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Merge branch 'develop' into pr/yazeed/3055

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Matthias 2020-08-24 07:21:48 +02:00
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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"

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.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
```

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.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
```

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.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

73
.github/workflows/ci.yml vendored
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@ -88,7 +88,7 @@ jobs:
run: |
cp config.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --print-all
- name: Flake8
run: |
@ -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 }}*'
@ -150,7 +150,7 @@ jobs:
run: |
cp config.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --print-all
- name: Flake8
run: |
@ -162,7 +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*'
@ -189,6 +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
@ -226,25 +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

4
Dockerfile
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@ -1,7 +1,7 @@
FROM python:3.8.3-slim-buster
FROM python:3.8.5-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 libffi-dev libatlas3-base libgfortran5 sqlite3 \
&& apt-get clean \
&& pip install --upgrade pip \
&& echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > /etc/pip.conf
# 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" ]

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

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

12
config.json.example
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@ -4,7 +4,7 @@
"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,
@ -76,6 +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": {

12
config_binance.json.example
View File

@ -4,7 +4,7 @@
"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,
@ -81,6 +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": {

8
config_full.json.example
View File

@ -9,7 +9,7 @@
"last_stake_amount_min_ratio": 0.5,
"dry_run": false,
"cancel_open_orders_on_exit": false,
"ticker_interval": "5m",
"timeframe": "5m",
"trailing_stop": false,
"trailing_stop_positive": 0.005,
"trailing_stop_positive_offset": 0.0051,
@ -64,8 +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": {
@ -121,7 +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"
},
@ -132,6 +135,7 @@
"process_throttle_secs": 5,
"heartbeat_interval": 60
},
"disable_dataframe_checks": false,
"strategy": "DefaultStrategy",
"strategy_path": "user_data/strategies/",
"dataformat_ohlcv": "json",

12
config_kraken.json.example
View File

@ -4,7 +4,7 @@
"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,
@ -87,6 +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": {

4
docs/advanced-hyperopt.md
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)

48
docs/advanced-setup.md
View File

@ -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.

93
docs/backtesting.md
View File

@ -12,7 +12,7 @@ real data. This is what we call
[backtesting](https://en.wikipedia.org/wiki/Backtesting).
Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
If no data is available for the exchange / pair / timeframe (ticker interval) combination, backtesting will ask you to download them first using `freqtrade download-data`.
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.
@ -35,7 +35,7 @@ freqtrade backtesting
#### With 1 min candle (OHLCV) data
```bash
freqtrade backtesting --ticker-interval 1m
freqtrade backtesting --timeframe 1m
```
#### Using a different on-disk historical candle (OHLCV) data source
@ -58,7 +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.
@ -66,7 +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.
@ -157,17 +157,32 @@ A backtesting result will look like that:
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 | 0 | 0 |
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 | 0 | 0 |
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 | 0 | 0 |
=============== SUMMARY METRICS ===============
| Metric | Value |
|-----------------------+---------------------|
| Backtesting from | 2019-01-01 00:00:00 |
| Backtesting to | 2019-05-01 00:00:00 |
| Total trades | 429 |
| First trade | 2019-01-01 18:30:00 |
| First trade Pair | EOS/USDT |
| Total Profit % | 152.41% |
| Trades per day | 3.575 |
| Best day | 25.27% |
| Worst day | -30.67% |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| | |
| Max Drawdown | 50.63% |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
===============================================
```
### Backtesting report table
The 1st table contains all trades the bot made, including "left open trades".
The 2nd table contains a recap of sell reasons.
This table can tell you which area needs some additional work (i.e. all `sell_signal` trades are losses, so we should disable the sell-signal or work on improving that).
The 3rd table contains all trades the bot had to `forcesell` at the end of the backtest period to present a full picture.
This is necessary to simulate realistic behaviour, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
These trades are also included in the first table, but are extracted separately for clarity.
The last line will give you the overall performance of your strategy,
here:
@ -196,6 +211,58 @@ On the other hand, if you set a too high `minimal_roi` like `"0": 0.55`
(55%), there is almost no chance that the bot will ever reach this profit.
Hence, keep in mind that your performance is an integral mix of all different elements of the strategy, your configuration, and the crypto-currency pairs you have set up.
### Sell reasons table
The 2nd table contains a recap of sell reasons.
This table can tell you which area needs some additional work (e.g. all or many of the `sell_signal` trades are losses, so you should work on improving the sell signal, or consider disabling it).
### Left open trades table
The 3rd table contains all trades the bot had to `forcesell` at the end of the backtesting period to present you the full picture.
This is necessary to simulate realistic behavior, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
These trades are also included in the first table, but are also shown separately in this table for clarity.
### Summary metrics
The last element of the backtest report is the summary metrics table.
It contains some useful key metrics about performance of your strategy on backtesting data.
```
=============== SUMMARY METRICS ===============
| Metric | Value |
|-----------------------+---------------------|
| Backtesting from | 2019-01-01 00:00:00 |
| Backtesting to | 2019-05-01 00:00:00 |
| Total trades | 429 |
| First trade | 2019-01-01 18:30:00 |
| First trade Pair | EOS/USDT |
| Total Profit % | 152.41% |
| Trades per day | 3.575 |
| Best day | 25.27% |
| Worst day | -30.67% |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| | |
| Max Drawdown | 50.63% |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
===============================================
```
- `Total trades`: Identical to the total trades of the backtest output table.
- `First trade`: First trade entered.
- `First trade pair`: Which pair was part of the first trade.
- `Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
- `Total Profit %`: Total profit per stake amount. Aligned to the TOTAL column of the first table.
- `Trades per day`: Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
- `Best day` / `Worst day`: Best and worst day based on daily profit.
- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
- `Max Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
- `Drawdown Start` / `Drawdown End`: Start and end datetimes for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
- `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
### Assumptions made by backtesting
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
@ -228,13 +295,13 @@ 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 timeframe (ticker interval) value 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
docs/bot-basics.md Normal file
View File

@ -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.

50
docs/bot-usage.md
View File

@ -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.
.
```
@ -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

109
docs/configuration.md
View File

@ -47,17 +47,17 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> **Datatype:** Positive Float as ratio.
| `ticker_interval` | The timeframe (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)
| `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
| `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#trailing-stop-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Boolean
| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-custom-positive-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `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
@ -83,7 +83,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#pairlists-and-pairlist-handlers)). <br> **Datatype:** List
| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#pairlists-and-pairlist-handlers)). <br> **Datatype:** List
| `exchange.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.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.
@ -102,11 +103,13 @@ 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 Integer
@ -123,7 +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`
@ -135,6 +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)
@ -214,7 +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
@ -268,26 +272,19 @@ 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.
`order_types` can be set in the configuration file or in the strategy.
`order_types` set in the configuration file overwrites values set in the strategy as a whole, so you need to configure the whole `order_types` dictionary in one place.
If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
`stoploss_on_exchange`) need to be present, otherwise the bot will fail to start.
`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`).
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$.
For information on (`emergencysell`,`stoploss_on_exchange`,`stoploss_on_exchange_interval`,`stoploss_on_exchange_limit_ratio`) please see stop loss documentation [stop loss on exchange](stoploss.md)
Syntax for Strategy:
@ -327,7 +324,10 @@ 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.
@ -455,6 +455,9 @@ 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
@ -549,13 +552,36 @@ A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting
When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
### Market order pricing
When using market orders, prices should be configured to use the "correct" side of the orderbook to allow realistic pricing detection.
Assuming both buy and sell are using market orders, a configuration similar to the following might be used
``` jsonc
"order_types": {
"buy": "market",
"sell": "market"
// ...
},
"bid_strategy": {
"price_side": "ask",
// ...
},
"ask_strategy":{
"price_side": "bid",
// ...
},
```
Obviously, if only one side is using limit orders, different pricing combinations can be used.
## Pairlists and Pairlist Handlers
Pairlist Handlers define the list of pairs (pairlist) that the bot should trade. They are configured in the `pairlists` section of the configuration settings.
In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list) Pairlist Handler).
Additionaly, [`PrecisionFilter`](#precisionfilter), [`PriceFilter`](#pricefilter), [`ShuffleFilter`](#shufflefilter) and [`SpreadFilter`](#spreadfilter) act as Pairlist Filters, removing certain pairs and/or moving their positions in the pairlist.
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.
@ -565,6 +591,7 @@ Inactive markets are always removed from the resulting pairlist. Explicitly blac
* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
* [`VolumePairList`](#volume-pair-list)
* [`AgeFilter`](#agefilter)
* [`PrecisionFilter`](#precisionfilter)
* [`PriceFilter`](#pricefilter)
* [`ShuffleFilter`](#shufflefilter)
@ -587,7 +614,7 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
#### Volume Pair List
`VolumePairList` employs sorting/filtering of pairs by their trading volume. I selects `number_assets` top pairs with sorting based on the `sort_key` (which can only be `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`).
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.
@ -605,25 +632,48 @@ The `refresh_period` setting allows to define the period (in seconds), at which
"number_assets": 20,
"sort_key": "quoteVolume",
"refresh_period": 1800,
],
}],
```
#### AgeFilter
Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`).
When pairs are first listed on an exchange they can suffer huge price drops and volatility
in the first few days while the pair goes through its price-discovery period. Bots can often
be caught out buying before the pair has finished dropping in price.
This filter allows freqtrade to ignore pairs until they have been listed for at least `min_days_listed` days.
#### PrecisionFilter
Filters low-value coins which would not allow setting stoplosses.
#### PriceFilter
The `PriceFilter` allows filtering of pairs by price.
The `PriceFilter` allows filtering of pairs by price. Currently the following price filters are supported:
Currently, only `low_price_ratio` setting is implemented, where a raise of 1 price unit (pip) is below the `low_price_ratio` ratio.
This option is disabled by default, and will only apply if set to <> 0.
* `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.
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.
For `PriceFiler` at least one of its `min_price`, `max_price` or `low_price_ratio` settings must be applied.
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.
Min price precision for SHITCOIN/BTC is 8 decimals. If its price is 0.00000011 - one price step above would be 0.00000012, which is ~9% higher than the previous price value. You may filter out this pair by using PriceFilter with `low_price_ratio` set to 0.09 (9%) or with `min_price` set to 0.00000011, correspondingly.
These pairs are dangerous since it may be impossible to place the desired stoploss - and often result in high losses. Here is what the PriceFilters takes over.
!!! Warning "Low priced pairs"
Low priced pairs with high "1 pip movements" are dangerous since they are often illiquid and it may also be impossible to place the desired stoploss, which can often result in high losses since price needs to be rounded to the next tradable price - so instead of having a stoploss of -5%, you could end up with a stoploss of -9% simply due to price rounding.
#### ShuffleFilter
@ -655,6 +705,7 @@ 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": "SpreadFilter", "max_spread_ratio": 0.005},

56
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@ -109,7 +109,7 @@ The following command will convert all candle (OHLCV) data available in `~/.freq
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

72
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@ -85,6 +85,35 @@ docker-compose exec freqtrade_develop /bin/bash
![image](https://user-images.githubusercontent.com/419355/65456522-ba671a80-de06-11e9-9598-df9ca0d8dcac.png)
## ErrorHandling
Freqtrade Exceptions all inherit from `FreqtradeException`.
This general class of error should however not be used directly. Instead, multiple specialized sub-Exceptions exist.
Below is an outline of exception inheritance hierarchy:
```
+ FreqtradeException
|
+---+ OperationalException
|
+---+ DependencyException
| |
| +---+ PricingError
| |
| +---+ ExchangeError
| |
| +---+ TemporaryError
| |
| +---+ DDosProtection
| |
| +---+ InvalidOrderException
| |
| +---+ RetryableOrderError
|
+---+ StrategyError
```
## Modules
### Dynamic Pairlist
@ -92,13 +121,13 @@ 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 +143,44 @@ 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 +190,6 @@ 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

14
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@ -6,7 +6,8 @@ This page explains how to use Edge Positioning module in your bot in order to en
Edge positioning is not compatible with dynamic (volume-based) whitelist.
!!! Note
Edge does not consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else are ignored in its calculation.
Edge does not consider anything other than *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
Therefore, it is important to understand that Edge can improve the performance of some trading strategies but *decrease* the performance of others.
## Introduction
@ -89,7 +90,7 @@ You can also use this value to evaluate the effectiveness of modifications to th
## How does it work?
If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over *N* trades for each stoploss. Here is an example:
Edge combines dynamic stoploss, dynamic positions, and whitelist generation into one isolated module which is then applied to the trading strategy. If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over *N* trades for each stoploss. Here is an example:
| Pair | Stoploss | Win Rate | Risk Reward Ratio | Expectancy |
|----------|:-------------:|-------------:|------------------:|-----------:|
@ -148,7 +149,6 @@ 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 +156,7 @@ 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 timeframe (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
@ -187,6 +187,12 @@ An example of its output:
| APPC/BTC | -0.02 | 0.44 | 2.28 | 1.27 | 0.44 | 25 | 43 |
| NEBL/BTC | -0.03 | 0.63 | 1.29 | 0.58 | 0.44 | 19 | 59 |
Edge produced the above table by comparing `calculate_since_number_of_days` to `minimum_expectancy` to find `min_trade_number` historical information based on the config file. The timerange Edge uses for its comparisons can be further limited by using the `--timerange` switch.
In live and dry-run modes, after the `process_throttle_secs` has passed, Edge will again process `calculate_since_number_of_days` against `minimum_expectancy` to find `min_trade_number`. If no `min_trade_number` is found, the bot will return "whitelist empty". Depending on the trade strategy being deployed, "whitelist empty" may be return much of the time - or *all* of the time. The use of Edge may also cause trading to occur in bursts, though this is rare.
If you encounter "whitelist empty" a lot, condsider tuning `calculate_since_number_of_days`, `minimum_expectancy` and `min_trade_number` to align to the trading frequency of your strategy.
### Update cached pairs with the latest data
Edge requires historic data the same way as backtesting does.

33
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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.

47
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@ -1,5 +1,9 @@
# Freqtrade FAQ
## Beginner Tips & Tricks
* When you work with your strategy & hyperopt file you should use a proper code editor like vscode or Pycharm. A good code editor will provide syntax highlighting as well as line numbers, making it easy to find syntax errors (most likely, pointed out by Freqtrade during startup).
## Freqtrade common issues
### The bot does not start
@ -15,10 +19,12 @@ This could have the following reasons:
### I have waited 5 minutes, why hasn't the bot made any trades yet?!
Depending on the buy strategy, the amount of whitelisted coins, the
* Depending on the buy strategy, the amount of whitelisted coins, the
situation of the market etc, it can take up to hours to find good entry
position for a trade. Be patient!
* Or it may because of a configuration error? Best check the logs, it's usually telling you if the bot is simply not getting buy signals (only heartbeat messages), or if there is something wrong (errors / exceptions in the log).
### I have made 12 trades already, why is my total profit negative?!
I understand your disappointment but unfortunately 12 trades is just
@ -45,6 +51,20 @@ 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.
@ -115,25 +135,27 @@ to find a great result (unless if you are very lucky), so you probably
have to run it for 10.000 or more. But it will take an eternity to
compute.
We recommend you to run it at least 10.000 epochs:
Since hyperopt uses Bayesian search, running for too many epochs may not produce greater results.
It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10.000 epocs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
```bash
freqtrade hyperopt -e 10000
freqtrade hyperopt -e 1000
```
or if you want intermediate result to see
```bash
for i in {1..100}; do freqtrade hyperopt -e 100; done
for i in {1..100}; do freqtrade hyperopt -e 1000; done
```
### Why it is so long to run hyperopt?
### Why does it take a long time to run hyperopt?
Finding a great Hyperopt results takes time.
* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) - or the Freqtrade [discord community](https://discord.gg/X89cVG). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
If you wonder why it takes a while to find great hyperopt results
* If you wonder why it can take from 20 minutes to days to do 1000 epocs here are some answers:
This answer was written during the under the release 0.15.1, when we had:
This answer was written during the release 0.15.1, when we had:
- 8 triggers
- 9 guards: let's say we evaluate even 10 values from each
@ -143,7 +165,14 @@ The following calculation is still very rough and not very precise
but it will give the idea. With only these triggers and guards there is
already 8\*10^9\*10 evaluations. A roughly total of 80 billion evals.
Did you run 100 000 evals? Congrats, you've done roughly 1 / 100 000 th
of the search space.
of the search space, assuming that the bot never tests the same parameters more than once.
* The time it takes to run 1000 hyperopt epocs depends on things like: The available cpu, harddisk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 10.0000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades.
Example: 4% profit 650 times vs 0,3% profit a trade 10.000 times in a year. If we assume you set the --timerange to 365 days.
Example:
`freqtrade --config config.json --strategy SampleStrategy --hyperopt SampleHyperopt -e 1000 --timerange 20190601-20200601`
## Edge module

22
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View File

@ -124,9 +124,9 @@ To avoid naming collisions in the search-space, please prefix all sell-spaces wi
#### Using timeframe as a part of the Strategy
The Strategy class exposes the timeframe (ticker interval) value as the `self.ticker_interval` attribute.
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
@ -265,7 +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
@ -370,6 +370,9 @@ By default, hyperopt prints colorized results -- epochs with positive profit are
You can use the `--print-all` command line option if you would like to see all results in the hyperopt output, not only the best ones. When `--print-all` is used, current best results are also colorized by default -- they are printed in bold (bright) style. This can also be switched off with the `--no-color` command line option.
!!! Note "Windows and color output"
Windows does not support color-output nativly, therefore it is automatically disabled. To have color-output for hyperopt running under windows, please consider using WSL.
### Understand Hyperopt ROI results
If you are optimizing ROI (i.e. if optimization search-space contains 'all', 'default' or 'roi'), your result will look as follows and include a ROI table:
@ -403,7 +406,7 @@ 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 timeframes, 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 | |
| ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
@ -412,7 +415,7 @@ 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 timeframe (ticker interval) used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe 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.
@ -487,7 +490,7 @@ 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
@ -498,8 +501,3 @@ 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).

2
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.

25
docs/plotting.md
View File

@ -31,7 +31,7 @@ usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[--plot-limit INT] [--db-url PATH]
[--trade-source {DB,file}] [--export EXPORT]
[--export-filename PATH]
[--timerange TIMERANGE] [-i TICKER_INTERVAL]
[--timerange TIMERANGE] [-i TIMEFRAME]
[--no-trades]
optional arguments:
@ -65,7 +65,7 @@ optional arguments:
_today.json`
--timerange TIMERANGE
Specify what timerange of data to use.
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
-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.
@ -224,10 +224,11 @@ Possible options for the `freqtrade plot-profit` subcommand:
```
usage: freqtrade plot-profit [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
[-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-p PAIRS [PAIRS ...]]
[--timerange TIMERANGE] [--export EXPORT]
[--export-filename PATH] [--db-url PATH]
[--trade-source {DB,file}] [-i TICKER_INTERVAL]
[--trade-source {DB,file}] [-i TIMEFRAME]
optional arguments:
-h, --help show this help message and exit
@ -250,7 +251,7 @@ 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`).
@ -261,14 +262,20 @@ Common arguments:
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
Strategy arguments:
-s NAME, --strategy NAME
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
The `-p/--pairs` argument, can be used to limit the pairs that are considered for this calculation.
@ -278,7 +285,7 @@ Examples:
Use custom backtest-export file
``` bash
freqtrade plot-profit -p LTC/BTC --export-filename user_data/backtest_results/backtest-result-Strategy005.json
freqtrade plot-profit -p LTC/BTC --export-filename user_data/backtest_results/backtest-result.json
```
Use custom database

2
docs/requirements-docs.txt
View File

@ -1,2 +1,2 @@
mkdocs-material==5.1.7
mkdocs-material==5.5.7
mdx_truly_sane_lists==1.2

74
docs/rest-api.md
View File

@ -11,7 +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!"
},
@ -44,7 +46,7 @@ secrets.token_hex()
### Configuration with docker
If you run your bot using docker, you'll need to have the bot listen to incomming connections. The security is then handled by docker.
If you run your bot using docker, you'll need to have the bot listen to incoming connections. The security is then handled by docker.
``` json
"api_server": {
@ -104,26 +106,29 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
## Available commands
| Command | Default | Description |
|----------|---------|-------------|
| `start` | | Starts the trader
| `stop` | | Stops the trader
| `stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `reload_conf` | | Reloads the configuration file
| `show_config` | | Shows part of the current configuration with relevant settings to operation
| `status` | | Lists all open trades
| `count` | | Displays number of trades used and available
| `profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
| `forcesell <trade_id>` | | Instantly sells the given trade (Ignoring `minimum_roi`).
| `forcesell all` | | Instantly sells all open trades (Ignoring `minimum_roi`).
| `forcebuy <pair> [rate]` | | Instantly buys the given pair. Rate is optional. (`forcebuy_enable` must be set to True)
| `performance` | | Show performance of each finished trade grouped by pair
| `balance` | | Show account balance per currency
| `daily <n>` | 7 | Shows profit or loss per day, over the last n days
| `whitelist` | | Show the current whitelist
| `blacklist [pair]` | | Show the current blacklist, or adds a pair to the blacklist.
| `edge` | | Show validated pairs by Edge if it is enabled.
| `version` | | Show version
| Command | Description |
|----------|-------------|
| `ping` | Simple command testing the API Readiness - requires no authentication.
| `start` | Starts the trader
| `stop` | Stops the trader
| `stopbuy` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `reload_config` | Reloads the configuration file
| `trades` | List last trades.
| `delete_trade <trade_id>` | Remove trade from the database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `show_config` | Shows part of the current configuration with relevant settings to operation
| `status` | Lists all open trades
| `count` | Displays number of trades used and available
| `profit` | Display a summary of your profit/loss from close trades and some stats about your performance
| `forcesell <trade_id>` | Instantly sells the given trade (Ignoring `minimum_roi`).
| `forcesell all` | Instantly sells all open trades (Ignoring `minimum_roi`).
| `forcebuy <pair> [rate]` | Instantly buys the given pair. Rate is optional. (`forcebuy_enable` must be set to True)
| `performance` | Show performance of each finished trade grouped by pair
| `balance` | Show account balance per currency
| `daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
| `whitelist` | Show the current whitelist
| `blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
| `edge` | Show validated pairs by Edge if it is enabled.
| `version` | Show version
Possible commands can be listed from the rest-client script using the `help` command.
@ -173,7 +178,7 @@ profit
Returns the profit summary
:returns: json object
reload_conf
reload_config
Reload configuration
:returns: json object
@ -195,7 +200,7 @@ stop
stopbuy
Stop buying (but handle sells gracefully).
use reload_conf to reset
use reload_config to reset
:returns: json object
version
@ -231,3 +236,26 @@ Since the access token has a short timeout (15 min) - the `token/refresh` reques
> 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.

42
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@ -13,6 +13,15 @@ Feel free to use a visual Database editor like SqliteBrowser if you feel more co
sudo apt-get install sqlite3
```
### Using sqlite3 via docker-compose
The freqtrade docker image does contain sqlite3, so you can edit the database without having to install anything on the host system.
``` bash
docker-compose exec freqtrade /bin/bash
sqlite3 <databasefile>.sqlite
```
## Open the DB
```bash
@ -70,7 +79,7 @@ CREATE TABLE trades
min_rate FLOAT,
sell_reason VARCHAR,
strategy VARCHAR,
ticker_interval INTEGER,
timeframe INTEGER,
PRIMARY KEY (id),
CHECK (is_open IN (0, 1))
);
@ -100,8 +109,8 @@ UPDATE trades
SET is_open=0,
close_date=<close_date>,
close_rate=<close_rate>,
close_profit=close_rate/open_rate-1,
close_profit_abs = (amount * <close_rate> * (1 - fee_close) - (amount * open_rate * 1 - fee_open),
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>;
```
@ -111,24 +120,39 @@ WHERE id=<trade_ID_to_update>;
```sql
UPDATE trades
SET is_open=0,
close_date='2017-12-20 03:08:45.103418',
close_date='2020-06-20 03:08:45.103418',
close_rate=0.19638016,
close_profit=0.0496,
close_profit_abs = (amount * 0.19638016 * (1 - fee_close) - (amount * open_rate * 1 - fee_open)
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')
```
## Remove trade from the database
Maybe you'd like to remove a trade from the database, because something went wrong.
```sql
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.

157
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@ -1,12 +1,68 @@
# 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.
!!! Info
All stoploss properties mentioned in this file can be set in the Strategy, or in the configuration. Configuration values will override the strategy values.
All stoploss properties mentioned in this file can be set in the Strategy, or in the configuration.
<ins>Configuration values will override the strategy values.</ins>
## Stop Loss On-Exchange/Freqtrade
Those stoploss modes can be *on exchange* or *off exchange*.
These modes can be configured with these values:
``` python
'emergencysell': 'market',
'stoploss_on_exchange': False
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_limit_ratio': 0.99
```
!!! Note
Stoploss on exchange is only supported for Binance (stop-loss-limit), Kraken (stop-loss-market) and FTX (stop limit and stop-market) as of now.
<ins>Do not set too low stoploss value if using stop loss on exchange!</ins>
If set to low/tight then you have greater risk of missing fill on the order and stoploss will not work
### stoploss_on_exchange and stoploss_on_exchange_limit_ratio
Enable or Disable stop loss on exchange.
If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfully. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
If `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.
`stoploss` defines the stop-price where the limit order is placed - and limit should be slightly below this.
If an exchange supports both limit and market stoploss orders, then the value of `stoploss` will be used to determine the stoploss type.
Calculation example: we bought the asset at 100$.
Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the limit order fill can happen between 95$ and 94.05$.
For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
### stoploss_on_exchange_interval
In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.
The bot cannot do these every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
### emergencysell
`emergencysell` is an optional value, which defaults to `market` and is used when creating stop loss on exchange orders fails.
The below is the default which is used if not changed in strategy or configuration file.
Example from strategy file:
``` python
order_types = {
'buy': 'limit',
'sell': 'limit',
'emergencysell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': True,
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_limit_ratio': 0.99
}
```
## Stop Loss Types
@ -17,29 +73,29 @@ At this stage the bot contains the following stoploss support modes:
3. Trailing stop loss, custom positive loss.
4. Trailing stop loss only once the trade has reached a certain offset.
Those stoploss modes can be *on exchange* or *off exchange*. If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfully. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.
For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
The bot cannot do this every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
!!! Note
Stoploss on exchange is only supported for Binance (stop-loss-limit) and Kraken (stop-loss-market) as of now.
## Static Stop Loss
### Static Stop Loss
This is very simple, you define a stop loss of x (as a ratio of price, i.e. x * 100% of price). This will try to sell the asset once the loss exceeds the defined loss.
## Trailing Stop Loss
Example of stop loss:
``` python
stoploss = -0.10
```
For example, simplified math:
* the bot buys an asset at a price of 100$
* the stop loss is defined at -10%
* the stop loss would get triggered once the asset drops below 90$
### Trailing Stop Loss
The initial value for this is `stoploss`, just as you would define your static Stop loss.
To enable trailing stoploss:
``` python
trailing_stop = True
stoploss = -0.10
trailing_stop = True
```
This will now activate an algorithm, which automatically moves the stop loss up every time the price of your asset increases.
@ -47,35 +103,43 @@ This will now activate an algorithm, which automatically moves the stop loss up
For example, simplified math:
* the bot buys an asset at a price of 100$
* the stop loss is defined at 2%
* the stop loss would get triggered once the asset dropps below 98$
* the stop loss is defined at -10%
* the stop loss would get triggered once the asset drops below 90$
* assuming the asset now increases to 102$
* the stop loss will now be 2% of 102$ or 99.96$
* now the asset drops in value to 101$, the stop loss will still be 99.96$ and would trigger at 99.96$.
* the stop loss will now be -10% of 102$ = 91.8$
* now the asset drops in value to 101$, the stop loss will still be 91.8$ and would trigger at 91.8$.
In summary: The stoploss will be adjusted to be always be 2% of the highest observed price.
In summary: The stoploss will be adjusted to be always be -10% of the highest observed price.
### Custom positive stoploss
### Trailing stop loss, custom positive loss
It is also possible to have a default stop loss, when you are in the red with your buy, but once your profit surpasses a certain percentage, the system will utilize a new stop loss, which can have a different value.
For example your default stop loss is 5%, but once you have 1.1% profit, it will be changed to be only a 1% stop loss, which trails the green candles until it goes below them.
It is also possible to have a default stop loss, when you are in the red with your buy (buy - fee), but once you hit positive result the system will utilize a new stop loss, which can have a different value.
For example, your default stop loss is -10%, but once you have more than 0% profit (example 0.1%) a different trailing stoploss will be used.
Both values require `trailing_stop` to be set to true.
!!! Note
If you want the stoploss to only be changed when you break even of making a profit (what most users want) please refer to next section with [offset enabled](#Trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset).
Both values require `trailing_stop` to be set to true and `trailing_stop_positive` with a value.
``` python
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.011
stoploss = -0.10
trailing_stop = True
trailing_stop_positive = 0.02
```
The 0.01 would translate to a 1% stop loss, once you hit 1.1% profit.
For example, simplified math:
* the bot buys an asset at a price of 100$
* the stop loss is defined at -10%
* the stop loss would get triggered once the asset drops below 90$
* assuming the asset now increases to 102$
* the stop loss will now be -2% of 102$ = 99.96$ (99.96$ stop loss will be locked in and will follow asset price increasements with -2%)
* now the asset drops in value to 101$, the stop loss will still be 99.96$ and would trigger at 99.96$
The 0.02 would translate to a -2% stop loss.
Before this, `stoploss` is used for the trailing stoploss.
Read the [next section](#trailing-only-once-offset-is-reached) to keep stoploss at 5% of the entry point.
!!! Tip
Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.
### Trailing only once offset is reached
### Trailing stop loss only once the trade has reached a certain offset
It is also possible to use a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
@ -84,24 +148,35 @@ 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:
Configuration (offset is buyprice + 3%):
``` python
stoploss = 0.05
stoploss = -0.10
trailing_stop = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.03
trailing_only_offset_is_reached = True
```
For example, simplified math:
* the bot buys an asset at a price of 100$
* the stop loss is defined at 5%
* the stop loss will remain at 95% until profit reaches +3%
* the stop loss is defined at -10%
* the stop loss would get triggered once the asset drops below 90$
* stoploss will remain at 90$ unless asset increases to or above our configured offset
* assuming the asset now increases to 103$ (where we have the offset configured)
* the stop loss will now be -2% of 103$ = 100.94$
* now the asset drops in value to 101$, the stop loss will still be 100.94$ and would trigger at 100.94$
!!! Tip
Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.
## 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).

133
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View File

@ -1,7 +1,12 @@
# Advanced Strategies
This page explains some advanced concepts available for strategies.
If you're just getting started, please be familiar with the methods described in the [Strategy Customization](strategy-customization.md) documentation first.
If you're just getting started, please be familiar with the methods described in the [Strategy Customization](strategy-customization.md) documentation and with the [Freqtrade basics](bot-basics.md) first.
[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
@ -89,3 +94,129 @@ class Awesomestrategy(IStrategy):
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
```
## Derived strategies
The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
``` python
class MyAwesomeStrategy(IStrategy):
...
stoploss = 0.13
trailing_stop = False
# All other attributes and methods are here as they
# should be in any custom strategy...
...
class MyAwesomeStrategy2(MyAwesomeStrategy):
# Override something
stoploss = 0.08
trailing_stop = True
```
Both attributes and methods may be overriden, altering behavior of the original strategy in a way you need.

229
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View File

@ -1,6 +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
@ -56,12 +58,12 @@ file as reference.**
!!! Note "Strategies and Backtesting"
To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware
that during backtesting the full time-interval is passed to the `populate_*()` methods at once.
that during backtesting the full time range is passed to the `populate_*()` methods at once.
It is therefore best to use vectorized operations (across the whole dataframe, not loops) and
avoid index referencing (`df.iloc[-1]`), but instead use `df.shift()` to get to the previous candle.
!!! Warning "Warning: Using future data"
Since backtesting passes the full time interval to the `populate_*()` methods, the strategy author
Since backtesting passes the full time range to the `populate_*()` methods, the strategy author
needs to take care to avoid having the strategy utilize data from the future.
Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
@ -139,10 +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.
@ -248,20 +250,20 @@ 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.
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 ...)
To use times based on candle duration (timeframe), the following snippet can be handy.
This will allow you to change the timeframe for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
``` python
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
}
```
@ -283,14 +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).
### Timeframe (ticker interval)
### Timeframe (formerly 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 well with one timeframe, but not with the others.
This setting is accessible within the strategy methods as the `self.ticker_interval` attribute.
This setting is accessible within the strategy methods as the `self.timeframe` attribute.
### Metadata dict
@ -326,15 +328,15 @@ class Awesomestrategy(IStrategy):
***
### Additional data (informative_pairs)
## Additional data (informative_pairs)
#### Get data for non-tradeable 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 below).
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.
The pairs need to be specified as tuples in the format `("pair", "timeframe")`, with pair as the first and timeframe as the second argument.
Sample:
@ -345,15 +347,17 @@ def informative_pairs(self):
]
```
A full sample can be found [in the DataProvider section](#complete-data-provider-sample).
!!! Warning
As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short.
All intervals and all pairs can be specified as long as they are available (and active) on the used exchange.
It is however better to use resampling to longer time-intervals when possible
All timeframes and all pairs can be specified as long as they are available (and active) on the used exchange.
It is however better to use resampling to longer timeframes whenever possible
to avoid hammering the exchange with too many requests and risk being blocked.
***
### Additional data (DataProvider)
## Additional data (DataProvider)
The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
@ -361,11 +365,16 @@ 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
!!! Warning "Hyperopt"
Dataprovider is available during hyperopt, however it can only be used in `populate_indicators()` within a strategy.
It is not available in `populate_buy()` and `populate_sell()` methods, nor in `populate_indicators()`, if this method located in the hyperopt file.
- [`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)
### Possible options for DataProvider
- [`available_pairs`](#available_pairs) - Property with tuples listing cached pairs with their timeframe (pair, timeframe).
- [`current_whitelist()`](#current_whitelist) - Returns a current list of whitelisted pairs. Useful for accessing dynamic whitelists (i.e. 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.
@ -373,9 +382,9 @@ Please always check the mode of operation to select the correct method to get da
- [`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:
### Example Usages
#### *available_pairs*
### *available_pairs*
``` python
if self.dp:
@ -383,27 +392,21 @@ if self.dp:
print(f"available {pair}, {timeframe}")
```
#### *current_whitelist()*
### *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.*
*Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to buy and sell.*
Due to the limited available data, it's very difficult to resample our `5m` candles into daily candles for use in a 14 day ATR. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
Due to the limited available data, it's very difficult to resample our `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
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...
ticker_interval = '5m'
# more strategy init stuff..
def informative_pairs(self):
# get access to all pairs available in whitelist.
@ -411,16 +414,9 @@ class SampleStrategy(IStrategy):
# 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)*
### *get_pair_dataframe(pair, timeframe)*
``` python
# fetch live / historical candle (OHLCV) data for the first informative pair
@ -431,14 +427,27 @@ if self.dp:
```
!!! Warning "Warning about backtesting"
Be carefull when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
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).
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)*
#### *orderbook(pair, maximum)*
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.timeframe)
```
!!! Note "No data available"
Returns an empty dataframe if the requested pair was not cached.
This should not happen when using whitelisted pairs.
### *orderbook(pair, maximum)*
``` python
if self.dp:
@ -449,10 +458,9 @@ if self.dp:
```
!!! Warning
The order book is not part of the historic data which means backtesting and hyperopt will not work if this
method is used.
The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used.
#### *ticker(pair)*
### *ticker(pair)*
``` python
if self.dp:
@ -469,8 +477,77 @@ if self.dp:
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.
!!! Warning "Warning about backtesting"
This method will always return up-to-date values - so usage during backtesting / hyperopt will lead to wrong results.
### Complete Data-provider sample
```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]
# Optionally Add additional "static" pairs
informative_pairs += [("ETH/USDT", "5m"),
("BTC/TUSD", "15m"),
]
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if not self.dp:
# Don't do anything if DataProvider is not available.
return dataframe
inf_tf = '1d'
# Get the informative pair
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
# Get the 14 day rsi
informative['rsi'] = ta.RSI(informative, timeperiod=14)
# Rename columns to be unique
informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
# Assuming inf_tf = '1d' - then the columns will now be:
# date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
# Combine the 2 dataframes
# all indicators on the informative sample MUST be calculated before this point
dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_{inf_tf}', how='left')
# FFill to have the 1d value available in every row throughout the day.
# Without this, comparisons would only work once per day.
dataframe = dataframe.ffill()
# Calculate rsi of the original dataframe (5m timeframe)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# Do other stuff
# ...
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
(dataframe['rsi_1d'] < 30) & # Ensure daily RSI is < 30
(dataframe['volume'] > 0) # Ensure this candle had volume (important for backtesting)
),
'buy'] = 1
```
***
### Additional data (Wallets)
## Additional data (Wallets)
The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
@ -486,14 +563,15 @@ if self.wallets:
total_eth = self.wallets.get_total('ETH')
```
#### Possible options for Wallets
### Possible options for Wallets
- `get_free(asset)` - currently available balance to trade
- `get_used(asset)` - currently tied up balance (open orders)
- `get_total(asset)` - total available balance - sum of the 2 above
***
### Additional data (Trades)
## Additional data (Trades)
A history of Trades can be retrieved in the strategy by querying the database.
@ -539,13 +617,13 @@ Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of
!!! Warning
Trade history is not available during backtesting or hyperopt.
### Prevent trades from happening for a specific pair
## Prevent trades from happening for a specific pair
Freqtrade locks pairs automatically for the current candle (until that candle is over) when a pair is sold, preventing an immediate re-buy of that pair.
Locked pairs will show the message `Pair <pair> is currently locked.`.
#### Locking pairs from within the strategy
### Locking pairs from within the strategy
Sometimes it may be desired to lock a pair after certain events happen (e.g. multiple losing trades in a row).
@ -557,12 +635,12 @@ 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.
##### Pair locking example
#### Pair locking example
``` python
from freqtrade.persistence import Trade
@ -584,7 +662,7 @@ if self.config['runmode'].value in ('live', 'dry_run'):
self.lock_pair(metadata['pair'], until=datetime.now(timezone.utc) + timedelta(hours=12))
```
### Print created dataframe
## Print created dataframe
To inspect the created dataframe, you can issue a print-statement in either `populate_buy_trend()` or `populate_sell_trend()`.
You may also want to print the pair so it's clear what data is currently shown.
@ -608,36 +686,7 @@ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
Printing more than a few rows is also possible (simply use `print(dataframe)` instead of `print(dataframe.tail())`), however not recommended, as that will be very verbose (~500 lines per pair every 5 seconds).
### Specify custom strategy location
If you want to use a strategy from a different directory you can pass `--strategy-path`
```bash
freqtrade trade --strategy AwesomeStrategy --strategy-path /some/directory
```
### Derived strategies
The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
``` python
class MyAwesomeStrategy(IStrategy):
...
stoploss = 0.13
trailing_stop = False
# All other attributes and methods are here as they
# should be in any custom strategy...
...
class MyAwesomeStrategy2(MyAwesomeStrategy):
# Override something
stoploss = 0.08
trailing_stop = True
```
Both attributes and methods may be overriden, altering behavior of the original strategy in a way you need.
### Common mistakes when developing strategies
## Common mistakes when developing strategies
Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.
This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods, since they all use data which is not available during dry/live runs, so these strategies will perform well during backtesting, but will fail / perform badly in real conditions.
@ -649,7 +698,7 @@ The following lists some common patterns which should be avoided to prevent frus
- don't use `dataframe['volume'].mean()`. This uses the full DataFrame for backtesting, including data from the future. Use `dataframe['volume'].rolling(<window>).mean()` instead
- don't use `.resample('1h')`. This uses the left border of the interval, so moves data from an hour to the start of the hour. Use `.resample('1h', label='right')` instead.
### Further strategy ideas
## Further strategy ideas
To get additional Ideas for strategies, head over to our [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
Feel free to use any of them as inspiration for your own strategies.

44
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
@ -85,10 +85,44 @@ Analyze a trades dataframe (also used below for plotting)
```python
from freqtrade.data.btanalysis import load_backtest_data
from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats
# Load backtest results
trades = load_backtest_data(config["user_data_dir"] / "backtest_results/backtest-result.json")
# if backtest_dir points to a directory, it'll automatically load the last backtest file.
backtest_dir = config["user_data_dir"] / "backtest_results"
# backtest_dir can also point to a specific file
# backtest_dir = config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"
```
```python
# You can get the full backtest statistics by using the following command.
# This contains all information used to generate the backtest result.
stats = load_backtest_stats(backtest_dir)
strategy = 'SampleStrategy'
# All statistics are available per strategy, so if `--strategy-list` was used during backtest, this will be reflected here as well.
# Example usages:
print(stats['strategy'][strategy]['results_per_pair'])
# Get pairlist used for this backtest
print(stats['strategy'][strategy]['pairlist'])
# Get market change (average change of all pairs from start to end of the backtest period)
print(stats['strategy'][strategy]['market_change'])
# Maximum drawdown ()
print(stats['strategy'][strategy]['max_drawdown'])
# Maximum drawdown start and end
print(stats['strategy'][strategy]['drawdown_start'])
print(stats['strategy'][strategy]['drawdown_end'])
# Get strategy comparison (only relevant if multiple strategies were compared)
print(stats['strategy_comparison'])
```
```python
# Load backtested trades as dataframe
trades = load_backtest_data(backtest_dir)
# Show value-counts per pair
trades.groupby("pair")["sell_reason"].value_counts()

58
docs/telegram-usage.md
View File

@ -47,28 +47,30 @@ Per default, the Telegram bot shows predefined commands. Some commands
are only available by sending them to the bot. The table below list the
official commands. You can ask at any moment for help with `/help`.
| Command | Default | Description |
|----------|---------|-------------|
| `/start` | | Starts the trader
| `/stop` | | Stops the trader
| `/stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `/reload_conf` | | 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 (**)
| `/count` | | Displays number of trades used and available
| `/profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
| `/forcesell <trade_id>` | | Instantly sells the given trade (Ignoring `minimum_roi`).
| `/forcesell all` | | Instantly sells all open trades (Ignoring `minimum_roi`).
| `/forcebuy <pair> [rate]` | | Instantly buys the given pair. Rate is optional. (`forcebuy_enable` must be set to True)
| `/performance` | | Show performance of each finished trade grouped by pair
| `/balance` | | Show account balance per currency
| `/daily <n>` | 7 | Shows profit or loss per day, over the last n days
| `/whitelist` | | Show the current whitelist
| `/blacklist [pair]` | | Show the current blacklist, or adds a pair to the blacklist.
| `/edge` | | Show validated pairs by Edge if it is enabled.
| `/help` | | Show help message
| `/version` | | Show version
| Command | Description |
|----------|-------------|
| `/start` | Starts the trader
| `/stop` | Stops the trader
| `/stopbuy` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `/reload_config` | Reloads the configuration file
| `/show_config` | Shows part of the current configuration with relevant settings to operation
| `/status` | Lists all open trades
| `/status table` | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
| `/trades [limit]` | List all recently closed trades in a table format.
| `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `/count` | Displays number of trades used and available
| `/profit` | Display a summary of your profit/loss from close trades and some stats about your performance
| `/forcesell <trade_id>` | Instantly sells the given trade (Ignoring `minimum_roi`).
| `/forcesell all` | Instantly sells all open trades (Ignoring `minimum_roi`).
| `/forcebuy <pair> [rate]` | Instantly buys the given pair. Rate is optional. (`forcebuy_enable` must be set to True)
| `/performance` | Show performance of each finished trade grouped by pair
| `/balance` | Show account balance per currency
| `/daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
| `/whitelist` | Show the current whitelist
| `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
| `/edge` | Show validated pairs by Edge if it is enabled.
| `/help` | Show help message
| `/version` | Show version
## Telegram commands in action
@ -85,14 +87,14 @@ 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.
@ -113,6 +115,7 @@ For each open trade, the bot will send you the following message.
### /status table
Return the status of all open trades in a table format.
```
ID Pair Since Profit
---- -------- ------- --------
@ -123,6 +126,7 @@ Return the status of all open trades in a table format.
### /count
Return the number of trades used and available.
```
current max
--------- -----
@ -208,15 +212,15 @@ 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.
Also supports multiple pairs, separated by a space.
Use `/reload_config` to reset the blacklist.
> Using blacklist `StaticPairList` with 2 pairs
>`DODGE/BTC`, `HOT/BTC`.
### /edge
Shows pairs validated by Edge along with their corresponding winrate, expectancy and stoploss values.
Shows pairs validated by Edge along with their corresponding win-rate, expectancy and stoploss values.
> **Edge only validated following pairs:**
```

12
docs/utils.md
View File

@ -62,7 +62,7 @@ $ freqtrade new-config --config config_binance.json
? Please insert your stake currency: BTC
? Please insert your stake amount: 0.05
? Please insert max_open_trades (Integer or 'unlimited'): 3
? Please insert your timeframe (ticker interval): 5m
? 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
@ -432,9 +432,9 @@ usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[--max-trades INT] [--min-avg-time FLOAT]
[--max-avg-time FLOAT] [--min-avg-profit FLOAT]
[--max-avg-profit FLOAT]
[--min-total-profit FLOAT]
[--max-total-profit FLOAT] [--no-color]
[--print-json] [--no-details]
[--min-total-profit FLOAT] [--max-total-profit FLOAT]
[--min-objective FLOAT] [--max-objective FLOAT]
[--no-color] [--print-json] [--no-details]
[--export-csv FILE]
optional arguments:
@ -453,6 +453,10 @@ optional arguments:
Select epochs on above total profit.
--max-total-profit FLOAT
Select epochs on below total profit.
--min-objective FLOAT
Select epochs on above objective (- is added by default).
--max-objective FLOAT
Select epochs on below objective (- is added by default).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--print-json Print best result detailization in JSON format.

4
docs/webhook-config.md
View File

@ -47,6 +47,7 @@ Different payloads can be configured for different events. Not all fields are ne
The fields in `webhook.webhookbuy` are filled when the bot executes a buy. Parameters are filled using string.format.
Possible parameters are:
* `trade_id`
* `exchange`
* `pair`
* `limit`
@ -63,6 +64,7 @@ Possible parameters are:
The fields in `webhook.webhookbuycancel` are filled when the bot cancels a buy order. Parameters are filled using string.format.
Possible parameters are:
* `trade_id`
* `exchange`
* `pair`
* `limit`
@ -79,6 +81,7 @@ Possible parameters are:
The fields in `webhook.webhooksell` are filled when the bot sells a trade. Parameters are filled using string.format.
Possible parameters are:
* `trade_id`
* `exchange`
* `pair`
* `gain`
@ -100,6 +103,7 @@ Possible parameters are:
The fields in `webhook.webhooksellcancel` are filled when the bot cancels a sell order. Parameters are filled using string.format.
Possible parameters are:
* `trade_id`
* `exchange`
* `pair`
* `gain`

3
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)

242
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 + ["position_stacking", "use_max_market_positions",
@ -54,15 +54,17 @@ 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", "no_trades"]
"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"]
@ -71,6 +73,7 @@ ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
"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",
"hyperopt_list_min_objective", "hyperopt_list_max_objective",
"print_colorized", "print_json", "hyperopt_list_no_details",
"export_csv"]
@ -78,7 +81,7 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
"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", "show-trades"]
@ -159,7 +162,7 @@ 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,
@ -181,25 +184,6 @@ class Arguments:
trade_cmd.set_defaults(func=start_trading)
self._build_args(optionlist=ARGS_TRADE, parser=trade_cmd)
# Add backtesting subcommand
backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.',
parents=[_common_parser, _strategy_parser])
backtesting_cmd.set_defaults(func=start_backtesting)
self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.',
parents=[_common_parser, _strategy_parser])
edge_cmd.set_defaults(func=start_edge)
self._build_args(optionlist=ARGS_EDGE, parser=edge_cmd)
# Add hyperopt subcommand
hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.',
parents=[_common_parser, _strategy_parser],
)
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.",
@ -213,79 +197,17 @@ class Arguments:
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.',
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-hyperopts subcommand
list_hyperopts_cmd = subparsers.add_parser(
'list-hyperopts',
help='Print available hyperopt classes.',
parents=[_common_parser],
)
list_hyperopts_cmd.set_defaults(func=start_list_hyperopts)
self._build_args(optionlist=ARGS_LIST_HYPEROPTS, parser=list_hyperopts_cmd)
# Add list-exchanges subcommand
list_exchanges_cmd = subparsers.add_parser(
'list-exchanges',
help='Print available exchanges.',
parents=[_common_parser],
)
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.',
parents=[_common_parser],
)
list_timeframes_cmd.set_defaults(func=start_list_timeframes)
self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
# Add list-markets subcommand
list_markets_cmd = subparsers.add_parser(
'list-markets',
help='Print markets on exchange.',
parents=[_common_parser],
)
list_markets_cmd.set_defaults(func=partial(start_list_markets, pairs_only=False))
self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_markets_cmd)
# Add list-pairs subcommand
list_pairs_cmd = subparsers.add_parser(
'list-pairs',
help='Print pairs on exchange.',
parents=[_common_parser],
)
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 test-pairlist subcommand
test_pairlist_cmd = subparsers.add_parser(
'test-pairlist',
help='Test your pairlist configuration.',
)
test_pairlist_cmd.set_defaults(func=start_test_pairlist)
self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_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(
@ -314,32 +236,33 @@ class Arguments:
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',
help='Plot candles with indicators.',
# 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])
backtesting_cmd.set_defaults(func=start_backtesting)
self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.',
parents=[_common_parser, _strategy_parser])
edge_cmd.set_defaults(func=start_edge)
self._build_args(optionlist=ARGS_EDGE, parser=edge_cmd)
# Add hyperopt subcommand
hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.',
parents=[_common_parser, _strategy_parser],
)
plot_dataframe_cmd.set_defaults(func=start_plot_dataframe)
self._build_args(optionlist=ARGS_PLOT_DATAFRAME, parser=plot_dataframe_cmd)
# Plot profit
plot_profit_cmd = subparsers.add_parser(
'plot-profit',
help='Generate plot showing profits.',
parents=[_common_parser],
)
plot_profit_cmd.set_defaults(func=start_plot_profit)
self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_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)
hyperopt_cmd.set_defaults(func=start_hyperopt)
self._build_args(optionlist=ARGS_HYPEROPT, parser=hyperopt_cmd)
# Add hyperopt-list subcommand
hyperopt_list_cmd = subparsers.add_parser(
@ -358,3 +281,92 @@ class Arguments:
)
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(
'list-exchanges',
help='Print available exchanges.',
parents=[_common_parser],
)
list_exchanges_cmd.set_defaults(func=start_list_exchanges)
self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
# Add list-hyperopts subcommand
list_hyperopts_cmd = subparsers.add_parser(
'list-hyperopts',
help='Print available hyperopt classes.',
parents=[_common_parser],
)
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(
'list-markets',
help='Print markets on exchange.',
parents=[_common_parser],
)
list_markets_cmd.set_defaults(func=partial(start_list_markets, pairs_only=False))
self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_markets_cmd)
# Add list-pairs subcommand
list_pairs_cmd = subparsers.add_parser(
'list-pairs',
help='Print pairs on exchange.',
parents=[_common_parser],
)
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',
help='Test your pairlist configuration.',
)
test_pairlist_cmd.set_defaults(func=start_test_pairlist)
self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_cmd)
# Add Plotting subcommand
plot_dataframe_cmd = subparsers.add_parser(
'plot-dataframe',
help='Plot candles with indicators.',
parents=[_common_parser, _strategy_parser],
)
plot_dataframe_cmd.set_defaults(func=start_plot_dataframe)
self._build_args(optionlist=ARGS_PLOT_DATAFRAME, parser=plot_dataframe_cmd)
# Plot profit
plot_profit_cmd = subparsers.add_parser(
'plot-profit',
help='Generate plot showing profits.',
parents=[_common_parser, _strategy_parser],
)
plot_profit_cmd.set_defaults(func=start_plot_profit)
self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)

4
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 timeframe (ticker interval):",
"name": "timeframe",
"message": "Please insert your desired timeframe (e.g. 5m):",
"default": "5m",
},
{

28
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(
@ -455,37 +455,49 @@ AVAILABLE_CLI_OPTIONS = {
),
"hyperopt_list_min_avg_time": Arg(
'--min-avg-time',
help='Select epochs on above average time.',
help='Select epochs above average time.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_avg_time": Arg(
'--max-avg-time',
help='Select epochs on under average time.',
help='Select epochs below average time.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_min_avg_profit": Arg(
'--min-avg-profit',
help='Select epochs on above average profit.',
help='Select epochs above average profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_avg_profit": Arg(
'--max-avg-profit',
help='Select epochs on below average profit.',
help='Select epochs below average profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_min_total_profit": Arg(
'--min-total-profit',
help='Select epochs on above total profit.',
help='Select epochs above total profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_total_profit": Arg(
'--max-total-profit',
help='Select epochs on below total profit.',
help='Select epochs below total profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_min_objective": Arg(
'--min-objective',
help='Select epochs above objective.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_objective": Arg(
'--max-objective',
help='Select epochs below objective.',
type=float,
metavar='FLOAT',
),

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'))

59
freqtrade/commands/hyperopt_commands.py
View File

@ -35,7 +35,9 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
'filter_min_objective': config.get('hyperopt_list_min_objective', None),
'filter_max_objective': config.get('hyperopt_list_max_objective', None),
}
results_file = (config['user_data_dir'] /
@ -45,7 +47,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
epochs = Hyperopt.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = _hyperopt_filter_epochs(epochs, filteroptions)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
if print_colorized:
colorama_init(autoreset=True)
@ -92,14 +94,16 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
'filter_min_objective': config.get('hyperopt_list_min_objective', None),
'filter_max_objective': config.get('hyperopt_list_max_objective', None)
}
# Previous evaluations
epochs = Hyperopt.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = _hyperopt_filter_epochs(epochs, filteroptions)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
filtered_epochs = len(epochs)
if n > filtered_epochs:
@ -119,7 +123,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
header_str="Epoch details")
def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
"""
Filter our items from the list of hyperopt results
"""
@ -127,6 +131,24 @@ def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
epochs = [x for x in epochs if x['is_best']]
if filteroptions['only_profitable']:
epochs = [x for x in epochs if x['results_metrics']['profit'] > 0]
epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
epochs = _hyperopt_filter_epochs_duration(epochs, filteroptions)
epochs = _hyperopt_filter_epochs_profit(epochs, filteroptions)
epochs = _hyperopt_filter_epochs_objective(epochs, filteroptions)
logger.info(f"{len(epochs)} " +
("best " if filteroptions['only_best'] else "") +
("profitable " if filteroptions['only_profitable'] else "") +
"epochs found.")
return epochs
def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_trades'] > 0:
epochs = [
x for x in epochs
@ -137,6 +159,11 @@ def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
x for x in epochs
if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
]
return epochs
def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_avg_time'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [
@ -149,6 +176,12 @@ def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
x for x in epochs
if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
]
return epochs
def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_avg_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [
@ -173,10 +206,18 @@ def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
x for x in epochs
if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
]
return epochs
logger.info(f"{len(epochs)} " +
("best " if filteroptions['only_best'] else "") +
("profitable " if filteroptions['only_profitable'] else "") +
"epochs found.")
def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_objective'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
if filteroptions['filter_max_objective'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]
return epochs

8
freqtrade/commands/list_commands.py
View File

@ -14,7 +14,7 @@ from freqtrade.configuration import setup_utils_configuration
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (available_exchanges, ccxt_exchanges,
market_is_active, symbol_is_pair)
market_is_active)
from freqtrade.misc import plural
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
@ -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)
@ -163,7 +163,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
tabular_data.append({'Id': v['id'], 'Symbol': v['symbol'],
'Base': v['base'], 'Quote': v['quote'],
'Active': market_is_active(v),
**({'Is pair': symbol_is_pair(v['symbol'])}
**({'Is pair': exchange.market_is_tradable(v)}
if not pairs_only else {})})
if (args.get('print_one_column', False) or

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

18
freqtrade/configuration/configuration.py
View File

@ -199,14 +199,14 @@ class Configuration:
config['exportfilename'] = Path(config['exportfilename'])
else:
config['exportfilename'] = (config['user_data_dir']
/ 'backtest_results/backtest-result.json')
/ 'backtest_results')
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 ...')
@ -242,8 +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: {} ...')
@ -334,6 +334,12 @@ class Configuration:
self._args_to_config(config, argname='hyperopt_list_max_total_profit',
logstring='Parameter --max-total-profit detected: {}')
self._args_to_config(config, argname='hyperopt_list_min_objective',
logstring='Parameter --min-objective detected: {}')
self._args_to_config(config, argname='hyperopt_list_max_objective',
logstring='Parameter --max-objective detected: {}')
self._args_to_config(config, argname='hyperopt_list_no_details',
logstring='Parameter --no-details detected: {}')

13
freqtrade/configuration/deprecated_settings.py
View File

@ -60,10 +60,21 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
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']

25
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
@ -19,15 +22,19 @@ ORDERBOOK_SIDES = ['ask', 'bid']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'PrecisionFilter', 'PriceFilter', 'ShuffleFilter', 'SpreadFilter']
'AgeFilter', 'PrecisionFilter', 'PriceFilter',
'ShuffleFilter', 'SpreadFilter']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz']
DRY_RUN_WALLET = 1000
DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
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']
LAST_BT_RESULT_FN = '.last_result.json'
USERPATH_HYPEROPTS = 'hyperopts'
USERPATH_STRATEGIES = 'strategies'
USERPATH_NOTEBOOKS = 'notebooks'
@ -68,7 +75,7 @@ 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'],
@ -152,7 +159,9 @@ CONF_SCHEMA = {
'emergencysell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss_on_exchange': {'type': 'boolean'},
'stoploss_on_exchange_interval': {'type': 'number'}
'stoploss_on_exchange_interval': {'type': 'number'},
'stoploss_on_exchange_limit_ratio': {'type': 'number', 'minimum': 0.0,
'maximum': 1.0}
},
'required': ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
},
@ -218,12 +227,16 @@ 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': {},
@ -282,7 +295,6 @@ 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'},
@ -299,6 +311,7 @@ CONF_SCHEMA = {
SCHEMA_TRADE_REQUIRED = [
'exchange',
'timeframe',
'max_open_trades',
'stake_currency',
'stake_amount',
@ -329,3 +342,7 @@ CANCEL_REASON = {
"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]

166
freqtrade/data/btanalysis.py
View File

@ -3,52 +3,123 @@ Helpers when analyzing backtest data
"""
import logging
from pathlib import Path
from typing import Dict, Union, Tuple
from typing import Dict, Union, Tuple, Any, Optional
import numpy as np
import pandas as pd
from datetime import timezone
from freqtrade import persistence
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.misc import json_load
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_date", "close_date", "index", "trade_duration",
"open_rate", "close_rate", "open_at_end", "sell_reason"]
def load_backtest_data(filename: Union[Path, str]) -> pd.DataFrame:
def get_latest_backtest_filename(directory: Union[Path, str]) -> str:
"""
Load backtest data file.
:param filename: pathlib.Path object, or string pointing to the file.
:return: a dataframe with the analysis results
Get latest backtest export based on '.last_result.json'.
:param directory: Directory to search for last result
:return: string containing the filename of the latest backtest result
:raises: ValueError in the following cases:
* Directory does not exist
* `directory/.last_result.json` does not exist
* `directory/.last_result.json` has the wrong content
"""
if isinstance(filename, str):
filename = Path(filename)
if isinstance(directory, str):
directory = Path(directory)
if not directory.is_dir():
raise ValueError(f"Directory '{directory}' does not exist.")
filename = directory / LAST_BT_RESULT_FN
if not filename.is_file():
raise ValueError(f"File {filename} does not exist.")
raise ValueError(
f"Directory '{directory}' does not seem to contain backtest statistics yet.")
with filename.open() as file:
data = json_load(file)
if 'latest_backtest' not in data:
raise ValueError(f"Invalid '{LAST_BT_RESULT_FN}' format.")
return data['latest_backtest']
def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
"""
Load backtest statistics file.
:param filename: pathlib.Path object, or string pointing to the file.
:return: a dictionary containing the resulting file.
"""
if isinstance(filename, str):
filename = Path(filename)
if filename.is_dir():
filename = filename / get_latest_backtest_filename(filename)
if not filename.is_file():
raise ValueError(f"File {filename} does not exist.")
logger.info(f"Loading backtest result from {filename}")
with filename.open() as file:
data = json_load(file)
return data
def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = None) -> pd.DataFrame:
"""
Load backtest data file.
:param filename: pathlib.Path object, or string pointing to a file or directory
:param strategy: Strategy to load - mainly relevant for multi-strategy backtests
Can also serve as protection to load the correct result.
:return: a dataframe with the analysis results
:raise: ValueError if loading goes wrong.
"""
data = load_backtest_stats(filename)
if not isinstance(data, list):
# new, nested format
if 'strategy' not in data:
raise ValueError("Unknown dataformat.")
if not strategy:
if len(data['strategy']) == 1:
strategy = list(data['strategy'].keys())[0]
else:
raise ValueError("Detected backtest result with more than one strategy. "
"Please specify a strategy.")
if strategy not in data['strategy']:
raise ValueError(f"Strategy {strategy} not available in the backtest result.")
data = data['strategy'][strategy]['trades']
df = pd.DataFrame(data)
df['open_date'] = pd.to_datetime(df['open_date'],
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
utc=True,
infer_datetime_format=True
)
else:
# old format - only with lists.
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
df['open_time'] = pd.to_datetime(df['open_time'],
df['open_date'] = pd.to_datetime(df['open_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['close_time'] = pd.to_datetime(df['close_time'],
df['close_date'] = pd.to_datetime(df['close_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['profit'] = df['close_rate'] - df['open_rate']
df = df.sort_values("open_time").reset_index(drop=True)
df['profit_abs'] = df['close_rate'] - df['open_rate']
df = df.sort_values("open_date").reset_index(drop=True)
return df
@ -62,9 +133,9 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF
"""
from freqtrade.exchange import timeframe_to_minutes
timeframe_min = timeframe_to_minutes(timeframe)
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time,
dates = [pd.Series(pd.date_range(row[1]['open_date'], row[1]['close_date'],
freq=f"{timeframe_min}min"))
for row in results[['open_time', 'close_time']].iterrows()]
for row in results[['open_date', 'close_date']].iterrows()]
deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date')
df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
@ -90,20 +161,25 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
return df_final[df_final['open_trades'] > max_open_trades]
def load_trades_from_db(db_url: str) -> pd.DataFrame:
def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataFrame:
"""
Load trades from a DB (using dburl)
:param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
:param strategy: Strategy to load - mainly relevant for multi-strategy backtests
Can also serve as protection to load the correct result.
:return: Dataframe containing Trades
"""
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",
"open_rate", "close_rate", "amount", "duration", "sell_reason",
columns = ["pair", "open_date", "close_date", "profit", "profit_percent",
"open_rate", "close_rate", "amount", "trade_duration", "sell_reason",
"fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
"stake_amount", "max_rate", "min_rate", "id", "exchange",
"stop_loss", "initial_stop_loss", "strategy", "ticker_interval"]
"stop_loss", "initial_stop_loss", "strategy", "timeframe"]
filters = []
if strategy:
filters.append(Trade.strategy == strategy)
trades = pd.DataFrame([(t.pair,
t.open_date.replace(tzinfo=timezone.utc),
@ -121,16 +197,16 @@ 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()],
for t in Trade.get_trades(filters).all()],
columns=columns)
return trades
def load_trades(source: str, db_url: str, exportfilename: Path,
no_trades: bool = False) -> pd.DataFrame:
no_trades: bool = False, strategy: Optional[str] = None) -> pd.DataFrame:
"""
Based on configuration option "trade_source":
* loads data from DB (using `db_url`)
@ -148,7 +224,7 @@ def load_trades(source: str, db_url: str, exportfilename: Path,
if source == "DB":
return load_trades_from_db(db_url)
elif source == "file":
return load_backtest_data(exportfilename)
return load_backtest_data(exportfilename, strategy)
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
@ -163,11 +239,31 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
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)]
trades = trades.loc[(trades['open_date'] >= trades_start) &
(trades['close_date'] <= trades_stop)]
return trades
def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
"""
Calculate market change based on "column".
Calculation is done by taking the first non-null and the last non-null element of each column
and calculating the pctchange as "(last - first) / first".
Then the results per pair are combined as mean.
:param data: Dict of Dataframes, dict key should be pair.
:param column: Column in the original dataframes to use
:return:
"""
tmp_means = []
for pair, df in data.items():
start = df[column].dropna().iloc[0]
end = df[column].dropna().iloc[-1]
tmp_means.append((end - start) / start)
return np.mean(tmp_means)
def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
column: str = "close") -> pd.DataFrame:
"""
@ -190,15 +286,19 @@ 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_date 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_date'
)[['profit_percent']].sum()
df.loc[:, col_name] = _trades_sum.cumsum()
# Set first value to 0
df.loc[df.iloc[0].name, col_name] = 0
@ -207,14 +307,14 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
return df
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time',
value_col: str = 'profitperc'
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
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 profitperc)
: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 'profitperc')
:param trades: DataFrame containing trades (requires columns close_date and profit_percent)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_percent')
:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:raise: ValueError if trade-dataframe was found empty.
"""

6
freqtrade/data/converter.py
View File

@ -197,7 +197,7 @@ def trades_to_ohlcv(trades: List, timeframe: str) -> DataFrame:
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):
@ -236,12 +236,12 @@ 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')])
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))

46
freqtrade/data/dataprovider.py
View File

@ -5,16 +5,17 @@ including ticker and orderbook data, live and historical candle (OHLCV) data
Common Interface for bot and strategy to access data.
"""
import logging
from typing import Any, Dict, List, Optional
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 DependencyException, OperationalException
from freqtrade.exceptions import ExchangeError, OperationalException
from freqtrade.exchange import Exchange
from freqtrade.state import RunMode
from freqtrade.typing import ListPairsWithTimeframes
logger = logging.getLogger(__name__)
@ -25,6 +26,18 @@ class DataProvider:
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: ListPairsWithTimeframes,
@ -55,7 +68,7 @@ class DataProvider:
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()
@ -67,7 +80,7 @@ class DataProvider:
: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']
)
@ -89,6 +102,20 @@ class DataProvider:
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
@ -105,17 +132,18 @@ class DataProvider:
"""
try:
return self._exchange.fetch_ticker(pair)
except DependencyException:
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:

5
freqtrade/data/history/history_utils.py
View File

@ -270,6 +270,11 @@ def _download_trades_history(exchange: Exchange,
# DEFAULT_TRADES_COLUMNS: 0 -> timestamp
# DEFAULT_TRADES_COLUMNS: 1 -> id
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

9
freqtrade/data/history/idatahandler.py
View File

@ -13,6 +13,7 @@ from typing import List, Optional, Type
from pandas import DataFrame
from freqtrade.configuration import TimeRange
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
@ -28,6 +29,14 @@ 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]:
"""

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

@ -8,7 +8,8 @@ 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, TradeList
@ -21,6 +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]:
"""

33
freqtrade/edge/edge_positioning.py
View File

@ -9,7 +9,7 @@ import utils_find_1st as utf1st
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, DATETIME_PRINT_FORMAT
from freqtrade.exceptions import OperationalException
from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.strategy.interface import SellType
@ -57,9 +57,7 @@ class Edge:
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
@ -100,14 +98,14 @@ class Edge:
datadir=self.config['datadir'],
pairs=pairs,
exchange=self.exchange,
timeframe=self.strategy.ticker_interval,
timeframe=self.strategy.timeframe,
timerange=self._timerange,
)
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'),
@ -123,12 +121,9 @@ class Edge:
# Print timeframe
min_date, max_date = get_timerange(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days) ...',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
logger.info(f'Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
trades: list = []
@ -157,7 +152,7 @@ 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)
@ -242,7 +237,7 @@ class Edge:
# All returned values are relative, they are defined as ratios.
stake = 0.015
result['trade_duration'] = result['close_time'] - result['open_time']
result['trade_duration'] = result['close_date'] - result['open_date']
result['trade_duration'] = result['trade_duration'].map(
lambda x: int(x.total_seconds() / 60))
@ -283,8 +278,8 @@ class Edge:
#
# Removing Pumps
if self.edge_config.get('remove_pumps', False):
results = results.groupby(['pair', 'stoploss']).apply(
lambda x: x[x['profit_abs'] < 2 * x['profit_abs'].std() + x['profit_abs'].mean()])
results = results[results['profit_abs'] < 2 * results['profit_abs'].std()
+ results['profit_abs'].mean()]
##########################################################################
# Removing trades having a duration more than X minutes (set in config)
@ -432,10 +427,8 @@ class Edge:
'stoploss': stoploss,
'profit_ratio': '',
'profit_abs': '',
'open_time': date_column[open_trade_index],
'close_time': date_column[exit_index],
'open_index': start_point + open_trade_index,
'close_index': start_point + exit_index,
'open_date': date_column[open_trade_index],
'close_date': date_column[exit_index],
'trade_duration': '',
'open_rate': round(open_price, 15),
'close_rate': round(exit_price, 15),

33
freqtrade/exceptions.py
View File

@ -21,7 +21,22 @@ class DependencyException(FreqtradeException):
"""
class InvalidOrderException(FreqtradeException):
class PricingError(DependencyException):
"""
Subclass of DependencyException.
Indicates that the price could not be determined.
Implicitly a buy / sell operation.
"""
class ExchangeError(DependencyException):
"""
Error raised out of the exchange.
Has multiple Errors to determine the appropriate error.
"""
class InvalidOrderException(ExchangeError):
"""
This is returned when the order is not valid. Example:
If stoploss on exchange order is hit, then trying to cancel the order
@ -29,7 +44,14 @@ 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 TemporaryError(ExchangeError):
"""
Temporary network or exchange related error.
This could happen when an exchange is congested, unavailable, or the user
@ -37,6 +59,13 @@ class TemporaryError(FreqtradeException):
"""
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.

3
freqtrade/exchange/__init__.py
View File

@ -12,8 +12,7 @@ from freqtrade.exchange.exchange import (timeframe_to_seconds,
timeframe_to_msecs,
timeframe_to_next_date,
timeframe_to_prev_date)
from freqtrade.exchange.exchange import (market_is_active,
symbol_is_pair)
from freqtrade.exchange.exchange import (market_is_active)
from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.binance import Binance
from freqtrade.exchange.bibox import Bibox

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.
@ -77,8 +80,8 @@ class Binance(Exchange):
'stop price: %s. limit: %s', pair, stop_price, rate)
return order
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to create {ordertype} sell order on market {pair}.'
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
except ccxt.InvalidOrder as 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

39
freqtrade/exchange/common.py
View File

@ -1,6 +1,10 @@
import asyncio
import logging
import time
from functools import wraps
from freqtrade.exceptions import TemporaryError
from freqtrade.exceptions import (DDosProtection, RetryableOrderError,
TemporaryError)
logger = logging.getLogger(__name__)
@ -88,6 +92,13 @@ 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)
@ -96,9 +107,13 @@ def retrier_async(f):
except TemporaryError as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
if count > 0:
logger.warning('retrying %s() still for %s times', f.__name__, count)
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.info(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 retrier(_func=None, retries=API_RETRY_COUNT):
def decorator(f):
@wraps(f)
def wrapper(*args, **kwargs):
count = kwargs.pop('count', API_RETRY_COUNT)
count = kwargs.pop('count', retries)
try:
return f(*args, **kwargs)
except TemporaryError as ex:
except (TemporaryError, RetryableOrderError) as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
if count > 0:
logger.warning('retrying %s() still for %s times', f.__name__, count)
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.info(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)

167
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.constants import ListPairsWithTimeframes
from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
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, safe_value_fallback
from freqtrade.typing import ListPairsWithTimeframes
from freqtrade.misc import deep_merge_dicts, safe_value_fallback2
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
@ -214,7 +222,7 @@ class Exchange:
if quote_currencies:
markets = {k: v for k, v in markets.items() if v['quote'] in quote_currencies}
if pairs_only:
markets = {k: v for k, v in markets.items() if symbol_is_pair(v['symbol'])}
markets = {k: v for k, v in markets.items() if self.market_is_tradable(v)}
if active_only:
markets = {k: v for k, v in markets.items() if market_is_active(v)}
return markets
@ -238,6 +246,19 @@ class Exchange:
"""
return self.markets.get(pair, {}).get('base', '')
def market_is_tradable(self, market: Dict[str, Any]) -> bool:
"""
Check if the market symbol is tradable by Freqtrade.
By default, checks if it's splittable by `/` and both sides correspond to base / quote
"""
symbol_parts = market['symbol'].split('/')
return (len(symbol_parts) == 2 and
len(symbol_parts[0]) > 0 and
len(symbol_parts[1]) > 0 and
symbol_parts[0] == market.get('base') and
symbol_parts[1] == market.get('quote')
)
def klines(self, pair_interval: Tuple[str, str], copy: bool = True) -> DataFrame:
if pair_interval in self._klines:
return self._klines[pair_interval].copy() if copy else self._klines[pair_interval]
@ -250,8 +271,8 @@ class Exchange:
api.urls['api'] = api.urls['test']
logger.info("Enabled Sandbox API on %s", name)
else:
logger.warning(name, "No Sandbox URL in CCXT, exiting. "
"Please check your config.json")
logger.warning(
f"No Sandbox URL in CCXT for {name}, exiting. Please check your config.json")
raise OperationalException(f'Exchange {name} does not provide a sandbox api')
def _load_async_markets(self, reload: bool = False) -> None:
@ -273,8 +294,8 @@ 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 +304,8 @@ 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.")
@ -347,7 +370,7 @@ 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:
"""
@ -470,6 +493,7 @@ class Exchange:
"id": order_id,
'pair': pair,
'price': rate,
'average': rate,
'amount': _amount,
'cost': _amount * rate,
'type': ordertype,
@ -514,15 +538,17 @@ class Exchange:
amount, rate_for_order, params)
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to create {ordertype} {side} order on market {pair}.'
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(
f'Could not create {ordertype} {side} order on market {pair}.'
f'Tried to {side} amount {amount} at rate {rate}.'
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
@ -602,6 +628,8 @@ 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
@ -616,6 +644,8 @@ class Exchange:
raise OperationalException(
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
@ -626,9 +656,11 @@ 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
@ -762,6 +794,8 @@ class Exchange:
raise OperationalException(
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 fetch historical candle (OHLCV) data '
f'for pair {pair} due to {e.__class__.__name__}. '
@ -798,6 +832,8 @@ class Exchange:
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
@ -887,14 +923,19 @@ class Exchange:
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, "
@ -924,7 +965,7 @@ class Exchange:
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 get_order()
: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
@ -939,12 +980,17 @@ class Exchange:
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 cancel_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
@ -956,7 +1002,7 @@ class Exchange:
"""
Cancel order returning a result.
Creates a fake result if cancel order returns a non-usable result
and get_order does not work (certain exchanges don't return cancelled orders)
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
@ -967,17 +1013,17 @@ class Exchange:
if self.is_cancel_order_result_suitable(corder):
return corder
except InvalidOrderException:
logger.warning(f"Could not cancel order {order_id}.")
logger.warning(f"Could not cancel order {order_id} for {pair}.")
try:
order = self.get_order(order_id, pair)
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:
@retrier(retries=5)
def fetch_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
try:
order = self._dry_run_open_orders[order_id]
@ -988,22 +1034,30 @@ 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 (pair: {pair} 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
f'Tried to get an invalid order (pair: {pair} 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 get_order_book(self, pair: str, limit: int = 100) -> dict:
"""
get order book level 2 from exchange
# Assign method to fetch_stoploss_order to allow easy overriding in other classes
fetch_stoploss_order = fetch_order
Notes:
20180619: bittrex doesnt support limits -.-
@retrier
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
"""
Get L2 order book from exchange.
Can be limited to a certain amount (if supported).
Returns a dict in the format
{'asks': [price, volume], 'bids': [price, volume]}
"""
try:
@ -1012,6 +1066,8 @@ 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
@ -1048,7 +1104,8 @@ class Exchange:
matched_trades = [trade for trade in my_trades if trade['order'] == order_id]
return matched_trades
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 {e.__class__.__name__}. Message: {e}') from e
@ -1065,6 +1122,8 @@ 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
@ -1099,19 +1158,22 @@ class Exchange:
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)
order['fee']['cost'] / safe_value_fallback2(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)
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')
fee_to_quote_rate = safe_value_fallback2(tick, tick, 'last', 'ask')
return round((order['fee']['cost'] * fee_to_quote_rate) / order['cost'], 8)
except DependencyException:
except ExchangeError:
return None
def extract_cost_curr_rate(self, order: Dict) -> Tuple[float, str, Optional[float]]:
@ -1124,7 +1186,6 @@ class Exchange:
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:
@ -1210,20 +1271,6 @@ def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def symbol_is_pair(market_symbol: str, base_currency: str = None,
quote_currency: str = None) -> bool:
"""
Check if the market symbol is a pair, i.e. that its symbol consists of the base currency and the
quote currency separated by '/' character. If base_currency and/or quote_currency is passed,
it also checks that the symbol contains appropriate base and/or quote currency part before
and after the separating character correspondingly.
"""
symbol_parts = market_symbol.split('/')
return (len(symbol_parts) == 2 and
(symbol_parts[0] == base_currency if base_currency else len(symbol_parts[0]) > 0) and
(symbol_parts[1] == quote_currency if quote_currency else len(symbol_parts[1]) > 0))
def market_is_active(market: Dict) -> bool:
"""
Return True if the market is active.

124
freqtrade/exchange/ftx.py
View File

@ -1,8 +1,14 @@
""" FTX exchange subclass """
import logging
from typing import Dict
from typing import Any, 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 +16,121 @@ logger = logging.getLogger(__name__)
class Ftx(Exchange):
_ft_has: Dict = {
"stoploss_on_exchange": True,
"ohlcv_candle_limit": 1500,
}
def market_is_tradable(self, market: Dict[str, Any]) -> bool:
"""
Check if the market symbol is tradable by Freqtrade.
Default checks + check if pair is spot pair (no futures trading yet).
"""
parent_check = super().market_is_tradable(market)
return (parent_check and
market.get('spot', False) is True)
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(retries=5)
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

26
freqtrade/exchange/kraken.py
View File

@ -1,11 +1,12 @@
""" Kraken exchange subclass """
import logging
from typing import Dict
from typing import Any, 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
@ -21,6 +22,16 @@ class Kraken(Exchange):
"trades_pagination_arg": "since",
}
def market_is_tradable(self, market: Dict[str, Any]) -> bool:
"""
Check if the market symbol is tradable by Freqtrade.
Default checks + check if pair is darkpool pair.
"""
parent_check = super().market_is_tradable(market)
return (parent_check and
market.get('darkpool', False) is False)
@retrier
def get_balances(self) -> dict:
if self._config['dry_run']:
@ -45,6 +56,8 @@ 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 +71,7 @@ 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.
@ -84,8 +98,8 @@ class Kraken(Exchange):
'stop price: %s.', pair, stop_price)
return order
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to create {ordertype} sell order on market {pair}.'
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:
@ -93,6 +107,8 @@ 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

177
freqtrade/freqtradebot.py
View File

@ -11,16 +11,16 @@ 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.misc import safe_value_fallback, safe_value_fallback2
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@ -119,6 +119,8 @@ class FreqtradeBot:
if self.config['cancel_open_orders_on_exit']:
self.cancel_all_open_orders()
self.check_for_open_trades()
self.rpc.cleanup()
persistence.cleanup()
@ -139,8 +141,8 @@ 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()
@ -151,6 +153,10 @@ class FreqtradeBot:
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()
@ -175,6 +181,24 @@ class FreqtradeBot:
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
@ -251,7 +275,7 @@ class FreqtradeBot:
rate = self._buy_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.info(f"Using cached buy rate for {pair}.")
logger.debug(f"Using cached buy rate for {pair}.")
return rate
bid_strategy = self.config.get('bid_strategy', {})
@ -260,12 +284,19 @@ class FreqtradeBot:
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.get_order_book(pair, order_book_top)
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[f"{bid_strategy['price_side']}s"][order_book_top - 1][0]
logger.info(f'...top {order_book_top} order book buy rate {order_book_rate:.8f}')
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(f"Using Last {bid_strategy['price_side'].capitalize()} / Last Price")
ticker = self.exchange.fetch_ticker(pair)
@ -413,8 +444,8 @@ class FreqtradeBot:
return False
# running get_signal on historical data fetched
dataframe = self.dataprovider.ohlcv(pair, self.strategy.ticker_interval)
(buy, sell) = self.strategy.get_signal(pair, self.strategy.ticker_interval, dataframe)
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:
stake_amount = self.get_trade_stake_amount(pair)
@ -445,7 +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()
@ -487,6 +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
amount = self.exchange.amount_to_precision(pair, amount)
order = self.exchange.buy(pair=pair, ordertype=order_type,
amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force)
@ -495,6 +532,7 @@ class FreqtradeBot:
# we assume the order is executed at the price requested
buy_limit_filled_price = buy_limit_requested
amount_requested = amount
if order_status == 'expired' or order_status == 'rejected':
order_tif = self.strategy.order_time_in_force['buy']
@ -515,15 +553,15 @@ class FreqtradeBot:
order['filled'], order['amount'], order['remaining']
)
stake_amount = order['cost']
amount = order['amount']
buy_limit_filled_price = order['price']
amount = safe_value_fallback(order, 'filled', 'amount')
buy_limit_filled_price = safe_value_fallback(order, 'average', 'price')
order_id = None
# in case of FOK the order may be filled immediately and fully
elif order_status == 'closed':
stake_amount = order['cost']
amount = order['amount']
buy_limit_filled_price = order['price']
amount = safe_value_fallback(order, 'filled', 'amount')
buy_limit_filled_price = safe_value_fallback(order, 'average', 'price')
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
@ -531,6 +569,7 @@ class FreqtradeBot:
pair=pair,
stake_amount=stake_amount,
amount=amount,
amount_requested=amount_requested,
fee_open=fee,
fee_close=fee,
open_rate=buy_limit_filled_price,
@ -539,7 +578,7 @@ 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
@ -561,6 +600,7 @@ class FreqtradeBot:
Sends rpc notification when a buy occured.
"""
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
@ -584,6 +624,7 @@ class FreqtradeBot:
current_rate = self.get_buy_rate(trade.pair, False)
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_CANCEL_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
@ -621,7 +662,7 @@ class FreqtradeBot:
trades_closed += 1
except DependencyException as exception:
logger.warning('Unable to sell trade: %s', exception)
logger.warning('Unable to sell trade %s: %s', trade.pair, exception)
# Updating wallets if any trade occured
if trades_closed:
@ -634,7 +675,7 @@ class FreqtradeBot:
"""
Helper generator to query orderbook in loop (used for early sell-order placing)
"""
order_book = self.exchange.get_order_book(pair, order_book_max)
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]
@ -652,7 +693,7 @@ class FreqtradeBot:
rate = self._sell_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.info(f"Using cached sell rate for {pair}.")
logger.debug(f"Using cached sell rate for {pair}.")
return rate
ask_strategy = self.config.get('ask_strategy', {})
@ -661,10 +702,15 @@ class FreqtradeBot:
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)[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
@ -684,23 +730,33 @@ class FreqtradeBot:
if (config_ask_strategy.get('use_sell_signal', True) or
config_ask_strategy.get('ignore_roi_if_buy_signal', False)):
(buy, sell) = self.strategy.get_signal(
trade.pair, self.strategy.ticker_interval,
self.dataprovider.ohlcv(trade.pair, self.strategy.ticker_interval))
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._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):
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
@ -714,7 +770,7 @@ class FreqtradeBot:
logger.debug('Found no sell signal for %s.', trade)
return False
def create_stoploss_order(self, trade: Trade, stop_price: float, rate: float) -> bool:
def create_stoploss_order(self, trade: Trade, stop_price: float) -> bool:
"""
Abstracts creating stoploss orders from the logic.
Handles errors and updates the trade database object.
@ -733,7 +789,7 @@ 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
@ -751,18 +807,18 @@ 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
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
@ -773,20 +829,17 @@ class FreqtradeBot:
return False
# If buy order is fulfilled but there is no stoploss, we add a stoploss on exchange
if (not stoploss_order):
if not stoploss_order:
stoploss = self.edge.stoploss(pair=trade.pair) if self.edge else self.strategy.stoploss
stop_price = trade.open_rate * (1 + stoploss)
if self.create_stoploss_order(trade=trade, stop_price=stop_price, rate=stop_price):
if self.create_stoploss_order(trade=trade, stop_price=stop_price):
trade.stoploss_last_update = datetime.now()
return False
# If stoploss order is canceled for some reason we add it
if stoploss_order and stoploss_order['status'] == 'canceled':
if self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss,
rate=trade.stop_loss):
if stoploss_order and stoploss_order['status'] in ('canceled', 'cancelled'):
if self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss):
return False
else:
trade.stoploss_order_id = None
@ -817,14 +870,13 @@ 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}")
# Create new stoploss order
if not self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss,
rate=trade.stop_loss):
if not self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss):
logger.warning(f"Could not create trailing stoploss order "
f"for pair {trade.pair}.")
@ -868,8 +920,8 @@ 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):
order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
except (ExchangeError):
logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
continue
@ -901,8 +953,8 @@ class FreqtradeBot:
for trade in Trade.get_open_order_trades():
try:
order = self.exchange.get_order(trade.open_order_id, trade.pair)
except (DependencyException, InvalidOrderException):
order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
except (ExchangeError):
logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
continue
@ -924,6 +976,12 @@ class FreqtradeBot:
reason = constants.CANCEL_REASON['TIMEOUT']
corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount)
# Avoid race condition where the order could not be cancelled coz its already filled.
# Simply bailing here is the only safe way - as this order will then be
# handled in the next iteration.
if corder.get('status') not in ('canceled', 'closed'):
logger.warning(f"Order {trade.open_order_id} for {trade.pair} not cancelled.")
return False
else:
# Order was cancelled already, so we can reuse the existing dict
corder = order
@ -932,7 +990,7 @@ class FreqtradeBot:
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')
filled_amount = safe_value_fallback2(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)
@ -1045,7 +1103,7 @@ 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}")
@ -1055,12 +1113,20 @@ 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']
@ -1072,7 +1138,7 @@ class FreqtradeBot:
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)
@ -1091,6 +1157,7 @@ class FreqtradeBot:
msg = {
'type': RPCMessageType.SELL_NOTIFICATION,
'trade_id': trade.id,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'gain': gain,
@ -1133,6 +1200,7 @@ class FreqtradeBot:
msg = {
'type': RPCMessageType.SELL_CANCEL_NOTIFICATION,
'trade_id': trade.id,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'gain': gain,
@ -1180,14 +1248,15 @@ class FreqtradeBot:
# Update trade with order values
logger.info('Found open order for %s', trade)
try:
order = action_order or self.exchange.get_order(order_id, trade.pair)
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):
if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
abs_tol=constants.MATH_CLOSE_PREC):
order['amount'] = new_amount
order.pop('filled', None)
trade.recalc_open_trade_price()
@ -1233,7 +1302,7 @@ class FreqtradeBot:
"""
# Init variables
if order_amount is None:
order_amount = order['amount']
order_amount = safe_value_fallback(order, 'filled', 'amount')
# Only run for closed orders
if trade.fee_updated(order.get('side', '')) or order['status'] == 'open':
return order_amount

8
freqtrade/loggers.py
View File

@ -11,7 +11,7 @@ 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
@ -28,6 +28,10 @@ def _set_loggers(verbosity: int = 0) -> None:
)
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)

16
freqtrade/misc.py
View File

@ -134,7 +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):
def safe_value_fallback(obj: dict, key1: str, key2: str, default_value=None):
"""
Search a value in obj, return this if it's not None.
Then search key2 in obj - return that if it's not none - then use default_value.
Else falls back to None.
"""
if key1 in obj and obj[key1] is not None:
return obj[key1]
else:
if key2 in obj and obj[key2] is not None:
return obj[key2]
return default_value
def safe_value_fallback2(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.

118
freqtrade/optimize/backtesting.py
View File

@ -13,17 +13,18 @@ from pandas import DataFrame
from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency)
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history
from freqtrade.data.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.optimize.optimize_reports import (show_backtest_results,
store_backtest_result)
from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
show_backtest_results,
store_backtest_stats)
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
logger = logging.getLogger(__name__)
@ -36,14 +37,15 @@ class BacktestResult(NamedTuple):
pair: str
profit_percent: float
profit_abs: float
open_time: datetime
close_time: datetime
open_index: int
close_index: int
open_date: datetime
open_rate: float
open_fee: float
close_date: datetime
close_rate: float
close_fee: float
amount: float
trade_duration: float
open_at_end: bool
open_rate: float
close_rate: float
sell_reason: SellType
@ -64,23 +66,8 @@ class Backtesting:
self.strategylist: List[IStrategy] = []
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
self.pairlists = PairListManager(self.exchange, self.config)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException("VolumePairList not allowed for backtesting.")
self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0:
raise OperationalException("No pair in whitelist.")
if config.get('fee'):
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
if self.config.get('runmode') != RunMode.HYPEROPT:
self.dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = self.dataprovider
dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = dataprovider
if self.config.get('strategy_list', None):
for strat in list(self.config['strategy_list']):
@ -94,12 +81,31 @@ class Backtesting:
self.strategylist.append(StrategyResolver.load_strategy(self.config))
validate_config_consistency(self.config)
if "ticker_interval" not in self.config:
if "timeframe" not in self.config:
raise OperationalException("Timeframe (ticker interval) needs to be set in either "
"configuration or as cli argument `--ticker-interval 5m`")
self.timeframe = str(self.config.get('ticker_interval'))
"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
@ -131,10 +137,10 @@ class Backtesting:
min_date, max_date = history.get_timerange(data)
logger.info(
'Loading data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
logger.info(f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
self.required_startup, min_date)
@ -219,7 +225,7 @@ class Backtesting:
open_rate=buy_row.open,
open_date=buy_row.date,
stake_amount=stake_amount,
amount=stake_amount / buy_row.open,
amount=round(stake_amount / buy_row.open, 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
@ -240,14 +246,15 @@ class Backtesting:
return BacktestResult(pair=pair,
profit_percent=trade.calc_profit_ratio(rate=closerate),
profit_abs=trade.calc_profit(rate=closerate),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=trade_dur,
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=False,
open_date=buy_row.date,
open_rate=buy_row.open,
open_fee=self.fee,
close_date=sell_row.date,
close_rate=closerate,
close_fee=self.fee,
amount=trade.amount,
trade_duration=trade_dur,
open_at_end=False,
sell_reason=sell.sell_type
)
if partial_ohlcv:
@ -256,15 +263,16 @@ class Backtesting:
bt_res = BacktestResult(pair=pair,
profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
open_time=buy_row.date,
close_time=sell_row.date,
open_date=buy_row.date,
open_rate=buy_row.open,
open_fee=self.fee,
close_date=sell_row.date,
close_rate=sell_row.open,
close_fee=self.fee,
amount=trade.amount,
trade_duration=int((
sell_row.date - buy_row.date).total_seconds() // 60),
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=True,
open_rate=buy_row.open,
close_rate=sell_row.open,
sell_reason=SellType.FORCE_SELL
)
logger.debug(f"{pair} - Force selling still open trade, "
@ -350,8 +358,8 @@ class Backtesting:
if trade_entry:
logger.debug(f"{pair} - Locking pair till "
f"close_time={trade_entry.close_time}")
lock_pair_until[pair] = trade_entry.close_time
f"close_date={trade_entry.close_date}")
lock_pair_until[pair] = trade_entry.close_date
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
@ -394,10 +402,9 @@ class Backtesting:
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = history.get_timerange(preprocessed)
logger.info(
'Backtesting with data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest(
processed=preprocessed,
@ -408,7 +415,10 @@ class Backtesting:
position_stacking=position_stacking,
)
stats = generate_backtest_stats(self.config, data, all_results,
min_date=min_date, max_date=max_date)
if self.config.get('export', False):
store_backtest_result(self.config['exportfilename'], all_results)
store_backtest_stats(self.config['exportfilename'], stats)
# Show backtest results
show_backtest_results(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)

75
freqtrade/optimize/hyperopt.py
View File

@ -4,27 +4,28 @@
This module contains the hyperopt logic
"""
import io
import locale
import logging
import random
import warnings
from math import ceil
from collections import OrderedDict
from math import ceil
from operator import itemgetter
from pathlib import Path
from pprint import pprint
from pprint import pformat
from typing import Any, Dict, List, Optional
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 pandas import DataFrame, json_normalize, isna
import progressbar
import tabulate
from os import path
import io
from pandas import DataFrame, isna, json_normalize
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import get_timerange
from freqtrade.exceptions import OperationalException
@ -32,9 +33,11 @@ from freqtrade.misc import plural, round_dict
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import \
IHyperOptLoss # noqa: F401
from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
HyperOptResolver)
from freqtrade.strategy import IStrategy
# Suppress scikit-learn FutureWarnings from skopt
with warnings.catch_warnings():
@ -230,6 +233,9 @@ 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
@ -244,11 +250,24 @@ 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:
@ -296,11 +315,16 @@ class Hyperopt:
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
trials.columns = ['Best', 'Epoch', 'Trades', 'W/D/L', 'Avg profit', 'Total profit',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
@ -374,7 +398,7 @@ class Hyperopt:
return
# Verification for overwrite
if path.isfile(csv_file):
if Path(csv_file).is_file():
logger.error(f"CSV file already exists: {csv_file}")
return
@ -542,9 +566,17 @@ class Hyperopt:
}
def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
wins = len(backtesting_results[backtesting_results.profit_percent > 0])
draws = len(backtesting_results[backtesting_results.profit_percent == 0])
losses = len(backtesting_results[backtesting_results.profit_percent < 0])
return {
'trade_count': len(backtesting_results.index),
'wins': wins,
'draws': draws,
'losses': losses,
'winsdrawslosses': f"{wins}/{draws}/{losses}",
'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
'median_profit': backtesting_results.profit_percent.median() * 100.0,
'total_profit': backtesting_results.profit_abs.sum(),
'profit': backtesting_results.profit_percent.sum() * 100.0,
'duration': backtesting_results.trade_duration.mean(),
@ -556,7 +588,10 @@ class Hyperopt:
"""
stake_cur = self.config['stake_currency']
return (f"{results_metrics['trade_count']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
f"Median profit {results_metrics['median_profit']: 6.2f}%. "
f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
f"Avg duration {results_metrics['duration']:5.1f} min."
@ -609,15 +644,17 @@ class Hyperopt:
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = get_timerange(data)
logger.info(
'Hyperopting with data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
dump(preprocessed, self.data_pickle_file)
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore
self.backtesting.strategy.dp = None # type: ignore
IStrategy.dp = None # type: ignore
self.epochs = self.load_previous_results(self.results_file)
@ -628,6 +665,10 @@ class Hyperopt:
self.dimensions: List[Dimension] = self.hyperopt_space()
self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.print_colorized:
colorama_init(autoreset=True)
try:
with Parallel(n_jobs=config_jobs) as parallel:
jobs = parallel._effective_n_jobs()

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_daily.py
View File

@ -43,7 +43,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
normalize=True)
sum_daily = (
results.resample(resample_freq, on='close_time').agg(
results.resample(resample_freq, on='close_date').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
)

2
freqtrade/optimize/hyperopt_loss_sortino_daily.py
View File

@ -45,7 +45,7 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
normalize=True)
sum_daily = (
results.resample(resample_freq, on='close_time').agg(
results.resample(resample_freq, on='close_date').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
)

494
freqtrade/optimize/optimize_reports.py
View File

@ -1,186 +1,166 @@
import logging
from datetime import timedelta
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Dict
from typing import Any, Dict, List
from arrow import Arrow
from pandas import DataFrame
from numpy import int64
from tabulate import tabulate
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import calculate_max_drawdown, calculate_market_change
from freqtrade.misc import file_dump_json
logger = logging.getLogger(__name__)
def store_backtest_result(recordfilename: Path, all_results: Dict[str, DataFrame]) -> None:
def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> None:
"""
Stores backtest results to file (one file per strategy)
:param recordfilename: Destination filename
:param all_results: Dict of Dataframes, one results dataframe per strategy
Stores backtest results
:param recordfilename: Path object, which can either be a filename or a directory.
Filenames will be appended with a timestamp right before the suffix
while for diectories, <directory>/backtest-result-<datetime>.json will be used as filename
:param stats: Dataframe containing the backtesting statistics
"""
for strategy, results in all_results.items():
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:
filename = recordfilename
if len(all_results) > 1:
# Inject strategy to filename
if recordfilename.is_dir():
filename = (recordfilename /
f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.json')
else:
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)
f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
).with_suffix(recordfilename.suffix)
file_dump_json(filename, stats)
latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
results: DataFrame, skip_nan: bool = False) -> str:
def _get_line_floatfmt() -> List[str]:
"""
Generates and returns a text table for the given backtest data and the results dataframe
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() if len(result) > 0 else 0.0,
'profit_mean_pct': result['profit_percent'].mean() * 100.0 if len(result) > 0 else 0.0,
'profit_sum': result['profit_percent'].sum(),
'profit_sum_pct': result['profit_percent'].sum() * 100.0,
'profit_total_abs': result['profit_abs'].sum(),
'profit_total': result['profit_percent'].sum() / max_open_trades,
'profit_total_pct': result['profit_percent'].sum() * 100.0 / max_open_trades,
'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(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 stake_currency: Stakecurrency used
:param max_open_trades: Max_open_trades parameter
:param results: Dataframe containing the backtest results
:return: pretty printed table with tabulate as string
: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_total_pct': 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',
@ -204,40 +184,264 @@ def generate_edge_table(results: dict) -> str:
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def show_backtest_results(config: Dict, btdata: Dict[str, DataFrame],
all_results: Dict[str, DataFrame]):
def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
if len(results) == 0:
return {
'backtest_best_day': 0,
'backtest_worst_day': 0,
'winning_days': 0,
'draw_days': 0,
'losing_days': 0,
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
daily_profit = results.resample('1d', on='close_date')['profit_percent'].sum()
worst = min(daily_profit)
best = max(daily_profit)
winning_days = sum(daily_profit > 0)
draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0)
winning_trades = results.loc[results['profit_percent'] > 0]
losing_trades = results.loc[results['profit_percent'] < 0]
return {
'backtest_best_day': best,
'backtest_worst_day': worst,
'winning_days': winning_days,
'draw_days': draw_days,
'losing_days': losing_days,
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta()),
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta()),
}
def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
all_results: Dict[str, DataFrame],
min_date: Arrow, max_date: Arrow
) -> Dict[str, Any]:
"""
:param config: Configuration object used for backtest
:param btdata: Backtest data
:param all_results: backtest result - dictionary with { Strategy: results}.
:param min_date: Backtest start date
:param max_date: Backtest end date
: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': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, results in all_results.items():
print(f"Result for strategy {strategy}")
table = generate_text_table(btdata, stake_currency=config['stake_currency'],
max_open_trades=config['max_open_trades'],
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)
daily_stats = generate_daily_stats(results)
results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
backtest_days = (max_date - min_date).days
strat_stats = {
'trades': results.to_dict(orient='records'),
'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
'profit_mean': results['profit_percent'].mean() if len(results) > 0 else 0,
'profit_total': results['profit_percent'].sum(),
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.datetime,
'backtest_start_ts': min_date.timestamp * 1000,
'backtest_end': max_date.datetime,
'backtest_end_ts': max_date.timestamp * 1000,
'backtest_days': backtest_days,
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'market_change': market_change,
'pairlist': list(btdata.keys()),
'stake_amount': config['stake_amount'],
'stake_currency': config['stake_currency'],
'max_open_trades': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe': config['timeframe'],
**daily_stats,
}
result['strategy'][strategy] = strat_stats
try:
max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
results, value_col='profit_percent')
strat_stats.update({
'max_drawdown': max_drawdown,
'drawdown_start': drawdown_start,
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end,
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
})
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_start_ts': 0,
'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_end_ts': 0,
})
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_total_pct'],
] 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 text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
min_trade = min(strat_results['trades'], key=lambda x: x['open_date'])
metrics = [
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
('Total trades', strat_results['total_trades']),
('First trade', min_trade['open_date'].strftime(DATETIME_PRINT_FORMAT)),
('First trade Pair', min_trade['pair']),
('Total Profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Trades per day', strat_results['trades_per_day']),
('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
('Days win/draw/lose', f"{strat_results['winning_days']} / "
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
('', ''), # Empty line to improve readability
('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
]
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
else:
return ''
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 = generate_text_table_sell_reason(stake_currency=config['stake_currency'],
max_open_trades=config['max_open_trades'],
results=results)
if isinstance(table, str):
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = generate_text_table(btdata,
stake_currency=config['stake_currency'],
max_open_trades=config['max_open_trades'],
results=results.loc[results.open_at_end], skip_nan=True)
if isinstance(table, str):
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str):
table = text_table_add_metrics(results)
if isinstance(table, str) and len(table) > 0:
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0]))
print()
if len(all_results) > 1:
if len(backtest_stats['strategy']) > 1:
# Print Strategy summary table
table = generate_text_table_strategy(config['stake_currency'],
config['max_open_trades'],
all_results=all_results)
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]))

83
freqtrade/pairlist/AgeFilter.py Normal file
View File

@ -0,0 +1,83 @@
"""
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 to be >= 1")
if self._min_days_listed > exchange.ohlcv_candle_limit:
raise OperationalException("AgeFilter requires min_days_listed to not exceed "
"exchange max request size "
f"({exchange.ohlcv_candle_limit})")
@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

61
freqtrade/pairlist/IPairList.py
View File

@ -3,10 +3,12 @@ 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
@ -25,6 +27,8 @@ class IPairList(ABC):
: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
@ -64,7 +68,7 @@ class IPairList(ABC):
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
"""
@ -75,16 +79,59 @@ 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)
return pairlist
def verify_blacklist(self, pairlist: List[str], logmethod) -> List[str]:
"""
@ -103,6 +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:
@ -112,6 +162,11 @@ class IPairList(ABC):
f"{self._exchange.name}. Removing it from whitelist..")
continue
if not self._exchange.market_is_tradable(markets[pair]):
logger.warning(f"Pair {pair} is not tradable with Freqtrade."
"Removing it from whitelist..")
continue
if self._exchange.get_pair_quote_currency(pair) != self._config['stake_currency']:
logger.warning(f"Pair {pair} is not compatible with your stake currency "
f"{self._config['stake_currency']}. Removing it from whitelist..")

34
freqtrade/pairlist/PrecisionFilter.py
View File

@ -2,11 +2,10 @@
Precision pair list filter
"""
import logging
from copy import deepcopy
from typing import Any, Dict, List
from typing import Any, Dict
from freqtrade.pairlist.IPairList import IPairList
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
@ -18,14 +17,21 @@ class PrecisionFilter(IPairList):
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
if 'stoploss' not in self._config:
raise OperationalException(
'PrecisionFilter can only work with stoploss defined. Please add the '
'stoploss key to your configuration (overwrites eventual strategy settings).')
self._stoploss = self._config['stoploss']
self._enabled = self._stoploss != 0
# Precalculate sanitized stoploss value to avoid recalculation for every pair
self._stoploss = 1 - abs(self._config['stoploss'])
self._stoploss = 1 - abs(self._stoploss)
@property
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
"""
return True
@ -36,16 +42,14 @@ class PrecisionFilter(IPairList):
"""
return f"{self.name} - Filtering untradable pairs."
def _validate_precision_filter(self, ticker: dict, stoploss: float) -> bool:
def _validate_pair(self, ticker: dict) -> bool:
"""
Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
low value pairs.
:param ticker: ticker dict as returned from ccxt.load_markets()
:param stoploss: stoploss value as set in the configuration
(already cleaned to be 1 - stoploss)
:return: True if the pair can stay, False if it should be removed
"""
stop_price = ticker['ask'] * stoploss
stop_price = ticker['ask'] * self._stoploss
# Adjust stop-prices to precision
sp = self._exchange.price_to_precision(ticker["symbol"], stop_price)
@ -60,15 +64,3 @@ class PrecisionFilter(IPairList):
return False
return True
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlists and assigns and returns them again.
"""
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
# Filter out assets which would not allow setting a stoploss
if not self._validate_precision_filter(tickers[p], self._stoploss):
pairlist.remove(p)
return pairlist

69
freqtrade/pairlist/PriceFilter.py
View File

@ -2,9 +2,9 @@
Price pair list filter
"""
import logging
from copy import deepcopy
from typing import Any, Dict, List
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
from freqtrade.pairlist.IPairList import IPairList
@ -19,12 +19,23 @@ class PriceFilter(IPairList):
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._low_price_ratio = pairlistconfig.get('low_price_ratio', 0)
if self._low_price_ratio < 0:
raise OperationalException("PriceFilter requires low_price_ratio to be >= 0")
self._min_price = pairlistconfig.get('min_price', 0)
if self._min_price < 0:
raise OperationalException("PriceFilter requires min_price to be >= 0")
self._max_price = pairlistconfig.get('max_price', 0)
if self._max_price < 0:
raise OperationalException("PriceFilter requires max_price to be >= 0")
self._enabled = ((self._low_price_ratio > 0) or
(self._min_price > 0) or
(self._max_price > 0))
@property
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
"""
return True
@ -33,40 +44,52 @@ class PriceFilter(IPairList):
"""
Short whitelist method description - used for startup-messages
"""
return f"{self.name} - Filtering pairs priced below {self._low_price_ratio * 100}%."
active_price_filters = []
if self._low_price_ratio != 0:
active_price_filters.append(f"below {self._low_price_ratio * 100}%")
if self._min_price != 0:
active_price_filters.append(f"below {self._min_price:.8f}")
if self._max_price != 0:
active_price_filters.append(f"above {self._max_price:.8f}")
def _validate_ticker_lowprice(self, ticker) -> bool:
if len(active_price_filters):
return f"{self.name} - Filtering pairs priced {' or '.join(active_price_filters)}."
return f"{self.name} - No price filters configured."
def _validate_pair(self, ticker) -> bool:
"""
Check if if one price-step (pip) is > than a certain barrier.
:param ticker: ticker dict as returned from ccxt.load_markets()
:return: True if the pair can stay, false if it should be removed
"""
if ticker['last'] is None:
if ticker['last'] is None or ticker['last'] == 0:
self.log_on_refresh(logger.info,
f"Removed {ticker['symbol']} from whitelist, because "
"ticker['last'] is empty (Usually no trade in the last 24h).")
return False
# Perform low_price_ratio check.
if self._low_price_ratio != 0:
compare = self._exchange.price_get_one_pip(ticker['symbol'], ticker['last'])
changeperc = compare / ticker['last']
if changeperc > self._low_price_ratio:
self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
f"because 1 unit is {changeperc * 100:.3f}%")
return False
# Perform min_price check.
if self._min_price != 0:
if ticker['last'] < self._min_price:
self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
f"because last price < {self._min_price:.8f}")
return False
# Perform max_price check.
if self._max_price != 0:
if ticker['last'] > self._max_price:
self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
f"because last price > {self._max_price:.8f}")
return False
return True
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
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
if self._low_price_ratio:
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
# Filter out assets which would not allow setting a stoploss
if not self._validate_ticker_lowprice(tickers[p]):
pairlist.remove(p)
return pairlist

7
freqtrade/pairlist/ShuffleFilter.py
View File

@ -3,7 +3,7 @@ Shuffle pair list filter
"""
import logging
import random
from typing import Dict, List
from typing import Any, Dict, List
from freqtrade.pairlist.IPairList import IPairList
@ -13,7 +13,8 @@ logger = logging.getLogger(__name__)
class ShuffleFilter(IPairList):
def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
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)
@ -24,7 +25,7 @@ class ShuffleFilter(IPairList):
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
"""
return False

28
freqtrade/pairlist/SpreadFilter.py
View File

@ -2,8 +2,7 @@
Spread pair list filter
"""
import logging
from copy import deepcopy
from typing import Dict, List
from typing import Any, Dict
from freqtrade.pairlist.IPairList import IPairList
@ -13,17 +12,19 @@ logger = logging.getLogger(__name__)
class SpreadFilter(IPairList):
def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
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._max_spread_ratio = pairlistconfig.get('max_spread_ratio', 0.005)
self._enabled = self._max_spread_ratio != 0
@property
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
"""
return True
@ -35,7 +36,7 @@ class SpreadFilter(IPairList):
return (f"{self.name} - Filtering pairs with ask/bid diff above "
f"{self._max_spread_ratio * 100}%.")
def _validate_spread(self, ticker: dict) -> bool:
def _validate_pair(self, ticker: dict) -> bool:
"""
Validate spread for the ticker
:param ticker: ticker dict as returned from ccxt.load_markets()
@ -51,20 +52,3 @@ class SpreadFilter(IPairList):
else:
return True
return False
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
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
ticker = tickers[p]
# Filter out assets
if not self._validate_spread(ticker):
pairlist.remove(p)
return pairlist

25
freqtrade/pairlist/StaticPairList.py
View File

@ -4,8 +4,9 @@ Static Pair List provider
Provides pair white list as it configured in config
"""
import logging
from typing import Dict, List
from typing import Any, Dict, List
from freqtrade.exceptions import OperationalException
from freqtrade.pairlist.IPairList import IPairList
@ -14,11 +15,20 @@ logger = logging.getLogger(__name__)
class StaticPairList(IPairList):
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)
if self._pairlist_pos != 0:
raise OperationalException(f"{self.name} can only be used in the first position "
"in the list of Pairlist Handlers.")
@property
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
"""
return False
@ -30,6 +40,15 @@ class StaticPairList(IPairList):
"""
return f"{self.name}"
def gen_pairlist(self, cached_pairlist: List[str], tickers: Dict) -> List[str]:
"""
Generate the pairlist
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:return: List of pairs
"""
return self._whitelist_for_active_markets(self._config['exchange']['pair_whitelist'])
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlist and returns the whitelist again.
@ -38,4 +57,4 @@ class StaticPairList(IPairList):
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
return self._whitelist_for_active_markets(self._config['exchange']['pair_whitelist'])
return pairlist

67
freqtrade/pairlist/VolumePairList.py
View File

@ -19,7 +19,8 @@ SORT_VALUES = ['askVolume', 'bidVolume', 'quoteVolume']
class VolumePairList(IPairList):
def __init__(self, exchange, pairlistmanager, config: Dict[str, Any], pairlistconfig: dict,
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)
@ -36,8 +37,8 @@ class VolumePairList(IPairList):
if not self._exchange.exchange_has('fetchTickers'):
raise OperationalException(
'Exchange does not support dynamic whitelist.'
'Please edit your config and restart the bot'
'Exchange does not support dynamic whitelist. '
'Please edit your config and restart the bot.'
)
if not self._validate_keys(self._sort_key):
@ -53,7 +54,7 @@ class VolumePairList(IPairList):
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
"""
return True
@ -67,6 +68,31 @@ class VolumePairList(IPairList):
"""
return f"{self.name} - top {self._pairlistconfig['number_assets']} volume pairs."
def gen_pairlist(self, cached_pairlist: List[str], tickers: Dict) -> List[str]:
"""
Generate the pairlist
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:return: List of pairs
"""
# Generate dynamic whitelist
# Must always run if this pairlist is not the first in the list.
if self._last_refresh + self.refresh_period < datetime.now().timestamp():
self._last_refresh = int(datetime.now().timestamp())
# Use fresh pairlist
# Check if pair quote currency equals to the stake currency.
filtered_tickers = [
v for k, v in tickers.items()
if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
and v[self._sort_key] is not None)]
pairlist = [s['symbol'] for s in filtered_tickers]
else:
# Use the cached pairlist if it's not time yet to refresh
pairlist = cached_pairlist
return pairlist
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlist and returns the whitelist again.
@ -75,36 +101,7 @@ class VolumePairList(IPairList):
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
# Generate dynamic whitelist
# Must always run if this pairlist is not the first in the list.
if (self._pairlist_pos != 0 or
(self._last_refresh + self.refresh_period < datetime.now().timestamp())):
self._last_refresh = int(datetime.now().timestamp())
pairs = self._gen_pair_whitelist(pairlist, tickers)
else:
pairs = pairlist
self.log_on_refresh(logger.info, f"Searching {self._number_pairs} pairs: {pairs}")
return pairs
def _gen_pair_whitelist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Updates the whitelist with with a dynamically generated list
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()).
:return: List of pairs
"""
if self._pairlist_pos == 0:
# If VolumePairList is the first in the list, use fresh pairlist
# Check if pair quote currency equals to the stake currency.
filtered_tickers = [
v for k, v in tickers.items()
if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
and v[self._sort_key] is not None)]
else:
# If other pairlist is in front, use the incoming pairlist.
# Use the incoming pairlist.
filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
if self._min_value > 0:
@ -119,4 +116,6 @@ class VolumePairList(IPairList):
# Limit pairlist to the requested number of pairs
pairs = pairs[:self._number_pairs]
self.log_on_refresh(logger.info, f"Searching {self._number_pairs} pairs: {pairs}")
return pairs

9
freqtrade/pairlist/pairlistmanager.py
View File

@ -10,7 +10,7 @@ from cachetools import TTLCache, cached
from freqtrade.exceptions import OperationalException
from freqtrade.pairlist.IPairList import IPairList
from freqtrade.resolvers import PairListResolver
from freqtrade.typing import ListPairsWithTimeframes
from freqtrade.constants import ListPairsWithTimeframes
logger = logging.getLogger(__name__)
@ -87,6 +87,9 @@ class PairListManager():
# Adjust whitelist if filters are using tickers
pairlist = self._prepare_whitelist(self._whitelist.copy(), tickers)
# Generate the pairlist with first Pairlist Handler in the chain
pairlist = self._pairlist_handlers[0].gen_pairlist(self._whitelist, tickers)
# Process all Pairlist Handlers in the chain
for pairlist_handler in self._pairlist_handlers:
pairlist = pairlist_handler.filter_pairlist(pairlist, tickers)
@ -128,6 +131,6 @@ class PairListManager():
def create_pair_list(self, pairs: List[str], timeframe: str = None) -> ListPairsWithTimeframes:
"""
Create list of pair tuples with (pair, ticker_interval)
Create list of pair tuples with (pair, timeframe)
"""
return [(pair, timeframe or self._config['ticker_interval']) for pair in pairs]
return [(pair, timeframe or self._config['timeframe']) for pair in pairs]

77
freqtrade/persistence.py
View File

@ -2,7 +2,7 @@
This module contains the class to persist trades into SQLite
"""
import logging
from datetime import datetime
from datetime import datetime, timezone
from decimal import Decimal
from typing import Any, Dict, List, Optional
@ -17,6 +17,7 @@ from sqlalchemy.orm.session import sessionmaker
from sqlalchemy.pool import StaticPool
from freqtrade.exceptions import OperationalException
from freqtrade.misc import safe_value_fallback
logger = logging.getLogger(__name__)
@ -86,7 +87,7 @@ def check_migrate(engine) -> None:
logger.debug(f'trying {table_back_name}')
# Check for latest column
if not has_column(cols, 'sell_order_status'):
if not has_column(cols, 'amount_requested'):
logger.info(f'Running database migration - backup available as {table_back_name}')
fee_open = get_column_def(cols, 'fee_open', 'fee')
@ -107,13 +108,19 @@ def check_migrate(engine) -> None:
min_rate = get_column_def(cols, 'min_rate', 'null')
sell_reason = get_column_def(cols, 'sell_reason', 'null')
strategy = get_column_def(cols, 'strategy', 'null')
ticker_interval = get_column_def(cols, 'ticker_interval', 'null')
# If ticker-interval existed use that, else null.
if has_column(cols, 'ticker_interval'):
timeframe = get_column_def(cols, 'timeframe', 'ticker_interval')
else:
timeframe = get_column_def(cols, 'timeframe', 'null')
open_trade_price = get_column_def(cols, 'open_trade_price',
f'amount * open_rate * (1 + {fee_open})')
close_profit_abs = get_column_def(
cols, 'close_profit_abs',
f"(amount * close_rate * (1 - {fee_close})) - {open_trade_price}")
sell_order_status = get_column_def(cols, 'sell_order_status', 'null')
amount_requested = get_column_def(cols, 'amount_requested', 'amount')
# Schema migration necessary
engine.execute(f"alter table trades rename to {table_back_name}")
@ -129,11 +136,11 @@ def check_migrate(engine) -> None:
fee_open, fee_open_cost, fee_open_currency,
fee_close, fee_close_cost, fee_open_currency, open_rate,
open_rate_requested, close_rate, close_rate_requested, close_profit,
stake_amount, amount, open_date, close_date, open_order_id,
stake_amount, amount, amount_requested, open_date, close_date, open_order_id,
stop_loss, stop_loss_pct, initial_stop_loss, initial_stop_loss_pct,
stoploss_order_id, stoploss_last_update,
max_rate, min_rate, sell_reason, sell_order_status, strategy,
ticker_interval, open_trade_price, close_profit_abs
timeframe, open_trade_price, close_profit_abs
)
select id, lower(exchange),
case
@ -148,14 +155,14 @@ def check_migrate(engine) -> None:
{fee_close_cost} fee_close_cost, {fee_close_currency} fee_close_currency,
open_rate, {open_rate_requested} open_rate_requested, close_rate,
{close_rate_requested} close_rate_requested, close_profit,
stake_amount, amount, open_date, close_date, open_order_id,
stake_amount, amount, {amount_requested}, open_date, close_date, open_order_id,
{stop_loss} stop_loss, {stop_loss_pct} stop_loss_pct,
{initial_stop_loss} initial_stop_loss,
{initial_stop_loss_pct} initial_stop_loss_pct,
{stoploss_order_id} stoploss_order_id, {stoploss_last_update} stoploss_last_update,
{max_rate} max_rate, {min_rate} min_rate, {sell_reason} sell_reason,
{sell_order_status} sell_order_status,
{strategy} strategy, {ticker_interval} ticker_interval,
{strategy} strategy, {timeframe} timeframe,
{open_trade_price} open_trade_price, {close_profit_abs} close_profit_abs
from {table_back_name}
""")
@ -210,6 +217,7 @@ class Trade(_DECL_BASE):
close_profit_abs = Column(Float)
stake_amount = Column(Float, nullable=False)
amount = Column(Float)
amount_requested = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
close_date = Column(DateTime)
open_order_id = Column(String)
@ -232,7 +240,7 @@ class Trade(_DECL_BASE):
sell_reason = Column(String, nullable=True)
sell_order_status = Column(String, nullable=True)
strategy = Column(String, nullable=True)
ticker_interval = Column(Integer, nullable=True)
timeframe = Column(Integer, nullable=True)
def __init__(self, **kwargs):
super().__init__(**kwargs)
@ -249,37 +257,59 @@ class Trade(_DECL_BASE):
'trade_id': self.id,
'pair': self.pair,
'is_open': self.is_open,
'exchange': self.exchange,
'amount': round(self.amount, 8),
'amount_requested': round(self.amount_requested, 8) if self.amount_requested else None,
'stake_amount': round(self.stake_amount, 8),
'strategy': self.strategy,
'ticker_interval': self.timeframe, # DEPRECATED
'timeframe': self.timeframe,
'fee_open': self.fee_open,
'fee_open_cost': self.fee_open_cost,
'fee_open_currency': self.fee_open_currency,
'fee_close': self.fee_close,
'fee_close_cost': self.fee_close_cost,
'fee_close_currency': self.fee_close_currency,
'open_date_hum': arrow.get(self.open_date).humanize(),
'open_date': self.open_date.strftime("%Y-%m-%d %H:%M:%S"),
'open_timestamp': int(self.open_date.replace(tzinfo=timezone.utc).timestamp() * 1000),
'open_rate': self.open_rate,
'open_rate_requested': self.open_rate_requested,
'open_trade_price': round(self.open_trade_price, 8),
'close_date_hum': (arrow.get(self.close_date).humanize()
if self.close_date else None),
'close_date': (self.close_date.strftime("%Y-%m-%d %H:%M:%S")
if self.close_date else None),
'open_rate': self.open_rate,
'open_rate_requested': self.open_rate_requested,
'open_trade_price': self.open_trade_price,
'close_timestamp': int(self.close_date.replace(
tzinfo=timezone.utc).timestamp() * 1000) if self.close_date else None,
'close_rate': self.close_rate,
'close_rate_requested': self.close_rate_requested,
'amount': round(self.amount, 8),
'stake_amount': round(self.stake_amount, 8),
'close_profit': self.close_profit,
'close_profit_abs': self.close_profit_abs,
'sell_reason': self.sell_reason,
'sell_order_status': self.sell_order_status,
'stop_loss': self.stop_loss,
'stop_loss': self.stop_loss, # Deprecated - should not be used
'stop_loss_abs': self.stop_loss,
'stop_loss_ratio': self.stop_loss_pct if self.stop_loss_pct else None,
'stop_loss_pct': (self.stop_loss_pct * 100) if self.stop_loss_pct else None,
'initial_stop_loss': self.initial_stop_loss,
'stoploss_order_id': self.stoploss_order_id,
'stoploss_last_update': (self.stoploss_last_update.strftime("%Y-%m-%d %H:%M:%S")
if self.stoploss_last_update else None),
'stoploss_last_update_timestamp': int(self.stoploss_last_update.replace(
tzinfo=timezone.utc).timestamp() * 1000) if self.stoploss_last_update else None,
'initial_stop_loss': self.initial_stop_loss, # Deprecated - should not be used
'initial_stop_loss_abs': self.initial_stop_loss,
'initial_stop_loss_ratio': (self.initial_stop_loss_pct
if self.initial_stop_loss_pct else None),
'initial_stop_loss_pct': (self.initial_stop_loss_pct * 100
if self.initial_stop_loss_pct else None),
'min_rate': self.min_rate,
'max_rate': self.max_rate,
'strategy': self.strategy,
'ticker_interval': self.ticker_interval,
'open_order_id': self.open_order_id,
}
@ -335,27 +365,27 @@ class Trade(_DECL_BASE):
def update(self, order: Dict) -> None:
"""
Updates this entity with amount and actual open/close rates.
:param order: order retrieved by exchange.get_order()
:param order: order retrieved by exchange.fetch_order()
:return: None
"""
order_type = order['type']
# Ignore open and cancelled orders
if order['status'] == 'open' or order['price'] is None:
if order['status'] == 'open' or safe_value_fallback(order, 'average', 'price') is None:
return
logger.info('Updating trade (id=%s) ...', self.id)
if order_type in ('market', 'limit') and order['side'] == 'buy':
# Update open rate and actual amount
self.open_rate = Decimal(order['price'])
self.amount = Decimal(order.get('filled', order['amount']))
self.open_rate = Decimal(safe_value_fallback(order, 'average', 'price'))
self.amount = Decimal(safe_value_fallback(order, 'filled', 'amount'))
self.recalc_open_trade_price()
logger.info('%s_BUY has been fulfilled for %s.', order_type.upper(), self)
self.open_order_id = None
elif order_type in ('market', 'limit') and order['side'] == 'sell':
self.close(order['price'])
self.close(safe_value_fallback(order, 'average', 'price'))
logger.info('%s_SELL has been fulfilled for %s.', order_type.upper(), self)
elif order_type in ('stop_loss_limit', 'stop-loss'):
elif order_type in ('stop_loss_limit', 'stop-loss', 'stop'):
self.stoploss_order_id = None
self.close_rate_requested = self.stop_loss
logger.info('%s is hit for %s.', order_type.upper(), self)
@ -544,6 +574,7 @@ class Trade(_DECL_BASE):
def get_best_pair():
"""
Get best pair with closed trade.
:returns: Tuple containing (pair, profit_sum)
"""
best_pair = Trade.session.query(
Trade.pair, func.sum(Trade.close_profit).label('profit_sum')

60
freqtrade/plot/plotting.py
View File

@ -8,12 +8,16 @@ from freqtrade.configuration import TimeRange
from freqtrade.data.btanalysis import (calculate_max_drawdown,
combine_dataframes_with_mean,
create_cum_profit,
extract_trades_of_period, load_trades)
extract_trades_of_period,
load_trades)
from freqtrade.data.converter import trim_dataframe
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history import load_data
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.misc import pair_to_filename
from freqtrade.resolvers import StrategyResolver
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.strategy import IStrategy
logger = logging.getLogger(__name__)
@ -44,25 +48,28 @@ def init_plotscript(config):
data = load_data(
datadir=config.get("datadir"),
pairs=pairs,
timeframe=config.get('ticker_interval', '5m'),
timeframe=config.get('timeframe', '5m'),
timerange=timerange,
data_format=config.get('dataformat_ohlcv', 'json'),
)
no_trades = False
filename = config.get('exportfilename')
if config.get('no_trades', False):
no_trades = True
elif not config['exportfilename'].is_file() and config['trade_source'] == 'file':
elif config['trade_source'] == 'file':
if not filename.is_dir() and not filename.is_file():
logger.warning("Backtest file is missing skipping trades.")
no_trades = True
trades = load_trades(
config['trade_source'],
db_url=config.get('db_url'),
exportfilename=config.get('exportfilename'),
no_trades=no_trades
exportfilename=filename,
no_trades=no_trades,
strategy=config.get("strategy"),
)
trades = trim_dataframe(trades, timerange, 'open_time')
trades = trim_dataframe(trades, timerange, 'open_date')
return {"ohlcv": data,
"trades": trades,
@ -161,11 +168,12 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
# Trades can be empty
if trades is not None and len(trades) > 0:
# Create description for sell summarizing the trade
trades['desc'] = trades.apply(lambda row: f"{round(row['profitperc'] * 100, 1)}%, "
f"{row['sell_reason']}, {row['duration']} min",
trades['desc'] = trades.apply(lambda row: f"{round(row['profit_percent'] * 100, 1)}%, "
f"{row['sell_reason']}, "
f"{row['trade_duration']} min",
axis=1)
trade_buys = go.Scatter(
x=trades["open_time"],
x=trades["open_date"],
y=trades["open_rate"],
mode='markers',
name='Trade buy',
@ -180,9 +188,9 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
)
trade_sells = go.Scatter(
x=trades.loc[trades['profitperc'] > 0, "close_time"],
y=trades.loc[trades['profitperc'] > 0, "close_rate"],
text=trades.loc[trades['profitperc'] > 0, "desc"],
x=trades.loc[trades['profit_percent'] > 0, "close_date"],
y=trades.loc[trades['profit_percent'] > 0, "close_rate"],
text=trades.loc[trades['profit_percent'] > 0, "desc"],
mode='markers',
name='Sell - Profit',
marker=dict(
@ -193,9 +201,9 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
)
)
trade_sells_loss = go.Scatter(
x=trades.loc[trades['profitperc'] <= 0, "close_time"],
y=trades.loc[trades['profitperc'] <= 0, "close_rate"],
text=trades.loc[trades['profitperc'] <= 0, "desc"],
x=trades.loc[trades['profit_percent'] <= 0, "close_date"],
y=trades.loc[trades['profit_percent'] <= 0, "close_rate"],
text=trades.loc[trades['profit_percent'] <= 0, "desc"],
mode='markers',
name='Sell - Loss',
marker=dict(
@ -414,9 +422,12 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
for pair in pairs:
profit_col = f'cum_profit_{pair}'
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col, timeframe)
try:
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col,
timeframe)
fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
except ValueError:
pass
return fig
@ -463,6 +474,8 @@ def load_and_plot_trades(config: Dict[str, Any]):
"""
strategy = StrategyResolver.load_strategy(config)
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
IStrategy.dp = DataProvider(config, exchange)
plot_elements = init_plotscript(config)
trades = plot_elements['trades']
pair_counter = 0
@ -483,7 +496,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
plot_config=strategy.plot_config if hasattr(strategy, 'plot_config') else {}
)
store_plot_file(fig, filename=generate_plot_filename(pair, config['ticker_interval']),
store_plot_file(fig, filename=generate_plot_filename(pair, config['timeframe']),
directory=config['user_data_dir'] / "plot")
logger.info('End of plotting process. %s plots generated', pair_counter)
@ -502,12 +515,15 @@ def plot_profit(config: Dict[str, Any]) -> None:
# Remove open pairs - we don't know the profit yet so can't calculate profit for these.
# Also, If only one open pair is left, then the profit-generation would fail.
trades = trades[(trades['pair'].isin(plot_elements["pairs"]))
& (~trades['close_time'].isnull())
& (~trades['close_date'].isnull())
]
if len(trades) == 0:
raise OperationalException("No trades found, cannot generate Profit-plot without "
"trades from either Backtest result or database.")
# Create an average close price of all the pairs that were involved.
# this could be useful to gauge the overall market trend
fig = generate_profit_graph(plot_elements["pairs"], plot_elements["ohlcv"],
trades, config.get('ticker_interval', '5m'))
trades, config.get('timeframe', '5m'))
store_plot_file(fig, filename='freqtrade-profit-plot.html',
directory=config['user_data_dir'] / "plot", auto_open=True)

13
freqtrade/resolvers/hyperopt_resolver.py
View File

@ -23,7 +23,7 @@ class HyperOptResolver(IResolver):
object_type = IHyperOpt
object_type_str = "Hyperopt"
user_subdir = USERPATH_HYPEROPTS
initial_search_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
initial_search_path = None
@staticmethod
def load_hyperopt(config: Dict) -> IHyperOpt:
@ -42,13 +42,13 @@ class HyperOptResolver(IResolver):
extra_dir=config.get('hyperopt_path'))
if not hasattr(hyperopt, 'populate_indicators'):
logger.warning("Hyperopt class does not provide populate_indicators() method. "
logger.info("Hyperopt class does not provide populate_indicators() method. "
"Using populate_indicators from the strategy.")
if not hasattr(hyperopt, 'populate_buy_trend'):
logger.warning("Hyperopt class does not provide populate_buy_trend() method. "
logger.info("Hyperopt class does not provide populate_buy_trend() method. "
"Using populate_buy_trend from the strategy.")
if not hasattr(hyperopt, 'populate_sell_trend'):
logger.warning("Hyperopt class does not provide populate_sell_trend() method. "
logger.info("Hyperopt class does not provide populate_sell_trend() method. "
"Using populate_sell_trend from the strategy.")
return hyperopt
@ -77,8 +77,9 @@ class HyperOptLossResolver(IResolver):
config, kwargs={},
extra_dir=config.get('hyperopt_path'))
# Assign ticker_interval to be used in hyperopt
hyperoptloss.__class__.ticker_interval = str(config['ticker_interval'])
# Assign timeframe to be used in hyperopt
hyperoptloss.__class__.ticker_interval = str(config['timeframe'])
hyperoptloss.__class__.timeframe = str(config['timeframe'])
if not hasattr(hyperoptloss, 'hyperopt_loss_function'):
raise OperationalException(

60
freqtrade/resolvers/strategy_resolver.py
View File

@ -50,39 +50,51 @@ class StrategyResolver(IResolver):
if 'ask_strategy' not in config:
config['ask_strategy'] = {}
if hasattr(strategy, 'ticker_interval') and not hasattr(strategy, 'timeframe'):
# Assign ticker_interval to timeframe to keep compatibility
if 'timeframe' not in config:
logger.warning(
"DEPRECATED: Please migrate to using 'timeframe' instead of 'ticker_interval'."
)
strategy.timeframe = strategy.ticker_interval
# Set attributes
# Check if we need to override configuration
# (Attribute name, default, ask_strategy)
attributes = [("minimal_roi", {"0": 10.0}, False),
("ticker_interval", None, False),
("stoploss", None, False),
("trailing_stop", None, False),
("trailing_stop_positive", None, False),
("trailing_stop_positive_offset", 0.0, False),
("trailing_only_offset_is_reached", None, False),
("process_only_new_candles", None, False),
("order_types", None, False),
("order_time_in_force", None, False),
("stake_currency", None, False),
("stake_amount", None, False),
("startup_candle_count", None, False),
("unfilledtimeout", None, False),
("use_sell_signal", True, True),
("sell_profit_only", False, True),
("ignore_roi_if_buy_signal", False, True),
# (Attribute name, default, subkey)
attributes = [("minimal_roi", {"0": 10.0}, None),
("timeframe", None, None),
("stoploss", None, None),
("trailing_stop", None, None),
("trailing_stop_positive", None, None),
("trailing_stop_positive_offset", 0.0, None),
("trailing_only_offset_is_reached", None, None),
("process_only_new_candles", None, None),
("order_types", None, None),
("order_time_in_force", None, None),
("stake_currency", None, None),
("stake_amount", None, None),
("startup_candle_count", None, None),
("unfilledtimeout", None, None),
("use_sell_signal", True, 'ask_strategy'),
("sell_profit_only", False, 'ask_strategy'),
("ignore_roi_if_buy_signal", False, 'ask_strategy'),
("disable_dataframe_checks", False, None),
]
for attribute, default, ask_strategy in attributes:
if ask_strategy:
StrategyResolver._override_attribute_helper(strategy, config['ask_strategy'],
for attribute, default, subkey in attributes:
if subkey:
StrategyResolver._override_attribute_helper(strategy, config.get(subkey, {}),
attribute, default)
else:
StrategyResolver._override_attribute_helper(strategy, config,
attribute, default)
# Assign deprecated variable - to not break users code relying on this.
strategy.ticker_interval = strategy.timeframe
# Loop this list again to have output combined
for attribute, _, exp in attributes:
if exp and attribute in config['ask_strategy']:
logger.info("Strategy using %s: %s", attribute, config['ask_strategy'][attribute])
for attribute, _, subkey in attributes:
if subkey and attribute in config[subkey]:
logger.info("Strategy using %s: %s", attribute, config[subkey][attribute])
elif attribute in config:
logger.info("Strategy using %s: %s", attribute, config[attribute])

43
freqtrade/rpc/api_server.py
View File

@ -16,7 +16,9 @@ from werkzeug.security import safe_str_cmp
from werkzeug.serving import make_server
from freqtrade.__init__ import __version__
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.rpc.rpc import RPC, RPCException
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
logger = logging.getLogger(__name__)
@ -31,7 +33,7 @@ class ArrowJSONEncoder(JSONEncoder):
elif isinstance(obj, date):
return obj.strftime("%Y-%m-%d")
elif isinstance(obj, datetime):
return obj.strftime("%Y-%m-%d %H:%M:%S")
return obj.strftime(DATETIME_PRINT_FORMAT)
iterable = iter(obj)
except TypeError:
pass
@ -55,7 +57,7 @@ def require_login(func: Callable[[Any, Any], Any]):
# Type should really be Callable[[ApiServer], Any], but that will create a circular dependency
def rpc_catch_errors(func: Callable[[Any], Any]):
def rpc_catch_errors(func: Callable[..., Any]):
def func_wrapper(obj, *args, **kwargs):
@ -89,7 +91,11 @@ class ApiServer(RPC):
self._config = freqtrade.config
self.app = Flask(__name__)
self._cors = CORS(self.app, resources={r"/api/*": {"origins": "*"}})
self._cors = CORS(self.app,
resources={r"/api/*": {
"supports_credentials": True,
"origins": self._config['api_server'].get('CORS_origins', [])}}
)
# Setup the Flask-JWT-Extended extension
self.app.config['JWT_SECRET_KEY'] = self._config['api_server'].get(
@ -101,6 +107,9 @@ class ApiServer(RPC):
# Register application handling
self.register_rest_rpc_urls()
if self._config.get('fiat_display_currency', None):
self._fiat_converter = CryptoToFiatConverter()
thread = threading.Thread(target=self.run, daemon=True)
thread.start()
@ -170,8 +179,8 @@ class ApiServer(RPC):
self.app.add_url_rule(f'{BASE_URI}/stop', 'stop', view_func=self._stop, methods=['POST'])
self.app.add_url_rule(f'{BASE_URI}/stopbuy', 'stopbuy',
view_func=self._stopbuy, methods=['POST'])
self.app.add_url_rule(f'{BASE_URI}/reload_conf', 'reload_conf',
view_func=self._reload_conf, methods=['POST'])
self.app.add_url_rule(f'{BASE_URI}/reload_config', 'reload_config',
view_func=self._reload_config, methods=['POST'])
# Info commands
self.app.add_url_rule(f'{BASE_URI}/balance', 'balance',
view_func=self._balance, methods=['GET'])
@ -192,6 +201,8 @@ class ApiServer(RPC):
view_func=self._ping, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/trades', 'trades',
view_func=self._trades, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/trades/<int:tradeid>', 'trades_delete',
view_func=self._trades_delete, methods=['DELETE'])
# Combined actions and infos
self.app.add_url_rule(f'{BASE_URI}/blacklist', 'blacklist', view_func=self._blacklist,
methods=['GET', 'POST'])
@ -302,12 +313,12 @@ class ApiServer(RPC):
@require_login
@rpc_catch_errors
def _reload_conf(self):
def _reload_config(self):
"""
Handler for /reload_conf.
Handler for /reload_config.
Triggers a config file reload
"""
msg = self._rpc_reload_conf()
msg = self._rpc_reload_config()
return self.rest_dump(msg)
@require_login
@ -358,7 +369,6 @@ class ApiServer(RPC):
Returns a cumulative profit statistics
:return: stats
"""
logger.info("LocalRPC - Profit Command Called")
stats = self._rpc_trade_statistics(self._config['stake_currency'],
self._config.get('fiat_display_currency')
@ -375,8 +385,6 @@ class ApiServer(RPC):
Returns a cumulative performance statistics
:return: stats
"""
logger.info("LocalRPC - performance Command Called")
stats = self._rpc_performance()
return self.rest_dump(stats)
@ -419,6 +427,19 @@ class ApiServer(RPC):
results = self._rpc_trade_history(limit)
return self.rest_dump(results)
@require_login
@rpc_catch_errors
def _trades_delete(self, tradeid):
"""
Handler for DELETE /trades/<tradeid> endpoint.
Removes the trade from the database (tries to cancel open orders first!)
get:
param:
tradeid: Numeric trade-id assigned to the trade.
"""
result = self._rpc_delete(tradeid)
return self.rest_dump(result)
@require_login
@rpc_catch_errors
def _whitelist(self):

175
freqtrade/rpc/rpc.py
View File

@ -6,12 +6,14 @@ from abc import abstractmethod
from datetime import date, datetime, timedelta
from enum import Enum
from math import isnan
from typing import Any, Dict, List, Optional, Tuple
from typing import Any, Dict, List, Optional, Tuple, Union
import arrow
from numpy import NAN, mean
from freqtrade.exceptions import DependencyException, TemporaryError
from freqtrade.exceptions import (ExchangeError,
PricingError)
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_msecs
from freqtrade.misc import shorten_date
from freqtrade.persistence import Trade
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
@ -101,10 +103,15 @@ class RPC:
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset'),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached'),
'ticker_interval': config['ticker_interval'],
'ticker_interval': config['timeframe'], # DEPRECATED
'timeframe': config['timeframe'],
'timeframe_ms': timeframe_to_msecs(config['timeframe']),
'timeframe_min': timeframe_to_minutes(config['timeframe']),
'exchange': config['exchange']['name'],
'strategy': config['strategy'],
'forcebuy_enabled': config.get('forcebuy_enable', False),
'ask_strategy': config.get('ask_strategy', {}),
'bid_strategy': config.get('bid_strategy', {}),
'state': str(self._freqtrade.state)
}
return val
@ -123,21 +130,36 @@ class RPC:
for trade in trades:
order = None
if trade.open_order_id:
order = self._freqtrade.exchange.get_order(trade.open_order_id, trade.pair)
order = self._freqtrade.exchange.fetch_order(trade.open_order_id, trade.pair)
# calculate profit and send message to user
try:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except DependencyException:
except (ExchangeError, PricingError):
current_rate = NAN
current_profit = trade.calc_profit_ratio(current_rate)
current_profit_abs = trade.calc_profit(current_rate)
# Calculate guaranteed profit (in case of trailing stop)
stoploss_entry_dist = trade.calc_profit(trade.stop_loss)
stoploss_entry_dist_ratio = trade.calc_profit_ratio(trade.stop_loss)
# calculate distance to stoploss
stoploss_current_dist = trade.stop_loss - current_rate
stoploss_current_dist_ratio = stoploss_current_dist / current_rate
fmt_close_profit = (f'{round(trade.close_profit * 100, 2):.2f}%'
if trade.close_profit else None)
if trade.close_profit is not None else None)
trade_dict = trade.to_json()
trade_dict.update(dict(
base_currency=self._freqtrade.config['stake_currency'],
close_profit=fmt_close_profit,
close_profit=trade.close_profit if trade.close_profit is not None else None,
close_profit_pct=fmt_close_profit,
current_rate=current_rate,
current_profit=round(current_profit * 100, 2),
current_profit=current_profit,
current_profit_pct=round(current_profit * 100, 2),
current_profit_abs=current_profit_abs,
stoploss_current_dist=stoploss_current_dist,
stoploss_current_dist_ratio=round(stoploss_current_dist_ratio, 8),
stoploss_entry_dist=stoploss_entry_dist,
stoploss_entry_dist_ratio=round(stoploss_entry_dist_ratio, 8),
open_order='({} {} rem={:.8f})'.format(
order['type'], order['side'], order['remaining']
) if order else None,
@ -156,7 +178,7 @@ class RPC:
# calculate profit and send message to user
try:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except DependencyException:
except (PricingError, ExchangeError):
current_rate = NAN
trade_percent = (100 * trade.calc_profit_ratio(current_rate))
trade_profit = trade.calc_profit(current_rate)
@ -202,22 +224,20 @@ class RPC:
]).order_by(Trade.close_date).all()
curdayprofit = sum(trade.close_profit_abs for trade in trades)
profit_days[profitday] = {
'amount': f'{curdayprofit:.8f}',
'amount': curdayprofit,
'trades': len(trades)
}
data = [
{
'date': key,
'abs_profit': f'{float(value["amount"]):.8f}',
'fiat_value': '{value:.3f}'.format(
value=self._fiat_converter.convert_amount(
'abs_profit': value["amount"],
'fiat_value': self._fiat_converter.convert_amount(
value['amount'],
stake_currency,
fiat_display_currency
) if self._fiat_converter else 0,
),
'trade_count': f'{value["trades"]}',
'trade_count': value["trades"],
}
for key, value in profit_days.items()
]
@ -230,9 +250,10 @@ class RPC:
def _rpc_trade_history(self, limit: int) -> Dict:
""" Returns the X last trades """
if limit > 0:
trades = Trade.get_trades().order_by(Trade.id.desc()).limit(limit)
trades = Trade.get_trades([Trade.is_open.is_(False)]).order_by(
Trade.id.desc()).limit(limit)
else:
trades = Trade.get_trades().order_by(Trade.id.desc()).all()
trades = Trade.get_trades([Trade.is_open.is_(False)]).order_by(Trade.id.desc()).all()
output = [trade.to_json() for trade in trades]
@ -251,6 +272,8 @@ class RPC:
profit_closed_coin = []
profit_closed_ratio = []
durations = []
winning_trades = 0
losing_trades = 0
for trade in trades:
current_rate: float = 0.0
@ -264,11 +287,15 @@ class RPC:
profit_ratio = trade.close_profit
profit_closed_coin.append(trade.close_profit_abs)
profit_closed_ratio.append(profit_ratio)
if trade.close_profit >= 0:
winning_trades += 1
else:
losing_trades += 1
else:
# Get current rate
try:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except DependencyException:
except (PricingError, ExchangeError):
current_rate = NAN
profit_ratio = trade.calc_profit_ratio(rate=current_rate)
@ -279,15 +306,11 @@ class RPC:
best_pair = Trade.get_best_pair()
if not best_pair:
raise RPCException('no closed trade')
bp_pair, bp_rate = best_pair
# Prepare data to display
profit_closed_coin_sum = round(sum(profit_closed_coin), 8)
profit_closed_percent = (round(mean(profit_closed_ratio) * 100, 2) if profit_closed_ratio
else 0.0)
profit_closed_ratio_mean = mean(profit_closed_ratio) if profit_closed_ratio else 0.0
profit_closed_ratio_sum = sum(profit_closed_ratio) if profit_closed_ratio else 0.0
profit_closed_fiat = self._fiat_converter.convert_amount(
profit_closed_coin_sum,
stake_currency,
@ -295,27 +318,43 @@ class RPC:
) if self._fiat_converter else 0
profit_all_coin_sum = round(sum(profit_all_coin), 8)
profit_all_percent = round(mean(profit_all_ratio) * 100, 2) if profit_all_ratio else 0.0
profit_all_ratio_mean = mean(profit_all_ratio) if profit_all_ratio else 0.0
profit_all_ratio_sum = sum(profit_all_ratio) if profit_all_ratio else 0.0
profit_all_fiat = self._fiat_converter.convert_amount(
profit_all_coin_sum,
stake_currency,
fiat_display_currency
) if self._fiat_converter else 0
first_date = trades[0].open_date if trades else None
last_date = trades[-1].open_date if trades else None
num = float(len(durations) or 1)
return {
'profit_closed_coin': profit_closed_coin_sum,
'profit_closed_percent': profit_closed_percent,
'profit_closed_percent': round(profit_closed_ratio_mean * 100, 2), # DEPRECATED
'profit_closed_percent_mean': round(profit_closed_ratio_mean * 100, 2),
'profit_closed_ratio_mean': profit_closed_ratio_mean,
'profit_closed_percent_sum': round(profit_closed_ratio_sum * 100, 2),
'profit_closed_ratio_sum': profit_closed_ratio_sum,
'profit_closed_fiat': profit_closed_fiat,
'profit_all_coin': profit_all_coin_sum,
'profit_all_percent': profit_all_percent,
'profit_all_percent': round(profit_all_ratio_mean * 100, 2), # DEPRECATED
'profit_all_percent_mean': round(profit_all_ratio_mean * 100, 2),
'profit_all_ratio_mean': profit_all_ratio_mean,
'profit_all_percent_sum': round(profit_all_ratio_sum * 100, 2),
'profit_all_ratio_sum': profit_all_ratio_sum,
'profit_all_fiat': profit_all_fiat,
'trade_count': len(trades),
'first_trade_date': arrow.get(trades[0].open_date).humanize(),
'latest_trade_date': arrow.get(trades[-1].open_date).humanize(),
'closed_trade_count': len([t for t in trades if not t.is_open]),
'first_trade_date': arrow.get(first_date).humanize() if first_date else '',
'first_trade_timestamp': int(first_date.timestamp() * 1000) if first_date else 0,
'latest_trade_date': arrow.get(last_date).humanize() if last_date else '',
'latest_trade_timestamp': int(last_date.timestamp() * 1000) if last_date else 0,
'avg_duration': str(timedelta(seconds=sum(durations) / num)).split('.')[0],
'best_pair': bp_pair,
'best_rate': round(bp_rate * 100, 2),
'best_pair': best_pair[0] if best_pair else '',
'best_rate': round(best_pair[1] * 100, 2) if best_pair else 0,
'winning_trades': winning_trades,
'losing_trades': losing_trades,
}
def _rpc_balance(self, stake_currency: str, fiat_display_currency: str) -> Dict:
@ -324,7 +363,7 @@ class RPC:
total = 0.0
try:
tickers = self._freqtrade.exchange.get_tickers()
except (TemporaryError, DependencyException):
except (ExchangeError):
raise RPCException('Error getting current tickers.')
self._freqtrade.wallets.update(require_update=False)
@ -345,7 +384,7 @@ class RPC:
if pair.startswith(stake_currency):
rate = 1.0 / rate
est_stake = rate * balance.total
except (TemporaryError, DependencyException):
except (ExchangeError):
logger.warning(f" Could not get rate for pair {coin}.")
continue
total = total + (est_stake or 0)
@ -391,9 +430,9 @@ class RPC:
return {'status': 'already stopped'}
def _rpc_reload_conf(self) -> Dict[str, str]:
""" Handler for reload_conf. """
self._freqtrade.state = State.RELOAD_CONF
def _rpc_reload_config(self) -> Dict[str, str]:
""" Handler for reload_config. """
self._freqtrade.state = State.RELOAD_CONFIG
return {'status': 'reloading config ...'}
def _rpc_stopbuy(self) -> Dict[str, str]:
@ -404,7 +443,7 @@ class RPC:
# Set 'max_open_trades' to 0
self._freqtrade.config['max_open_trades'] = 0
return {'status': 'No more buy will occur from now. Run /reload_conf to reset.'}
return {'status': 'No more buy will occur from now. Run /reload_config to reset.'}
def _rpc_forcesell(self, trade_id: str) -> Dict[str, str]:
"""
@ -414,7 +453,7 @@ class RPC:
def _exec_forcesell(trade: Trade) -> None:
# Check if there is there is an open order
if trade.open_order_id:
order = self._freqtrade.exchange.get_order(trade.open_order_id, trade.pair)
order = self._freqtrade.exchange.fetch_order(trade.open_order_id, trade.pair)
# Cancel open LIMIT_BUY orders and close trade
if order and order['status'] == 'open' \
@ -483,7 +522,7 @@ class RPC:
# check if valid pair
# check if pair already has an open pair
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair.is_(pair)]).first()
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
if trade:
raise RPCException(f'position for {pair} already open - id: {trade.id}')
@ -492,11 +531,51 @@ class RPC:
# execute buy
if self._freqtrade.execute_buy(pair, stakeamount, price):
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair.is_(pair)]).first()
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
return trade
else:
return None
def _rpc_delete(self, trade_id: str) -> Dict[str, Union[str, int]]:
"""
Handler for delete <id>.
Delete the given trade and close eventually existing open orders.
"""
with self._freqtrade._sell_lock:
c_count = 0
trade = Trade.get_trades(trade_filter=[Trade.id == trade_id]).first()
if not trade:
logger.warning('delete trade: Invalid argument received')
raise RPCException('invalid argument')
# Try cancelling regular order if that exists
if trade.open_order_id:
try:
self._freqtrade.exchange.cancel_order(trade.open_order_id, trade.pair)
c_count += 1
except (ExchangeError):
pass
# cancel stoploss on exchange ...
if (self._freqtrade.strategy.order_types.get('stoploss_on_exchange')
and trade.stoploss_order_id):
try:
self._freqtrade.exchange.cancel_stoploss_order(trade.stoploss_order_id,
trade.pair)
c_count += 1
except (ExchangeError):
pass
Trade.session.delete(trade)
Trade.session.flush()
self._freqtrade.wallets.update()
return {
'result': 'success',
'trade_id': trade_id,
'result_msg': f'Deleted trade {trade_id}. Closed {c_count} open orders.',
'cancel_order_count': c_count,
}
def _rpc_performance(self) -> List[Dict[str, Any]]:
"""
Handler for performance.
@ -529,16 +608,26 @@ class RPC:
def _rpc_blacklist(self, add: List[str] = None) -> Dict:
""" Returns the currently active blacklist"""
errors = {}
if add:
stake_currency = self._freqtrade.config.get('stake_currency')
for pair in add:
if (self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency
and pair not in self._freqtrade.pairlists.blacklist):
if self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency:
if pair not in self._freqtrade.pairlists.blacklist:
self._freqtrade.pairlists.blacklist.append(pair)
else:
errors[pair] = {
'error_msg': f'Pair {pair} already in pairlist.'}
else:
errors[pair] = {
'error_msg': f"Pair {pair} does not match stake currency."
}
res = {'method': self._freqtrade.pairlists.name_list,
'length': len(self._freqtrade.pairlists.blacklist),
'blacklist': self._freqtrade.pairlists.blacklist,
'errors': errors,
}
return res

4
freqtrade/rpc/rpc_manager.py
View File

@ -72,7 +72,7 @@ class RPCManager:
minimal_roi = config['minimal_roi']
stoploss = config['stoploss']
trailing_stop = config['trailing_stop']
ticker_interval = config['ticker_interval']
timeframe = config['timeframe']
exchange_name = config['exchange']['name']
strategy_name = config.get('strategy', '')
self.send_msg({
@ -81,7 +81,7 @@ class RPCManager:
f'*Stake per trade:* `{stake_amount} {stake_currency}`\n'
f'*Minimum ROI:* `{minimal_roi}`\n'
f'*{"Trailing " if trailing_stop else ""}Stoploss:* `{stoploss}`\n'
f'*Ticker Interval:* `{ticker_interval}`\n'
f'*Timeframe:* `{timeframe}`\n'
f'*Strategy:* `{strategy_name}`'
})
self.send_msg({

238
freqtrade/rpc/telegram.py
View File

@ -3,7 +3,9 @@
"""
This module manage Telegram communication
"""
import json
import logging
import arrow
from typing import Any, Callable, Dict
from tabulate import tabulate
@ -19,7 +21,6 @@ logger = logging.getLogger(__name__)
logger.debug('Included module rpc.telegram ...')
MAX_TELEGRAM_MESSAGE_LENGTH = 4096
@ -29,6 +30,7 @@ def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]:
:param command_handler: Telegram CommandHandler
:return: decorated function
"""
def wrapper(self, *args, **kwargs):
""" Decorator logic """
update = kwargs.get('update') or args[0]
@ -91,11 +93,13 @@ class Telegram(RPC):
CommandHandler('stop', self._stop),
CommandHandler('forcesell', self._forcesell),
CommandHandler('forcebuy', self._forcebuy),
CommandHandler('trades', self._trades),
CommandHandler('delete', self._delete_trade),
CommandHandler('performance', self._performance),
CommandHandler('daily', self._daily),
CommandHandler('count', self._count),
CommandHandler('reload_conf', self._reload_conf),
CommandHandler('show_config', self._show_config),
CommandHandler(['reload_config', 'reload_conf'], self._reload_config),
CommandHandler(['show_config', 'show_conf'], self._show_config),
CommandHandler('stopbuy', self._stopbuy),
CommandHandler('whitelist', self._whitelist),
CommandHandler('blacklist', self._blacklist),
@ -133,7 +137,7 @@ class Telegram(RPC):
else:
msg['stake_amount_fiat'] = 0
message = ("*{exchange}:* Buying {pair}\n"
message = ("\N{LARGE BLUE CIRCLE} *{exchange}:* Buying {pair}\n"
"*Amount:* `{amount:.8f}`\n"
"*Open Rate:* `{limit:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
@ -144,7 +148,8 @@ class Telegram(RPC):
message += ")`"
elif msg['type'] == RPCMessageType.BUY_CANCEL_NOTIFICATION:
message = "*{exchange}:* Cancelling Open Buy Order for {pair}".format(**msg)
message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling Open Buy Order for {pair}".format(**msg))
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
msg['amount'] = round(msg['amount'], 8)
@ -153,7 +158,9 @@ class Telegram(RPC):
microsecond=0) - msg['open_date'].replace(microsecond=0)
msg['duration_min'] = msg['duration'].total_seconds() / 60
message = ("*{exchange}:* Selling {pair}\n"
msg['emoji'] = self._get_sell_emoji(msg)
message = ("{emoji} *{exchange}:* Selling {pair}\n"
"*Amount:* `{amount:.8f}`\n"
"*Open Rate:* `{open_rate:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
@ -172,14 +179,14 @@ class Telegram(RPC):
' / {profit_fiat:.3f} {fiat_currency})`').format(**msg)
elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
message = ("*{exchange}:* Cancelling Open Sell Order "
message = ("\N{WARNING SIGN} *{exchange}:* Cancelling Open Sell Order "
"for {pair}. Reason: {reason}").format(**msg)
elif msg['type'] == RPCMessageType.STATUS_NOTIFICATION:
message = '*Status:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.WARNING_NOTIFICATION:
message = '*Warning:* `{status}`'.format(**msg)
message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.CUSTOM_NOTIFICATION:
message = '{status}'.format(**msg)
@ -189,6 +196,20 @@ class Telegram(RPC):
self._send_msg(message)
def _get_sell_emoji(self, msg):
"""
Get emoji for sell-side
"""
if float(msg['profit_percent']) >= 5.0:
return "\N{ROCKET}"
elif float(msg['profit_percent']) >= 0.0:
return "\N{EIGHT SPOKED ASTERISK}"
elif msg['sell_reason'] == "stop_loss":
return"\N{WARNING SIGN}"
else:
return "\N{CROSS MARK}"
@authorized_only
def _status(self, update: Update, context: CallbackContext) -> None:
"""
@ -215,13 +236,15 @@ class Telegram(RPC):
"*Open Rate:* `{open_rate:.8f}`",
"*Close Rate:* `{close_rate}`" if r['close_rate'] else "",
"*Current Rate:* `{current_rate:.8f}`",
"*Close Profit:* `{close_profit}`" if r['close_profit'] else "",
"*Current Profit:* `{current_profit:.2f}%`",
("*Close Profit:* `{close_profit_pct}`"
if r['close_profit_pct'] is not None else ""),
"*Current Profit:* `{current_profit_pct:.2f}%`",
# Adding initial stoploss only if it is different from stoploss
"*Initial Stoploss:* `{initial_stop_loss:.8f}` " +
("`({initial_stop_loss_pct:.2f}%)`" if r['initial_stop_loss_pct'] else "")
if r['stop_loss'] != r['initial_stop_loss'] else "",
("`({initial_stop_loss_pct:.2f}%)`") if (
r['stop_loss'] != r['initial_stop_loss']
and r['initial_stop_loss_pct'] is not None) else "",
# Adding stoploss and stoploss percentage only if it is not None
"*Stoploss:* `{stop_loss:.8f}` " +
@ -282,8 +305,8 @@ class Telegram(RPC):
)
stats_tab = tabulate(
[[day['date'],
f"{day['abs_profit']} {stats['stake_currency']}",
f"{day['fiat_value']} {stats['fiat_display_currency']}",
f"{day['abs_profit']:.8f} {stats['stake_currency']}",
f"{day['fiat_value']:.3f} {stats['fiat_display_currency']}",
f"{day['trade_count']} trades"] for day in stats['data']],
headers=[
'Day',
@ -309,15 +332,16 @@ class Telegram(RPC):
stake_cur = self._config['stake_currency']
fiat_disp_cur = self._config.get('fiat_display_currency', '')
try:
stats = self._rpc_trade_statistics(
stake_cur,
fiat_disp_cur)
profit_closed_coin = stats['profit_closed_coin']
profit_closed_percent = stats['profit_closed_percent']
profit_closed_percent_mean = stats['profit_closed_percent_mean']
profit_closed_percent_sum = stats['profit_closed_percent_sum']
profit_closed_fiat = stats['profit_closed_fiat']
profit_all_coin = stats['profit_all_coin']
profit_all_percent = stats['profit_all_percent']
profit_all_percent_mean = stats['profit_all_percent_mean']
profit_all_percent_sum = stats['profit_all_percent_sum']
profit_all_fiat = stats['profit_all_fiat']
trade_count = stats['trade_count']
first_trade_date = stats['first_trade_date']
@ -325,22 +349,33 @@ class Telegram(RPC):
avg_duration = stats['avg_duration']
best_pair = stats['best_pair']
best_rate = stats['best_rate']
if stats['trade_count'] == 0:
markdown_msg = 'No trades yet.'
else:
# Message to display
markdown_msg = "*ROI:* Close trades\n" \
f"∙ `{profit_closed_coin:.8f} {stake_cur} "\
f"({profit_closed_percent:.2f}%)`\n" \
f"∙ `{profit_closed_fiat:.3f} {fiat_disp_cur}`\n" \
f"*ROI:* All trades\n" \
f"∙ `{profit_all_coin:.8f} {stake_cur} ({profit_all_percent:.2f}%)`\n" \
f"∙ `{profit_all_fiat:.3f} {fiat_disp_cur}`\n" \
f"*Total Trade Count:* `{trade_count}`\n" \
f"*First Trade opened:* `{first_trade_date}`\n" \
f"*Latest Trade opened:* `{latest_trade_date}`\n" \
f"*Avg. Duration:* `{avg_duration}`\n" \
f"*Best Performing:* `{best_pair}: {best_rate:.2f}%`"
if stats['closed_trade_count'] > 0:
markdown_msg = ("*ROI:* Closed trades\n"
f"∙ `{profit_closed_coin:.8f} {stake_cur} "
f"({profit_closed_percent_mean:.2f}%) "
f"({profit_closed_percent_sum} \N{GREEK CAPITAL LETTER SIGMA}%)`\n"
f"∙ `{profit_closed_fiat:.3f} {fiat_disp_cur}`\n")
else:
markdown_msg = "`No closed trade` \n"
markdown_msg += (f"*ROI:* All trades\n"
f"∙ `{profit_all_coin:.8f} {stake_cur} "
f"({profit_all_percent_mean:.2f}%) "
f"({profit_all_percent_sum} \N{GREEK CAPITAL LETTER SIGMA}%)`\n"
f"∙ `{profit_all_fiat:.3f} {fiat_disp_cur}`\n"
f"*Total Trade Count:* `{trade_count}`\n"
f"*First Trade opened:* `{first_trade_date}`\n"
f"*Latest Trade opened:* `{latest_trade_date}\n`"
f"*Win / Loss:* `{stats['winning_trades']} / {stats['losing_trades']}`"
)
if stats['closed_trade_count'] > 0:
markdown_msg += (f"\n*Avg. Duration:* `{avg_duration}`\n"
f"*Best Performing:* `{best_pair}: {best_rate:.2f}%`")
self._send_msg(markdown_msg)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _balance(self, update: Update, context: CallbackContext) -> None:
@ -359,11 +394,11 @@ class Telegram(RPC):
)
for currency in result['currencies']:
if currency['est_stake'] > 0.0001:
curr_output = "*{currency}:*\n" \
"\t`Available: {free: .8f}`\n" \
"\t`Balance: {balance: .8f}`\n" \
"\t`Pending: {used: .8f}`\n" \
"\t`Est. {stake}: {est_stake: .8f}`\n".format(**currency)
curr_output = ("*{currency}:*\n"
"\t`Available: {free: .8f}`\n"
"\t`Balance: {balance: .8f}`\n"
"\t`Pending: {used: .8f}`\n"
"\t`Est. {stake}: {est_stake: .8f}`\n").format(**currency)
else:
curr_output = "*{currency}:* not showing <1$ amount \n".format(**currency)
@ -374,9 +409,9 @@ class Telegram(RPC):
else:
output += curr_output
output += "\n*Estimated Value*:\n" \
"\t`{stake}: {total: .8f}`\n" \
"\t`{symbol}: {value: .2f}`\n".format(**result)
output += ("\n*Estimated Value*:\n"
"\t`{stake}: {total: .8f}`\n"
"\t`{symbol}: {value: .2f}`\n").format(**result)
self._send_msg(output)
except RPCException as e:
self._send_msg(str(e))
@ -406,15 +441,15 @@ class Telegram(RPC):
self._send_msg('Status: `{status}`'.format(**msg))
@authorized_only
def _reload_conf(self, update: Update, context: CallbackContext) -> None:
def _reload_config(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /reload_conf.
Handler for /reload_config.
Triggers a config file reload
:param bot: telegram bot
:param update: message update
:return: None
"""
msg = self._rpc_reload_conf()
msg = self._rpc_reload_config()
self._send_msg('Status: `{status}`'.format(**msg))
@authorized_only
@ -464,6 +499,62 @@ class Telegram(RPC):
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _trades(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /trades <n>
Returns last n recent trades.
:param bot: telegram bot
:param update: message update
:return: None
"""
stake_cur = self._config['stake_currency']
try:
nrecent = int(context.args[0])
except (TypeError, ValueError, IndexError):
nrecent = 10
try:
trades = self._rpc_trade_history(
nrecent
)
trades_tab = tabulate(
[[arrow.get(trade['open_date']).humanize(),
trade['pair'],
f"{(100 * trade['close_profit']):.2f}% ({trade['close_profit_abs']})"]
for trade in trades['trades']],
headers=[
'Open Date',
'Pair',
f'Profit ({stake_cur})',
],
tablefmt='simple')
message = (f"<b>{min(trades['trades_count'], nrecent)} recent trades</b>:\n"
+ (f"<pre>{trades_tab}</pre>" if trades['trades_count'] > 0 else ''))
self._send_msg(message, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _delete_trade(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /delete <id>.
Delete the given trade
:param bot: telegram bot
:param update: message update
:return: None
"""
trade_id = context.args[0] if len(context.args) > 0 else None
try:
msg = self._rpc_delete(trade_id)
self._send_msg((
'`{result_msg}`\n'
'Please make sure to take care of this asset on the exchange manually.'
).format(**msg))
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _performance(self, update: Update, context: CallbackContext) -> None:
"""
@ -532,6 +623,11 @@ class Telegram(RPC):
try:
blacklist = self._rpc_blacklist(context.args)
errmsgs = []
for pair, error in blacklist['errors'].items():
errmsgs.append(f"Error adding `{pair}` to blacklist: `{error['error_msg']}`")
if errmsgs:
self._send_msg('\n'.join(errmsgs))
message = f"Blacklist contains {blacklist['length']} pairs\n"
message += f"`{', '.join(blacklist['blacklist'])}`"
@ -564,32 +660,34 @@ class Telegram(RPC):
:param update: message update
:return: None
"""
forcebuy_text = "*/forcebuy <pair> [<rate>]:* `Instantly buys the given pair. " \
"Optionally takes a rate at which to buy.` \n"
message = "*/start:* `Starts the trader`\n" \
"*/stop:* `Stops the trader`\n" \
"*/status [table]:* `Lists all open trades`\n" \
" *table :* `will display trades in a table`\n" \
" `pending buy orders are marked with an asterisk (*)`\n" \
" `pending sell orders are marked with a double asterisk (**)`\n" \
"*/profit:* `Lists cumulative profit from all finished trades`\n" \
"*/forcesell <trade_id>|all:* `Instantly sells the given trade or all trades, " \
"regardless of profit`\n" \
f"{forcebuy_text if self._config.get('forcebuy_enable', False) else '' }" \
"*/performance:* `Show performance of each finished trade grouped by pair`\n" \
"*/daily <n>:* `Shows profit or loss per day, over the last n days`\n" \
"*/count:* `Show number of trades running compared to allowed number of trades`" \
"\n" \
"*/balance:* `Show account balance per currency`\n" \
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n" \
"*/reload_conf:* `Reload configuration file` \n" \
"*/show_config:* `Show running configuration` \n" \
"*/whitelist:* `Show current whitelist` \n" \
"*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs " \
"to the blacklist.` \n" \
"*/edge:* `Shows validated pairs by Edge if it is enabled` \n" \
"*/help:* `This help message`\n" \
"*/version:* `Show version`"
forcebuy_text = ("*/forcebuy <pair> [<rate>]:* `Instantly buys the given pair. "
"Optionally takes a rate at which to buy.` \n")
message = ("*/start:* `Starts the trader`\n"
"*/stop:* `Stops the trader`\n"
"*/status [table]:* `Lists all open trades`\n"
" *table :* `will display trades in a table`\n"
" `pending buy orders are marked with an asterisk (*)`\n"
" `pending sell orders are marked with a double asterisk (**)`\n"
"*/trades [limit]:* `Lists last closed trades (limited to 10 by default)`\n"
"*/profit:* `Lists cumulative profit from all finished trades`\n"
"*/forcesell <trade_id>|all:* `Instantly sells the given trade or all trades, "
"regardless of profit`\n"
f"{forcebuy_text if self._config.get('forcebuy_enable', False) else ''}"
"*/delete <trade_id>:* `Instantly delete the given trade in the database`\n"
"*/performance:* `Show performance of each finished trade grouped by pair`\n"
"*/daily <n>:* `Shows profit or loss per day, over the last n days`\n"
"*/count:* `Show number of trades running compared to allowed number of trades`"
"\n"
"*/balance:* `Show account balance per currency`\n"
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n"
"*/reload_config:* `Reload configuration file` \n"
"*/show_config:* `Show running configuration` \n"
"*/whitelist:* `Show current whitelist` \n"
"*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs "
"to the blacklist.` \n"
"*/edge:* `Shows validated pairs by Edge if it is enabled` \n"
"*/help:* `This help message`\n"
"*/version:* `Show version`")
self._send_msg(message)
@ -631,8 +729,10 @@ class Telegram(RPC):
f"*Stake per trade:* `{val['stake_amount']} {val['stake_currency']}`\n"
f"*Max open Trades:* `{val['max_open_trades']}`\n"
f"*Minimum ROI:* `{val['minimal_roi']}`\n"
f"*Ask strategy:* ```\n{json.dumps(val['ask_strategy'])}```\n"
f"*Bid strategy:* ```\n{json.dumps(val['bid_strategy'])}```\n"
f"{sl_info}"
f"*Ticker Interval:* `{val['ticker_interval']}`\n"
f"*Timeframe:* `{val['timeframe']}`\n"
f"*Strategy:* `{val['strategy']}`\n"
f"*Current state:* `{val['state']}`"
)

2
freqtrade/state.py
View File

@ -12,7 +12,7 @@ class State(Enum):
"""
RUNNING = 1
STOPPED = 2
RELOAD_CONF = 3
RELOAD_CONFIG = 3
def __str__(self):
return f"{self.name.lower()}"

182
freqtrade/strategy/interface.py
View File

@ -7,20 +7,19 @@ import warnings
from abc import ABC, abstractmethod
from datetime import datetime, timezone
from enum import Enum
from typing import Dict, NamedTuple, Optional, Tuple
from typing import Dict, List, NamedTuple, Optional, Tuple
import arrow
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import StrategyError
from freqtrade.exceptions import StrategyError, OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.persistence import Trade
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.typing import ListPairsWithTimeframes
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
@ -45,6 +44,10 @@ class SellType(Enum):
EMERGENCY_SELL = "emergency_sell"
NONE = ""
def __str__(self):
# explicitly convert to String to help with exporting data.
return self.value
class SellCheckTuple(NamedTuple):
"""
@ -62,7 +65,7 @@ class IStrategy(ABC):
Attributes you can use:
minimal_roi -> Dict: Minimal ROI designed for the strategy
stoploss -> float: optimal stoploss designed for the strategy
ticker_interval -> str: value of the timeframe (ticker interval) to use with the strategy
timeframe -> str: value of the timeframe (ticker interval) to use with the strategy
"""
# Strategy interface version
# Default to version 2
@ -85,8 +88,9 @@ class IStrategy(ABC):
trailing_stop_positive_offset: float = 0.0
trailing_only_offset_is_reached = False
# associated ticker interval
ticker_interval: str
# associated timeframe
ticker_interval: str # DEPRECATED
timeframe: str
# Optional order types
order_types: Dict = {
@ -106,6 +110,9 @@ class IStrategy(ABC):
# run "populate_indicators" only for new candle
process_only_new_candles: bool = False
# Disable checking the dataframe (converts the error into a warning message)
disable_dataframe_checks: bool = False
# Count of candles the strategy requires before producing valid signals
startup_candle_count: int = 0
@ -187,6 +194,63 @@ class IStrategy(ABC):
"""
return False
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.
"""
pass
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
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 trade: trade object.
: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
"""
return True
def informative_pairs(self) -> ListPairsWithTimeframes:
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
@ -200,6 +264,10 @@ class IStrategy(ABC):
"""
return []
###
# END - Intended to be overridden by strategy
###
def get_strategy_name(self) -> str:
"""
Returns strategy class name
@ -269,6 +337,8 @@ class IStrategy(ABC):
# Defs that only make change on new candle data.
dataframe = self.analyze_ticker(dataframe, metadata)
self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
if self.dp:
self.dp._set_cached_df(pair, self.timeframe, dataframe)
else:
logger.debug("Skipping TA Analysis for already analyzed candle")
dataframe['buy'] = 0
@ -280,14 +350,53 @@ class IStrategy(ABC):
return dataframe
def analyze_pair(self, pair: str) -> None:
"""
Fetch data for this pair from dataprovider and analyze.
Stores the dataframe into the dataprovider.
The analyzed dataframe is then accessible via `dp.get_analyzed_dataframe()`.
:param pair: Pair to analyze.
"""
if not self.dp:
raise OperationalException("DataProvider not found.")
dataframe = self.dp.ohlcv(pair, self.timeframe)
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty candle (OHLCV) data for pair %s', pair)
return
try:
df_len, df_close, df_date = self.preserve_df(dataframe)
dataframe = strategy_safe_wrapper(
self._analyze_ticker_internal, message=""
)(dataframe, {'pair': pair})
self.assert_df(dataframe, df_len, df_close, df_date)
except StrategyError as error:
logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
return
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return
def analyze(self, pairs: List[str]) -> None:
"""
Analyze all pairs using analyze_pair().
:param pairs: List of pairs to analyze
"""
for pair in pairs:
self.analyze_pair(pair)
@staticmethod
def preserve_df(dataframe: DataFrame) -> Tuple[int, float, datetime]:
""" keep some data for dataframes """
return len(dataframe), dataframe["close"].iloc[-1], dataframe["date"].iloc[-1]
@staticmethod
def assert_df(dataframe: DataFrame, df_len: int, df_close: float, df_date: datetime):
""" make sure data is unmodified """
def assert_df(self, dataframe: DataFrame, df_len: int, df_close: float, df_date: datetime):
"""
Ensure dataframe (length, last candle) was not modified, and has all elements we need.
"""
message = ""
if df_len != len(dataframe):
message = "length"
@ -296,64 +405,48 @@ class IStrategy(ABC):
elif df_date != dataframe["date"].iloc[-1]:
message = "last date"
if message:
if self.disable_dataframe_checks:
logger.warning(f"Dataframe returned from strategy has mismatching {message}.")
else:
raise StrategyError(f"Dataframe returned from strategy has mismatching {message}.")
def get_signal(self, pair: str, interval: str, dataframe: DataFrame) -> Tuple[bool, bool]:
def get_signal(self, pair: str, timeframe: str, dataframe: DataFrame) -> Tuple[bool, bool]:
"""
Calculates current signal based several technical analysis indicators
Calculates current signal based based on the buy / sell columns of the dataframe.
Used by Bot to get the signal to buy or sell
:param pair: pair in format ANT/BTC
:param interval: Interval to use (in min)
:param dataframe: Dataframe to analyze
:param timeframe: timeframe to use
:param dataframe: Analyzed dataframe to get signal from.
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty candle (OHLCV) data for pair %s', pair)
return False, False
try:
df_len, df_close, df_date = self.preserve_df(dataframe)
dataframe = strategy_safe_wrapper(
self._analyze_ticker_internal, message=""
)(dataframe, {'pair': pair})
self.assert_df(dataframe, df_len, df_close, df_date)
except StrategyError as error:
logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
return False, False
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
logger.warning(f'Empty candle (OHLCV) data for pair {pair}')
return False, False
latest_date = dataframe['date'].max()
latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
interval_minutes = timeframe_to_minutes(interval)
# Explicitly convert to arrow object to ensure the below comparison does not fail
latest_date = arrow.get(latest_date)
# Check if dataframe is out of date
timeframe_minutes = timeframe_to_minutes(timeframe)
offset = self.config.get('exchange', {}).get('outdated_offset', 5)
if latest_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + offset))):
if latest_date < (arrow.utcnow().shift(minutes=-(timeframe_minutes * 2 + offset))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
int((arrow.utcnow() - latest_date).total_seconds() // 60)
pair, int((arrow.utcnow() - latest_date).total_seconds() // 60)
)
return False, False
# Check if dataframe has new candle
if (arrow.utcnow() - latest_date).total_seconds() // 60 >= interval_minutes:
if (arrow.utcnow() - latest_date).total_seconds() // 60 >= timeframe_minutes:
logger.warning('Old candle for pair %s. Last candle is %s minutes old',
pair, int((arrow.utcnow() - latest_date).total_seconds() // 60))
return False, False
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
logger.debug(
'trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'],
pair,
str(buy),
str(sell)
)
logger.debug('trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'], pair, str(buy), str(sell))
return buy, sell
def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
@ -499,7 +592,8 @@ class IStrategy(ABC):
def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given candle (OHLCV) data
Populates indicators for given candle (OHLCV) data (for multiple pairs)
Does not run advice_buy or advise_sell!
Used by optimize operations only, not during dry / live runs.
Using .copy() to get a fresh copy of the dataframe for every strategy run.
Has positive effects on memory usage for whatever reason - also when

6
freqtrade/strategy/strategy_wrapper.py
View File

@ -5,7 +5,7 @@ from freqtrade.exceptions import StrategyError
logger = logging.getLogger(__name__)
def strategy_safe_wrapper(f, message: str = "", default_retval=None):
def strategy_safe_wrapper(f, message: str = "", default_retval=None, supress_error=False):
"""
Wrapper around user-provided methods and functions.
Caches all exceptions and returns either the default_retval (if it's not None) or raises
@ -20,7 +20,7 @@ def strategy_safe_wrapper(f, message: str = "", default_retval=None):
f"Strategy caused the following exception: {error}"
f"{f}"
)
if default_retval is None:
if default_retval is None and not supress_error:
raise StrategyError(str(error)) from error
return default_retval
except Exception as error:
@ -28,7 +28,7 @@ def strategy_safe_wrapper(f, message: str = "", default_retval=None):
f"{message}"
f"Unexpected error {error} calling {f}"
)
if default_retval is None:
if default_retval is None and not supress_error:
raise StrategyError(str(error)) from error
return default_retval

12
freqtrade/templates/base_config.json.j2
View File

@ -4,7 +4,7 @@
"stake_amount": {{ stake_amount }},
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "{{ fiat_display_currency }}",
"ticker_interval": "{{ ticker_interval }}",
"timeframe": "{{ timeframe }}",
"dry_run": {{ dry_run | lower }},
"cancel_open_orders_on_exit": false,
"unfilledtimeout": {
@ -53,6 +53,16 @@
"token": "{{ telegram_token }}",
"chat_id": "{{ 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": {

4
freqtrade/templates/base_strategy.py.j2
View File

@ -51,8 +51,8 @@ class {{ strategy }}(IStrategy):
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal ticker interval for the strategy.
ticker_interval = '5m'
# Optimal timeframe for the strategy.
timeframe = '5m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False

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