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Merge pull request #2997 from freqtrade/new_release

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New release 2020.02
This commit is contained in:
Matthias 2020-02-29 19:33:12 +01:00 committed by GitHub
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33
.github/workflows/ci.yml vendored
View File

@ -7,6 +7,8 @@ on:
- develop
- github_actions_tests
tags:
release:
types: [published]
pull_request:
schedule:
- cron: '0 5 * * 4'
@ -18,7 +20,7 @@ jobs:
strategy:
matrix:
os: [ ubuntu-18.04, macos-latest ]
python-version: [3.7]
python-version: [3.7, 3.8]
steps:
- uses: actions/checkout@v1
@ -68,7 +70,7 @@ jobs:
pytest --random-order --cov=freqtrade --cov-config=.coveragerc
- name: Coveralls
if: startsWith(matrix.os, 'ubuntu')
if: (startsWith(matrix.os, 'ubuntu') && matrix.python-version == '3.8')
env:
# Coveralls token. Not used as secret due to github not providing secrets to forked repositories
COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@ -191,15 +193,40 @@ jobs:
deploy:
needs: [ build, build_windows, docs_check ]
runs-on: ubuntu-18.04
if: (github.event_name == 'push' || github.event_name == 'schedule') && github.repository == 'freqtrade/freqtrade'
if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
steps:
- uses: actions/checkout@v1
- name: Set up Python
uses: actions/setup-python@v1
with:
python-version: 3.8
- name: Extract branch name
shell: bash
run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})"
id: extract_branch
- name: Build distribution
run: |
pip install -U setuptools wheel
python setup.py sdist bdist_wheel
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@master
if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
with:
user: __token__
password: ${{ secrets.pypi_test_password }}
repository_url: https://test.pypi.org/legacy/
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@master
if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
with:
user: __token__
password: ${{ secrets.pypi_password }}
- name: Build and test and push docker image
env:
IMAGE_NAME: freqtradeorg/freqtrade

6
CONTRIBUTING.md
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@ -48,7 +48,7 @@ pytest tests/test_<file_name>.py::test_<method_name>
#### Run Flake8
```bash
flake8 freqtrade
flake8 freqtrade tests scripts
```
We receive a lot of code that fails the `flake8` checks.
@ -109,11 +109,11 @@ Exceptions:
Contributors may be given commit privileges. Preference will be given to those with:
1. Past contributions to FreqTrade and other related open-source projects. Contributions to FreqTrade include both code (both accepted and pending) and friendly participation in the issue tracker and Pull request reviews. Quantity and quality are considered.
1. Past contributions to Freqtrade and other related open-source projects. Contributions to Freqtrade include both code (both accepted and pending) and friendly participation in the issue tracker and Pull request reviews. Quantity and quality are considered.
1. A coding style that the other core committers find simple, minimal, and clean.
1. Access to resources for cross-platform development and testing.
1. Time to devote to the project regularly.
Being a Committer does not grant write permission on `develop` or `master` for security reasons (Users trust FreqTrade with their Exchange API keys).
Being a Committer does not grant write permission on `develop` or `master` for security reasons (Users trust Freqtrade with their Exchange API keys).
After being Committer for some time, a Committer may be named Core Committer and given full repository access.

2
Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.7.6-slim-stretch
FROM python:3.8.1-slim-buster
RUN apt-get update \
&& apt-get -y install curl build-essential libssl-dev \

2
README.md
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@ -1,6 +1,6 @@
# Freqtrade
[![Build Status](https://travis-ci.org/freqtrade/freqtrade.svg?branch=develop)](https://travis-ci.org/freqtrade/freqtrade)
[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
[![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
[![Documentation](https://readthedocs.org/projects/freqtrade/badge/)](https://www.freqtrade.io)
[![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)

12
bin/freqtrade
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@ -1,11 +1,11 @@
#!/usr/bin/env python3
import sys
import warnings
import logging
from freqtrade.main import main
logger = logging.getLogger(__name__)
warnings.warn(
"Deprecated - To continue to run the bot like this, please run `pip install -e .` again.",
DeprecationWarning)
main(sys.argv[1:])
logger.error("DEPRECATED installation detected, please run `pip install -e .` again.")
sys.exit(2)

2
build_helpers/publish_docker.sh
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@ -23,7 +23,7 @@ if [ $? -ne 0 ]; then
fi
# Run backtest
docker run --rm -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy DefaultStrategy
docker run --rm -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy DefaultStrategy
if [ $? -ne 0 ]; then
echo "failed running backtest"

2
config.json.example
View File

@ -44,7 +44,7 @@
"DASH/BTC",
"ZEC/BTC",
"XLM/BTC",
"NXT/BTC",
"XRP/BTC",
"TRX/BTC",
"ADA/BTC",
"XMR/BTC"

8
config_full.json.example
View File

@ -62,8 +62,8 @@
"refresh_period": 1800
},
{"method": "PrecisionFilter"},
{"method": "PriceFilter", "low_price_ratio": 0.01
}
{"method": "PriceFilter", "low_price_ratio": 0.01},
{"method": "SpreadFilter", "max_spread_ratio": 0.005}
],
"exchange": {
"name": "bittrex",
@ -129,5 +129,7 @@
"heartbeat_interval": 60
},
"strategy": "DefaultStrategy",
"strategy_path": "user_data/strategies/"
"strategy_path": "user_data/strategies/",
"dataformat_ohlcv": "json",
"dataformat_trades": "jsongz"
}

14
docker-compose.yml
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@ -3,6 +3,18 @@ version: '3'
services:
freqtrade:
image: freqtradeorg/freqtrade:master
# Build step - only needed when additional dependencies are needed
# build:
# context: .
# dockerfile: "./Dockerfile.technical"
restart: unless-stopped
container_name: freqtrade
volumes:
- "./user_data:/freqtrade/user_data"
- "./config.json:/freqtrade/config.json"
# Default command used when running `docker compose up`
command: >
trade
--logfile /freqtrade/user_data/freqtrade.log
--db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
--config /freqtrade/user_data/config.json
--strategy SampleStrategy

28
docs/advanced-hyperopt.md
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@ -4,6 +4,34 @@ This page explains some advanced Hyperopt topics that may require higher
coding skills and Python knowledge than creation of an ordinal hyperoptimization
class.
## Derived hyperopt classes
Custom hyperop classes can be derived in the same way [it can be done for strategies](strategy-customization.md#derived-strategies).
Applying to hyperoptimization, as an example, you may override how dimensions are defined in your optimization hyperspace:
```python
class MyAwesomeHyperOpt(IHyperOpt):
...
# Uses default stoploss dimension
class MyAwesomeHyperOpt2(MyAwesomeHyperOpt):
@staticmethod
def stoploss_space() -> List[Dimension]:
# Override boundaries for stoploss
return [
Real(-0.33, -0.01, name='stoploss'),
]
```
and then quickly switch between hyperopt classes, running optimization process with hyperopt class you need in each particular case:
```
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt ...
or
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 ...
```
## Creating and using a custom loss function
To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.

74
docs/backtesting.md
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@ -119,40 +119,40 @@ A backtesting result will look like that:
```
========================================================= BACKTESTING REPORT ========================================================
| pair | buy count | avg profit % | cum profit % | tot profit BTC | tot profit % | avg duration | profit | loss |
|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 | 21 |
| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 | 8 |
| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 | 14 |
| DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 | 7 |
| ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 | 10 |
| EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 | 20 |
| ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 | 15 |
| ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 | 17 |
| IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 | 18 |
| LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 | 9 |
| LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 | 21 |
| NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 | 7 |
| NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 | 13 |
| REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 | 5 |
| XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 | 9 |
| XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 | 11 |
| XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 | 23 |
| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 | 15 |
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 |
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 | 0 | 21 |
| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 | 0 | 8 |
| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 | 0 | 14 |
| DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 | 0 | 7 |
| ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 | 0 | 10 |
| EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 | 0 | 20 |
| ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 | 0 | 15 |
| ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 | 0 | 17 |
| IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 | 0 | 18 |
| LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 | 0 | 9 |
| LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 | 0 | 21 |
| NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 | 0 | 7 |
| NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 | 0 | 13 |
| REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 | 0 | 5 |
| XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 | 0 | 9 |
| XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 | 0 | 11 |
| XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 | 0 | 23 |
| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 | 0 | 15 |
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 |
========================================================= SELL REASON STATS =========================================================
| Sell Reason | Count | Profit | Loss |
|:-------------------|--------:|---------:|-------:|
| trailing_stop_loss | 205 | 150 | 55 |
| stop_loss | 166 | 0 | 166 |
| sell_signal | 56 | 36 | 20 |
| force_sell | 2 | 0 | 2 |
| Sell Reason | Sells | Wins | Draws | Losses |
|:-------------------|--------:|------:|-------:|--------:|
| trailing_stop_loss | 205 | 150 | 0 | 55 |
| stop_loss | 166 | 0 | 0 | 166 |
| sell_signal | 56 | 36 | 0 | 20 |
| force_sell | 2 | 0 | 0 | 2 |
====================================================== LEFT OPEN TRADES REPORT ======================================================
| pair | buy count | avg profit % | cum profit % | tot profit BTC | tot profit % | avg duration | profit | loss |
|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 | 0 |
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 | 0 |
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 | 0 |
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
| 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 |
```
The 1st table contains all trades the bot made, including "left open trades".
@ -237,11 +237,11 @@ There will be an additional table comparing win/losses of the different strategi
Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
```
=========================================================== Strategy Summary ===========================================================
| Strategy | buy count | avg profit % | cum profit % | tot profit BTC | tot profit % | avg duration | profit | loss |
|:------------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 |
| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 825 |
=========================================================== STRATEGY SUMMARY ===========================================================
| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |
|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|
| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 |
| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 825 |
```
## Next step

52
docs/bot-usage.md
View File

@ -58,9 +58,10 @@ Common arguments:
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
@ -71,6 +72,7 @@ Strategy arguments:
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
.
```
@ -242,12 +244,15 @@ optional arguments:
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified.
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
@ -280,7 +285,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[--hyperopt NAME] [--hyperopt-path PATH] [--eps]
[-e INT]
[--spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
[--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]]
[--dmmp] [--print-all] [--no-color] [--print-json]
[-j JOBS] [--random-state INT] [--min-trades INT]
[--continue] [--hyperopt-loss NAME]
@ -308,9 +313,9 @@ optional arguments:
Allow buying the same pair multiple times (position
stacking).
-e INT, --epochs INT Specify number of epochs (default: 100).
--spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]
--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]
Specify which parameters to hyperopt. Space-separated
list. Default: `all`.
list.
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
@ -337,17 +342,21 @@ optional arguments:
generate completely different results, since the
target for optimization is different. Built-in
Hyperopt-loss-functions are: DefaultHyperOptLoss,
OnlyProfitHyperOptLoss, SharpeHyperOptLoss (default:
OnlyProfitHyperOptLoss, SharpeHyperOptLoss,
SharpeHyperOptLossDaily.(default:
`DefaultHyperOptLoss`).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified.
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
@ -358,6 +367,7 @@ Strategy arguments:
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
## Edge commands
@ -394,12 +404,15 @@ optional arguments:
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified.
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
@ -410,6 +423,7 @@ Strategy arguments:
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
To understand edge and how to read the results, please read the [edge documentation](edge.md).

164
docs/configuration.md
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@ -40,75 +40,79 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| Parameter | Description |
|------------|-------------|
| `max_open_trades` | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades). [More information below](#configuring-amount-per-trade).<br> ***Datatype:*** *Positive integer or -1.*
| `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *String*
| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Positive float or `"unlimited"`.*
| `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> ***Datatype:*** *Positive float between `0.1` and `1.0`.*
| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> ***Datatype:*** *Float (as ratio)*
| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> ***Datatype:*** *Positive Float as ratio.*
| `ticker_interval` | The ticker interval to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *String*
| `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*
| `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*
| `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*
| `max_open_trades` | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
| `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Positive float or `"unlimited"`.
| `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> **Datatype:** Positive float between `0.1` and `1.0`.
| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> **Datatype:** Positive Float as ratio.
| `ticker_interval` | The ticker interval to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `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
| `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
| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook).
| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> ***Datatype:*** *Boolean*
| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.* <br> ***Datatype:*** *Positive Integer*
| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.* <br> ***Datatype:*** *Float (as ratio)*
| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> ***Datatype:*** *Boolean*
| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> ***Datatype:*** *Positive Integer*
| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> ***Datatype:*** *Positive Integer*
| `ask_strategy.use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
| `ask_strategy.sell_profit_only` | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
| `ask_strategy.ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> ***Datatype:*** *Dict*
| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Dict*
| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> ***Datatype:*** *String*
| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> ***Datatype:*** *Boolean*
| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#dynamic-pairlists)). <br> ***Datatype:*** *List*
| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#dynamic-pairlists)). <br> ***Datatype:*** *List*
| `exchange.ccxt_config` | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> ***Datatype:*** *Dict*
| `exchange.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*
| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.* <br> **Datatype:** Float (as ratio)
| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> **Datatype:** Boolean
| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `ask_strategy.use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `ask_strategy.sell_profit_only` | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `ask_strategy.ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> **Datatype:** Boolean
| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#dynamic-pairlists)). <br> **Datatype:** List
| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#dynamic-pairlists)). <br> **Datatype:** List
| `exchange.ccxt_config` | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
| `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
| `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
| `pairlists` | Define one or more pairlists to be used. [More information below](#dynamic-pairlists). <br>*Defaults to `StaticPairList`.* <br> ***Datatype:*** *List of Dicts*
| `telegram.enabled` | Enable the usage of Telegram. <br> ***Datatype:*** *Boolean*
| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `webhook.enabled` | Enable usage of Webhook notifications <br> ***Datatype:*** *Boolean*
| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
| `webhook.webhooksell` | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
| `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.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*
| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> ***Datatype:*** *ClassName*
| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> ***Datatype:*** *String*
| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> ***Datatype:*** *Positive Integer*
| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> ***Datatype:*** *Positive Integer or 0*
| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> ***Datatype:*** *Boolean*
| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> ***Datatype:*** *String*
| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> ***Datatype:*** *String*
| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `pairlists` | Define one or more pairlists to be used. [More information below](#dynamic-pairlists). <br>*Defaults to `StaticPairList`.* <br> **Datatype:** List of Dicts
| `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `webhook.enabled` | Enable usage of Webhook notifications <br> **Datatype:** Boolean
| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhookbuycancel` | Payload to send on buy order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhooksell` | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhooksellcancel` | Payload to send on sell order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `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.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
| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Intege
| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
| `dataformat_ohlcv` | Data format to use to store OHLCV historic data. <br> *Defaults to `json`*. <br> **Datatype:** String
| `dataformat_trades` | Data format to use to store trades historic data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
### Parameters in the strategy
@ -278,7 +282,7 @@ If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
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%.
`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$.
@ -503,6 +507,7 @@ Inactive markets and blacklisted pairs are always removed from the resulting `pa
* [`VolumePairList`](#volume-pair-list)
* [`PrecisionFilter`](#precision-filter)
* [`PriceFilter`](#price-pair-filter)
* [`SpreadFilter`](#spread-filter)
!!! Tip "Testing pairlists"
Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) subcommand to test your configuration quickly.
@ -551,6 +556,11 @@ Min price precision is 8 decimals. If price is 0.00000011 - one step would be 0.
These pairs are dangerous since it may be impossible to place the desired stoploss - and often result in high losses.
#### Spread Filter
Removes pairs that have a difference between asks and bids above the specified ratio (default `0.005`).
Example:
If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027 the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005`
### Full Pairlist example
The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting by `quoteVolume` and applies both [`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter), filtering all assets where 1 priceunit is > 1%.
@ -602,12 +612,25 @@ Once you will be happy with your bot performance running in the Dry-run mode, yo
!!! Note
A simulated wallet is available during dry-run mode, and will assume a starting capital of `dry_run_wallet` (defaults to 1000).
### Considerations for dry-run
* API-keys may or may not be provided. Only Read-Only operations (i.e. operations that do not alter account state) on the exchange are performed in the dry-run mode.
* Wallets (`/balance`) are simulated.
* Orders are simulated, and will not be posted to the exchange.
* In combination with `stoploss_on_exchange`, the stop_loss price is assumed to be filled.
* Open orders (not trades, which are stored in the database) are reset on bot restart.
## Switch to production mode
In production mode, the bot will engage your money. Be careful, since a wrong
strategy can lose all your money. Be aware of what you are doing when
you run it in production mode.
### Setup your exchange account
You will need to create API Keys (usually you get `key` and `secret`, some exchanges require an additional `password`) from the Exchange website and you'll need to insert this into the appropriate fields in the configuration or when asked by the `freqtrade new-config` command.
API Keys are usually only required for live trading (trading for real money, bot running in "production mode", executing real orders on the exchange) and are not required for the bot running in dry-run (trade simulation) mode. When you setup the bot in dry-run mode, you may fill these fields with empty values.
### To switch your bot in production mode
**Edit your `config.json` file.**
@ -629,9 +652,6 @@ you run it in production mode.
}
```
!!! Note
If you have an exchange API key yet, [see our tutorial](installation.md#setup-your-exchange-account).
You should also make sure to read the [Exchanges](exchanges.md) section of the documentation to be aware of potential configuration details specific to your exchange.
### Using proxy with Freqtrade
@ -656,7 +676,7 @@ freqtrade
## Embedding Strategies
FreqTrade provides you with with an easy way to embed the strategy into your configuration file.
Freqtrade provides you with with an easy way to embed the strategy into your configuration file.
This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
in your chosen config file.

146
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@ -12,6 +12,152 @@ Otherwise `--exchange` becomes mandatory.
If you already have backtesting data available in your data-directory and would like to refresh this data up to today, use `--days xx` with a number slightly higher than the missing number of days. Freqtrade will keep the available data and only download the missing data.
Be carefull though: If the number is too small (which would result in a few missing days), the whole dataset will be removed and only xx days will be downloaded.
### Usage
```
usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
[--pairs-file FILE] [--days INT] [--dl-trades] [--exchange EXCHANGE]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
[--erase] [--data-format-ohlcv {json,jsongz}] [--data-format-trades {json,jsongz}]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-separated.
--pairs-file FILE File containing a list of pairs to download.
--days INT Download data for given number of days.
--dl-trades Download trades instead of OHLCV data. The bot will resample trades to the desired timeframe as specified as
--timeframes/-t.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no config is provided.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
Specify which tickers to download. Space-separated list. Default: `1m 5m`.
--erase Clean all existing data for the selected exchange/pairs/timeframes.
--data-format-ohlcv {json,jsongz}
Storage format for downloaded ohlcv data. (default: `json`).
--data-format-trades {json,jsongz}
Storage format for downloaded trades data. (default: `jsongz`).
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: `config.json`). 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.
```
### Data format
Freqtrade currently supports 2 dataformats, `json` (plain "text" json files) and `jsongz` (a gzipped version of json files).
By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
This can be changed via the `--data-format-ohlcv` and `--data-format-trades` parameters respectivly.
If the default dataformat has been changed during download, then the keys `dataformat_ohlcv` and `dataformat_trades` in the configuration file need to be adjusted to the selected dataformat as well.
!!! Note
You can convert between data-formats using the [convert-data](#subcommand-convert-data) and [convert-trade-data](#subcommand-convert-trade-data) methods.
#### Subcommand convert data
```
usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz} --format-to {json,jsongz}
[--erase]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
separated.
--format-from {json,jsongz}
Source format for data conversion.
--format-to {json,jsongz}
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
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: `config.json`).
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 converting data
The following command will convert all ohlcv (candle) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
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
```
#### Subcommand convert-trade data
```
usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz} --format-to {json,jsongz}
[--erase]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
separated.
--format-from {json,jsongz}
Source format for data conversion.
--format-to {json,jsongz}
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
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: `config.json`).
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 converting trades
The following command will convert all available trade-data in `~/.freqtrade/data/kraken` from jsongz to json.
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
```
### Pairs file
In alternative to the whitelist from `config.json`, a `pairs.json` file can be used.

4
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@ -1,6 +1,6 @@
# Development Help
This page is intended for developers of FreqTrade, people who want to contribute to the FreqTrade codebase or documentation, or people who want to understand the source code of the application they're running.
This page is intended for developers of Freqtrade, people who want to contribute to the Freqtrade codebase or documentation, or people who want to understand the source code of the application they're running.
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel in [slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) where you can ask questions.
@ -153,7 +153,7 @@ In VolumePairList, this implements different methods of sorting, does early vali
## Implement a new Exchange (WIP)
!!! Note
This section is a Work in Progress and is not a complete guide on how to test a new exchange with FreqTrade.
This section is a Work in Progress and is not a complete guide on how to test a new exchange with Freqtrade.
Most exchanges supported by CCXT should work out of the box.

134
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@ -1,4 +1,4 @@
# Using FreqTrade with Docker
# Using Freqtrade with Docker
## Install Docker
@ -8,13 +8,141 @@ Start by downloading and installing Docker CE for your platform:
* [Windows](https://docs.docker.com/docker-for-windows/install/)
* [Linux](https://docs.docker.com/install/)
Optionally, [docker-compose](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
## Download the official FreqTrade docker image
## Freqtrade with docker-compose
Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
!!! Note
The following section assumes that docker and docker-compose is installed and available to the logged in user.
!!! Note
All below comands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
### Docker quick start
Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
``` bash
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
# Create configuration - Requires answering interactive questions
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
The above snippet creates a new directory called "ft_userdata", downloads the latest compose file and pulls the freqtrade image.
The last 2 steps in the snippet create the directory with user-data, as well as (interactively) the default configuration based on your selections.
!!! Note
You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
#### Adding your strategy
The configuration is now available as `user_data/config.json`.
You should now copy your strategy to `user_data/strategies/` - and add the Strategy class name to the `docker-compose.yml` file, replacing `SampleStrategy`. If you wish to run the bot with the SampleStrategy, just leave it as it is.
!!! Warning
The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
Please always backtest the strategy and use dry-run for some time before risking real money!
Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
``` bash
docker-compose up -d
```
#### Docker-compose logs
Logs will be written to `user_data/freqtrade.log`.
Alternatively, you can check the latest logs using `docker-compose logs -f`.
#### Database
The database will be in the user_data directory as well, and will be called `user_data/tradesv3.sqlite`.
#### Updating freqtrade with docker-compose
To update freqtrade when using docker-compose is as simple as running the following 2 commands:
``` bash
# Download the latest image
docker-compose pull
# Restart the image
docker-compose up -d
```
This will first pull the latest image, and will then restart the container with the just pulled version.
!!! Note
You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
#### Going from here
Advanced users may edit the docker-compose file further to include all possible options or arguments.
All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
!!! Note "`docker-compose run --rm`"
Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
##### Example: Download data with docker-compose
Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
``` bash
docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
```
Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
##### Example: Backtest with docker-compose
Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
``` bash
docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
```
Head over to the [Backtesting Documentation](backtesting.md) to learn more.
#### Additional dependencies with docker-compose
If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) for an example).
You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
``` yaml
image: freqtrade_custom
build:
context: .
dockerfile: "./Dockerfile.<yourextension>"
```
You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
## Freqtrade with docker without docker-compose
!!! Warning
The below documentation is provided for completeness and assumes that you are somewhat familiar with running docker containers. If you're just starting out with docker, we recommend to follow the [Freqtrade with docker-compose](#freqtrade-with-docker-compose) instructions.
### Download the official Freqtrade docker image
Pull the image from docker hub.
Branches / tags available can be checked out on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
Branches / tags available can be checked out on [Dockerhub tags page](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
```bash
docker pull freqtradeorg/freqtrade:develop

26
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@ -145,19 +145,19 @@ Edge module has following configuration options:
| Parameter | Description |
|------------|-------------|
| `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*
| `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br> **Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br> ***Datatype:*** *Float*
| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> ***Datatype:*** *Float*
| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> ***Datatype:*** *Float*
| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> ***Datatype:*** *Integer*
| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your ticker interval. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> ***Datatype:*** *Integer*
| `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*
| `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
| `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br> **Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br> **Datatype:** Float
| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your ticker interval. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
| `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

9
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@ -5,7 +5,7 @@ This page combines common gotchas and informations which are exchange-specific a
## Binance
!!! Tip "Stoploss on Exchange"
Binance is currently the only exchange supporting `stoploss_on_exchange`. It provides great advantages, so we recommend to benefit from it.
Binance supports `stoploss_on_exchange` and uses stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
### Blacklists
@ -22,6 +22,9 @@ Binance has been split into 3, and users must use the correct ccxt exchange ID f
## Kraken
!!! Tip "Stoploss on Exchange"
Kraken supports `stoploss_on_exchange` and uses stop-loss-market orders. It provides great advantages, so we recommend to benefit from it, however since the resulting order is a stoploss-market order, sell-rates are not guaranteed, which makes this feature less secure than on other exchanges. This limitation is based on kraken's policy [source](https://blog.kraken.com/post/1234/announcement-delisting-pairs-and-temporary-suspension-of-advanced-order-types/) and [source2](https://blog.kraken.com/post/1494/kraken-enables-advanced-orders-and-adds-10-currency-pairs/) - which has stoploss-limit orders disabled.
### Historic Kraken data
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.
@ -29,6 +32,10 @@ To download data for the Kraken exchange, using `--dl-trades` is mandatory, othe
## Bittrex
### Order types
Bittrex does not support market orders. If you have a message at the bot startup about this, you should change order type values set in your configuration and/or in the strategy from `"market"` to `"limit"`. See some more details on this [here in the FAQ](faq.md#im-getting-the-exchange-bittrex-does-not-support-market-orders-message-and-cannot-run-my-strategy).
### Restricted markets
Bittrex split its exchange into US and International versions.

18
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@ -45,12 +45,28 @@ the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-c
You can use the `/forcesell all` command from Telegram.
### I get the message "RESTRICTED_MARKET"
### I'm getting the "RESTRICTED_MARKET" message in the log
Currently known to happen for US Bittrex users.
Read [the Bittrex section about restricted markets](exchanges.md#restricted-markets) for more information.
### I'm getting the "Exchange Bittrex does not support market orders." message and cannot run my strategy
As the message says, Bittrex does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Probably your strategy was written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Bittrex).
To fix it for Bittrex, redefine order types in the strategy to use "limit" instead of "market":
```
order_types = {
...
'stoploss': 'limit',
...
}
```
Same fix should be done in the configuration file, if order types are defined in your custom config rather than in the strategy.
### How do I search the bot logs for something?
By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility subcommands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.

12
docs/hyperopt.md
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@ -75,8 +75,8 @@ Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts
There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing:
- Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
- Inside `populate_buy_trend()` - applying the parameters.
* Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
* Inside `populate_buy_trend()` - applying the parameters.
There you have two different types of indicators: 1. `guards` and 2. `triggers`.
@ -182,7 +182,7 @@ add it to the `populate_indicators()` method in your custom hyperopt file.
Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
By default, FreqTrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
By default, Freqtrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
A different loss function can be specified by using the `--hyperopt-loss <Class-name>` argument.
This class should be in its own file within the `user_data/hyperopts/` directory.
@ -192,6 +192,7 @@ Currently, the following loss functions are builtin:
* `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function)
* `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on the trade returns)
* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on daily trade returns)
Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
@ -372,6 +373,7 @@ In order to use this best ROI table found by Hyperopt in backtesting and for liv
118: 0
}
```
As stated in the comment, you can also use it as the value of the `minimal_roi` setting in the configuration file.
#### Default ROI Search Space
@ -379,7 +381,7 @@ As stated in the comment, you can also use it as the value of the `minimal_roi`
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
| # step | 1m | | 5m | | 1h | | 1d | |
|---|---|---|---|---|---|---|---|---|
| ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
| 1 | 0 | 0.01161...0.11992 | 0 | 0.03...0.31 | 0 | 0.06883...0.71124 | 0 | 0.12178...1.25835 |
| 2 | 2...8 | 0.00774...0.04255 | 10...40 | 0.02...0.11 | 120...480 | 0.04589...0.25238 | 2880...11520 | 0.08118...0.44651 |
| 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 |
@ -416,6 +418,7 @@ In order to use this best stoploss value found by Hyperopt in backtesting and fo
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.27996
```
As stated in the comment, you can also use it as the value of the `stoploss` setting in the configuration file.
#### Default Stoploss Search Space
@ -452,6 +455,7 @@ In order to use these best trailing stop parameters found by Hyperopt in backtes
trailing_stop_positive_offset = 0.06038
trailing_only_offset_is_reached = True
```
As stated in the comment, you can also use it as the values of the corresponding settings in the configuration file.
#### Default Trailing Stop Search Space

9
docs/index.md
View File

@ -1,5 +1,5 @@
# Freqtrade
[![Build Status](https://travis-ci.org/freqtrade/freqtrade.svg?branch=develop)](https://travis-ci.org/freqtrade/freqtrade)
[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
[![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
[![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
@ -51,12 +51,15 @@ To run this bot we recommend you a cloud instance with a minimum of:
### Software requirements
- Docker (Recommended)
Alternatively
- Python 3.6.x
- pip (pip3)
- git
- TA-Lib
- virtualenv (Recommended)
- Docker (Recommended)
## Support
@ -67,4 +70,4 @@ Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODc
## Ready to try?
Begin by reading our installation guide [here](installation).
Begin by reading our installation guide [for docker](docker.md), or for [installation without docker](installation.md).

39
docs/installation.md
View File

@ -2,6 +2,8 @@
This page explains how to prepare your environment for running the bot.
Please consider using the prebuilt [docker images](docker.md) to get started quickly while trying out freqtrade evaluating how it operates.
## Prerequisite
### Requirements
@ -14,15 +16,7 @@ Click each one for install guide:
* [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
### API keys
Before running your bot in production you will need to setup few
external API. In production mode, the bot will require valid Exchange API
credentials. We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot) (optional but recommended).
### Setup your exchange account
You will need to create API Keys (Usually you get `key` and `secret`) from the Exchange website and insert this into the appropriate fields in the configuration or when asked by the installation script.
We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
## Quick start
@ -31,7 +25,7 @@ Freqtrade provides the Linux/MacOS Easy Installation script to install all depen
!!! Note
Windows installation is explained [here](#windows).
The easiest way to install and run Freqtrade is to clone the bot GitHub repository and then run the Easy Installation script, if it's available for your platform.
The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the Easy Installation script, if it's available for your platform.
!!! Note "Version considerations"
When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `master` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
@ -42,11 +36,12 @@ The easiest way to install and run Freqtrade is to clone the bot GitHub reposito
This can be achieved with the following commands:
```bash
git clone git@github.com:freqtrade/freqtrade.git
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
git checkout master # Optional, see (1)
./setup.sh --install
```
(1) This command switches the cloned repository to the use of the `master` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout master`/`git checkout develop` commands.
## Easy Installation Script (Linux/MacOS)
@ -64,11 +59,11 @@ usage:
** --install **
With this option, the script will install everything you need to run the bot:
With this option, the script will install the bot and most dependencies:
You will need to have git and python3.6+ installed beforehand for this to work.
* Mandatory software as: `ta-lib`
* Setup your virtualenv
* Configure your `config.json` file
* Setup your virtualenv under `.env/`
This option is a combination of installation tasks, `--reset` and `--config`.
@ -82,7 +77,7 @@ This option will hard reset your branch (only if you are on either `master` or `
** --config **
Use this option to configure the `config.json` configuration file. The script will interactively ask you questions to setup your bot and create your `config.json`.
DEPRECATED - use `freqtrade new-config -c config.json` instead.
------
@ -129,6 +124,17 @@ bash setup.sh -i
#### 1. Install TA-Lib
Use the provided ta-lib installation script
```bash
sudo ./build_helpers/install_ta-lib.sh
```
!!! Note
This will use the ta-lib tar.gz included in this repository.
##### TA-Lib manual installation
Official webpage: https://mrjbq7.github.io/ta-lib/install.html
```bash
@ -184,7 +190,8 @@ python3 -m pip install -e .
# Initialize the user_directory
freqtrade create-userdir --userdir user_data/
cp config.json.example config.json
# Create a new configuration file
freqtrade new-config --config config.json
```
> *To edit the config please refer to [Bot Configuration](configuration.md).*

2
docs/requirements-docs.txt
View File

@ -1,2 +1,2 @@
mkdocs-material==4.6.0
mkdocs-material==4.6.3
mdx_truly_sane_lists==1.2

2
docs/rest-api.md
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@ -74,7 +74,7 @@ docker run -d \
## Consuming the API
You can consume the API by using the script `scripts/rest_client.py`.
The client script only requires the `requests` module, so FreqTrade does not need to be installed on the system.
The client script only requires the `requests` module, so Freqtrade does not need to be installed on the system.
``` bash
python3 scripts/rest_client.py <command> [optional parameters]

2
docs/stoploss.md
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@ -27,7 +27,7 @@ So this parameter will tell the bot how often it should update the stoploss orde
This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
!!! Note
Stoploss on exchange is only supported for Binance as of now.
Stoploss on exchange is only supported for Binance (stop-loss-limit) and Kraken (stop-loss-market) as of now.
## Static Stop Loss

31
docs/strategy-customization.md
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@ -346,7 +346,7 @@ if self.dp:
``` python
if self.dp:
if self.dp.runmode in ('live', 'dry_run'):
if self.dp.runmode.value in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
@ -422,7 +422,7 @@ from freqtrade.persistence import Trade
The following example queries for the current pair and trades from today, however other filters can easily be added.
``` python
if self.config['runmode'] in ('live', 'dry_run'):
if self.config['runmode'].value in ('live', 'dry_run'):
trades = Trade.get_trades([Trade.pair == metadata['pair'],
Trade.open_date > datetime.utcnow() - timedelta(days=1),
Trade.is_open == False,
@ -434,7 +434,7 @@ if self.config['runmode'] in ('live', 'dry_run'):
Get amount of stake_currency currently invested in Trades:
``` python
if self.config['runmode'] in ('live', 'dry_run'):
if self.config['runmode'].value in ('live', 'dry_run'):
total_stakes = Trade.total_open_trades_stakes()
```
@ -442,7 +442,7 @@ Retrieve performance per pair.
Returns a List of dicts per pair.
``` python
if self.config['runmode'] in ('live', 'dry_run'):
if self.config['runmode'].value in ('live', 'dry_run'):
performance = Trade.get_overall_performance()
```
@ -487,7 +487,7 @@ from datetime import timedelta, datetime, timezone
# --------
# Within populate indicators (or populate_buy):
if self.config['runmode'] in ('live', 'dry_run'):
if self.config['runmode'].value in ('live', 'dry_run'):
# fetch closed trades for the last 2 days
trades = Trade.get_trades([Trade.pair == metadata['pair'],
Trade.open_date > datetime.utcnow() - timedelta(days=2),
@ -532,6 +532,27 @@ If you want to use a strategy from a different directory you can pass `--strateg
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
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.

28
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@ -1,24 +1,28 @@
# Strategy analysis example
Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.
The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.
## Setup
```python
from pathlib import Path
from freqtrade.configuration import Configuration
# Customize these according to your needs.
# Initialize empty configuration object
config = Configuration.from_files([])
# Optionally, use existing configuration file
# config = Configuration.from_files(["config.json"])
# Define some constants
timeframe = "5m"
config["ticker_interval"] = "5m"
# Name of the strategy class
strategy_name = 'SampleStrategy'
# Path to user data
user_data_dir = Path('user_data')
# Location of the strategy
strategy_location = user_data_dir / 'strategies'
config["strategy"] = "SampleStrategy"
# Location of the data
data_location = Path(user_data_dir, 'data', 'binance')
data_location = Path(config['user_data_dir'], 'data', 'binance')
# Pair to analyze - Only use one pair here
pair = "BTC_USDT"
```
@ -29,7 +33,7 @@ pair = "BTC_USDT"
from freqtrade.data.history import load_pair_history
candles = load_pair_history(datadir=data_location,
timeframe=timeframe,
timeframe=config["ticker_interval"],
pair=pair)
# Confirm success
@ -44,9 +48,7 @@ candles.head()
```python
# Load strategy using values set above
from freqtrade.resolvers import StrategyResolver
strategy = StrategyResolver.load_strategy({'strategy': strategy_name,
'user_data_dir': user_data_dir,
'strategy_path': strategy_location})
strategy = StrategyResolver.load_strategy(config)
# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(candles, {'pair': pair})
@ -86,7 +88,7 @@ Analyze a trades dataframe (also used below for plotting)
from freqtrade.data.btanalysis import load_backtest_data
# Load backtest results
trades = load_backtest_data(user_data_dir / "backtest_results/backtest-result.json")
trades = load_backtest_data(config["user_data_dir"] / "backtest_results/backtest-result.json")
# Show value-counts per pair
trades.groupby("pair")["sell_reason"].value_counts()

2
docs/telegram-usage.md
View File

@ -55,7 +55,7 @@ official commands. You can ask at any moment for help with `/help`.
| `/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
| `/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`).

185
docs/utils.md
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@ -36,6 +36,38 @@ optional arguments:
└── sample_strategy.py
```
## Create new config
Creates a new configuration file, asking some questions which are important selections for a configuration.
```
usage: freqtrade new-config [-h] [-c PATH]
optional arguments:
-h, --help show this help message 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.
```
!!! Warning
Only vital questions are asked. Freqtrade offers a lot more configuration possibilities, which are listed in the [Configuration documentation](configuration.md#configuration-parameters)
### Create config examples
```
$ freqtrade new-config --config config_binance.json
? Do you want to enable Dry-run (simulated trades)? Yes
? Please insert your stake currency: BTC
? Please insert your stake amount: 0.05
? Please insert max_open_trades (Integer or 'unlimited'): 5
? Please insert your ticker interval: 15m
? Please insert your display Currency (for reporting): USD
? Select exchange binance
? Do you want to enable Telegram? No
```
## Create new strategy
Creates a new strategy from a template similar to SampleStrategy.
@ -108,26 +140,62 @@ With custom user directory
freqtrade new-hyperopt --userdir ~/.freqtrade/ --hyperopt AwesomeHyperopt
```
## List Strategies
## List Strategies and List Hyperopts
Use the `list-strategies` subcommand to see all strategies in one particular directory.
Use the `list-strategies` subcommand to see all strategies in one particular directory and the `list-hyperopts` subcommand to list custom Hyperopts.
These subcommands are useful for finding problems in your environment with loading strategies or hyperopt classes: modules with strategies or hyperopt classes that contain errors and failed to load are printed in red (LOAD FAILED), while strategies or hyperopt classes with duplicate names are printed in yellow (DUPLICATE NAME).
```
freqtrade list-strategies --help
usage: freqtrade list-strategies [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--strategy-path PATH] [-1]
usage: freqtrade list-strategies [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[--strategy-path PATH] [-1] [--no-color]
optional arguments:
-h, --help show this help message and exit
--strategy-path PATH Specify additional strategy lookup path.
-1, --one-column Print output in one column.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
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.
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
to read config from stdin.
Specify configuration file (default: `config.json`).
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.
```
```
usage: freqtrade list-hyperopts [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[--hyperopt-path PATH] [-1] [--no-color]
optional arguments:
-h, --help show this help message and exit
--hyperopt-path PATH Specify additional lookup path for Hyperopt and
Hyperopt Loss functions.
-1, --one-column Print output in one column.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
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: `config.json`).
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
@ -135,20 +203,34 @@ Common arguments:
```
!!! Warning
Using this command will try to load all python files from a directory. This can be a security risk if untrusted files reside in this directory, since all module-level code is executed.
Using these commands will try to load all python files from a directory. This can be a security risk if untrusted files reside in this directory, since all module-level code is executed.
Example: search default strategy directory within userdir
Example: Search default strategies and hyperopts directories (within the default userdir).
``` bash
freqtrade list-strategies
freqtrade list-hyperopts
```
Example: Search strategies and hyperopts directory within the userdir.
``` bash
freqtrade list-strategies --userdir ~/.freqtrade/
freqtrade list-hyperopts --userdir ~/.freqtrade/
```
Example: search dedicated strategy path
Example: Search dedicated strategy path.
``` bash
freqtrade list-strategies --strategy-path ~/.freqtrade/strategies/
```
Example: Search dedicated hyperopt path.
``` bash
freqtrade list-hyperopt --hyperopt-path ~/.freqtrade/hyperopts/
```
## List Exchanges
Use the `list-exchanges` subcommand to see the exchanges available for the bot.
@ -179,20 +261,31 @@ All exchanges supported by the ccxt library: _1btcxe, acx, adara, allcoin, anxpr
Use the `list-timeframes` subcommand to see the list of ticker intervals (timeframes) available for the exchange.
```
usage: freqtrade list-timeframes [-h] [--exchange EXCHANGE] [-1]
usage: freqtrade list-timeframes [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--exchange EXCHANGE] [-1]
optional arguments:
-h, --help show this help message and exit
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no config is provided.
-1, --one-column Print output in one column.
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: `config.json`). 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: see the timeframes for the 'binance' exchange, set in the configuration file:
```
$ freqtrade -c config_binance.json list-timeframes
$ freqtrade list-timeframes -c config_binance.json
...
Timeframes available for the exchange `binance`: 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d, 3d, 1w, 1M
```
@ -216,14 +309,16 @@ You can print info about any pair/market with these subcommands - and you can fi
These subcommands have same usage and same set of available options:
```
usage: freqtrade list-markets [-h] [--exchange EXCHANGE] [--print-list]
[--print-json] [-1] [--print-csv]
usage: freqtrade list-markets [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [--exchange EXCHANGE]
[--print-list] [--print-json] [-1] [--print-csv]
[--base BASE_CURRENCY [BASE_CURRENCY ...]]
[--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
[-a]
usage: freqtrade list-pairs [-h] [--exchange EXCHANGE] [--print-list]
[--print-json] [-1] [--print-csv]
usage: freqtrade list-pairs [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [--exchange EXCHANGE]
[--print-list] [--print-json] [-1] [--print-csv]
[--base BASE_CURRENCY [BASE_CURRENCY ...]]
[--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]] [-a]
@ -242,6 +337,22 @@ optional arguments:
Specify quote currency(-ies). Space-separated list.
-a, --all Print all pairs or market symbols. By default only
active ones are shown.
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: `config.json`).
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.
```
By default, only active pairs/markets are shown. Active pairs/markets are those that can currently be traded
@ -263,7 +374,7 @@ $ freqtrade list-pairs --quote USD --print-json
human-readable list with summary:
```
$ freqtrade -c config_binance.json list-pairs --all --base BTC ETH --quote USDT USD --print-list
$ freqtrade list-pairs -c config_binance.json --all --base BTC ETH --quote USDT USD --print-list
```
* Print all markets on exchange "Kraken", in the tabular format:
@ -311,17 +422,49 @@ You can list the hyperoptimization epochs the Hyperopt module evaluated previous
```
usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [--best]
[--profitable] [--no-color] [--print-json]
[--no-details]
[--profitable] [--min-trades INT]
[--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]
optional arguments:
-h, --help show this help message and exit
--best Select only best epochs.
--profitable Select only profitable epochs.
--min-trades INT Select epochs with more than INT trades.
--max-trades INT Select epochs with less than INT trades.
--min-avg-time FLOAT Select epochs on above average time.
--max-avg-time FLOAT Select epochs on under average time.
--min-avg-profit FLOAT
Select epochs on above average profit.
--max-avg-profit FLOAT
Select epochs on below average profit.
--min-total-profit FLOAT
Select epochs on above total profit.
--max-total-profit FLOAT
Select epochs on below total profit.
--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.
--no-details Do not print best epoch details.
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: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
### Examples

50
docs/webhook-config.md
View File

@ -15,11 +15,21 @@ Sample configuration (tested using IFTTT).
"value2": "limit {limit:8f}",
"value3": "{stake_amount:8f} {stake_currency}"
},
"webhookbuycancel": {
"value1": "Cancelling Open Buy Order for {pair}",
"value2": "limit {limit:8f}",
"value3": "{stake_amount:8f} {stake_currency}"
},
"webhooksell": {
"value1": "Selling {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency}"
},
"webhooksellcancel": {
"value1": "Cancelling Open Sell Order for {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency}"
},
"webhookstatus": {
"value1": "Status: {status}",
"value2": "",
@ -40,10 +50,29 @@ Possible parameters are:
* `exchange`
* `pair`
* `limit`
* `amount`
* `open_date`
* `stake_amount`
* `stake_currency`
* `fiat_currency`
* `order_type`
* `current_rate`
### Webhookbuycancel
The fields in `webhook.webhookbuycancel` are filled when the bot cancels a buy order. Parameters are filled using string.format.
Possible parameters are:
* `exchange`
* `pair`
* `limit`
* `amount`
* `open_date`
* `stake_amount`
* `stake_currency`
* `fiat_currency`
* `order_type`
* `current_rate`
### Webhooksell
@ -66,6 +95,27 @@ Possible parameters are:
* `open_date`
* `close_date`
### Webhooksellcancel
The fields in `webhook.webhooksellcancel` are filled when the bot cancels a sell order. Parameters are filled using string.format.
Possible parameters are:
* `exchange`
* `pair`
* `gain`
* `limit`
* `amount`
* `open_rate`
* `current_rate`
* `profit_amount`
* `profit_percent`
* `stake_currency`
* `fiat_currency`
* `sell_reason`
* `order_type`
* `open_date`
* `close_date`
### Webhookstatus
The fields in `webhook.webhookstatus` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.

18
freqtrade/__init__.py
View File

@ -1,13 +1,27 @@
""" FreqTrade bot """
__version__ = '2020.01'
""" Freqtrade bot """
__version__ = '2020.02'
if __version__ == 'develop':
try:
import subprocess
__version__ = 'develop-' + subprocess.check_output(
['git', 'log', '--format="%h"', '-n 1'],
stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
# from datetime import datetime
# last_release = subprocess.check_output(
# ['git', 'tag']
# ).decode('utf-8').split()[-1].split(".")
# # Releases are in the format "2020.1" - we increment the latest version for dev.
# prefix = f"{last_release[0]}.{int(last_release[1]) + 1}"
# dev_version = int(datetime.now().timestamp() // 1000)
# __version__ = f"{prefix}.dev{dev_version}"
# subprocess.check_output(
# ['git', 'log', '--format="%h"', '-n 1'],
# stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
except Exception:
# git not available, ignore
pass

5
freqtrade/commands/__init__.py
View File

@ -7,13 +7,16 @@ Note: Be careful with file-scoped imports in these subfiles.
as they are parsed on startup, nothing containing optional modules should be loaded.
"""
from freqtrade.commands.arguments import Arguments
from freqtrade.commands.data_commands import start_download_data
from freqtrade.commands.build_config_commands import start_new_config
from freqtrade.commands.data_commands import (start_convert_data,
start_download_data)
from freqtrade.commands.deploy_commands import (start_create_userdir,
start_new_hyperopt,
start_new_strategy)
from freqtrade.commands.hyperopt_commands import (start_hyperopt_list,
start_hyperopt_show)
from freqtrade.commands.list_commands import (start_list_exchanges,
start_list_hyperopts,
start_list_markets,
start_list_strategies,
start_list_timeframes)

92
freqtrade/commands/arguments.py
View File

@ -6,8 +6,8 @@ from functools import partial
from pathlib import Path
from typing import Any, Dict, List, Optional
from freqtrade import constants
from freqtrade.commands.cli_options import AVAILABLE_CLI_OPTIONS
from freqtrade.constants import DEFAULT_CONFIG
ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"]
@ -30,7 +30,9 @@ ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column"]
ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column", "print_colorized"]
ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column", "print_colorized"]
ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"]
@ -43,12 +45,17 @@ ARGS_TEST_PAIRLIST = ["config", "quote_currencies", "print_one_column", "list_pa
ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
ARGS_BUILD_CONFIG = ["config"]
ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
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_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
"timeframes", "erase"]
"timeframes", "erase", "dataformat_ohlcv", "dataformat_trades"]
ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
"db_url", "trade_source", "export", "exportfilename",
@ -57,15 +64,20 @@ ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
"trade_source", "ticker_interval"]
ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable", "print_colorized",
"print_json", "hyperopt_list_no_details"]
ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
"hyperopt_list_min_trades", "hyperopt_list_max_trades",
"hyperopt_list_min_avg_time", "hyperopt_list_max_avg_time",
"hyperopt_list_min_avg_profit", "hyperopt_list_max_avg_profit",
"hyperopt_list_min_total_profit", "hyperopt_list_max_total_profit",
"print_colorized", "print_json", "hyperopt_list_no_details"]
ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
"print_json", "hyperopt_show_no_header"]
NO_CONF_REQURIED = ["download-data", "list-timeframes", "list-markets", "list-pairs",
"list-strategies", "hyperopt-list", "hyperopt-show", "plot-dataframe",
"plot-profit"]
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies",
"list-hyperopts", "hyperopt-list", "hyperopt-show",
"plot-dataframe", "plot-profit"]
NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
@ -99,10 +111,23 @@ class Arguments:
# Workaround issue in argparse with action='append' and default value
# (see https://bugs.python.org/issue16399)
# Allow no-config for certain commands (like downloading / plotting)
if ('config' in parsed_arg and parsed_arg.config is None and
((Path.cwd() / constants.DEFAULT_CONFIG).is_file() or
not ('command' in parsed_arg and parsed_arg.command in NO_CONF_REQURIED))):
parsed_arg.config = [constants.DEFAULT_CONFIG]
if ('config' in parsed_arg and parsed_arg.config is None):
conf_required = ('command' in parsed_arg and parsed_arg.command in NO_CONF_REQURIED)
if 'user_data_dir' in parsed_arg and parsed_arg.user_data_dir is not None:
user_dir = parsed_arg.user_data_dir
else:
# Default case
user_dir = 'user_data'
# Try loading from "user_data/config.json"
cfgfile = Path(user_dir) / DEFAULT_CONFIG
if cfgfile.is_file():
parsed_arg.config = [str(cfgfile)]
else:
# Else use "config.json".
cfgfile = Path.cwd() / DEFAULT_CONFIG
if cfgfile.is_file() or not conf_required:
parsed_arg.config = [DEFAULT_CONFIG]
return parsed_arg
@ -130,11 +155,13 @@ class Arguments:
self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
self._build_args(optionlist=['version'], parser=self.parser)
from freqtrade.commands import (start_create_userdir, start_download_data,
from freqtrade.commands import (start_create_userdir, start_convert_data,
start_download_data,
start_hyperopt_list, start_hyperopt_show,
start_list_exchanges, start_list_markets,
start_list_strategies, start_new_hyperopt,
start_new_strategy, start_list_timeframes,
start_list_exchanges, start_list_hyperopts,
start_list_markets, start_list_strategies,
start_list_timeframes, start_new_config,
start_new_hyperopt, start_new_strategy,
start_plot_dataframe, start_plot_profit,
start_backtesting, start_hyperopt, start_edge,
start_test_pairlist, start_trading)
@ -177,6 +204,12 @@ class Arguments:
create_userdir_cmd.set_defaults(func=start_create_userdir)
self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
# add new-config subcommand
build_config_cmd = subparsers.add_parser('new-config',
help="Create new config")
build_config_cmd.set_defaults(func=start_new_config)
self._build_args(optionlist=ARGS_BUILD_CONFIG, parser=build_config_cmd)
# add new-strategy subcommand
build_strategy_cmd = subparsers.add_parser('new-strategy',
help="Create new strategy")
@ -198,6 +231,15 @@ class Arguments:
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',
@ -251,6 +293,24 @@ class Arguments:
download_data_cmd.set_defaults(func=start_download_data)
self._build_args(optionlist=ARGS_DOWNLOAD_DATA, parser=download_data_cmd)
# Add convert-data subcommand
convert_data_cmd = subparsers.add_parser(
'convert-data',
help='Convert OHLCV data from one format to another.',
parents=[_common_parser],
)
convert_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=True))
self._build_args(optionlist=ARGS_CONVERT_DATA_OHLCV, parser=convert_data_cmd)
# Add convert-trade-data subcommand
convert_trade_data_cmd = subparsers.add_parser(
'convert-trade-data',
help='Convert trade-data from one format to another.',
parents=[_common_parser],
)
convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))
self._build_args(optionlist=ARGS_CONVERT_DATA, parser=convert_trade_data_cmd)
# Add Plotting subcommand
plot_dataframe_cmd = subparsers.add_parser(
'plot-dataframe',

193
freqtrade/commands/build_config_commands.py Normal file
View File

@ -0,0 +1,193 @@
import logging
from pathlib import Path
from typing import Any, Dict
from questionary import Separator, prompt
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
from freqtrade.exchange import available_exchanges, MAP_EXCHANGE_CHILDCLASS
from freqtrade.misc import render_template
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def validate_is_int(val):
try:
_ = int(val)
return True
except Exception:
return False
def validate_is_float(val):
try:
_ = float(val)
return True
except Exception:
return False
def ask_user_overwrite(config_path: Path) -> bool:
questions = [
{
"type": "confirm",
"name": "overwrite",
"message": f"File {config_path} already exists. Overwrite?",
"default": False,
},
]
answers = prompt(questions)
return answers['overwrite']
def ask_user_config() -> Dict[str, Any]:
"""
Ask user a few questions to build the configuration.
Interactive questions built using https://github.com/tmbo/questionary
:returns: Dict with keys to put into template
"""
questions = [
{
"type": "confirm",
"name": "dry_run",
"message": "Do you want to enable Dry-run (simulated trades)?",
"default": True,
},
{
"type": "text",
"name": "stake_currency",
"message": "Please insert your stake currency:",
"default": 'BTC',
},
{
"type": "text",
"name": "stake_amount",
"message": "Please insert your stake amount:",
"default": "0.01",
"validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_float(val),
},
{
"type": "text",
"name": "max_open_trades",
"message": f"Please insert max_open_trades (Integer or '{UNLIMITED_STAKE_AMOUNT}'):",
"default": "3",
"validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_int(val)
},
{
"type": "text",
"name": "ticker_interval",
"message": "Please insert your ticker interval:",
"default": "5m",
},
{
"type": "text",
"name": "fiat_display_currency",
"message": "Please insert your display Currency (for reporting):",
"default": 'USD',
},
{
"type": "select",
"name": "exchange_name",
"message": "Select exchange",
"choices": [
"binance",
"binanceje",
"binanceus",
"bittrex",
"kraken",
Separator(),
"other",
],
},
{
"type": "autocomplete",
"name": "exchange_name",
"message": "Type your exchange name (Must be supported by ccxt)",
"choices": available_exchanges(),
"when": lambda x: x["exchange_name"] == 'other'
},
{
"type": "password",
"name": "exchange_key",
"message": "Insert Exchange Key",
"when": lambda x: not x['dry_run']
},
{
"type": "password",
"name": "exchange_secret",
"message": "Insert Exchange Secret",
"when": lambda x: not x['dry_run']
},
{
"type": "confirm",
"name": "telegram",
"message": "Do you want to enable Telegram?",
"default": False,
},
{
"type": "password",
"name": "telegram_token",
"message": "Insert Telegram token",
"when": lambda x: x['telegram']
},
{
"type": "text",
"name": "telegram_chat_id",
"message": "Insert Telegram chat id",
"when": lambda x: x['telegram']
},
]
answers = prompt(questions)
if not answers:
# Interrupted questionary sessions return an empty dict.
raise OperationalException("User interrupted interactive questions.")
return answers
def deploy_new_config(config_path: Path, selections: Dict[str, Any]) -> None:
"""
Applies selections to the template and writes the result to config_path
:param config_path: Path object for new config file. Should not exist yet
:param selecions: Dict containing selections taken by the user.
"""
from jinja2.exceptions import TemplateNotFound
try:
exchange_template = MAP_EXCHANGE_CHILDCLASS.get(
selections['exchange_name'], selections['exchange_name'])
selections['exchange'] = render_template(
templatefile=f"subtemplates/exchange_{exchange_template}.j2",
arguments=selections
)
except TemplateNotFound:
selections['exchange'] = render_template(
templatefile=f"subtemplates/exchange_generic.j2",
arguments=selections
)
config_text = render_template(templatefile='base_config.json.j2',
arguments=selections)
logger.info(f"Writing config to `{config_path}`.")
config_path.write_text(config_text)
def start_new_config(args: Dict[str, Any]) -> None:
"""
Create a new strategy from a template
Asking the user questions to fill out the templateaccordingly.
"""
config_path = Path(args['config'][0])
if config_path.exists():
overwrite = ask_user_overwrite(config_path)
if overwrite:
config_path.unlink()
else:
raise OperationalException(
f"Configuration file `{config_path}` already exists. "
"Please delete it or use a different configuration file name.")
selections = ask_user_config()
deploy_new_config(config_path, selections)

77
freqtrade/commands/cli_options.py
View File

@ -59,7 +59,8 @@ AVAILABLE_CLI_OPTIONS = {
),
"config": Arg(
'-c', '--config',
help=f'Specify configuration file (default: `{constants.DEFAULT_CONFIG}`). '
help=f'Specify configuration file (default: `userdir/{constants.DEFAULT_CONFIG}` '
f'or `config.json` whichever exists). '
f'Multiple --config options may be used. '
f'Can be set to `-` to read config from stdin.',
action='append',
@ -256,7 +257,7 @@ AVAILABLE_CLI_OPTIONS = {
help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
'Different functions can generate completely different results, '
'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss.'
'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily.'
'(default: `%(default)s`).',
metavar='NAME',
default=constants.DEFAULT_HYPEROPT_LOSS,
@ -332,6 +333,30 @@ AVAILABLE_CLI_OPTIONS = {
'desired timeframe as specified as --timeframes/-t.',
action='store_true',
),
"format_from": Arg(
'--format-from',
help='Source format for data conversion.',
choices=constants.AVAILABLE_DATAHANDLERS,
required=True,
),
"format_to": Arg(
'--format-to',
help='Destination format for data conversion.',
choices=constants.AVAILABLE_DATAHANDLERS,
required=True,
),
"dataformat_ohlcv": Arg(
'--data-format-ohlcv',
help='Storage format for downloaded ohlcv data. (default: `%(default)s`).',
choices=constants.AVAILABLE_DATAHANDLERS,
default='json'
),
"dataformat_trades": Arg(
'--data-format-trades',
help='Storage format for downloaded trades data. (default: `%(default)s`).',
choices=constants.AVAILABLE_DATAHANDLERS,
default='jsongz'
),
"exchange": Arg(
'--exchange',
help=f'Exchange name (default: `{constants.DEFAULT_EXCHANGE}`). '
@ -398,6 +423,54 @@ AVAILABLE_CLI_OPTIONS = {
help='Select only best epochs.',
action='store_true',
),
"hyperopt_list_min_trades": Arg(
'--min-trades',
help='Select epochs with more than INT trades.',
type=check_int_positive,
metavar='INT',
),
"hyperopt_list_max_trades": Arg(
'--max-trades',
help='Select epochs with less than INT trades.',
type=check_int_positive,
metavar='INT',
),
"hyperopt_list_min_avg_time": Arg(
'--min-avg-time',
help='Select epochs on above average time.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_avg_time": Arg(
'--max-avg-time',
help='Select epochs on under average time.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_min_avg_profit": Arg(
'--min-avg-profit',
help='Select epochs on above average profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_avg_profit": Arg(
'--max-avg-profit',
help='Select epochs on below average profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_min_total_profit": Arg(
'--min-total-profit',
help='Select epochs on above total profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_total_profit": Arg(
'--max-total-profit',
help='Select epochs on below total profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_no_details": Arg(
'--no-details',
help='Do not print best epoch details.',

35
freqtrade/commands/data_commands.py
View File

@ -5,6 +5,8 @@ from typing import Any, Dict, List
import arrow
from freqtrade.configuration import TimeRange, setup_utils_configuration
from freqtrade.data.converter import (convert_ohlcv_format,
convert_trades_format)
from freqtrade.data.history import (convert_trades_to_ohlcv,
refresh_backtest_ohlcv_data,
refresh_backtest_trades_data)
@ -37,22 +39,32 @@ def start_download_data(args: Dict[str, Any]) -> None:
pairs_not_available: List[str] = []
# Init exchange
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
# Manual validations of relevant settings
exchange.validate_pairs(config['pairs'])
for timeframe in config['timeframes']:
exchange.validate_timeframes(timeframe)
try:
if config.get('download_trades'):
pairs_not_available = refresh_backtest_trades_data(
exchange, pairs=config["pairs"], datadir=config['datadir'],
timerange=timerange, erase=config.get("erase"))
timerange=timerange, erase=bool(config.get("erase")),
data_format=config['dataformat_trades'])
# Convert downloaded trade data to different timeframes
convert_trades_to_ohlcv(
pairs=config["pairs"], timeframes=config["timeframes"],
datadir=config['datadir'], timerange=timerange, erase=config.get("erase"))
datadir=config['datadir'], timerange=timerange, erase=bool(config.get("erase")),
data_format_ohlcv=config['dataformat_ohlcv'],
data_format_trades=config['dataformat_trades'],
)
else:
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=config["pairs"], timeframes=config["timeframes"],
datadir=config['datadir'], timerange=timerange, erase=config.get("erase"))
datadir=config['datadir'], timerange=timerange, erase=bool(config.get("erase")),
data_format=config['dataformat_ohlcv'])
except KeyboardInterrupt:
sys.exit("SIGINT received, aborting ...")
@ -61,3 +73,18 @@ def start_download_data(args: Dict[str, Any]) -> None:
if pairs_not_available:
logger.info(f"Pairs [{','.join(pairs_not_available)}] not available "
f"on exchange {exchange.name}.")
def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
"""
Convert data from one format to another
"""
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if ohlcv:
convert_ohlcv_format(config,
convert_from=args['format_from'], convert_to=args['format_to'],
erase=args['erase'])
else:
convert_trades_format(config,
convert_from=args['format_from'], convert_to=args['format_to'],
erase=args['erase'])

8
freqtrade/commands/deploy_commands.py
View File

@ -6,7 +6,7 @@ from typing import Any, Dict
from freqtrade.configuration import setup_utils_configuration
from freqtrade.configuration.directory_operations import (copy_sample_files,
create_userdata_dir)
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGY
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.exceptions import OperationalException
from freqtrade.misc import render_template
from freqtrade.state import RunMode
@ -28,7 +28,7 @@ def start_create_userdir(args: Dict[str, Any]) -> None:
sys.exit(1)
def deploy_new_strategy(strategy_name, strategy_path: Path, subtemplate: str):
def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: str) -> None:
"""
Deploy new strategy from template to strategy_path
"""
@ -57,7 +57,7 @@ def start_new_strategy(args: Dict[str, Any]) -> None:
if args["strategy"] == "DefaultStrategy":
raise OperationalException("DefaultStrategy is not allowed as name.")
new_path = config['user_data_dir'] / USERPATH_STRATEGY / (args["strategy"] + ".py")
new_path = config['user_data_dir'] / USERPATH_STRATEGIES / (args["strategy"] + ".py")
if new_path.exists():
raise OperationalException(f"`{new_path}` already exists. "
@ -69,7 +69,7 @@ def start_new_strategy(args: Dict[str, Any]) -> None:
raise OperationalException("`new-strategy` requires --strategy to be set.")
def deploy_new_hyperopt(hyperopt_name, hyperopt_path: Path, subtemplate: str):
def deploy_new_hyperopt(hyperopt_name: str, hyperopt_path: Path, subtemplate: str) -> None:
"""
Deploys a new hyperopt template to hyperopt_path
"""

94
freqtrade/commands/hyperopt_commands.py Normal file → Executable file
View File

@ -19,13 +19,24 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
only_best = config.get('hyperopt_list_best', False)
only_profitable = config.get('hyperopt_list_profitable', False)
print_colorized = config.get('print_colorized', False)
print_json = config.get('print_json', False)
no_details = config.get('hyperopt_list_no_details', False)
no_header = False
filteroptions = {
'only_best': config.get('hyperopt_list_best', False),
'only_profitable': config.get('hyperopt_list_profitable', False),
'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', 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)
}
trials_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
@ -33,7 +44,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
trials = Hyperopt.load_previous_results(trials_file)
total_epochs = len(trials)
trials = _hyperopt_filter_trials(trials, only_best, only_profitable)
trials = _hyperopt_filter_trials(trials, filteroptions)
# TODO: fetch the interval for epochs to print from the cli option
epoch_start, epoch_stop = 0, None
@ -44,7 +55,8 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
try:
# Human-friendly indexes used here (starting from 1)
for val in trials[epoch_start:epoch_stop]:
Hyperopt.print_results_explanation(val, total_epochs, not only_best, print_colorized)
Hyperopt.print_results_explanation(val, total_epochs,
not filteroptions['only_best'], print_colorized)
except KeyboardInterrupt:
print('User interrupted..')
@ -63,8 +75,18 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
only_best = config.get('hyperopt_list_best', False)
only_profitable = config.get('hyperopt_list_profitable', False)
filteroptions = {
'only_best': config.get('hyperopt_list_best', False),
'only_profitable': config.get('hyperopt_list_profitable', False),
'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', 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)
}
no_header = config.get('hyperopt_show_no_header', False)
trials_file = (config['user_data_dir'] /
@ -74,7 +96,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
trials = Hyperopt.load_previous_results(trials_file)
total_epochs = len(trials)
trials = _hyperopt_filter_trials(trials, only_best, only_profitable)
trials = _hyperopt_filter_trials(trials, filteroptions)
trials_epochs = len(trials)
n = config.get('hyperopt_show_index', -1)
@ -97,18 +119,66 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
header_str="Epoch details")
def _hyperopt_filter_trials(trials: List, only_best: bool, only_profitable: bool) -> List:
def _hyperopt_filter_trials(trials: List, filteroptions: dict) -> List:
"""
Filter our items from the list of hyperopt results
"""
if only_best:
if filteroptions['only_best']:
trials = [x for x in trials if x['is_best']]
if only_profitable:
if filteroptions['only_profitable']:
trials = [x for x in trials if x['results_metrics']['profit'] > 0]
if filteroptions['filter_min_trades'] > 0:
trials = [
x for x in trials
if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
]
if filteroptions['filter_max_trades'] > 0:
trials = [
x for x in trials
if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
]
if filteroptions['filter_min_avg_time'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
]
if filteroptions['filter_max_avg_time'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
]
if filteroptions['filter_min_avg_profit'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['avg_profit']
> filteroptions['filter_min_avg_profit']
]
if filteroptions['filter_max_avg_profit'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['avg_profit']
< filteroptions['filter_max_avg_profit']
]
if filteroptions['filter_min_total_profit'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
]
if filteroptions['filter_max_total_profit'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
]
logger.info(f"{len(trials)} " +
("best " if only_best else "") +
("profitable " if only_profitable else "") +
("best " if filteroptions['only_best'] else "") +
("profitable " if filteroptions['only_profitable'] else "") +
"epochs found.")
return trials

61
freqtrade/commands/list_commands.py
View File

@ -3,13 +3,15 @@ import logging
import sys
from collections import OrderedDict
from pathlib import Path
from typing import Any, Dict
from typing import Any, Dict, List
from colorama import init as colorama_init
from colorama import Fore, Style
import rapidjson
from tabulate import tabulate
from freqtrade.configuration import setup_utils_configuration
from freqtrade.constants import USERPATH_STRATEGY
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)
@ -36,22 +38,63 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
print(f"Exchanges available for Freqtrade: {', '.join(exchanges)}")
def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
if print_colorized:
colorama_init(autoreset=True)
red = Fore.RED
yellow = Fore.YELLOW
reset = Style.RESET_ALL
else:
red = ''
yellow = ''
reset = ''
names = [s['name'] for s in objs]
objss_to_print = [{
'name': s['name'] if s['name'] else "--",
'location': s['location'].name,
'status': (red + "LOAD FAILED" + reset if s['class'] is None
else "OK" if names.count(s['name']) == 1
else yellow + "DUPLICATE NAME" + reset)
} for s in objs]
print(tabulate(objss_to_print, headers='keys', tablefmt='pipe'))
def start_list_strategies(args: Dict[str, Any]) -> None:
"""
Print Strategies available in a directory
Print files with Strategy custom classes available in the directory
"""
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGY))
strategies = StrategyResolver.search_all_objects(directory)
directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
strategy_objs = StrategyResolver.search_all_objects(directory, not args['print_one_column'])
# Sort alphabetically
strategies = sorted(strategies, key=lambda x: x['name'])
strats_to_print = [{'name': s['name'], 'location': s['location'].name} for s in strategies]
strategy_objs = sorted(strategy_objs, key=lambda x: x['name'])
if args['print_one_column']:
print('\n'.join([s['name'] for s in strategies]))
print('\n'.join([s['name'] for s in strategy_objs]))
else:
print(tabulate(strats_to_print, headers='keys', tablefmt='pipe'))
_print_objs_tabular(strategy_objs, config.get('print_colorized', False))
def start_list_hyperopts(args: Dict[str, Any]) -> None:
"""
Print files with HyperOpt custom classes available in the directory
"""
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
directory = Path(config.get('hyperopt_path', config['user_data_dir'] / USERPATH_HYPEROPTS))
hyperopt_objs = HyperOptResolver.search_all_objects(directory, not args['print_one_column'])
# Sort alphabetically
hyperopt_objs = sorted(hyperopt_objs, key=lambda x: x['name'])
if args['print_one_column']:
print('\n'.join([s['name'] for s in hyperopt_objs]))
else:
_print_objs_tabular(hyperopt_objs, config.get('print_colorized', False))
def start_list_timeframes(args: Dict[str, Any]) -> None:

2
freqtrade/commands/plot_commands.py
View File

@ -5,7 +5,7 @@ from freqtrade.exceptions import OperationalException
from freqtrade.state import RunMode
def validate_plot_args(args: Dict[str, Any]):
def validate_plot_args(args: Dict[str, Any]) -> None:
if not args.get('datadir') and not args.get('config'):
raise OperationalException(
"You need to specify either `--datadir` or `--config` "

2
freqtrade/configuration/check_exchange.py
View File

@ -10,7 +10,7 @@ from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
def remove_credentials(config: Dict[str, Any]):
def remove_credentials(config: Dict[str, Any]) -> None:
"""
Removes exchange keys from the configuration and specifies dry-run
Used for backtesting / hyperopt / edge and utils.

31
freqtrade/configuration/configuration.py
View File

@ -310,6 +310,30 @@ class Configuration:
self._args_to_config(config, argname='hyperopt_list_profitable',
logstring='Parameter --profitable detected: {}')
self._args_to_config(config, argname='hyperopt_list_min_trades',
logstring='Parameter --min-trades detected: {}')
self._args_to_config(config, argname='hyperopt_list_max_trades',
logstring='Parameter --max-trades detected: {}')
self._args_to_config(config, argname='hyperopt_list_min_avg_time',
logstring='Parameter --min-avg-time detected: {}')
self._args_to_config(config, argname='hyperopt_list_max_avg_time',
logstring='Parameter --max-avg-time detected: {}')
self._args_to_config(config, argname='hyperopt_list_min_avg_profit',
logstring='Parameter --min-avg-profit detected: {}')
self._args_to_config(config, argname='hyperopt_list_max_avg_profit',
logstring='Parameter --max-avg-profit detected: {}')
self._args_to_config(config, argname='hyperopt_list_min_total_profit',
logstring='Parameter --min-total-profit detected: {}')
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_no_details',
logstring='Parameter --no-details detected: {}')
@ -340,9 +364,16 @@ class Configuration:
self._args_to_config(config, argname='days',
logstring='Detected --days: {}')
self._args_to_config(config, argname='download_trades',
logstring='Detected --dl-trades: {}')
self._args_to_config(config, argname='dataformat_ohlcv',
logstring='Using "{}" to store OHLCV data.')
self._args_to_config(config, argname='dataformat_trades',
logstring='Using "{}" to store trades data.')
def _process_runmode(self, config: Dict[str, Any]) -> None:
if not self.runmode:

4
freqtrade/configuration/deprecated_settings.py
View File

@ -13,7 +13,7 @@ logger = logging.getLogger(__name__)
def check_conflicting_settings(config: Dict[str, Any],
section1: str, name1: str,
section2: str, name2: str):
section2: str, name2: str) -> None:
section1_config = config.get(section1, {})
section2_config = config.get(section2, {})
if name1 in section1_config and name2 in section2_config:
@ -28,7 +28,7 @@ def check_conflicting_settings(config: Dict[str, Any],
def process_deprecated_setting(config: Dict[str, Any],
section1: str, name1: str,
section2: str, name2: str):
section2: str, name2: str) -> None:
section2_config = config.get(section2, {})
if name2 in section2_config:

2
freqtrade/configuration/directory_operations.py
View File

@ -23,7 +23,7 @@ def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Pat
return folder
def create_userdata_dir(directory: str, create_dir=False) -> Path:
def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
"""
Create userdata directory structure.
if create_dir is True, then the parent-directory will be created if it does not exist.

5
freqtrade/configuration/timerange.py
View File

@ -7,6 +7,7 @@ from typing import Optional
import arrow
logger = logging.getLogger(__name__)
@ -30,7 +31,7 @@ class TimeRange:
return (self.starttype == other.starttype and self.stoptype == other.stoptype
and self.startts == other.startts and self.stopts == other.stopts)
def subtract_start(self, seconds) -> None:
def subtract_start(self, seconds: int) -> None:
"""
Subtracts <seconds> from startts if startts is set.
:param seconds: Seconds to subtract from starttime
@ -59,7 +60,7 @@ class TimeRange:
self.starttype = 'date'
@staticmethod
def parse_timerange(text: Optional[str]):
def parse_timerange(text: Optional[str]) -> 'TimeRange':
"""
Parse the value of the argument --timerange to determine what is the range desired
:param text: value from --timerange

30
freqtrade/constants.py
View File

@ -17,20 +17,24 @@ REQUIRED_ORDERTIF = ['buy', 'sell']
REQUIRED_ORDERTYPES = ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'PrecisionFilter', 'PriceFilter']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'PrecisionFilter', 'PriceFilter', 'SpreadFilter']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz']
DRY_RUN_WALLET = 1000
MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
USERPATH_HYPEROPTS = 'hyperopts'
USERPATH_STRATEGY = 'strategies'
USERPATH_STRATEGIES = 'strategies'
USERPATH_NOTEBOOKS = 'notebooks'
# Soure files with destination directories within user-directory
USER_DATA_FILES = {
'sample_strategy.py': USERPATH_STRATEGY,
'sample_strategy.py': USERPATH_STRATEGIES,
'sample_hyperopt_advanced.py': USERPATH_HYPEROPTS,
'sample_hyperopt_loss.py': USERPATH_HYPEROPTS,
'sample_hyperopt.py': USERPATH_HYPEROPTS,
'strategy_analysis_example.ipynb': 'notebooks',
'strategy_analysis_example.ipynb': USERPATH_NOTEBOOKS,
}
SUPPORTED_FIAT = [
@ -189,7 +193,9 @@ CONF_SCHEMA = {
'properties': {
'enabled': {'type': 'boolean'},
'webhookbuy': {'type': 'object'},
'webhookbuycancel': {'type': 'object'},
'webhooksell': {'type': 'object'},
'webhooksellcancel': {'type': 'object'},
'webhookstatus': {'type': 'object'},
},
},
@ -213,11 +219,22 @@ CONF_SCHEMA = {
'forcebuy_enable': {'type': 'boolean'},
'internals': {
'type': 'object',
'default': {},
'properties': {
'process_throttle_secs': {'type': 'integer'},
'interval': {'type': 'integer'},
'sd_notify': {'type': 'boolean'},
}
},
'dataformat_ohlcv': {
'type': 'string',
'enum': AVAILABLE_DATAHANDLERS,
'default': 'json'
},
'dataformat_trades': {
'type': 'string',
'enum': AVAILABLE_DATAHANDLERS,
'default': 'jsongz'
}
},
'definitions': {
@ -288,9 +305,14 @@ SCHEMA_TRADE_REQUIRED = [
'unfilledtimeout',
'stoploss',
'minimal_roi',
'internals',
'dataformat_ohlcv',
'dataformat_trades',
]
SCHEMA_MINIMAL_REQUIRED = [
'exchange',
'dry_run',
'dataformat_ohlcv',
'dataformat_trades',
]

7
freqtrade/data/btanalysis.py
View File

@ -3,7 +3,7 @@ Helpers when analyzing backtest data
"""
import logging
from pathlib import Path
from typing import Dict
from typing import Dict, Union
import numpy as np
import pandas as pd
@ -20,7 +20,7 @@ BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "du
"open_rate", "close_rate", "open_at_end", "sell_reason"]
def load_backtest_data(filename) -> pd.DataFrame:
def load_backtest_data(filename: Union[Path, str]) -> pd.DataFrame:
"""
Load backtest data file.
:param filename: pathlib.Path object, or string pointing to the file.
@ -151,7 +151,8 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> p
return trades
def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame], column: str = "close"):
def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame],
column: str = "close") -> pd.DataFrame:
"""
Combine multiple dataframes "column"
:param tickers: Dict of Dataframes, dict key should be pair.

122
freqtrade/data/converter.py
View File

@ -2,10 +2,13 @@
Functions to convert data from one format to another
"""
import logging
from datetime import datetime, timezone
from typing import Any, Dict
import pandas as pd
from pandas import DataFrame, to_datetime
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
logger = logging.getLogger(__name__)
@ -24,7 +27,7 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
:return: DataFrame
"""
logger.debug("Parsing tickerlist to dataframe")
cols = ['date', 'open', 'high', 'low', 'close', 'volume']
cols = DEFAULT_DATAFRAME_COLUMNS
frame = DataFrame(ticker, columns=cols)
frame['date'] = to_datetime(frame['date'],
@ -37,9 +40,29 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
# and fail with exception...
frame = frame.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
'volume': 'float'})
return clean_ohlcv_dataframe(frame, timeframe, pair,
fill_missing=fill_missing,
drop_incomplete=drop_incomplete)
def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
"""
Clense a ohlcv dataframe by
* Grouping it by date (removes duplicate tics)
* dropping last candles if requested
* Filling up missing data (if requested)
:param data: DataFrame containing ohlcv data.
:param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
:param pair: Pair this data is for (used to warn if fillup was necessary)
:param fill_missing: fill up missing candles with 0 candles
(see ohlcv_fill_up_missing_data for details)
:param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
:return: DataFrame
"""
# group by index and aggregate results to eliminate duplicate ticks
frame = frame.groupby(by='date', as_index=False, sort=True).agg({
data = data.groupby(by='date', as_index=False, sort=True).agg({
'open': 'first',
'high': 'max',
'low': 'min',
@ -48,13 +71,13 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
})
# eliminate partial candle
if drop_incomplete:
frame.drop(frame.tail(1).index, inplace=True)
data.drop(data.tail(1).index, inplace=True)
logger.debug('Dropping last candle')
if fill_missing:
return ohlcv_fill_up_missing_data(frame, timeframe, pair)
return ohlcv_fill_up_missing_data(data, timeframe, pair)
else:
return frame
return data
def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame:
@ -92,8 +115,26 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
return df
def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date') -> DataFrame:
"""
Trim dataframe based on given timerange
:param df: Dataframe to trim
:param timerange: timerange (use start and end date if available)
:param: df_date_col: Column in the dataframe to use as Date column
:return: trimmed dataframe
"""
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
df = df.loc[df[df_date_col] >= start, :]
if timerange.stoptype == 'date':
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
df = df.loc[df[df_date_col] <= stop, :]
return df
def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
"""
TODO: This should get a dedicated test
Gets order book list, returns dataframe with below format per suggested by creslin
-------------------------------------------------------------------
b_sum b_size bids asks a_size a_sum
@ -116,12 +157,13 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
return frame
def trades_to_ohlcv(trades: list, timeframe: str) -> list:
def trades_to_ohlcv(trades: list, timeframe: str) -> DataFrame:
"""
Converts trades list to ohlcv list
TODO: This should get a dedicated test
:param trades: List of trades, as returned by ccxt.fetch_trades.
:param timeframe: Ticker timeframe to resample data to
:return: ohlcv timeframe as list (as returned by ccxt.fetch_ohlcv)
:return: ohlcv Dataframe.
"""
from freqtrade.exchange import timeframe_to_minutes
ticker_minutes = timeframe_to_minutes(timeframe)
@ -131,8 +173,68 @@ def trades_to_ohlcv(trades: list, timeframe: str) -> list:
df_new = df['price'].resample(f'{ticker_minutes}min').ohlc()
df_new['volume'] = df['amount'].resample(f'{ticker_minutes}min').sum()
df_new['date'] = df_new.index.astype("int64") // 10 ** 6
df_new['date'] = df_new.index
# Drop 0 volume rows
df_new = df_new.dropna()
columns = ["date", "open", "high", "low", "close", "volume"]
return list(zip(*[df_new[x].values.tolist() for x in columns]))
return df_new[DEFAULT_DATAFRAME_COLUMNS]
def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
"""
Convert trades from one format to another format.
:param config: Config dictionary
:param convert_from: Source format
:param convert_to: Target format
:param erase: Erase souce data (does not apply if source and target format are identical)
"""
from freqtrade.data.history.idatahandler import get_datahandler
src = get_datahandler(config['datadir'], convert_from)
trg = get_datahandler(config['datadir'], convert_to)
if 'pairs' not in config:
config['pairs'] = src.trades_get_pairs(config['datadir'])
logger.info(f"Converting trades for {config['pairs']}")
for pair in config['pairs']:
data = src.trades_load(pair=pair)
logger.info(f"Converting {len(data)} trades for {pair}")
trg.trades_store(pair, data)
if erase and convert_from != convert_to:
logger.info(f"Deleting source Trade data for {pair}.")
src.trades_purge(pair=pair)
def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
"""
Convert ohlcv from one format to another format.
:param config: Config dictionary
:param convert_from: Source format
:param convert_to: Target format
:param erase: Erase souce data (does not apply if source and target format are identical)
"""
from freqtrade.data.history.idatahandler import get_datahandler
src = get_datahandler(config['datadir'], convert_from)
trg = get_datahandler(config['datadir'], convert_to)
timeframes = config.get('timeframes', [config.get('ticker_interval')])
logger.info(f"Converting OHLCV for timeframe {timeframes}")
if 'pairs' not in config:
config['pairs'] = []
# Check timeframes or fall back to ticker_interval.
for timeframe in timeframes:
config['pairs'].extend(src.ohlcv_get_pairs(config['datadir'],
timeframe))
logger.info(f"Converting OHLCV for {config['pairs']}")
for timeframe in timeframes:
for pair in config['pairs']:
data = src.ohlcv_load(pair=pair, timeframe=timeframe,
timerange=None,
fill_missing=False,
drop_incomplete=False,
startup_candles=0)
logger.info(f"Converting {len(data)} candles for {pair}")
trg.ohlcv_store(pair=pair, timeframe=timeframe, data=data)
if erase and convert_from != convert_to:
logger.info(f"Deleting source data for {pair} / {timeframe}")
src.ohlcv_purge(pair=pair, timeframe=timeframe)

14
freqtrade/data/history/__init__.py Normal file
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@ -0,0 +1,14 @@
"""
Handle historic data (ohlcv).
Includes:
* load data for a pair (or a list of pairs) from disk
* download data from exchange and store to disk
"""
from .history_utils import (convert_trades_to_ohlcv, # noqa: F401
get_timerange, load_data, load_pair_history,
refresh_backtest_ohlcv_data,
refresh_backtest_trades_data, refresh_data,
validate_backtest_data)
from .idatahandler import get_datahandler # noqa: F401

304
freqtrade/data/history.py → freqtrade/data/history/history_utils.py
View File

@ -1,138 +1,31 @@
"""
Handle historic data (ohlcv).
Includes:
* load data for a pair (or a list of pairs) from disk
* download data from exchange and store to disk
"""
import logging
import operator
from copy import deepcopy
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
from typing import Dict, List, Optional, Tuple
import arrow
from pandas import DataFrame
from freqtrade import misc
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (Exchange, timeframe_to_minutes,
timeframe_to_seconds)
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]:
"""
Trim tickerlist based on given timerange
"""
if not tickerlist:
return tickerlist
start_index = 0
stop_index = len(tickerlist)
if timerange.starttype == 'date':
while (start_index < len(tickerlist) and
tickerlist[start_index][0] < timerange.startts * 1000):
start_index += 1
if timerange.stoptype == 'date':
while (stop_index > 0 and
tickerlist[stop_index-1][0] > timerange.stopts * 1000):
stop_index -= 1
if start_index > stop_index:
raise ValueError(f'The timerange [{timerange.startts},{timerange.stopts}] is incorrect')
return tickerlist[start_index:stop_index]
def trim_dataframe(df: DataFrame, timerange: TimeRange, df_date_col: str = 'date') -> DataFrame:
"""
Trim dataframe based on given timerange
:param df: Dataframe to trim
:param timerange: timerange (use start and end date if available)
:param: df_date_col: Column in the dataframe to use as Date column
:return: trimmed dataframe
"""
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
df = df.loc[df[df_date_col] >= start, :]
if timerange.stoptype == 'date':
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
df = df.loc[df[df_date_col] <= stop, :]
return df
def load_tickerdata_file(datadir: Path, pair: str, timeframe: str,
timerange: Optional[TimeRange] = None) -> List[Dict]:
"""
Load a pair from file, either .json.gz or .json
:return: tickerlist or None if unsuccessful
"""
filename = pair_data_filename(datadir, pair, timeframe)
pairdata = misc.file_load_json(filename)
if not pairdata:
return []
if timerange:
pairdata = trim_tickerlist(pairdata, timerange)
return pairdata
def store_tickerdata_file(datadir: Path, pair: str,
timeframe: str, data: list, is_zip: bool = False):
"""
Stores tickerdata to file
"""
filename = pair_data_filename(datadir, pair, timeframe)
misc.file_dump_json(filename, data, is_zip=is_zip)
def load_trades_file(datadir: Path, pair: str,
timerange: Optional[TimeRange] = None) -> List[Dict]:
"""
Load a pair from file, either .json.gz or .json
:return: tradelist or empty list if unsuccesful
"""
filename = pair_trades_filename(datadir, pair)
tradesdata = misc.file_load_json(filename)
if not tradesdata:
return []
return tradesdata
def store_trades_file(datadir: Path, pair: str,
data: list, is_zip: bool = True):
"""
Stores tickerdata to file
"""
filename = pair_trades_filename(datadir, pair)
misc.file_dump_json(filename, data, is_zip=is_zip)
def _validate_pairdata(pair, pairdata, timerange: TimeRange):
if timerange.starttype == 'date' and pairdata[0][0] > timerange.startts * 1000:
logger.warning('Missing data at start for pair %s, data starts at %s',
pair, arrow.get(pairdata[0][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
if timerange.stoptype == 'date' and pairdata[-1][0] < timerange.stopts * 1000:
logger.warning('Missing data at end for pair %s, data ends at %s',
pair, arrow.get(pairdata[-1][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
def load_pair_history(pair: str,
timeframe: str,
datadir: Path,
datadir: Path, *,
timerange: Optional[TimeRange] = None,
fill_up_missing: bool = True,
drop_incomplete: bool = True,
startup_candles: int = 0,
data_format: str = None,
data_handler: IDataHandler = None,
) -> DataFrame:
"""
Load cached ticker history for the given pair.
@ -140,39 +33,34 @@ def load_pair_history(pair: str,
:param pair: Pair to load data for
:param timeframe: Ticker timeframe (e.g. "5m")
:param datadir: Path to the data storage location.
:param data_format: Format of the data. Ignored if data_handler is set.
:param timerange: Limit data to be loaded to this timerange
:param fill_up_missing: Fill missing values with "No action"-candles
:param drop_incomplete: Drop last candle assuming it may be incomplete.
:param startup_candles: Additional candles to load at the start of the period
:param data_handler: Initialized data-handler to use.
Will be initialized from data_format if not set
:return: DataFrame with ohlcv data, or empty DataFrame
"""
timerange_startup = deepcopy(timerange)
if startup_candles > 0 and timerange_startup:
timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
data_handler = get_datahandler(datadir, data_format, data_handler)
pairdata = load_tickerdata_file(datadir, pair, timeframe, timerange=timerange_startup)
if pairdata:
if timerange_startup:
_validate_pairdata(pair, pairdata, timerange_startup)
return parse_ticker_dataframe(pairdata, timeframe, pair=pair,
return data_handler.ohlcv_load(pair=pair,
timeframe=timeframe,
timerange=timerange,
fill_missing=fill_up_missing,
drop_incomplete=drop_incomplete)
else:
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
drop_incomplete=drop_incomplete,
startup_candles=startup_candles,
)
return DataFrame()
def load_data(datadir: Path,
timeframe: str,
pairs: List[str],
pairs: List[str], *,
timerange: Optional[TimeRange] = None,
fill_up_missing: bool = True,
startup_candles: int = 0,
fail_without_data: bool = False
fail_without_data: bool = False,
data_format: str = 'json',
) -> Dict[str, DataFrame]:
"""
Load ticker history data for a list of pairs.
@ -184,17 +72,22 @@ def load_data(datadir: Path,
:param fill_up_missing: Fill missing values with "No action"-candles
:param startup_candles: Additional candles to load at the start of the period
:param fail_without_data: Raise OperationalException if no data is found.
:param data_format: Data format which should be used. Defaults to json
:return: dict(<pair>:<Dataframe>)
"""
result: Dict[str, DataFrame] = {}
if startup_candles > 0 and timerange:
logger.info(f'Using indicator startup period: {startup_candles} ...')
data_handler = get_datahandler(datadir, data_format)
for pair in pairs:
hist = load_pair_history(pair=pair, timeframe=timeframe,
datadir=datadir, timerange=timerange,
fill_up_missing=fill_up_missing,
startup_candles=startup_candles)
startup_candles=startup_candles,
data_handler=data_handler
)
if not hist.empty:
result[pair] = hist
@ -207,6 +100,7 @@ def refresh_data(datadir: Path,
timeframe: str,
pairs: List[str],
exchange: Exchange,
data_format: str = None,
timerange: Optional[TimeRange] = None,
) -> None:
"""
@ -218,70 +112,50 @@ def refresh_data(datadir: Path,
:param exchange: Exchange object
:param timerange: Limit data to be loaded to this timerange
"""
data_handler = get_datahandler(datadir, data_format)
for pair in pairs:
_download_pair_history(pair=pair, timeframe=timeframe,
datadir=datadir, timerange=timerange,
exchange=exchange)
exchange=exchange, data_handler=data_handler)
def pair_data_filename(datadir: Path, pair: str, timeframe: str) -> Path:
pair_s = pair.replace("/", "_")
filename = datadir.joinpath(f'{pair_s}-{timeframe}.json')
return filename
def pair_trades_filename(datadir: Path, pair: str) -> Path:
pair_s = pair.replace("/", "_")
filename = datadir.joinpath(f'{pair_s}-trades.json.gz')
return filename
def _load_cached_data_for_updating(datadir: Path, pair: str, timeframe: str,
timerange: Optional[TimeRange]) -> Tuple[List[Any],
Optional[int]]:
def _load_cached_data_for_updating(pair: str, timeframe: str, timerange: Optional[TimeRange],
data_handler: IDataHandler) -> Tuple[DataFrame, Optional[int]]:
"""
Load cached data to download more data.
If timerange is passed in, checks whether data from an before the stored data will be
downloaded.
If that's the case then what's available should be completely overwritten.
Only used by download_pair_history().
Otherwise downloads always start at the end of the available data to avoid data gaps.
Note: Only used by download_pair_history().
"""
since_ms = None
# user sets timerange, so find the start time
start = None
if timerange:
if timerange.starttype == 'date':
since_ms = timerange.startts * 1000
elif timerange.stoptype == 'line':
num_minutes = timerange.stopts * timeframe_to_minutes(timeframe)
since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
# TODO: convert to date for conversion
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
# read the cached file
# Intentionally don't pass timerange in - since we need to load the full dataset.
data = load_tickerdata_file(datadir, pair, timeframe)
# remove the last item, could be incomplete candle
if data:
data.pop()
else:
data = []
if data:
if since_ms and since_ms < data[0][0]:
data = data_handler.ohlcv_load(pair, timeframe=timeframe,
timerange=None, fill_missing=False,
drop_incomplete=True, warn_no_data=False)
if not data.empty:
if start and start < data.iloc[0]['date']:
# Earlier data than existing data requested, redownload all
data = []
data = DataFrame(columns=DEFAULT_DATAFRAME_COLUMNS)
else:
# a part of the data was already downloaded, so download unexist data only
since_ms = data[-1][0] + 1
start = data.iloc[-1]['date']
return (data, since_ms)
start_ms = int(start.timestamp() * 1000) if start else None
return data, start_ms
def _download_pair_history(datadir: Path,
exchange: Exchange,
pair: str,
pair: str, *,
timeframe: str = '5m',
timerange: Optional[TimeRange] = None) -> bool:
timerange: Optional[TimeRange] = None,
data_handler: IDataHandler = None) -> bool:
"""
Download latest candles from the exchange for the pair and timeframe passed in parameters
The data is downloaded starting from the last correct data that
@ -295,16 +169,22 @@ def _download_pair_history(datadir: Path,
:param timerange: range of time to download
:return: bool with success state
"""
data_handler = get_datahandler(datadir, data_handler=data_handler)
try:
logger.info(
f'Download history data for pair: "{pair}", timeframe: {timeframe} '
f'and store in {datadir}.'
)
data, since_ms = _load_cached_data_for_updating(datadir, pair, timeframe, timerange)
# data, since_ms = _load_cached_data_for_updating_old(datadir, pair, timeframe, timerange)
data, since_ms = _load_cached_data_for_updating(pair, timeframe, timerange,
data_handler=data_handler)
logger.debug("Current Start: %s", misc.format_ms_time(data[1][0]) if data else 'None')
logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
logger.debug("Current Start: %s",
f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
logger.debug("Current End: %s",
f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
# Default since_ms to 30 days if nothing is given
new_data = exchange.get_historic_ohlcv(pair=pair,
@ -313,12 +193,20 @@ def _download_pair_history(datadir: Path,
int(arrow.utcnow().shift(
days=-30).float_timestamp) * 1000
)
data.extend(new_data)
# TODO: Maybe move parsing to exchange class (?)
new_dataframe = parse_ticker_dataframe(new_data, timeframe, pair,
fill_missing=False, drop_incomplete=True)
if data.empty:
data = new_dataframe
else:
data = data.append(new_dataframe)
logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
logger.debug("New End: %s", misc.format_ms_time(data[-1][0]))
logger.debug("New Start: %s",
f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
logger.debug("New End: %s",
f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
store_tickerdata_file(datadir, pair, timeframe, data=data)
data_handler.ohlcv_store(pair, timeframe, data=data)
return True
except Exception as e:
@ -331,13 +219,14 @@ def _download_pair_history(datadir: Path,
def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes: List[str],
datadir: Path, timerange: Optional[TimeRange] = None,
erase=False) -> List[str]:
erase: bool = False, data_format: str = None) -> List[str]:
"""
Refresh stored ohlcv data for backtesting and hyperopt operations.
Used by freqtrade download-data subcommand.
:return: List of pairs that are not available.
"""
pairs_not_available = []
data_handler = get_datahandler(datadir, data_format)
for pair in pairs:
if pair not in exchange.markets:
pairs_not_available.append(pair)
@ -345,23 +234,23 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
continue
for timeframe in timeframes:
dl_file = pair_data_filename(datadir, pair, timeframe)
if erase and dl_file.exists():
if erase:
if data_handler.ohlcv_purge(pair, timeframe):
logger.info(
f'Deleting existing data for pair {pair}, interval {timeframe}.')
dl_file.unlink()
logger.info(f'Downloading pair {pair}, interval {timeframe}.')
_download_pair_history(datadir=datadir, exchange=exchange,
pair=pair, timeframe=str(timeframe),
timerange=timerange)
timerange=timerange, data_handler=data_handler)
return pairs_not_available
def _download_trades_history(datadir: Path,
exchange: Exchange,
pair: str,
timerange: Optional[TimeRange] = None) -> bool:
def _download_trades_history(exchange: Exchange,
pair: str, *,
timerange: Optional[TimeRange] = None,
data_handler: IDataHandler
) -> bool:
"""
Download trade history from the exchange.
Appends to previously downloaded trades data.
@ -370,7 +259,7 @@ def _download_trades_history(datadir: Path,
since = timerange.startts * 1000 if timerange and timerange.starttype == 'date' else None
trades = load_trades_file(datadir, pair)
trades = data_handler.trades_load(pair)
from_id = trades[-1]['id'] if trades else None
@ -385,7 +274,7 @@ def _download_trades_history(datadir: Path,
from_id=from_id,
)
trades.extend(new_trades[1])
store_trades_file(datadir, pair, trades)
data_handler.trades_store(pair, data=trades)
logger.debug("New Start: %s", trades[0]['datetime'])
logger.debug("New End: %s", trades[-1]['datetime'])
@ -401,47 +290,52 @@ def _download_trades_history(datadir: Path,
def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir: Path,
timerange: TimeRange, erase=False) -> List[str]:
timerange: TimeRange, erase: bool = False,
data_format: str = 'jsongz') -> List[str]:
"""
Refresh stored trades data for backtesting and hyperopt operations.
Used by freqtrade download-data subcommand.
:return: List of pairs that are not available.
"""
pairs_not_available = []
data_handler = get_datahandler(datadir, data_format=data_format)
for pair in pairs:
if pair not in exchange.markets:
pairs_not_available.append(pair)
logger.info(f"Skipping pair {pair}...")
continue
dl_file = pair_trades_filename(datadir, pair)
if erase and dl_file.exists():
logger.info(
f'Deleting existing data for pair {pair}.')
dl_file.unlink()
if erase:
if data_handler.trades_purge(pair):
logger.info(f'Deleting existing data for pair {pair}.')
logger.info(f'Downloading trades for pair {pair}.')
_download_trades_history(datadir=datadir, exchange=exchange,
_download_trades_history(exchange=exchange,
pair=pair,
timerange=timerange)
timerange=timerange,
data_handler=data_handler)
return pairs_not_available
def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
datadir: Path, timerange: TimeRange, erase=False) -> None:
datadir: Path, timerange: TimeRange, erase: bool = False,
data_format_ohlcv: str = 'json',
data_format_trades: str = 'jsongz') -> None:
"""
Convert stored trades data to ohlcv data
"""
data_handler_trades = get_datahandler(datadir, data_format=data_format_trades)
data_handler_ohlcv = get_datahandler(datadir, data_format=data_format_ohlcv)
for pair in pairs:
trades = load_trades_file(datadir, pair)
trades = data_handler_trades.trades_load(pair)
for timeframe in timeframes:
ohlcv_file = pair_data_filename(datadir, pair, timeframe)
if erase and ohlcv_file.exists():
if erase:
if data_handler_ohlcv.ohlcv_purge(pair, timeframe):
logger.info(f'Deleting existing data for pair {pair}, interval {timeframe}.')
ohlcv_file.unlink()
ohlcv = trades_to_ohlcv(trades, timeframe)
# Store ohlcv
store_tickerdata_file(datadir, pair, timeframe, data=ohlcv)
data_handler_ohlcv.ohlcv_store(pair, timeframe, data=ohlcv)
def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:

220
freqtrade/data/history/idatahandler.py Normal file
View File

@ -0,0 +1,220 @@
"""
Abstract datahandler interface.
It's subclasses handle and storing data from disk.
"""
import logging
from abc import ABC, abstractclassmethod, abstractmethod
from copy import deepcopy
from datetime import datetime, timezone
from pathlib import Path
from typing import Dict, List, Optional, Type
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.data.converter import clean_ohlcv_dataframe, trim_dataframe
from freqtrade.exchange import timeframe_to_seconds
logger = logging.getLogger(__name__)
class IDataHandler(ABC):
def __init__(self, datadir: Path) -> None:
self._datadir = datadir
@abstractclassmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
"""
Returns a list of all pairs with ohlcv data available in this datadir
for the specified timeframe
:param datadir: Directory to search for ohlcv files
:param timeframe: Timeframe to search pairs for
:return: List of Pairs
"""
@abstractmethod
def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
"""
Store data in json format "values".
format looks as follows:
[[<date>,<open>,<high>,<low>,<close>]]
:param pair: Pair - used to generate filename
:timeframe: Timeframe - used to generate filename
:data: Dataframe containing OHLCV data
:return: None
"""
@abstractmethod
def _ohlcv_load(self, pair: str, timeframe: str,
timerange: Optional[TimeRange] = None,
) -> DataFrame:
"""
Internal method used to load data for one pair from disk.
Implements the loading and conversion to a Pandas dataframe.
Timerange trimming and dataframe validation happens outside of this method.
:param pair: Pair to load data
:param timeframe: Ticker timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange.
Optionally implemented by subclasses to avoid loading
all data where possible.
:return: DataFrame with ohlcv data, or empty DataFrame
"""
@abstractmethod
def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
"""
Remove data for this pair
:param pair: Delete data for this pair.
:param timeframe: Ticker timeframe (e.g. "5m")
:return: True when deleted, false if file did not exist.
"""
@abstractmethod
def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
"""
Append data to existing data structures
:param pair: Pair
:param timeframe: Timeframe this ohlcv data is for
:param data: Data to append.
"""
@abstractclassmethod
def trades_get_pairs(cls, datadir: Path) -> List[str]:
"""
Returns a list of all pairs for which trade data is available in this
:param datadir: Directory to search for ohlcv files
:return: List of Pairs
"""
@abstractmethod
def trades_store(self, pair: str, data: List[Dict]) -> None:
"""
Store trades data (list of Dicts) to file
:param pair: Pair - used for filename
:param data: List of Dicts containing trade data
"""
@abstractmethod
def trades_append(self, pair: str, data: List[Dict]):
"""
Append data to existing files
:param pair: Pair - used for filename
:param data: List of Dicts containing trade data
"""
@abstractmethod
def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> List[Dict]:
"""
Load a pair from file, either .json.gz or .json
:param pair: Load trades for this pair
:param timerange: Timerange to load trades for - currently not implemented
:return: List of trades
"""
@abstractmethod
def trades_purge(self, pair: str) -> bool:
"""
Remove data for this pair
:param pair: Delete data for this pair.
:return: True when deleted, false if file did not exist.
"""
def ohlcv_load(self, pair, timeframe: str,
timerange: Optional[TimeRange] = None,
fill_missing: bool = True,
drop_incomplete: bool = True,
startup_candles: int = 0,
warn_no_data: bool = True
) -> DataFrame:
"""
Load cached ticker history for the given pair.
:param pair: Pair to load data for
:param timeframe: Ticker timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange
:param fill_missing: Fill missing values with "No action"-candles
:param drop_incomplete: Drop last candle assuming it may be incomplete.
:param startup_candles: Additional candles to load at the start of the period
:param warn_no_data: Log a warning message when no data is found
:return: DataFrame with ohlcv data, or empty DataFrame
"""
# Fix startup period
timerange_startup = deepcopy(timerange)
if startup_candles > 0 and timerange_startup:
timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
pairdf = self._ohlcv_load(pair, timeframe,
timerange=timerange_startup)
if pairdf.empty:
if warn_no_data:
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
)
return pairdf
else:
enddate = pairdf.iloc[-1]['date']
if timerange_startup:
self._validate_pairdata(pair, pairdf, timerange_startup)
pairdf = trim_dataframe(pairdf, timerange_startup)
# incomplete candles should only be dropped if we didn't trim the end beforehand.
return clean_ohlcv_dataframe(pairdf, timeframe,
pair=pair,
fill_missing=fill_missing,
drop_incomplete=(drop_incomplete and
enddate == pairdf.iloc[-1]['date']))
def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
"""
Validates pairdata for missing data at start end end and logs warnings.
:param pairdata: Dataframe to validate
:param timerange: Timerange specified for start and end dates
"""
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
if pairdata.iloc[0]['date'] > start:
logger.warning(f"Missing data at start for pair {pair}, "
f"data starts at {pairdata.iloc[0]['date']:%Y-%m-%d %H:%M:%S}")
if timerange.stoptype == 'date':
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
if pairdata.iloc[-1]['date'] < stop:
logger.warning(f"Missing data at end for pair {pair}, "
f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
"""
Get datahandler class.
Could be done using Resolvers, but since this may be called often and resolvers
are rather expensive, doing this directly should improve performance.
:param datatype: datatype to use.
:return: Datahandler class
"""
if datatype == 'json':
from .jsondatahandler import JsonDataHandler
return JsonDataHandler
elif datatype == 'jsongz':
from .jsondatahandler import JsonGzDataHandler
return JsonGzDataHandler
else:
raise ValueError(f"No datahandler for datatype {datatype} available.")
def get_datahandler(datadir: Path, data_format: str = None,
data_handler: IDataHandler = None) -> IDataHandler:
"""
:param datadir: Folder to save data
:data_format: dataformat to use
:data_handler: returns this datahandler if it exists or initializes a new one
"""
if not data_handler:
HandlerClass = get_datahandlerclass(data_format or 'json')
data_handler = HandlerClass(datadir)
return data_handler

179
freqtrade/data/history/jsondatahandler.py Normal file
View File

@ -0,0 +1,179 @@
import re
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
from pandas import DataFrame, read_json, to_datetime
from freqtrade import misc
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
from .idatahandler import IDataHandler
class JsonDataHandler(IDataHandler):
_use_zip = False
_columns = DEFAULT_DATAFRAME_COLUMNS
@classmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
"""
Returns a list of all pairs with ohlcv data available in this datadir
for the specified timeframe
:param datadir: Directory to search for ohlcv files
:param timeframe: Timeframe to search pairs for
:return: List of Pairs
"""
_tmp = [re.search(r'^(\S+)(?=\-' + timeframe + '.json)', p.name)
for p in datadir.glob(f"*{timeframe}.{cls._get_file_extension()}")]
# Check if regex found something and only return these results
return [match[0].replace('_', '/') for match in _tmp if match]
def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
"""
Store data in json format "values".
format looks as follows:
[[<date>,<open>,<high>,<low>,<close>]]
:param pair: Pair - used to generate filename
:timeframe: Timeframe - used to generate filename
:data: Dataframe containing OHLCV data
:return: None
"""
filename = self._pair_data_filename(self._datadir, pair, timeframe)
_data = data.copy()
# Convert date to int
_data['date'] = _data['date'].astype(np.int64) // 1000 // 1000
# Reset index, select only appropriate columns and save as json
_data.reset_index(drop=True).loc[:, self._columns].to_json(
filename, orient="values",
compression='gzip' if self._use_zip else None)
def _ohlcv_load(self, pair: str, timeframe: str,
timerange: Optional[TimeRange] = None,
) -> DataFrame:
"""
Internal method used to load data for one pair from disk.
Implements the loading and conversion to a Pandas dataframe.
Timerange trimming and dataframe validation happens outside of this method.
:param pair: Pair to load data
:param timeframe: Ticker timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange.
Optionally implemented by subclasses to avoid loading
all data where possible.
:return: DataFrame with ohlcv data, or empty DataFrame
"""
filename = self._pair_data_filename(self._datadir, pair, timeframe)
if not filename.exists():
return DataFrame(columns=self._columns)
pairdata = read_json(filename, orient='values')
pairdata.columns = self._columns
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
pairdata['date'] = to_datetime(pairdata['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
return pairdata
def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
"""
Remove data for this pair
:param pair: Delete data for this pair.
:param timeframe: Ticker timeframe (e.g. "5m")
:return: True when deleted, false if file did not exist.
"""
filename = self._pair_data_filename(self._datadir, pair, timeframe)
if filename.exists():
filename.unlink()
return True
return False
def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
"""
Append data to existing data structures
:param pair: Pair
:param timeframe: Timeframe this ohlcv data is for
:param data: Data to append.
"""
raise NotImplementedError()
@classmethod
def trades_get_pairs(cls, datadir: Path) -> List[str]:
"""
Returns a list of all pairs for which trade data is available in this
:param datadir: Directory to search for ohlcv files
:return: List of Pairs
"""
_tmp = [re.search(r'^(\S+)(?=\-trades.json)', p.name)
for p in datadir.glob(f"*trades.{cls._get_file_extension()}")]
# Check if regex found something and only return these results to avoid exceptions.
return [match[0].replace('_', '/') for match in _tmp if match]
def trades_store(self, pair: str, data: List[Dict]) -> None:
"""
Store trades data (list of Dicts) to file
:param pair: Pair - used for filename
:param data: List of Dicts containing trade data
"""
filename = self._pair_trades_filename(self._datadir, pair)
misc.file_dump_json(filename, data, is_zip=self._use_zip)
def trades_append(self, pair: str, data: List[Dict]):
"""
Append data to existing files
:param pair: Pair - used for filename
:param data: List of Dicts containing trade data
"""
raise NotImplementedError()
def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> List[Dict]:
"""
Load a pair from file, either .json.gz or .json
# TODO: respect timerange ...
:param pair: Load trades for this pair
:param timerange: Timerange to load trades for - currently not implemented
:return: List of trades
"""
filename = self._pair_trades_filename(self._datadir, pair)
tradesdata = misc.file_load_json(filename)
if not tradesdata:
return []
return tradesdata
def trades_purge(self, pair: str) -> bool:
"""
Remove data for this pair
:param pair: Delete data for this pair.
:return: True when deleted, false if file did not exist.
"""
filename = self._pair_trades_filename(self._datadir, pair)
if filename.exists():
filename.unlink()
return True
return False
@classmethod
def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
pair_s = misc.pair_to_filename(pair)
filename = datadir.joinpath(f'{pair_s}-{timeframe}.{cls._get_file_extension()}')
return filename
@classmethod
def _get_file_extension(cls):
return "json.gz" if cls._use_zip else "json"
@classmethod
def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
pair_s = misc.pair_to_filename(pair)
filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}')
return filename
class JsonGzDataHandler(JsonDataHandler):
_use_zip = True

5
freqtrade/edge/edge_positioning.py
View File

@ -1,7 +1,7 @@
# pragma pylint: disable=W0603
""" Edge positioning package """
import logging
from typing import Any, Dict, NamedTuple
from typing import Any, Dict, List, NamedTuple
import arrow
import numpy as np
@ -110,6 +110,7 @@ class Edge:
timeframe=self.strategy.ticker_interval,
timerange=self._timerange,
startup_candles=self.strategy.startup_candle_count,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
if not data:
@ -181,7 +182,7 @@ class Edge:
'strategy stoploss is returned instead.')
return self.strategy.stoploss
def adjust(self, pairs) -> list:
def adjust(self, pairs: List[str]) -> list:
"""
Filters out and sorts "pairs" according to Edge calculated pairs
"""

18
freqtrade/exchange/__init__.py
View File

@ -1,18 +1,20 @@
from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS # noqa: F401
from freqtrade.exchange.exchange import Exchange # noqa: F401
from freqtrade.exchange.exchange import (get_exchange_bad_reason, # noqa: F401
# flake8: noqa: F401
from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS
from freqtrade.exchange.exchange import Exchange
from freqtrade.exchange.exchange import (get_exchange_bad_reason,
is_exchange_bad,
is_exchange_known_ccxt,
is_exchange_officially_supported,
ccxt_exchanges,
available_exchanges)
from freqtrade.exchange.exchange import (timeframe_to_seconds, # noqa: F401
from freqtrade.exchange.exchange import (timeframe_to_seconds,
timeframe_to_minutes,
timeframe_to_msecs,
timeframe_to_next_date,
timeframe_to_prev_date)
from freqtrade.exchange.exchange import (market_is_active, # noqa: F401
from freqtrade.exchange.exchange import (market_is_active,
symbol_is_pair)
from freqtrade.exchange.kraken import Kraken # noqa: F401
from freqtrade.exchange.binance import Binance # noqa: F401
from freqtrade.exchange.bibox import Bibox # noqa: F401
from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.binance import Binance
from freqtrade.exchange.bibox import Bibox
from freqtrade.exchange.ftx import Ftx

18
freqtrade/exchange/binance.py
View File

@ -32,13 +32,23 @@ class Binance(Exchange):
return super().get_order_book(pair, limit)
def stoploss_limit(self, pair: str, amount: float, stop_price: float, rate: float) -> Dict:
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_loss_limit' and stop_loss > float(order['info']['stopPrice'])
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
"""
creates a stoploss limit order.
this stoploss-limit is binance-specific.
It may work with a limited number of other exchanges, but this has not been tested yet.
"""
# Limit price threshold: As limit price should always be below stop-price
limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
rate = stop_price * limit_price_pct
ordertype = "stop_loss_limit"
stop_price = self.price_to_precision(pair, stop_price)
@ -61,8 +71,8 @@ class Binance(Exchange):
rate = self.price_to_precision(pair, rate)
order = self._api.create_order(pair, ordertype, 'sell',
amount, rate, params)
order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
amount=amount, price=stop_price, params=params)
logger.info('stoploss limit order added for %s. '
'stop price: %s. limit: %s', pair, stop_price, rate)
return order

79
freqtrade/exchange/exchange.py
View File

@ -24,6 +24,10 @@ from freqtrade.exceptions import (DependencyException, InvalidOrderException,
from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
from freqtrade.misc import deep_merge_dicts
CcxtModuleType = Any
logger = logging.getLogger(__name__)
@ -51,7 +55,7 @@ class Exchange:
}
_ft_has: Dict = {}
def __init__(self, config: dict, validate: bool = True) -> None:
def __init__(self, config: Dict[str, Any], validate: bool = True) -> None:
"""
Initializes this module with the given config,
it does basic validation whether the specified exchange and pairs are valid.
@ -62,8 +66,6 @@ class Exchange:
self._config.update(config)
self._cached_ticker: Dict[str, Any] = {}
# Holds last candle refreshed time of each pair
self._pairs_last_refresh_time: Dict[Tuple[str, str], int] = {}
# Timestamp of last markets refresh
@ -135,7 +137,7 @@ class Exchange:
if self._api_async and inspect.iscoroutinefunction(self._api_async.close):
asyncio.get_event_loop().run_until_complete(self._api_async.close())
def _init_ccxt(self, exchange_config: dict, ccxt_module=ccxt,
def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt,
ccxt_kwargs: dict = None) -> ccxt.Exchange:
"""
Initialize ccxt with given config and return valid
@ -224,13 +226,13 @@ class Exchange:
markets = self.markets
return sorted(set([x['quote'] for _, x in markets.items()]))
def klines(self, pair_interval: Tuple[str, str], copy=True) -> DataFrame:
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]
else:
return DataFrame()
def set_sandbox(self, api, exchange_config: dict, name: str):
def set_sandbox(self, api: ccxt.Exchange, exchange_config: dict, name: str) -> None:
if exchange_config.get('sandbox'):
if api.urls.get('test'):
api.urls['api'] = api.urls['test']
@ -240,7 +242,7 @@ class Exchange:
"Please check your config.json")
raise OperationalException(f'Exchange {name} does not provide a sandbox api')
def _load_async_markets(self, reload=False) -> None:
def _load_async_markets(self, reload: bool = False) -> None:
try:
if self._api_async:
asyncio.get_event_loop().run_until_complete(
@ -273,7 +275,7 @@ class Exchange:
except ccxt.BaseError:
logger.exception("Could not reload markets.")
def validate_stakecurrency(self, stake_currency) -> None:
def validate_stakecurrency(self, stake_currency: str) -> None:
"""
Checks stake-currency against available currencies on the exchange.
:param stake_currency: Stake-currency to validate
@ -319,7 +321,7 @@ class Exchange:
f"Please check if you are impacted by this restriction "
f"on the exchange and eventually remove {pair} from your whitelist.")
def get_valid_pair_combination(self, curr_1, curr_2) -> str:
def get_valid_pair_combination(self, curr_1: str, curr_2: str) -> str:
"""
Get valid pair combination of curr_1 and curr_2 by trying both combinations.
"""
@ -373,7 +375,7 @@ class Exchange:
raise OperationalException(
f'Time in force policies are not supported for {self.name} yet.')
def validate_required_startup_candles(self, startup_candles) -> None:
def validate_required_startup_candles(self, startup_candles: int) -> None:
"""
Checks if required startup_candles is more than ohlcv_candle_limit.
Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
@ -392,7 +394,7 @@ class Exchange:
"""
return endpoint in self._api.has and self._api.has[endpoint]
def amount_to_precision(self, pair, amount: float) -> float:
def amount_to_precision(self, pair: str, amount: float) -> float:
'''
Returns the amount to buy or sell to a precision the Exchange accepts
Reimplementation of ccxt internal methods - ensuring we can test the result is correct
@ -406,7 +408,7 @@ class Exchange:
return amount
def price_to_precision(self, pair, price: float) -> float:
def price_to_precision(self, pair: str, price: float) -> float:
'''
Returns the price rounded up to the precision the Exchange accepts.
Partial Reimplementation of ccxt internal method decimal_to_precision(),
@ -494,7 +496,7 @@ class Exchange:
raise OperationalException(e) from e
def buy(self, pair: str, ordertype: str, amount: float,
rate: float, time_in_force) -> Dict:
rate: float, time_in_force: str) -> Dict:
if self._config['dry_run']:
dry_order = self.dry_run_order(pair, ordertype, "buy", amount, rate)
@ -507,7 +509,7 @@ class Exchange:
return self.create_order(pair, ordertype, 'buy', amount, rate, params)
def sell(self, pair: str, ordertype: str, amount: float,
rate: float, time_in_force='gtc') -> Dict:
rate: float, time_in_force: str = 'gtc') -> Dict:
if self._config['dry_run']:
dry_order = self.dry_run_order(pair, ordertype, "sell", amount, rate)
@ -519,9 +521,17 @@ class Exchange:
return self.create_order(pair, ordertype, 'sell', amount, rate, params)
def stoploss_limit(self, pair: str, amount: float, stop_price: float, rate: float) -> Dict:
def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
"""
creates a stoploss limit order.
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
raise OperationalException(f"stoploss is not implemented for {self.name}.")
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
"""
creates a stoploss order.
The precise ordertype is determined by the order_types dict or exchange default.
Since ccxt does not unify stoploss-limit orders yet, this needs to be implemented in each
exchange's subclass.
The exception below should never raise, since we disallow
@ -529,7 +539,7 @@ class Exchange:
Note: Changes to this interface need to be applied to all sub-classes too.
"""
raise OperationalException(f"stoploss_limit is not implemented for {self.name}.")
raise OperationalException(f"stoploss is not implemented for {self.name}.")
@retrier
def get_balance(self, currency: str) -> float:
@ -579,28 +589,17 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def fetch_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
if refresh or pair not in self._cached_ticker.keys():
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")
data = self._api.fetch_ticker(pair)
try:
self._cached_ticker[pair] = {
'bid': float(data['bid']),
'ask': float(data['ask']),
}
except KeyError:
logger.debug("Could not cache ticker data for %s", pair)
return data
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load ticker due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
else:
logger.info("returning cached ticker-data for %s", pair)
return self._cached_ticker[pair]
def get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int) -> List:
@ -728,10 +727,11 @@ class Exchange:
f'Exchange {self._api.name} does not support fetching historical candlestick data.'
f'Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(f'Could not load ticker history due to {e.__class__.__name__}. '
f'Message: {e}') from e
raise TemporaryError(f'Could not load ticker history for pair {pair} due to '
f'{e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(f'Could not fetch ticker data. Msg: {e}') from e
raise OperationalException(f'Could not fetch ticker data for pair {pair}. '
f'Msg: {e}') from e
@retrier_async
async def _async_fetch_trades(self, pair: str,
@ -976,8 +976,8 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def get_fee(self, symbol, type='', side='', amount=1,
price=1, taker_or_maker='maker') -> float:
def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
price: float = 1, taker_or_maker: str = 'maker') -> float:
try:
# validate that markets are loaded before trying to get fee
if self._api.markets is None or len(self._api.markets) == 0:
@ -1000,7 +1000,7 @@ def get_exchange_bad_reason(exchange_name: str) -> str:
return BAD_EXCHANGES.get(exchange_name, "")
def is_exchange_known_ccxt(exchange_name: str, ccxt_module=None) -> bool:
def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
return exchange_name in ccxt_exchanges(ccxt_module)
@ -1008,14 +1008,14 @@ def is_exchange_officially_supported(exchange_name: str) -> bool:
return exchange_name in ['bittrex', 'binance']
def ccxt_exchanges(ccxt_module=None) -> List[str]:
def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
"""
Return the list of all exchanges known to ccxt
"""
return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
def available_exchanges(ccxt_module=None) -> List[str]:
def available_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
"""
Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
"""
@ -1075,7 +1075,8 @@ 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):
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,
@ -1088,7 +1089,7 @@ def symbol_is_pair(market_symbol: str, base_currency: str = None, quote_currency
(symbol_parts[1] == quote_currency if quote_currency else len(symbol_parts[1]) > 0))
def market_is_active(market):
def market_is_active(market: Dict) -> bool:
"""
Return True if the market is active.
"""

14
freqtrade/exchange/ftx.py Normal file
View File

@ -0,0 +1,14 @@
""" FTX exchange subclass """
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Ftx(Exchange):
_ft_has: Dict = {
"ohlcv_candle_limit": 1500,
}

52
freqtrade/exchange/kraken.py
View File

@ -4,7 +4,8 @@ from typing import Dict
import ccxt
from freqtrade.exceptions import OperationalException, TemporaryError
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.exchange import retrier
@ -15,6 +16,7 @@ class Kraken(Exchange):
_params: Dict = {"trading_agreement": "agree"}
_ft_has: Dict = {
"stoploss_on_exchange": True,
"trades_pagination": "id",
"trades_pagination_arg": "since",
}
@ -48,3 +50,51 @@ class Kraken(Exchange):
f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
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-loss' and stop_loss > float(order['price'])
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
"""
Creates a stoploss market order.
Stoploss market orders is the only stoploss type supported by kraken.
"""
ordertype = "stop-loss"
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()
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 DependencyException(
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.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

235
freqtrade/freqtradebot.py
View File

@ -6,11 +6,11 @@ import logging
import traceback
from datetime import datetime
from math import isclose
from os import getpid
from threading import Lock
from typing import Any, Dict, List, Optional, Tuple
import arrow
from cachetools import TTLCache
from requests.exceptions import RequestException
from freqtrade import __version__, constants, persistence
@ -52,9 +52,8 @@ class FreqtradeBot:
# Init objects
self.config = config
self._heartbeat_msg = 0
self.heartbeat_interval = self.config.get('internals', {}).get('heartbeat_interval', 60)
self._sell_rate_cache = TTLCache(maxsize=100, ttl=5)
self._buy_rate_cache = TTLCache(maxsize=100, ttl=5)
self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
@ -159,11 +158,6 @@ class FreqtradeBot:
self.check_handle_timedout()
Trade.session.flush()
if (self.heartbeat_interval
and (arrow.utcnow().timestamp - self._heartbeat_msg > self.heartbeat_interval)):
logger.info(f"Bot heartbeat. PID={getpid()}")
self._heartbeat_msg = arrow.utcnow().timestamp
def _refresh_whitelist(self, trades: List[Trade] = []) -> List[str]:
"""
Refresh whitelist from pairlist or edge and extend it with trades.
@ -234,11 +228,20 @@ class FreqtradeBot:
return trades_created
def get_buy_rate(self, pair: str, tick: Dict = None) -> float:
def get_buy_rate(self, pair: str, refresh: bool) -> float:
"""
Calculates bid target between current ask price and last price
:param pair: Pair to get rate for
:param refresh: allow cached data
:return: float: Price
"""
if not refresh:
rate = self._buy_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.info(f"Using cached buy rate for {pair}.")
return rate
config_bid_strategy = self.config.get('bid_strategy', {})
if 'use_order_book' in config_bid_strategy and\
config_bid_strategy.get('use_order_book', False):
@ -251,11 +254,8 @@ class FreqtradeBot:
logger.info('...top %s order book buy rate %0.8f', order_book_top, order_book_rate)
used_rate = order_book_rate
else:
if not tick:
logger.info('Using Last Ask / Last Price')
ticker = self.exchange.fetch_ticker(pair)
else:
ticker = tick
if ticker['ask'] < ticker['last']:
ticker_rate = ticker['ask']
else:
@ -263,9 +263,11 @@ class FreqtradeBot:
ticker_rate = ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
used_rate = ticker_rate
self._buy_rate_cache[pair] = used_rate
return used_rate
def get_trade_stake_amount(self, pair) -> float:
def get_trade_stake_amount(self, pair: str) -> float:
"""
Calculate stake amount for the trade
:return: float: Stake amount
@ -404,7 +406,7 @@ class FreqtradeBot:
stake_amount = self.get_trade_stake_amount(pair)
if not stake_amount:
logger.debug("Stake amount is 0, ignoring possible trade for {pair}.")
logger.debug(f"Stake amount is 0, ignoring possible trade for {pair}.")
return False
logger.info(f"Buy signal found: about create a new trade with stake_amount: "
@ -414,10 +416,12 @@ class FreqtradeBot:
if ((bid_check_dom.get('enabled', False)) and
(bid_check_dom.get('bids_to_ask_delta', 0) > 0)):
if self._check_depth_of_market_buy(pair, bid_check_dom):
logger.info(f'Executing Buy for {pair}.')
return self.execute_buy(pair, stake_amount)
else:
return False
logger.info(f'Executing Buy for {pair}')
return self.execute_buy(pair, stake_amount)
else:
return False
@ -427,23 +431,30 @@ class FreqtradeBot:
Checks depth of market before executing a buy
"""
conf_bids_to_ask_delta = conf.get('bids_to_ask_delta', 0)
logger.info('checking depth of market for %s', pair)
logger.info(f"Checking depth of market for {pair} ...")
order_book = self.exchange.get_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()
bids_ask_delta = order_book_bids / order_book_asks
logger.info('bids: %s, asks: %s, delta: %s', order_book_bids,
order_book_asks, bids_ask_delta)
logger.info(
f"Bids: {order_book_bids}, Asks: {order_book_asks}, Delta: {bids_ask_delta}, "
f"Bid Price: {order_book['bids'][0][0]}, Ask Price: {order_book['asks'][0][0]}, "
f"Immediate Bid Quantity: {order_book['bids'][0][1]}, "
f"Immediate Ask Quantity: {order_book['asks'][0][1]}."
)
if bids_ask_delta >= conf_bids_to_ask_delta:
logger.info(f"Bids to asks delta for {pair} DOES satisfy condition.")
return True
else:
logger.info(f"Bids to asks delta for {pair} does not satisfy condition.")
return False
def execute_buy(self, pair: str, stake_amount: float, price: Optional[float] = None) -> bool:
"""
Executes a limit buy for the given pair
:param pair: pair for which we want to create a LIMIT_BUY
:return: None
:return: True if a buy order is created, false if it fails.
"""
time_in_force = self.strategy.order_time_in_force['buy']
@ -451,7 +462,7 @@ class FreqtradeBot:
buy_limit_requested = price
else:
# Calculate price
buy_limit_requested = self.get_buy_rate(pair)
buy_limit_requested = self.get_buy_rate(pair, True)
min_stake_amount = self._get_min_pair_stake_amount(pair, buy_limit_requested)
if min_stake_amount is not None and min_stake_amount > stake_amount:
@ -518,8 +529,6 @@ class FreqtradeBot:
ticker_interval=timeframe_to_minutes(self.config['ticker_interval'])
)
self._notify_buy(trade, order_type)
# Update fees if order is closed
if order_status == 'closed':
self.update_trade_state(trade, order)
@ -530,9 +539,11 @@ class FreqtradeBot:
# Updating wallets
self.wallets.update()
self._notify_buy(trade, order_type)
return True
def _notify_buy(self, trade: Trade, order_type: str):
def _notify_buy(self, trade: Trade, order_type: str) -> None:
"""
Sends rpc notification when a buy occured.
"""
@ -545,6 +556,32 @@ class FreqtradeBot:
'stake_amount': trade.stake_amount,
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
'amount': trade.amount,
'open_date': trade.open_date or datetime.utcnow(),
'current_rate': trade.open_rate_requested,
}
# Send the message
self.rpc.send_msg(msg)
def _notify_buy_cancel(self, trade: Trade, order_type: str) -> None:
"""
Sends rpc notification when a buy cancel occured.
"""
current_rate = self.get_buy_rate(trade.pair, False)
msg = {
'type': RPCMessageType.BUY_CANCEL_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
'limit': trade.open_rate,
'order_type': order_type,
'stake_amount': trade.stake_amount,
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
'amount': trade.amount,
'open_date': trade.open_date,
'current_rate': current_rate,
}
# Send the message
@ -586,8 +623,17 @@ class FreqtradeBot:
The orderbook portion is only used for rpc messaging, which would otherwise fail
for BitMex (has no bid/ask in fetch_ticker)
or remain static in any other case since it's not updating.
:param pair: Pair to get rate for
:param refresh: allow cached data
:return: Bid rate
"""
if not refresh:
rate = self._sell_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.info(f"Using cached sell rate for {pair}.")
return rate
config_ask_strategy = self.config.get('ask_strategy', {})
if config_ask_strategy.get('use_order_book', False):
logger.debug('Using order book to get sell rate')
@ -596,7 +642,8 @@ class FreqtradeBot:
rate = order_book['bids'][0][0]
else:
rate = self.exchange.fetch_ticker(pair, refresh)['bid']
rate = self.exchange.fetch_ticker(pair)['bid']
self._sell_rate_cache[pair] = rate
return rate
def handle_trade(self, trade: Trade) -> bool:
@ -620,7 +667,7 @@ class FreqtradeBot:
self.dataprovider.ohlcv(trade.pair, self.strategy.ticker_interval))
if config_ask_strategy.get('use_order_book', False):
logger.info('Using order book for selling...')
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)
@ -629,7 +676,7 @@ class FreqtradeBot:
for i in range(order_book_min, order_book_max + 1):
order_book_rate = order_book['asks'][i - 1][0]
logger.info(' order book asks top %s: %0.8f', i, order_book_rate)
logger.debug(' order book asks top %s: %0.8f', i, order_book_rate)
sell_rate = order_book_rate
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
@ -651,13 +698,10 @@ class FreqtradeBot:
Force-sells the pair (using EmergencySell reason) in case of Problems creating the order.
:return: True if the order succeeded, and False in case of problems.
"""
# Limit price threshold: As limit price should always be below stop-price
LIMIT_PRICE_PCT = self.strategy.order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
try:
stoploss_order = self.exchange.stoploss_limit(pair=trade.pair, amount=trade.amount,
stoploss_order = self.exchange.stoploss(pair=trade.pair, amount=trade.amount,
stop_price=stop_price,
rate=rate * LIMIT_PRICE_PCT)
order_types=self.strategy.order_types)
trade.stoploss_order_id = str(stoploss_order['id'])
return True
except InvalidOrderException as e:
@ -689,8 +733,24 @@ class FreqtradeBot:
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':
trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
trade.update(stoploss_order)
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair,
timeframe_to_next_date(self.config['ticker_interval']))
self._notify_sell(trade, "stoploss")
return True
if trade.open_order_id or not trade.is_open:
# Trade has an open Buy or Sell order, Stoploss-handling can't happen in this case
# as the Amount on the exchange is tied up in another trade.
# The trade can be closed already (sell-order fill confirmation came in this iteration)
return False
# If buy order is fulfilled but there is no stoploss, we add a stoploss on exchange
if (not trade.open_order_id and not stoploss_order):
if (not stoploss_order):
stoploss = self.edge.stoploss(pair=trade.pair) if self.edge else self.strategy.stoploss
@ -709,16 +769,6 @@ class FreqtradeBot:
trade.stoploss_order_id = None
logger.warning('Stoploss order was cancelled, but unable to recreate one.')
# We check if stoploss order is fulfilled
if stoploss_order and stoploss_order['status'] == 'closed':
trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
trade.update(stoploss_order)
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair,
timeframe_to_next_date(self.config['ticker_interval']))
self._notify_sell(trade, "stoploss")
return True
# Finally we check if stoploss on exchange should be moved up because of trailing.
if stoploss_order and self.config.get('trailing_stop', False):
# if trailing stoploss is enabled we check if stoploss value has changed
@ -728,7 +778,7 @@ class FreqtradeBot:
return False
def handle_trailing_stoploss_on_exchange(self, trade: Trade, order):
def handle_trailing_stoploss_on_exchange(self, trade: Trade, order: dict) -> None:
"""
Check to see if stoploss on exchange should be updated
in case of trailing stoploss on exchange
@ -736,8 +786,7 @@ class FreqtradeBot:
:param order: Current on exchange stoploss order
:return: None
"""
if trade.stop_loss > float(order['info']['stopPrice']):
if self.exchange.stoploss_adjust(trade.stop_loss, order):
# we check if the update is neccesary
update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat:
@ -751,10 +800,8 @@ class FreqtradeBot:
f"for pair {trade.pair}")
# Create new stoploss order
if self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss,
if not self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss,
rate=trade.stop_loss):
return False
else:
logger.warning(f"Could not create trailing stoploss order "
f"for pair {trade.pair}.")
@ -769,8 +816,8 @@ class FreqtradeBot:
)
if should_sell.sell_flag:
logger.info(f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}')
self.execute_sell(trade, sell_rate, should_sell.sell_type)
logger.info('executed sell, reason: %s', should_sell.sell_type)
return True
return False
@ -813,41 +860,37 @@ class FreqtradeBot:
if ((order['side'] == 'buy' and order['status'] == 'canceled')
or (self._check_timed_out('buy', order))):
self.handle_timedout_limit_buy(trade, order)
self.wallets.update()
order_type = self.strategy.order_types['buy']
self._notify_buy_cancel(trade, order_type)
elif ((order['side'] == 'sell' and order['status'] == 'canceled')
or (self._check_timed_out('sell', order))):
self.handle_timedout_limit_sell(trade, order)
self.wallets.update()
def handle_buy_order_full_cancel(self, trade: Trade, reason: str) -> None:
"""Close trade in database and send message"""
Trade.session.delete(trade)
Trade.session.flush()
logger.info('Buy order %s for %s.', reason, trade)
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'Unfilled buy order for {trade.pair} {reason}'
})
order_type = self.strategy.order_types['sell']
self._notify_sell_cancel(trade, order_type)
def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
"""
Buy timeout - cancel order
:return: True if order was fully cancelled
"""
reason = "cancelled due to timeout"
if order['status'] != 'canceled':
reason = "cancelled due to timeout"
corder = self.exchange.cancel_order(trade.open_order_id, trade.pair)
logger.info('Buy order %s for %s.', reason, trade)
else:
# Order was cancelled already, so we can reuse the existing dict
corder = order
reason = "canceled on Exchange"
reason = "cancelled on exchange"
logger.info('Buy order %s for %s.', reason, trade)
if corder.get('remaining', order['remaining']) == order['amount']:
# if trade is not partially completed, just delete the trade
self.handle_buy_order_full_cancel(trade, reason)
Trade.session.delete(trade)
Trade.session.flush()
return True
# if trade is partially complete, edit the stake details for the trade
@ -882,24 +925,22 @@ class FreqtradeBot:
Sell timeout - cancel order and update trade
:return: True if order was fully cancelled
"""
if order['remaining'] == order['amount']:
# if trade is not partially completed, just cancel the trade
if order['remaining'] == order['amount']:
if order["status"] != "canceled":
reason = "due to timeout"
reason = "cancelled due to timeout"
# if trade is not partially completed, just delete the trade
self.exchange.cancel_order(trade.open_order_id, trade.pair)
logger.info('Sell order timeout for %s.', trade)
logger.info('Sell order %s for %s.', reason, trade)
else:
reason = "on exchange"
logger.info('Sell order canceled on exchange for %s.', trade)
reason = "cancelled on exchange"
logger.info('Sell order %s for %s.', reason, trade)
trade.close_rate = None
trade.close_profit = None
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'Unfilled sell order for {trade.pair} cancelled {reason}'
})
return True
@ -931,13 +972,13 @@ class FreqtradeBot:
raise DependencyException(
f"Not enough amount to sell. Trade-amount: {amount}, Wallet: {wallet_amount}")
def execute_sell(self, trade: Trade, limit: float, sell_reason: SellType) -> None:
def execute_sell(self, trade: Trade, limit: float, sell_reason: SellType) -> bool:
"""
Executes a limit sell for the given trade and limit
:param trade: Trade instance
:param limit: limit rate for the sell order
:param sellreason: Reason the sell was triggered
:return: None
:return: True if it succeeds (supported) False (not supported)
"""
sell_type = 'sell'
if sell_reason in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
@ -983,7 +1024,9 @@ class FreqtradeBot:
self._notify_sell(trade, order_type)
def _notify_sell(self, trade: Trade, order_type: str):
return True
def _notify_sell(self, trade: Trade, order_type: str) -> None:
"""
Sends rpc notification when a sell occured.
"""
@ -999,7 +1042,7 @@ class FreqtradeBot:
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'gain': gain,
'limit': trade.close_rate_requested,
'limit': profit_rate,
'order_type': order_type,
'amount': trade.amount,
'open_rate': trade.open_rate,
@ -1010,6 +1053,44 @@ class FreqtradeBot:
'open_date': trade.open_date,
'close_date': trade.close_date or datetime.utcnow(),
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
}
if 'fiat_display_currency' in self.config:
msg.update({
'fiat_currency': self.config['fiat_display_currency'],
})
# Send the message
self.rpc.send_msg(msg)
def _notify_sell_cancel(self, trade: Trade, order_type: str) -> None:
"""
Sends rpc notification when a sell cancel occured.
"""
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
current_rate = self.get_sell_rate(trade.pair, False)
profit_percent = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_percent > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_CANCEL_NOTIFICATION,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'gain': gain,
'limit': profit_rate,
'order_type': order_type,
'amount': trade.amount,
'open_rate': trade.open_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_percent': profit_percent,
'sell_reason': trade.sell_reason,
'open_date': trade.open_date,
'close_date': trade.close_date,
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
}
if 'fiat_display_currency' in self.config:
@ -1024,7 +1105,7 @@ class FreqtradeBot:
# Common update trade state methods
#
def update_trade_state(self, trade, action_order: dict = None):
def update_trade_state(self, trade: Trade, action_order: dict = None) -> None:
"""
Checks trades with open orders and updates the amount if necessary
"""

4
freqtrade/main.py
View File

@ -38,8 +38,8 @@ def main(sysargv: List[str] = None) -> None:
# No subcommand was issued.
raise OperationalException(
"Usage of Freqtrade requires a subcommand to be specified.\n"
"To have the previous behavior (bot executing trades in live/dry-run modes, "
"depending on the value of the `dry_run` setting in the config), run freqtrade "
"To have the bot executing trades in live/dry-run modes, "
"depending on the value of the `dry_run` setting in the config, run Freqtrade "
"as `freqtrade trade [options...]`.\n"
"To see the full list of options available, please use "
"`freqtrade --help` or `freqtrade <command> --help`."

20
freqtrade/misc.py
View File

@ -6,6 +6,7 @@ import logging
import re
from datetime import datetime
from pathlib import Path
from typing import Any
from typing.io import IO
import numpy as np
@ -40,28 +41,30 @@ def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
return dates.dt.to_pydatetime()
def file_dump_json(filename: Path, data, is_zip=False) -> None:
def file_dump_json(filename: Path, data: Any, is_zip: bool = False) -> None:
"""
Dump JSON data into a file
:param filename: file to create
:param data: JSON Data to save
:return:
"""
logger.info(f'dumping json to "{filename}"')
if is_zip:
if filename.suffix != '.gz':
filename = filename.with_suffix('.gz')
logger.info(f'dumping json to "{filename}"')
with gzip.open(filename, 'w') as fp:
rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
else:
logger.info(f'dumping json to "{filename}"')
with open(filename, 'w') as fp:
rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
logger.debug(f'done json to "{filename}"')
def json_load(datafile: IO):
def json_load(datafile: IO) -> Any:
"""
load data with rapidjson
Use this to have a consistent experience,
@ -90,6 +93,12 @@ def file_load_json(file):
return pairdata
def pair_to_filename(pair: str) -> str:
for ch in ['/', '-', ' ', '.', '@', '$', '+', ':']:
pair = pair.replace(ch, '_')
return pair
def format_ms_time(date: int) -> str:
"""
convert MS date to readable format.
@ -125,11 +134,11 @@ def round_dict(d, n):
return {k: (round(v, n) if isinstance(v, float) else v) for k, v in d.items()}
def plural(num, singular: str, plural: str = None) -> str:
def plural(num: float, singular: str, plural: str = None) -> str:
return singular if (num == 1 or num == -1) else plural or singular + 's'
def render_template(templatefile: str, arguments: dict = {}):
def render_template(templatefile: str, arguments: dict = {}) -> str:
from jinja2 import Environment, PackageLoader, select_autoescape
@ -138,5 +147,4 @@ def render_template(templatefile: str, arguments: dict = {}):
autoescape=select_autoescape(['html', 'xml'])
)
template = env.get_template(templatefile)
return template.render(**arguments)

21
freqtrade/optimize/backtesting.py
View File

@ -9,11 +9,13 @@ from datetime import datetime, timedelta
from pathlib import Path
from typing import Any, Dict, List, NamedTuple, Optional
import arrow
from pandas import DataFrame
from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency)
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
@ -24,7 +26,7 @@ from freqtrade.optimize.optimize_reports import (
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
from freqtrade.strategy.interface import IStrategy, SellType
from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
logger = logging.getLogger(__name__)
@ -117,6 +119,7 @@ class Backtesting:
timerange=timerange,
startup_candles=self.required_startup,
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
min_date, max_date = history.get_timerange(data)
@ -148,7 +151,7 @@ class Backtesting:
logger.info(f'Dumping backtest results to {recordfilename}')
file_dump_json(recordfilename, records)
def _get_ticker_list(self, processed) -> Dict[str, DataFrame]:
def _get_ticker_list(self, processed: Dict) -> Dict[str, DataFrame]:
"""
Helper function to convert a processed tickerlist into a list for performance reasons.
@ -175,7 +178,8 @@ class Backtesting:
ticker[pair] = [x for x in ticker_data.itertuples()]
return ticker
def _get_close_rate(self, sell_row, trade: Trade, sell, trade_dur) -> float:
def _get_close_rate(self, sell_row, trade: Trade, sell: SellCheckTuple,
trade_dur: int) -> float:
"""
Get close rate for backtesting result
"""
@ -280,7 +284,7 @@ class Backtesting:
return None
def backtest(self, processed: Dict, stake_amount: float,
start_date, end_date,
start_date: arrow.Arrow, end_date: arrow.Arrow,
max_open_trades: int = 0, position_stacking: bool = False) -> DataFrame:
"""
Implement backtesting functionality
@ -395,7 +399,7 @@ class Backtesting:
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = history.trim_dataframe(df, timerange)
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = history.get_timerange(preprocessed)
logger.info(
@ -426,7 +430,10 @@ class Backtesting:
results=results))
print(' SELL REASON STATS '.center(133, '='))
print(generate_text_table_sell_reason(data, results))
print(generate_text_table_sell_reason(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results))
print(' LEFT OPEN TRADES REPORT '.center(133, '='))
print(generate_text_table(data,
@ -436,7 +443,7 @@ class Backtesting:
print()
if len(all_results) > 1:
# Print Strategy summary table
print(' Strategy Summary '.center(133, '='))
print(' STRATEGY SUMMARY '.center(133, '='))
print(generate_text_table_strategy(self.config['stake_currency'],
self.config['max_open_trades'],
all_results=all_results))

22
freqtrade/optimize/hyperopt.py
View File

@ -22,7 +22,8 @@ from joblib import (Parallel, cpu_count, delayed, dump, load,
wrap_non_picklable_objects)
from pandas import DataFrame
from freqtrade.data.history import get_timerange, trim_dataframe
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import get_timerange
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural, round_dict
from freqtrade.optimize.backtesting import Backtesting
@ -59,6 +60,7 @@ class Hyperopt:
hyperopt = Hyperopt(config)
hyperopt.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
@ -117,11 +119,11 @@ class Hyperopt:
self.print_json = self.config.get('print_json', False)
@staticmethod
def get_lock_filename(config) -> str:
def get_lock_filename(config: Dict[str, Any]) -> str:
return str(config['user_data_dir'] / 'hyperopt.lock')
def clean_hyperopt(self):
def clean_hyperopt(self) -> None:
"""
Remove hyperopt pickle files to restart hyperopt.
"""
@ -158,7 +160,7 @@ class Hyperopt:
f"saved to '{self.trials_file}'.")
@staticmethod
def _read_trials(trials_file) -> List:
def _read_trials(trials_file: Path) -> List:
"""
Read hyperopt trials file
"""
@ -189,7 +191,7 @@ class Hyperopt:
return result
@staticmethod
def print_epoch_details(results, total_epochs, print_json: bool,
def print_epoch_details(results, total_epochs: int, print_json: bool,
no_header: bool = False, header_str: str = None) -> None:
"""
Display details of the hyperopt result
@ -218,7 +220,7 @@ class Hyperopt:
Hyperopt._params_pretty_print(params, 'trailing', "Trailing stop:")
@staticmethod
def _params_update_for_json(result_dict, params, space: str):
def _params_update_for_json(result_dict, params, space: str) -> None:
if space in params:
space_params = Hyperopt._space_params(params, space)
if space in ['buy', 'sell']:
@ -235,7 +237,7 @@ class Hyperopt:
result_dict.update(space_params)
@staticmethod
def _params_pretty_print(params, space: str, header: str):
def _params_pretty_print(params, space: str, header: str) -> None:
if space in params:
space_params = Hyperopt._space_params(params, space, 5)
if space == 'stoploss':
@ -251,7 +253,7 @@ class Hyperopt:
return round_dict(d, r) if r else d
@staticmethod
def is_best_loss(results, current_best_loss) -> bool:
def is_best_loss(results, current_best_loss: float) -> bool:
return results['loss'] < current_best_loss
def print_results(self, results) -> None:
@ -438,7 +440,7 @@ class Hyperopt:
random_state=self.random_state,
)
def fix_optimizer_models_list(self):
def fix_optimizer_models_list(self) -> None:
"""
WORKAROUND: Since skopt is not actively supported, this resolves problems with skopt
memory usage, see also: https://github.com/scikit-optimize/scikit-optimize/pull/746
@ -460,7 +462,7 @@ class Hyperopt:
wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
@staticmethod
def load_previous_results(trials_file) -> List:
def load_previous_results(trials_file: Path) -> List:
"""
Load data for epochs from the file if we have one
"""

2
freqtrade/optimize/hyperopt_interface.py
View File

@ -207,7 +207,7 @@ class IHyperOpt(ABC):
# so this intermediate parameter is used as the value of the difference between
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
# generate_trailing_params() method.
# # This is similar to the hyperspace dimensions used for constructing the ROI tables.
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),

9
freqtrade/optimize/hyperopt_loss_sharpe.py
View File

@ -28,18 +28,19 @@ class SharpeHyperOptLoss(IHyperOptLoss):
Uses Sharpe Ratio calculation.
"""
total_profit = results.profit_percent
total_profit = results["profit_percent"]
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_yearly_return = total_profit.sum() / days_period
expected_returns_mean = total_profit.sum() / days_period
up_stdev = np.std(total_profit)
if (np.std(total_profit) != 0.):
sharp_ratio = expected_yearly_return / np.std(total_profit) * np.sqrt(365)
sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
sharp_ratio = -20.
# print(expected_yearly_return, np.std(total_profit), sharp_ratio)
# print(expected_returns_mean, up_stdev, sharp_ratio)
return -sharp_ratio

62
freqtrade/optimize/hyperopt_loss_sharpe_daily.py Normal file
View File

@ -0,0 +1,62 @@
"""
SharpeHyperOptLossDaily
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
import math
from datetime import datetime
from pandas import DataFrame, date_range
from freqtrade.optimize.hyperopt import IHyperOptLoss
class SharpeHyperOptLossDaily(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation uses the Sharpe Ratio calculation.
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Sharpe Ratio calculation.
"""
resample_freq = '1D'
slippage_per_trade_ratio = 0.0005
days_in_year = 365
annual_risk_free_rate = 0.0
risk_free_rate = annual_risk_free_rate / days_in_year
# apply slippage per trade to profit_percent
results.loc[:, 'profit_percent_after_slippage'] = \
results['profit_percent'] - slippage_per_trade_ratio
# create the index within the min_date and end max_date
t_index = date_range(start=min_date, end=max_date, freq=resample_freq,
normalize=True)
sum_daily = (
results.resample(resample_freq, on='close_time').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
)
total_profit = sum_daily["profit_percent_after_slippage"] - risk_free_rate
expected_returns_mean = total_profit.mean()
up_stdev = total_profit.std()
if (up_stdev != 0.):
sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
sharp_ratio = -20.
# print(t_index, sum_daily, total_profit)
# print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
return -sharp_ratio

66
freqtrade/optimize/optimize_reports.py
View File

@ -19,9 +19,18 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
f'tot profit {stake_currency}', 'tot profit %', 'avg duration',
'profit', 'loss']
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():
@ -37,6 +46,7 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
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])
])
@ -51,6 +61,7 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
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
@ -58,7 +69,9 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
floatfmt=floatfmt, tablefmt="pipe") # type: ignore
def generate_text_table_sell_reason(data: Dict[str, Dict], results: DataFrame) -> str:
def generate_text_table_sell_reason(
data: Dict[str, Dict], stake_currency: str, max_open_trades: int, results: DataFrame
) -> str:
"""
Generate small table outlining Backtest results
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
@ -66,13 +79,39 @@ def generate_text_table_sell_reason(data: Dict[str, Dict], results: DataFrame) -
:return: pretty printed table with tabulate as string
"""
tabular_data = []
headers = ['Sell Reason', 'Count', 'Profit', 'Loss', 'Profit %']
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]
profit = len(result[result['profit_abs'] >= 0])
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)
tabular_data.append([reason.value, count, profit, loss, profit_mean])
profit_sum = round(result["profit_percent"].sum() * 100.0, 2)
profit_tot = result['profit_abs'].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,
]
)
return tabulate(tabular_data, headers=headers, tablefmt="pipe")
@ -88,9 +127,9 @@ def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
f'tot profit {stake_currency}', 'tot profit %', 'avg duration',
'profit', 'loss']
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,
@ -102,6 +141,7 @@ def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
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
@ -113,9 +153,9 @@ def generate_edge_table(results: dict) -> str:
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', '.d')
tabular_data = []
headers = ['pair', 'stoploss', 'win rate', 'risk reward ratio',
'required risk reward', 'expectancy', 'total number of trades',
'average duration (min)']
headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio',
'Required Risk Reward', 'Expectancy', 'Total Number of Trades',
'Average Duration (min)']
for result in results.items():
if result[1].nb_trades > 0:

5
freqtrade/pairlist/IPairList.py
View File

@ -7,7 +7,7 @@ Provides lists as configured in config.json
import logging
from abc import ABC, abstractmethod, abstractproperty
from copy import deepcopy
from typing import Dict, List
from typing import Any, Dict, List
from freqtrade.exchange import market_is_active
@ -16,7 +16,8 @@ logger = logging.getLogger(__name__)
class IPairList(ABC):
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:
"""
:param exchange: Exchange instance

6
freqtrade/pairlist/PrecisionFilter.py
View File

@ -48,10 +48,10 @@ class PrecisionFilter(IPairList):
"""
Filters and sorts pairlists and assigns and returns them again.
"""
stoploss = None
if self._config.get('stoploss') is not None:
stoploss = self._config.get('stoploss')
if stoploss is not None:
# Precalculate sanitized stoploss value to avoid recalculation for every pair
stoploss = 1 - abs(self._config.get('stoploss'))
stoploss = 1 - abs(stoploss)
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
ticker = tickers.get(p)

5
freqtrade/pairlist/PriceFilter.py
View File

@ -1,6 +1,6 @@
import logging
from copy import deepcopy
from typing import Dict, List
from typing import Any, Dict, List
from freqtrade.pairlist.IPairList import IPairList
@ -9,7 +9,8 @@ logger = logging.getLogger(__name__)
class PriceFilter(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)

59
freqtrade/pairlist/SpreadFilter.py Normal file
View File

@ -0,0 +1,59 @@
import logging
from copy import deepcopy
from typing import Dict, List
from freqtrade.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
class SpreadFilter(IPairList):
def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._max_spread_ratio = pairlistconfig.get('max_spread_ratio', 0.005)
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries 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 ask/bid diff above "
f"{self._max_spread_ratio * 100}%.")
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
spread = None
for p in deepcopy(pairlist):
ticker = tickers.get(p)
assert ticker is not None
if 'bid' in ticker and 'ask' in ticker:
spread = 1 - ticker['bid'] / ticker['ask']
if not ticker or spread > self._max_spread_ratio:
logger.info(f"Removed {ticker['symbol']} from whitelist, "
f"because spread {spread * 100:.3f}% >"
f"{self._max_spread_ratio * 100}%")
pairlist.remove(p)
else:
pairlist.remove(p)
return pairlist

12
freqtrade/pairlist/VolumePairList.py
View File

@ -6,7 +6,7 @@ Provides lists as configured in config.json
"""
import logging
from datetime import datetime
from typing import Dict, List
from typing import Any, Dict, List
from freqtrade.exceptions import OperationalException
from freqtrade.pairlist.IPairList import IPairList
@ -18,7 +18,7 @@ SORT_VALUES = ['askVolume', 'bidVolume', 'quoteVolume']
class VolumePairList(IPairList):
def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
def __init__(self, exchange, pairlistmanager, config: Dict[str, Any], pairlistconfig: dict,
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
@ -28,6 +28,7 @@ class VolumePairList(IPairList):
'for "pairlist.config.number_assets"')
self._number_pairs = self._pairlistconfig['number_assets']
self._sort_key = self._pairlistconfig.get('sort_key', 'quoteVolume')
self._min_value = self._pairlistconfig.get('min_value', 0)
self.refresh_period = self._pairlistconfig.get('refresh_period', 1800)
if not self._exchange.exchange_has('fetchTickers'):
@ -73,11 +74,13 @@ class VolumePairList(IPairList):
tickers,
self._config['stake_currency'],
self._sort_key,
self._min_value
)
else:
return pairlist
def _gen_pair_whitelist(self, pairlist, tickers, base_currency: str, key: str) -> List[str]:
def _gen_pair_whitelist(self, pairlist: List[str], tickers: Dict,
base_currency: str, key: str, min_val: int) -> List[str]:
"""
Updates the whitelist with with a dynamically generated list
:param base_currency: base currency as str
@ -96,6 +99,9 @@ class VolumePairList(IPairList):
# If other pairlist is in front, use the incomming pairlist.
filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
if min_val > 0:
filtered_tickers = list(filter(lambda t: t[key] > min_val, filtered_tickers))
sorted_tickers = sorted(filtered_tickers, reverse=True, key=lambda t: t[key])
# Validate whitelist to only have active market pairs

13
freqtrade/persistence.py
View File

@ -64,11 +64,11 @@ def init(db_url: str, clean_open_orders: bool = False) -> None:
clean_dry_run_db()
def has_column(columns, searchname: str) -> bool:
def has_column(columns: List, searchname: str) -> bool:
return len(list(filter(lambda x: x["name"] == searchname, columns))) == 1
def get_column_def(columns, column: str, default: str) -> str:
def get_column_def(columns: List, column: str, default: str) -> str:
return default if not has_column(columns, column) else column
@ -246,14 +246,15 @@ class Trade(_DECL_BASE):
if self.initial_stop_loss_pct else None),
}
def adjust_min_max_rates(self, current_price: float):
def adjust_min_max_rates(self, current_price: float) -> None:
"""
Adjust the max_rate and min_rate.
"""
self.max_rate = max(current_price, self.max_rate or self.open_rate)
self.min_rate = min(current_price, self.min_rate or self.open_rate)
def adjust_stop_loss(self, current_price: float, stoploss: float, initial: bool = False):
def adjust_stop_loss(self, current_price: float, stoploss: float,
initial: bool = False) -> None:
"""
This adjusts the stop loss to it's most recently observed setting
:param current_price: Current rate the asset is traded
@ -317,10 +318,10 @@ class Trade(_DECL_BASE):
elif order_type in ('market', 'limit') and order['side'] == 'sell':
self.close(order['price'])
logger.info('%s_SELL has been fulfilled for %s.', order_type.upper(), self)
elif order_type == 'stop_loss_limit':
elif order_type in ('stop_loss_limit', 'stop-loss'):
self.stoploss_order_id = None
self.close_rate_requested = self.stop_loss
logger.info('STOP_LOSS_LIMIT is hit for %s.', self)
logger.info('%s is hit for %s.', order_type.upper(), self)
self.close(order['average'])
else:
raise ValueError(f'Unknown order type: {order_type}')

16
freqtrade/plot/plotting.py
View File

@ -3,11 +3,14 @@ from pathlib import Path
from typing import Any, Dict, List
import pandas as pd
from freqtrade.configuration import TimeRange
from freqtrade.data import history
from freqtrade.data.btanalysis import (combine_tickers_with_mean,
create_cum_profit,
extract_trades_of_period, load_trades)
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import load_data
from freqtrade.misc import pair_to_filename
from freqtrade.resolvers import StrategyResolver
logger = logging.getLogger(__name__)
@ -36,18 +39,19 @@ def init_plotscript(config):
# Set timerange to use
timerange = TimeRange.parse_timerange(config.get("timerange"))
tickers = history.load_data(
tickers = load_data(
datadir=config.get("datadir"),
pairs=pairs,
timeframe=config.get('ticker_interval', '5m'),
timerange=timerange,
data_format=config.get('dataformat_ohlcv', 'json'),
)
trades = load_trades(config['trade_source'],
db_url=config.get('db_url'),
exportfilename=config.get('exportfilename'),
)
trades = history.trim_dataframe(trades, timerange, 'open_time')
trades = trim_dataframe(trades, timerange, 'open_time')
return {"tickers": tickers,
"trades": trades,
"pairs": pairs,
@ -370,12 +374,12 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
return fig
def generate_plot_filename(pair, timeframe) -> str:
def generate_plot_filename(pair: str, timeframe: str) -> str:
"""
Generate filenames per pair/timeframe to be used for storing plots
"""
pair_name = pair.replace("/", "_")
file_name = 'freqtrade-plot-' + pair_name + '-' + timeframe + '.html'
pair_s = pair_to_filename(pair)
file_name = 'freqtrade-plot-' + pair_s + '-' + timeframe + '.html'
logger.info('Generate plot file for %s', pair)

31
freqtrade/resolvers/iresolver.py
View File

@ -7,7 +7,7 @@ import importlib.util
import inspect
import logging
from pathlib import Path
from typing import Any, Dict, Generator, List, Optional, Tuple, Type, Union
from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union
from freqtrade.exceptions import OperationalException
@ -22,13 +22,15 @@ class IResolver:
object_type: Type[Any]
object_type_str: str
user_subdir: Optional[str] = None
initial_search_path: Path
initial_search_path: Optional[Path]
@classmethod
def build_search_paths(cls, config, user_subdir: Optional[str] = None,
def build_search_paths(cls, config: Dict[str, Any], user_subdir: Optional[str] = None,
extra_dir: Optional[str] = None) -> List[Path]:
abs_paths: List[Path] = [cls.initial_search_path]
abs_paths: List[Path] = []
if cls.initial_search_path:
abs_paths.append(cls.initial_search_path)
if user_subdir:
abs_paths.insert(0, config['user_data_dir'].joinpath(user_subdir))
@ -40,12 +42,14 @@ class IResolver:
return abs_paths
@classmethod
def _get_valid_object(cls, module_path: Path,
object_name: Optional[str]) -> Generator[Any, None, None]:
def _get_valid_object(cls, module_path: Path, object_name: Optional[str],
enum_failed: bool = False) -> Iterator[Any]:
"""
Generator returning objects with matching object_type and object_name in the path given.
:param module_path: absolute path to the module
:param object_name: Class name of the object
:param enum_failed: If True, will return None for modules which fail.
Otherwise, failing modules are skipped.
:return: generator containing matching objects
"""
@ -58,10 +62,13 @@ class IResolver:
except (ModuleNotFoundError, SyntaxError) as err:
# Catch errors in case a specific module is not installed
logger.warning(f"Could not import {module_path} due to '{err}'")
if enum_failed:
return iter([None])
valid_objects_gen = (
obj for name, obj in inspect.getmembers(module, inspect.isclass)
if (object_name is None or object_name == name) and cls.object_type in obj.__bases__
if ((object_name is None or object_name == name) and
issubclass(obj, cls.object_type) and obj is not cls.object_type)
)
return valid_objects_gen
@ -135,10 +142,13 @@ class IResolver:
)
@classmethod
def search_all_objects(cls, directory: Path) -> List[Dict[str, Any]]:
def search_all_objects(cls, directory: Path,
enum_failed: bool) -> List[Dict[str, Any]]:
"""
Searches a directory for valid objects
:param directory: Path to search
:param enum_failed: If True, will return None for modules which fail.
Otherwise, failing modules are skipped.
:return: List of dicts containing 'name', 'class' and 'location' entires
"""
logger.debug(f"Searching for {cls.object_type.__name__} '{directory}'")
@ -150,9 +160,10 @@ class IResolver:
continue
module_path = entry.resolve()
logger.debug(f"Path {module_path}")
for obj in cls._get_valid_object(module_path, object_name=None):
for obj in cls._get_valid_object(module_path, object_name=None,
enum_failed=enum_failed):
objects.append(
{'name': obj.__name__,
{'name': obj.__name__ if obj is not None else '',
'class': obj,
'location': entry,
})

15
freqtrade/resolvers/strategy_resolver.py
View File

@ -9,10 +9,10 @@ from base64 import urlsafe_b64decode
from collections import OrderedDict
from inspect import getfullargspec
from pathlib import Path
from typing import Dict, Optional
from typing import Any, Dict, Optional
from freqtrade.constants import (REQUIRED_ORDERTIF, REQUIRED_ORDERTYPES,
USERPATH_STRATEGY)
USERPATH_STRATEGIES)
from freqtrade.exceptions import OperationalException
from freqtrade.resolvers import IResolver
from freqtrade.strategy.interface import IStrategy
@ -26,11 +26,11 @@ class StrategyResolver(IResolver):
"""
object_type = IStrategy
object_type_str = "Strategy"
user_subdir = USERPATH_STRATEGY
initial_search_path = Path(__file__).parent.parent.joinpath('strategy').resolve()
user_subdir = USERPATH_STRATEGIES
initial_search_path = None
@staticmethod
def load_strategy(config: Optional[Dict] = None) -> IStrategy:
def load_strategy(config: Dict[str, Any] = None) -> IStrategy:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
@ -96,7 +96,8 @@ class StrategyResolver(IResolver):
return strategy
@staticmethod
def _override_attribute_helper(strategy, config, attribute: str, default):
def _override_attribute_helper(strategy, config: Dict[str, Any],
attribute: str, default: Any):
"""
Override attributes in the strategy.
Prevalence:
@ -140,7 +141,7 @@ class StrategyResolver(IResolver):
"""
abs_paths = StrategyResolver.build_search_paths(config,
user_subdir=USERPATH_STRATEGY,
user_subdir=USERPATH_STRATEGIES,
extra_dir=extra_dir)
if ":" in strategy_name:

12
freqtrade/rpc/rpc.py
View File

@ -26,7 +26,9 @@ class RPCMessageType(Enum):
WARNING_NOTIFICATION = 'warning'
CUSTOM_NOTIFICATION = 'custom'
BUY_NOTIFICATION = 'buy'
BUY_CANCEL_NOTIFICATION = 'buy_cancel'
SELL_NOTIFICATION = 'sell'
SELL_CANCEL_NOTIFICATION = 'sell_cancel'
def __repr__(self):
return self.value
@ -39,6 +41,7 @@ class RPCException(Exception):
raise RPCException('*Status:* `no active trade`')
"""
def __init__(self, message: str) -> None:
super().__init__(self)
self.message = message
@ -139,7 +142,8 @@ class RPC:
results.append(trade_dict)
return results
def _rpc_status_table(self, stake_currency, fiat_display_currency: str) -> Tuple[List, List]:
def _rpc_status_table(self, stake_currency: str,
fiat_display_currency: str) -> Tuple[List, List]:
trades = Trade.get_open_trades()
if not trades:
raise RPCException('no active trade')
@ -164,7 +168,9 @@ class RPC:
profit_str += f" ({fiat_profit:.2f})"
trades_list.append([
trade.id,
trade.pair,
trade.pair + ('*' if (trade.open_order_id is not None
and trade.close_rate_requested is None) else '')
+ ('**' if (trade.close_rate_requested is not None) else ''),
shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
profit_str
])
@ -385,7 +391,7 @@ class RPC:
return {'status': 'No more buy will occur from now. Run /reload_conf to reset.'}
def _rpc_forcesell(self, trade_id) -> Dict[str, str]:
def _rpc_forcesell(self, trade_id: str) -> Dict[str, str]:
"""
Handler for forcesell <id>.
Sells the given trade at current price

2
freqtrade/rpc/rpc_manager.py
View File

@ -61,7 +61,7 @@ class RPCManager:
except NotImplementedError:
logger.error(f"Message type {msg['type']} not implemented by handler {mod.name}.")
def startup_messages(self, config, pairlist) -> None:
def startup_messages(self, config: Dict[str, Any], pairlist) -> None:
if config['dry_run']:
self.send_msg({
'type': RPCMessageType.WARNING_NOTIFICATION,

20
freqtrade/rpc/telegram.py
View File

@ -134,13 +134,18 @@ class Telegram(RPC):
msg['stake_amount_fiat'] = 0
message = ("*{exchange}:* Buying {pair}\n"
"at rate `{limit:.8f}\n"
"({stake_amount:.6f} {stake_currency}").format(**msg)
"*Amount:* `{amount:.8f}`\n"
"*Open Rate:* `{limit:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
"*Total:* `({stake_amount:.6f} {stake_currency}").format(**msg)
if msg.get('fiat_currency', None):
message += ", {stake_amount_fiat:.3f} {fiat_currency}".format(**msg)
message += ")`"
elif msg['type'] == RPCMessageType.BUY_CANCEL_NOTIFICATION:
message = "*{exchange}:* Cancelling Open Buy Order for {pair}".format(**msg)
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
msg['amount'] = round(msg['amount'], 8)
msg['profit_percent'] = round(msg['profit_percent'] * 100, 2)
@ -149,10 +154,10 @@ class Telegram(RPC):
msg['duration_min'] = msg['duration'].total_seconds() / 60
message = ("*{exchange}:* Selling {pair}\n"
"*Rate:* `{limit:.8f}`\n"
"*Amount:* `{amount:.8f}`\n"
"*Open Rate:* `{open_rate:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
"*Close Rate:* `{limit:.8f}`\n"
"*Sell Reason:* `{sell_reason}`\n"
"*Duration:* `{duration} ({duration_min:.1f} min)`\n"
"*Profit:* `{profit_percent:.2f}%`").format(**msg)
@ -163,8 +168,11 @@ class Telegram(RPC):
and self._fiat_converter):
msg['profit_fiat'] = self._fiat_converter.convert_amount(
msg['profit_amount'], msg['stake_currency'], msg['fiat_currency'])
message += ('` ({gain}: {profit_amount:.8f} {stake_currency}`'
'` / {profit_fiat:.3f} {fiat_currency})`').format(**msg)
message += (' `({gain}: {profit_amount:.8f} {stake_currency}'
' / {profit_fiat:.3f} {fiat_currency})`').format(**msg)
elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
message = "*{exchange}:* Cancelling Open Sell Order for {pair}".format(**msg)
elif msg['type'] == RPCMessageType.STATUS_NOTIFICATION:
message = '*Status:* `{status}`'.format(**msg)
@ -553,6 +561,8 @@ class Telegram(RPC):
"*/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" \

4
freqtrade/rpc/webhook.py
View File

@ -41,8 +41,12 @@ class Webhook(RPC):
if msg['type'] == RPCMessageType.BUY_NOTIFICATION:
valuedict = self._config['webhook'].get('webhookbuy', None)
elif msg['type'] == RPCMessageType.BUY_CANCEL_NOTIFICATION:
valuedict = self._config['webhook'].get('webhookbuycancel', None)
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
valuedict = self._config['webhook'].get('webhooksell', None)
elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
valuedict = self._config['webhook'].get('webhooksellcancel', None)
elif msg['type'] in(RPCMessageType.STATUS_NOTIFICATION,
RPCMessageType.CUSTOM_NOTIFICATION,
RPCMessageType.WARNING_NOTIFICATION):

4
freqtrade/strategy/interface.py
View File

@ -180,7 +180,7 @@ class IStrategy(ABC):
if pair not in self._pair_locked_until or self._pair_locked_until[pair] < until:
self._pair_locked_until[pair] = until
def unlock_pair(self, pair) -> None:
def unlock_pair(self, pair: str) -> None:
"""
Unlocks a pair previously locked using lock_pair.
Not used by freqtrade itself, but intended to be used if users lock pairs
@ -439,7 +439,7 @@ class IStrategy(ABC):
else:
return current_profit > roi
def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
def tickerdata_to_dataframe(self, tickerdata: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given ticker data
Used by optimize operations only, not during dry / live runs.

58
freqtrade/templates/base_config.json.j2 Normal file
View File

@ -0,0 +1,58 @@
{
"max_open_trades": {{ max_open_trades }},
"stake_currency": "{{ stake_currency }}",
"stake_amount": {{ stake_amount }},
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "{{ fiat_display_currency }}",
"ticker_interval": "{{ ticker_interval }}",
"dry_run": {{ dry_run | lower }},
"unfilledtimeout": {
"buy": 10,
"sell": 30
},
"bid_strategy": {
"ask_last_balance": 0.0,
"use_order_book": false,
"order_book_top": 1,
"check_depth_of_market": {
"enabled": false,
"bids_to_ask_delta": 1
}
},
"ask_strategy": {
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 9,
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false
},
{{ exchange | indent(4) }},
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": {{ telegram | lower }},
"token": "{{ telegram_token }}",
"chat_id": "{{ telegram_chat_id }}"
},
"initial_state": "running",
"forcebuy_enable": false,
"internals": {
"process_throttle_secs": 5
}
}

13
freqtrade/templates/sample_hyperopt_advanced.py
View File

@ -230,7 +230,7 @@ class AdvancedSampleHyperOpt(IHyperOpt):
'stoploss' optimization hyperspace.
"""
return [
Real(-0.5, -0.02, name='stoploss'),
Real(-0.35, -0.02, name='stoploss'),
]
@staticmethod
@ -249,8 +249,15 @@ class AdvancedSampleHyperOpt(IHyperOpt):
# other 'trailing' hyperspace parameters.
Categorical([True], name='trailing_stop'),
Real(0.02, 0.35, name='trailing_stop_positive'),
Real(0.01, 0.1, name='trailing_stop_positive_offset'),
Real(0.01, 0.35, name='trailing_stop_positive'),
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
# so this intermediate parameter is used as the value of the difference between
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
# generate_trailing_params() method.
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]

124
freqtrade/templates/sample_strategy.py
View File

@ -124,24 +124,70 @@ class SampleStrategy(IStrategy):
# Momentum Indicators
# ------------------------------------
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Aroon, Aroon Oscillator
# aroon = ta.AROON(dataframe)
# dataframe['aroonup'] = aroon['aroonup']
# dataframe['aroondown'] = aroon['aroondown']
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# # Awesome oscillator
# # Awesome Oscillator
# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# # Commodity Channel Index: values Oversold:<-100, Overbought:>100
# # Keltner Channel
# keltner = qtpylib.keltner_channel(dataframe)
# dataframe["kc_upperband"] = keltner["upper"]
# dataframe["kc_lowerband"] = keltner["lower"]
# dataframe["kc_middleband"] = keltner["mid"]
# dataframe["kc_percent"] = (
# (dataframe["close"] - dataframe["kc_lowerband"]) /
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
# )
# dataframe["kc_width"] = (
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
# )
# # Ultimate Oscillator
# dataframe['uo'] = ta.ULTOSC(dataframe)
# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
# dataframe['cci'] = ta.CCI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stochastic Slow
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stochastic RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
@ -151,60 +197,58 @@ class SampleStrategy(IStrategy):
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # ROC
# dataframe['roc'] = ta.ROC(dataframe)
# # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stoch
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stoch RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# Overlap Studies
# ------------------------------------
# Bollinger bands
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
)
# Bollinger Bands - Weighted (EMA based instead of SMA)
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
# qtpylib.typical_price(dataframe), window=20, stds=2
# )
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
# dataframe["wbb_percent"] = (
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
# )
# dataframe["wbb_width"] = (
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
# dataframe["wbb_middleband"]
# )
# # EMA - Exponential Moving Average
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# # SMA - Simple Moving Average
# dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
# SAR Parabol
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
@ -264,7 +308,7 @@ class SampleStrategy(IStrategy):
# # Chart type
# # ------------------------------------
# # Heikinashi stategy
# # Heikin Ashi Strategy
# heikinashi = qtpylib.heikinashi(dataframe)
# dataframe['ha_open'] = heikinashi['open']
# dataframe['ha_close'] = heikinashi['close']

28
freqtrade/templates/strategy_analysis_example.ipynb
View File

@ -6,7 +6,8 @@
"source": [
"# Strategy analysis example\n",
"\n",
"Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data."
"Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.\n",
"The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location."
]
},
{
@ -23,18 +24,21 @@
"outputs": [],
"source": [
"from pathlib import Path\n",
"from freqtrade.configuration import Configuration\n",
"\n",
"# Customize these according to your needs.\n",
"\n",
"# Initialize empty configuration object\n",
"config = Configuration.from_files([])\n",
"# Optionally, use existing configuration file\n",
"# config = Configuration.from_files([\"config.json\"])\n",
"\n",
"# Define some constants\n",
"timeframe = \"5m\"\n",
"config[\"ticker_interval\"] = \"5m\"\n",
"# Name of the strategy class\n",
"strategy_name = 'SampleStrategy'\n",
"# Path to user data\n",
"user_data_dir = Path('user_data')\n",
"# Location of the strategy\n",
"strategy_location = user_data_dir / 'strategies'\n",
"config[\"strategy\"] = \"SampleStrategy\"\n",
"# Location of the data\n",
"data_location = Path(user_data_dir, 'data', 'binance')\n",
"data_location = Path(config['user_data_dir'], 'data', 'binance')\n",
"# Pair to analyze - Only use one pair here\n",
"pair = \"BTC_USDT\""
]
@ -49,7 +53,7 @@
"from freqtrade.data.history import load_pair_history\n",
"\n",
"candles = load_pair_history(datadir=data_location,\n",
" timeframe=timeframe,\n",
" timeframe=config[\"ticker_interval\"],\n",
" pair=pair)\n",
"\n",
"# Confirm success\n",
@ -73,9 +77,7 @@
"source": [
"# Load strategy using values set above\n",
"from freqtrade.resolvers import StrategyResolver\n",
"strategy = StrategyResolver.load_strategy({'strategy': strategy_name,\n",
" 'user_data_dir': user_data_dir,\n",
" 'strategy_path': strategy_location})\n",
"strategy = StrategyResolver.load_strategy(config)\n",
"\n",
"# Generate buy/sell signals using strategy\n",
"df = strategy.analyze_ticker(candles, {'pair': pair})\n",
@ -137,7 +139,7 @@
"from freqtrade.data.btanalysis import load_backtest_data\n",
"\n",
"# Load backtest results\n",
"trades = load_backtest_data(user_data_dir / \"backtest_results/backtest-result.json\")\n",
"trades = load_backtest_data(config[\"user_data_dir\"] / \"backtest_results/backtest-result.json\")\n",
"\n",
"# Show value-counts per pair\n",
"trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"

41
freqtrade/templates/subtemplates/exchange_binance.j2 Normal file
View File

@ -0,0 +1,41 @@
"exchange": {
"name": "{{ exchange_name | lower }}",
"key": "{{ exchange_key }}",
"secret": "{{ exchange_secret }}",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 200
},
"pair_whitelist": [
"ALGO/BTC",
"ATOM/BTC",
"BAT/BTC",
"BCH/BTC",
"BRD/BTC",
"EOS/BTC",
"ETH/BTC",
"IOTA/BTC",
"LINK/BTC",
"LTC/BTC",
"NEO/BTC",
"NXS/BTC",
"XMR/BTC",
"XRP/BTC",
"XTZ/BTC"
],
"pair_blacklist": [
"BNB/BTC",
"BNB/BUSD",
"BNB/ETH",
"BNB/EUR",
"BNB/NGN",
"BNB/PAX",
"BNB/RUB",
"BNB/TRY",
"BNB/TUSD",
"BNB/USDC",
"BNB/USDS",
"BNB/USDT",
]
}

31
freqtrade/templates/subtemplates/exchange_bittrex.j2 Normal file
View File

@ -0,0 +1,31 @@
"order_types": {
"buy": "limit",
"sell": "limit",
"emergencysell": "limit",
"stoploss": "limit",
"stoploss_on_exchange": false
},
"exchange": {
"name": "{{ exchange_name | lower }}",
"key": "{{ exchange_key }}",
"secret": "{{ exchange_secret }}",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 500
},
"pair_whitelist": [
"ETH/BTC",
"LTC/BTC",
"ETC/BTC",
"DASH/BTC",
"ZEC/BTC",
"XLM/BTC",
"XRP/BTC",
"TRX/BTC",
"ADA/BTC",
"XMR/BTC"
],
"pair_blacklist": [
]
}

15
freqtrade/templates/subtemplates/exchange_generic.j2 Normal file
View File

@ -0,0 +1,15 @@
"exchange": {
"name": "{{ exchange_name | lower }}",
"key": "{{ exchange_key }}",
"secret": "{{ exchange_secret }}",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": true
},
"pair_whitelist": [
],
"pair_blacklist": [
]
}

36
freqtrade/templates/subtemplates/exchange_kraken.j2 Normal file
View File

@ -0,0 +1,36 @@
"download_trades": true,
"exchange": {
"name": "kraken",
"key": "{{ exchange_key }}",
"secret": "{{ exchange_secret }}",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 1000
},
"pair_whitelist": [
"ADA/EUR",
"ATOM/EUR",
"BAT/EUR",
"BCH/EUR",
"BTC/EUR",
"DAI/EUR",
"DASH/EUR",
"EOS/EUR",
"ETC/EUR",
"ETH/EUR",
"LINK/EUR",
"LTC/EUR",
"QTUM/EUR",
"REP/EUR",
"WAVES/EUR",
"XLM/EUR",
"XMR/EUR",
"XRP/EUR",
"XTZ/EUR",
"ZEC/EUR"
],
"pair_blacklist": [
]
}

123
freqtrade/templates/subtemplates/indicators_full.j2
View File

@ -2,24 +2,70 @@
# Momentum Indicators
# ------------------------------------
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Aroon, Aroon Oscillator
# aroon = ta.AROON(dataframe)
# dataframe['aroonup'] = aroon['aroonup']
# dataframe['aroondown'] = aroon['aroondown']
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# # Awesome oscillator
# # Awesome Oscillator
# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# # Commodity Channel Index: values Oversold:<-100, Overbought:>100
# # Keltner Channel
# keltner = qtpylib.keltner_channel(dataframe)
# dataframe["kc_upperband"] = keltner["upper"]
# dataframe["kc_lowerband"] = keltner["lower"]
# dataframe["kc_middleband"] = keltner["mid"]
# dataframe["kc_percent"] = (
# (dataframe["close"] - dataframe["kc_lowerband"]) /
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
# )
# dataframe["kc_width"] = (
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
# )
# # Ultimate Oscillator
# dataframe['uo'] = ta.ULTOSC(dataframe)
# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
# dataframe['cci'] = ta.CCI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stochastic Slow
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stochastic RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
@ -29,60 +75,57 @@ dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # ROC
# dataframe['roc'] = ta.ROC(dataframe)
# # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stoch
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stoch RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# Overlap Studies
# ------------------------------------
# Bollinger bands
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
)
# Bollinger Bands - Weighted (EMA based instead of SMA)
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
# qtpylib.typical_price(dataframe), window=20, stds=2
# )
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
# dataframe["wbb_percent"] = (
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
# )
# dataframe["wbb_width"] = (
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
# )
# # EMA - Exponential Moving Average
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# # SMA - Simple Moving Average
# dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
# SAR Parabol
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
@ -142,7 +185,7 @@ dataframe['htleadsine'] = hilbert['leadsine']
# # Chart type
# # ------------------------------------
# # Heikinashi stategy
# # Heikin Ashi Strategy
# heikinashi = qtpylib.heikinashi(dataframe)
# dataframe['ha_open'] = heikinashi['open']
# dataframe['ha_close'] = heikinashi['close']

4
freqtrade/vendor/qtpylib/indicators.py vendored
View File

@ -288,9 +288,9 @@ def rolling_min(series, window=14, min_periods=None):
def rolling_max(series, window=14, min_periods=None):
min_periods = window if min_periods is None else min_periods
try:
return series.rolling(window=window, min_periods=min_periods).min()
return series.rolling(window=window, min_periods=min_periods).max()
except Exception as e: # noqa: F841
return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
return pd.Series(series).rolling(window=window, min_periods=min_periods).max()
# ---------------------------------------------

10
freqtrade/wallets.py
View File

@ -30,24 +30,21 @@ class Wallets:
self._last_wallet_refresh = 0
self.update()
def get_free(self, currency) -> float:
def get_free(self, currency: str) -> float:
balance = self._wallets.get(currency)
if balance and balance.free:
return balance.free
else:
return 0
def get_used(self, currency) -> float:
def get_used(self, currency: str) -> float:
balance = self._wallets.get(currency)
if balance and balance.used:
return balance.used
else:
return 0
def get_total(self, currency) -> float:
def get_total(self, currency: str) -> float:
balance = self._wallets.get(currency)
if balance and balance.total:
return balance.total
@ -87,7 +84,6 @@ class Wallets:
self._wallets = _wallets
def _update_live(self) -> None:
balances = self._exchange.get_balances()
for currency in balances:

72
freqtrade/worker.py
View File

@ -4,6 +4,7 @@ Main Freqtrade worker class.
import logging
import time
import traceback
from os import getpid
from typing import Any, Callable, Dict, Optional
import sdnotify
@ -22,16 +23,19 @@ class Worker:
Freqtradebot worker class
"""
def __init__(self, args: Dict[str, Any], config=None) -> None:
def __init__(self, args: Dict[str, Any], config: Dict[str, Any] = None) -> None:
"""
Init all variables and objects the bot needs to work
"""
logger.info('Starting worker %s', __version__)
logger.info(f"Starting worker {__version__}")
self._args = args
self._config = config
self._init(False)
self.last_throttle_start_time: float = 0
self._heartbeat_msg: float = 0
# Tell systemd that we completed initialization phase
if self._sd_notify:
logger.debug("sd_notify: READY=1")
@ -48,22 +52,14 @@ class Worker:
# Init the instance of the bot
self.freqtrade = FreqtradeBot(self._config)
self._throttle_secs = self._config.get('internals', {}).get(
'process_throttle_secs',
constants.PROCESS_THROTTLE_SECS
)
internals_config = self._config.get('internals', {})
self._throttle_secs = internals_config.get('process_throttle_secs',
constants.PROCESS_THROTTLE_SECS)
self._heartbeat_interval = internals_config.get('heartbeat_interval', 60)
self._sd_notify = sdnotify.SystemdNotifier() if \
self._config.get('internals', {}).get('sd_notify', False) else None
@property
def state(self) -> State:
return self.freqtrade.state
@state.setter
def state(self, value: State) -> None:
self.freqtrade.state = value
def run(self) -> None:
state = None
while True:
@ -71,31 +67,33 @@ class Worker:
if state == State.RELOAD_CONF:
self._reconfigure()
def _worker(self, old_state: Optional[State], throttle_secs: Optional[float] = None) -> State:
def _worker(self, old_state: Optional[State]) -> State:
"""
Trading routine that must be run at each loop
The main routine that runs each throttling iteration and handles the states.
:param old_state: the previous service state from the previous call
:return: current service state
"""
state = self.freqtrade.state
if throttle_secs is None:
throttle_secs = self._throttle_secs
# Log state transition
if state != old_state:
self.freqtrade.notify_status(f'{state.name.lower()}')
logger.info('Changing state to: %s', state.name)
logger.info(f"Changing state to: {state.name}")
if state == State.RUNNING:
self.freqtrade.startup()
# Reset heartbeat timestamp to log the heartbeat message at
# first throttling iteration when the state changes
self._heartbeat_msg = 0
if state == State.STOPPED:
# Ping systemd watchdog before sleeping in the stopped state
if self._sd_notify:
logger.debug("sd_notify: WATCHDOG=1\\nSTATUS=State: STOPPED.")
self._sd_notify.notify("WATCHDOG=1\nSTATUS=State: STOPPED.")
time.sleep(throttle_secs)
self._throttle(func=self._process_stopped, throttle_secs=self._throttle_secs)
elif state == State.RUNNING:
# Ping systemd watchdog before throttling
@ -103,28 +101,40 @@ class Worker:
logger.debug("sd_notify: WATCHDOG=1\\nSTATUS=State: RUNNING.")
self._sd_notify.notify("WATCHDOG=1\nSTATUS=State: RUNNING.")
self._throttle(func=self._process, min_secs=throttle_secs)
self._throttle(func=self._process_running, throttle_secs=self._throttle_secs)
if self._heartbeat_interval:
now = time.time()
if (now - self._heartbeat_msg) > self._heartbeat_interval:
logger.info(f"Bot heartbeat. PID={getpid()}, "
f"version='{__version__}', state='{state.name}'")
self._heartbeat_msg = now
return state
def _throttle(self, func: Callable[..., Any], min_secs: float, *args, **kwargs) -> Any:
def _throttle(self, func: Callable[..., Any], throttle_secs: float, *args, **kwargs) -> Any:
"""
Throttles the given callable that it
takes at least `min_secs` to finish execution.
:param func: Any callable
:param min_secs: minimum execution time in seconds
:return: Any
:param throttle_secs: throttling interation execution time limit in seconds
:return: Any (result of execution of func)
"""
start = time.time()
self.last_throttle_start_time = time.time()
logger.debug("========================================")
result = func(*args, **kwargs)
end = time.time()
duration = max(min_secs - (end - start), 0.0)
logger.debug('Throttling %s for %.2f seconds', func.__name__, duration)
time.sleep(duration)
time_passed = time.time() - self.last_throttle_start_time
sleep_duration = max(throttle_secs - time_passed, 0.0)
logger.debug(f"Throttling with '{func.__name__}()': sleep for {sleep_duration:.2f} s, "
f"last iteration took {time_passed:.2f} s.")
time.sleep(sleep_duration)
return result
def _process(self) -> None:
logger.debug("========================================")
def _process_stopped(self) -> None:
# Maybe do here something in the future...
pass
def _process_running(self) -> None:
try:
self.freqtrade.process()
except TemporaryError as error:

4
mkdocs.yml
View File

@ -1,8 +1,8 @@
site_name: Freqtrade
nav:
- About: index.md
- Installation: installation.md
- Home: index.md
- Installation Docker: docker.md
- Installation: installation.md
- Configuration: configuration.md
- Strategy Customization: strategy-customization.md
- Stoploss: stoploss.md

13
requirements-common.txt
View File

@ -1,18 +1,18 @@
# requirements without requirements installable via conda
# mainly used for Raspberry pi installs
ccxt==1.21.91
ccxt==1.22.95
SQLAlchemy==1.3.13
python-telegram-bot==12.3.0
python-telegram-bot==12.4.2
arrow==0.15.5
cachetools==4.0.0
requests==2.22.0
requests==2.23.0
urllib3==1.25.8
wrapt==1.11.2
wrapt==1.12.0
jsonschema==3.2.0
TA-Lib==0.4.17
tabulate==0.8.6
coinmarketcap==5.0.3
jinja2==2.10.3
jinja2==2.11.1
# find first, C search in arrays
py_find_1st==1.1.4
@ -28,3 +28,6 @@ flask==1.1.1
# Support for colorized terminal output
colorama==0.4.3
# Building config files interactively
questionary==1.5.1
prompt-toolkit==3.0.3

4
requirements-dev.txt
View File

@ -3,12 +3,12 @@
-r requirements-plot.txt
-r requirements-hyperopt.txt
coveralls==1.10.0
coveralls==1.11.1
flake8==3.7.9
flake8-type-annotations==0.1.0
flake8-tidy-imports==4.0.0
mypy==0.761
pytest==5.3.4
pytest==5.3.5
pytest-asyncio==0.10.0
pytest-cov==2.8.1
pytest-mock==2.0.0

2
requirements-hyperopt.txt
View File

@ -4,6 +4,6 @@
# Required for hyperopt
scipy==1.4.1
scikit-learn==0.22.1
scikit-optimize==0.5.2
scikit-optimize==0.7.4
filelock==3.0.12
joblib==0.14.1

2
requirements-plot.txt
View File

@ -1,5 +1,5 @@
# Include all requirements to run the bot.
-r requirements.txt
plotly==4.5.0
plotly==4.5.1

2
requirements.txt
View File

@ -2,4 +2,4 @@
-r requirements-common.txt
numpy==1.18.1
pandas==0.25.3
pandas==1.0.1

2
setup.py
View File

@ -79,6 +79,8 @@ setup(name='freqtrade',
'sdnotify',
'colorama',
'jinja2',
'questionary',
'prompt-toolkit',
# from requirements.txt
'numpy',
'pandas',

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