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Merge branch 'develop' into pr/orehunt/2951

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Matthias 2020-07-19 20:12:39 +02:00
commit e116277383
182 changed files with 9296 additions and 3460 deletions
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17
.dependabot/config.yml
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@ -1,17 +0,0 @@
version: 1
update_configs:
- package_manager: "python"
directory: "/"
update_schedule: "weekly"
allowed_updates:
- match:
update_type: "all"
target_branch: "develop"
- package_manager: "docker"
directory: "/"
update_schedule: "daily"
allowed_updates:
- match:
update_type: "all"

33
.github/ISSUE_TEMPLATE.md vendored
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@ -1,33 +0,0 @@
## Step 1: Have you search for this issue before posting it?
If you have discovered a bug in the bot, please [search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue).
If it hasn't been reported, please create a new issue.
## Step 2: Describe your environment
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Branch: Master | Develop
* Last Commit ID: _____ (`git log --format="%H" -n 1`)
## Step 3: Describe the problem:
*Explain the problem you have encountered*
### Steps to reproduce:
1. _____
2. _____
3. _____
### Observed Results:
* What happened?
* What did you expect to happen?
### Relevant code exceptions or logs:
```
// paste your log here
```

48
.github/ISSUE_TEMPLATE/bug_report.md vendored Normal file
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@ -0,0 +1,48 @@
---
name: Bug report
about: Create a report to help us improve
title: ''
labels: "Triage Needed"
assignees: ''
---
<!--
Have you searched for similar issues before posting it?
If you have discovered a bug in the bot, please [search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue).
If it hasn't been reported, please create a new issue.
Please do not use bug reports to request new features.
-->
## Describe your environment
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
Note: All issues other than enhancement requests will be closed without further comment if the above template is deleted or not filled out.
## Describe the problem:
*Explain the problem you have encountered*
### Steps to reproduce:
1. _____
2. _____
3. _____
### Observed Results:
* What happened?
* What did you expect to happen?
### Relevant code exceptions or logs
Note: Please copy/paste text of the messages, no screenshots of logs please.
```
// paste your log here
```

27
.github/ISSUE_TEMPLATE/feature_request.md vendored Normal file
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@ -0,0 +1,27 @@
---
name: Feature request
about: Suggest an idea for this project
title: ''
labels: ''
assignees: ''
---
<!--
Note: this section will not show up in the issue.
Have you search for this feature before requesting it? It's highly likely that a similar request was already filed.
-->
## Describe your environment
(if applicable)
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
## Describe the enhancement
*Explain the enhancement you would like*

25
.github/ISSUE_TEMPLATE/question.md vendored Normal file
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@ -0,0 +1,25 @@
---
name: BQuestion
about: Ask a question you could not find an answer in the docs
title: ''
labels: "Question"
assignees: ''
---
<!--
Have you searched for similar issues before posting it?
Did you have a VERY good look at the [documentation](https://www.freqtrade.io/en/latest/) and are sure that the question is not explained there
Please do not use the question template to report bugs or to request new features.
-->
## Describe your environment
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
## Your question
*Ask the question you have not been able to find an answer in our [Documentation](https://www.freqtrade.io/en/latest/)*

13
.github/dependabot.yml vendored Normal file
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@ -0,0 +1,13 @@
version: 2
updates:
- package-ecosystem: docker
directory: "/"
schedule:
interval: daily
open-pull-requests-limit: 10
- package-ecosystem: pip
directory: "/"
schedule:
interval: weekly
open-pull-requests-limit: 10
target-branch: develop

74
.github/workflows/ci.yml vendored
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@ -100,7 +100,7 @@ jobs:
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
job_name: '*Freqtrade CI ${{ matrix.os }}*'
@ -115,7 +115,7 @@ jobs:
strategy:
matrix:
os: [ windows-latest ]
python-version: [3.7]
python-version: [3.7, 3.8]
steps:
- uses: actions/checkout@v2
@ -130,8 +130,7 @@ jobs:
if: startsWith(runner.os, 'Windows')
with:
path: ~\AppData\Local\pip\Cache
key: ${{ runner.os }}-pip
restore-keys: ${{ runner.os }}-pip
key: ${{ matrix.os }}-${{ matrix.python-version }}-pip
- name: Installation
run: |
@ -163,7 +162,7 @@ jobs:
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
job_name: '*Freqtrade CI windows*'
@ -190,6 +189,29 @@ jobs:
channel: '#notifications'
url: ${{ secrets.SLACK_WEBHOOK }}
cleanup-prior-runs:
runs-on: ubuntu-latest
steps:
- name: Cleanup previous runs on this branch
uses: rokroskar/workflow-run-cleanup-action@v0.2.2
if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/master' && github.repository == 'freqtrade/freqtrade'"
env:
GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
# Notify on slack only once - when CI completes (and after deploy) in case it's successfull
notify-complete:
needs: [ build, build_windows, docs_check ]
runs-on: ubuntu-latest
steps:
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
job_name: '*Freqtrade CI*'
channel: '#notifications'
url: ${{ secrets.SLACK_WEBHOOK }}
deploy:
needs: [ build, build_windows, docs_check ]
runs-on: ubuntu-18.04
@ -227,25 +249,45 @@ jobs:
user: __token__
password: ${{ secrets.pypi_password }}
- name: Dockerhub login
env:
DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
run: |
echo "${DOCKER_PASSWORD}" | docker login --username ${DOCKER_USERNAME} --password-stdin
- name: Build and test and push docker image
env:
IMAGE_NAME: freqtradeorg/freqtrade
DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
run: |
build_helpers/publish_docker.sh
- name: Build raspberry image for ${{ steps.extract_branch.outputs.branch }}_pi
uses: elgohr/Publish-Docker-Github-Action@2.7
# We need docker experimental to pull the ARM image.
- name: Switch docker to experimental
run: |
docker version -f '{{.Server.Experimental}}'
echo $'{\n "experimental": true\n}' | sudo tee /etc/docker/daemon.json
sudo systemctl restart docker
docker version -f '{{.Server.Experimental}}'
- name: Set up Docker Buildx
id: buildx
uses: crazy-max/ghaction-docker-buildx@v1
with:
name: freqtradeorg/freqtrade:${{ steps.extract_branch.outputs.branch }}_pi
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_PASSWORD }}
dockerfile: Dockerfile.pi
# cache: true
cache: ${{ github.event_name != 'schedule' }}
tag_names: true
buildx-version: latest
qemu-version: latest
- name: Available platforms
run: echo ${{ steps.buildx.outputs.platforms }}
- name: Build Raspberry docker image
env:
IMAGE_NAME: freqtradeorg/freqtrade
BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}_pi
run: |
build_helpers/publish_docker_pi.sh
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0

1
.gitignore vendored
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@ -6,7 +6,6 @@ user_data/*
!user_data/strategy/sample_strategy.py
!user_data/notebooks
user_data/notebooks/*
!user_data/notebooks/*example.ipynb
freqtrade-plot.html
freqtrade-profit-plot.html

5
.travis.yml
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@ -1,4 +1,3 @@
sudo: true
os:
- linux
dist: xenial
@ -11,10 +10,10 @@ env:
global:
- IMAGE_NAME=freqtradeorg/freqtrade
install:
- cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
- cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies; cd ..
- export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
- export TA_LIBRARY_PATH=${HOME}/dependencies/lib
- export TA_INCLUDE_PATH=${HOME}/dependencies/lib/include
- export TA_INCLUDE_PATH=${HOME}/dependencies/include
- pip install -r requirements-dev.txt
- pip install -e .
jobs:

4
Dockerfile
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@ -1,7 +1,7 @@
FROM python:3.8.2-slim-buster
FROM python:3.8.4-slim-buster
RUN apt-get update \
&& apt-get -y install curl build-essential libssl-dev \
&& apt-get -y install curl build-essential libssl-dev sqlite3 \
&& apt-get clean \
&& pip install --upgrade pip

29
Dockerfile.armhf Normal file
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@ -0,0 +1,29 @@
FROM --platform=linux/arm/v7 python:3.7.7-slim-buster
RUN apt-get update \
&& apt-get -y install curl build-essential libssl-dev libatlas3-base libgfortran5 sqlite3 \
&& apt-get clean \
&& pip install --upgrade pip \
&& echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > /etc/pip.conf
# Prepare environment
RUN mkdir /freqtrade
WORKDIR /freqtrade
# Install TA-lib
COPY build_helpers/* /tmp/
RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
ENV LD_LIBRARY_PATH /usr/local/lib
# Install dependencies
COPY requirements.txt requirements-common.txt /freqtrade/
RUN pip install numpy --no-cache-dir \
&& pip install -r requirements.txt --no-cache-dir
# Install and execute
COPY . /freqtrade/
RUN pip install -e . --no-cache-dir
ENTRYPOINT ["freqtrade"]
# Default to trade mode
CMD [ "trade" ]

41
Dockerfile.pi
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@ -1,41 +0,0 @@
FROM balenalib/raspberrypi3-debian:stretch
RUN [ "cross-build-start" ]
RUN apt-get update \
&& apt-get -y install wget curl build-essential libssl-dev libffi-dev \
&& apt-get clean
# Prepare environment
RUN mkdir /freqtrade
WORKDIR /freqtrade
# Install TA-lib
COPY build_helpers/ta-lib-0.4.0-src.tar.gz /freqtrade/
RUN tar -xzf /freqtrade/ta-lib-0.4.0-src.tar.gz \
&& cd /freqtrade/ta-lib/ \
&& ./configure \
&& make \
&& make install \
&& rm /freqtrade/ta-lib-0.4.0-src.tar.gz
ENV LD_LIBRARY_PATH /usr/local/lib
# Install berryconda
RUN wget -q https://github.com/jjhelmus/berryconda/releases/download/v2.0.0/Berryconda3-2.0.0-Linux-armv7l.sh \
&& bash ./Berryconda3-2.0.0-Linux-armv7l.sh -b \
&& rm Berryconda3-2.0.0-Linux-armv7l.sh
# Install dependencies
COPY requirements-common.txt /freqtrade/
RUN ~/berryconda3/bin/conda install -y numpy pandas \
&& ~/berryconda3/bin/pip install -r requirements-common.txt --no-cache-dir
# Install and execute
COPY . /freqtrade/
RUN ~/berryconda3/bin/pip install -e . --no-cache-dir
RUN [ "cross-build-end" ]
ENTRYPOINT ["/root/berryconda3/bin/python","./freqtrade/main.py"]
CMD [ "trade" ]

1
MANIFEST.in
View File

@ -2,3 +2,4 @@ include LICENSE
include README.md
include config.json.example
recursive-include freqtrade *.py
recursive-include freqtrade/templates/ *.j2 *.ipynb

51
README.md
View File

@ -68,40 +68,43 @@ For any other type of installation please refer to [Installation doc](https://ww
### Bot commands
```
usage: freqtrade [-h] [-v] [--logfile FILE] [--version] [-c PATH] [-d PATH]
[-s NAME] [--strategy-path PATH] [--dynamic-whitelist [INT]]
[--db-url PATH] [--sd-notify]
{backtesting,edge,hyperopt} ...
usage: freqtrade [-h] [-V]
{trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
...
Free, open source crypto trading bot
positional arguments:
{backtesting,edge,hyperopt}
{trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
trade Trade module.
create-userdir Create user-data directory.
new-config Create new config
new-hyperopt Create new hyperopt
new-strategy Create new strategy
download-data Download backtesting data.
convert-data Convert candle (OHLCV) data from one format to
another.
convert-trade-data Convert trade data from one format to another.
backtesting Backtesting module.
edge Edge module.
hyperopt Hyperopt module.
hyperopt-list List Hyperopt results
hyperopt-show Show details of Hyperopt results
list-exchanges Print available exchanges.
list-hyperopts Print available hyperopt classes.
list-markets Print markets on exchange.
list-pairs Print pairs on exchange.
list-strategies Print available strategies.
list-timeframes Print available timeframes for the exchange.
show-trades Show trades.
test-pairlist Test your pairlist configuration.
plot-dataframe Plot candles with indicators.
plot-profit Generate plot showing profits.
optional arguments:
-h, --help show this help message and exit
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified
--version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: None). Multiple
--config options may be used.
-d PATH, --datadir PATH
Path to backtest data.
-s NAME, --strategy NAME
Specify strategy class name (default:
DefaultStrategy).
--strategy-path PATH Specify additional strategy lookup path.
--dynamic-whitelist [INT]
Dynamically generate and update whitelist based on 24h
BaseVolume (default: 20). DEPRECATED.
--db-url PATH Override trades database URL, this is useful if
dry_run is enabled or in custom deployments (default:
None).
--sd-notify Notify systemd service manager.
-V, --version show program's version number and exit
```
### Telegram RPC commands

11
bin/freqtrade
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@ -1,11 +0,0 @@
#!/usr/bin/env python3
import sys
import logging
logger = logging.getLogger(__name__)
logger.error("DEPRECATED installation detected, please run `pip install -e .` again.")
sys.exit(2)

BIN
build_helpers/TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl
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10
build_helpers/install_windows.ps1
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@ -3,7 +3,15 @@
# 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
pip install build_helpers\TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl
$pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
if ($pyv -eq '3.7') {
pip install build_helpers\TA_Lib-0.4.18-cp37-cp37m-win_amd64.whl
}
if ($pyv -eq '3.8') {
pip install build_helpers\TA_Lib-0.4.18-cp38-cp38-win_amd64.whl
}
pip install -r requirements-dev.txt
pip install -e .

8
build_helpers/publish_docker.sh
View File

@ -42,14 +42,6 @@ if [ "${TAG}" = "develop" ]; then
docker tag freqtrade:$TAG ${IMAGE_NAME}:latest
fi
# Login
docker login -u $DOCKER_USERNAME -p $DOCKER_PASSWORD
if [ $? -ne 0 ]; then
echo "failed login"
return 1
fi
# Show all available images
docker images

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

15
config.json.example
View File

@ -4,8 +4,9 @@
"stake_amount": 0.05,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
"ticker_interval": "5m",
"timeframe": "5m",
"dry_run": false,
"cancel_open_orders_on_exit": false,
"trailing_stop": false,
"unfilledtimeout": {
"buy": 10,
@ -23,7 +24,7 @@
"ask_strategy":{
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 9,
"order_book_max": 1,
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false
@ -75,6 +76,16 @@
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
},
"api_server": {
"enabled": false,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"verbosity": "info",
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "",
"password": ""
},
"initial_state": "running",
"forcebuy_enable": false,
"internals": {

15
config_binance.json.example
View File

@ -4,8 +4,9 @@
"stake_amount": 0.05,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
"ticker_interval": "5m",
"timeframe": "5m",
"dry_run": true,
"cancel_open_orders_on_exit": false,
"trailing_stop": false,
"unfilledtimeout": {
"buy": 10,
@ -23,7 +24,7 @@
"ask_strategy":{
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 9,
"order_book_max": 1,
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false
@ -80,6 +81,16 @@
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
},
"api_server": {
"enabled": false,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"verbosity": "info",
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "",
"password": ""
},
"initial_state": "running",
"forcebuy_enable": false,
"internals": {

14
config_full.json.example
View File

@ -8,7 +8,8 @@
"amend_last_stake_amount": false,
"last_stake_amount_min_ratio": 0.5,
"dry_run": false,
"ticker_interval": "5m",
"cancel_open_orders_on_exit": false,
"timeframe": "5m",
"trailing_stop": false,
"trailing_stop_positive": 0.005,
"trailing_stop_positive_offset": 0.0051,
@ -25,6 +26,7 @@
"sell": 30
},
"bid_strategy": {
"price_side": "bid",
"use_order_book": false,
"ask_last_balance": 0.0,
"order_book_top": 1,
@ -34,9 +36,10 @@
}
},
"ask_strategy":{
"price_side": "ask",
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 9,
"order_book_max": 1,
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false
@ -61,8 +64,9 @@
"sort_key": "quoteVolume",
"refresh_period": 1800
},
{"method": "AgeFilter", "min_days_listed": 10},
{"method": "PrecisionFilter"},
{"method": "PriceFilter", "low_price_ratio": 0.01},
{"method": "PriceFilter", "low_price_ratio": 0.01, "min_price": 0.00000010},
{"method": "SpreadFilter", "max_spread_ratio": 0.005}
],
"exchange": {
@ -118,6 +122,9 @@
"enabled": false,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"verbosity": "info",
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "freqtrader",
"password": "SuperSecurePassword"
},
@ -128,6 +135,7 @@
"process_throttle_secs": 5,
"heartbeat_interval": 60
},
"disable_dataframe_checks": false,
"strategy": "DefaultStrategy",
"strategy_path": "user_data/strategies/",
"dataformat_ohlcv": "json",

15
config_kraken.json.example
View File

@ -4,8 +4,9 @@
"stake_amount": 10,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "EUR",
"ticker_interval": "5m",
"timeframe": "5m",
"dry_run": true,
"cancel_open_orders_on_exit": false,
"trailing_stop": false,
"unfilledtimeout": {
"buy": 10,
@ -23,7 +24,7 @@
"ask_strategy":{
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 9,
"order_book_max": 1,
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false
@ -86,6 +87,16 @@
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
},
"api_server": {
"enabled": false,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"verbosity": "info",
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "",
"password": ""
},
"initial_state": "running",
"forcebuy_enable": false,
"internals": {

3
docker-compose.yml
View File

@ -3,6 +3,7 @@ version: '3'
services:
freqtrade:
image: freqtradeorg/freqtrade:master
# image: freqtradeorg/freqtrade:develop
# Build step - only needed when additional dependencies are needed
# build:
# context: .
@ -14,7 +15,7 @@ services:
# Default command used when running `docker compose up`
command: >
trade
--logfile /freqtrade/user_data/freqtrade.log
--logfile /freqtrade/user_data/logs/freqtrade.log
--db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
--config /freqtrade/user_data/config.json
--strategy SampleStrategy

4
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View File

@ -63,8 +63,8 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
* 0.25: Avoiding trade loss
* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
"""
total_profit = results.profit_percent.sum()
trade_duration = results.trade_duration.mean()
total_profit = results['profit_percent'].sum()
trade_duration = results['trade_duration'].mean()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)

72
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@ -4,6 +4,54 @@ This page explains some advanced tasks and configuration options that can be per
If you do not know what things mentioned here mean, you probably do not need it.
## Running multiple instances of Freqtrade
This section will show you how to run multiple bots at the same time, on the same machine.
### Things to consider
* Use different database files.
* Use different Telegram bots (requires multiple different configuration files; applies only when Telegram is enabled).
* Use different ports (applies only when Freqtrade REST API webserver is enabled).
### Different database files
In order to keep track of your trades, profits, etc., freqtrade is using a SQLite database where it stores various types of information such as the trades you performed in the past and the current position(s) you are holding at any time. This allows you to keep track of your profits, but most importantly, keep track of ongoing activity if the bot process would be restarted or would be terminated unexpectedly.
Freqtrade will, by default, use separate database files for dry-run and live bots (this assumes no database-url is given in either configuration nor via command line argument).
For live trading mode, the default database will be `tradesv3.sqlite` and for dry-run it will be `tradesv3.dryrun.sqlite`.
The optional argument to the trade command used to specify the path of these files is `--db-url`, which requires a valid SQLAlchemy url.
So when you are starting a bot with only the config and strategy arguments in dry-run mode, the following 2 commands would have the same outcome.
``` bash
freqtrade trade -c MyConfig.json -s MyStrategy
# is equivalent to
freqtrade trade -c MyConfig.json -s MyStrategy --db-url sqlite:///tradesv3.dryrun.sqlite
```
It means that if you are running the trade command in two different terminals, for example to test your strategy both for trades in USDT and in another instance for trades in BTC, you will have to run them with different databases.
If you specify the URL of a database which does not exist, freqtrade will create one with the name you specified. So to test your custom strategy with BTC and USDT stake currencies, you could use the following commands (in 2 separate terminals):
``` bash
# Terminal 1:
freqtrade trade -c MyConfigBTC.json -s MyCustomStrategy --db-url sqlite:///user_data/tradesBTC.dryrun.sqlite
# Terminal 2:
freqtrade trade -c MyConfigUSDT.json -s MyCustomStrategy --db-url sqlite:///user_data/tradesUSDT.dryrun.sqlite
```
Conversely, if you wish to do the same thing in production mode, you will also have to create at least one new database (in addition to the default one) and specify the path to the "live" databases, for example:
``` bash
# Terminal 1:
freqtrade trade -c MyConfigBTC.json -s MyCustomStrategy --db-url sqlite:///user_data/tradesBTC.live.sqlite
# Terminal 2:
freqtrade trade -c MyConfigUSDT.json -s MyCustomStrategy --db-url sqlite:///user_data/tradesUSDT.live.sqlite
```
For more information regarding usage of the sqlite databases, for example to manually enter or remove trades, please refer to the [SQL Cheatsheet](sql_cheatsheet.md).
## Configure the bot running as a systemd service
Copy the `freqtrade.service` file to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
@ -37,30 +85,30 @@ as the watchdog.
## Advanced Logging
On many Linux systems the bot can be configured to send its log messages to `syslog` or `journald` system services. Logging to a remote `syslog` server is also available on Windows. The special values for the `--logfilename` command line option can be used for this.
On many Linux systems the bot can be configured to send its log messages to `syslog` or `journald` system services. Logging to a remote `syslog` server is also available on Windows. The special values for the `--logfile` command line option can be used for this.
### Logging to syslog
To send Freqtrade log messages to a local or remote `syslog` service use the `--logfilename` command line option with the value in the following format:
To send Freqtrade log messages to a local or remote `syslog` service use the `--logfile` command line option with the value in the following format:
* `--logfilename syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
* `--logfile syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
So, the following are the examples of possible usages:
* `--logfilename syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
* `--logfilename syslog` -- same as above, the shortcut for `/dev/log`.
* `--logfilename syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
* `--logfilename syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
* `--logfilename syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
* `--logfile syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
* `--logfile syslog` -- same as above, the shortcut for `/dev/log`.
* `--logfile syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
* `--logfile syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
* `--logfile syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
Log messages are send to `syslog` with the `user` facility. So you can see them with the following commands:
* `tail -f /var/log/user`, or
* install a comprehensive graphical viewer (for instance, 'Log File Viewer' for Ubuntu).
On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfilename syslog` or `--logfilename journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
```
@ -78,9 +126,9 @@ $RepeatedMsgReduction on
This needs the `systemd` python package installed as the dependency, which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
To send Freqtrade log messages to `journald` system service use the `--logfilename` command line option with the value in the following format:
To send Freqtrade log messages to `journald` system service use the `--logfile` command line option with the value in the following format:
* `--logfilename journald` -- send log messages to `journald`.
* `--logfile journald` -- send log messages to `journald`.
Log messages are send to `journald` with the `user` facility. So you can see them with the following commands:
@ -89,4 +137,4 @@ Log messages are send to `journald` with the `user` facility. So you can see the
There are many other options in the `journalctl` utility to filter the messages, see manual pages for this utility.
On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfilename syslog` or `--logfilename journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.

31
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@ -11,30 +11,34 @@ Now you have good Buy and Sell strategies and some historic data, you want to te
real data. This is what we call
[backtesting](https://en.wikipedia.org/wiki/Backtesting).
Backtesting will use the crypto-currencies (pairs) from your config file and load ticker data from `user_data/data/<exchange>` by default.
If no data is available for the exchange / pair / ticker interval combination, backtesting will ask you to download them first using `freqtrade download-data`.
Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
If no data is available for the exchange / pair / timeframe combination, backtesting will ask you to download them first using `freqtrade download-data`.
For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
!!! Tip "Using dynamic pairlists for backtesting"
While using dynamic pairlists during backtesting is not possible, a dynamic pairlist using current data can be generated via the [`test-pairlist`](utils.md#test-pairlist) command, and needs to be specified as `"pair_whitelist"` attribute in the configuration.
!!! Warning "Using dynamic pairlists for backtesting"
Using dynamic pairlists is possible, however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
Also, when using pairlists other than StaticPairlist, reproducability of backtesting-results cannot be guaranteed.
Please read the [pairlists documentation](configuration.md#pairlists) for more information.
To achieve reproducible results, best generate a pairlist via the [`test-pairlist`](utils.md#test-pairlist) command and use that as static pairlist.
### Run a backtesting against the currencies listed in your config file
#### With 5 min tickers (Per default)
#### With 5 min candle (OHLCV) data (per default)
```bash
freqtrade backtesting
```
#### With 1 min tickers
#### With 1 min candle (OHLCV) data
```bash
freqtrade backtesting --ticker-interval 1m
freqtrade backtesting --timeframe 1m
```
#### Using a different on-disk ticker-data source
#### Using a different on-disk historical candle (OHLCV) data source
Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory.
You can then use this data for backtesting as follows:
@ -54,7 +58,7 @@ Where `-s SampleStrategy` refers to the class name within the strategy file `sam
#### Comparing multiple Strategies
```bash
freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --ticker-interval 5m
freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timeframe 5m
```
Where `SampleStrategy1` and `AwesomeStrategy` refer to class names of strategies.
@ -62,7 +66,7 @@ Where `SampleStrategy1` and `AwesomeStrategy` refer to class names of strategies
#### Exporting trades to file
```bash
freqtrade backtesting --export trades
freqtrade backtesting --export trades --config config.json --strategy SampleStrategy
```
The exported trades can be used for [further analysis](#further-backtest-result-analysis), or can be used by the plotting script `plot_dataframe.py` in the scripts directory.
@ -198,7 +202,7 @@ Since backtesting lacks some detailed information about what happens within a ca
- Buys happen at open-price
- Sell signal sells happen at open-price of the following candle
- Low happens before high for stoploss, protecting capital first.
- Low happens before high for stoploss, protecting capital first
- ROI
- sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
- sells are never "below the candle", so a ROI of 2% may result in a sell at 2.4% if low was at 2.4% profit
@ -208,6 +212,7 @@ Since backtesting lacks some detailed information about what happens within a ca
- High happens first - adjusting stoploss
- Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
- Sell-reason does not explain if a trade was positive or negative, just what triggered the sell (this can look odd if negative ROI values are used)
- Stoploss (and trailing stoploss) is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` and/or `trailing_stop` sell reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes.
Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.
Also, keep in mind that past results don't guarantee future success.
@ -223,13 +228,13 @@ You can then load the trades to perform further analysis as shown in our [data a
To compare multiple strategies, a list of Strategies can be provided to backtesting.
This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple
This is limited to 1 timeframe value per run. However, data is only loaded once from disk so if you have multiple
strategies you'd like to compare, this will give a nice runtime boost.
All listed Strategies need to be in the same directory.
``` bash
freqtrade backtesting --timerange 20180401-20180410 --ticker-interval 5m --strategy-list Strategy001 Strategy002 --export trades
freqtrade backtesting --timerange 20180401-20180410 --timeframe 5m --strategy-list Strategy001 Strategy002 --export trades
```
This will save the results to `user_data/backtest_results/backtest-result-<strategy>.json`, injecting the strategy-name into the target filename.

58
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@ -0,0 +1,58 @@
# Freqtrade basics
This page provides you some basic concepts on how Freqtrade works and operates.
## Freqtrade terminology
* Trade: Open position.
* Open Order: Order which is currently placed on the exchange, and is not yet complete.
* Pair: Tradable pair, usually in the format of Quote/Base (e.g. XRP/USDT).
* Timeframe: Candle length to use (e.g. `"5m"`, `"1h"`, ...).
* Indicators: Technical indicators (SMA, EMA, RSI, ...).
* Limit order: Limit orders which execute at the defined limit price or better.
* Market order: Guaranteed to fill, may move price depending on the order size.
## Fee handling
All profit calculations of Freqtrade include fees. For Backtesting / Hyperopt / Dry-run modes, the exchange default fee is used (lowest tier on the exchange). For live operations, fees are used as applied by the exchange (this includes BNB rebates etc.).
## Bot execution logic
Starting freqtrade in dry-run or live mode (using `freqtrade trade`) will start the bot and start the bot iteration loop.
By default, loop runs every few seconds (`internals.process_throttle_secs`) and does roughly the following in the following sequence:
* Fetch open trades from persistence.
* Calculate current list of tradable pairs.
* Download ohlcv data for the pairlist including all [informative pairs](strategy-customization.md#get-data-for-non-tradeable-pairs)
This step is only executed once per Candle to avoid unnecessary network traffic.
* Call `bot_loop_start()` strategy callback.
* Analyze strategy per pair.
* Call `populate_indicators()`
* Call `populate_buy_trend()`
* Call `populate_sell_trend()`
* Check timeouts for open orders.
* Calls `check_buy_timeout()` strategy callback for open buy orders.
* Calls `check_sell_timeout()` strategy callback for open sell orders.
* Verifies existing positions and eventually places sell orders.
* Considers stoploss, ROI and sell-signal.
* Determine sell-price based on `ask_strategy` configuration setting.
* Before a sell order is placed, `confirm_trade_exit()` strategy callback is called.
* Check if trade-slots are still available (if `max_open_trades` is reached).
* Verifies buy signal trying to enter new positions.
* Determine buy-price based on `bid_strategy` configuration setting.
* Before a buy order is placed, `confirm_trade_entry()` strategy callback is called.
This loop will be repeated again and again until the bot is stopped.
## Backtesting / Hyperopt execution logic
[backtesting](backtesting.md) or [hyperopt](hyperopt.md) do only part of the above logic, since most of the trading operations are fully simulated.
* Load historic data for configured pairlist.
* Calculate indicators (calls `populate_indicators()`).
* Calls `populate_buy_trend()` and `populate_sell_trend()`
* Loops per candle simulating entry and exit points.
* Generate backtest report output
!!! Note
Both Backtesting and Hyperopt include exchange default Fees in the calculation. Custom fees can be passed to backtesting / hyperopt by specifying the `--fee` argument.

56
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@ -9,22 +9,35 @@ This page explains the different parameters of the bot and how to run it.
```
usage: freqtrade [-h] [-V]
{trade,backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
{trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
...
Free, open source crypto trading bot
positional arguments:
{trade,backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
{trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
trade Trade module.
create-userdir Create user-data directory.
new-config Create new config
new-hyperopt Create new hyperopt
new-strategy Create new strategy
download-data Download backtesting data.
convert-data Convert candle (OHLCV) data from one format to
another.
convert-trade-data Convert trade data from one format to another.
backtesting Backtesting module.
edge Edge module.
hyperopt Hyperopt module.
create-userdir Create user-data directory.
hyperopt-list List Hyperopt results
hyperopt-show Show details of Hyperopt results
list-exchanges Print available exchanges.
list-timeframes Print available ticker intervals (timeframes) for the
exchange.
download-data Download backtesting data.
list-hyperopts Print available hyperopt classes.
list-markets Print markets on exchange.
list-pairs Print pairs on exchange.
list-strategies Print available strategies.
list-timeframes Print available timeframes for the exchange.
show-trades Show trades.
test-pairlist Test your pairlist configuration.
plot-dataframe Plot candles with indicators.
plot-profit Generate plot showing profits.
@ -72,7 +85,6 @@ Strategy arguments:
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
.
```
@ -144,10 +156,10 @@ It is recommended to use version control to keep track of changes to your strate
### How to use **--strategy**?
This parameter will allow you to load your custom strategy class.
Per default without `--strategy` or `-s` the bot will load the
`DefaultStrategy` included with the bot (`freqtrade/strategy/default_strategy.py`).
To test the bot installation, you can use the `SampleStrategy` installed by the `create-userdir` subcommand (usually `user_data/strategy/sample_strategy.py`).
The bot will search your strategy file within `user_data/strategies` and `freqtrade/strategy`.
The bot will search your strategy file within `user_data/strategies`.
To use other directories, please read the next section about `--strategy-path`.
To load a strategy, simply pass the class name (e.g.: `CustomStrategy`) in this parameter.
@ -197,7 +209,7 @@ Backtesting also uses the config specified via `-c/--config`.
```
usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-i TICKER_INTERVAL]
[--strategy-path PATH] [-i TIMEFRAME]
[--timerange TIMERANGE] [--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[--eps] [--dmmp]
@ -206,7 +218,7 @@ usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
optional arguments:
-h, --help show this help message and exit
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--timerange TIMERANGE
@ -280,7 +292,7 @@ to find optimal parameter values for your strategy.
```
usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[-i TICKER_INTERVAL] [--timerange TIMERANGE]
[-i TIMEFRAME] [--timerange TIMERANGE]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[--hyperopt NAME] [--hyperopt-path PATH] [--eps]
@ -292,7 +304,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
optional arguments:
-h, --help show this help message and exit
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--timerange TIMERANGE
@ -323,7 +335,7 @@ optional arguments:
--print-all Print all results, not only the best ones.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--print-json Print best results in JSON format.
--print-json Print output in JSON format.
-j JOBS, --job-workers JOBS
The number of concurrently running jobs for
hyperoptimization (hyperopt worker processes). If -1
@ -341,11 +353,11 @@ optional arguments:
class (IHyperOptLoss). Different functions can
generate completely different results, since the
target for optimization is different. Built-in
Hyperopt-loss-functions are:
DefaultHyperOptLoss, OnlyProfitHyperOptLoss,
SharpeHyperOptLoss, SharpeHyperOptLossDaily,
SortinoHyperOptLoss, SortinoHyperOptLossDaily.
(default: `DefaultHyperOptLoss`).
Hyperopt-loss-functions are: DefaultHyperOptLoss,
OnlyProfitHyperOptLoss, SharpeHyperOptLoss,
SharpeHyperOptLossDaily, SortinoHyperOptLoss,
SortinoHyperOptLossDaily.(default:
`DefaultHyperOptLoss`).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
@ -378,13 +390,13 @@ To know your trade expectancy and winrate against historical data, you can use E
```
usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[-i TICKER_INTERVAL] [--timerange TIMERANGE]
[-i TIMEFRAME] [--timerange TIMERANGE]
[--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
[--fee FLOAT] [--stoplosses STOPLOSS_RANGE]
optional arguments:
-h, --help show this help message and exit
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--timerange TIMERANGE

251
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@ -34,37 +34,40 @@ The prevelance for all Options is as follows:
- CLI arguments override any other option
- Configuration files are used in sequence (last file wins), and override Strategy configurations.
- Strategy configurations are only used if they are not set via configuration or via command line arguments. These options are market with [Strategy Override](#parameters-in-the-strategy) in the below table.
- Strategy configurations are only used if they are not set via configuration or via command line arguments. These options are marked with [Strategy Override](#parameters-in-the-strategy) in the below table.
Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
| Parameter | Description |
|------------|-------------|
| `max_open_trades` | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation which can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
| `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Positive float or `"unlimited"`.
| `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> **Datatype:** Positive float between `0.1` and `1.0`.
| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> **Datatype:** Positive Float as ratio.
| `ticker_interval` | The ticker interval to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `timeframe` | The timeframe (former ticker interval) to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in the Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
| `cancel_open_orders_on_exit` | Cancel open orders when the `/stop` RPC command is issued, `Ctrl+C` is pressed or the bot dies unexpectedly. When set to `true`, this allows you to use `/stop` to cancel unfilled and partially filled orders in the event of a market crash. It does not impact open positions. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `minimal_roi` | **Required.** Set the threshold in percent the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
| `stoploss` | **Required.** Value of the stoploss in percent used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float (as ratio)
| `minimal_roi` | **Required.** Set the threshold as ratio the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
| `stoploss` | **Required.** Value as ratio of the stoploss used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float (as ratio)
| `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Boolean
| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md). [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
| `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. [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. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook).
| `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.** Set the bidding price. More information [below](#buy-price-without-orderbook-enabled).
| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.* <br> **Datatype:** Float (as ratio)
| `ask_strategy.price_side` | Select the side of the spread the bot should look at to get the sell rate. [More information below](#sell-price-side).<br> *Defaults to `ask`.* <br> **Datatype:** String (either `ask` or `bid`).
| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> **Datatype:** Boolean
| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
@ -78,14 +81,15 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#dynamic-pairlists)). <br> **Datatype:** List
| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#dynamic-pairlists)). <br> **Datatype:** List
| `exchange.ccxt_config` | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#pairlists-and-pairlist-handlers)). <br> **Datatype:** List
| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#pairlists-and-pairlist-handlers)). <br> **Datatype:** List
| `exchange.ccxt_config` | Additional CCXT parameters passed to both ccxt instances (sync and async). This is usually the correct place for ccxt configurations. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
| `exchange.ccxt_sync_config` | Additional CCXT parameters passed to the regular (sync) ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
| `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
| `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `pairlists` | Define one or more pairlists to be used. [More information below](#dynamic-pairlists). <br>*Defaults to `StaticPairList`.* <br> **Datatype:** List of Dicts
| `pairlists` | Define one or more pairlists to be used. [More information below](#pairlists-and-pairlist-handlers). <br>*Defaults to `StaticPairList`.* <br> **Datatype:** List of Dicts
| `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
@ -99,20 +103,22 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4
| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>**Datatype:** Integer between 1024 and 65535
| `api_server.verbosity` | Logging verbosity. `info` will print all RPC Calls, while "error" will only display errors. <br>**Datatype:** Enum, either `info` or `error`. Defaults to `info`.
| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
| `initial_state` | Defines the initial application state. More information below. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> **Datatype:** Boolean
| `disable_dataframe_checks` | Disable checking the OHLCV dataframe returned from the strategy methods for correctness. Only use when intentionally changing the dataframe and understand what you are doing. [Strategy Override](#parameters-in-the-strategy).<br> *Defaults to `False`*. <br> **Datatype:** Boolean
| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Intege
| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
| `dataformat_ohlcv` | Data format to use to store OHLCV historic data. <br> *Defaults to `json`*. <br> **Datatype:** String
| `dataformat_trades` | Data format to use to store trades historic data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
### Parameters in the strategy
@ -120,7 +126,7 @@ The following parameters can be set in either configuration file or strategy.
Values set in the configuration file always overwrite values set in the strategy.
* `minimal_roi`
* `ticker_interval`
* `timeframe`
* `stoploss`
* `trailing_stop`
* `trailing_stop_positive`
@ -132,6 +138,7 @@ Values set in the configuration file always overwrite values set in the strategy
* `stake_currency`
* `stake_amount`
* `unfilledtimeout`
* `disable_dataframe_checks`
* `use_sell_signal` (ask_strategy)
* `sell_profit_only` (ask_strategy)
* `ignore_roi_if_buy_signal` (ask_strategy)
@ -211,7 +218,7 @@ To allow the bot to trade all the available `stake_currency` in your account (mi
### Understand minimal_roi
The `minimal_roi` configuration parameter is a JSON object where the key is a duration
in minutes and the value is the minimum ROI in percent.
in minutes and the value is the minimum ROI as ratio.
See the example below:
```json
@ -265,10 +272,10 @@ the static list of pairs) if we should buy.
### Understand order_types
The `order_types` configuration parameter maps actions (`buy`, `sell`, `stoploss`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
The `order_types` configuration parameter maps actions (`buy`, `sell`, `stoploss`, `emergencysell`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
This allows to buy using limit orders, sell using
limit-orders, and create stoplosses using using market orders. It also allows to set the
limit-orders, and create stoplosses using market orders. It also allows to set the
stoploss "on exchange" which means stoploss order would be placed immediately once
the buy order is fulfilled.
If `stoploss_on_exchange` and `trailing_stop` are both set, then the bot will use `stoploss_on_exchange_interval` to check and update the stoploss on exchange periodically.
@ -281,8 +288,12 @@ If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
`emergencysell` is an optional value, which defaults to `market` and is used when creating stoploss on exchange orders fails.
The below is the default which is used if this is not configured in either strategy or configuration file.
Since `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.
`stoploss` defines the stop-price - and limit should be slightly below this. This defaults to 0.99 / 1% (configurable via `stoploss_on_exchange_limit_ratio`).
Not all Exchanges support `stoploss_on_exchange`. If an exchange supports both limit and market stoploss orders, then the value of `stoploss` will be used to determine the stoploss type.
If `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.
`stoploss` defines the stop-price - and limit should be slightly below this.
This defaults to 0.99 / 1% (configurable via `stoploss_on_exchange_limit_ratio`).
Calculation example: we bought the asset at 100$.
Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the stoploss will happen between 95$ and 94.05$.
@ -324,7 +335,10 @@ Configuration:
refer to [the stoploss documentation](stoploss.md).
!!! Note
If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new order.
If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new stoploss order.
!!! Warning "Using market orders"
Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
!!! Warning "Warning: stoploss_on_exchange failures"
If stoploss on exchange creation fails for some reason, then an "emergency sell" is initiated. By default, this will sell the asset using a market order. The order-type for the emergency-sell can be changed by setting the `emergencysell` value in the `order_types` dictionary - however this is not advised.
@ -411,7 +425,7 @@ Advanced options can be configured using the `_ft_has_params` setting, which wil
Available options are listed in the exchange-class as `_ft_has_default`.
For example, to test the order type `FOK` with Kraken, and modify candle_limit to 200 (so you only get 200 candles per call):
For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
```json
"exchange": {
@ -452,6 +466,9 @@ Prices are always retrieved right before an order is placed, either by querying
!!! Note
Orderbook data used by Freqtrade are the data retrieved from exchange by the ccxt's function `fetch_order_book()`, i.e. are usually data from the L2-aggregated orderbook, while the ticker data are the structures returned by the ccxt's `fetch_ticker()`/`fetch_tickers()` functions. Refer to the ccxt library [documentation](https://github.com/ccxt/ccxt/wiki/Manual#market-data) for more details.
!!! Warning "Using market orders"
Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
### Buy price
#### Check depth of market
@ -463,56 +480,136 @@ Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) s
!!! Note
A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
#### Buy price side
The configuration setting `bid_strategy.price_side` defines the side of the spread the bot looks for when buying.
The following displays an orderbook.
``` explanation
...
103
102
101 # ask
-------------Current spread
99 # bid
98
97
...
```
If `bid_strategy.price_side` is set to `"bid"`, then the bot will use 99 as buying price.
In line with that, if `bid_strategy.price_side` is set to `"ask"`, then the bot will use 101 as buying price.
Using `ask` price often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
Taker fees instead of maker fees will most likely apply even when using limit buy orders.
Also, prices at the "ask" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
#### Buy price with Orderbook enabled
When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the `bid` (buy) side of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the configured side (`bid_strategy.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
#### Buy price without Orderbook enabled
When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `ask` (sell) price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `ask` price is not below the `last` price), it calculates a rate between `ask` and `last` price.
The following section uses `side` as the configured `bid_strategy.price_side`.
The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `ask` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
Using `ask` price often guarantees quicker success in the bid, but the bot can also end up paying more than what would have been necessary.
The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
### Sell price
#### Sell price side
The configuration setting `ask_strategy.price_side` defines the side of the spread the bot looks for when selling.
The following displays an orderbook:
``` explanation
...
103
102
101 # ask
-------------Current spread
99 # bid
98
97
...
```
If `ask_strategy.price_side` is set to `"ask"`, then the bot will use 101 as selling price.
In line with that, if `ask_strategy.price_side` is set to `"bid"`, then the bot will use 99 as selling price.
#### Sell price with Orderbook enabled
When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the `ask` orderbook side are validated for a profitable sell-possibility based on the strategy configuration and the sell order is placed at the first profitable spot.
When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the configured orderbook side are validated for a profitable sell-possibility based on the strategy configuration (`minimal_roi` conditions) and the sell order is placed at the first profitable spot.
!!! Note
Using `order_book_max` higher than `order_book_min` only makes sense when ask_strategy.price_side is set to `"ask"`.
The idea here is to place the sell order early, to be ahead in the queue.
A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting `ask_strategy.order_book_min` and `ask_strategy.order_book_max` to the same number.
!!! Warning "Orderbook and stoploss_on_exchange"
Using `ask_strategy.order_book_max` higher than 1 may increase the risk, since an eventual [stoploss on exchange](#understand-order_types) will be needed to be cancelled as soon as the order is placed.
!!! Warning "Order_book_max > 1 - increased risks for stoplosses!"
Using `ask_strategy.order_book_max` higher than 1 will increase the risk the stoploss on exchange is cancelled too early, since an eventual [stoploss on exchange](#understand-order_types) will be cancelled as soon as the order is placed.
Also, the sell order will remain on the exchange for `unfilledtimeout.sell` (or until it's filled) - which can lead to missed stoplosses (with or without using stoploss on exchange).
!!! Warning "Order_book_max > 1 in dry-run"
Using `ask_strategy.order_book_max` higher than 1 will result in improper dry-run results (significantly better than real orders executed on exchange), since dry-run assumes orders to be filled almost instantly.
It is therefore advised to not use this setting for dry-runs.
#### Sell price without Orderbook enabled
When not using orderbook (`ask_strategy.use_order_book=False`), the `bid` price from the ticker will be used as the sell price.
When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
## Pairlists
### Market order pricing
Pairlists define the list of pairs that the bot should trade.
There are [`StaticPairList`](#static-pair-list) and dynamic Whitelists available.
When using market orders, prices should be configured to use the "correct" side of the orderbook to allow realistic pricing detection.
Assuming both buy and sell are using market orders, a configuration similar to the following might be used
[`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter) act as filters, removing low-value pairs.
``` jsonc
"order_types": {
"buy": "market",
"sell": "market"
// ...
},
"bid_strategy": {
"price_side": "ask",
// ...
},
"ask_strategy":{
"price_side": "bid",
// ...
},
```
All pairlists can be chained, and a combination of all pairlists will become your new whitelist. Pairlists are executed in the sequence they are configured. You should always configure either `StaticPairList` or `DynamicPairList` as starting pairlists.
Obviously, if only one side is using limit orders, different pricing combinations can be used.
Inactive markets and blacklisted pairs are always removed from the resulting `pair_whitelist`.
## Pairlists and Pairlist Handlers
### Available Pairlists
Pairlist Handlers define the list of pairs (pairlist) that the bot should trade. They are configured in the `pairlists` section of the configuration settings.
In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list) Pairlist Handler).
Additionaly, [`AgeFilter`](#agefilter), [`PrecisionFilter`](#precisionfilter), [`PriceFilter`](#pricefilter), [`ShuffleFilter`](#shufflefilter) and [`SpreadFilter`](#spreadfilter) act as Pairlist Filters, removing certain pairs and/or moving their positions in the pairlist.
If multiple Pairlist Handlers are used, they are chained and a combination of all Pairlist Handlers forms the resulting pairlist the bot uses for trading and backtesting. Pairlist Handlers are executed in the sequence they are configured. You should always configure either `StaticPairList` or `VolumePairList` as the starting Pairlist Handler.
Inactive markets are always removed from the resulting pairlist. Explicitly blacklisted pairs (those in the `pair_blacklist` configuration setting) are also always removed from the resulting pairlist.
### Available Pairlist Handlers
* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
* [`VolumePairList`](#volume-pair-list)
* [`PrecisionFilter`](#precision-filter)
* [`PriceFilter`](#price-pair-filter)
* [`SpreadFilter`](#spread-filter)
* [`AgeFilter`](#agefilter)
* [`PrecisionFilter`](#precisionfilter)
* [`PriceFilter`](#pricefilter)
* [`ShuffleFilter`](#shufflefilter)
* [`SpreadFilter`](#spreadfilter)
!!! Tip "Testing pairlists"
Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) subcommand to test your configuration quickly.
Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) utility subcommand to test your configuration quickly.
#### Static Pair List
@ -528,16 +625,16 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
#### Volume Pair List
`VolumePairList` selects `number_assets` top pairs based on `sort_key`, which can be one of `askVolume`, `bidVolume` and `quoteVolume` and defaults to `quoteVolume`.
`VolumePairList` employs sorting/filtering of pairs by their trading volume. It selects `number_assets` top pairs with sorting based on the `sort_key` (which can only be `quoteVolume`).
`VolumePairList` considers outputs of previous pairlists unless it's the first configured pairlist, it does not consider `pair_whitelist`, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
When used in the chain of Pairlist Handlers in a non-leading position (after StaticPairList and other Pairlist Filters), `VolumePairList` considers outputs of previous Pairlist Handlers, adding its sorting/selection of the pairs by the trading volume.
`refresh_period` allows setting the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
When used on the leading position of the chain of Pairlist Handlers, it does not consider `pair_whitelist` configuration setting, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
`VolumePairList` is based on the ticker data, as reported by the ccxt library:
The `refresh_period` setting allows to define the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
`VolumePairList` is based on the ticker data from exchange, as reported by the ccxt library:
* The `bidVolume` is the volume (amount) of current best bid in the orderbook.
* The `askVolume` is the volume (amount) of current best ask in the orderbook.
* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
```json
@ -546,32 +643,63 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
"number_assets": 20,
"sort_key": "quoteVolume",
"refresh_period": 1800,
],
}],
```
#### Precision Filter
#### AgeFilter
Filters low-value coins which would not allow setting a stoploss.
Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`).
#### Price Pair Filter
When pairs are first listed on an exchange they can suffer huge price drops and volatility
in the first few days while the pair goes through its price-discovery period. Bots can often
be caught out buying before the pair has finished dropping in price.
The `PriceFilter` allows filtering of pairs by price.
Currently, only `low_price_ratio` is implemented, where a raise of 1 price unit (pip) is below the `low_price_ratio` ratio.
This filter allows freqtrade to ignore pairs until they have been listed for at least `min_days_listed` days.
#### PrecisionFilter
Filters low-value coins which would not allow setting stoplosses.
#### PriceFilter
The `PriceFilter` allows filtering of pairs by price. Currently the following price filters are supported:
* `min_price`
* `max_price`
* `low_price_ratio`
The `min_price` setting removes pairs where the price is below the specified price. This is useful if you wish to avoid trading very low-priced pairs.
This option is disabled by default, and will only apply if set to <> 0.
Calculation example:
Min price precision is 8 decimals. If price is 0.00000011 - one step would be 0.00000012 - which is almost 10% higher than the previous value.
The `max_price` setting removes pairs where the price is above the specified price. This is useful if you wish to trade only low-priced pairs.
This option is disabled by default, and will only apply if set to <> 0.
The `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.
Calculation example:
Min price precision is 8 decimals. If price is 0.00000011 - one step would be 0.00000012 - which is almost 10% higher than the previous value.
These pairs are dangerous since it may be impossible to place the desired stoploss - and often result in high losses.
#### Spread Filter
Removes pairs that have a difference between asks and bids above the specified ratio (default `0.005`).
#### ShuffleFilter
Shuffles (randomizes) pairs in the pairlist. It can be used for preventing the bot from trading some of the pairs more frequently then others when you want all pairs be treated with the same priority.
!!! Tip
You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order.
#### SpreadFilter
Removes pairs that have a difference between asks and bids above the specified ratio, `max_spread_ratio` (defaults to `0.005`).
Example:
If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027 the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005`
### Full Pairlist example
If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027, the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005` and this pair will be filtered out.
The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting by `quoteVolume` and applies both [`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter), filtering all assets where 1 priceunit is > 1%.
### Full example of Pairlist Handlers
The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies both [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#price-filter), filtering all assets where 1 priceunit is > 1%. Then the `SpreadFilter` is applied and pairs are finally shuffled with the random seed set to some predefined value.
```json
"exchange": {
@ -584,8 +712,11 @@ The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets,
"number_assets": 20,
"sort_key": "quoteVolume",
},
{"method": "AgeFilter", "min_days_listed": 10},
{"method": "PrecisionFilter"},
{"method": "PriceFilter", "low_price_ratio": 0.01}
{"method": "PriceFilter", "low_price_ratio": 0.01},
{"method": "SpreadFilter", "max_spread_ratio": 0.005},
{"method": "ShuffleFilter", "seed": 42}
],
```

68
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@ -33,7 +33,7 @@ optional arguments:
Specify which tickers to download. Space-separated list. Default: `1m 5m`.
--erase Clean all existing data for the selected exchange/pairs/timeframes.
--data-format-ohlcv {json,jsongz}
Storage format for downloaded ohlcv data. (default: `json`).
Storage format for downloaded candle (OHLCV) data. (default: `json`).
--data-format-trades {json,jsongz}
Storage format for downloaded trades data. (default: `jsongz`).
@ -105,11 +105,11 @@ Common arguments:
##### Example converting data
The following command will convert all ohlcv (candle) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
The following command will convert all candle (OHLCV) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
It'll also remove original json data files (`--erase` parameter).
``` bash
freqtrade convert-data --format-from json --format-to jsongz --data-dir ~/.freqtrade/data/binance -t 5m 15m --erase
freqtrade convert-data --format-from json --format-to jsongz --datadir ~/.freqtrade/data/binance -t 5m 15m --erase
```
#### Subcommand convert-trade data
@ -155,7 +155,59 @@ The following command will convert all available trade-data in `~/.freqtrade/dat
It'll also remove original jsongz data files (`--erase` parameter).
``` bash
freqtrade convert-trade-data --format-from jsongz --format-to json --data-dir ~/.freqtrade/data/kraken --erase
freqtrade convert-trade-data --format-from jsongz --format-to json --datadir ~/.freqtrade/data/kraken --erase
```
### Subcommand list-data
You can get a list of downloaded data using the `list-data` subcommand.
```
usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [--exchange EXCHANGE]
[--data-format-ohlcv {json,jsongz}]
[-p PAIRS [PAIRS ...]]
optional arguments:
-h, --help show this help message and exit
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
--data-format-ohlcv {json,jsongz}
Storage format for downloaded candle (OHLCV) data.
(default: `json`).
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
separated.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
#### Example list-data
```bash
> freqtrade list-data --userdir ~/.freqtrade/user_data/
Found 33 pair / timeframe combinations.
pairs timeframe
---------- -----------------------------------------
ADA/BTC 5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d
ADA/ETH 5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d
ETH/BTC 5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d
ETH/USDT 5m, 15m, 30m, 1h, 2h, 4h
```
### Pairs file
@ -192,15 +244,15 @@ Then run:
freqtrade download-data --exchange binance
```
This will download ticker data for all the currency pairs you defined in `pairs.json`.
This will download historical candle (OHLCV) data for all the currency pairs you defined in `pairs.json`.
### Other Notes
- To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
- To change the exchange used to download the tickers, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
- To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
- To download ticker data for only 10 days, use `--days 10` (defaults to 30 days).
- Use `--timeframes` to specify which tickers to download. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute tickers.
- To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
- Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
- To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
### Trades (tick) data

10
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@ -24,3 +24,13 @@ and in freqtrade 2019.7 (master branch).
`--live` in the context of backtesting allowed to download the latest tick data for backtesting.
Did only download the latest 500 candles, so was ineffective in getting good backtest data.
Removed in 2019-7-dev (develop branch) and in freqtrade 2019-8 (master branch)
### Allow running multiple pairlists in sequence
The former `"pairlist"` section in the configuration has been removed, and is replaced by `"pairlists"` - being a list to specify a sequence of pairlists.
The old section of configuration parameters (`"pairlist"`) has been deprecated in 2019.11 and has been removed in 2020.4.
### deprecation of bidVolume and askVolume from volumepairlist
Since only quoteVolume can be compared between assets, the other options (bidVolume, askVolume) have been deprecated in 2020.4.

49
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@ -92,13 +92,13 @@ docker-compose exec freqtrade_develop /bin/bash
You have a great idea for a new pair selection algorithm you would like to try out? Great.
Hopefully you also want to contribute this back upstream.
Whatever your motivations are - This should get you off the ground in trying to develop a new Pairlist provider.
Whatever your motivations are - This should get you off the ground in trying to develop a new Pairlist Handler.
First of all, have a look at the [VolumePairList](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/pairlist/VolumePairList.py) provider, and best copy this file with a name of your new Pairlist Provider.
First of all, have a look at the [VolumePairList](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/pairlist/VolumePairList.py) Handler, and best copy this file with a name of your new Pairlist Handler.
This is a simple provider, which however serves as a good example on how to start developing.
This is a simple Handler, which however serves as a good example on how to start developing.
Next, modify the classname of the provider (ideally align this with the Filename).
Next, modify the classname of the Handler (ideally align this with the module filename).
The base-class provides an instance of the exchange (`self._exchange`) the pairlist manager (`self._pairlistmanager`), as well as the main configuration (`self._config`), the pairlist dedicated configuration (`self._pairlistconfig`) and the absolute position within the list of pairlists.
@ -114,28 +114,44 @@ Now, let's step through the methods which require actions:
#### Pairlist configuration
Configuration for PairListProvider is done in the bot configuration file in the element `"pairlist"`.
This Pairlist-object may contain configurations with additional configurations for the configured pairlist.
By convention, `"number_assets"` is used to specify the maximum number of pairs to keep in the whitelist. Please follow this to ensure a consistent user experience.
Configuration for the chain of Pairlist Handlers is done in the bot configuration file in the element `"pairlists"`, an array of configuration parameters for each Pairlist Handlers in the chain.
Additional elements can be configured as needed. `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successfull and dynamic.
By convention, `"number_assets"` is used to specify the maximum number of pairs to keep in the pairlist. Please follow this to ensure a consistent user experience.
Additional parameters can be configured as needed. For instance, `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successfull and dynamic.
#### short_desc
Returns a description used for Telegram messages.
This should contain the name of the Provider, as well as a short description containing the number of assets. Please follow the format `"PairlistName - top/bottom X pairs"`.
This should contain the name of the Pairlist Handler, as well as a short description containing the number of assets. Please follow the format `"PairlistName - top/bottom X pairs"`.
#### gen_pairlist
Override this method if the Pairlist Handler can be used as the leading Pairlist Handler in the chain, defining the initial pairlist which is then handled by all Pairlist Handlers in the chain. Examples are `StaticPairList` and `VolumePairList`.
This is called with each iteration of the bot (only if the Pairlist Handler is at the first location) - so consider implementing caching for compute/network heavy calculations.
It must return the resulting pairlist (which may then be passed into the chain of Pairlist Handlers).
Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filtering. Use this if you limit your result to a certain number of pairs - so the endresult is not shorter than expected.
#### filter_pairlist
Override this method and run all calculations needed in this method.
This method is called for each Pairlist Handler in the chain by the pairlist manager.
This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
It get's passed a pairlist (which can be the result of previous pairlists) as well as `tickers`, a pre-fetched version of `get_tickers()`.
It must return the resulting pairlist (which may then be passed into the next pairlist filter).
The default implementation in the base class simply calls the `_validate_pair()` method for each pair in the pairlist, but you may override it. So you should either implement the `_validate_pair()` in your Pairlist Handler or override `filter_pairlist()` to do something else.
If overridden, it must return the resulting pairlist (which may then be passed into the next Pairlist Handler in the chain).
Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filters. Use this if you limit your result to a certain number of pairs - so the endresult is not shorter than expected.
In `VolumePairList`, this implements different methods of sorting, does early validation so only the expected number of pairs is returned.
##### sample
``` python
@ -145,11 +161,6 @@ Validations are optional, the parent class exposes a `_verify_blacklist(pairlist
return pairs
```
#### _gen_pair_whitelist
This is a simple method used by `VolumePairList` - however serves as a good example.
In VolumePairList, this implements different methods of sorting, does early validation so only the expected number of pairs is returned.
## Implement a new Exchange (WIP)
!!! Note
@ -165,7 +176,7 @@ Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need
### Incomplete candles
While fetching OHLCV data, we're may end up getting incomplete candles (Depending on the exchange).
While fetching candle (OHLCV) data, we may end up getting incomplete candles (depending on the exchange).
To demonstrate this, we'll use daily candles (`"1d"`) to keep things simple.
We query the api (`ct.fetch_ohlcv()`) for the timeframe and look at the date of the last entry. If this entry changes or shows the date of a "incomplete" candle, then we should drop this since having incomplete candles is problematic because indicators assume that only complete candles are passed to them, and will generate a lot of false buy signals. By default, we're therefore removing the last candle assuming it's incomplete.
@ -174,14 +185,14 @@ To check how the new exchange behaves, you can use the following snippet:
``` python
import ccxt
from datetime import datetime
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.converter import ohlcv_to_dataframe
ct = ccxt.binance()
timeframe = "1d"
pair = "XLM/BTC" # Make sure to use a pair that exists on that exchange!
raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
# convert to dataframe
df1 = parse_ticker_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
df1 = ohlcv_to_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
print(df1.tail(1))
print(datetime.utcnow())

5
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@ -22,6 +22,9 @@ Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.co
!!! Note
All below comands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
!!! Note "Docker on Raspberry"
If you're running freqtrade on a Raspberry PI, you must change the image from `freqtradeorg/freqtrade:master` to `freqtradeorg/freqtrade:master_pi` or `freqtradeorg/freqtrade:develop_pi`, otherwise the image will not work.
### Docker quick start
Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
@ -65,7 +68,7 @@ docker-compose up -d
#### Docker-compose logs
Logs will be written to `user_data/freqtrade.log`.
Logs will be written to `user_data/logs/freqtrade.log`.
Alternatively, you can check the latest logs using `docker-compose logs -f`.
#### Database

5
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@ -79,7 +79,7 @@ So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
Expectancy = (5 X 0.28) – 0.72 = 0.68
```
Superficially, this means that on average you expect this strategy’s trades to return .68 times the size of your loses. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
Superficially, this means that on average you expect this strategy’s trades to return 1.68 times the size of your loses. Said another way, you can expect to win $1.68 for every $1 you lose. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
It is important to remember that any system with an expectancy greater than 0 is profitable using past data. The key is finding one that will be profitable in the future.
@ -148,7 +148,6 @@ Edge module has following configuration options:
| `enabled` | If true, then Edge will run periodically. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `process_throttle_secs` | How often should Edge run in seconds. <br>*Defaults to `3600` (once per hour).* <br> **Datatype:** Integer
| `calculate_since_number_of_days` | Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy. <br> **Note** that it downloads historical data so increasing this number would lead to slowing down the bot. <br>*Defaults to `7`.* <br> **Datatype:** Integer
| `capital_available_percentage` | **DEPRECATED - [replaced with `tradable_balance_ratio`](configuration.md#Available balance)** This is the percentage of the total capital on exchange in stake currency. <br>As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital. <br>*Defaults to `0.5`.* <br> **Datatype:** Float
| `allowed_risk` | Ratio of allowed risk per trade. <br>*Defaults to `0.01` (1%)).* <br> **Datatype:** Float
| `stoploss_range_min` | Minimum stoploss. <br>*Defaults to `-0.01`.* <br> **Datatype:** Float
| `stoploss_range_max` | Maximum stoploss. <br>*Defaults to `-0.10`.* <br> **Datatype:** Float
@ -156,7 +155,7 @@ Edge module has following configuration options:
| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your ticker interval. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
| `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> **Datatype:** Boolean
## Running Edge independently

53
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@ -30,6 +30,15 @@ Binance has been split into 3, and users must use the correct ccxt exchange ID f
The Kraken API does only provide 720 historic candles, which is sufficient for Freqtrade dry-run and live trade modes, but is a problem for backtesting.
To download data for the Kraken exchange, using `--dl-trades` is mandatory, otherwise the bot will download the same 720 candles over and over, and you'll not have enough backtest data.
Due to the heavy rate-limiting applied by Kraken, the following configuration section should be used to download data:
``` json
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 3100
},
```
## Bittrex
### Order types
@ -62,6 +71,30 @@ res = [ f"{x['MarketCurrency']}/{x['BaseCurrency']}" for x in ct.publicGetMarket
print(res)
```
## FTX
!!! Tip "Stoploss on Exchange"
FTX supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide.
### Using subaccounts
To use subaccounts with FTX, you need to edit the configuration and add the following:
``` json
"exchange": {
"ccxt_config": {
"headers": {
"FTX-SUBACCOUNT": "name"
}
},
}
```
!!! Note
Older versions of freqtrade may require this key to be added to `"ccxt_async_config"` as well.
## All exchanges
Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
@ -74,23 +107,13 @@ Should you experience constant errors with Nonce (like `InvalidNonce`), it is be
$ pip3 install web3
```
### Send incomplete candles to the strategy
### Getting latest price / Incomplete candles
Most exchanges return incomplete candles via their ohlcv / klines interface.
By default, Freqtrade assumes that incomplete candles are returned and removes the last candle assuming it's an incomplete candle.
Most exchanges return current incomplete candle via their OHLCV/klines API interface.
By default, Freqtrade assumes that incomplete candle is fetched from the exchange and removes the last candle assuming it's the incomplete candle.
Whether your exchange returns incomplete candles or not can be checked using [the helper script](developer.md#Incomplete-candles) from the Contributor documentation.
If the exchange does return incomplete candles and you would like to have incomplete candles in your strategy, you can set the following parameter in the configuration file.
Due to the danger of repainting, Freqtrade does not allow you to use this incomplete candle.
``` json
{
"exchange": {
"_ft_has_params": {"ohlcv_partial_candle": false}
}
}
```
!!! Warning "Danger of repainting"
Changing this parameter makes the strategy responsible to avoid repainting and handle this accordingly. Doing this is therefore not recommended, and should only be performed by experienced users who are fully aware of the impact this setting has.
However, if it is based on the need for the latest price for your strategy - then this requirement can be acquired using the [data provider](strategy-customization.md#possible-options-for-dataprovider) from within the strategy.

16
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@ -45,6 +45,20 @@ the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-c
You can use the `/forcesell all` command from Telegram.
### I want to run multiple bots on the same machine
Please look at the [advanced setup documentation Page](advanced-setup.md#running-multiple-instances-of-freqtrade).
### I'm getting "Missing data fillup" messages in the log
This message is just a warning that the latest candles had missing candles in them.
Depending on the exchange, this can indicate that the pair didn't have a trade for the timeframe you are using - and the exchange does only return candles with volume.
On low volume pairs, this is a rather common occurance.
If this happens for all pairs in the pairlist, this might indicate a recent exchange downtime. Please check your exchange's public channels for details.
Irrespectively of the reason, Freqtrade will fill up these candles with "empty" candles, where open, high, low and close are set to the previous candle close - and volume is empty. In a chart, this will look like a `_` - and is aligned with how exchanges usually represent 0 volume candles.
### I'm getting the "RESTRICTED_MARKET" message in the log
Currently known to happen for US Bittrex users.
@ -100,7 +114,7 @@ $ tail -f /path/to/mylogfile.log | grep 'something'
```
from a separate terminal window.
On Windows, the `--logfilename` option is also supported by Freqtrade and you can use the `findstr` command to search the log for the string of interest:
On Windows, the `--logfile` option is also supported by Freqtrade and you can use the `findstr` command to search the log for the string of interest:
```
> type \path\to\mylogfile.log | findstr "something"
```

58
docs/hyperopt.md
View File

@ -6,9 +6,7 @@ algorithms included in the `scikit-optimize` package to accomplish this. The
search will burn all your CPU cores, make your laptop sound like a fighter jet
and still take a long time.
In general, the search for best parameters starts with a few random combinations and then uses Bayesian search with a
ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace
that minimizes the value of the [loss function](#loss-functions).
In general, the search for best parameters starts with a few random combinations (see [below](#reproducible-results) for more details) and then uses Bayesian search with a ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the [loss function](#loss-functions).
Hyperopt requires historic data to be available, just as backtesting does.
To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
@ -16,6 +14,24 @@ To learn how to get data for the pairs and exchange you're interested in, head o
!!! Bug
Hyperopt can crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
## Install hyperopt dependencies
Since Hyperopt dependencies are not needed to run the bot itself, are heavy, can not be easily built on some platforms (like Raspberry PI), they are not installed by default. Before you run Hyperopt, you need to install the corresponding dependencies, as described in this section below.
!!! Note
Since Hyperopt is a resource intensive process, running it on a Raspberry Pi is not recommended nor supported.
### Docker
The docker-image includes hyperopt dependencies, no further action needed.
### Easy installation script (setup.sh) / Manual installation
```bash
source .env/bin/activate
pip install -r requirements-hyperopt.txt
```
## Prepare Hyperopting
Before we start digging into Hyperopt, we recommend you to take a look at
@ -47,6 +63,9 @@ Optional - can also be loaded from a strategy:
!!! Note
Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
!!! Note
You always have to provide a strategy to Hyperopt, even if your custom Hyperopt class contains all methods.
Rarely you may also need to override:
* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
@ -103,10 +122,11 @@ Place the corresponding settings into the following methods
The configuration and rules are the same than for buy signals.
To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
#### Using ticker-interval as part of the Strategy
#### Using timeframe as a part of the Strategy
The Strategy exposes the ticker-interval as `self.ticker_interval`. The same value is available as class-attribute `HyperoptName.ticker_interval`.
In the case of the linked sample-value this would be `SampleHyperOpt.ticker_interval`.
The Strategy class exposes the timeframe value as the `self.timeframe` attribute.
The same value is available as class-attribute `HyperoptName.timeframe`.
In the case of the linked sample-value this would be `SampleHyperOpt.timeframe`.
## Solving a Mystery
@ -159,6 +179,9 @@ So let's write the buy strategy using these values:
dataframe['macd'], dataframe['macdsignal']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@ -222,11 +245,11 @@ The `--spaces all` option determines that all possible parameters should be opti
!!! Warning
When switching parameters or changing configuration options, make sure to not use the argument `--continue` so temporary results can be removed.
### Execute Hyperopt with Different Ticker-Data Source
### Execute Hyperopt with different historical data source
If you would like to hyperopt parameters using an alternate ticker data that
you have on-disk, use the `--datadir PATH` option. Default hyperopt will
use data from directory `user_data/data`.
If you would like to hyperopt parameters using an alternate historical data set that
you have on-disk, use the `--datadir PATH` option. By default, hyperopt
uses data from directory `user_data/data`.
### Running Hyperopt with Smaller Testset
@ -242,7 +265,7 @@ freqtrade hyperopt --timerange 20180401-20180501
Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
```bash
freqtrade hyperopt --strategy SampleStrategy --customhyperopt SampleHyperopt
freqtrade hyperopt --strategy SampleStrategy --hyperopt SampleHyperopt
```
### Running Hyperopt with Smaller Search Space
@ -289,7 +312,7 @@ You can also enable position stacking in the configuration file by explicitly se
### Reproducible results
The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with a leading asterisk sign at the Hyperopt output.
The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with an asterisk character (`*`) in the first column in the Hyperopt output.
The initial state for generation of these random values (random state) is controlled by the value of the `--random-state` command line option. You can set it to some arbitrary value of your choice to obtain reproducible results.
@ -380,7 +403,7 @@ As stated in the comment, you can also use it as the value of the `minimal_roi`
#### Default ROI Search Space
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the timeframe used. By default the values vary in the following ranges (for some of the most used timeframes, values are rounded to 5 digits after the decimal point):
| # step | 1m | | 5m | | 1h | | 1d | |
| ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
@ -389,7 +412,7 @@ If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace f
| 3 | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 |
| 4 | 6...44 | 0.0 | 30...220 | 0.0 | 360...2640 | 0.0 | 8640...63360 | 0.0 |
These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the ticker interval used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the ticker interval used.
These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
@ -464,7 +487,7 @@ As stated in the comment, you can also use it as the values of the corresponding
If you are optimizing trailing stop values, Freqtrade creates the 'trailing' optimization hyperspace for you. By default, the `trailing_stop` parameter is always set to True in that hyperspace, the value of the `trailing_only_offset_is_reached` vary between True and False, the values of the `trailing_stop_positive` and `trailing_stop_positive_offset` parameters vary in the ranges 0.02...0.35 and 0.01...0.1 correspondingly, which is sufficient in most cases.
Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py).
Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
## Show details of Hyperopt results
@ -475,8 +498,3 @@ After you run Hyperopt for the desired amount of epochs, you can later list all
Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same set of arguments `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
## Next Step
Now you have a perfect bot and want to control it from Telegram. Your
next step is to learn the [Telegram usage](telegram-usage.md).

6
docs/installation.md
View File

@ -13,7 +13,7 @@ Click each one for install guide:
* [Python >= 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/)
* [pip](https://pip.pypa.io/en/stable/installing/)
* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
* [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
* [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
@ -248,14 +248,14 @@ git clone https://github.com/freqtrade/freqtrade.git
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‑0.4.17‑cp36‑cp36m‑win32.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 precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version)
```cmd
>cd \path\freqtrade-develop
>python -m venv .env
>.env\Scripts\activate.bat
REM optionally install ta-lib from wheel
REM >pip install TA_Lib‑0.4.17‑cp36‑cp36m‑win32.whl
REM >pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl
>pip install -r requirements.txt
>pip install -e .
>freqtrade

76
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View File

@ -23,44 +23,64 @@ The `freqtrade plot-dataframe` subcommand shows an interactive graph with three
Possible arguments:
```
usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-p PAIRS [PAIRS ...]] [--indicators1 INDICATORS1 [INDICATORS1 ...]]
[--indicators2 INDICATORS2 [INDICATORS2 ...]] [--plot-limit INT] [--db-url PATH]
[--trade-source {DB,file}] [--export EXPORT] [--export-filename PATH] [--timerange TIMERANGE]
[-i TICKER_INTERVAL]
usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-p PAIRS [PAIRS ...]]
[--indicators1 INDICATORS1 [INDICATORS1 ...]]
[--indicators2 INDICATORS2 [INDICATORS2 ...]]
[--plot-limit INT] [--db-url PATH]
[--trade-source {DB,file}] [--export EXPORT]
[--export-filename PATH]
[--timerange TIMERANGE] [-i TIMEFRAME]
[--no-trades]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-separated.
Show profits for only these pairs. Pairs are space-
separated.
--indicators1 INDICATORS1 [INDICATORS1 ...]
Set indicators from your strategy you want in the first row of the graph. Space-separated list. Example:
Set indicators from your strategy you want in the
first row of the graph. Space-separated list. Example:
`ema3 ema5`. Default: `['sma', 'ema3', 'ema5']`.
--indicators2 INDICATORS2 [INDICATORS2 ...]
Set indicators from your strategy you want in the third row of the graph. Space-separated list. Example:
Set indicators from your strategy you want in the
third row of the graph. Space-separated list. Example:
`fastd fastk`. Default: `['macd', 'macdsignal']`.
--plot-limit INT Specify tick limit for plotting. Notice: too high values cause huge files. Default: 750.
--db-url PATH Override trades database URL, this is useful in custom deployments (default: `sqlite:///tradesv3.sqlite`
for Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for Dry Run).
--plot-limit INT Specify tick limit for plotting. Notice: too high
values cause huge files. Default: 750.
--db-url PATH Override trades database URL, this is useful in custom
deployments (default: `sqlite:///tradesv3.sqlite` for
Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
Dry Run).
--trade-source {DB,file}
Specify the source for trades (Can be DB or file (backtest file)) Default: file
--export EXPORT Export backtest results, argument are: trades. Example: `--export=trades`
Specify the source for trades (Can be DB or file
(backtest file)) Default: file
--export EXPORT Export backtest results, argument are: trades.
Example: `--export=trades`
--export-filename PATH
Save backtest results to the file with this filename. Requires `--export` to be set as well. Example:
`--export-filename=user_data/backtest_results/backtest_today.json`
Save backtest results to the file with this filename.
Requires `--export` to be set as well. Example:
`--export-filename=user_data/backtest_results/backtest
_today.json`
--timerange TIMERANGE
Specify what timerange of data to use.
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
Specify ticker interval (`1m`, `5m`, `30m`, `1h`, `1d`).
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--no-trades Skip using trades from backtesting file and DB.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
@ -68,9 +88,9 @@ Common arguments:
Strategy arguments:
-s NAME, --strategy NAME
Specify strategy class name which will be used by the bot.
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
Example:
@ -196,6 +216,7 @@ The first graph is good to get a grip of how the overall market progresses.
The second graph will show if your algorithm works or doesn't.
Perhaps you want an algorithm that steadily makes small profits, or one that acts less often, but makes big swings.
This graph will also highlight the start (and end) of the Max drawdown period.
The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
@ -206,7 +227,7 @@ usage: freqtrade plot-profit [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
[--timerange TIMERANGE] [--export EXPORT]
[--export-filename PATH] [--db-url PATH]
[--trade-source {DB,file}] [-i TICKER_INTERVAL]
[--trade-source {DB,file}] [-i TIMEFRAME]
optional arguments:
-h, --help show this help message and exit
@ -229,7 +250,7 @@ optional arguments:
--trade-source {DB,file}
Specify the source for trades (Can be DB or file
(backtest file)) Default: file
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
@ -240,9 +261,10 @@ Common arguments:
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH

2
docs/requirements-docs.txt
View File

@ -1,2 +1,2 @@
mkdocs-material==4.6.3
mkdocs-material==5.4.0
mdx_truly_sane_lists==1.2

62
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@ -11,6 +11,9 @@ Sample configuration:
"enabled": true,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"verbosity": "info",
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "Freqtrader",
"password": "SuperSecret1!"
},
@ -29,7 +32,7 @@ This should return the response:
{"status":"pong"}
```
All other endpoints return sensitive info and require authentication, so are not available through a web browser.
All other endpoints return sensitive info and require authentication and are therefore not available through a web browser.
To generate a secure password, either use a password manager, or use the below code snipped.
@ -38,6 +41,9 @@ import secrets
secrets.token_hex()
```
!!! Hint
Use the same method to also generate a JWT secret key (`jwt_secret_key`).
### Configuration with docker
If you run your bot using docker, you'll need to have the bot listen to incomming connections. The security is then handled by docker.
@ -105,7 +111,7 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
| `start` | | Starts the trader
| `stop` | | Stops the trader
| `stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `reload_conf` | | Reloads the configuration file
| `reload_config` | | Reloads the configuration file
| `show_config` | | Shows part of the current configuration with relevant settings to operation
| `status` | | Lists all open trades
| `count` | | Displays number of trades used and available
@ -169,7 +175,7 @@ profit
Returns the profit summary
:returns: json object
reload_conf
reload_config
Reload configuration
:returns: json object
@ -191,7 +197,7 @@ stop
stopbuy
Stop buying (but handle sells gracefully).
use reload_conf to reset
use reload_config to reset
:returns: json object
version
@ -202,3 +208,51 @@ whitelist
Show the current whitelist
:returns: json object
```
## Advanced API usage using JWT tokens
!!! Note
The below should be done in an application (a Freqtrade REST API client, which fetches info via API), and is not intended to be used on a regular basis.
Freqtrade's REST API also offers JWT (JSON Web Tokens).
You can login using the following command, and subsequently use the resulting access_token.
``` bash
> curl -X POST --user Freqtrader http://localhost:8080/api/v1/token/login
{"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk2ODEsIm5iZiI6MTU4OTExOTY4MSwianRpIjoiMmEwYmY0NWUtMjhmOS00YTUzLTlmNzItMmM5ZWVlYThkNzc2IiwiZXhwIjoxNTg5MTIwNTgxLCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.qt6MAXYIa-l556OM7arBvYJ0SDI9J8bIk3_glDujF5g","refresh_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk2ODEsIm5iZiI6MTU4OTExOTY4MSwianRpIjoiZWQ1ZWI3YjAtYjMwMy00YzAyLTg2N2MtNWViMjIxNWQ2YTMxIiwiZXhwIjoxNTkxNzExNjgxLCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJ0eXBlIjoicmVmcmVzaCJ9.d1AT_jYICyTAjD0fiQAr52rkRqtxCjUGEMwlNuuzgNQ"}
> access_token="eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk2ODEsIm5iZiI6MTU4OTExOTY4MSwianRpIjoiMmEwYmY0NWUtMjhmOS00YTUzLTlmNzItMmM5ZWVlYThkNzc2IiwiZXhwIjoxNTg5MTIwNTgxLCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.qt6MAXYIa-l556OM7arBvYJ0SDI9J8bIk3_glDujF5g"
# Use access_token for authentication
> curl -X GET --header "Authorization: Bearer ${access_token}" http://localhost:8080/api/v1/count
```
Since the access token has a short timeout (15 min) - the `token/refresh` request should be used periodically to get a fresh access token:
``` bash
> curl -X POST --header "Authorization: Bearer ${refresh_token}"http://localhost:8080/api/v1/token/refresh
{"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk5NzQsIm5iZiI6MTU4OTExOTk3NCwianRpIjoiMDBjNTlhMWUtMjBmYS00ZTk0LTliZjAtNWQwNTg2MTdiZDIyIiwiZXhwIjoxNTg5MTIwODc0LCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.1seHlII3WprjjclY6DpRhen0rqdF4j6jbvxIhUFaSbs"}
```
## CORS
All web-based frontends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
Users can configure this themselves via the `CORS_origins` configuration setting.
It consists of a list of allowed sites that are allowed to consume resources from the bot's API.
Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - this would mean that the following configuration becomes necessary:
```jsonc
{
//...
"jwt_secret_key": "somethingrandom",
"CORS_origins": ["https://frequi.freqtrade.io"],
//...
}
```
!!! Note
We strongly recommend to also set `jwt_secret_key` to something random and known only to yourself to avoid unauthorized access to your bot.

128
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View File

@ -1,13 +1,29 @@
# SQL Helper
This page contains some help if you want to edit your sqlite db.
## Install sqlite3
**Ubuntu/Debian installation**
Sqlite3 is a terminal based sqlite application.
Feel free to use a visual Database editor like SqliteBrowser if you feel more comfortable with that.
### Ubuntu/Debian installation
```bash
sudo apt-get install sqlite3
```
### Using sqlite3 via docker-compose
The freqtrade docker image does contain sqlite3, so you can edit the database without having to install anything on the host system.
``` bash
docker-compose exec freqtrade /bin/bash
sqlite3 <databasefile>.sqlite
```
## Open the DB
```bash
sqlite3
.open <filepath>
@ -16,45 +32,61 @@ sqlite3
## Table structure
### List tables
```bash
.tables
```
### Display table structure
```bash
.schema <table_name>
```
### Trade table structure
```sql
CREATE TABLE trades (
id INTEGER NOT NULL,
exchange VARCHAR NOT NULL,
pair VARCHAR NOT NULL,
is_open BOOLEAN NOT NULL,
fee_open FLOAT NOT NULL,
fee_close FLOAT NOT NULL,
open_rate FLOAT,
open_rate_requested FLOAT,
close_rate FLOAT,
close_rate_requested FLOAT,
close_profit FLOAT,
stake_amount FLOAT NOT NULL,
amount FLOAT,
open_date DATETIME NOT NULL,
close_date DATETIME,
open_order_id VARCHAR,
stop_loss FLOAT,
initial_stop_loss FLOAT,
stoploss_order_id VARCHAR,
stoploss_last_update DATETIME,
max_rate FLOAT,
sell_reason VARCHAR,
strategy VARCHAR,
ticker_interval INTEGER,
PRIMARY KEY (id),
CHECK (is_open IN (0, 1))
CREATE TABLE trades
id INTEGER NOT NULL,
exchange VARCHAR NOT NULL,
pair VARCHAR NOT NULL,
is_open BOOLEAN NOT NULL,
fee_open FLOAT NOT NULL,
fee_open_cost FLOAT,
fee_open_currency VARCHAR,
fee_close FLOAT NOT NULL,
fee_close_cost FLOAT,
fee_close_currency VARCHAR,
open_rate FLOAT,
open_rate_requested FLOAT,
open_trade_price FLOAT,
close_rate FLOAT,
close_rate_requested FLOAT,
close_profit FLOAT,
close_profit_abs FLOAT,
stake_amount FLOAT NOT NULL,
amount FLOAT,
open_date DATETIME NOT NULL,
close_date DATETIME,
open_order_id VARCHAR,
stop_loss FLOAT,
stop_loss_pct FLOAT,
initial_stop_loss FLOAT,
initial_stop_loss_pct FLOAT,
stoploss_order_id VARCHAR,
stoploss_last_update DATETIME,
max_rate FLOAT,
min_rate FLOAT,
sell_reason VARCHAR,
strategy VARCHAR,
timeframe INTEGER,
PRIMARY KEY (id),
CHECK (is_open IN (0, 1))
);
CREATE INDEX ix_trades_stoploss_order_id ON trades (stoploss_order_id);
CREATE INDEX ix_trades_pair ON trades (pair);
CREATE INDEX ix_trades_is_open ON trades (is_open);
```
## Get all trades in the table
@ -67,42 +99,60 @@ SELECT * FROM trades;
!!! Warning
Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, forcesell <tradeid> should be used to accomplish the same thing.
It is strongly advised to backup your database file before making any manual changes.
It is strongly advised to backup your database file before making any manual changes.
!!! Note
This should not be necessary after /forcesell, as forcesell orders are closed automatically by the bot on the next iteration.
```sql
UPDATE trades
SET is_open=0, close_date=<close_date>, close_rate=<close_rate>, close_profit=close_rate/open_rate-1, sell_reason=<sell_reason>
SET is_open=0,
close_date=<close_date>,
close_rate=<close_rate>,
close_profit = close_rate / open_rate - 1,
close_profit_abs = (amount * <close_rate> * (1 - fee_close) - (amount * (open_rate * 1 - fee_open))),
sell_reason=<sell_reason>
WHERE id=<trade_ID_to_update>;
```
##### Example
### Example
```sql
UPDATE trades
SET is_open=0, close_date='2017-12-20 03:08:45.103418', close_rate=0.19638016, close_profit=0.0496, sell_reason='force_sell'
SET is_open=0,
close_date='2020-06-20 03:08:45.103418',
close_rate=0.19638016,
close_profit=0.0496,
close_profit_abs = (amount * 0.19638016 * (1 - fee_close) - (amount * open_rate * (1 - fee_open)))
sell_reason='force_sell'
WHERE id=31;
```
## Insert manually a new trade
## Manually insert a new trade
```sql
INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date)
VALUES ('bittrex', 'ETH/BTC', 1, 0.0025, 0.0025, <open_rate>, <stake_amount>, <amount>, '<datetime>')
VALUES ('binance', 'ETH/BTC', 1, 0.0025, 0.0025, <open_rate>, <stake_amount>, <amount>, '<datetime>')
```
##### Example:
### Insert trade example
```sql
INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date)
VALUES ('bittrex', 'ETH/BTC', 1, 0.0025, 0.0025, 0.00258580, 0.002, 0.7715262081, '2017-11-28 12:44:24.000000')
VALUES ('binance', 'ETH/BTC', 1, 0.0025, 0.0025, 0.00258580, 0.002, 0.7715262081, '2020-06-28 12:44:24.000000')
```
## Fix wrong fees in the table
If your DB was created before [PR#200](https://github.com/freqtrade/freqtrade/pull/200) was merged (before 12/23/17).
## Remove trade from the database
Maybe you'd like to remove a trade from the database, because something went wrong.
```sql
UPDATE trades SET fee=0.0025 WHERE fee=0.005;
DELETE FROM trades WHERE id = <tradeid>;
```
```sql
DELETE FROM trades WHERE id = 31;
```
!!! Warning
This will remove this trade from the database. Please make sure you got the correct id and **NEVER** run this query without the `where` clause.

8
docs/stoploss.md
View File

@ -1,6 +1,6 @@
# Stop Loss
The `stoploss` configuration parameter is loss in percentage that should trigger a sale.
The `stoploss` configuration parameter is loss as ratio that should trigger a sale.
For example, value `-0.10` will cause immediate sell if the profit dips below -10% for a given trade. This parameter is optional.
Most of the strategy files already include the optimal `stoploss` value.
@ -27,7 +27,7 @@ So this parameter will tell the bot how often it should update the stoploss orde
This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
!!! Note
Stoploss on exchange is only supported for Binance (stop-loss-limit) and Kraken (stop-loss-market) as of now.
Stoploss on exchange is only supported for Binance (stop-loss-limit), Kraken (stop-loss-market) and FTX (stop limit and stop-market) as of now.
## Static Stop Loss
@ -84,7 +84,7 @@ This option can be used with or without `trailing_stop_positive`, but uses `trai
``` python
trailing_stop_positive_offset = 0.011
trailing_only_offset_is_reached = true
trailing_only_offset_is_reached = True
```
Simplified example:
@ -101,7 +101,7 @@ Simplified example:
## Changing stoploss on open trades
A stoploss on an open trade can be changed by changing the value in the configuration or strategy and use the `/reload_conf` command (alternatively, completely stopping and restarting the bot also works).
A stoploss on an open trade can be changed by changing the value in the configuration or strategy and use the `/reload_config` command (alternatively, completely stopping and restarting the bot also works).
The new stoploss value will be applied to open trades (and corresponding log-messages will be generated).

201
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View File

@ -0,0 +1,201 @@
# Advanced Strategies
This page explains some advanced concepts available for strategies.
If you're just getting started, please be familiar with the methods described in the [Strategy Customization](strategy-customization.md) documentation and with the [Freqtrade basics](bot-basics.md) first.
[Freqtrade basics](bot-basics.md) describes in which sequence each method described below is called, which can be helpful to understand which method to use for your custom needs.
!!! Note
All callback methods described below should only be implemented in a strategy if they are actually used.
## Custom order timeout rules
Simple, timebased order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
However, freqtrade also offers a custom callback for both ordertypes, which allows you to decide based on custom criteria if a order did time out or not.
!!! Note
Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
### Custom order timeout example
A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below.
It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
The function must return either `True` (cancel order) or `False` (keep order alive).
``` python
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
class Awesomestrategy(IStrategy):
# ... populate_* methods
# Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
unfilledtimeout = {
'buy': 60 * 25,
'sell': 60 * 25
}
def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
return True
return False
def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
return True
return False
```
!!! Note
For the above example, `unfilledtimeout` must be set to something bigger than 24h, otherwise that type of timeout will apply first.
### Custom order timeout example (using additional data)
``` python
from datetime import datetime
from freqtrade.persistence import Trade
class Awesomestrategy(IStrategy):
# ... populate_* methods
# Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
unfilledtimeout = {
'buy': 60 * 25,
'sell': 60 * 25
}
def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
ob = self.dp.orderbook(pair, 1)
current_price = ob['bids'][0][0]
# Cancel buy order if price is more than 2% above the order.
if current_price > order['price'] * 1.02:
return True
return False
def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
ob = self.dp.orderbook(pair, 1)
current_price = ob['asks'][0][0]
# Cancel sell order if price is more than 2% below the order.
if current_price < order['price'] * 0.98:
return True
return False
```
## Bot loop start callback
A simple callback which is called once at the start of every bot throttling iteration.
This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
``` python
import requests
class Awesomestrategy(IStrategy):
# ... populate_* methods
def bot_loop_start(self, **kwargs) -> None:
"""
Called at the start of the bot iteration (one loop).
Might be used to perform pair-independent tasks
(e.g. gather some remote resource for comparison)
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
"""
if self.config['runmode'].value in ('live', 'dry_run'):
# Assign this to the class by using self.*
# can then be used by populate_* methods
self.remote_data = requests.get('https://some_remote_source.example.com')
```
## Bot order confirmation
### Trade entry (buy order) confirmation
`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
``` python
class Awesomestrategy(IStrategy):
# ... populate_* methods
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, **kwargs) -> bool:
"""
Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be bought.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process
"""
return True
```
### Trade exit (sell order) confirmation
`confirm_trade_exit()` can be used to abort a trade exit (sell) at the latest second (maybe because the price is not what we expect).
``` python
from freqtrade.persistence import Trade
class Awesomestrategy(IStrategy):
# ... populate_* methods
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be sold.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process
"""
if sell_reason == 'force_sell' and trade.calc_profit_ratio(rate) < 0:
# Reject force-sells with negative profit
# This is just a sample, please adjust to your needs
# (this does not necessarily make sense, assuming you know when you're force-selling)
return False
return True
```

230
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View File

@ -1,7 +1,8 @@
# Strategy Customization
This page explains where to customize your strategies, and add new
indicators.
This page explains how to customize your strategies, add new indicators and set up trading rules.
Please familiarize yourself with [Freqtrade basics](bot-basics.md) first, which provides overall info on how the bot operates.
## Install a custom strategy file
@ -84,7 +85,7 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
@ -140,10 +141,10 @@ By letting the bot know how much history is needed, backtest trades can start at
#### Example
Let's try to backtest 1 month (January 2019) of 5m candles using the an example strategy with EMA100, as above.
Let's try to backtest 1 month (January 2019) of 5m candles using an example strategy with EMA100, as above.
``` bash
freqtrade backtesting --timerange 20190101-20190201 --ticker-interval 5m
freqtrade backtesting --timerange 20190101-20190201 --timeframe 5m
```
Assuming `startup_candle_count` is set to 100, backtesting knows it needs 100 candles to generate valid buy signals. It will load data from `20190101 - (100 * 5m)` - which is ~2019-12-31 15:30:00.
@ -249,7 +250,7 @@ minimal_roi = {
While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
To use times based on candle duration (ticker_interval or timeframe), the following snippet can be handy.
To use times based on candle duration (timeframe), the following snippet can be handy.
This will allow you to change the ticket_interval for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
``` python
@ -257,12 +258,12 @@ from freqtrade.exchange import timeframe_to_minutes
class AwesomeStrategy(IStrategy):
ticker_interval = "1d"
ticker_interval_mins = timeframe_to_minutes(ticker_interval)
timeframe = "1d"
timeframe_mins = timeframe_to_minutes(timeframe)
minimal_roi = {
"0": 0.05, # 5% for the first 3 candles
str(ticker_interval_mins * 3)): 0.02, # 2% after 3 candles
str(ticker_interval_mins * 6)): 0.01, # 1% After 6 candles
str(timeframe_mins * 3)): 0.02, # 2% after 3 candles
str(timeframe_mins * 6)): 0.01, # 1% After 6 candles
}
```
@ -284,13 +285,14 @@ If your exchange supports it, it's recommended to also set `"stoploss_on_exchang
For more information on order_types please look [here](configuration.md#understand-order_types).
### Ticker interval
### Timeframe (ticker interval)
This is the set of candles the bot should download and use for the analysis.
Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
Please note that the same buy/sell signals may work with one interval, but not the other.
This setting is accessible within the strategy by using `self.ticker_interval`.
Please note that the same buy/sell signals may work well with one timeframe, but not with the others.
This setting is accessible within the strategy methods as the `self.timeframe` attribute.
### Metadata dict
@ -324,67 +326,14 @@ class Awesomestrategy(IStrategy):
!!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
### Additional data (DataProvider)
***
The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
All methods return `None` in case of failure (do not raise an exception).
Please always check the mode of operation to select the correct method to get data (samples see below).
#### Possible options for DataProvider
- `available_pairs` - Property with tuples listing cached pairs with their intervals (pair, interval).
- `ohlcv(pair, timeframe)` - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.
- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
- `get_pair_dataframe(pair, timeframe)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
- `orderbook(pair, maximum)` - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
- `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on Market data structure.
- `runmode` - Property containing the current runmode.
#### Example: fetch live ohlcv / historic data for the first informative pair
``` python
if self.dp:
inf_pair, inf_timeframe = self.informative_pairs()[0]
informative = self.dp.get_pair_dataframe(pair=inf_pair,
timeframe=inf_timeframe)
```
!!! Warning "Warning about backtesting"
Be carefull when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
for the backtesting runmode) provides the full time-range in one go,
so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).
!!! Warning "Warning in hyperopt"
This option cannot currently be used during hyperopt.
#### Orderbook
``` python
if self.dp:
if self.dp.runmode.value in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
```
!!! Warning
The order book is not part of the historic data which means backtesting and hyperopt will not work if this
method is used.
#### Available Pairs
``` python
if self.dp:
for pair, ticker in self.dp.available_pairs:
print(f"available {pair}, {ticker}")
```
### Additional data (informative_pairs)
#### Get data for non-tradeable pairs
Data for additional, informative pairs (reference pairs) can be beneficial for some strategies.
Ohlcv data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see above).
Ohlcv data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see below).
These parts will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting.
The pairs need to be specified as tuples in the format `("pair", "interval")`, with pair as the first and time interval as the second argument.
@ -404,6 +353,146 @@ def informative_pairs(self):
It is however better to use resampling to longer time-intervals when possible
to avoid hammering the exchange with too many requests and risk being blocked.
***
### Additional data (DataProvider)
The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
All methods return `None` in case of failure (do not raise an exception).
Please always check the mode of operation to select the correct method to get data (samples see below).
#### Possible options for DataProvider
- [`available_pairs`](#available_pairs) - Property with tuples listing cached pairs with their intervals (pair, interval).
- [`current_whitelist()`](#current_whitelist) - Returns a current list of whitelisted pairs. Useful for accessing dynamic whitelists (ie. VolumePairlist)
- [`get_pair_dataframe(pair, timeframe)`](#get_pair_dataframepair-timeframe) - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
- [`get_analyzed_dataframe(pair, timeframe)`](#get_analyzed_dataframepair-timeframe) - Returns the analyzed dataframe (after calling `populate_indicators()`, `populate_buy()`, `populate_sell()`) and the time of the latest analysis.
- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
- `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on the Market data structure.
- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
- [`orderbook(pair, maximum)`](#orderbookpair-maximum) - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
- [`ticker(pair)`](#tickerpair) - Returns current ticker data for the pair. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#price-tickers) for more details on the Ticker data structure.
- `runmode` - Property containing the current runmode.
#### Example Usages:
#### *available_pairs*
``` python
if self.dp:
for pair, timeframe in self.dp.available_pairs:
print(f"available {pair}, {timeframe}")
```
#### *current_whitelist()*
Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume.
The strategy might look something like this:
*Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day ATR to buy and sell.*
Due to the limited available data, it's very difficult to resample our `5m` candles into daily candles for use in a 14 day ATR. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
Since we can't resample our data we will have to use an informative pair; and since our whitelist will be dynamic we don't know which pair(s) to use.
This is where calling `self.dp.current_whitelist()` comes in handy.
```python
class SampleStrategy(IStrategy):
# strategy init stuff...
timeframe = '5m'
# more strategy init stuff..
def informative_pairs(self):
# get access to all pairs available in whitelist.
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1d') for pair in pairs]
return informative_pairs
def populate_indicators(self, dataframe, metadata):
# Get the informative pair
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
# Get the 14 day ATR.
atr = ta.ATR(informative, timeperiod=14)
# Do other stuff
```
#### *get_pair_dataframe(pair, timeframe)*
``` python
# fetch live / historical candle (OHLCV) data for the first informative pair
if self.dp:
inf_pair, inf_timeframe = self.informative_pairs()[0]
informative = self.dp.get_pair_dataframe(pair=inf_pair,
timeframe=inf_timeframe)
```
!!! Warning "Warning about backtesting"
Be careful when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
for the backtesting runmode) provides the full time-range in one go,
so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).
!!! Warning "Warning in hyperopt"
This option cannot currently be used during hyperopt.
#### *get_analyzed_dataframe(pair, timeframe)*
This method is used by freqtrade internally to determine the last signal.
It can also be used in specific callbacks to get the signal that caused the action (see [Advanced Strategy Documentation](strategy-advanced.md) for more details on available callbacks).
``` python
# fetch current dataframe
if self.dp:
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
timeframe=self.ticker_interval)
```
!!! Note "No data available"
Returns an empty dataframe if the requested pair was not cached.
This should not happen when using whitelisted pairs.
!!! Warning "Warning in hyperopt"
This option cannot currently be used during hyperopt.
#### *orderbook(pair, maximum)*
``` python
if self.dp:
if self.dp.runmode.value in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
```
!!! Warning
The order book is not part of the historic data which means backtesting and hyperopt will not work if this
method is used.
#### *ticker(pair)*
``` python
if self.dp:
if self.dp.runmode.value in ('live', 'dry_run'):
ticker = self.dp.ticker(metadata['pair'])
dataframe['last_price'] = ticker['last']
dataframe['volume24h'] = ticker['quoteVolume']
dataframe['vwap'] = ticker['vwap']
```
!!! Warning
Although the ticker data structure is a part of the ccxt Unified Interface, the values returned by this method can
vary for different exchanges. For instance, many exchanges do not return `vwap` values, the FTX exchange
does not always fills in the `last` field (so it can be None), etc. So you need to carefully verify the ticker
data returned from the exchange and add appropriate error handling / defaults.
***
### Additional data (Wallets)
The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
@ -426,6 +515,7 @@ if self.wallets:
- `get_used(asset)` - currently tied up balance (open orders)
- `get_total(asset)` - total available balance - sum of the 2 above
***
### Additional data (Trades)
A history of Trades can be retrieved in the strategy by querying the database.
@ -490,7 +580,7 @@ Locks can also be lifted manually, by calling `self.unlock_pair(pair)`.
To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
!!! Note
Locked pairs are not persisted, so a restart of the bot, or calling `/reload_conf` will reset locked pairs.
Locked pairs are not persisted, so a restart of the bot, or calling `/reload_config` will reset locked pairs.
!!! Warning
Locking pairs is not functioning during backtesting.

4
docs/strategy_analysis_example.md
View File

@ -18,7 +18,7 @@ config = Configuration.from_files([])
# config = Configuration.from_files(["config.json"])
# Define some constants
config["ticker_interval"] = "5m"
config["timeframe"] = "5m"
# Name of the strategy class
config["strategy"] = "SampleStrategy"
# Location of the data
@ -33,7 +33,7 @@ pair = "BTC_USDT"
from freqtrade.data.history import load_pair_history
candles = load_pair_history(datadir=data_location,
timeframe=config["ticker_interval"],
timeframe=config["timeframe"],
pair=pair)
# Confirm success

8
docs/telegram-usage.md
View File

@ -52,7 +52,7 @@ official commands. You can ask at any moment for help with `/help`.
| `/start` | | Starts the trader
| `/stop` | | Stops the trader
| `/stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `/reload_conf` | | Reloads the configuration file
| `/reload_config` | | Reloads the configuration file
| `/show_config` | | Shows part of the current configuration with relevant settings to operation
| `/status` | | Lists all open trades
| `/status table` | | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
@ -85,14 +85,14 @@ Below, example of Telegram message you will receive for each command.
### /stopbuy
> **status:** `Setting max_open_trades to 0. Run /reload_conf to reset.`
> **status:** `Setting max_open_trades to 0. Run /reload_config to reset.`
Prevents the bot from opening new trades by temporarily setting "max_open_trades" to 0. Open trades will be handled via their regular rules (ROI / Sell-signal, stoploss, ...).
After this, give the bot time to close off open trades (can be checked via `/status table`).
Once all positions are sold, run `/stop` to completely stop the bot.
`/reload_conf` resets "max_open_trades" to the value set in the configuration and resets this command.
`/reload_config` resets "max_open_trades" to the value set in the configuration and resets this command.
!!! Warning
The stop-buy signal is ONLY active while the bot is running, and is not persisted anyway, so restarting the bot will cause this to reset.
@ -209,7 +209,7 @@ Shows the current whitelist
Shows the current blacklist.
If Pair is set, then this pair will be added to the pairlist.
Also supports multiple pairs, seperated by a space.
Use `/reload_conf` to reset the blacklist.
Use `/reload_config` to reset the blacklist.
> Using blacklist `StaticPairList` with 2 pairs
>`DODGE/BTC`, `HOT/BTC`.

87
docs/utils.md
View File

@ -61,8 +61,8 @@ $ freqtrade new-config --config config_binance.json
? Do you want to enable Dry-run (simulated trades)? Yes
? Please insert your stake currency: BTC
? Please insert your stake amount: 0.05
? Please insert max_open_trades (Integer or 'unlimited'): 5
? Please insert your ticker interval: 15m
? Please insert max_open_trades (Integer or 'unlimited'): 3
? Please insert your desired timeframe (e.g. 5m): 5m
? Please insert your display Currency (for reporting): USD
? Select exchange binance
? Do you want to enable Telegram? No
@ -77,7 +77,7 @@ Results will be located in `user_data/strategies/<strategyclassname>.py`.
``` output
usage: freqtrade new-strategy [-h] [--userdir PATH] [-s NAME]
[--template {full,minimal}]
[--template {full,minimal,advanced}]
optional arguments:
-h, --help show this help message and exit
@ -86,10 +86,10 @@ optional arguments:
-s NAME, --strategy NAME
Specify strategy class name which will be used by the
bot.
--template {full,minimal}
Use a template which is either `minimal` or `full`
(containing multiple sample indicators). Default:
`full`.
--template {full,minimal,advanced}
Use a template which is either `minimal`, `full`
(containing multiple sample indicators) or `advanced`.
Default: `full`.
```
@ -105,6 +105,12 @@ With custom user directory
freqtrade new-strategy --userdir ~/.freqtrade/ --strategy AwesomeStrategy
```
Using the advanced template (populates all optional functions and methods)
```bash
freqtrade new-strategy --strategy AwesomeStrategy --template advanced
```
## Create new hyperopt
Creates a new hyperopt from a template similar to SampleHyperopt.
@ -114,7 +120,7 @@ Results will be located in `user_data/hyperopts/<classname>.py`.
``` output
usage: freqtrade new-hyperopt [-h] [--userdir PATH] [--hyperopt NAME]
[--template {full,minimal}]
[--template {full,minimal,advanced}]
optional arguments:
-h, --help show this help message and exit
@ -122,10 +128,10 @@ optional arguments:
Path to userdata directory.
--hyperopt NAME Specify hyperopt class name which will be used by the
bot.
--template {full,minimal}
Use a template which is either `minimal` or `full`
(containing multiple sample indicators). Default:
`full`.
--template {full,minimal,advanced}
Use a template which is either `minimal`, `full`
(containing multiple sample indicators) or `advanced`.
Default: `full`.
```
### Sample usage of new-hyperopt
@ -258,7 +264,7 @@ All exchanges supported by the ccxt library: _1btcxe, acx, adara, allcoin, anxpr
## List Timeframes
Use the `list-timeframes` subcommand to see the list of ticker intervals (timeframes) available for the exchange.
Use the `list-timeframes` subcommand to see the list of timeframes (ticker intervals) available for the exchange.
```
usage: freqtrade list-timeframes [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--exchange EXCHANGE] [-1]
@ -429,6 +435,7 @@ usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[--min-total-profit FLOAT]
[--max-total-profit FLOAT] [--no-color]
[--print-json] [--no-details]
[--export-csv FILE]
optional arguments:
-h, --help show this help message and exit
@ -450,6 +457,8 @@ optional arguments:
useful if you are redirecting output to a file.
--print-json Print best result detailization in JSON format.
--no-details Do not print best epoch details.
--export-csv FILE Export to CSV-File. This will disable table print.
Example: --export-csv hyperopt.csv
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
@ -458,9 +467,10 @@ Common arguments:
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
@ -511,3 +521,48 @@ Prints JSON data with details for the last best epoch (i.e., the best of all epo
```
freqtrade hyperopt-show --best -n -1 --print-json --no-header
```
## Show trades
Print selected (or all) trades from database to screen.
```
usage: freqtrade show-trades [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [--db-url PATH]
[--trade-ids TRADE_IDS [TRADE_IDS ...]]
[--print-json]
optional arguments:
-h, --help show this help message and exit
--db-url PATH Override trades database URL, this is useful in custom
deployments (default: `sqlite:///tradesv3.sqlite` for
Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
Dry Run).
--trade-ids TRADE_IDS [TRADE_IDS ...]
Specify the list of trade ids.
--print-json Print output in JSON format.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
### Examples
Print trades with id 2 and 3 as json
``` bash
freqtrade show-trades --db-url sqlite:///tradesv3.sqlite --trade-ids 2 3 --print-json
```

8
docs/webhook-config.md
View File

@ -23,12 +23,12 @@ Sample configuration (tested using IFTTT).
"webhooksell": {
"value1": "Selling {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency}"
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
},
"webhooksellcancel": {
"value1": "Cancelling Open Sell Order for {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency}"
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
},
"webhookstatus": {
"value1": "Status: {status}",
@ -87,7 +87,7 @@ Possible parameters are:
* `open_rate`
* `current_rate`
* `profit_amount`
* `profit_percent`
* `profit_ratio`
* `stake_currency`
* `fiat_currency`
* `sell_reason`
@ -108,7 +108,7 @@ Possible parameters are:
* `open_rate`
* `current_rate`
* `profit_amount`
* `profit_percent`
* `profit_ratio`
* `stake_currency`
* `fiat_currency`
* `sell_reason`

2
environment.yml
View File

@ -45,7 +45,7 @@ dependencies:
- pip:
# Required for app
- cython
- coinmarketcap
- pycoingecko
- ccxt
- TA-Lib
- py_find_1st

9
freqtrade/__init__.py
View File

@ -24,4 +24,11 @@ if __version__ == 'develop':
# stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
except Exception:
# git not available, ignore
pass
try:
# Try Fallback to freqtrade_commit file (created by CI while building docker image)
from pathlib import Path
versionfile = Path('./freqtrade_commit')
if versionfile.is_file():
__version__ = f"docker-{versionfile.read_text()[:8]}"
except Exception:
pass

6
freqtrade/commands/__init__.py
View File

@ -9,7 +9,8 @@ Note: Be careful with file-scoped imports in these subfiles.
from freqtrade.commands.arguments import Arguments
from freqtrade.commands.build_config_commands import start_new_config
from freqtrade.commands.data_commands import (start_convert_data,
start_download_data)
start_download_data,
start_list_data)
from freqtrade.commands.deploy_commands import (start_create_userdir,
start_new_hyperopt,
start_new_strategy)
@ -19,7 +20,8 @@ from freqtrade.commands.list_commands import (start_list_exchanges,
start_list_hyperopts,
start_list_markets,
start_list_strategies,
start_list_timeframes)
start_list_timeframes,
start_show_trades)
from freqtrade.commands.optimize_commands import (start_backtesting,
start_edge, start_hyperopt)
from freqtrade.commands.pairlist_commands import start_test_pairlist

215
freqtrade/commands/arguments.py
View File

@ -15,7 +15,7 @@ ARGS_STRATEGY = ["strategy", "strategy_path"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
ARGS_COMMON_OPTIMIZE = ["ticker_interval", "timerange",
ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange",
"max_open_trades", "stake_amount", "fee"]
ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + [
@ -55,30 +55,35 @@ ARGS_BUILD_HYPEROPT = ["user_data_dir", "hyperopt", "template"]
ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase"]
ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs"]
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
"timeframes", "erase", "dataformat_ohlcv", "dataformat_trades"]
ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
"db_url", "trade_source", "export", "exportfilename",
"timerange", "ticker_interval"]
"timerange", "timeframe", "no_trades"]
ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
"trade_source", "ticker_interval"]
"trade_source", "timeframe"]
ARGS_SHOW_TRADES = ["db_url", "trade_ids", "print_json"]
ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
"hyperopt_list_min_trades", "hyperopt_list_max_trades",
"hyperopt_list_min_avg_time", "hyperopt_list_max_avg_time",
"hyperopt_list_min_avg_profit", "hyperopt_list_max_avg_profit",
"hyperopt_list_min_total_profit", "hyperopt_list_max_total_profit",
"print_colorized", "print_json", "hyperopt_list_no_details"]
"print_colorized", "print_json", "hyperopt_list_no_details",
"export_csv"]
ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
"print_json", "hyperopt_show_no_header"]
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies",
"list-markets", "list-pairs", "list-strategies", "list-data",
"list-hyperopts", "hyperopt-list", "hyperopt-show",
"plot-dataframe", "plot-profit"]
"plot-dataframe", "plot-profit", "show-trades"]
NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
@ -157,13 +162,13 @@ class Arguments:
self._build_args(optionlist=['version'], parser=self.parser)
from freqtrade.commands import (start_create_userdir, start_convert_data,
start_download_data,
start_download_data, start_list_data,
start_hyperopt_list, start_hyperopt_show,
start_list_exchanges, start_list_hyperopts,
start_list_markets, start_list_strategies,
start_list_timeframes, start_new_config,
start_new_hyperopt, start_new_strategy,
start_plot_dataframe, start_plot_profit,
start_plot_dataframe, start_plot_profit, start_show_trades,
start_backtesting, start_hyperopt, start_edge,
start_test_pairlist, start_trading)
@ -179,6 +184,67 @@ class Arguments:
trade_cmd.set_defaults(func=start_trading)
self._build_args(optionlist=ARGS_TRADE, parser=trade_cmd)
# add create-userdir subcommand
create_userdir_cmd = subparsers.add_parser('create-userdir',
help="Create user-data directory.",
)
create_userdir_cmd.set_defaults(func=start_create_userdir)
self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
# add new-config subcommand
build_config_cmd = subparsers.add_parser('new-config',
help="Create new config")
build_config_cmd.set_defaults(func=start_new_config)
self._build_args(optionlist=ARGS_BUILD_CONFIG, parser=build_config_cmd)
# add new-hyperopt subcommand
build_hyperopt_cmd = subparsers.add_parser('new-hyperopt',
help="Create new hyperopt")
build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
# add new-strategy subcommand
build_strategy_cmd = subparsers.add_parser('new-strategy',
help="Create new strategy")
build_strategy_cmd.set_defaults(func=start_new_strategy)
self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
# Add download-data subcommand
download_data_cmd = subparsers.add_parser(
'download-data',
help='Download backtesting data.',
parents=[_common_parser],
)
download_data_cmd.set_defaults(func=start_download_data)
self._build_args(optionlist=ARGS_DOWNLOAD_DATA, parser=download_data_cmd)
# Add convert-data subcommand
convert_data_cmd = subparsers.add_parser(
'convert-data',
help='Convert candle (OHLCV) data from one format to another.',
parents=[_common_parser],
)
convert_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=True))
self._build_args(optionlist=ARGS_CONVERT_DATA_OHLCV, parser=convert_data_cmd)
# Add convert-trade-data subcommand
convert_trade_data_cmd = subparsers.add_parser(
'convert-trade-data',
help='Convert trade data from one format to another.',
parents=[_common_parser],
)
convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))
self._build_args(optionlist=ARGS_CONVERT_DATA, parser=convert_trade_data_cmd)
# Add list-data subcommand
list_data_cmd = subparsers.add_parser(
'list-data',
help='List downloaded data.',
parents=[_common_parser],
)
list_data_cmd.set_defaults(func=start_list_data)
self._build_args(optionlist=ARGS_LIST_DATA, parser=list_data_cmd)
# Add backtesting subcommand
backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.',
parents=[_common_parser, _strategy_parser])
@ -198,48 +264,23 @@ class Arguments:
hyperopt_cmd.set_defaults(func=start_hyperopt)
self._build_args(optionlist=ARGS_HYPEROPT, parser=hyperopt_cmd)
# add create-userdir subcommand
create_userdir_cmd = subparsers.add_parser('create-userdir',
help="Create user-data directory.",
)
create_userdir_cmd.set_defaults(func=start_create_userdir)
self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
# add new-config subcommand
build_config_cmd = subparsers.add_parser('new-config',
help="Create new config")
build_config_cmd.set_defaults(func=start_new_config)
self._build_args(optionlist=ARGS_BUILD_CONFIG, parser=build_config_cmd)
# add new-strategy subcommand
build_strategy_cmd = subparsers.add_parser('new-strategy',
help="Create new strategy")
build_strategy_cmd.set_defaults(func=start_new_strategy)
self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
# add new-hyperopt subcommand
build_hyperopt_cmd = subparsers.add_parser('new-hyperopt',
help="Create new hyperopt")
build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
# Add list-strategies subcommand
list_strategies_cmd = subparsers.add_parser(
'list-strategies',
help='Print available strategies.',
# Add hyperopt-list subcommand
hyperopt_list_cmd = subparsers.add_parser(
'hyperopt-list',
help='List Hyperopt results',
parents=[_common_parser],
)
list_strategies_cmd.set_defaults(func=start_list_strategies)
self._build_args(optionlist=ARGS_LIST_STRATEGIES, parser=list_strategies_cmd)
hyperopt_list_cmd.set_defaults(func=start_hyperopt_list)
self._build_args(optionlist=ARGS_HYPEROPT_LIST, parser=hyperopt_list_cmd)
# Add list-hyperopts subcommand
list_hyperopts_cmd = subparsers.add_parser(
'list-hyperopts',
help='Print available hyperopt classes.',
# Add hyperopt-show subcommand
hyperopt_show_cmd = subparsers.add_parser(
'hyperopt-show',
help='Show details of Hyperopt results',
parents=[_common_parser],
)
list_hyperopts_cmd.set_defaults(func=start_list_hyperopts)
self._build_args(optionlist=ARGS_LIST_HYPEROPTS, parser=list_hyperopts_cmd)
hyperopt_show_cmd.set_defaults(func=start_hyperopt_show)
self._build_args(optionlist=ARGS_HYPEROPT_SHOW, parser=hyperopt_show_cmd)
# Add list-exchanges subcommand
list_exchanges_cmd = subparsers.add_parser(
@ -250,14 +291,14 @@ class Arguments:
list_exchanges_cmd.set_defaults(func=start_list_exchanges)
self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
# Add list-timeframes subcommand
list_timeframes_cmd = subparsers.add_parser(
'list-timeframes',
help='Print available ticker intervals (timeframes) for the exchange.',
# Add list-hyperopts subcommand
list_hyperopts_cmd = subparsers.add_parser(
'list-hyperopts',
help='Print available hyperopt classes.',
parents=[_common_parser],
)
list_timeframes_cmd.set_defaults(func=start_list_timeframes)
self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
list_hyperopts_cmd.set_defaults(func=start_list_hyperopts)
self._build_args(optionlist=ARGS_LIST_HYPEROPTS, parser=list_hyperopts_cmd)
# Add list-markets subcommand
list_markets_cmd = subparsers.add_parser(
@ -277,6 +318,33 @@ class Arguments:
list_pairs_cmd.set_defaults(func=partial(start_list_markets, pairs_only=True))
self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_pairs_cmd)
# Add list-strategies subcommand
list_strategies_cmd = subparsers.add_parser(
'list-strategies',
help='Print available strategies.',
parents=[_common_parser],
)
list_strategies_cmd.set_defaults(func=start_list_strategies)
self._build_args(optionlist=ARGS_LIST_STRATEGIES, parser=list_strategies_cmd)
# Add list-timeframes subcommand
list_timeframes_cmd = subparsers.add_parser(
'list-timeframes',
help='Print available timeframes for the exchange.',
parents=[_common_parser],
)
list_timeframes_cmd.set_defaults(func=start_list_timeframes)
self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
# Add show-trades subcommand
show_trades = subparsers.add_parser(
'show-trades',
help='Show trades.',
parents=[_common_parser],
)
show_trades.set_defaults(func=start_show_trades)
self._build_args(optionlist=ARGS_SHOW_TRADES, parser=show_trades)
# Add test-pairlist subcommand
test_pairlist_cmd = subparsers.add_parser(
'test-pairlist',
@ -285,33 +353,6 @@ class Arguments:
test_pairlist_cmd.set_defaults(func=start_test_pairlist)
self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_cmd)
# Add download-data subcommand
download_data_cmd = subparsers.add_parser(
'download-data',
help='Download backtesting data.',
parents=[_common_parser],
)
download_data_cmd.set_defaults(func=start_download_data)
self._build_args(optionlist=ARGS_DOWNLOAD_DATA, parser=download_data_cmd)
# Add convert-data subcommand
convert_data_cmd = subparsers.add_parser(
'convert-data',
help='Convert OHLCV data from one format to another.',
parents=[_common_parser],
)
convert_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=True))
self._build_args(optionlist=ARGS_CONVERT_DATA_OHLCV, parser=convert_data_cmd)
# Add convert-trade-data subcommand
convert_trade_data_cmd = subparsers.add_parser(
'convert-trade-data',
help='Convert trade-data from one format to another.',
parents=[_common_parser],
)
convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))
self._build_args(optionlist=ARGS_CONVERT_DATA, parser=convert_trade_data_cmd)
# Add Plotting subcommand
plot_dataframe_cmd = subparsers.add_parser(
'plot-dataframe',
@ -329,21 +370,3 @@ class Arguments:
)
plot_profit_cmd.set_defaults(func=start_plot_profit)
self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)
# Add hyperopt-list subcommand
hyperopt_list_cmd = subparsers.add_parser(
'hyperopt-list',
help='List Hyperopt results',
parents=[_common_parser],
)
hyperopt_list_cmd.set_defaults(func=start_hyperopt_list)
self._build_args(optionlist=ARGS_HYPEROPT_LIST, parser=hyperopt_list_cmd)
# Add hyperopt-show subcommand
hyperopt_show_cmd = subparsers.add_parser(
'hyperopt-show',
help='Show details of Hyperopt results',
parents=[_common_parser],
)
hyperopt_show_cmd.set_defaults(func=start_hyperopt_show)
self._build_args(optionlist=ARGS_HYPEROPT_SHOW, parser=hyperopt_show_cmd)

6
freqtrade/commands/build_config_commands.py
View File

@ -75,8 +75,8 @@ def ask_user_config() -> Dict[str, Any]:
},
{
"type": "text",
"name": "ticker_interval",
"message": "Please insert your ticker interval:",
"name": "timeframe",
"message": "Please insert your desired timeframe (e.g. 5m):",
"default": "5m",
},
{
@ -163,7 +163,7 @@ def deploy_new_config(config_path: Path, selections: Dict[str, Any]) -> None:
)
except TemplateNotFound:
selections['exchange'] = render_template(
templatefile=f"subtemplates/exchange_generic.j2",
templatefile="subtemplates/exchange_generic.j2",
arguments=selections
)

69
freqtrade/commands/cli_options.py
View File

@ -110,8 +110,8 @@ AVAILABLE_CLI_OPTIONS = {
action='store_true',
),
# Optimize common
"ticker_interval": Arg(
'-i', '--ticker-interval',
"timeframe": Arg(
'-i', '--timeframe', '--ticker-interval',
help='Specify ticker interval (`1m`, `5m`, `30m`, `1h`, `1d`).',
),
"timerange": Arg(
@ -195,8 +195,7 @@ AVAILABLE_CLI_OPTIONS = {
metavar='INT',
default=constants.HYPEROPT_EPOCH,
),
"effort":
Arg(
"effort": Arg(
'--effort',
help=('The higher the number, the longer will be the search if'
'no epochs are defined (default: %(default)d).'),
@ -204,28 +203,29 @@ AVAILABLE_CLI_OPTIONS = {
metavar='FLOAT',
default=constants.HYPEROPT_EFFORT,
),
"mode":
Arg('--mode',
"mode": Arg(
'--mode',
help='Switches hyperopt to use one optimizer per job, use it'
'when backtesting iterations are cheap (default: %(default)s).',
'when backtesting iterations are cheap (default: %(default)s).',
metavar='NAME',
default=constants.HYPEROPT_MODE),
"n_points":
Arg('--n-points',
"n_points": Arg(
'--n-points',
help='Controls how many points to ask to the optimizer '
'increase if cpu usage of each core '
'appears low (default: %(default)d).',
'increase if cpu usage of each core '
'appears low (default: %(default)d).',
type=int,
metavar='INT',
default=constants.HYPEROPT_N_POINTS),
"lie_strat":
Arg('--lie-strat',
default=constants.HYPEROPT_N_POINTS
),
"lie_strat": Arg(
'--lie-strat',
help='Sets the strategy that the optimizer uses to lie '
'when asking for more than one point, '
'no effect if n_point is one (default: %(default)s).',
default=constants.HYPEROPT_LIE_STRAT),
"spaces":
Arg(
'when asking for more than one point, '
'no effect if n_point is one (default: %(default)s).',
default=constants.HYPEROPT_LIE_STRAT
),
"spaces": Arg(
'--spaces',
help='Specify which parameters to hyperopt. Space-separated list.',
choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'default'],
@ -247,10 +247,17 @@ AVAILABLE_CLI_OPTIONS = {
),
"print_json": Arg(
'--print-json',
help='Print best result detailization in JSON format.',
help='Print output in JSON format.',
action='store_true',
default=False,
),
"export_csv": Arg(
'--export-csv',
help='Export to CSV-File.'
' This will disable table print.'
' Example: --export-csv hyperopt.csv',
metavar='FILE',
),
"hyperopt_jobs": Arg(
'-j', '--job-workers',
help='The number of concurrently running jobs for hyperoptimization '
@ -378,7 +385,7 @@ AVAILABLE_CLI_OPTIONS = {
),
"dataformat_ohlcv": Arg(
'--data-format-ohlcv',
help='Storage format for downloaded ohlcv data. (default: `%(default)s`).',
help='Storage format for downloaded candle (OHLCV) data. (default: `%(default)s`).',
choices=constants.AVAILABLE_DATAHANDLERS,
default='json'
),
@ -395,8 +402,8 @@ AVAILABLE_CLI_OPTIONS = {
),
"timeframes": Arg(
'-t', '--timeframes',
help=f'Specify which tickers to download. Space-separated list. '
f'Default: `1m 5m`.',
help='Specify which tickers to download. Space-separated list. '
'Default: `1m 5m`.',
choices=['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h',
'6h', '8h', '12h', '1d', '3d', '1w'],
default=['1m', '5m'],
@ -410,9 +417,9 @@ AVAILABLE_CLI_OPTIONS = {
# Templating options
"template": Arg(
'--template',
help='Use a template which is either `minimal` or '
'`full` (containing multiple sample indicators). Default: `%(default)s`.',
choices=['full', 'minimal'],
help='Use a template which is either `minimal`, '
'`full` (containing multiple sample indicators) or `advanced`. Default: `%(default)s`.',
choices=['full', 'minimal', 'advanced'],
default='full',
),
# Plot dataframe
@ -436,6 +443,11 @@ AVAILABLE_CLI_OPTIONS = {
metavar='INT',
default=750,
),
"no_trades": Arg(
'--no-trades',
help='Skip using trades from backtesting file and DB.',
action='store_true',
),
"trade_source": Arg(
'--trade-source',
help='Specify the source for trades (Can be DB or file (backtest file)) '
@ -443,6 +455,11 @@ AVAILABLE_CLI_OPTIONS = {
choices=["DB", "file"],
default="file",
),
"trade_ids": Arg(
'--trade-ids',
help='Specify the list of trade ids.',
nargs='+',
),
# hyperopt-list, hyperopt-show
"hyperopt_list_profitable": Arg(
'--profitable',

29
freqtrade/commands/data_commands.py
View File

@ -1,5 +1,6 @@
import logging
import sys
from collections import defaultdict
from typing import Any, Dict, List
import arrow
@ -11,6 +12,7 @@ from freqtrade.data.history import (convert_trades_to_ohlcv,
refresh_backtest_ohlcv_data,
refresh_backtest_trades_data)
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.resolvers import ExchangeResolver
from freqtrade.state import RunMode
@ -88,3 +90,30 @@ def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
convert_trades_format(config,
convert_from=args['format_from'], convert_to=args['format_to'],
erase=args['erase'])
def start_list_data(args: Dict[str, Any]) -> None:
"""
List available backtest data
"""
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
from freqtrade.data.history.idatahandler import get_datahandler
from tabulate import tabulate
dhc = get_datahandler(config['datadir'], config['dataformat_ohlcv'])
paircombs = dhc.ohlcv_get_available_data(config['datadir'])
if args['pairs']:
paircombs = [comb for comb in paircombs if comb[0] in args['pairs']]
print(f"Found {len(paircombs)} pair / timeframe combinations.")
groupedpair = defaultdict(list)
for pair, timeframe in sorted(paircombs, key=lambda x: (x[0], timeframe_to_minutes(x[1]))):
groupedpair[pair].append(timeframe)
if groupedpair:
print(tabulate([(pair, ', '.join(timeframes)) for pair, timeframes in groupedpair.items()],
headers=("Pair", "Timeframe"),
tablefmt='psql', stralign='right'))

53
freqtrade/commands/deploy_commands.py
View File

@ -8,7 +8,7 @@ from freqtrade.configuration.directory_operations import (copy_sample_files,
create_userdata_dir)
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.exceptions import OperationalException
from freqtrade.misc import render_template
from freqtrade.misc import render_template, render_template_with_fallback
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
@ -32,10 +32,27 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
"""
Deploy new strategy from template to strategy_path
"""
indicators = render_template(templatefile=f"subtemplates/indicators_{subtemplate}.j2",)
buy_trend = render_template(templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",)
sell_trend = render_template(templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",)
plot_config = render_template(templatefile=f"subtemplates/plot_config_{subtemplate}.j2",)
fallback = 'full'
indicators = render_template_with_fallback(
templatefile=f"subtemplates/indicators_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/indicators_{fallback}.j2",
)
buy_trend = render_template_with_fallback(
templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/buy_trend_{fallback}.j2",
)
sell_trend = render_template_with_fallback(
templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/sell_trend_{fallback}.j2",
)
plot_config = render_template_with_fallback(
templatefile=f"subtemplates/plot_config_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/plot_config_{fallback}.j2",
)
additional_methods = render_template_with_fallback(
templatefile=f"subtemplates/strategy_methods_{subtemplate}.j2",
templatefallbackfile="subtemplates/strategy_methods_empty.j2",
)
strategy_text = render_template(templatefile='base_strategy.py.j2',
arguments={"strategy": strategy_name,
@ -43,6 +60,7 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
"buy_trend": buy_trend,
"sell_trend": sell_trend,
"plot_config": plot_config,
"additional_methods": additional_methods,
})
logger.info(f"Writing strategy to `{strategy_path}`.")
@ -73,14 +91,23 @@ def deploy_new_hyperopt(hyperopt_name: str, hyperopt_path: Path, subtemplate: st
"""
Deploys a new hyperopt template to hyperopt_path
"""
buy_guards = render_template(
templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",)
sell_guards = render_template(
templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",)
buy_space = render_template(
templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",)
sell_space = render_template(
templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",)
fallback = 'full'
buy_guards = render_template_with_fallback(
templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/hyperopt_buy_guards_{fallback}.j2",
)
sell_guards = render_template_with_fallback(
templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/hyperopt_sell_guards_{fallback}.j2",
)
buy_space = render_template_with_fallback(
templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/hyperopt_buy_space_{fallback}.j2",
)
sell_space = render_template_with_fallback(
templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/hyperopt_sell_space_{fallback}.j2",
)
strategy_text = render_template(templatefile='base_hyperopt.py.j2',
arguments={"hyperopt": hyperopt_name,

117
freqtrade/commands/hyperopt_commands.py
View File

@ -21,6 +21,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
print_colorized = config.get('print_colorized', False)
print_json = config.get('print_json', False)
export_csv = config.get('export_csv', None)
no_details = config.get('hyperopt_list_no_details', False)
no_header = False
@ -37,29 +38,35 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
}
trials_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
results_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
# Previous evaluations
trials = Hyperopt.load_previous_results(trials_file)
total_epochs = len(trials)
epochs = Hyperopt.load_previous_results(results_file)
total_epochs = len(epochs)
trials = _hyperopt_filter_trials(trials, filteroptions)
epochs = _hyperopt_filter_epochs(epochs, filteroptions)
if print_colorized:
colorama_init(autoreset=True)
try:
Hyperopt.print_result_table(config, trials, total_epochs,
not filteroptions['only_best'], print_colorized)
except KeyboardInterrupt:
print('User interrupted..')
if not export_csv:
try:
print(Hyperopt.get_result_table(config, epochs, total_epochs,
not filteroptions['only_best'], print_colorized, 0))
except KeyboardInterrupt:
print('User interrupted..')
if trials and not no_details:
sorted_trials = sorted(trials, key=itemgetter('loss'))
results = sorted_trials[0]
if epochs and not no_details:
sorted_epochs = sorted(epochs, key=itemgetter('loss'))
results = sorted_epochs[0]
Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
if epochs and export_csv:
Hyperopt.export_csv_file(
config, epochs, total_epochs, not filteroptions['only_best'], export_csv
)
def start_hyperopt_show(args: Dict[str, Any]) -> None:
"""
@ -71,8 +78,8 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
print_json = config.get('print_json', False)
no_header = config.get('hyperopt_show_no_header', False)
trials_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
results_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
n = config.get('hyperopt_show_index', -1)
filteroptions = {
@ -89,89 +96,87 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
}
# Previous evaluations
trials = Hyperopt.load_previous_results(trials_file)
total_epochs = len(trials)
epochs = Hyperopt.load_previous_results(results_file)
total_epochs = len(epochs)
trials = _hyperopt_filter_trials(trials, filteroptions)
trials_epochs = len(trials)
epochs = _hyperopt_filter_epochs(epochs, filteroptions)
filtered_epochs = len(epochs)
if n > trials_epochs:
if n > filtered_epochs:
raise OperationalException(
f"The index of the epoch to show should be less than {trials_epochs + 1}.")
if n < -trials_epochs:
f"The index of the epoch to show should be less than {filtered_epochs + 1}.")
if n < -filtered_epochs:
raise OperationalException(
f"The index of the epoch to show should be greater than {-trials_epochs - 1}.")
f"The index of the epoch to show should be greater than {-filtered_epochs - 1}.")
# Translate epoch index from human-readable format to pythonic
if n > 0:
n -= 1
if trials:
val = trials[n]
if epochs:
val = epochs[n]
Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,
header_str="Epoch details")
def _hyperopt_filter_trials(trials: List, filteroptions: dict) -> List:
def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
"""
Filter our items from the list of hyperopt results
"""
if filteroptions['only_best']:
trials = [x for x in trials if x['is_best']]
epochs = [x for x in epochs if x['is_best']]
if filteroptions['only_profitable']:
trials = [x for x in trials if x['results_metrics']['profit'] > 0]
epochs = [x for x in epochs if x['results_metrics']['profit'] > 0]
if filteroptions['filter_min_trades'] > 0:
trials = [
x for x in trials
epochs = [
x for x in epochs
if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
]
if filteroptions['filter_max_trades'] > 0:
trials = [
x for x in trials
epochs = [
x for x in epochs
if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
]
if filteroptions['filter_min_avg_time'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [
x for x in epochs
if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
]
if filteroptions['filter_max_avg_time'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [
x for x in epochs
if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
]
if filteroptions['filter_min_avg_profit'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['avg_profit']
> filteroptions['filter_min_avg_profit']
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [
x for x in epochs
if x['results_metrics']['avg_profit'] > filteroptions['filter_min_avg_profit']
]
if filteroptions['filter_max_avg_profit'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['avg_profit']
< filteroptions['filter_max_avg_profit']
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [
x for x in epochs
if x['results_metrics']['avg_profit'] < filteroptions['filter_max_avg_profit']
]
if filteroptions['filter_min_total_profit'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [
x for x in epochs
if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
]
if filteroptions['filter_max_total_profit'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [
x for x in epochs
if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
]
logger.info(f"{len(trials)} " +
logger.info(f"{len(epochs)} " +
("best " if filteroptions['only_best'] else "") +
("profitable " if filteroptions['only_profitable'] else "") +
"epochs found.")
return trials
return epochs

31
freqtrade/commands/list_commands.py
View File

@ -102,8 +102,8 @@ def start_list_timeframes(args: Dict[str, Any]) -> None:
Print ticker intervals (timeframes) available on Exchange
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
# Do not use ticker_interval set in the config
config['ticker_interval'] = None
# Do not use timeframe set in the config
config['timeframe'] = None
# Init exchange
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
@ -197,3 +197,30 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
args.get('list_pairs_print_json', False) or
args.get('print_csv', False)):
print(f"{summary_str}.")
def start_show_trades(args: Dict[str, Any]) -> None:
"""
Show trades
"""
from freqtrade.persistence import init, Trade
import json
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if 'db_url' not in config:
raise OperationalException("--db-url is required for this command.")
logger.info(f'Using DB: "{config["db_url"]}"')
init(config['db_url'], clean_open_orders=False)
tfilter = []
if config.get('trade_ids'):
tfilter.append(Trade.id.in_(config['trade_ids']))
trades = Trade.get_trades(tfilter).all()
logger.info(f"Printing {len(trades)} Trades: ")
if config.get('print_json', False):
print(json.dumps([trade.to_json() for trade in trades], indent=4))
else:
for trade in trades:
print(trade)

13
freqtrade/commands/optimize_commands.py
View File

@ -17,10 +17,15 @@ def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[
"""
config = setup_utils_configuration(args, method)
if method == RunMode.BACKTEST:
if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
raise DependencyException('stake amount could not be "%s" for backtesting' %
constants.UNLIMITED_STAKE_AMOUNT)
no_unlimited_runmodes = {
RunMode.BACKTEST: 'backtesting',
RunMode.HYPEROPT: 'hyperoptimization',
}
if (method in no_unlimited_runmodes.keys() and
config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT):
raise DependencyException(
f'The value of `stake_amount` cannot be set as "{constants.UNLIMITED_STAKE_AMOUNT}" '
f'for {no_unlimited_runmodes[method]}')
return config

1
freqtrade/commands/pairlist_commands.py
View File

@ -25,7 +25,6 @@ def start_test_pairlist(args: Dict[str, Any]) -> None:
results = {}
for curr in quote_currencies:
config['stake_currency'] = curr
# Do not use ticker_interval set in the config
pairlists = PairListManager(exchange, config)
pairlists.refresh_pairlist()
results[curr] = pairlists.whitelist

3
freqtrade/commands/trade_commands.py
View File

@ -18,6 +18,9 @@ def start_trading(args: Dict[str, Any]) -> int:
try:
worker = Worker(args)
worker.run()
except Exception as e:
logger.error(str(e))
logger.exception("Fatal exception!")
except KeyboardInterrupt:
logger.info('SIGINT received, aborting ...')
finally:

21
freqtrade/configuration/configuration.py
View File

@ -196,6 +196,7 @@ class Configuration:
if self.args.get('exportfilename'):
self._args_to_config(config, argname='exportfilename',
logstring='Storing backtest results to {} ...')
config['exportfilename'] = Path(config['exportfilename'])
else:
config['exportfilename'] = (config['user_data_dir']
/ 'backtest_results/backtest-result.json')
@ -203,9 +204,9 @@ class Configuration:
def _process_optimize_options(self, config: Dict[str, Any]) -> None:
# This will override the strategy configuration
self._args_to_config(config, argname='ticker_interval',
logstring='Parameter -i/--ticker-interval detected ... '
'Using ticker_interval: {} ...')
self._args_to_config(config, argname='timeframe',
logstring='Parameter -i/--timeframe detected ... '
'Using timeframe: {} ...')
self._args_to_config(config, argname='position_stacking',
logstring='Parameter --enable-position-stacking detected ...')
@ -241,8 +242,8 @@ class Configuration:
self._args_to_config(config, argname='strategy_list',
logstring='Using strategy list of {} strategies', logfun=len)
self._args_to_config(config, argname='ticker_interval',
logstring='Overriding ticker interval with Command line argument')
self._args_to_config(config, argname='timeframe',
logstring='Overriding timeframe with Command line argument')
self._args_to_config(config, argname='export',
logstring='Parameter --export detected: {} ...')
@ -294,6 +295,9 @@ class Configuration:
self._args_to_config(config, argname='print_json',
logstring='Parameter --print-json detected ...')
self._args_to_config(config, argname='export_csv',
logstring='Parameter --export-csv detected: {}')
self._args_to_config(config, argname='hyperopt_jobs',
logstring='Parameter -j/--job-workers detected: {}')
@ -359,14 +363,21 @@ class Configuration:
self._args_to_config(config, argname='indicators2',
logstring='Using indicators2: {}')
self._args_to_config(config, argname='trade_ids',
logstring='Filtering on trade_ids: {}')
self._args_to_config(config, argname='plot_limit',
logstring='Limiting plot to: {}')
self._args_to_config(config, argname='trade_source',
logstring='Using trades from: {}')
self._args_to_config(config, argname='erase',
logstring='Erase detected. Deleting existing data.')
self._args_to_config(config, argname='no_trades',
logstring='Parameter --no-trades detected.')
self._args_to_config(config, argname='timeframes',
logstring='timeframes --timeframes: {}')

36
freqtrade/configuration/deprecated_settings.py
View File

@ -58,35 +58,23 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
process_deprecated_setting(config, 'ask_strategy', 'ignore_roi_if_buy_signal',
'experimental', 'ignore_roi_if_buy_signal')
if not config.get('pairlists') and not config.get('pairlists'):
config['pairlists'] = [{'method': 'StaticPairList'}]
logger.warning(
"DEPRECATED: "
"Pairlists must be defined explicitly in the future."
"Defaulting to StaticPairList for now.")
if config.get('pairlist', {}).get("method") == 'VolumePairList':
logger.warning(
"DEPRECATED: "
f"Using VolumePairList in pairlist is deprecated and must be moved to pairlists. "
"Please refer to the docs on configuration details")
pl = {'method': 'VolumePairList'}
pl.update(config.get('pairlist', {}).get('config'))
config['pairlists'].append(pl)
if config.get('pairlist', {}).get('config', {}).get('precision_filter'):
logger.warning(
"DEPRECATED: "
f"Using precision_filter setting is deprecated and has been replaced by"
"PrecisionFilter. Please refer to the docs on configuration details")
config['pairlists'].append({'method': 'PrecisionFilter'})
if (config.get('edge', {}).get('enabled', False)
and 'capital_available_percentage' in config.get('edge', {})):
logger.warning(
raise OperationalException(
"DEPRECATED: "
"Using 'edge.capital_available_percentage' has been deprecated in favor of "
"'tradable_balance_ratio'. Please migrate your configuration to "
"'tradable_balance_ratio' and remove 'capital_available_percentage' "
"from the edge configuration."
)
if 'ticker_interval' in config:
logger.warning(
"DEPRECATED: "
"Please use 'timeframe' instead of 'ticker_interval."
)
if 'timeframe' in config:
raise OperationalException(
"Both 'timeframe' and 'ticker_interval' detected."
"Please remove 'ticker_interval' from your configuration to continue operating."
)
config['timeframe'] = config['ticker_interval']

4
freqtrade/configuration/directory_operations.py
View File

@ -33,8 +33,8 @@ def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
:param create_dir: Create directory if it does not exist.
:return: Path object containing the directory
"""
sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "notebooks",
"plot", "strategies", ]
sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "logs",
"notebooks", "plot", "strategies", ]
folder = Path(directory)
if not folder.is_dir():
if create_dir:

33
freqtrade/configuration/load_config.py
View File

@ -1,13 +1,15 @@
"""
This module contain functions to load the configuration file
"""
import rapidjson
import logging
import re
import sys
from pathlib import Path
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
import rapidjson
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
@ -15,6 +17,26 @@ logger = logging.getLogger(__name__)
CONFIG_PARSE_MODE = rapidjson.PM_COMMENTS | rapidjson.PM_TRAILING_COMMAS
def log_config_error_range(path: str, errmsg: str) -> str:
"""
Parses configuration file and prints range around error
"""
if path != '-':
offsetlist = re.findall(r'(?<=Parse\serror\sat\soffset\s)\d+', errmsg)
if offsetlist:
offset = int(offsetlist[0])
text = Path(path).read_text()
# Fetch an offset of 80 characters around the error line
subtext = text[offset-min(80, offset):offset+80]
segments = subtext.split('\n')
if len(segments) > 3:
# Remove first and last lines, to avoid odd truncations
return '\n'.join(segments[1:-1])
else:
return subtext
return ''
def load_config_file(path: str) -> Dict[str, Any]:
"""
Loads a config file from the given path
@ -29,5 +51,12 @@ def load_config_file(path: str) -> Dict[str, Any]:
raise OperationalException(
f'Config file "{path}" not found!'
' Please create a config file or check whether it exists.')
except rapidjson.JSONDecodeError as e:
err_range = log_config_error_range(path, str(e))
raise OperationalException(
f'{e}\n'
f'Please verify the following segment of your configuration:\n{err_range}'
if err_range else 'Please verify your configuration file for syntax errors.'
)
return config

2
freqtrade/configuration/timerange.py
View File

@ -45,7 +45,7 @@ class TimeRange:
"""
Adjust startts by <startup_candles> candles.
Applies only if no startup-candles have been available.
:param timeframe_secs: Ticker timeframe in seconds e.g. `timeframe_to_seconds('5m')`
:param timeframe_secs: Timeframe in seconds e.g. `timeframe_to_seconds('5m')`
:param startup_candles: Number of candles to move start-date forward
:param min_date: Minimum data date loaded. Key kriterium to decide if start-time
has to be moved

53
freqtrade/constants.py
View File

@ -3,6 +3,9 @@
"""
bot constants
"""
from typing import List, Tuple
DEFAULT_CONFIG = 'config.json'
DEFAULT_EXCHANGE = 'bittrex'
PROCESS_THROTTLE_SECS = 5 # sec
@ -19,14 +22,19 @@ UNLIMITED_STAKE_AMOUNT = 'unlimited'
DEFAULT_AMOUNT_RESERVE_PERCENT = 0.05
REQUIRED_ORDERTIF = ['buy', 'sell']
REQUIRED_ORDERTYPES = ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
ORDERBOOK_SIDES = ['ask', 'bid']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'PrecisionFilter', 'PriceFilter', 'SpreadFilter']
'AgeFilter', 'PrecisionFilter', 'PriceFilter',
'ShuffleFilter', 'SpreadFilter']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz']
DRY_RUN_WALLET = 1000
MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
# Don't modify sequence of DEFAULT_TRADES_COLUMNS
# it has wide consequences for stored trades files
DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost']
USERPATH_HYPEROPTS = 'hyperopts'
USERPATH_STRATEGIES = 'strategies'
@ -46,7 +54,7 @@ SUPPORTED_FIAT = [
"EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY",
"KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN",
"RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD",
"BTC", "XBT", "ETH", "XRP", "LTC", "BCH", "USDT"
"BTC", "ETH", "XRP", "LTC", "BCH"
]
MINIMAL_CONFIG = {
@ -68,7 +76,7 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
'ticker_interval': {'type': 'string'},
'timeframe': {'type': 'string'},
'stake_currency': {'type': 'string'},
'stake_amount': {
'type': ['number', 'string'],
@ -88,6 +96,7 @@ CONF_SCHEMA = {
'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
'dry_run': {'type': 'boolean'},
'dry_run_wallet': {'type': 'number', 'default': DRY_RUN_WALLET},
'cancel_open_orders_on_exit': {'type': 'boolean', 'default': False},
'process_only_new_candles': {'type': 'boolean'},
'minimal_roi': {
'type': 'object',
@ -117,15 +126,16 @@ CONF_SCHEMA = {
'minimum': 0,
'maximum': 1,
'exclusiveMaximum': False,
'use_order_book': {'type': 'boolean'},
'order_book_top': {'type': 'integer', 'maximum': 20, 'minimum': 1},
'check_depth_of_market': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'bids_to_ask_delta': {'type': 'number', 'minimum': 0},
}
},
},
'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'bid'},
'use_order_book': {'type': 'boolean'},
'order_book_top': {'type': 'integer', 'maximum': 20, 'minimum': 1},
'check_depth_of_market': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'bids_to_ask_delta': {'type': 'number', 'minimum': 0},
}
},
},
'required': ['ask_last_balance']
@ -133,6 +143,7 @@ CONF_SCHEMA = {
'ask_strategy': {
'type': 'object',
'properties': {
'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'ask'},
'use_order_book': {'type': 'boolean'},
'order_book_min': {'type': 'integer', 'minimum': 1},
'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
@ -215,12 +226,16 @@ CONF_SCHEMA = {
},
'username': {'type': 'string'},
'password': {'type': 'string'},
'jwt_secret_key': {'type': 'string'},
'CORS_origins': {'type': 'array', 'items': {'type': 'string'}},
'verbosity': {'type': 'string', 'enum': ['error', 'info']},
},
'required': ['enabled', 'listen_ip_address', 'listen_port', 'username', 'password']
},
'db_url': {'type': 'string'},
'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
'forcebuy_enable': {'type': 'boolean'},
'disable_dataframe_checks': {'type': 'boolean'},
'internals': {
'type': 'object',
'default': {},
@ -279,7 +294,6 @@ CONF_SCHEMA = {
'process_throttle_secs': {'type': 'integer', 'minimum': 600},
'calculate_since_number_of_days': {'type': 'integer'},
'allowed_risk': {'type': 'number'},
'capital_available_percentage': {'type': 'number'},
'stoploss_range_min': {'type': 'number'},
'stoploss_range_max': {'type': 'number'},
'stoploss_range_step': {'type': 'number'},
@ -296,6 +310,7 @@ CONF_SCHEMA = {
SCHEMA_TRADE_REQUIRED = [
'exchange',
'timeframe',
'max_open_trades',
'stake_currency',
'stake_amount',
@ -303,6 +318,7 @@ SCHEMA_TRADE_REQUIRED = [
'last_stake_amount_min_ratio',
'dry_run',
'dry_run_wallet',
'ask_strategy',
'bid_strategy',
'unfilledtimeout',
'stoploss',
@ -318,3 +334,14 @@ SCHEMA_MINIMAL_REQUIRED = [
'dataformat_ohlcv',
'dataformat_trades',
]
CANCEL_REASON = {
"TIMEOUT": "cancelled due to timeout",
"PARTIALLY_FILLED": "partially filled - keeping order open",
"ALL_CANCELLED": "cancelled (all unfilled and partially filled open orders cancelled)",
"CANCELLED_ON_EXCHANGE": "cancelled on exchange",
}
# List of pairs with their timeframes
PairWithTimeframe = Tuple[str, str]
ListPairsWithTimeframes = List[PairWithTimeframe]

84
freqtrade/data/btanalysis.py
View File

@ -3,7 +3,7 @@ Helpers when analyzing backtest data
"""
import logging
from pathlib import Path
from typing import Dict, Union
from typing import Dict, Union, Tuple
import numpy as np
import pandas as pd
@ -16,7 +16,7 @@ from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
# must align with columns in backtest.py
BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "duration",
BT_DATA_COLUMNS = ["pair", "profit_percent", "open_time", "close_time", "index", "duration",
"open_rate", "close_rate", "open_at_end", "sell_reason"]
@ -99,11 +99,11 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
persistence.init(db_url, clean_open_orders=False)
columns = ["pair", "open_time", "close_time", "profit", "profitperc",
columns = ["pair", "open_time", "close_time", "profit", "profit_percent",
"open_rate", "close_rate", "amount", "duration", "sell_reason",
"fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
"stake_amount", "max_rate", "min_rate", "id", "exchange",
"stop_loss", "initial_stop_loss", "strategy", "ticker_interval"]
"stop_loss", "initial_stop_loss", "strategy", "timeframe"]
trades = pd.DataFrame([(t.pair,
t.open_date.replace(tzinfo=timezone.utc),
@ -111,7 +111,7 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
t.calc_profit(), t.calc_profit_ratio(),
t.open_rate, t.close_rate, t.amount,
(round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
if t.close_date else None),
if t.close_date else None),
t.sell_reason,
t.fee_open, t.fee_close,
t.open_rate_requested,
@ -121,7 +121,7 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
t.min_rate,
t.id, t.exchange,
t.stop_loss, t.initial_stop_loss,
t.strategy, t.ticker_interval
t.strategy, t.timeframe
)
for t in Trade.get_trades().all()],
columns=columns)
@ -129,39 +129,56 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
return trades
def load_trades(source: str, db_url: str, exportfilename: str) -> pd.DataFrame:
def load_trades(source: str, db_url: str, exportfilename: Path,
no_trades: bool = False) -> pd.DataFrame:
"""
Based on configuration option "trade_source":
* loads data from DB (using `db_url`)
* loads data from backtestfile (using `exportfilename`)
:param source: "DB" or "file" - specify source to load from
:param db_url: sqlalchemy formatted url to a database
:param exportfilename: Json file generated by backtesting
:param no_trades: Skip using trades, only return backtesting data columns
:return: DataFrame containing trades
"""
if no_trades:
df = pd.DataFrame(columns=BT_DATA_COLUMNS)
return df
if source == "DB":
return load_trades_from_db(db_url)
elif source == "file":
return load_backtest_data(Path(exportfilename))
return load_backtest_data(exportfilename)
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> pd.DataFrame:
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
date_index=False) -> pd.DataFrame:
"""
Compare trades and backtested pair DataFrames to get trades performed on backtested period
:return: the DataFrame of a trades of period
"""
trades = trades.loc[(trades['open_time'] >= dataframe.iloc[0]['date']) &
(trades['close_time'] <= dataframe.iloc[-1]['date'])]
if date_index:
trades_start = dataframe.index[0]
trades_stop = dataframe.index[-1]
else:
trades_start = dataframe.iloc[0]['date']
trades_stop = dataframe.iloc[-1]['date']
trades = trades.loc[(trades['open_time'] >= trades_start) &
(trades['close_time'] <= trades_stop)]
return trades
def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame],
column: str = "close") -> pd.DataFrame:
def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
column: str = "close") -> pd.DataFrame:
"""
Combine multiple dataframes "column"
:param tickers: Dict of Dataframes, dict key should be pair.
:param data: Dict of Dataframes, dict key should be pair.
:param column: Column in the original dataframes to use
:return: DataFrame with the column renamed to the dict key, and a column
named mean, containing the mean of all pairs.
"""
df_comb = pd.concat([tickers[pair].set_index('date').rename(
{column: pair}, axis=1)[pair] for pair in tickers], axis=1)
df_comb = pd.concat([data[pair].set_index('date').rename(
{column: pair}, axis=1)[pair] for pair in data], axis=1)
df_comb['mean'] = df_comb.mean(axis=1)
@ -173,18 +190,49 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
"""
Adds a column `col_name` with the cumulative profit for the given trades array.
:param df: DataFrame with date index
:param trades: DataFrame containing trades (requires columns close_time and profitperc)
:param trades: DataFrame containing trades (requires columns close_time and profit_percent)
:param col_name: Column name that will be assigned the results
:param timeframe: Timeframe used during the operations
:return: Returns df with one additional column, col_name, containing the cumulative profit.
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
from freqtrade.exchange import timeframe_to_minutes
timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to timeframe to make sure trades match candles
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time')[['profitperc']].sum()
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time'
)[['profit_percent']].sum()
df.loc[:, col_name] = _trades_sum.cumsum()
# Set first value to 0
df.loc[df.iloc[0].name, col_name] = 0
# FFill to get continuous
df[col_name] = df[col_name].ffill()
return df
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time',
value_col: str = 'profit_percent'
) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
"""
Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_time and profit_percent)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_time')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_percent')
:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
profit_results = trades.sort_values(date_col).reset_index(drop=True)
max_drawdown_df = pd.DataFrame()
max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
idxmin = max_drawdown_df['drawdown'].idxmin()
if idxmin == 0:
raise ValueError("No losing trade, therefore no drawdown.")
high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
low_date = profit_results.loc[idxmin, date_col]
return abs(min(max_drawdown_df['drawdown'])), high_date, low_date

97
freqtrade/data/converter.py
View File

@ -1,24 +1,27 @@
"""
Functions to convert data from one format to another
"""
import itertools
import logging
from datetime import datetime, timezone
from typing import Any, Dict
from operator import itemgetter
from typing import Any, Dict, List
import pandas as pd
from pandas import DataFrame, to_datetime
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS,
DEFAULT_TRADES_COLUMNS)
logger = logging.getLogger(__name__)
def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
fill_missing: bool = True, drop_incomplete: bool = True) -> DataFrame:
"""
Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
:param ticker: ticker list, as returned by exchange.async_get_candle_history
Converts a list with candle (OHLCV) data (in format returned by ccxt.fetch_ohlcv)
to a Dataframe
:param ohlcv: list with candle (OHLCV) data, as returned by exchange.async_get_candle_history
:param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
:param pair: Pair this data is for (used to warn if fillup was necessary)
:param fill_missing: fill up missing candles with 0 candles
@ -26,21 +29,18 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
:param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
:return: DataFrame
"""
logger.debug("Parsing tickerlist to dataframe")
logger.debug(f"Converting candle (OHLCV) data to dataframe for pair {pair}.")
cols = DEFAULT_DATAFRAME_COLUMNS
frame = DataFrame(ticker, columns=cols)
df = DataFrame(ohlcv, columns=cols)
frame['date'] = to_datetime(frame['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
df['date'] = to_datetime(df['date'], unit='ms', utc=True, infer_datetime_format=True)
# Some exchanges return int values for volume and even for ohlc.
# Some exchanges return int values for Volume and even for OHLC.
# Convert them since TA-LIB indicators used in the strategy assume floats
# and fail with exception...
frame = frame.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
'volume': 'float'})
return clean_ohlcv_dataframe(frame, timeframe, pair,
df = df.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
'volume': 'float'})
return clean_ohlcv_dataframe(df, timeframe, pair,
fill_missing=fill_missing,
drop_incomplete=drop_incomplete)
@ -49,11 +49,11 @@ def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
"""
Clense a ohlcv dataframe by
Clense a OHLCV dataframe by
* Grouping it by date (removes duplicate tics)
* dropping last candles if requested
* Filling up missing data (if requested)
:param data: DataFrame containing ohlcv data.
:param data: DataFrame containing candle (OHLCV) data.
:param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
:param pair: Pair this data is for (used to warn if fillup was necessary)
:param fill_missing: fill up missing candles with 0 candles
@ -88,16 +88,16 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
"""
from freqtrade.exchange import timeframe_to_minutes
ohlc_dict = {
ohlcv_dict = {
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'volume': 'sum'
}
ticker_minutes = timeframe_to_minutes(timeframe)
timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to create "NAN" values
df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)
df = dataframe.resample(f'{timeframe_minutes}min', on='date').agg(ohlcv_dict)
# Forwardfill close for missing columns
df['close'] = df['close'].fillna(method='ffill')
@ -157,26 +157,47 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
return frame
def trades_to_ohlcv(trades: list, timeframe: str) -> DataFrame:
def trades_remove_duplicates(trades: List[List]) -> List[List]:
"""
Converts trades list to ohlcv list
Removes duplicates from the trades list.
Uses itertools.groupby to avoid converting to pandas.
Tests show it as being pretty efficient on lists of 4M Lists.
:param trades: List of Lists with constants.DEFAULT_TRADES_COLUMNS as columns
:return: same format as above, but with duplicates removed
"""
return [i for i, _ in itertools.groupby(sorted(trades, key=itemgetter(0)))]
def trades_dict_to_list(trades: List[Dict]) -> List[List]:
"""
Convert fetch_trades result into a List (to be more memory efficient).
:param trades: List of trades, as returned by ccxt.fetch_trades.
:return: List of Lists, with constants.DEFAULT_TRADES_COLUMNS as columns
"""
return [[t[col] for col in DEFAULT_TRADES_COLUMNS] for t in trades]
def trades_to_ohlcv(trades: List, timeframe: str) -> DataFrame:
"""
Converts trades list to OHLCV list
TODO: This should get a dedicated test
:param trades: List of trades, as returned by ccxt.fetch_trades.
:param timeframe: Ticker timeframe to resample data to
:return: ohlcv Dataframe.
:param timeframe: Timeframe to resample data to
:return: OHLCV Dataframe.
"""
from freqtrade.exchange import timeframe_to_minutes
ticker_minutes = timeframe_to_minutes(timeframe)
df = pd.DataFrame(trades)
df['datetime'] = pd.to_datetime(df['datetime'])
df = df.set_index('datetime')
timeframe_minutes = timeframe_to_minutes(timeframe)
df = pd.DataFrame(trades, columns=DEFAULT_TRADES_COLUMNS)
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms',
utc=True,)
df = df.set_index('timestamp')
df_new = df['price'].resample(f'{ticker_minutes}min').ohlc()
df_new['volume'] = df['amount'].resample(f'{ticker_minutes}min').sum()
df_new = df['price'].resample(f'{timeframe_minutes}min').ohlc()
df_new['volume'] = df['amount'].resample(f'{timeframe_minutes}min').sum()
df_new['date'] = df_new.index
# Drop 0 volume rows
df_new = df_new.dropna()
return df_new[DEFAULT_DATAFRAME_COLUMNS]
return df_new.loc[:, DEFAULT_DATAFRAME_COLUMNS]
def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
@ -206,7 +227,7 @@ def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to:
def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
"""
Convert ohlcv from one format to another format.
Convert OHLCV from one format to another
:param config: Config dictionary
:param convert_from: Source format
:param convert_to: Target format
@ -215,16 +236,16 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
from freqtrade.data.history.idatahandler import get_datahandler
src = get_datahandler(config['datadir'], convert_from)
trg = get_datahandler(config['datadir'], convert_to)
timeframes = config.get('timeframes', [config.get('ticker_interval')])
logger.info(f"Converting OHLCV for timeframe {timeframes}")
timeframes = config.get('timeframes', [config.get('timeframe')])
logger.info(f"Converting candle (OHLCV) for timeframe {timeframes}")
if 'pairs' not in config:
config['pairs'] = []
# Check timeframes or fall back to ticker_interval.
# Check timeframes or fall back to timeframe.
for timeframe in timeframes:
config['pairs'].extend(src.ohlcv_get_pairs(config['datadir'],
timeframe))
logger.info(f"Converting OHLCV for {config['pairs']}")
logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")
for timeframe in timeframes:
for pair in config['pairs']:

86
freqtrade/data/dataprovider.py
View File

@ -1,15 +1,19 @@
"""
Dataprovider
Responsible to provide data to the bot
including Klines, tickers, historic data
including ticker and orderbook data, live and historical candle (OHLCV) data
Common Interface for bot and strategy to access data.
"""
import logging
from datetime import datetime, timezone
from typing import Any, Dict, List, Optional, Tuple
from arrow import Arrow
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe
from freqtrade.data.history import load_pair_history
from freqtrade.exceptions import ExchangeError, OperationalException
from freqtrade.exchange import Exchange
from freqtrade.state import RunMode
@ -18,13 +22,26 @@ logger = logging.getLogger(__name__)
class DataProvider:
def __init__(self, config: dict, exchange: Exchange) -> None:
def __init__(self, config: dict, exchange: Exchange, pairlists=None) -> None:
self._config = config
self._exchange = exchange
self._pairlists = pairlists
self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
def _set_cached_df(self, pair: str, timeframe: str, dataframe: DataFrame) -> None:
"""
Store cached Dataframe.
Using private method as this should never be used by a user
(but the class is exposed via `self.dp` to the strategy)
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param dataframe: analyzed dataframe
"""
self.__cached_pairs[(pair, timeframe)] = (dataframe, Arrow.utcnow().datetime)
def refresh(self,
pairlist: List[Tuple[str, str]],
helping_pairs: List[Tuple[str, str]] = None) -> None:
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
"""
Refresh data, called with each cycle
"""
@ -34,7 +51,7 @@ class DataProvider:
self._exchange.refresh_latest_ohlcv(pairlist)
@property
def available_pairs(self) -> List[Tuple[str, str]]:
def available_pairs(self) -> ListPairsWithTimeframes:
"""
Return a list of tuples containing (pair, timeframe) for which data is currently cached.
Should be whitelist + open trades.
@ -43,48 +60,62 @@ class DataProvider:
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get ohlcv data for the given pair as 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: Ticker timeframe to get 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['ticker_interval']),
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:
"""
Get stored historic ohlcv data
Get stored historical candle (OHLCV) data
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
"""
return load_pair_history(pair=pair,
timeframe=timeframe or self._config['ticker_interval'],
timeframe=timeframe or self._config['timeframe'],
datadir=self._config['datadir']
)
def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Return pair ohlcv data, either live or cached historical -- depending
Return pair candle (OHLCV) data, either live or cached historical -- depending
on the runmode.
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
:return: Dataframe for this pair
"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
# Get live ohlcv data.
# Get live OHLCV data.
data = self.ohlcv(pair=pair, timeframe=timeframe)
else:
# Get historic ohlcv data (cached on disk).
# Get historical OHLCV data (cached on disk).
data = self.historic_ohlcv(pair=pair, timeframe=timeframe)
if len(data) == 0:
logger.warning(f"No data found for ({pair}, {timeframe}).")
return data
def get_analyzed_dataframe(self, pair: str, timeframe: str) -> Tuple[DataFrame, datetime]:
"""
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
:return: Tuple of (Analyzed Dataframe, lastrefreshed) for the requested pair / timeframe
combination.
Returns empty dataframe and Epoch 0 (1970-01-01) if no dataframe was cached.
"""
if (pair, timeframe) in self.__cached_pairs:
return self.__cached_pairs[(pair, timeframe)]
else:
return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
def market(self, pair: str) -> Optional[Dict[str, Any]]:
"""
Return market data for the pair
@ -95,19 +126,24 @@ class DataProvider:
def ticker(self, pair: str):
"""
Return last ticker data
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
"""
# TODO: Implement me
pass
try:
return self._exchange.fetch_ticker(pair)
except ExchangeError:
return {}
def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
"""
fetch latest orderbook data
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.get_order_book(pair, maximum)
return self._exchange.fetch_l2_order_book(pair, maximum)
@property
def runmode(self) -> RunMode:
@ -116,3 +152,17 @@ class DataProvider:
can be "live", "dry-run", "backtest", "edgecli", "hyperopt" or "other".
"""
return RunMode(self._config.get('runmode', RunMode.OTHER))
def current_whitelist(self) -> List[str]:
"""
fetch latest available whitelist.
Useful when you have a large whitelist and need to call each pair as an informative pair.
As available pairs does not show whitelist until after informative pairs have been cached.
:return: list of pairs in whitelist
"""
if self._pairlists:
return self._pairlists.whitelist
else:
raise OperationalException("Dataprovider was not initialized with a pairlist provider.")

61
freqtrade/data/history/history_utils.py
View File

@ -9,10 +9,13 @@ from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
from freqtrade.data.converter import (ohlcv_to_dataframe,
trades_remove_duplicates,
trades_to_ohlcv)
from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import Exchange
from freqtrade.misc import format_ms_time
logger = logging.getLogger(__name__)
@ -28,10 +31,10 @@ def load_pair_history(pair: str,
data_handler: IDataHandler = None,
) -> DataFrame:
"""
Load cached ticker history for the given pair.
Load cached ohlcv history for the given pair.
:param pair: Pair to load data for
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param datadir: Path to the data storage location.
:param data_format: Format of the data. Ignored if data_handler is set.
:param timerange: Limit data to be loaded to this timerange
@ -63,10 +66,10 @@ def load_data(datadir: Path,
data_format: str = 'json',
) -> Dict[str, DataFrame]:
"""
Load ticker history data for a list of pairs.
Load ohlcv history data for a list of pairs.
:param datadir: Path to the data storage location.
:param timeframe: Ticker Timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param pairs: List of pairs to load
:param timerange: Limit data to be loaded to this timerange
:param fill_up_missing: Fill missing values with "No action"-candles
@ -104,10 +107,10 @@ def refresh_data(datadir: Path,
timerange: Optional[TimeRange] = None,
) -> None:
"""
Refresh ticker history data for a list of pairs.
Refresh ohlcv history data for a list of pairs.
:param datadir: Path to the data storage location.
:param timeframe: Ticker Timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param pairs: List of pairs to load
:param exchange: Exchange object
:param timerange: Limit data to be loaded to this timerange
@ -165,7 +168,7 @@ def _download_pair_history(datadir: Path,
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pair: pair to download
:param timeframe: Ticker Timeframe (e.g 5m)
:param timeframe: Timeframe (e.g "5m")
:param timerange: range of time to download
:return: bool with success state
"""
@ -194,8 +197,8 @@ def _download_pair_history(datadir: Path,
days=-30).float_timestamp) * 1000
)
# TODO: Maybe move parsing to exchange class (?)
new_dataframe = parse_ticker_dataframe(new_data, timeframe, pair,
fill_missing=False, drop_incomplete=True)
new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,
fill_missing=False, drop_incomplete=True)
if data.empty:
data = new_dataframe
else:
@ -257,27 +260,45 @@ def _download_trades_history(exchange: Exchange,
"""
try:
since = timerange.startts * 1000 if timerange and timerange.starttype == 'date' else None
since = timerange.startts * 1000 if \
(timerange and timerange.starttype == 'date') else int(arrow.utcnow().shift(
days=-30).float_timestamp) * 1000
trades = data_handler.trades_load(pair)
from_id = trades[-1]['id'] if trades else None
# TradesList columns are defined in constants.DEFAULT_TRADES_COLUMNS
# DEFAULT_TRADES_COLUMNS: 0 -> timestamp
# DEFAULT_TRADES_COLUMNS: 1 -> id
logger.debug("Current Start: %s", trades[0]['datetime'] if trades else 'None')
logger.debug("Current End: %s", trades[-1]['datetime'] if trades else 'None')
if trades and since < trades[0][0]:
# since is before the first trade
logger.info(f"Start earlier than available data. Redownloading trades for {pair}...")
trades = []
from_id = trades[-1][1] if trades else None
if trades and since < trades[-1][0]:
# Reset since to the last available point
# - 5 seconds (to ensure we're getting all trades)
since = trades[-1][0] - (5 * 1000)
logger.info(f"Using last trade date -5s - Downloading trades for {pair} "
f"since: {format_ms_time(since)}.")
logger.debug(f"Current Start: {format_ms_time(trades[0][0]) if trades else 'None'}")
logger.debug(f"Current End: {format_ms_time(trades[-1][0]) if trades else 'None'}")
logger.info(f"Current Amount of trades: {len(trades)}")
# Default since_ms to 30 days if nothing is given
new_trades = exchange.get_historic_trades(pair=pair,
since=since if since else
int(arrow.utcnow().shift(
days=-30).float_timestamp) * 1000,
since=since,
from_id=from_id,
)
trades.extend(new_trades[1])
# Remove duplicates to make sure we're not storing data we don't need
trades = trades_remove_duplicates(trades)
data_handler.trades_store(pair, data=trades)
logger.debug("New Start: %s", trades[0]['datetime'])
logger.debug("New End: %s", trades[-1]['datetime'])
logger.debug(f"New Start: {format_ms_time(trades[0][0])}")
logger.debug(f"New End: {format_ms_time(trades[-1][0])}")
logger.info(f"New Amount of trades: {len(trades)}")
return True
@ -362,7 +383,7 @@ def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
:param pair: pair used for log output.
:param min_date: start-date of the data
:param max_date: end-date of the data
:param timeframe_min: ticker Timeframe in minutes
:param timeframe_min: Timeframe in minutes
"""
# total difference in minutes / timeframe-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_min)

81
freqtrade/data/history/idatahandler.py
View File

@ -8,22 +8,35 @@ from abc import ABC, abstractclassmethod, abstractmethod
from copy import deepcopy
from datetime import datetime, timezone
from pathlib import Path
from typing import Dict, List, Optional, Type
from typing import List, Optional, Type
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.data.converter import clean_ohlcv_dataframe, trim_dataframe
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.converter import (clean_ohlcv_dataframe,
trades_remove_duplicates, trim_dataframe)
from freqtrade.exchange import timeframe_to_seconds
logger = logging.getLogger(__name__)
# Type for trades list
TradeList = List[List]
class IDataHandler(ABC):
def __init__(self, datadir: Path) -> None:
self._datadir = datadir
@abstractclassmethod
def ohlcv_get_available_data(cls, datadir: Path) -> ListPairsWithTimeframes:
"""
Returns a list of all pairs with ohlcv data available in this datadir
:param datadir: Directory to search for ohlcv files
:return: List of Tuples of (pair, timeframe)
"""
@abstractclassmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
"""
@ -55,7 +68,7 @@ class IDataHandler(ABC):
Implements the loading and conversion to a Pandas dataframe.
Timerange trimming and dataframe validation happens outside of this method.
:param pair: Pair to load data
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange.
Optionally implemented by subclasses to avoid loading
all data where possible.
@ -67,7 +80,7 @@ class IDataHandler(ABC):
"""
Remove data for this pair
:param pair: Delete data for this pair.
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:return: True when deleted, false if file did not exist.
"""
@ -89,23 +102,25 @@ class IDataHandler(ABC):
"""
@abstractmethod
def trades_store(self, pair: str, data: List[Dict]) -> None:
def trades_store(self, pair: str, data: TradeList) -> None:
"""
Store trades data (list of Dicts) to file
:param pair: Pair - used for filename
:param data: List of Dicts containing trade data
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
@abstractmethod
def trades_append(self, pair: str, data: List[Dict]):
def trades_append(self, pair: str, data: TradeList):
"""
Append data to existing files
:param pair: Pair - used for filename
:param data: List of Dicts containing trade data
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
@abstractmethod
def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> List[Dict]:
def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
"""
Load a pair from file, either .json.gz or .json
:param pair: Load trades for this pair
@ -121,6 +136,16 @@ class IDataHandler(ABC):
:return: True when deleted, false if file did not exist.
"""
def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
"""
Load a pair from file, either .json.gz or .json
Removes duplicates in the process.
:param pair: Load trades for this pair
:param timerange: Timerange to load trades for - currently not implemented
:return: List of trades
"""
return trades_remove_duplicates(self._trades_load(pair, timerange=timerange))
def ohlcv_load(self, pair, timeframe: str,
timerange: Optional[TimeRange] = None,
fill_missing: bool = True,
@ -129,10 +154,10 @@ class IDataHandler(ABC):
warn_no_data: bool = True
) -> DataFrame:
"""
Load cached ticker history for the given pair.
Load cached candle (OHLCV) data for the given pair.
:param pair: Pair to load data for
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange
:param fill_missing: Fill missing values with "No action"-candles
:param drop_incomplete: Drop last candle assuming it may be incomplete.
@ -147,12 +172,7 @@ class IDataHandler(ABC):
pairdf = self._ohlcv_load(pair, timeframe,
timerange=timerange_startup)
if pairdf.empty:
if warn_no_data:
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
)
if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
return pairdf
else:
enddate = pairdf.iloc[-1]['date']
@ -160,13 +180,30 @@ class IDataHandler(ABC):
if timerange_startup:
self._validate_pairdata(pair, pairdf, timerange_startup)
pairdf = trim_dataframe(pairdf, timerange_startup)
if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
return pairdf
# incomplete candles should only be dropped if we didn't trim the end beforehand.
return clean_ohlcv_dataframe(pairdf, timeframe,
pair=pair,
fill_missing=fill_missing,
drop_incomplete=(drop_incomplete and
enddate == pairdf.iloc[-1]['date']))
pairdf = clean_ohlcv_dataframe(pairdf, timeframe,
pair=pair,
fill_missing=fill_missing,
drop_incomplete=(drop_incomplete and
enddate == pairdf.iloc[-1]['date']))
self._check_empty_df(pairdf, pair, timeframe, warn_no_data)
return pairdf
def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str, warn_no_data: bool):
"""
Warn on empty dataframe
"""
if pairdf.empty:
if warn_no_data:
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
)
return True
return False
def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
"""

45
freqtrade/data/history/jsondatahandler.py
View File

@ -1,15 +1,20 @@
import logging
import re
from pathlib import Path
from typing import Dict, List, Optional
from typing import List, Optional
import numpy as np
from pandas import DataFrame, read_json, to_datetime
from freqtrade import misc
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS,
ListPairsWithTimeframes)
from freqtrade.data.converter import trades_dict_to_list
from .idatahandler import IDataHandler
from .idatahandler import IDataHandler, TradeList
logger = logging.getLogger(__name__)
class JsonDataHandler(IDataHandler):
@ -17,6 +22,18 @@ class JsonDataHandler(IDataHandler):
_use_zip = False
_columns = DEFAULT_DATAFRAME_COLUMNS
@classmethod
def ohlcv_get_available_data(cls, datadir: Path) -> ListPairsWithTimeframes:
"""
Returns a list of all pairs with ohlcv data available in this datadir
:param datadir: Directory to search for ohlcv files
:return: List of Tuples of (pair, timeframe)
"""
_tmp = [re.search(r'^([a-zA-Z_]+)\-(\d+\S+)(?=.json)', p.name)
for p in datadir.glob(f"*.{cls._get_file_extension()}")]
return [(match[1].replace('_', '/'), match[2]) for match in _tmp
if match and len(match.groups()) > 1]
@classmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
"""
@ -60,7 +77,7 @@ class JsonDataHandler(IDataHandler):
Implements the loading and conversion to a Pandas dataframe.
Timerange trimming and dataframe validation happens outside of this method.
:param pair: Pair to load data
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange.
Optionally implemented by subclasses to avoid loading
all data where possible.
@ -83,7 +100,7 @@ class JsonDataHandler(IDataHandler):
"""
Remove data for this pair
:param pair: Delete data for this pair.
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:return: True when deleted, false if file did not exist.
"""
filename = self._pair_data_filename(self._datadir, pair, timeframe)
@ -113,24 +130,26 @@ class JsonDataHandler(IDataHandler):
# Check if regex found something and only return these results to avoid exceptions.
return [match[0].replace('_', '/') for match in _tmp if match]
def trades_store(self, pair: str, data: List[Dict]) -> None:
def trades_store(self, pair: str, data: TradeList) -> None:
"""
Store trades data (list of Dicts) to file
:param pair: Pair - used for filename
:param data: List of Dicts containing trade data
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
filename = self._pair_trades_filename(self._datadir, pair)
misc.file_dump_json(filename, data, is_zip=self._use_zip)
def trades_append(self, pair: str, data: List[Dict]):
def trades_append(self, pair: str, data: TradeList):
"""
Append data to existing files
:param pair: Pair - used for filename
:param data: List of Dicts containing trade data
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
raise NotImplementedError()
def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> List[Dict]:
def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
"""
Load a pair from file, either .json.gz or .json
# TODO: respect timerange ...
@ -140,9 +159,15 @@ class JsonDataHandler(IDataHandler):
"""
filename = self._pair_trades_filename(self._datadir, pair)
tradesdata = misc.file_load_json(filename)
if not tradesdata:
return []
if isinstance(tradesdata[0], dict):
# Convert trades dict to list
logger.info("Old trades format detected - converting")
tradesdata = trades_dict_to_list(tradesdata)
pass
return tradesdata
def trades_purge(self, pair: str) -> bool:

67
freqtrade/edge/edge_positioning.py
View File

@ -8,10 +8,10 @@ import numpy as np
import utils_find_1st as utf1st
from pandas import DataFrame
from freqtrade import constants
from freqtrade.configuration import TimeRange
from freqtrade.data import history
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
from freqtrade.exceptions import OperationalException
from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.strategy.interface import SellType
logger = logging.getLogger(__name__)
@ -54,12 +54,10 @@ class Edge:
if self.config['max_open_trades'] != float('inf'):
logger.critical('max_open_trades should be -1 in config !')
if self.config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT:
if self.config['stake_amount'] != UNLIMITED_STAKE_AMOUNT:
raise OperationalException('Edge works only with unlimited stake amount')
# Deprecated capital_available_percentage. Will use tradable_balance_ratio in the future.
self._capital_percentage: float = self.edge_config.get(
'capital_available_percentage', self.config['tradable_balance_ratio'])
self._capital_ratio: float = self.config['tradable_balance_ratio']
self._allowed_risk: float = self.edge_config.get('allowed_risk')
self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14)
self._last_updated: int = 0 # Timestamp of pairs last updated time
@ -96,18 +94,18 @@ class Edge:
logger.info('Using local backtesting data (using whitelist in given config) ...')
if self._refresh_pairs:
history.refresh_data(
refresh_data(
datadir=self.config['datadir'],
pairs=pairs,
exchange=self.exchange,
timeframe=self.strategy.ticker_interval,
timeframe=self.strategy.timeframe,
timerange=self._timerange,
)
data = history.load_data(
data = load_data(
datadir=self.config['datadir'],
pairs=pairs,
timeframe=self.strategy.ticker_interval,
timeframe=self.strategy.timeframe,
timerange=self._timerange,
startup_candles=self.strategy.startup_candle_count,
data_format=self.config.get('dataformat_ohlcv', 'json'),
@ -119,10 +117,10 @@ class Edge:
logger.critical("No data found. Edge is stopped ...")
return False
preprocessed = self.strategy.tickerdata_to_dataframe(data)
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Print timeframe
min_date, max_date = history.get_timerange(preprocessed)
min_date, max_date = get_timerange(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days) ...',
min_date.isoformat(),
@ -137,10 +135,10 @@ class Edge:
pair_data = pair_data.sort_values(by=['date'])
pair_data = pair_data.reset_index(drop=True)
ticker_data = self.strategy.advise_sell(
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
trades += self._find_trades_for_stoploss_range(ticker_data, pair, self._stoploss_range)
trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
# If no trade found then exit
if len(trades) == 0:
@ -157,7 +155,7 @@ class Edge:
def stake_amount(self, pair: str, free_capital: float,
total_capital: float, capital_in_trade: float) -> float:
stoploss = self.stoploss(pair)
available_capital = (total_capital + capital_in_trade) * self._capital_percentage
available_capital = (total_capital + capital_in_trade) * self._capital_ratio
allowed_capital_at_risk = available_capital * self._allowed_risk
max_position_size = abs(allowed_capital_at_risk / stoploss)
position_size = min(max_position_size, free_capital)
@ -238,19 +236,9 @@ class Edge:
:param result Dataframe
:return: result Dataframe
"""
# stake and fees
# stake = 0.015
# 0.05% is 0.0005
# fee = 0.001
# we set stake amount to an arbitrary amount.
# as it doesn't change the calculation.
# all returned values are relative. they are percentages.
# We set stake amount to an arbitrary amount, as it doesn't change the calculation.
# All returned values are relative, they are defined as ratios.
stake = 0.015
fee = self.fee
open_fee = fee / 2
close_fee = fee / 2
result['trade_duration'] = result['close_time'] - result['open_time']
@ -261,16 +249,16 @@ class Edge:
# Buy Price
result['buy_vol'] = stake / result['open_rate'] # How many target are we buying
result['buy_fee'] = stake * open_fee
result['buy_fee'] = stake * self.fee
result['buy_spend'] = stake + result['buy_fee'] # How much we're spending
# Sell price
result['sell_sum'] = result['buy_vol'] * result['close_rate']
result['sell_fee'] = result['sell_sum'] * close_fee
result['sell_fee'] = result['sell_sum'] * self.fee
result['sell_take'] = result['sell_sum'] - result['sell_fee']
# profit_percent
result['profit_percent'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
# profit_ratio
result['profit_ratio'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
# Absolute profit
result['profit_abs'] = result['sell_take'] - result['buy_spend']
@ -316,7 +304,7 @@ class Edge:
}
# Group by (pair and stoploss) by applying above aggregator
df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
df = results.groupby(['pair', 'stoploss'])[['profit_abs', 'trade_duration']].agg(
groupby_aggregator).reset_index(col_level=1)
# Dropping level 0 as we don't need it
@ -358,11 +346,11 @@ class Edge:
# Returning a list of pairs in order of "expectancy"
return final
def _find_trades_for_stoploss_range(self, ticker_data, pair, stoploss_range):
buy_column = ticker_data['buy'].values
sell_column = ticker_data['sell'].values
date_column = ticker_data['date'].values
ohlc_columns = ticker_data[['open', 'high', 'low', 'close']].values
def _find_trades_for_stoploss_range(self, df, pair, stoploss_range):
buy_column = df['buy'].values
sell_column = df['sell'].values
date_column = df['date'].values
ohlc_columns = df[['open', 'high', 'low', 'close']].values
result: list = []
for stoploss in stoploss_range:
@ -399,9 +387,8 @@ class Edge:
# trade opens in reality on the next candle
open_trade_index += 1
stop_price_percentage = stoploss + 1
open_price = ohlc_columns[open_trade_index, 0]
stop_price = (open_price * stop_price_percentage)
stop_price = (open_price * (stoploss + 1))
# Searching for the index where stoploss is hit
stop_index = utf1st.find_1st(
@ -441,7 +428,7 @@ class Edge:
trade = {'pair': pair,
'stoploss': stoploss,
'profit_percent': '',
'profit_ratio': '',
'profit_abs': '',
'open_time': date_column[open_trade_index],
'close_time': date_column[exit_index],

38
freqtrade/exceptions.py
View File

@ -21,6 +21,14 @@ class DependencyException(FreqtradeException):
"""
class PricingError(DependencyException):
"""
Subclass of DependencyException.
Indicates that the price could not be determined.
Implicitly a buy / sell operation.
"""
class InvalidOrderException(FreqtradeException):
"""
This is returned when the order is not valid. Example:
@ -29,9 +37,37 @@ class InvalidOrderException(FreqtradeException):
"""
class TemporaryError(FreqtradeException):
class RetryableOrderError(InvalidOrderException):
"""
This is returned when the order is not found.
This Error will be repeated with increasing backof (in line with DDosError).
"""
class ExchangeError(DependencyException):
"""
Error raised out of the exchange.
Has multiple Errors to determine the appropriate error.
"""
class TemporaryError(ExchangeError):
"""
Temporary network or exchange related error.
This could happen when an exchange is congested, unavailable, or the user
has networking problems. Usually resolves itself after a time.
"""
class DDosProtection(TemporaryError):
"""
Temporary error caused by DDOS protection.
Bot will wait for a second and then retry.
"""
class StrategyError(FreqtradeException):
"""
Errors with custom user-code deteced.
Usually caused by errors in the strategy.
"""

17
freqtrade/exchange/binance.py
View File

@ -4,9 +4,11 @@ from typing import Dict
import ccxt
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
logger = logging.getLogger(__name__)
@ -20,7 +22,7 @@ class Binance(Exchange):
"trades_pagination_arg": "fromId",
}
def get_order_book(self, pair: str, limit: int = 100) -> dict:
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
"""
get order book level 2 from exchange
@ -30,7 +32,7 @@ class Binance(Exchange):
# get next-higher step in the limit_range list
limit = min(list(filter(lambda x: limit <= x, limit_range)))
return super().get_order_book(pair, limit)
return super().fetch_l2_order_book(pair, limit)
def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
"""
@ -39,6 +41,7 @@ class Binance(Exchange):
"""
return order['type'] == 'stop_loss_limit' and stop_loss > float(order['info']['stopPrice'])
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
"""
creates a stoploss limit order.
@ -72,12 +75,12 @@ class Binance(Exchange):
rate = self.price_to_precision(pair, rate)
order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
amount=amount, price=stop_price, params=params)
amount=amount, price=rate, params=params)
logger.info('stoploss limit order added for %s. '
'stop price: %s. limit: %s', pair, stop_price, rate)
return order
except ccxt.InsufficientFunds as e:
raise DependencyException(
raise ExchangeError(
f'Insufficient funds to create {ordertype} sell order on market {pair}.'
f'Tried to sell amount {amount} at rate {rate}. '
f'Message: {e}') from e
@ -88,6 +91,8 @@ class Binance(Exchange):
f'Could not create {ordertype} sell order on market {pair}. '
f'Tried to sell amount {amount} at rate {rate}. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e

63
freqtrade/exchange/common.py
View File

@ -1,6 +1,10 @@
import asyncio
import logging
import time
from functools import wraps
from freqtrade.exceptions import DependencyException, TemporaryError
from freqtrade.exceptions import (DDosProtection, RetryableOrderError,
TemporaryError)
logger = logging.getLogger(__name__)
@ -88,17 +92,28 @@ MAP_EXCHANGE_CHILDCLASS = {
}
def calculate_backoff(retrycount, max_retries):
"""
Calculate backoff
"""
return (max_retries - retrycount) ** 2 + 1
def retrier_async(f):
async def wrapper(*args, **kwargs):
count = kwargs.pop('count', API_RETRY_COUNT)
try:
return await f(*args, **kwargs)
except (TemporaryError, DependencyException) as ex:
except TemporaryError as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
if count > 0:
count -= 1
kwargs.update({'count': count})
logger.warning('retrying %s() still for %s times', f.__name__, count)
if isinstance(ex, DDosProtection):
backoff_delay = calculate_backoff(count + 1, API_RETRY_COUNT)
logger.debug(f"Applying DDosProtection backoff delay: {backoff_delay}")
await asyncio.sleep(backoff_delay)
return await wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
@ -106,19 +121,31 @@ def retrier_async(f):
return wrapper
def retrier(f):
def wrapper(*args, **kwargs):
count = kwargs.pop('count', API_RETRY_COUNT)
try:
return f(*args, **kwargs)
except (TemporaryError, DependencyException) as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
if count > 0:
count -= 1
kwargs.update({'count': count})
logger.warning('retrying %s() still for %s times', f.__name__, count)
return wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
raise ex
return wrapper
def retrier(_func=None, retries=API_RETRY_COUNT):
def decorator(f):
@wraps(f)
def wrapper(*args, **kwargs):
count = kwargs.pop('count', retries)
try:
return f(*args, **kwargs)
except (TemporaryError, RetryableOrderError) as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
if count > 0:
count -= 1
kwargs.update({'count': count})
logger.warning('retrying %s() still for %s times', f.__name__, count)
if isinstance(ex, DDosProtection) or isinstance(ex, RetryableOrderError):
# increasing backoff
backoff_delay = calculate_backoff(count + 1, retries)
logger.debug(f"Applying DDosProtection backoff delay: {backoff_delay}")
time.sleep(backoff_delay)
return wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
raise ex
return wrapper
# Support both @retrier and @retrier(retries=2) syntax
if _func is None:
return decorator
else:
return decorator(_func)

346
freqtrade/exchange/exchange.py
View File

@ -18,12 +18,13 @@ from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE,
TRUNCATE, decimal_to_precision)
from pandas import DataFrame
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
RetryableOrderError, TemporaryError)
from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
from freqtrade.misc import deep_merge_dicts
from freqtrade.misc import deep_merge_dicts, safe_value_fallback
CcxtModuleType = Any
@ -79,7 +80,7 @@ class Exchange:
if config['dry_run']:
logger.info('Instance is running with dry_run enabled')
logger.info(f"Using CCXT {ccxt.__version__}")
exchange_config = config['exchange']
# Deep merge ft_has with default ft_has options
@ -98,12 +99,14 @@ class Exchange:
# Initialize ccxt objects
ccxt_config = self._ccxt_config.copy()
ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}),
ccxt_config)
self._api = self._init_ccxt(
exchange_config, ccxt_kwargs=ccxt_config)
ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}), ccxt_config)
ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_sync_config', {}), ccxt_config)
self._api = self._init_ccxt(exchange_config, ccxt_kwargs=ccxt_config)
ccxt_async_config = self._ccxt_config.copy()
ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}),
ccxt_async_config)
ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_async_config', {}),
ccxt_async_config)
self._api_async = self._init_ccxt(
@ -113,7 +116,7 @@ class Exchange:
if validate:
# Check if timeframe is available
self.validate_timeframes(config.get('ticker_interval'))
self.validate_timeframes(config.get('timeframe'))
# Initial markets load
self._load_markets()
@ -184,11 +187,16 @@ class Exchange:
def timeframes(self) -> List[str]:
return list((self._api.timeframes or {}).keys())
@property
def ohlcv_candle_limit(self) -> int:
"""exchange ohlcv candle limit"""
return int(self._ohlcv_candle_limit)
@property
def markets(self) -> Dict:
"""exchange ccxt markets"""
if not self._api.markets:
logger.warning("Markets were not loaded. Loading them now..")
logger.info("Markets were not loaded. Loading them now..")
self._load_markets()
return self._api.markets
@ -273,8 +281,8 @@ class Exchange:
except ccxt.BaseError as e:
logger.warning('Unable to initialize markets. Reason: %s', e)
def _reload_markets(self) -> None:
"""Reload markets both sync and async, if refresh interval has passed"""
def reload_markets(self) -> None:
"""Reload markets both sync and async if refresh interval has passed """
# Check whether markets have to be reloaded
if (self._last_markets_refresh > 0) and (
self._last_markets_refresh + self.markets_refresh_interval
@ -283,6 +291,8 @@ class Exchange:
logger.debug("Performing scheduled market reload..")
try:
self._api.load_markets(reload=True)
# Also reload async markets to avoid issues with newly listed pairs
self._load_async_markets(reload=True)
self._last_markets_refresh = arrow.utcnow().timestamp
except ccxt.BaseError:
logger.exception("Could not reload markets.")
@ -347,11 +357,11 @@ class Exchange:
for pair in [f"{curr_1}/{curr_2}", f"{curr_2}/{curr_1}"]:
if pair in self.markets and self.markets[pair].get('active'):
return pair
raise DependencyException(f"Could not combine {curr_1} and {curr_2} to get a valid pair.")
raise ExchangeError(f"Could not combine {curr_1} and {curr_2} to get a valid pair.")
def validate_timeframes(self, timeframe: Optional[str]) -> None:
"""
Checks if ticker interval from config is a supported timeframe on the exchange
Check if timeframe from config is a supported timeframe on the exchange
"""
if not hasattr(self._api, "timeframes") or self._api.timeframes is None:
# If timeframes attribute is missing (or is None), the exchange probably
@ -364,11 +374,10 @@ class Exchange:
if timeframe and (timeframe not in self.timeframes):
raise OperationalException(
f"Invalid ticker interval '{timeframe}'. This exchange supports: {self.timeframes}")
f"Invalid timeframe '{timeframe}'. This exchange supports: {self.timeframes}")
if timeframe and timeframe_to_minutes(timeframe) < 1:
raise OperationalException(
f"Timeframes < 1m are currently not supported by Freqtrade.")
raise OperationalException("Timeframes < 1m are currently not supported by Freqtrade.")
def validate_ordertypes(self, order_types: Dict) -> None:
"""
@ -452,6 +461,17 @@ class Exchange:
price = ceil(big_price) / pow(10, symbol_prec)
return price
def price_get_one_pip(self, pair: str, price: float) -> float:
"""
Get's the "1 pip" value for this pair.
Used in PriceFilter to calculate the 1pip movements.
"""
precision = self.markets[pair]['precision']['price']
if self.precisionMode == TICK_SIZE:
return precision
else:
return 1 / pow(10, precision)
def dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
rate: float, params: Dict = {}) -> Dict[str, Any]:
order_id = f'dry_run_{side}_{randint(0, 10**6)}'
@ -461,26 +481,31 @@ class Exchange:
'pair': pair,
'price': rate,
'amount': _amount,
"cost": _amount * rate,
'cost': _amount * rate,
'type': ordertype,
'side': side,
'remaining': _amount,
'datetime': arrow.utcnow().isoformat(),
'status': "closed" if ordertype == "market" else "open",
'fee': None,
"info": {}
'info': {}
}
self._store_dry_order(dry_order)
self._store_dry_order(dry_order, pair)
# Copy order and close it - so the returned order is open unless it's a market order
return dry_order
def _store_dry_order(self, dry_order: Dict) -> None:
def _store_dry_order(self, dry_order: Dict, pair: str) -> None:
closed_order = dry_order.copy()
if closed_order["type"] in ["market", "limit"]:
if closed_order['type'] in ["market", "limit"]:
closed_order.update({
"status": "closed",
"filled": closed_order["amount"],
"remaining": 0
'status': 'closed',
'filled': closed_order['amount'],
'remaining': 0,
'fee': {
'currency': self.get_pair_quote_currency(pair),
'cost': dry_order['cost'] * self.get_fee(pair),
'rate': self.get_fee(pair)
}
})
if closed_order["type"] in ["stop_loss_limit"]:
closed_order["info"].update({"stopPrice": closed_order["price"]})
@ -499,15 +524,17 @@ class Exchange:
amount, rate_for_order, params)
except ccxt.InsufficientFunds as e:
raise DependencyException(
raise ExchangeError(
f'Insufficient funds to create {ordertype} {side} order on market {pair}.'
f'Tried to {side} amount {amount} at rate {rate}.'
f'Message: {e}') from e
except ccxt.InvalidOrder as e:
raise DependencyException(
raise ExchangeError(
f'Could not create {ordertype} {side} order on market {pair}.'
f'Tried to {side} amount {amount} at rate {rate}.'
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place {side} order due to {e.__class__.__name__}. Message: {e}') from e
@ -587,6 +614,8 @@ class Exchange:
balances.pop("used", None)
return balances
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
@ -599,8 +628,10 @@ class Exchange:
return self._api.fetch_tickers()
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching tickers in batch.'
f'Exchange {self._api.name} does not support fetching tickers in batch. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load tickers due to {e.__class__.__name__}. Message: {e}') from e
@ -611,9 +642,11 @@ class Exchange:
def fetch_ticker(self, pair: str) -> dict:
try:
if pair not in self._api.markets or not self._api.markets[pair].get('active'):
raise DependencyException(f"Pair {pair} not available")
raise ExchangeError(f"Pair {pair} not available")
data = self._api.fetch_ticker(pair)
return data
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load ticker due to {e.__class__.__name__}. Message: {e}') from e
@ -623,13 +656,13 @@ class Exchange:
def get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int) -> List:
"""
Gets candle history using asyncio and returns the list of candles.
Handles all async doing.
Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
Get candle history using asyncio and returns the list of candles.
Handles all async work for this.
Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
:param pair: Pair to download
:param timeframe: Ticker Timeframe to get
:param timeframe: Timeframe to get data for
:param since_ms: Timestamp in milliseconds to get history from
:returns List of tickers
:returns List with candle (OHLCV) data
"""
return asyncio.get_event_loop().run_until_complete(
self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
@ -649,26 +682,27 @@ class Exchange:
pair, timeframe, since) for since in
range(since_ms, arrow.utcnow().timestamp * 1000, one_call)]
tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
results = await asyncio.gather(*input_coroutines, return_exceptions=True)
# Combine tickers
# Combine gathered results
data: List = []
for p, timeframe, ticker in tickers:
for p, timeframe, res in results:
if p == pair:
data.extend(ticker)
data.extend(res)
# Sort data again after extending the result - above calls return in "async order"
data = sorted(data, key=lambda x: x[0])
logger.info("downloaded %s with length %s.", pair, len(data))
logger.info("Downloaded data for %s with length %s.", pair, len(data))
return data
def refresh_latest_ohlcv(self, pair_list: List[Tuple[str, str]]) -> List[Tuple[str, List]]:
def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes) -> List[Tuple[str, List]]:
"""
Refresh in-memory ohlcv asynchronously and set `_klines` with the result
Refresh in-memory OHLCV asynchronously and set `_klines` with the result
Loops asynchronously over pair_list and downloads all pairs async (semi-parallel).
Only used in the dataprovider.refresh() method.
:param pair_list: List of 2 element tuples containing pair, interval to refresh
:return: Returns a List of ticker-dataframes.
:return: TODO: return value is only used in the tests, get rid of it
"""
logger.debug("Refreshing ohlcv data for %d pairs", len(pair_list))
logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
input_coroutines = []
@ -679,15 +713,15 @@ class Exchange:
input_coroutines.append(self._async_get_candle_history(pair, timeframe))
else:
logger.debug(
"Using cached ohlcv data for pair %s, timeframe %s ...",
"Using cached candle (OHLCV) data for pair %s, timeframe %s ...",
pair, timeframe
)
tickers = asyncio.get_event_loop().run_until_complete(
results = asyncio.get_event_loop().run_until_complete(
asyncio.gather(*input_coroutines, return_exceptions=True))
# handle caching
for res in tickers:
for res in results:
if isinstance(res, Exception):
logger.warning("Async code raised an exception: %s", res.__class__.__name__)
continue
@ -698,13 +732,14 @@ class Exchange:
if ticks:
self._pairs_last_refresh_time[(pair, timeframe)] = ticks[-1][0] // 1000
# keeping parsed dataframe in cache
self._klines[(pair, timeframe)] = parse_ticker_dataframe(
self._klines[(pair, timeframe)] = ohlcv_to_dataframe(
ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=self._ohlcv_partial_candle)
return tickers
return results
def _now_is_time_to_refresh(self, pair: str, timeframe: str) -> bool:
# Calculating ticker interval in seconds
# Timeframe in seconds
interval_in_sec = timeframe_to_seconds(timeframe)
return not ((self._pairs_last_refresh_time.get((pair, timeframe), 0)
@ -714,11 +749,11 @@ class Exchange:
async def _async_get_candle_history(self, pair: str, timeframe: str,
since_ms: Optional[int] = None) -> Tuple[str, str, List]:
"""
Asynchronously gets candle histories using fetch_ohlcv
Asynchronously get candle history data using fetch_ohlcv
returns tuple: (pair, timeframe, ohlcv_list)
"""
try:
# fetch ohlcv asynchronously
# Fetch OHLCV asynchronously
s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
logger.debug(
"Fetching pair %s, interval %s, since %s %s...",
@ -728,9 +763,9 @@ class Exchange:
data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
since=since_ms)
# Because some exchange sort Tickers ASC and other DESC.
# Ex: Bittrex returns a list of tickers ASC (oldest first, newest last)
# when GDAX returns a list of tickers DESC (newest first, oldest last)
# 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)
# while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
# Only sort if necessary to save computing time
try:
if data and data[0][0] > data[-1][0]:
@ -743,19 +778,22 @@ class Exchange:
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching historical candlestick data.'
f'Message: {e}') from e
f'Exchange {self._api.name} does not support fetching historical '
f'candle (OHLCV) data. Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(f'Could not load ticker history for pair {pair} due to '
f'{e.__class__.__name__}. Message: {e}') from e
raise TemporaryError(f'Could not fetch historical candle (OHLCV) data '
f'for pair {pair} due to {e.__class__.__name__}. '
f'Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(f'Could not fetch ticker data for pair {pair}. '
f'Msg: {e}') from e
raise OperationalException(f'Could not fetch historical candle (OHLCV) data '
f'for pair {pair}. Message: {e}') from e
@retrier_async
async def _async_fetch_trades(self, pair: str,
since: Optional[int] = None,
params: Optional[dict] = None) -> List[Dict]:
params: Optional[dict] = None) -> List[List]:
"""
Asyncronously gets trade history using fetch_trades.
Handles exchange errors, does one call to the exchange.
@ -775,11 +813,13 @@ class Exchange:
'(' + arrow.get(since // 1000).isoformat() + ') ' if since is not None else ''
)
trades = await self._api_async.fetch_trades(pair, since=since, limit=1000)
return trades
return trades_dict_to_list(trades)
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching historical trade data.'
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(f'Could not load trade history due to {e.__class__.__name__}. '
f'Message: {e}') from e
@ -789,7 +829,7 @@ class Exchange:
async def _async_get_trade_history_id(self, pair: str,
until: int,
since: Optional[int] = None,
from_id: Optional[str] = None) -> Tuple[str, List[Dict]]:
from_id: Optional[str] = None) -> Tuple[str, List[List]]:
"""
Asyncronously gets trade history using fetch_trades
use this when exchange uses id-based iteration (check `self._trades_pagination`)
@ -800,7 +840,7 @@ class Exchange:
returns tuple: (pair, trades-list)
"""
trades: List[Dict] = []
trades: List[List] = []
if not from_id:
# Fetch first elements using timebased method to get an ID to paginate on
@ -809,7 +849,9 @@ class Exchange:
# e.g. Binance returns the "last 1000" candles within a 1h time interval
# - so we will miss the first trades.
t = await self._async_fetch_trades(pair, since=since)
from_id = t[-1]['id']
# DEFAULT_TRADES_COLUMNS: 0 -> timestamp
# DEFAULT_TRADES_COLUMNS: 1 -> id
from_id = t[-1][1]
trades.extend(t[:-1])
while True:
t = await self._async_fetch_trades(pair,
@ -817,21 +859,21 @@ class Exchange:
if len(t):
# Skip last id since its the key for the next call
trades.extend(t[:-1])
if from_id == t[-1]['id'] or t[-1]['timestamp'] > until:
if from_id == t[-1][1] or t[-1][0] > until:
logger.debug(f"Stopping because from_id did not change. "
f"Reached {t[-1]['timestamp']} > {until}")
f"Reached {t[-1][0]} > {until}")
# Reached the end of the defined-download period - add last trade as well.
trades.extend(t[-1:])
break
from_id = t[-1]['id']
from_id = t[-1][1]
else:
break
return (pair, trades)
async def _async_get_trade_history_time(self, pair: str, until: int,
since: Optional[int] = None) -> Tuple[str, List]:
since: Optional[int] = None) -> Tuple[str, List[List]]:
"""
Asyncronously gets trade history using fetch_trades,
when the exchange uses time-based iteration (check `self._trades_pagination`)
@ -841,16 +883,18 @@ class Exchange:
returns tuple: (pair, trades-list)
"""
trades: List[Dict] = []
trades: List[List] = []
# DEFAULT_TRADES_COLUMNS: 0 -> timestamp
# DEFAULT_TRADES_COLUMNS: 1 -> id
while True:
t = await self._async_fetch_trades(pair, since=since)
if len(t):
since = t[-1]['timestamp']
since = t[-1][1]
trades.extend(t)
# Reached the end of the defined-download period
if until and t[-1]['timestamp'] > until:
if until and t[-1][0] > until:
logger.debug(
f"Stopping because until was reached. {t[-1]['timestamp']} > {until}")
f"Stopping because until was reached. {t[-1][0]} > {until}")
break
else:
break
@ -860,19 +904,24 @@ class Exchange:
async def _async_get_trade_history(self, pair: str,
since: Optional[int] = None,
until: Optional[int] = None,
from_id: Optional[str] = None) -> Tuple[str, List[Dict]]:
from_id: Optional[str] = None) -> Tuple[str, List[List]]:
"""
Async wrapper handling downloading trades using either time or id based methods.
"""
logger.debug(f"_async_get_trade_history(), pair: {pair}, "
f"since: {since}, until: {until}, from_id: {from_id}")
if until is None:
until = ccxt.Exchange.milliseconds()
logger.debug(f"Exchange milliseconds: {until}")
if self._trades_pagination == 'time':
return await self._async_get_trade_history_time(
pair=pair, since=since,
until=until or ccxt.Exchange.milliseconds())
pair=pair, since=since, until=until)
elif self._trades_pagination == 'id':
return await self._async_get_trade_history_id(
pair=pair, since=since,
until=until or ccxt.Exchange.milliseconds(), from_id=from_id
pair=pair, since=since, until=until, from_id=from_id
)
else:
raise OperationalException(f"Exchange {self.name} does use neither time, "
@ -883,14 +932,14 @@ class Exchange:
until: Optional[int] = None,
from_id: Optional[str] = None) -> Tuple[str, List]:
"""
Gets candle history using asyncio and returns the list of candles.
Handles all async doing.
Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
Get trade history data using asyncio.
Handles all async work and returns the list of candles.
Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
:param pair: Pair to download
:param since: Timestamp in milliseconds to get history from
:param until: Timestamp in milliseconds. Defaults to current timestamp if not defined.
:param from_id: Download data starting with ID (if id is known)
:returns List of tickers
:returns List of trade data
"""
if not self.exchange_has("fetchTrades"):
raise OperationalException("This exchange does not suport downloading Trades.")
@ -899,24 +948,68 @@ class Exchange:
self._async_get_trade_history(pair=pair, since=since,
until=until, from_id=from_id))
def check_order_canceled_empty(self, order: Dict) -> bool:
"""
Verify if an order has been cancelled without being partially filled
:param order: Order dict as returned from fetch_order()
:return: True if order has been cancelled without being filled, False otherwise.
"""
return order.get('status') in ('closed', 'canceled') and order.get('filled') == 0.0
@retrier
def cancel_order(self, order_id: str, pair: str) -> None:
def cancel_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
return
return {}
try:
return self._api.cancel_order(order_id, pair)
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Could not cancel order. Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
# Assign method to fetch_stoploss_order to allow easy overriding in other classes
cancel_stoploss_order = cancel_order
def is_cancel_order_result_suitable(self, corder) -> bool:
if not isinstance(corder, dict):
return False
required = ('fee', 'status', 'amount')
return all(k in corder for k in required)
def cancel_order_with_result(self, order_id: str, pair: str, amount: float) -> Dict:
"""
Cancel 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: Orderid 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
"""
try:
corder = self.cancel_order(order_id, pair)
if self.is_cancel_order_result_suitable(corder):
return corder
except InvalidOrderException:
logger.warning(f"Could not cancel order {order_id}.")
try:
order = self.fetch_order(order_id, pair)
except InvalidOrderException:
logger.warning(f"Could not fetch cancelled order {order_id}.")
order = {'fee': {}, 'status': 'canceled', 'amount': amount, 'info': {}}
return order
@retrier
def get_order(self, order_id: str, pair: str) -> Dict:
def fetch_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
try:
order = self._dry_run_open_orders[order_id]
@ -927,17 +1020,25 @@ class Exchange:
f'Tried to get an invalid dry-run-order (id: {order_id}). Message: {e}') from e
try:
return self._api.fetch_order(order_id, pair)
except ccxt.OrderNotFound as e:
raise RetryableOrderError(
f'Order not found (id: {order_id}). Message: {e}') from e
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Tried to get an invalid order (id: {order_id}). Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
# Assign method to fetch_stoploss_order to allow easy overriding in other classes
fetch_stoploss_order = fetch_order
@retrier
def get_order_book(self, pair: str, limit: int = 100) -> dict:
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
"""
get order book level 2 from exchange
@ -951,6 +1052,8 @@ class Exchange:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching order book.'
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get order book due to {e.__class__.__name__}. Message: {e}') from e
@ -987,10 +1090,11 @@ class Exchange:
matched_trades = [trade for trade in my_trades if trade['order'] == order_id]
return matched_trades
except ccxt.NetworkError as e:
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get trades due to networking error. Message: {e}') from e
f'Could not get trades due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@ -1004,12 +1108,72 @@ class Exchange:
return self._api.calculate_fee(symbol=symbol, type=type, side=side, amount=amount,
price=price, takerOrMaker=taker_or_maker)['rate']
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get fee info due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@staticmethod
def order_has_fee(order: Dict) -> bool:
"""
Verifies if the passed in order dict has the needed keys to extract fees,
and that these keys (currency, cost) are not empty.
:param order: Order or trade (one trade) dict
:return: True if the fee substructure contains currency and cost, false otherwise
"""
if not isinstance(order, dict):
return False
return ('fee' in order and order['fee'] is not None
and (order['fee'].keys() >= {'currency', 'cost'})
and order['fee']['currency'] is not None
and order['fee']['cost'] is not None
)
def calculate_fee_rate(self, order: Dict) -> Optional[float]:
"""
Calculate fee rate if it's not given by the exchange.
:param order: Order or trade (one trade) dict
"""
if order['fee'].get('rate') is not None:
return order['fee'].get('rate')
fee_curr = order['fee']['currency']
# Calculate fee based on order details
if fee_curr in self.get_pair_base_currency(order['symbol']):
# Base currency - divide by amount
return round(
order['fee']['cost'] / safe_value_fallback(order, order, 'filled', 'amount'), 8)
elif fee_curr in self.get_pair_quote_currency(order['symbol']):
# Quote currency - divide by cost
return round(order['fee']['cost'] / order['cost'], 8) if order['cost'] else None
else:
# If Fee currency is a different currency
if not order['cost']:
# If cost is None or 0.0 -> falsy, return None
return None
try:
comb = self.get_valid_pair_combination(fee_curr, self._config['stake_currency'])
tick = self.fetch_ticker(comb)
fee_to_quote_rate = safe_value_fallback(tick, tick, 'last', 'ask')
return round((order['fee']['cost'] * fee_to_quote_rate) / order['cost'], 8)
except ExchangeError:
return None
def extract_cost_curr_rate(self, order: Dict) -> Tuple[float, str, Optional[float]]:
"""
Extract tuple of cost, currency, rate.
Requires order_has_fee to run first!
:param order: Order or trade (one trade) dict
:return: Tuple with cost, currency, rate of the given fee dict
"""
return (order['fee']['cost'],
order['fee']['currency'],
self.calculate_fee_rate(order))
# calculate rate ? (order['fee']['cost'] / (order['amount'] * order['price']))
def is_exchange_bad(exchange_name: str) -> bool:
return exchange_name in BAD_EXCHANGES

112
freqtrade/exchange/ftx.py
View File

@ -2,7 +2,13 @@
import logging
from typing import Dict
import ccxt
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
logger = logging.getLogger(__name__)
@ -10,5 +16,111 @@ logger = logging.getLogger(__name__)
class Ftx(Exchange):
_ft_has: Dict = {
"stoploss_on_exchange": True,
"ohlcv_candle_limit": 1500,
}
def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return order['type'] == 'stop' and stop_loss > float(order['price'])
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
"""
Creates a stoploss order.
depending on order_types.stoploss configuration, uses 'market' or limit order.
Limit orders are defined by having orderPrice set, otherwise a market order is used.
"""
limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
limit_rate = stop_price * limit_price_pct
ordertype = "stop"
stop_price = self.price_to_precision(pair, stop_price)
if self._config['dry_run']:
dry_order = self.dry_run_order(
pair, ordertype, "sell", amount, stop_price)
return dry_order
try:
params = self._params.copy()
if order_types.get('stoploss', 'market') == 'limit':
# set orderPrice to place limit order, otherwise it's a market order
params['orderPrice'] = limit_rate
amount = self.amount_to_precision(pair, amount)
order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
amount=amount, price=stop_price, params=params)
logger.info('stoploss order added for %s. '
'stop price: %s.', pair, stop_price)
return order
except ccxt.InsufficientFunds as e:
raise ExchangeError(
f'Insufficient funds to create {ordertype} sell order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Could not create {ordertype} sell order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def fetch_stoploss_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
try:
order = self._dry_run_open_orders[order_id]
return order
except KeyError as e:
# Gracefully handle errors with dry-run orders.
raise InvalidOrderException(
f'Tried to get an invalid dry-run-order (id: {order_id}). Message: {e}') from e
try:
orders = self._api.fetch_orders(pair, None, params={'type': 'stop'})
order = [order for order in orders if order['id'] == order_id]
if len(order) == 1:
return order[0]
else:
raise InvalidOrderException(f"Could not get stoploss order for id {order_id}")
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Tried to get an invalid order (id: {order_id}). Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def cancel_stoploss_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
return {}
try:
return self._api.cancel_order(order_id, pair, params={'type': 'stop'})
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Could not cancel order. Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e

14
freqtrade/exchange/kraken.py
View File

@ -4,10 +4,11 @@ from typing import Dict
import ccxt
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.exchange import retrier
from freqtrade.exchange.common import retrier
logger = logging.getLogger(__name__)
@ -45,6 +46,8 @@ class Kraken(Exchange):
balances[bal]['free'] = balances[bal]['total'] - balances[bal]['used']
return balances
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
@ -58,6 +61,7 @@ class Kraken(Exchange):
"""
return order['type'] == 'stop-loss' and stop_loss > float(order['price'])
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
"""
Creates a stoploss market order.
@ -84,7 +88,7 @@ class Kraken(Exchange):
'stop price: %s.', pair, stop_price)
return order
except ccxt.InsufficientFunds as e:
raise DependencyException(
raise ExchangeError(
f'Insufficient funds to create {ordertype} sell order on market {pair}.'
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
@ -93,6 +97,8 @@ class Kraken(Exchange):
f'Could not create {ordertype} sell order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e

582
freqtrade/freqtradebot.py
View File

@ -7,25 +7,27 @@ import traceback
from datetime import datetime
from math import isclose
from threading import Lock
from typing import Any, Dict, List, Optional, Tuple
from typing import Any, Dict, List, Optional
import arrow
from cachetools import TTLCache
from requests.exceptions import RequestException
from freqtrade import __version__, constants, persistence
from freqtrade.configuration import validate_config_consistency
from freqtrade.data.converter import order_book_to_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.edge import Edge
from freqtrade.exceptions import DependencyException, InvalidOrderException
from freqtrade.exceptions import (DependencyException, ExchangeError,
InvalidOrderException, PricingError)
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date
from freqtrade.misc import safe_value_fallback
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.rpc import RPCManager, RPCMessageType
from freqtrade.state import State
from freqtrade.strategy.interface import IStrategy, SellType
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
@ -52,8 +54,11 @@ class FreqtradeBot:
# Init objects
self.config = config
self._sell_rate_cache = TTLCache(maxsize=100, ttl=5)
self._buy_rate_cache = TTLCache(maxsize=100, ttl=5)
# Cache values for 1800 to avoid frequent polling of the exchange for prices
# Caching only applies to RPC methods, so prices for open trades are still
# refreshed once every iteration.
self._sell_rate_cache = TTLCache(maxsize=100, ttl=1800)
self._buy_rate_cache = TTLCache(maxsize=100, ttl=1800)
self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
@ -66,20 +71,20 @@ class FreqtradeBot:
self.wallets = Wallets(self.config, self.exchange)
self.dataprovider = DataProvider(self.config, self.exchange)
self.pairlists = PairListManager(self.exchange, self.config)
self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists)
# Attach Dataprovider to Strategy baseclass
IStrategy.dp = self.dataprovider
# Attach Wallets to Strategy baseclass
IStrategy.wallets = self.wallets
self.pairlists = PairListManager(self.exchange, self.config)
# Initializing Edge only if enabled
self.edge = Edge(self.config, self.exchange, self.strategy) if \
self.config.get('edge', {}).get('enabled', False) else None
self.active_pair_whitelist = self._refresh_whitelist()
self.active_pair_whitelist = self._refresh_active_whitelist()
# Set initial bot state from config
initial_state = self.config.get('initial_state')
@ -111,6 +116,11 @@ class FreqtradeBot:
"""
logger.info('Cleaning up modules ...')
if self.config['cancel_open_orders_on_exit']:
self.cancel_all_open_orders()
self.check_for_open_trades()
self.rpc.cleanup()
persistence.cleanup()
@ -131,18 +141,26 @@ class FreqtradeBot:
:return: True if one or more trades has been created or closed, False otherwise
"""
# Check whether markets have to be reloaded
self.exchange._reload_markets()
# Check whether markets have to be reloaded and reload them when it's needed
self.exchange.reload_markets()
# Query trades from persistence layer
trades = Trade.get_open_trades()
self.active_pair_whitelist = self._refresh_whitelist(trades)
self.active_pair_whitelist = self._refresh_active_whitelist(trades)
# Refreshing candles
self.dataprovider.refresh(self._create_pair_whitelist(self.active_pair_whitelist),
self.dataprovider.refresh(self.pairlists.create_pair_list(self.active_pair_whitelist),
self.strategy.informative_pairs())
strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
self.strategy.analyze(self.active_pair_whitelist)
with self._sell_lock:
# Check and handle any timed out open orders
self.check_handle_timedout()
# Protect from collisions with forcesell.
# Without this, freqtrade my try to recreate stoploss_on_exchange orders
# while selling is in process, since telegram messages arrive in an different thread.
@ -154,13 +172,37 @@ class FreqtradeBot:
if self.get_free_open_trades():
self.enter_positions()
# Check and handle any timed out open orders
self.check_handle_timedout()
Trade.session.flush()
def _refresh_whitelist(self, trades: List[Trade] = []) -> List[str]:
def process_stopped(self) -> None:
"""
Refresh whitelist from pairlist or edge and extend it with trades.
Close all orders that were left open
"""
if self.config['cancel_open_orders_on_exit']:
self.cancel_all_open_orders()
def check_for_open_trades(self):
"""
Notify the user when the bot is stopped
and there are still open trades active.
"""
open_trades = Trade.get_trades([Trade.is_open == 1]).all()
if len(open_trades) != 0:
msg = {
'type': RPCMessageType.WARNING_NOTIFICATION,
'status': f"{len(open_trades)} open trades active.\n\n"
f"Handle these trades manually on {self.exchange.name}, "
f"or '/start' the bot again and use '/stopbuy' "
f"to handle open trades gracefully. \n"
f"{'Trades are simulated.' if self.config['dry_run'] else ''}",
}
self.rpc.send_msg(msg)
def _refresh_active_whitelist(self, trades: List[Trade] = []) -> List[str]:
"""
Refresh active whitelist from pairlist or edge and extend it with
pairs that have open trades.
"""
# Refresh whitelist
self.pairlists.refresh_pairlist()
@ -172,17 +214,11 @@ class FreqtradeBot:
_whitelist = self.edge.adjust(_whitelist)
if trades:
# Extend active-pair whitelist with pairs from open trades
# It ensures that tickers are downloaded for open trades
# Extend active-pair whitelist with pairs of open trades
# It ensures that candle (OHLCV) data are downloaded for open trades as well
_whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
return _whitelist
def _create_pair_whitelist(self, pairs: List[str]) -> List[Tuple[str, str]]:
"""
Create pair-whitelist tuple with (pair, ticker_interval)
"""
return [(pair, self.config['ticker_interval']) for pair in pairs]
def get_free_open_trades(self):
"""
Return the number of free open trades slots or 0 if
@ -242,25 +278,32 @@ class FreqtradeBot:
logger.info(f"Using cached buy rate for {pair}.")
return rate
config_bid_strategy = self.config.get('bid_strategy', {})
if 'use_order_book' in config_bid_strategy and\
config_bid_strategy.get('use_order_book', False):
logger.info('Getting price from order book')
order_book_top = config_bid_strategy.get('order_book_top', 1)
order_book = self.exchange.get_order_book(pair, order_book_top)
bid_strategy = self.config.get('bid_strategy', {})
if 'use_order_book' in bid_strategy and bid_strategy.get('use_order_book', False):
logger.info(
f"Getting price from order book {bid_strategy['price_side'].capitalize()} side."
)
order_book_top = bid_strategy.get('order_book_top', 1)
order_book = self.exchange.fetch_l2_order_book(pair, order_book_top)
logger.debug('order_book %s', order_book)
# top 1 = index 0
order_book_rate = order_book['bids'][order_book_top - 1][0]
logger.info('...top %s order book buy rate %0.8f', order_book_top, order_book_rate)
used_rate = order_book_rate
try:
rate_from_l2 = order_book[f"{bid_strategy['price_side']}s"][order_book_top - 1][0]
except (IndexError, KeyError) as e:
logger.warning(
"Buy Price from orderbook could not be determined."
f"Orderbook: {order_book}"
)
raise PricingError from e
logger.info(f'...top {order_book_top} order book buy rate {rate_from_l2:.8f}')
used_rate = rate_from_l2
else:
logger.info('Using Last Ask / Last Price')
logger.info(f"Using Last {bid_strategy['price_side'].capitalize()} / Last Price")
ticker = self.exchange.fetch_ticker(pair)
if ticker['ask'] < ticker['last']:
ticker_rate = ticker['ask']
else:
ticker_rate = ticker[bid_strategy['price_side']]
if ticker['last'] and ticker_rate > ticker['last']:
balance = self.config['bid_strategy']['ask_last_balance']
ticker_rate = ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
ticker_rate = ticker_rate + balance * (ticker['last'] - ticker_rate)
used_rate = ticker_rate
self._buy_rate_cache[pair] = used_rate
@ -394,16 +437,17 @@ class FreqtradeBot:
logger.info(f"Pair {pair} is currently locked.")
return False
# get_free_open_trades is checked before create_trade is called
# but it is still used here to prevent opening too many trades within one iteration
if not self.get_free_open_trades():
logger.debug(f"Can't open a new trade for {pair}: max number of trades is reached.")
return False
# running get_signal on historical data fetched
(buy, sell) = self.strategy.get_signal(
pair, self.strategy.ticker_interval,
self.dataprovider.ohlcv(pair, self.strategy.ticker_interval))
analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(pair, self.strategy.timeframe)
(buy, sell) = self.strategy.get_signal(pair, self.strategy.timeframe, analyzed_df)
if buy and not sell:
if not self.get_free_open_trades():
logger.debug("Can't open a new trade: max number of trades is reached.")
return False
stake_amount = self.get_trade_stake_amount(pair)
if not stake_amount:
logger.debug(f"Stake amount is 0, ignoring possible trade for {pair}.")
@ -432,7 +476,7 @@ class FreqtradeBot:
"""
conf_bids_to_ask_delta = conf.get('bids_to_ask_delta', 0)
logger.info(f"Checking depth of market for {pair} ...")
order_book = self.exchange.get_order_book(pair, 1000)
order_book = self.exchange.fetch_l2_order_book(pair, 1000)
order_book_data_frame = order_book_to_dataframe(order_book['bids'], order_book['asks'])
order_book_bids = order_book_data_frame['b_size'].sum()
order_book_asks = order_book_data_frame['a_size'].sum()
@ -474,6 +518,12 @@ class FreqtradeBot:
amount = stake_amount / buy_limit_requested
order_type = self.strategy.order_types['buy']
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,
time_in_force=time_in_force):
logger.info(f"User requested abortion of buying {pair}")
return False
order = self.exchange.buy(pair=pair, ordertype=order_type,
amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force)
@ -526,7 +576,7 @@ class FreqtradeBot:
exchange=self.exchange.id,
open_order_id=order_id,
strategy=self.strategy.get_strategy_name(),
ticker_interval=timeframe_to_minutes(self.config['ticker_interval'])
timeframe=timeframe_to_minutes(self.config['timeframe'])
)
# Update fees if order is closed
@ -598,14 +648,13 @@ class FreqtradeBot:
trades_closed = 0
for trade in trades:
try:
self.update_trade_state(trade)
if (self.strategy.order_types.get('stoploss_on_exchange') and
self.handle_stoploss_on_exchange(trade)):
trades_closed += 1
continue
# Check if we can sell our current pair
if trade.open_order_id is None and self.handle_trade(trade):
if trade.open_order_id is None and trade.is_open and self.handle_trade(trade):
trades_closed += 1
except DependencyException as exception:
@ -617,9 +666,18 @@ class FreqtradeBot:
return trades_closed
def _order_book_gen(self, pair: str, side: str, order_book_max: int = 1,
order_book_min: int = 1):
"""
Helper generator to query orderbook in loop (used for early sell-order placing)
"""
order_book = self.exchange.fetch_l2_order_book(pair, order_book_max)
for i in range(order_book_min, order_book_max + 1):
yield order_book[side][i - 1][0]
def get_sell_rate(self, pair: str, refresh: bool) -> float:
"""
Get sell rate - either using get-ticker bid or first bid based on orderbook
Get sell rate - either using ticker bid or first bid based on orderbook
The orderbook portion is only used for rpc messaging, which would otherwise fail
for BitMex (has no bid/ask in fetch_ticker)
or remain static in any other case since it's not updating.
@ -634,15 +692,21 @@ class FreqtradeBot:
logger.info(f"Using cached sell rate for {pair}.")
return rate
config_ask_strategy = self.config.get('ask_strategy', {})
if config_ask_strategy.get('use_order_book', False):
logger.debug('Using order book to get sell rate')
order_book = self.exchange.get_order_book(pair, 1)
rate = order_book['bids'][0][0]
ask_strategy = self.config.get('ask_strategy', {})
if ask_strategy.get('use_order_book', False):
# This code is only used for notifications, selling uses the generator directly
logger.info(
f"Getting price from order book {ask_strategy['price_side'].capitalize()} side."
)
try:
rate = next(self._order_book_gen(pair, f"{ask_strategy['price_side']}s"))
except (IndexError, KeyError) as e:
logger.warning("Sell Price at location from orderbook could not be determined.")
raise PricingError from e
else:
rate = self.exchange.fetch_ticker(pair)['bid']
rate = self.exchange.fetch_ticker(pair)[ask_strategy['price_side']]
if rate is None:
raise PricingError(f"Sell-Rate for {pair} was empty.")
self._sell_rate_cache[pair] = rate
return rate
@ -661,23 +725,34 @@ class FreqtradeBot:
config_ask_strategy = self.config.get('ask_strategy', {})
if (config_ask_strategy.get('use_sell_signal', True) or
config_ask_strategy.get('ignore_roi_if_buy_signal')):
(buy, sell) = self.strategy.get_signal(
trade.pair, self.strategy.ticker_interval,
self.dataprovider.ohlcv(trade.pair, self.strategy.ticker_interval))
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)
if config_ask_strategy.get('use_order_book', False):
logger.debug(f'Using order book for selling {trade.pair}...')
# logger.debug('Order book %s',orderBook)
order_book_min = config_ask_strategy.get('order_book_min', 1)
order_book_max = config_ask_strategy.get('order_book_max', 1)
logger.debug(f'Using order book between {order_book_min} and {order_book_max} '
f'for selling {trade.pair}...')
order_book = self.exchange.get_order_book(trade.pair, order_book_max)
order_book = self._order_book_gen(trade.pair, f"{config_ask_strategy['price_side']}s",
order_book_min=order_book_min,
order_book_max=order_book_max)
for i in range(order_book_min, order_book_max + 1):
order_book_rate = order_book['asks'][i - 1][0]
logger.debug(' order book asks top %s: %0.8f', i, order_book_rate)
sell_rate = order_book_rate
try:
sell_rate = next(order_book)
except (IndexError, KeyError) as e:
logger.warning(
f"Sell Price at location {i} from orderbook could not be determined."
)
raise PricingError from e
logger.debug(f" order book {config_ask_strategy['price_side']} top {i}: "
f"{sell_rate:0.8f}")
# Assign sell-rate to cache - otherwise sell-rate is never updated in the cache,
# resulting in outdated RPC messages
self._sell_rate_cache[trade.pair] = sell_rate
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True
@ -710,7 +785,7 @@ class FreqtradeBot:
logger.warning('Selling the trade forcefully')
self.execute_sell(trade, trade.stop_loss, sell_reason=SellType.EMERGENCY_SELL)
except DependencyException:
except ExchangeError:
trade.stoploss_order_id = None
logger.exception('Unable to place a stoploss order on exchange.')
return False
@ -728,18 +803,18 @@ class FreqtradeBot:
try:
# First we check if there is already a stoploss on exchange
stoploss_order = self.exchange.get_order(trade.stoploss_order_id, trade.pair) \
if trade.stoploss_order_id else None
stoploss_order = self.exchange.fetch_stoploss_order(
trade.stoploss_order_id, trade.pair) if trade.stoploss_order_id else None
except InvalidOrderException as exception:
logger.warning('Unable to fetch stoploss order: %s', exception)
# We check if stoploss order is fulfilled
if stoploss_order and stoploss_order['status'] == 'closed':
if stoploss_order and stoploss_order['status'] in ('closed', 'triggered'):
trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
trade.update(stoploss_order)
self.update_trade_state(trade, stoploss_order, sl_order=True)
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair,
timeframe_to_next_date(self.config['ticker_interval']))
timeframe_to_next_date(self.config['timeframe']))
self._notify_sell(trade, "stoploss")
return True
@ -761,7 +836,7 @@ class FreqtradeBot:
return False
# If stoploss order is canceled for some reason we add it
if stoploss_order and stoploss_order['status'] == 'canceled':
if stoploss_order and stoploss_order['status'] in ('canceled', 'cancelled'):
if self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss,
rate=trade.stop_loss):
return False
@ -794,7 +869,7 @@ class FreqtradeBot:
logger.info('Trailing stoploss: cancelling current stoploss on exchange (id:{%s}) '
'in order to add another one ...', order['id'])
try:
self.exchange.cancel_order(order['id'], trade.pair)
self.exchange.cancel_stoploss_order(order['id'], trade.pair)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {order['id']} "
f"for pair {trade.pair}")
@ -845,107 +920,136 @@ class FreqtradeBot:
try:
if not trade.open_order_id:
continue
order = self.exchange.get_order(trade.open_order_id, trade.pair)
except (RequestException, DependencyException, InvalidOrderException):
logger.info(
'Cannot query order for %s due to %s',
trade,
traceback.format_exc())
order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
except (ExchangeError, InvalidOrderException):
logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
continue
# Check if trade is still actually open
if float(order.get('remaining', 0.0)) == 0.0:
self.wallets.update()
continue
fully_cancelled = self.update_trade_state(trade, order)
if ((order['side'] == 'buy' and order['status'] == 'canceled')
or (self._check_timed_out('buy', order))):
self.handle_timedout_limit_buy(trade, order)
self.wallets.update()
order_type = self.strategy.order_types['buy']
self._notify_buy_cancel(trade, order_type)
if (order['side'] == 'buy' and (order['status'] == 'open' or fully_cancelled) and (
fully_cancelled
or self._check_timed_out('buy', order)
or strategy_safe_wrapper(self.strategy.check_buy_timeout,
default_retval=False)(pair=trade.pair,
trade=trade,
order=order))):
self.handle_cancel_buy(trade, order, constants.CANCEL_REASON['TIMEOUT'])
elif ((order['side'] == 'sell' and order['status'] == 'canceled')
or (self._check_timed_out('sell', order))):
self.handle_timedout_limit_sell(trade, order)
self.wallets.update()
order_type = self.strategy.order_types['sell']
self._notify_sell_cancel(trade, order_type)
elif (order['side'] == 'sell' and (order['status'] == 'open' or fully_cancelled) and (
fully_cancelled
or self._check_timed_out('sell', order)
or strategy_safe_wrapper(self.strategy.check_sell_timeout,
default_retval=False)(pair=trade.pair,
trade=trade,
order=order))):
self.handle_cancel_sell(trade, order, constants.CANCEL_REASON['TIMEOUT'])
def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
def cancel_all_open_orders(self) -> None:
"""
Buy timeout - cancel order
Cancel all orders that are currently open
:return: None
"""
for trade in Trade.get_open_order_trades():
try:
order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
except (DependencyException, InvalidOrderException):
logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
continue
if order['side'] == 'buy':
self.handle_cancel_buy(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
elif order['side'] == 'sell':
self.handle_cancel_sell(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
def handle_cancel_buy(self, trade: Trade, order: Dict, reason: str) -> bool:
"""
Buy cancel - cancel order
:return: True if order was fully cancelled
"""
if order['status'] != 'canceled':
reason = "cancelled due to timeout"
corder = self.exchange.cancel_order(trade.open_order_id, trade.pair)
logger.info('Buy order %s for %s.', reason, trade)
was_trade_fully_canceled = False
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in ('canceled', 'closed'):
reason = constants.CANCEL_REASON['TIMEOUT']
corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount)
else:
# Order was cancelled already, so we can reuse the existing dict
corder = order
reason = "cancelled on exchange"
logger.info('Buy order %s for %s.', reason, trade)
reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
if corder.get('remaining', order['remaining']) == order['amount']:
logger.info('Buy order %s for %s.', reason, trade)
# Using filled to determine the filled amount
filled_amount = safe_value_fallback(corder, order, 'filled', 'filled')
if isclose(filled_amount, 0.0, abs_tol=constants.MATH_CLOSE_PREC):
logger.info('Buy order fully cancelled. Removing %s from database.', trade)
# if trade is not partially completed, just delete the trade
Trade.session.delete(trade)
Trade.session.flush()
return True
was_trade_fully_canceled = True
else:
# if trade is partially complete, edit the stake details for the trade
# and close the order
# cancel_order may not contain the full order dict, so we need to fallback
# to the order dict aquired before cancelling.
# we need to fall back to the values from order if corder does not contain these keys.
trade.amount = filled_amount
trade.stake_amount = trade.amount * trade.open_rate
self.update_trade_state(trade, corder, trade.amount)
# if trade is partially complete, edit the stake details for the trade
# and close the order
# cancel_order may not contain the full order dict, so we need to fallback
# to the order dict aquired before cancelling.
# we need to fall back to the values from order if corder does not contain these keys.
trade.amount = order['amount'] - corder.get('remaining', order['remaining'])
trade.stake_amount = trade.amount * trade.open_rate
# verify if fees were taken from amount to avoid problems during selling
try:
new_amount = self.get_real_amount(trade, corder if 'fee' in corder else order,
trade.amount)
if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
trade.amount = new_amount
# Fee was applied, so set to 0
trade.fee_open = 0
trade.recalc_open_trade_price()
except DependencyException as e:
logger.warning("Could not update trade amount: %s", e)
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'Remaining buy order for {trade.pair} cancelled due to timeout'
})
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'Remaining buy order for {trade.pair} cancelled due to timeout'
})
return False
self.wallets.update()
self._notify_buy_cancel(trade, order_type=self.strategy.order_types['buy'])
return was_trade_fully_canceled
def handle_timedout_limit_sell(self, trade: Trade, order: Dict) -> bool:
def handle_cancel_sell(self, trade: Trade, order: Dict, reason: str) -> str:
"""
Sell timeout - cancel order and update trade
:return: True if order was fully cancelled
Sell cancel - cancel order and update trade
:return: Reason for cancel
"""
# if trade is not partially completed, just cancel the trade
if order['remaining'] == order['amount']:
if order["status"] != "canceled":
reason = "cancelled due to timeout"
# if trade is not partially completed, just delete the trade
self.exchange.cancel_order(trade.open_order_id, trade.pair)
# if trade is not partially completed, just cancel the order
if order['remaining'] == order['amount'] or order.get('filled') == 0.0:
if not self.exchange.check_order_canceled_empty(order):
try:
# if trade is not partially completed, just delete the order
self.exchange.cancel_order(trade.open_order_id, trade.pair)
except InvalidOrderException:
logger.exception(f"Could not cancel sell order {trade.open_order_id}")
return 'error cancelling order'
logger.info('Sell order %s for %s.', reason, trade)
else:
reason = "cancelled on exchange"
reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
logger.info('Sell order %s for %s.', reason, trade)
trade.close_rate = None
trade.close_rate_requested = None
trade.close_profit = None
trade.close_profit_abs = None
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
else:
# TODO: figure out how to handle partially complete sell orders
reason = constants.CANCEL_REASON['PARTIALLY_FILLED']
return True
# TODO: figure out how to handle partially complete sell orders
return False
self.wallets.update()
self._notify_sell_cancel(
trade,
order_type=self.strategy.order_types['sell'],
reason=reason
)
return reason
def _safe_sell_amount(self, pair: str, amount: float) -> float:
"""
@ -966,7 +1070,7 @@ class FreqtradeBot:
if wallet_amount >= amount:
return amount
elif wallet_amount > amount * 0.98:
logger.info(f"{pair} - Falling back to wallet-amount.")
logger.info(f"{pair} - Falling back to wallet-amount {wallet_amount} -> {amount}.")
return wallet_amount
else:
raise DependencyException(
@ -993,7 +1097,7 @@ class FreqtradeBot:
# First cancelling stoploss on exchange ...
if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
try:
self.exchange.cancel_order(trade.stoploss_order_id, trade.pair)
self.exchange.cancel_stoploss_order(trade.stoploss_order_id, trade.pair)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
@ -1003,12 +1107,20 @@ class FreqtradeBot:
order_type = self.strategy.order_types.get("emergencysell", "market")
amount = self._safe_sell_amount(trade.pair, trade.amount)
time_in_force = self.strategy.order_time_in_force['sell']
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,
time_in_force=time_in_force,
sell_reason=sell_reason.value):
logger.info(f"User requested abortion of selling {trade.pair}")
return False
# Execute sell and update trade record
order = self.exchange.sell(pair=str(trade.pair),
ordertype=order_type,
amount=amount, rate=limit,
time_in_force=self.strategy.order_time_in_force['sell']
time_in_force=time_in_force
)
trade.open_order_id = order['id']
@ -1016,11 +1128,11 @@ class FreqtradeBot:
trade.sell_reason = sell_reason.value
# In case of market sell orders the order can be closed immediately
if order.get('status', 'unknown') == 'closed':
trade.update(order)
self.update_trade_state(trade, order)
Trade.session.flush()
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair, timeframe_to_next_date(self.config['ticker_interval']))
self.strategy.lock_pair(trade.pair, timeframe_to_next_date(self.config['timeframe']))
self._notify_sell(trade, order_type)
@ -1032,10 +1144,10 @@ class FreqtradeBot:
"""
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
# Use cached ticker here - it was updated seconds ago.
# Use cached rates here - it was updated seconds ago.
current_rate = self.get_sell_rate(trade.pair, False)
profit_percent = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_percent > 0 else "loss"
profit_ratio = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_ratio > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_NOTIFICATION,
@ -1048,7 +1160,7 @@ class FreqtradeBot:
'open_rate': trade.open_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_percent': profit_percent,
'profit_ratio': profit_ratio,
'sell_reason': trade.sell_reason,
'open_date': trade.open_date,
'close_date': trade.close_date or datetime.utcnow(),
@ -1064,15 +1176,20 @@ class FreqtradeBot:
# Send the message
self.rpc.send_msg(msg)
def _notify_sell_cancel(self, trade: Trade, order_type: str) -> None:
def _notify_sell_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
"""
Sends rpc notification when a sell cancel occured.
"""
if trade.sell_order_status == reason:
return
else:
trade.sell_order_status = reason
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
current_rate = self.get_sell_rate(trade.pair, False)
profit_percent = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_percent > 0 else "loss"
profit_ratio = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_ratio > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_CANCEL_NOTIFICATION,
@ -1085,12 +1202,13 @@ class FreqtradeBot:
'open_rate': trade.open_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_percent': profit_percent,
'profit_ratio': profit_ratio,
'sell_reason': trade.sell_reason,
'open_date': trade.open_date,
'close_date': trade.close_date,
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
'reason': reason,
}
if 'fiat_display_currency' in self.config:
@ -1105,84 +1223,134 @@ class FreqtradeBot:
# Common update trade state methods
#
def update_trade_state(self, trade: Trade, action_order: dict = None) -> None:
def update_trade_state(self, trade: Trade, action_order: dict = None,
order_amount: float = None, sl_order: bool = False) -> bool:
"""
Checks trades with open orders and updates the amount if necessary
Handles closing both buy and sell orders.
:return: True if order has been cancelled without being filled partially, False otherwise
"""
# Get order details for actual price per unit
if trade.open_order_id:
# Update trade with order values
logger.info('Found open order for %s', trade)
try:
order = action_order or self.exchange.get_order(trade.open_order_id, trade.pair)
except InvalidOrderException as exception:
logger.warning('Unable to fetch order %s: %s', trade.open_order_id, exception)
return
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order)
if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
order['amount'] = new_amount
# Fee was applied, so set to 0
trade.fee_open = 0
trade.recalc_open_trade_price()
order_id = trade.open_order_id
elif trade.stoploss_order_id and sl_order:
order_id = trade.stoploss_order_id
else:
return False
# Update trade with order values
logger.info('Found open order for %s', trade)
try:
order = action_order or self.exchange.fetch_order(order_id, trade.pair)
except InvalidOrderException as exception:
logger.warning('Unable to fetch order %s: %s', order_id, exception)
return False
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order, order_amount)
if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
order['amount'] = new_amount
order.pop('filled', None)
trade.recalc_open_trade_price()
except DependencyException as exception:
logger.warning("Could not update trade amount: %s", exception)
except DependencyException as exception:
logger.warning("Could not update trade amount: %s", exception)
if self.exchange.check_order_canceled_empty(order):
# Trade has been cancelled on exchange
# Handling of this will happen in check_handle_timeout.
return True
trade.update(order)
trade.update(order)
# Updating wallets when order is closed
if not trade.is_open:
self.wallets.update()
return False
# Updating wallets when order is closed
if not trade.is_open:
self.wallets.update()
def apply_fee_conditional(self, trade: Trade, trade_base_currency: str,
amount: float, fee_abs: float) -> float:
"""
Applies the fee to amount (either from Order or from Trades).
Can eat into dust if more than the required asset is available.
"""
self.wallets.update()
if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount:
# Eat into dust if we own more than base currency
logger.info(f"Fee amount for {trade} was in base currency - "
f"Eating Fee {fee_abs} into dust.")
elif fee_abs != 0:
real_amount = self.exchange.amount_to_precision(trade.pair, amount - fee_abs)
logger.info(f"Applying fee on amount for {trade} "
f"(from {amount} to {real_amount}).")
return real_amount
return amount
def get_real_amount(self, trade: Trade, order: Dict, order_amount: float = None) -> float:
"""
Get real amount for the trade
Detect and update trade fee.
Calls trade.update_fee() uppon correct detection.
Returns modified amount if the fee was taken from the destination currency.
Necessary for exchanges which charge fees in base currency (e.g. binance)
:return: identical (or new) amount for the trade
"""
# Init variables
if order_amount is None:
order_amount = order['amount']
# Only run for closed orders
if trade.fee_open == 0 or order['status'] == 'open':
if trade.fee_updated(order.get('side', '')) or order['status'] == 'open':
return order_amount
trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
# use fee from order-dict if possible
if ('fee' in order and order['fee'] is not None and
(order['fee'].keys() >= {'currency', 'cost'})):
if (order['fee']['currency'] is not None and
order['fee']['cost'] is not None and
trade_base_currency == order['fee']['currency']):
new_amount = order_amount - order['fee']['cost']
logger.info("Applying fee on amount for %s (from %s to %s) from Order",
trade, order['amount'], new_amount)
return new_amount
if self.exchange.order_has_fee(order):
fee_cost, fee_currency, fee_rate = self.exchange.extract_cost_curr_rate(order)
logger.info(f"Fee for Trade {trade} [{order.get('side')}]: "
f"{fee_cost:.8g} {fee_currency} - rate: {fee_rate}")
# Fallback to Trades
trade.update_fee(fee_cost, fee_currency, fee_rate, order.get('side', ''))
if trade_base_currency == fee_currency:
# Apply fee to amount
return self.apply_fee_conditional(trade, trade_base_currency,
amount=order_amount, fee_abs=fee_cost)
return order_amount
return self.fee_detection_from_trades(trade, order, order_amount)
def fee_detection_from_trades(self, trade: Trade, order: Dict, order_amount: float) -> float:
"""
fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee.
"""
trades = self.exchange.get_trades_for_order(trade.open_order_id, trade.pair,
trade.open_date)
if len(trades) == 0:
logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)
return order_amount
fee_currency = None
amount = 0
fee_abs = 0
fee_abs = 0.0
fee_cost = 0.0
trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
fee_rate_array: List[float] = []
for exectrade in trades:
amount += exectrade['amount']
if ("fee" in exectrade and exectrade['fee'] is not None and
(exectrade['fee'].keys() >= {'currency', 'cost'})):
if self.exchange.order_has_fee(exectrade):
fee_cost_, fee_currency, fee_rate_ = self.exchange.extract_cost_curr_rate(exectrade)
fee_cost += fee_cost_
if fee_rate_ is not None:
fee_rate_array.append(fee_rate_)
# only applies if fee is in quote currency!
if (exectrade['fee']['currency'] is not None and
exectrade['fee']['cost'] is not None and
trade_base_currency == exectrade['fee']['currency']):
fee_abs += exectrade['fee']['cost']
if trade_base_currency == fee_currency:
fee_abs += fee_cost_
# Ensure at least one trade was found:
if fee_currency:
# fee_rate should use mean
fee_rate = sum(fee_rate_array) / float(len(fee_rate_array)) if fee_rate_array else None
trade.update_fee(fee_cost, fee_currency, fee_rate, order.get('side', ''))
if not isclose(amount, order_amount, abs_tol=constants.MATH_CLOSE_PREC):
logger.warning(f"Amount {amount} does not match amount {trade.amount}")
raise DependencyException("Half bought? Amounts don't match")
real_amount = amount - fee_abs
if fee_abs != 0:
logger.info(f"Applying fee on amount for {trade} "
f"(from {order_amount} to {real_amount}) from Trades")
return real_amount
return self.apply_fee_conditional(trade, trade_base_currency,
amount=amount, fee_abs=fee_abs)
else:
return amount

14
freqtrade/loggers.py
View File

@ -11,23 +11,27 @@ from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def _set_loggers(verbosity: int = 0) -> None:
def _set_loggers(verbosity: int = 0, api_verbosity: str = 'info') -> None:
"""
Set the logging level for third party libraries
:return: None
"""
logging.getLogger('requests').setLevel(
logging.INFO if verbosity <= 1 else logging.DEBUG
logging.INFO if verbosity <= 1 else logging.DEBUG
)
logging.getLogger("urllib3").setLevel(
logging.INFO if verbosity <= 1 else logging.DEBUG
logging.INFO if verbosity <= 1 else logging.DEBUG
)
logging.getLogger('ccxt.base.exchange').setLevel(
logging.INFO if verbosity <= 2 else logging.DEBUG
logging.INFO if verbosity <= 2 else logging.DEBUG
)
logging.getLogger('telegram').setLevel(logging.INFO)
logging.getLogger('werkzeug').setLevel(
logging.ERROR if api_verbosity == 'error' else logging.INFO
)
def setup_logging(config: Dict[str, Any]) -> None:
"""
@ -77,5 +81,5 @@ def setup_logging(config: Dict[str, Any]) -> None:
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=log_handlers
)
_set_loggers(verbosity)
_set_loggers(verbosity, config.get('api_server', {}).get('verbosity', 'info'))
logger.info('Verbosity set to %s', verbosity)

39
freqtrade/misc.py
View File

@ -81,13 +81,13 @@ def file_load_json(file):
gzipfile = file
# Try gzip file first, otherwise regular json file.
if gzipfile.is_file():
logger.debug('Loading ticker data from file %s', gzipfile)
with gzip.open(gzipfile) as tickerdata:
pairdata = json_load(tickerdata)
logger.debug(f"Loading historical data from file {gzipfile}")
with gzip.open(gzipfile) as datafile:
pairdata = json_load(datafile)
elif file.is_file():
logger.debug('Loading ticker data from file %s', file)
with open(file) as tickerdata:
pairdata = json_load(tickerdata)
logger.debug(f"Loading historical data from file {file}")
with open(file) as datafile:
pairdata = json_load(datafile)
else:
return None
return pairdata
@ -134,6 +134,21 @@ def round_dict(d, n):
return {k: (round(v, n) if isinstance(v, float) else v) for k, v in d.items()}
def safe_value_fallback(dict1: dict, dict2: dict, key1: str, key2: str, default_value=None):
"""
Search a value in dict1, return this if it's not None.
Fall back to dict2 - return key2 from dict2 if it's not None.
Else falls back to None.
"""
if key1 in dict1 and dict1[key1] is not None:
return dict1[key1]
else:
if key2 in dict2 and dict2[key2] is not None:
return dict2[key2]
return default_value
def plural(num: float, singular: str, plural: str = None) -> str:
return singular if (num == 1 or num == -1) else plural or singular + 's'
@ -148,3 +163,15 @@ def render_template(templatefile: str, arguments: dict = {}) -> str:
)
template = env.get_template(templatefile)
return template.render(**arguments)
def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
arguments: dict = {}) -> str:
"""
Use templatefile if possible, otherwise fall back to templatefallbackfile
"""
from jinja2.exceptions import TemplateNotFound
try:
return render_template(templatefile, arguments)
except TemplateNotFound:
return render_template(templatefallbackfile, arguments)

154
freqtrade/optimize/backtesting.py
View File

@ -6,8 +6,7 @@ This module contains the backtesting logic
import logging
from copy import deepcopy
from datetime import datetime, timedelta
from pathlib import Path
from typing import Any, Dict, List, NamedTuple, Optional
from typing import Any, Dict, List, NamedTuple, Optional, Tuple
import arrow
from pandas import DataFrame
@ -19,10 +18,10 @@ from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.misc import file_dump_json
from freqtrade.optimize.optimize_reports import (
generate_text_table, generate_text_table_sell_reason,
generate_text_table_strategy)
from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
show_backtest_results,
store_backtest_result)
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
@ -66,11 +65,6 @@ class Backtesting:
self.strategylist: List[IStrategy] = []
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
if config.get('fee'):
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(symbol=self.config['exchange']['pair_whitelist'][0])
if self.config.get('runmode') != RunMode.HYPEROPT:
self.dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = self.dataprovider
@ -87,12 +81,31 @@ class Backtesting:
self.strategylist.append(StrategyResolver.load_strategy(self.config))
validate_config_consistency(self.config)
if "ticker_interval" not in self.config:
raise OperationalException("Ticker-interval needs to be set in either configuration "
"or as cli argument `--ticker-interval 5m`")
self.timeframe = str(self.config.get('ticker_interval'))
if "timeframe" not in self.config:
raise OperationalException("Timeframe (ticker interval) needs to be set in either "
"configuration or as cli argument `--timeframe 5m`")
self.timeframe = str(self.config.get('timeframe'))
self.timeframe_min = timeframe_to_minutes(self.timeframe)
self.pairlists = PairListManager(self.exchange, self.config)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException("VolumePairList not allowed for backtesting.")
if len(self.strategylist) > 1 and 'PrecisionFilter' in self.pairlists.name_list:
raise OperationalException(
"PrecisionFilter not allowed for backtesting multiple strategies."
)
self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0:
raise OperationalException("No pair in whitelist.")
if config.get('fee', None) is not None:
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
# Load one (first) strategy
@ -108,13 +121,13 @@ class Backtesting:
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
def load_bt_data(self):
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
datadir=self.config['datadir'],
pairs=self.config['exchange']['pair_whitelist'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=timerange,
startup_candles=self.required_startup,
@ -134,49 +147,33 @@ class Backtesting:
return data, timerange
def _store_backtest_result(self, recordfilename: Path, results: DataFrame,
strategyname: Optional[str] = None) -> None:
records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
for index, t in results.iterrows()]
if records:
if strategyname:
# Inject strategyname to filename
recordfilename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{strategyname}').with_suffix(recordfilename.suffix)
logger.info(f'Dumping backtest results to {recordfilename}')
file_dump_json(recordfilename, records)
def _get_ticker_list(self, processed: Dict) -> Dict[str, DataFrame]:
def _get_ohlcv_as_lists(self, processed: Dict) -> Dict[str, DataFrame]:
"""
Helper function to convert a processed tickerlist into a list for performance reasons.
Helper function to convert a processed dataframes into lists for performance reasons.
Used by backtest() - so keep this optimized for performance.
"""
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
ticker: Dict = {}
# Create ticker dict
data: Dict = {}
# Create dict with data
for pair, pair_data in processed.items():
pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
ticker_data = self.strategy.advise_sell(
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
# To avoid using data from future, we use buy/sell signals shifted
# from the previous candle
df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
ticker_data.drop(ticker_data.head(1).index, inplace=True)
df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker[pair] = [x for x in ticker_data.itertuples()]
return ticker
data[pair] = [x for x in df_analyzed.itertuples()]
return data
def _get_close_rate(self, sell_row, trade: Trade, sell: SellCheckTuple,
trade_dur: int) -> float:
@ -220,7 +217,7 @@ class Backtesting:
def _get_sell_trade_entry(
self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict,
partial_ohlcv: List, trade_count_lock: Dict,
stake_amount: float, max_open_trades: int) -> Optional[BacktestResult]:
trade = Trade(
@ -235,7 +232,7 @@ class Backtesting:
)
logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
# calculate win/lose forwards from buy point
for sell_row in partial_ticker:
for sell_row in partial_ohlcv:
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
@ -259,9 +256,9 @@ class Backtesting:
close_rate=closerate,
sell_reason=sell.sell_type
)
if partial_ticker:
if partial_ohlcv:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ticker[-1]
sell_row = partial_ohlcv[-1]
bt_res = BacktestResult(pair=pair,
profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
@ -308,8 +305,9 @@ class Backtesting:
trades = []
trade_count_lock: Dict = {}
# Dict of ticker-lists for performance (looping lists is a lot faster than dataframes)
ticker: Dict = self._get_ticker_list(processed)
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed)
lock_pair_until: Dict = {}
# Indexes per pair, so some pairs are allowed to have a missing start.
@ -319,12 +317,12 @@ class Backtesting:
# Loop timerange and get candle for each pair at that point in time
while tmp < end_date:
for i, pair in enumerate(ticker):
for i, pair in enumerate(data):
if pair not in indexes:
indexes[pair] = 0
try:
row = ticker[pair][indexes[pair]]
row = data[pair][indexes[pair]]
except IndexError:
# missing Data for one pair at the end.
# Warnings for this are shown during data loading
@ -352,7 +350,7 @@ class Backtesting:
# since indexes has been incremented before, we need to go one step back to
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]-1:],
trade_entry = self._get_sell_trade_entry(pair, row, data[pair][indexes[pair]-1:],
trade_count_lock, stake_amount,
max_open_trades)
@ -395,7 +393,7 @@ class Backtesting:
self._set_strategy(strat)
# need to reprocess data every time to populate signals
preprocessed = self.strategy.tickerdata_to_dataframe(data)
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
@ -416,44 +414,8 @@ class Backtesting:
position_stacking=position_stacking,
)
for strategy, results in all_results.items():
if self.config.get('export', False):
self._store_backtest_result(Path(self.config['exportfilename']), results,
strategy if len(self.strategylist) > 1 else None)
print(f"Result for strategy {strategy}")
table = generate_text_table(data, stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results)
if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = generate_text_table_sell_reason(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results)
if isinstance(table, str):
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = generate_text_table(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results.loc[results.open_at_end], skip_nan=True)
if isinstance(table, str):
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str):
print('=' * len(table.splitlines()[0]))
print()
if len(all_results) > 1:
# Print Strategy summary table
table = generate_text_table_strategy(self.config['stake_currency'],
self.config['max_open_trades'],
all_results=all_results)
print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
print(table)
print('=' * len(table.splitlines()[0]))
print('\nFor more details, please look at the detail tables above')
if self.config.get('export', False):
store_backtest_result(self.config['exportfilename'], all_results)
# Show backtest results
stats = generate_backtest_stats(self.config, data, all_results)
show_backtest_results(self.config, stats)

4
freqtrade/optimize/default_hyperopt_loss.py
View File

@ -42,8 +42,8 @@ class DefaultHyperOptLoss(IHyperOptLoss):
* 0.25: Avoiding trade loss
* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
"""
total_profit = results.profit_percent.sum()
trade_duration = results.trade_duration.mean()
total_profit = results['profit_percent'].sum()
trade_duration = results['trade_duration'].mean()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)

298
freqtrade/optimize/hyperopt.py
View File

@ -3,40 +3,45 @@
This module contains the hyperopt logic
"""
import os
import io
import locale
import logging
import os
import random
import sys
import warnings
from collections import OrderedDict, deque
from math import factorial, log
from numpy import iinfo, int32
from multiprocessing import Manager
from operator import itemgetter
from os import path
from pathlib import Path
from pprint import pprint
from typing import Any, Dict, List, Optional, Tuple, Set
from pprint import pformat
from queue import Queue
from typing import Any, Dict, List, Optional, Set, Tuple
import progressbar
import rapidjson
import tabulate
from colorama import Fore, Style
from colorama import init as colorama_init
from joblib import (Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects)
from joblib import parallel_backend
from multiprocessing import Manager
from queue import Queue
from pandas import DataFrame, json_normalize, isna
from tabulate import tabulate
from joblib import (Parallel, cpu_count, delayed, dump, load, parallel_backend,
wrap_non_picklable_objects)
from numpy import iinfo, int32
from pandas import DataFrame, isna, json_normalize
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
import freqtrade.optimize.hyperopt_backend as backend
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import get_timerange
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural, round_dict
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
import freqtrade.optimize.hyperopt_backend as backend
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver, HyperOptResolver)
from freqtrade.optimize.hyperopt_loss_interface import \
IHyperOptLoss # noqa: F401
from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
HyperOptResolver)
# Suppress scikit-learn FutureWarnings from skopt
with warnings.catch_warnings():
@ -49,6 +54,9 @@ with warnings.catch_warnings():
# from sklearn.ensemble import HistGradientBoostingRegressor
# from xgboost import XGBoostRegressor
progressbar.streams.wrap_stderr()
progressbar.streams.wrap_stdout()
logger = logging.getLogger(__name__)
# supported strategies when asking for multiple points to the optimizer
@ -81,12 +89,12 @@ class Hyperopt:
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
self.trials_file = (self.config['user_data_dir'] / 'hyperopt_results' /
'hyperopt_results.pickle')
self.results_file = (self.config['user_data_dir'] / 'hyperopt_results' /
'hyperopt_results.pickle')
self.opts_file = (self.config['user_data_dir'] / 'hyperopt_results' /
'hyperopt_optimizers.pickle')
self.tickerdata_pickle = (self.config['user_data_dir'] / 'hyperopt_results' /
'hyperopt_tickerdata.pkl')
self.data_pickle_file = (self.config['user_data_dir'] / 'hyperopt_results' /
'hyperopt_tickerdata.pkl')
self.n_jobs = self.config.get('hyperopt_jobs', -1)
if self.n_jobs < 0:
@ -115,7 +123,7 @@ class Hyperopt:
else:
logger.info("Continuing on previous hyperopt results.")
self.num_trials_saved = 0
self.num_epochs_saved = 0
# evaluations
self.trials: List = []
@ -149,6 +157,7 @@ class Hyperopt:
self.config['ask_strategy']['use_sell_signal'] = True
self.print_all = self.config.get('print_all', False)
self.hyperopt_table_header = 0
self.print_colorized = self.config.get('print_colorized', False)
self.print_json = self.config.get('print_json', False)
@ -222,7 +231,7 @@ class Hyperopt:
"""
Remove hyperopt pickle files to restart hyperopt.
"""
for f in [self.tickerdata_pickle, self.trials_file, self.opts_file]:
for f in [self.data_pickle_file, self.results_file, self.opts_file]:
p = Path(f)
if p.is_file():
logger.info(f"Removing `{p}`.")
@ -241,20 +250,18 @@ class Hyperopt:
# and the values are taken from the list of parameters.
return {d.name: v for d, v in zip(dimensions, raw_params)}
def save_trials(self, final: bool = False) -> None:
def _save_results(self) -> None:
"""
Save hyperopt trials
Save hyperopt results to file
"""
num_trials = len(self.trials)
if num_trials > self.num_trials_saved:
logger.debug(f"\nSaving {num_trials} {plural(num_trials, 'epoch')}.")
# save_trials(self.trials, trials_path, self.num_trials_saved)
dump(self.trials, self.trials_file)
self.num_trials_saved = num_trials
num_epochs = len(self.epochs)
if num_epochs > self.num_epochs_saved:
logger.debug(f"Saving {num_epochs} {plural(num_epochs, 'epoch')}.")
dump(self.epochs, self.results_file)
self.num_epochs_saved = num_epochs
self.save_opts()
if final:
logger.info(f"{num_trials} {plural(num_trials, 'epoch')} "
f"saved to '{self.trials_file}'.")
logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
def save_opts(self) -> None:
"""
@ -286,13 +293,13 @@ class Hyperopt:
dump(opts, self.opts_file)
@staticmethod
def _read_trials(trials_file: Path) -> List:
def _read_results(results_file: Path) -> List:
"""
Read hyperopt trials file
Read hyperopt results from file
"""
logger.info("Reading Trials from '%s'", trials_file)
trials = load(trials_file)
return trials
logger.info("Reading epochs from '%s'", results_file)
data = load(results_file)
return data
def _get_params_details(self, params: Dict) -> Dict:
"""
@ -352,6 +359,9 @@ class Hyperopt:
if space in ['buy', 'sell']:
result_dict.setdefault('params', {}).update(space_params)
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
# OrderedDict is used to keep the numeric order of the items
@ -366,11 +376,24 @@ class Hyperopt:
def _params_pretty_print(params, space: str, header: str) -> None:
if space in params:
space_params = Hyperopt._space_params(params, space, 5)
params_result = f"\n# {header}\n"
if space == 'stoploss':
print(header, space_params.get('stoploss'))
params_result += f"stoploss = {space_params.get('stoploss')}"
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
minimal_roi_result = rapidjson.dumps(
OrderedDict(
(str(k), v) for k, v in space_params.items()
),
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
params_result += f"minimal_roi = {minimal_roi_result}"
else:
print(header)
pprint(space_params, indent=4)
params_result += f"{space}_params = {pformat(space_params, indent=4)}"
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
params_result = params_result.replace("\n", "\n ")
print(params_result)
@staticmethod
def _space_params(params, space: str, r: int = None) -> Dict:
@ -387,24 +410,16 @@ class Hyperopt:
Log results if it is better than any previous evaluation
"""
is_best = results['is_best']
if self.print_all or is_best:
self.print_results_explanation(results, self.epochs_limit(), self.print_all,
self.print_colorized)
@staticmethod
def print_results_explanation(results, total_epochs, highlight_best: bool,
print_colorized: bool) -> None:
"""
Log results explanation string
"""
explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
# Colorize output
if print_colorized:
if results['total_profit'] > 0:
explanation_str = Fore.GREEN + explanation_str
if highlight_best and results['is_best']:
explanation_str = Style.BRIGHT + explanation_str
print(explanation_str)
if self.print_all or is_best:
print(
self.get_result_table(
self.config, results, self.total_epochs,
self.print_all, self.print_colorized,
self.hyperopt_table_header
)
)
self.hyperopt_table_header = 2
@staticmethod
def _format_explanation_string(results, total_epochs) -> str:
@ -414,13 +429,15 @@ class Hyperopt:
f"Objective: {results['loss']:.5f}")
@staticmethod
def print_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool) -> None:
def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> str:
"""
Log result table
"""
if not results:
return
return ''
tabulate.PRESERVE_WHITESPACE = True
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
@ -431,37 +448,130 @@ class Hyperopt:
trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials['Objective'] = trials['Objective'].astype(str)
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
trials['Epoch'] = trials['Epoch'].apply(
lambda x: "{}/{}".format(x, total_epochs))
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else x)
trials['Profit'] = trials['Profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else x)
trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{: 11.8f} '.format(x) + config['stake_currency'] if not isna(x) else x)
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f}m'.format(x) if not isna(x) else x)
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
)
trials['Profit'] = trials.apply(
lambda x: '{:,.8f} {} {}'.format(
x['Total profit'], config['stake_currency'],
'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
).rjust(25+len(config['stake_currency']))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
axis=1
)
trials = trials.drop(columns=['Total profit'])
if print_colorized:
for i in range(len(trials)):
if trials.loc[i]['is_profit']:
for z in range(len(trials.loc[i])-3):
trials.iat[i, z] = "{}{}{}".format(Fore.GREEN,
str(trials.loc[i][z]), Fore.RESET)
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
str(trials.loc[i][j]), Fore.RESET)
if trials.loc[i]['is_best'] and highlight_best:
for z in range(len(trials.loc[i])-3):
trials.iat[i, z] = "{}{}{}".format(Style.BRIGHT,
str(trials.loc[i][z]), Style.RESET_ALL)
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
str(trials.loc[i][j]), Style.RESET_ALL)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
if remove_header > 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='orgtbl',
headers='keys', stralign="right"
)
print(tabulate(trials.to_dict(orient='list'), headers='keys', tablefmt='psql',
stralign="right"))
table = table.split("\n", remove_header)[remove_header]
elif remove_header < 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
table = "\n".join(table.split("\n")[0:remove_header])
else:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
return table
@staticmethod
def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
csv_file: str) -> None:
"""
Log result to csv-file
"""
if not results:
return
# Verification for overwrite
if path.isfile(csv_file):
logger.error(f"CSV file already exists: {csv_file}")
return
try:
io.open(csv_file, 'w+').close()
except IOError:
logger.error(f"Failed to create CSV file: {csv_file}")
return
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
trials = trials[base_metrics + param_metrics]
base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
param_columns = list(results[0]['params_dict'].keys())
trials.columns = base_columns + param_columns
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[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['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str)
trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
)
trials['Profit'] = trials['Profit'].apply(
lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
logger.info(f"CSV file created: {csv_file}")
def has_space(self, space: str) -> bool:
"""
@ -536,7 +646,7 @@ class Hyperopt:
self.backtesting.strategy.trailing_only_offset_is_reached = \
d['trailing_only_offset_is_reached']
processed = load(self.tickerdata_pickle)
processed = load(self.data_pickle_file)
min_date, max_date = get_timerange(processed)
@ -898,7 +1008,7 @@ class Hyperopt:
self.print_results(v)
self.trials.append(v)
# Save results and optimizers after every batch
self.save_trials()
self._save_results()
# track new points if in multi mode
if self.multi:
self.track_points(trials=self.trials[frame_start:])
@ -925,19 +1035,20 @@ class Hyperopt:
return False
@staticmethod
def load_previous_results(trials_file: Path) -> List:
def load_previous_results(results_file: Path) -> List:
"""
Load data for epochs from the file if we have one
"""
trials: List = []
if trials_file.is_file() and trials_file.stat().st_size > 0:
trials = Hyperopt._read_trials(trials_file)
if trials[0].get('is_best') is None:
epochs: List = []
if results_file.is_file() and results_file.stat().st_size > 0:
epochs = Hyperopt._read_results(results_file)
# Detection of some old format, without 'is_best' field saved
if epochs[0].get('is_best') is None:
raise OperationalException(
"The file with Hyperopt results is incompatible with this version "
"of Freqtrade and cannot be loaded.")
logger.info(f"Loaded {len(trials)} previous evaluations from disk.")
return trials
logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
return epochs
@staticmethod
def load_previous_optimizers(opts_file: Path) -> List:
@ -1204,7 +1315,7 @@ class Hyperopt:
self.hyperopt_table_header = -1
data, timerange = self.backtesting.load_bt_data()
preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
@ -1216,12 +1327,13 @@ class Hyperopt:
'Hyperopting with data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
dump(preprocessed, self.tickerdata_pickle)
dump(preprocessed, self.data_pickle_file)
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore
self.trials = self.load_previous_results(self.trials_file)
self.epochs = self.load_previous_results(self.results_file)
self.setup_epochs()
logger.info(f"Found {cpu_count()} CPU cores. Let's make them scream!")
@ -1242,11 +1354,13 @@ class Hyperopt:
self.main_loop(jobs_scheduler)
self.save_trials(final=True)
self._save_results()
logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
if self.trials:
sorted_trials = sorted(self.trials, key=itemgetter('loss'))
results = sorted_trials[0]
if self.epochs:
sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
results = sorted_epochs[0]
self.print_epoch_details(results, self.epochs_limit(), self.print_json)
else:
# This is printed when Ctrl+C is pressed quickly, before first epochs have

11
freqtrade/optimize/hyperopt_interface.py
View File

@ -31,13 +31,15 @@ class IHyperOpt(ABC):
Class attributes you can use:
ticker_interval -> int: value of the ticker interval to use for the strategy
"""
ticker_interval: str
ticker_interval: str # DEPRECATED
timeframe: str
def __init__(self, config: dict) -> None:
self.config = config
# Assign ticker_interval to be used in hyperopt
IHyperOpt.ticker_interval = str(config['ticker_interval'])
IHyperOpt.ticker_interval = str(config['timeframe']) # DEPRECATED
IHyperOpt.timeframe = str(config['timeframe'])
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
@ -218,9 +220,10 @@ class IHyperOpt(ABC):
# Why do I still need such shamanic mantras in modern python?
def __getstate__(self):
state = self.__dict__.copy()
state['ticker_interval'] = self.ticker_interval
state['timeframe'] = self.timeframe
return state
def __setstate__(self, state):
self.__dict__.update(state)
IHyperOpt.ticker_interval = state['ticker_interval']
IHyperOpt.ticker_interval = state['timeframe']
IHyperOpt.timeframe = state['timeframe']

2
freqtrade/optimize/hyperopt_loss_interface.py
View File

@ -14,7 +14,7 @@ class IHyperOptLoss(ABC):
Interface for freqtrade hyperopt Loss functions.
Defines the custom loss function (`hyperopt_loss_function()` which is evaluated every epoch.)
"""
ticker_interval: str
timeframe: str
@staticmethod
@abstractmethod

2
freqtrade/optimize/hyperopt_loss_onlyprofit.py
View File

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

2
freqtrade/optimize/hyperopt_loss_sharpe.py
View File

@ -36,7 +36,7 @@ class SharpeHyperOptLoss(IHyperOptLoss):
expected_returns_mean = total_profit.sum() / days_period
up_stdev = np.std(total_profit)
if (np.std(total_profit) != 0.):
if up_stdev != 0:
sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.

2
freqtrade/optimize/hyperopt_loss_sharpe_daily.py
View File

@ -51,7 +51,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
expected_returns_mean = total_profit.mean()
up_stdev = total_profit.std()
if (up_stdev != 0.):
if up_stdev != 0:
sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.

2
freqtrade/optimize/hyperopt_loss_sortino.py
View File

@ -39,7 +39,7 @@ class SortinoHyperOptLoss(IHyperOptLoss):
results.loc[total_profit < 0, 'downside_returns'] = results['profit_percent']
down_stdev = np.std(results['downside_returns'])
if np.std(total_profit) != 0.0:
if down_stdev != 0:
sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.

2
freqtrade/optimize/hyperopt_loss_sortino_daily.py
View File

@ -59,7 +59,7 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
# where P = sum_daily["profit_percent_after_slippage"]
down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))
if (down_stdev != 0.):
if down_stdev != 0:
sortino_ratio = expected_returns_mean / down_stdev * math.sqrt(days_in_year)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.

377
freqtrade/optimize/optimize_reports.py
View File

@ -1,157 +1,171 @@
import logging
from datetime import timedelta
from typing import Dict
from pathlib import Path
from typing import Any, Dict, List
from pandas import DataFrame
from tabulate import tabulate
from freqtrade.misc import file_dump_json
def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
results: DataFrame, skip_nan: bool = False) -> str:
logger = logging.getLogger(__name__)
def store_backtest_result(recordfilename: Path, all_results: Dict[str, DataFrame]) -> None:
"""
Generates and returns a text table for the given backtest data and the results dataframe
Stores backtest results to file (one file per strategy)
:param recordfilename: Destination filename
:param all_results: Dict of Dataframes, one results dataframe per strategy
"""
for strategy, results in all_results.items():
records = backtest_result_to_list(results)
if records:
filename = recordfilename
if len(all_results) > 1:
# Inject strategy to filename
filename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{strategy}').with_suffix(recordfilename.suffix)
logger.info(f'Dumping backtest results to {filename}')
file_dump_json(filename, records)
def backtest_result_to_list(results: DataFrame) -> List[List]:
"""
Converts a list of Backtest-results to list
:param results: Dataframe containing results for one strategy
:return: List of Lists containing the trades
"""
return [[t.pair, t.profit_percent, t.open_time.timestamp(),
t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value]
for index, t in results.iterrows()]
def _get_line_floatfmt() -> List[str]:
"""
Generate floatformat (goes in line with _generate_result_line())
"""
return ['s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', 'd', 'd', 'd']
def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
"""
Generate header lines (goes in line with _generate_result_line())
"""
return [first_column, 'Buys', 'Avg Profit %', 'Cum Profit %',
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
'Wins', 'Draws', 'Losses']
def _generate_result_line(result: DataFrame, max_open_trades: int, first_column: str) -> Dict:
"""
Generate one result dict, with "first_column" as key.
"""
return {
'key': first_column,
'trades': len(result),
'profit_mean': result['profit_percent'].mean(),
'profit_mean_pct': result['profit_percent'].mean() * 100.0,
'profit_sum': result['profit_percent'].sum(),
'profit_sum_pct': result['profit_percent'].sum() * 100.0,
'profit_total_abs': result['profit_abs'].sum(),
'profit_total_pct': result['profit_percent'].sum() * 100.0 / max_open_trades,
'duration_avg': str(timedelta(
minutes=round(result['trade_duration'].mean()))
) if not result.empty else '0:00',
# 'duration_max': str(timedelta(
# minutes=round(result['trade_duration'].max()))
# ) if not result.empty else '0:00',
# 'duration_min': str(timedelta(
# minutes=round(result['trade_duration'].min()))
# ) if not result.empty else '0:00',
'wins': len(result[result['profit_abs'] > 0]),
'draws': len(result[result['profit_abs'] == 0]),
'losses': len(result[result['profit_abs'] < 0]),
}
def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
results: DataFrame, skip_nan: bool = False) -> List[Dict]:
"""
Generates and returns a list for the given backtest data and the results dataframe
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades
:param results: Dataframe containing the backtest results
:param skip_nan: Print "left open" open trades
:return: pretty printed table with tabulate as string
:return: List of Dicts containing the metrics per pair
"""
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = [
'Pair',
'Buys',
'Avg Profit %',
'Cum Profit %',
f'Tot Profit {stake_currency}',
'Tot Profit %',
'Avg Duration',
'Wins',
'Draws',
'Losses'
]
for pair in data:
result = results[results.pair == pair]
if skip_nan and result.profit_abs.isnull().all():
result = results[results['pair'] == pair]
if skip_nan and result['profit_abs'].isnull().all():
continue
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_percent.sum() * 100.0,
result.profit_abs.sum(),
result.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
len(result[result.profit_abs > 0]),
len(result[result.profit_abs == 0]),
len(result[result.profit_abs < 0])
])
tabular_data.append(_generate_result_line(result, max_open_trades, pair))
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs == 0]),
len(results[results.profit_abs < 0])
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
tabular_data.append(_generate_result_line(results, max_open_trades, 'TOTAL'))
return tabular_data
def generate_text_table_sell_reason(
data: Dict[str, Dict], stake_currency: str, max_open_trades: int, results: DataFrame
) -> str:
def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
"""
Generate small table outlining Backtest results
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
:param results: Dataframe containing the backtest results
:return: pretty printed table with tabulate as string
:param max_open_trades: Max_open_trades parameter
:param results: Dataframe containing the backtest result for one strategy
:return: List of Dicts containing the metrics per Sell reason
"""
tabular_data = []
headers = [
"Sell Reason",
"Sells",
"Wins",
"Draws",
"Losses",
"Avg Profit %",
"Cum Profit %",
f"Tot Profit {stake_currency}",
"Tot Profit %",
]
for reason, count in results['sell_reason'].value_counts().iteritems():
result = results.loc[results['sell_reason'] == reason]
wins = len(result[result['profit_abs'] > 0])
draws = len(result[result['profit_abs'] == 0])
loss = len(result[result['profit_abs'] < 0])
profit_mean = round(result['profit_percent'].mean() * 100.0, 2)
profit_sum = round(result["profit_percent"].sum() * 100.0, 2)
profit_tot = result['profit_abs'].sum()
profit_mean = result['profit_percent'].mean()
profit_sum = result["profit_percent"].sum()
profit_percent_tot = round(result['profit_percent'].sum() * 100.0 / max_open_trades, 2)
tabular_data.append(
[
reason.value,
count,
wins,
draws,
loss,
profit_mean,
profit_sum,
profit_tot,
profit_percent_tot,
]
{
'sell_reason': reason.value,
'trades': count,
'wins': len(result[result['profit_abs'] > 0]),
'draws': len(result[result['profit_abs'] == 0]),
'losses': len(result[result['profit_abs'] < 0]),
'profit_mean': profit_mean,
'profit_mean_pct': round(profit_mean * 100, 2),
'profit_sum': profit_sum,
'profit_sum_pct': round(profit_sum * 100, 2),
'profit_total_abs': result['profit_abs'].sum(),
'profit_pct_total': profit_percent_tot,
}
)
return tabulate(tabular_data, headers=headers, tablefmt="orgtbl", stralign="right")
return tabular_data
def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
all_results: Dict) -> str:
def generate_strategy_metrics(stake_currency: str, max_open_trades: int,
all_results: Dict) -> List[Dict]:
"""
Generate summary table per strategy
Generate summary per strategy
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades used for backtest
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:return: pretty printed table with tabulate as string
:return: List of Dicts containing the metrics per Strategy
"""
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['Strategy', 'Buys', 'Avg Profit %', 'Cum Profit %',
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
'Wins', 'Draws', 'Losses']
for strategy, results in all_results.items():
tabular_data.append([
strategy,
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs == 0]),
len(results[results.profit_abs < 0])
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
tabular_data.append(_generate_result_line(results, max_open_trades, strategy))
return tabular_data
def generate_edge_table(results: dict) -> str:
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', '.d')
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd')
tabular_data = []
headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio',
'Required Risk Reward', 'Expectancy', 'Total Number of Trades',
@ -173,3 +187,148 @@ def generate_edge_table(results: dict) -> str:
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
all_results: Dict[str, DataFrame]) -> Dict[str, Any]:
"""
:param config: Configuration object used for backtest
:param btdata: Backtest data
:param all_results: backtest result - dictionary with { Strategy: results}.
:return:
Dictionary containing results per strategy and a stratgy summary.
"""
stake_currency = config['stake_currency']
max_open_trades = config['max_open_trades']
result: Dict[str, Any] = {'strategy': {}}
for strategy, results in all_results.items():
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
results=results.loc[results['open_at_end']],
skip_nan=True)
strat_stats = {
'trades': backtest_result_to_list(results),
'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
}
result['strategy'][strategy] = strat_stats
strategy_results = generate_strategy_metrics(stake_currency=stake_currency,
max_open_trades=max_open_trades,
all_results=all_results)
result['strategy_comparison'] = strategy_results
return result
###
# Start output section
###
def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
:param stake_currency: stake-currency - used to correctly name headers
:return: pretty printed table with tabulate as string
"""
headers = _get_line_header('Pair', stake_currency)
floatfmt = _get_line_floatfmt()
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
] for t in pair_results]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str:
"""
Generate small table outlining Backtest results
:param sell_reason_stats: Sell reason metrics
:param stake_currency: Stakecurrency used
:return: pretty printed table with tabulate as string
"""
headers = [
'Sell Reason',
'Sells',
'Wins',
'Draws',
'Losses',
'Avg Profit %',
'Cum Profit %',
f'Tot Profit {stake_currency}',
'Tot Profit %',
]
output = [[
t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_pct_total'],
] for t in sell_reason_stats]
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
def text_table_strategy(strategy_results, stake_currency: str) -> str:
"""
Generate summary table per strategy
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades used for backtest
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:return: pretty printed table with tabulate as string
"""
floatfmt = _get_line_floatfmt()
headers = _get_line_header('Strategy', stake_currency)
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
] for t in strategy_results]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def show_backtest_results(config: Dict, backtest_stats: Dict):
stake_currency = config['stake_currency']
for strategy, results in backtest_stats['strategy'].items():
# Print results
print(f"Result for strategy {strategy}")
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
stake_currency=stake_currency)
if isinstance(table, str):
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
if isinstance(table, str):
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str):
print('=' * len(table.splitlines()[0]))
print()
if len(backtest_stats['strategy']) > 1:
# Print Strategy summary table
table = text_table_strategy(backtest_stats['strategy_comparison'], stake_currency)
print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
print(table)
print('=' * len(table.splitlines()[0]))
print('\nFor more details, please look at the detail tables above')

84
freqtrade/pairlist/AgeFilter.py Normal file
View File

@ -0,0 +1,84 @@
"""
Minimum age (days listed) pair list filter
"""
import logging
import arrow
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural
from freqtrade.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
class AgeFilter(IPairList):
# Checked symbols cache (dictionary of ticker symbol => timestamp)
_symbolsChecked: Dict[str, int] = {}
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._min_days_listed = pairlistconfig.get('min_days_listed', 10)
if self._min_days_listed < 1:
raise OperationalException("AgeFilter requires min_days_listed must be >= 1")
if self._min_days_listed > exchange.ohlcv_candle_limit:
raise OperationalException("AgeFilter requires min_days_listed must not exceed "
"exchange max request size "
f"({exchange.ohlcv_candle_limit})")
self._enabled = self._min_days_listed >= 1
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requires tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return True
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
"""
return (f"{self.name} - Filtering pairs with age less than "
f"{self._min_days_listed} {plural(self._min_days_listed, 'day')}.")
def _validate_pair(self, ticker: dict) -> bool:
"""
Validate age for the ticker
:param ticker: ticker dict as returned from ccxt.load_markets()
:return: True if the pair can stay, False if it should be removed
"""
# Check symbol in cache
if ticker['symbol'] in self._symbolsChecked:
return True
since_ms = int(arrow.utcnow()
.floor('day')
.shift(days=-self._min_days_listed)
.float_timestamp) * 1000
daily_candles = self._exchange.get_historic_ohlcv(pair=ticker['symbol'],
timeframe='1d',
since_ms=since_ms)
if daily_candles is not None:
if len(daily_candles) > self._min_days_listed:
# We have fetched at least the minimum required number of daily candles
# Add to cache, store the time we last checked this symbol
self._symbolsChecked[ticker['symbol']] = int(arrow.utcnow().float_timestamp) * 1000
return True
else:
self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
f"because age {len(daily_candles)} is less than "
f"{self._min_days_listed} "
f"{plural(self._min_days_listed, 'day')}")
return False
return False

107
freqtrade/pairlist/IPairList.py
View File

@ -1,16 +1,17 @@
"""
Static List provider
Provides lists as configured in config.json
"""
PairList Handler base class
"""
import logging
from abc import ABC, abstractmethod, abstractproperty
from copy import deepcopy
from typing import Any, Dict, List
from cachetools import TTLCache, cached
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import market_is_active
logger = logging.getLogger(__name__)
@ -21,16 +22,21 @@ class IPairList(ABC):
pairlist_pos: int) -> None:
"""
:param exchange: Exchange instance
:param pairlistmanager: Instanciating Pairlist manager
:param pairlistmanager: Instantiated Pairlist manager
:param config: Global bot configuration
:param pairlistconfig: Configuration for this pairlist - can be empty.
:param pairlist_pos: Position of the filter in the pairlist-filter-list
:param pairlistconfig: Configuration for this Pairlist Handler - can be empty.
:param pairlist_pos: Position of the Pairlist Handler in the chain
"""
self._enabled = True
self._exchange = exchange
self._pairlistmanager = pairlistmanager
self._config = config
self._pairlistconfig = pairlistconfig
self._pairlist_pos = pairlist_pos
self.refresh_period = self._pairlistconfig.get('refresh_period', 1800)
self._last_refresh = 0
self._log_cache = TTLCache(maxsize=1024, ttl=self.refresh_period)
@property
def name(self) -> str:
@ -40,11 +46,29 @@ class IPairList(ABC):
"""
return self.__class__.__name__
def log_on_refresh(self, logmethod, message: str) -> None:
"""
Logs message - not more often than "refresh_period" to avoid log spamming
Logs the log-message as debug as well to simplify debugging.
:param logmethod: Function that'll be called. Most likely `logger.info`.
:param message: String containing the message to be sent to the function.
:return: None.
"""
@cached(cache=self._log_cache)
def _log_on_refresh(message: str):
logmethod(message)
# Log as debug first
logger.debug(message)
# Call hidden function.
_log_on_refresh(message)
@abstractproperty
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
If no Pairlist requires tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
@ -55,33 +79,68 @@ class IPairList(ABC):
-> Please overwrite in subclasses
"""
@abstractmethod
def _validate_pair(self, ticker) -> bool:
"""
Check one pair against Pairlist Handler's specific conditions.
Either implement it in the Pairlist Handler or override the generic
filter_pairlist() method.
:param ticker: ticker dict as returned from ccxt.load_markets()
:return: True if the pair can stay, false if it should be removed
"""
raise NotImplementedError()
def gen_pairlist(self, cached_pairlist: List[str], tickers: Dict) -> List[str]:
"""
Generate the pairlist.
This method is called once by the pairlistmanager in the refresh_pairlist()
method to supply the starting pairlist for the chain of the Pairlist Handlers.
Pairlist Filters (those Pairlist Handlers that cannot be used at the first
position in the chain) shall not override this base implementation --
it will raise the exception if a Pairlist Handler is used at the first
position in the chain.
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:return: List of pairs
"""
raise OperationalException("This Pairlist Handler should not be used "
"at the first position in the list of Pairlist Handlers.")
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
-> Please overwrite in subclasses
This generic implementation calls self._validate_pair() for each pair
in the pairlist.
Some Pairlist Handlers override this generic implementation and employ
own filtration.
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
if self._enabled:
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
# Filter out assets
if not self._validate_pair(tickers[p]):
pairlist.remove(p)
@staticmethod
def verify_blacklist(pairlist: List[str], blacklist: List[str]) -> List[str]:
"""
Verify and remove items from pairlist - returning a filtered pairlist.
"""
for pair in deepcopy(pairlist):
if pair in blacklist:
logger.warning(f"Pair {pair} in your blacklist. Removing it from whitelist...")
pairlist.remove(pair)
return pairlist
def _verify_blacklist(self, pairlist: List[str]) -> List[str]:
def verify_blacklist(self, pairlist: List[str], logmethod) -> List[str]:
"""
Proxy method to verify_blacklist for easy access for child classes.
:param pairlist: Pairlist to validate
:param logmethod: Function that'll be called, `logger.info` or `logger.warning`.
:return: pairlist - blacklisted pairs
"""
return IPairList.verify_blacklist(pairlist, self._pairlistmanager.blacklist)
return self._pairlistmanager.verify_blacklist(pairlist, logmethod)
def _whitelist_for_active_markets(self, pairlist: List[str]) -> List[str]:
"""
@ -91,6 +150,9 @@ class IPairList(ABC):
black_listed
"""
markets = self._exchange.markets
if not markets:
raise OperationalException(
'Markets not loaded. Make sure that exchange is initialized correctly.')
sanitized_whitelist: List[str] = []
for pair in pairlist:
@ -113,6 +175,5 @@ class IPairList(ABC):
if pair not in sanitized_whitelist:
sanitized_whitelist.append(pair)
sanitized_whitelist = self._verify_blacklist(sanitized_whitelist)
# We need to remove pairs that are unknown
return sanitized_whitelist

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