter_error(mocker, whitelist_conf) -> None:
PairListManager(MagicMock, whitelist_conf) PairListManager(MagicMock, whitelist_conf)
def test_PerformanceFilter_error(mocker, whitelist_conf, caplog) -> None:
whitelist_conf['pairlists'] = [{"method": "StaticPairList"}, {"method": "PerformanceFilter"}]
if hasattr(Trade, 'query'):
del Trade.query
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
exchange = get_patched_exchange(mocker, whitelist_conf)
pm = PairListManager(exchange, whitelist_conf)
pm.refresh_pairlist()
assert log_has("PerformanceFilter is not available in this mode.", caplog)
def test_gen_pair_whitelist_not_supported(mocker, default_conf, tickers) -> None: def test_gen_pair_whitelist_not_supported(mocker, default_conf, tickers) -> None:
default_conf['pairlists'] = [{'method': 'VolumePairList', 'number_assets': 10}] default_conf['pairlists'] = [{'method': 'VolumePairList', 'number_assets': 10}]
@ -787,6 +805,10 @@ def test_spreadfilter_invalid_data(mocker, default_conf, markets, tickers, caplo
"[{'PriceFilter': 'PriceFilter - Filtering pairs priced below 0.00002000.'}]", "[{'PriceFilter': 'PriceFilter - Filtering pairs priced below 0.00002000.'}]",
None None
), ),
({"method": "PriceFilter", "max_value": 0.00002000},
"[{'PriceFilter': 'PriceFilter - Filtering pairs priced Value above 0.00002000.'}]",
None
),
({"method": "PriceFilter"}, ({"method": "PriceFilter"},
"[{'PriceFilter': 'PriceFilter - No price filters configured.'}]", "[{'PriceFilter': 'PriceFilter - No price filters configured.'}]",
None None
@ -803,6 +825,10 @@ def test_spreadfilter_invalid_data(mocker, default_conf, markets, tickers, caplo
None, None,
"PriceFilter requires max_price to be >= 0" "PriceFilter requires max_price to be >= 0"
), # OperationalException expected ), # OperationalException expected
({"method": "PriceFilter", "max_value": -1.00010000},
None,
"PriceFilter requires max_value to be >= 0"
), # OperationalException expected
({"method": "RangeStabilityFilter", "lookback_days": 10, "min_rate_of_change": 0.01}, ({"method": "RangeStabilityFilter", "lookback_days": 10, "min_rate_of_change": 0.01},
"[{'RangeStabilityFilter': 'RangeStabilityFilter - Filtering pairs with rate of change below " "[{'RangeStabilityFilter': 'RangeStabilityFilter - Filtering pairs with rate of change below "
"0.01 over the last days.'}]", "0.01 over the last days.'}]",

29
tests/rpc/test_fiat_convert.py
View File

@ -1,6 +1,7 @@
# pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors, # pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors,
# pragma pylint: disable=protected-access, C0103 # pragma pylint: disable=protected-access, C0103
import datetime
from unittest.mock import MagicMock from unittest.mock import MagicMock
import pytest import pytest
@ -21,6 +22,12 @@ def test_fiat_convert_is_supported(mocker):
def test_fiat_convert_find_price(mocker): def test_fiat_convert_find_price(mocker):
fiat_convert = CryptoToFiatConverter() fiat_convert = CryptoToFiatConverter()
fiat_convert._cryptomap = {}
fiat_convert._backoff = 0
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._load_cryptomap',
return_value=None)
assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='EUR') == 0.0
with pytest.raises(ValueError, match=r'The fiat ABC is not supported.'): with pytest.raises(ValueError, match=r'The fiat ABC is not supported.'):
fiat_convert._find_price(crypto_symbol='BTC', fiat_symbol='ABC') fiat_convert._find_price(crypto_symbol='BTC', fiat_symbol='ABC')
@ -115,6 +122,28 @@ def test_fiat_convert_without_network(mocker):
CryptoToFiatConverter._coingekko = cmc_temp CryptoToFiatConverter._coingekko = cmc_temp
def test_fiat_too_many_requests_response(mocker, caplog):
# Because CryptoToFiatConverter is a Singleton we reset the listings
req_exception = "429 Too Many Requests"
listmock = MagicMock(return_value="{}", side_effect=RequestException(req_exception))
mocker.patch.multiple(
'freqtrade.rpc.fiat_convert.CoinGeckoAPI',
get_coins_list=listmock,
)
# with pytest.raises(RequestEsxception):
fiat_convert = CryptoToFiatConverter()
fiat_convert._cryptomap = {}
fiat_convert._load_cryptomap()
length_cryptomap = len(fiat_convert._cryptomap)
assert length_cryptomap == 0
assert fiat_convert._backoff > datetime.datetime.now().timestamp()
assert log_has(
'Too many requests for Coingecko API, backing off and trying again later.',
caplog
)
def test_fiat_invalid_response(mocker, caplog): def test_fiat_invalid_response(mocker, caplog):
# Because CryptoToFiatConverter is a Singleton we reset the listings # Because CryptoToFiatConverter is a Singleton we reset the listings
listmock = MagicMock(return_value="{'novalidjson':DEADBEEFf}") listmock = MagicMock(return_value="{'novalidjson':DEADBEEFf}")

13
tests/rpc/test_rpc.py
View File

@ -199,28 +199,31 @@ def test_rpc_status_table(default_conf, ticker, fee, mocker) -> None:
freqtradebot.enter_positions() freqtradebot.enter_positions()
result, headers = rpc._rpc_status_table(default_conf['stake_currency'], 'USD') result, headers, fiat_profit_sum = rpc._rpc_status_table(default_conf['stake_currency'], 'USD')
assert "Since" in headers assert "Since" in headers
assert "Pair" in headers assert "Pair" in headers
assert 'instantly' == result[0][2] assert 'instantly' == result[0][2]
assert 'ETH/BTC' in result[0][1] assert 'ETH/BTC' in result[0][1]
assert '-0.41%' == result[0][3] assert '-0.41%' == result[0][3]
assert isnan(fiat_profit_sum)
# Test with fiatconvert # Test with fiatconvert
rpc._fiat_converter = CryptoToFiatConverter() rpc._fiat_converter = CryptoToFiatConverter()
result, headers = rpc._rpc_status_table(default_conf['stake_currency'], 'USD') result, headers, fiat_profit_sum = rpc._rpc_status_table(default_conf['stake_currency'], 'USD')
assert "Since" in headers assert "Since" in headers
assert "Pair" in headers assert "Pair" in headers
assert 'instantly' == result[0][2] assert 'instantly' == result[0][2]
assert 'ETH/BTC' in result[0][1] assert 'ETH/BTC' in result[0][1]
assert '-0.41% (-0.06)' == result[0][3] assert '-0.41% (-0.06)' == result[0][3]
assert '-0.06' == f'{fiat_profit_sum:.2f}'
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_sell_rate', mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_sell_rate',
MagicMock(side_effect=ExchangeError("Pair 'ETH/BTC' not available"))) MagicMock(side_effect=ExchangeError("Pair 'ETH/BTC' not available")))
result, headers = rpc._rpc_status_table(default_conf['stake_currency'], 'USD') result, headers, fiat_profit_sum = rpc._rpc_status_table(default_conf['stake_currency'], 'USD')
assert 'instantly' == result[0][2] assert 'instantly' == result[0][2]
assert 'ETH/BTC' in result[0][1] assert 'ETH/BTC' in result[0][1]
assert 'nan%' == result[0][3] assert 'nan%' == result[0][3]
assert isnan(fiat_profit_sum)
def test_rpc_daily_profit(default_conf, update, ticker, fee, def test_rpc_daily_profit(default_conf, update, ticker, fee,
@ -419,7 +422,7 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
assert stats['trade_count'] == 2 assert stats['trade_count'] == 2
assert stats['first_trade_date'] == 'just now' assert stats['first_trade_date'] == 'just now'
assert stats['latest_trade_date'] == 'just now' assert stats['latest_trade_date'] == 'just now'
assert stats['avg_duration'] == '0:00:00' assert stats['avg_duration'] in ('0:00:00', '0:00:01')
assert stats['best_pair'] == 'ETH/BTC' assert stats['best_pair'] == 'ETH/BTC'
assert prec_satoshi(stats['best_rate'], 6.2) assert prec_satoshi(stats['best_rate'], 6.2)
@ -430,7 +433,7 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
assert stats['trade_count'] == 2 assert stats['trade_count'] == 2
assert stats['first_trade_date'] == 'just now' assert stats['first_trade_date'] == 'just now'
assert stats['latest_trade_date'] == 'just now' assert stats['latest_trade_date'] == 'just now'
assert stats['avg_duration'] == '0:00:00' assert stats['avg_duration'] in ('0:00:00', '0:00:01')
assert stats['best_pair'] == 'ETH/BTC' assert stats['best_pair'] == 'ETH/BTC'
assert prec_satoshi(stats['best_rate'], 6.2) assert prec_satoshi(stats['best_rate'], 6.2)
assert isnan(stats['profit_all_coin']) assert isnan(stats['profit_all_coin'])

17
tests/rpc/test_rpc_apiserver.py
View File

@ -710,7 +710,7 @@ def test_api_stats(botclient, mocker, ticker, fee, markets,):
assert 'draws' in rc.json()['durations'] assert 'draws' in rc.json()['durations']
def test_api_performance(botclient, mocker, ticker, fee): def test_api_performance(botclient, fee):
ftbot, client = botclient ftbot, client = botclient
patch_get_signal(ftbot, (True, False)) patch_get_signal(ftbot, (True, False))
@ -728,6 +728,7 @@ def test_api_performance(botclient, mocker, ticker, fee):
) )
trade.close_profit = trade.calc_profit_ratio() trade.close_profit = trade.calc_profit_ratio()
trade.close_profit_abs = trade.calc_profit()
Trade.query.session.add(trade) Trade.query.session.add(trade)
trade = Trade( trade = Trade(
@ -743,14 +744,16 @@ def test_api_performance(botclient, mocker, ticker, fee):
close_rate=0.391 close_rate=0.391
) )
trade.close_profit = trade.calc_profit_ratio() trade.close_profit = trade.calc_profit_ratio()
trade.close_profit_abs = trade.calc_profit()
Trade.query.session.add(trade) Trade.query.session.add(trade)
Trade.query.session.flush() Trade.query.session.flush()
rc = client_get(client, f"{BASE_URI}/performance") rc = client_get(client, f"{BASE_URI}/performance")
assert_response(rc) assert_response(rc)
assert len(rc.json()) == 2 assert len(rc.json()) == 2
assert rc.json() == [{'count': 1, 'pair': 'LTC/ETH', 'profit': 7.61}, assert rc.json() == [{'count': 1, 'pair': 'LTC/ETH', 'profit': 7.61, 'profit_abs': 0.01872279},
{'count': 1, 'pair': 'XRP/ETH', 'profit': -5.57}] {'count': 1, 'pair': 'XRP/ETH', 'profit': -5.57, 'profit_abs': -0.1150375}]
def test_api_status(botclient, mocker, ticker, fee, markets): def test_api_status(botclient, mocker, ticker, fee, markets):
@ -1142,6 +1145,14 @@ def test_api_plot_config(botclient):
assert_response(rc) assert_response(rc)
assert rc.json() == ftbot.strategy.plot_config assert rc.json() == ftbot.strategy.plot_config
assert isinstance(rc.json()['main_plot'], dict) assert isinstance(rc.json()['main_plot'], dict)
assert isinstance(rc.json()['subplots'], dict)
ftbot.strategy.plot_config = {'main_plot': {'sma': {}}}
rc = client_get(client, f"{BASE_URI}/plot_config")
assert_response(rc)
assert isinstance(rc.json()['main_plot'], dict)
assert isinstance(rc.json()['subplots'], dict)
def test_api_strategies(botclient): def test_api_strategies(botclient):

52
tests/rpc/test_rpc_telegram.py
View File

@ -4,6 +4,7 @@
import re import re
from datetime import datetime from datetime import datetime
from functools import reduce
from random import choice, randint from random import choice, randint
from string import ascii_uppercase from string import ascii_uppercase
from unittest.mock import ANY, MagicMock from unittest.mock import ANY, MagicMock
@ -54,6 +55,14 @@ class DummyCls(Telegram):
raise Exception('test') raise Exception('test')
def get_telegram_testobject_with_inline(mocker, default_conf, mock=True, ftbot=None):
inline_msg_mock = MagicMock()
telegram, ftbot, msg_mock = get_telegram_testobject(mocker, default_conf)
mocker.patch('freqtrade.rpc.telegram.Telegram._send_inline_msg', inline_msg_mock)
return telegram, ftbot, msg_mock, inline_msg_mock
def get_telegram_testobject(mocker, default_conf, mock=True, ftbot=None): def get_telegram_testobject(mocker, default_conf, mock=True, ftbot=None):
msg_mock = MagicMock() msg_mock = MagicMock()
if mock: if mock:
@ -902,6 +911,33 @@ def test_forcebuy_handle_exception(default_conf, update, mocker) -> None:
assert msg_mock.call_args_list[0][0][0] == 'Forcebuy not enabled.' assert msg_mock.call_args_list[0][0][0] == 'Forcebuy not enabled.'
def test_forcebuy_no_pair(default_conf, update, mocker) -> None:
mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
fbuy_mock = MagicMock(return_value=None)
mocker.patch('freqtrade.rpc.RPC._rpc_forcebuy', fbuy_mock)
telegram, freqtradebot, _, inline_msg_mock = get_telegram_testobject_with_inline(mocker,
default_conf)
patch_get_signal(freqtradebot, (True, False))
context = MagicMock()
context.args = []
telegram._forcebuy(update=update, context=context)
assert fbuy_mock.call_count == 0
assert inline_msg_mock.call_count == 1
assert inline_msg_mock.call_args_list[0][0][0] == 'Which pair?'
assert inline_msg_mock.call_args_list[0][1]['callback_query_handler'] == 'forcebuy'
keyboard = inline_msg_mock.call_args_list[0][1]['keyboard']
assert reduce(lambda acc, x: acc + len(x), keyboard, 0) == 4
update = MagicMock()
update.callback_query = MagicMock()
update.callback_query.data = 'XRP/USDT'
telegram._forcebuy_inline(update, None)
assert fbuy_mock.call_count == 1
def test_performance_handle(default_conf, update, ticker, fee, def test_performance_handle(default_conf, update, ticker, fee,
limit_buy_order, limit_sell_order, mocker) -> None: limit_buy_order, limit_sell_order, mocker) -> None:
@ -929,7 +965,7 @@ def test_performance_handle(default_conf, update, ticker, fee,
telegram._performance(update=update, context=MagicMock()) telegram._performance(update=update, context=MagicMock())
assert msg_mock.call_count == 1 assert msg_mock.call_count == 1
assert 'Performance' in msg_mock.call_args_list[0][0][0] assert 'Performance' in msg_mock.call_args_list[0][0][0]
assert '<code>ETH/BTC\t6.20% (1)</code>' in msg_mock.call_args_list[0][0][0] assert '<code>ETH/BTC\t0.00006217 BTC (6.20%) (1)</code>' in msg_mock.call_args_list[0][0][0]
def test_count_handle(default_conf, update, ticker, fee, mocker) -> None: def test_count_handle(default_conf, update, ticker, fee, mocker) -> None:
@ -969,6 +1005,11 @@ def test_telegram_lock_handle(default_conf, update, ticker, fee, mocker) -> None
) )
telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
patch_get_signal(freqtradebot, (True, False)) patch_get_signal(freqtradebot, (True, False))
telegram._locks(update=update, context=MagicMock())
assert msg_mock.call_count == 1
assert 'No active locks.' in msg_mock.call_args_list[0][0][0]
msg_mock.reset_mock()
PairLocks.lock_pair('ETH/BTC', arrow.utcnow().shift(minutes=4).datetime, 'randreason') PairLocks.lock_pair('ETH/BTC', arrow.utcnow().shift(minutes=4).datetime, 'randreason')
PairLocks.lock_pair('XRP/BTC', arrow.utcnow().shift(minutes=20).datetime, 'deadbeef') PairLocks.lock_pair('XRP/BTC', arrow.utcnow().shift(minutes=20).datetime, 'deadbeef')
@ -1102,6 +1143,15 @@ def test_edge_enabled(edge_conf, update, mocker) -> None:
assert '<b>Edge only validated following pairs:</b>\n<pre>' in msg_mock.call_args_list[0][0][0] assert '<b>Edge only validated following pairs:</b>\n<pre>' in msg_mock.call_args_list[0][0][0]
assert 'Pair Winrate Expectancy Stoploss' in msg_mock.call_args_list[0][0][0] assert 'Pair Winrate Expectancy Stoploss' in msg_mock.call_args_list[0][0][0]
msg_mock.reset_mock()
mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
return_value={}))
telegram._edge(update=update, context=MagicMock())
assert msg_mock.call_count == 1
assert '<b>Edge only validated following pairs:</b>' in msg_mock.call_args_list[0][0][0]
assert 'Winrate' not in msg_mock.call_args_list[0][0][0]
def test_telegram_trades(mocker, update, default_conf, fee): def test_telegram_trades(mocker, update, default_conf, fee):

9
tests/strategy/test_default_strategy.py
View File

@ -36,10 +36,11 @@ def test_default_strategy(result, fee):
) )
assert strategy.confirm_trade_entry(pair='ETH/BTC', order_type='limit', amount=0.1, assert strategy.confirm_trade_entry(pair='ETH/BTC', order_type='limit', amount=0.1,
rate=20000, time_in_force='gtc') is True rate=20000, time_in_force='gtc',
current_time=datetime.utcnow()) is True
assert strategy.confirm_trade_exit(pair='ETH/BTC', trade=trade, order_type='limit', amount=0.1, assert strategy.confirm_trade_exit(pair='ETH/BTC', trade=trade, order_type='limit', amount=0.1,
rate=20000, time_in_force='gtc', sell_reason='roi') is True rate=20000, time_in_force='gtc', sell_reason='roi',
current_time=datetime.utcnow()) is True
assert strategy.custom_stoploss(pair='ETH/BTC', trade=trade, current_time=datetime.now(), assert strategy.custom_stoploss(pair='ETH/BTC', trade=trade, current_time=datetime.now(),
current_rate=20_000, current_profit=0.05, dataframe=None current_rate=20_000, current_profit=0.05) == strategy.stoploss
) == strategy.stoploss

16
tests/strategy/test_interface.py
View File

@ -360,7 +360,7 @@ def test_stop_loss_reached(default_conf, fee, profit, adjusted, expected, traili
now = arrow.utcnow().datetime now = arrow.utcnow().datetime
sl_flag = strategy.stop_loss_reached(current_rate=trade.open_rate * (1 + profit), trade=trade, sl_flag = strategy.stop_loss_reached(current_rate=trade.open_rate * (1 + profit), trade=trade,
current_time=now, current_profit=profit, current_time=now, current_profit=profit,
force_stoploss=0, high=None, dataframe=None) force_stoploss=0, high=None)
assert isinstance(sl_flag, SellCheckTuple) assert isinstance(sl_flag, SellCheckTuple)
assert sl_flag.sell_type == expected assert sl_flag.sell_type == expected
if expected == SellType.NONE: if expected == SellType.NONE:
@ -371,7 +371,7 @@ def test_stop_loss_reached(default_conf, fee, profit, adjusted, expected, traili
sl_flag = strategy.stop_loss_reached(current_rate=trade.open_rate * (1 + profit2), trade=trade, sl_flag = strategy.stop_loss_reached(current_rate=trade.open_rate * (1 + profit2), trade=trade,
current_time=now, current_profit=profit2, current_time=now, current_profit=profit2,
force_stoploss=0, high=None, dataframe=None) force_stoploss=0, high=None)
assert sl_flag.sell_type == expected2 assert sl_flag.sell_type == expected2
if expected2 == SellType.NONE: if expected2 == SellType.NONE:
assert sl_flag.sell_flag is False assert sl_flag.sell_flag is False
@ -399,27 +399,27 @@ def test_custom_sell(default_conf, fee, caplog) -> None:
) )
now = arrow.utcnow().datetime now = arrow.utcnow().datetime
res = strategy.should_sell(None, trade, 1, now, False, False, None, None, 0) res = strategy.should_sell(trade, 1, now, False, False, None, None, 0)
assert res.sell_flag is False assert res.sell_flag is False
assert res.sell_type == SellType.NONE assert res.sell_type == SellType.NONE
strategy.custom_sell = MagicMock(return_value=True) strategy.custom_sell = MagicMock(return_value=True)
res = strategy.should_sell(None, trade, 1, now, False, False, None, None, 0) res = strategy.should_sell(trade, 1, now, False, False, None, None, 0)
assert res.sell_flag is True assert res.sell_flag is True
assert res.sell_type == SellType.CUSTOM_SELL assert res.sell_type == SellType.CUSTOM_SELL
assert res.sell_reason == 'custom_sell' assert res.sell_reason == 'custom_sell'
strategy.custom_sell = MagicMock(return_value='hello world') strategy.custom_sell = MagicMock(return_value='hello world')
res = strategy.should_sell(None, trade, 1, now, False, False, None, None, 0) res = strategy.should_sell(trade, 1, now, False, False, None, None, 0)
assert res.sell_type == SellType.CUSTOM_SELL assert res.sell_type == SellType.CUSTOM_SELL
assert res.sell_flag is True assert res.sell_flag is True
assert res.sell_reason == 'hello world' assert res.sell_reason == 'hello world'
caplog.clear() caplog.clear()
strategy.custom_sell = MagicMock(return_value='h' * 100) strategy.custom_sell = MagicMock(return_value='h' * 100)
res = strategy.should_sell(None, trade, 1, now, False, False, None, None, 0) res = strategy.should_sell(trade, 1, now, False, False, None, None, 0)
assert res.sell_type == SellType.CUSTOM_SELL assert res.sell_type == SellType.CUSTOM_SELL
assert res.sell_flag is True assert res.sell_flag is True
assert res.sell_reason == 'h' * 64 assert res.sell_reason == 'h' * 64
@ -636,7 +636,7 @@ def test_hyperopt_parameters():
assert len(list(intpar.range)) == 1 assert len(list(intpar.range)) == 1
# Range contains ONLY the default / value. # Range contains ONLY the default / value.
assert list(intpar.range) == [intpar.value] assert list(intpar.range) == [intpar.value]
intpar.hyperopt = True intpar.in_space = True
assert len(list(intpar.range)) == 6 assert len(list(intpar.range)) == 6
assert list(intpar.range) == [0, 1, 2, 3, 4, 5] assert list(intpar.range) == [0, 1, 2, 3, 4, 5]
@ -671,4 +671,4 @@ def test_auto_hyperopt_interface(default_conf):
strategy.sell_rsi = IntParameter([0, 10], default=5, space='buy') strategy.sell_rsi = IntParameter([0, 10], default=5, space='buy')
with pytest.raises(OperationalException, match=r"Inconclusive parameter.*"): with pytest.raises(OperationalException, match=r"Inconclusive parameter.*"):
[x for x in strategy.enumerate_parameters('sell')] [x for x in strategy._detect_parameters('sell')]

17
tests/test_freqtradebot.py
View File

@ -1371,7 +1371,8 @@ def test_handle_stoploss_on_exchange_trailing_error(mocker, default_conf, fee, c
} }
mocker.patch('freqtrade.exchange.Binance.cancel_stoploss_order', mocker.patch('freqtrade.exchange.Binance.cancel_stoploss_order',
side_effect=InvalidOrderException()) side_effect=InvalidOrderException())
mocker.patch('freqtrade.exchange.Binance.fetch_stoploss_order', stoploss_order_hanging) mocker.patch('freqtrade.exchange.Binance.fetch_stoploss_order',
return_value=stoploss_order_hanging)
freqtrade.handle_trailing_stoploss_on_exchange(trade, stoploss_order_hanging) freqtrade.handle_trailing_stoploss_on_exchange(trade, stoploss_order_hanging)
assert log_has_re(r"Could not cancel stoploss order abcd for pair ETH/BTC.*", caplog) assert log_has_re(r"Could not cancel stoploss order abcd for pair ETH/BTC.*", caplog)
@ -2430,13 +2431,22 @@ def test_handle_cancel_buy(mocker, caplog, default_conf, limit_buy_order) -> Non
freqtrade._notify_buy_cancel = MagicMock() freqtrade._notify_buy_cancel = MagicMock()
trade = MagicMock() trade = MagicMock()
trade.pair = 'LTC/ETH' trade.pair = 'LTC/USDT'
trade.open_rate = 200
limit_buy_order['filled'] = 0.0 limit_buy_order['filled'] = 0.0
limit_buy_order['status'] = 'open' limit_buy_order['status'] = 'open'
reason = CANCEL_REASON['TIMEOUT'] reason = CANCEL_REASON['TIMEOUT']
assert freqtrade.handle_cancel_buy(trade, limit_buy_order, reason) assert freqtrade.handle_cancel_buy(trade, limit_buy_order, reason)
assert cancel_order_mock.call_count == 1 assert cancel_order_mock.call_count == 1
cancel_order_mock.reset_mock()
caplog.clear()
limit_buy_order['filled'] = 0.01
assert not freqtrade.handle_cancel_buy(trade, limit_buy_order, reason)
assert cancel_order_mock.call_count == 0
assert log_has_re("Order .* for .* not cancelled, as the filled amount.* unsellable.*", caplog)
caplog.clear()
cancel_order_mock.reset_mock() cancel_order_mock.reset_mock()
limit_buy_order['filled'] = 2 limit_buy_order['filled'] = 2
assert not freqtrade.handle_cancel_buy(trade, limit_buy_order, reason) assert not freqtrade.handle_cancel_buy(trade, limit_buy_order, reason)
@ -2491,7 +2501,8 @@ def test_handle_cancel_buy_corder_empty(mocker, default_conf, limit_buy_order,
freqtrade._notify_buy_cancel = MagicMock() freqtrade._notify_buy_cancel = MagicMock()
trade = MagicMock() trade = MagicMock()
trade.pair = 'LTC/ETH' trade.pair = 'LTC/USDT'
trade.open_rate = 200
limit_buy_order['filled'] = 0.0 limit_buy_order['filled'] = 0.0
limit_buy_order['status'] = 'open' limit_buy_order['status'] = 'open'
reason = CANCEL_REASON['TIMEOUT'] reason = CANCEL_REASON['TIMEOUT']

2
tests/test_integration.py
View File

@ -63,7 +63,7 @@ def test_may_execute_sell_stoploss_on_exchange_multi(default_conf, ticker, fee,
amount_to_precision=lambda s, x, y: y, amount_to_precision=lambda s, x, y: y,
price_to_precision=lambda s, x, y: y, price_to_precision=lambda s, x, y: y,
fetch_stoploss_order=stoploss_order_mock, fetch_stoploss_order=stoploss_order_mock,
cancel_stoploss_order=cancel_order_mock, cancel_stoploss_order_with_result=cancel_order_mock,
) )
mocker.patch.multiple( mocker.patch.multiple(

59
tests/test_persistence.py
View File

@ -7,7 +7,7 @@ from unittest.mock import MagicMock
import arrow import arrow
import pytest import pytest
from sqlalchemy import create_engine from sqlalchemy import create_engine, inspect
from freqtrade import constants from freqtrade import constants
from freqtrade.exceptions import DependencyException, OperationalException from freqtrade.exceptions import DependencyException, OperationalException
@ -627,6 +627,63 @@ def test_migrate_new(mocker, default_conf, fee, caplog):
assert orders[1].order_id == 'stop_order_id222' assert orders[1].order_id == 'stop_order_id222'
assert orders[1].ft_order_side == 'stoploss' assert orders[1].ft_order_side == 'stoploss'
caplog.clear()
# Drop latest column
engine.execute("alter table orders rename to orders_bak")
inspector = inspect(engine)
for index in inspector.get_indexes('orders_bak'):
engine.execute(f"drop index {index['name']}")
# Recreate table
engine.execute("""
CREATE TABLE orders (
id INTEGER NOT NULL,
ft_trade_id INTEGER,
ft_order_side VARCHAR NOT NULL,
ft_pair VARCHAR NOT NULL,
ft_is_open BOOLEAN NOT NULL,
order_id VARCHAR NOT NULL,
status VARCHAR,
symbol VARCHAR,
order_type VARCHAR,
side VARCHAR,
price FLOAT,
amount FLOAT,
filled FLOAT,
remaining FLOAT,
cost FLOAT,
order_date DATETIME,
order_filled_date DATETIME,
order_update_date DATETIME,
PRIMARY KEY (id),
CONSTRAINT _order_pair_order_id UNIQUE (ft_pair, order_id),
FOREIGN KEY(ft_trade_id) REFERENCES trades (id)
)
""")
engine.execute("""
insert into orders ( id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id, status,
symbol, order_type, side, price, amount, filled, remaining, cost, order_date,
order_filled_date, order_update_date)
select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id, status,
symbol, order_type, side, price, amount, filled, remaining, cost, order_date,
order_filled_date, order_update_date
from orders_bak
""")
# Run init to test migration
init_db(default_conf['db_url'], default_conf['dry_run'])
assert log_has("trying orders_bak1", caplog)
orders = Order.query.all()
assert len(orders) == 2
assert orders[0].order_id == 'buy_order'
assert orders[0].ft_order_side == 'buy'
assert orders[1].order_id == 'stop_order_id222'
assert orders[1].ft_order_side == 'stoploss'
def test_migrate_mid_state(mocker, default_conf, fee, caplog): def test_migrate_mid_state(mocker, default_conf, fee, caplog):
""" """

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tests/testdata/strategy_SampleStrategy.fthypt vendored Normal file
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