adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge branch 'develop' into pr/orehunt/3059

Browse Source
This commit is contained in:
Matthias 2021-03-24 20:11:31 +01:00
commit cf0cd6ff38
179 changed files with 7122 additions and 4103 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
.devcontainer/Dockerfile
View File

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

2
.github/ISSUE_TEMPLATE/question.md vendored
View File

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

35
.github/workflows/ci.yml vendored
View File

@ -61,6 +61,12 @@ jobs:
- name: Tests
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc
if: matrix.python-version != '3.9'
- name: Tests incl. ccxt compatibility tests
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
if: matrix.python-version == '3.9'
- name: Coveralls
if: (startsWith(matrix.os, 'ubuntu-20') && matrix.python-version == '3.8')
@ -73,13 +79,13 @@ jobs:
- name: Backtesting
run: |
cp config.json.example config.json
cp config_bittrex.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
- name: Hyperopt
run: |
cp config.json.example config.json
cp config_bittrex.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
@ -96,7 +102,7 @@ jobs:
mypy freqtrade scripts
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
uses: lazy-actions/slatify@v3.0.0
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
@ -111,7 +117,7 @@ jobs:
strategy:
matrix:
os: [ macos-latest ]
python-version: [3.7, 3.8]
python-version: [3.7, 3.8, 3.9]
steps:
- uses: actions/checkout@v2
@ -140,8 +146,9 @@ jobs:
run: |
cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
- name: Installation - *nix
- name: Installation - macOS
run: |
brew install hdf5 c-blosc
python -m pip install --upgrade pip
export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
export TA_LIBRARY_PATH=${HOME}/dependencies/lib
@ -164,13 +171,13 @@ jobs:
- name: Backtesting
run: |
cp config.json.example config.json
cp config_bittrex.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
- name: Hyperopt
run: |
cp config.json.example config.json
cp config_bittrex.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
@ -187,7 +194,7 @@ jobs:
mypy freqtrade scripts
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
uses: lazy-actions/slatify@v3.0.0
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
@ -231,13 +238,13 @@ jobs:
- name: Backtesting
run: |
cp config.json.example config.json
cp config_bittrex.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
- name: Hyperopt
run: |
cp config.json.example config.json
cp config_bittrex.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
@ -250,7 +257,7 @@ jobs:
mypy freqtrade scripts
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
uses: lazy-actions/slatify@v3.0.0
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
@ -281,7 +288,7 @@ jobs:
mkdocs build
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
uses: lazy-actions/slatify@v3.0.0
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
@ -304,7 +311,7 @@ jobs:
runs-on: ubuntu-20.04
steps:
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
uses: lazy-actions/slatify@v3.0.0
if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
@ -391,7 +398,7 @@ jobs:
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
uses: lazy-actions/slatify@v3.0.0
if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}

1
.gitignore vendored
View File

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

4
.travis.yml
View File

@ -26,12 +26,12 @@ jobs:
# - coveralls || true
name: pytest
- script:
- cp config.json.example config.json
- cp config_bittrex.json.example config.json
- freqtrade create-userdir --userdir user_data
- freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
name: backtest
- script:
- cp config.json.example config.json
- cp config_bittrex.json.example config.json
- freqtrade create-userdir --userdir user_data
- freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily
name: hyperopt

2
CONTRIBUTING.md
View File

@ -12,7 +12,7 @@ Few pointers for contributions:
- New features need to contain unit tests, must conform to PEP8 (max-line-length = 100) and should be documented with the introduction PR.
- PR's can be declared as `[WIP]` - which signify Work in Progress Pull Requests (which are not finished).
If you are unsure, discuss the feature on our [discord server](https://discord.gg/MA9v74M), on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/zt-jaut7r4m-Y17k4x5mcQES9a9swKuxbg) or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
If you are unsure, discuss the feature on our [discord server](https://discord.gg/MA9v74M), on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw) or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
## Getting started

6
Dockerfile
View File

@ -1,4 +1,4 @@
FROM python:3.8.6-slim-buster as base
FROM python:3.9.2-slim-buster as base
# Setup env
ENV LANG C.UTF-8
@ -40,7 +40,9 @@ COPY --from=python-deps /root/.local /root/.local
# Install and execute
COPY . /freqtrade/
RUN pip install -e . --no-cache-dir \
&& mkdir /freqtrade/user_data/
&& mkdir /freqtrade/user_data/ \
&& freqtrade install-ui
ENTRYPOINT ["freqtrade"]
# Default to trade mode
CMD [ "trade" ]

6
Dockerfile.armhf
View File

@ -41,7 +41,11 @@ COPY --from=python-deps /root/.local /root/.local
# Install and execute
COPY . /freqtrade/
RUN pip install -e . --no-cache-dir
RUN apt-get install -y libhdf5-serial-dev \
&& apt-get clean \
&& pip install -e . --no-cache-dir \
&& freqtrade install-ui
ENTRYPOINT ["freqtrade"]
# Default to trade mode
CMD [ "trade" ]

3
MANIFEST.in
View File

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

23
README.md
View File

@ -22,12 +22,21 @@ expect.
We strongly recommend you to have coding and Python knowledge. Do not
hesitate to read the source code and understand the mechanism of this bot.
## Exchange marketplaces supported
## Supported Exchange marketplaces
Please read the [exchange specific notes](docs/exchanges.md) to learn about eventual, special configurations needed for each exchange.
- [X] [Bittrex](https://bittrex.com/)
- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#blacklists))
- [X] [Kraken](https://kraken.com/)
- [ ] [113 others to tests](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
- [X] [FTX](https://ftx.com)
- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
### Community tested
Exchanges confirmed working by the community:
- [X] [Bitvavo](https://bitvavo.com/)
## Documentation
@ -39,7 +48,7 @@ Please find the complete documentation on our [website](https://www.freqtrade.io
- [x] **Based on Python 3.7+**: For botting on any operating system - Windows, macOS and Linux.
- [x] **Persistence**: Persistence is achieved through sqlite.
- [x] **Dry-run**: Run the bot without playing money.
- [x] **Dry-run**: Run the bot without paying money.
- [x] **Backtesting**: Run a simulation of your buy/sell strategy.
- [x] **Strategy Optimization by machine learning**: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
- [x] **Edge position sizing** Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market. [Learn more](https://www.freqtrade.io/en/latest/edge/).
@ -113,7 +122,7 @@ Telegram is not mandatory. However, this is a great way to control your bot. Mor
- `/start`: Starts the trader.
- `/stop`: Stops the trader.
- `/stopbuy`: Stop entering new trades.
- `/status [table]`: Lists all open trades.
- `/status <trade_id>|[table]`: Lists all or specific open trades.
- `/profit`: Lists cumulative profit from all finished trades
- `/forcesell <trade_id>|all`: Instantly sells the given trade (Ignoring `minimum_roi`).
- `/performance`: Show performance of each finished trade grouped by pair
@ -138,7 +147,7 @@ For any questions not covered by the documentation or for further information ab
Please check out our [discord server](https://discord.gg/MA9v74M).
You can also join our [Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/zt-jaut7r4m-Y17k4x5mcQES9a9swKuxbg).
You can also join our [Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw).
### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
@ -169,7 +178,7 @@ to understand the requirements before sending your pull-requests.
Coding is not a necessity to contribute - maybe start with improving our documentation?
Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.
**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [discord](https://discord.gg/MA9v74M) or [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/zt-jaut7r4m-Y17k4x5mcQES9a9swKuxbg). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [discord](https://discord.gg/MA9v74M) or [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
**Important:** Always create your PR against the `develop` branch, not `stable`.
@ -191,5 +200,5 @@ To run this bot we recommend you a cloud instance with a minimum of:
- [pip](https://pip.pypa.io/en/stable/installing/)
- [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
- [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)
- [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
- [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
- [Docker](https://www.docker.com/products/docker) (Recommended)

4
build_helpers/publish_docker.sh
View File

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

11
config_binance.json.example
View File

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

13
config.json.example → config_bittrex.json.example
View File

@ -41,13 +41,13 @@
"ETH/BTC",
"LTC/BTC",
"ETC/BTC",
"DASH/BTC",
"ZEC/BTC",
"RVN/BTC",
"CRO/BTC",
"XLM/BTC",
"XRP/BTC",
"TRX/BTC",
"ADA/BTC",
"XMR/BTC"
"DOT/BTC"
],
"pair_blacklist": [
"DOGE/BTC"
@ -79,12 +79,13 @@
"enabled": false,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"verbosity": "info",
"verbosity": "error",
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "",
"password": ""
"username": "freqtrader",
"password": "SuperSecurePassword"
},
"bot_name": "freqtrade",
"initial_state": "running",
"forcebuy_enable": false,
"internals": {

34
config_full.json.example
View File

@ -42,12 +42,15 @@
"order_book_max": 1,
"use_sell_signal": true,
"sell_profit_only": false,
"sell_profit_offset": 0.0,
"ignore_roi_if_buy_signal": false
},
"order_types": {
"buy": "limit",
"sell": "limit",
"emergencysell": "market",
"forcesell": "market",
"forcebuy": "market",
"stoploss": "market",
"stoploss_on_exchange": false,
"stoploss_on_exchange_interval": 60
@ -103,28 +106,33 @@
}
],
"exchange": {
"name": "bittrex",
"name": "binance",
"sandbox": false,
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"password": "",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": false,
"enableRateLimit": true,
"rateLimit": 500,
"aiohttp_trust_env": false
},
"pair_whitelist": [
"ALGO/BTC",
"ATOM/BTC",
"BAT/BTC",
"BCH/BTC",
"BRD/BTC",
"EOS/BTC",
"ETH/BTC",
"IOTA/BTC",
"LINK/BTC",
"LTC/BTC",
"ETC/BTC",
"DASH/BTC",
"ZEC/BTC",
"XLM/BTC",
"NXT/BTC",
"TRX/BTC",
"ADA/BTC",
"XMR/BTC"
"NEO/BTC",
"NXS/BTC",
"XMR/BTC",
"XRP/BTC",
"XTZ/BTC"
],
"pair_blacklist": [
"DOGE/BTC"
@ -147,7 +155,7 @@
"remove_pumps": false
},
"telegram": {
"enabled": true,
"enabled": false,
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id",
"notification_settings": {
@ -164,12 +172,14 @@
"enabled": false,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"verbosity": "info",
"verbosity": "error",
"enable_openapi": false,
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "freqtrader",
"password": "SuperSecurePassword"
},
"bot_name": "freqtrade",
"db_url": "sqlite:///tradesv3.sqlite",
"initial_state": "running",
"forcebuy_enable": false,

7
config_kraken.json.example
View File

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

5
docker-compose.yml
View File

@ -14,6 +14,11 @@ services:
container_name: freqtrade
volumes:
- "./user_data:/freqtrade/user_data"
# Expose api on port 8080 (localhost only)
# Please read the https://www.freqtrade.io/en/latest/rest-api/ documentation
# before enabling this.
# ports:
# - "127.0.0.1:8080:8080"
# Default command used when running `docker compose up`
command: >
trade

63
docs/advanced-hyperopt.md
View File

@ -6,7 +6,7 @@ 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).
Custom hyperopt 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:
@ -32,6 +32,51 @@ or
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...
```
## Sharing methods with your strategy
Hyperopt classes provide access to the Strategy via the `strategy` class attribute.
This can be a great way to reduce code duplication if used correctly, but will also complicate usage for inexperienced users.
``` python
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
import freqtrade.vendor.qtpylib.indicators as qtpylib
class MyAwesomeStrategy(IStrategy):
buy_params = {
'rsi-value': 30,
'adx-value': 35,
}
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return self.buy_strategy_generator(self.buy_params, dataframe, metadata)
@staticmethod
def buy_strategy_generator(params, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
qtpylib.crossed_above(dataframe['rsi'], params['rsi-value']) &
dataframe['adx'] > params['adx-value']) &
dataframe['volume'] > 0
)
, 'buy'] = 1
return dataframe
class MyAwesomeHyperOpt(IHyperOpt):
...
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
# Call strategy's buy strategy generator
return self.StrategyClass.buy_strategy_generator(params, dataframe, metadata)
return populate_buy_trend
```
## 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.
@ -40,6 +85,11 @@ For the sample below, you then need to add the command line parameter `--hyperop
A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in [userdata/hyperopts](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py).
``` python
from datetime import datetime
from typing import Dict
from pandas import DataFrame
from freqtrade.optimize.hyperopt import IHyperOptLoss
TARGET_TRADES = 600
@ -54,6 +104,7 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
config: Dict, processed: Dict[str, DataFrame],
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results
@ -63,7 +114,7 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
* 0.25: Avoiding trade loss
* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
"""
total_profit = results['profit_percent'].sum()
total_profit = results['profit_ratio'].sum()
trade_duration = results['trade_duration'].mean()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
@ -77,10 +128,12 @@ Currently, the arguments are:
* `results`: DataFrame containing the result
The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):
`pair, profit_percent, profit_abs, open_date, open_rate, open_fee, close_date, close_rate, close_fee, amount, trade_duration, open_at_end, sell_reason`
`pair, profit_ratio, profit_abs, open_date, open_rate, fee_open, close_date, close_rate, fee_close, amount, trade_duration, is_open, sell_reason, stake_amount, min_rate, max_rate, stop_loss_ratio, stop_loss_abs`
* `trade_count`: Amount of trades (identical to `len(results)`)
* `min_date`: Start date of the hyperopting TimeFrame
* `min_date`: End date of the hyperopting TimeFrame
* `min_date`: Start date of the timerange used
* `min_date`: End date of the timerange used
* `config`: Config object used (Note: Not all strategy-related parameters will be updated here if they are part of a hyperopt space).
* `processed`: Dict of Dataframes with the pair as keys containing the data used for backtesting.
This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.

239
docs/backtesting.md
View File

@ -5,11 +5,97 @@ This page explains how to validate your strategy performance by using Backtestin
Backtesting requires historic data to be available.
To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
## Backtesting command reference
```
usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-i TIMEFRAME]
[--timerange TIMERANGE]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[--eps] [--dmmp] [--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
[--export EXPORT] [--export-filename PATH]
optional arguments:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--data-format-ohlcv {json,jsongz,hdf5}
Storage format for downloaded candle (OHLCV) data.
(default: `None`).
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--stake-amount STAKE_AMOUNT
Override the value of the `stake_amount` configuration
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
number).
--enable-protections, --enableprotections
Enable protections for backtesting.Will slow
backtesting down by a considerable amount, but will
include configured protections
--dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
Starting balance, used for backtesting / hyperopt and
dry-runs.
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
Provide a space-separated list of strategies to
backtest. Please note that ticker-interval needs to be
set either in config or via command line. When using
this together with `--export trades`, the strategy-
name is injected into the filename (so `backtest-
data.json` becomes `backtest-data-
DefaultStrategy.json`
--export EXPORT Export backtest results, argument are: trades.
Example: `--export=trades`
--export-filename PATH
Save backtest results to the file with this filename.
Requires `--export` to be set as well. Example:
`--export-filename=user_data/backtest_results/backtest
_today.json`
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
Strategy arguments:
-s NAME, --strategy NAME
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
## Test your strategy with Backtesting
Now you have good Buy and Sell strategies and some historic data, you want to test it against
real data. This is what we call
[backtesting](https://en.wikipedia.org/wiki/Backtesting).
real data. This is what we call [backtesting](https://en.wikipedia.org/wiki/Backtesting).
Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
If no data is available for the exchange / pair / timeframe combination, backtesting will ask you to download them first using `freqtrade download-data`.
@ -17,45 +103,65 @@ For details on downloading, please refer to the [Data Downloading](data-download
The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
All profit calculations include fees, and freqtrade will use the exchange's default fees for the calculation.
!!! Warning "Using dynamic pairlists for backtesting"
Using dynamic pairlists is possible, however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
Also, when using pairlists other than StaticPairlist, reproducability of backtesting-results cannot be guaranteed.
Please read the [pairlists documentation](configuration.md#pairlists) for more information.
Please read the [pairlists documentation](plugins.md#pairlists) for more information.
To achieve reproducible results, best generate a pairlist via the [`test-pairlist`](utils.md#test-pairlist) command and use that as static pairlist.
### Run a backtesting against the currencies listed in your config file
### Starting balance
#### With 5 min candle (OHLCV) data (per default)
Backtesting will require a starting balance, which can be provided as `--dry-run-wallet <balance>` or `--starting-balance <balance>` command line argument, or via `dry_run_wallet` configuration setting.
This amount must be higher than `stake_amount`, otherwise the bot will not be able to simulate any trade.
### Dynamic stake amount
Backtesting supports [dynamic stake amount](configuration.md#dynamic-stake-amount) by configuring `stake_amount` as `"unlimited"`, which will split the starting balance into `max_open_trades` pieces.
Profits from early trades will result in subsequent higher stake amounts, resulting in compounding of profits over the backtesting period.
### Example backtesting commands
With 5 min candle (OHLCV) data (per default)
```bash
freqtrade backtesting
freqtrade backtesting --strategy AwesomeStrategy
```
#### With 1 min candle (OHLCV) data
Where `--strategy AwesomeStrategy` / `-s AwesomeStrategy` refers to the class name of the strategy, which is within a python file in the `user_data/strategies` directory.
---
With 1 min candle (OHLCV) data
```bash
freqtrade backtesting --timeframe 1m
freqtrade backtesting --strategy AwesomeStrategy --timeframe 1m
```
#### Using a different on-disk historical candle (OHLCV) data source
---
Providing a custom starting balance of 1000 (in stake currency)
```bash
freqtrade backtesting --strategy AwesomeStrategy --dry-run-wallet 1000
```
---
Using a different on-disk historical candle (OHLCV) data source
Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory.
You can then use this data for backtesting as follows:
```bash
freqtrade --datadir user_data/data/bittrex-20180101 backtesting
freqtrade backtesting --strategy AwesomeStrategy --datadir user_data/data/bittrex-20180101
```
#### With a (custom) strategy file
---
```bash
freqtrade backtesting -s SampleStrategy
```
Where `-s SampleStrategy` refers to the class name within the strategy file `sample_strategy.py` found in the `freqtrade/user_data/strategies` directory.
#### Comparing multiple Strategies
Comparing multiple Strategies
```bash
freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timeframe 5m
@ -63,23 +169,29 @@ freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timefram
Where `SampleStrategy1` and `AwesomeStrategy` refer to class names of strategies.
#### Exporting trades to file
---
Exporting trades to file
```bash
freqtrade backtesting --export trades --config config.json --strategy SampleStrategy
freqtrade backtesting --strategy backtesting --export trades --config config.json
```
The exported trades can be used for [further analysis](#further-backtest-result-analysis), or can be used by the plotting script `plot_dataframe.py` in the scripts directory.
#### Exporting trades to file specifying a custom filename
---
Exporting trades to file specifying a custom filename
```bash
freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json
freqtrade backtesting --strategy backtesting --export trades --export-filename=backtest_samplestrategy.json
```
Please also read about the [strategy startup period](strategy-customization.md#strategy-startup-period).
#### Supplying custom fee value
---
Supplying custom fee value
Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.
To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
@ -94,26 +206,26 @@ freqtrade backtesting --fee 0.001
!!! Note
Only supply this option (or the corresponding configuration parameter) if you want to experiment with different fee values. By default, Backtesting fetches the default fee from the exchange pair/market info.
#### Running backtest with smaller testset by using timerange
---
Use the `--timerange` argument to change how much of the testset you want to use.
Running backtest with smaller test-set by using timerange
Use the `--timerange` argument to change how much of the test-set you want to use.
For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your inputdata.
For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your input data.
```bash
freqtrade backtesting --timerange=20190501-
```
You can also specify particular dates or a range span indexed by start and stop.
You can also specify particular date ranges.
The full timerange specification:
- Use tickframes till 2018/01/31: `--timerange=-20180131`
- Use tickframes since 2018/01/31: `--timerange=20180131-`
- Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
- Use tickframes between POSIX timestamps 1527595200 1527618600:
`--timerange=1527595200-1527618600`
- Use data until 2018/01/31: `--timerange=-20180131`
- Use data since 2018/01/31: `--timerange=20180131-`
- Use data since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
- Use data between POSIX / epoch timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`
## Understand the backtesting result
@ -165,19 +277,30 @@ A backtesting result will look like that:
| Max open trades | 3 |
| | |
| Total trades | 429 |
| Total Profit % | 152.41% |
| Starting balance | 0.01000000 BTC |
| Final balance | 0.01762792 BTC |
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| Trades per day | 3.575 |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
| Best Pair | LSK/BTC 26.26% |
| Worst Pair | ZEC/BTC -10.18% |
| Best Trade | LSK/BTC 4.25% |
| Worst Trade | ZEC/BTC -10.25% |
| Best day | 25.27% |
| Worst day | -30.67% |
| Best day | 0.00076 BTC |
| Worst day | -0.00036 BTC |
| Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| | |
| Max Drawdown | 50.63% |
| Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC |
| Drawdown | 50.63% |
| Drawdown | 0.0015 BTC |
| Drawdown high | 0.0013 BTC |
| Drawdown low | -0.0002 BTC |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
@ -198,9 +321,9 @@ here:
The bot has made `429` trades for an average duration of `4:12:00`, with a performance of `76.20%` (profit), that means it has
earned a total of `0.00762792 BTC` starting with a capital of 0.01 BTC.
The column `avg profit %` shows the average profit for all trades made while the column `cum profit %` sums up all the profits/losses.
The column `tot profit %` shows instead the total profit % in relation to allocated capital (`max_open_trades * stake_amount`).
In the above results we have `max_open_trades=2` and `stake_amount=0.005` in config so `tot_profit %` will be `(76.20/100) * (0.005 * 2) =~ 0.00762792 BTC`.
The column `Avg Profit %` shows the average profit for all trades made while the column `Cum Profit %` sums up all the profits/losses.
The column `Tot Profit %` shows instead the total profit % in relation to the starting balance.
In the above results, we have a starting balance of 0.01 BTC and an absolute profit of 0.00762792 BTC - so the `Tot Profit %` will be `(0.00762792 / 0.01) * 100 ~= 76.2%`.
Your strategy performance is influenced by your buy strategy, your sell strategy, and also by the `minimal_roi` and `stop_loss` you have set.
@ -241,19 +364,30 @@ It contains some useful key metrics about performance of your strategy on backte
| Max open trades | 3 |
| | |
| Total trades | 429 |
| Total Profit % | 152.41% |
| Starting balance | 0.01000000 BTC |
| Final balance | 0.01762792 BTC |
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| Trades per day | 3.575 |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
| Best Pair | LSK/BTC 26.26% |
| Worst Pair | ZEC/BTC -10.18% |
| Best Trade | LSK/BTC 4.25% |
| Worst Trade | ZEC/BTC -10.25% |
| Best day | 25.27% |
| Worst day | -30.67% |
| Best day | 0.00076 BTC |
| Worst day | -0.00036 BTC |
| Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| | |
| Max Drawdown | 50.63% |
| Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC |
| Drawdown | 50.63% |
| Drawdown | 0.0015 BTC |
| Drawdown high | 0.0013 BTC |
| Drawdown low | -0.0002 BTC |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
@ -262,15 +396,23 @@ It contains some useful key metrics about performance of your strategy on backte
```
- `Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
- `Max open trades`: Setting of `max_open_trades` (or `--max-open-trades`) - to clearly see settings for this.
- `Max open trades`: Setting of `max_open_trades` (or `--max-open-trades`) - or number of pairs in the pairlist (whatever is lower).
- `Total trades`: Identical to the total trades of the backtest output table.
- `Total Profit %`: Total profit per stake amount. Aligned to the TOTAL column of the first table.
- `Starting balance`: Start balance - as given by dry-run-wallet (config or command line).
- `Final balance`: Final balance - starting balance + absolute profit.
- `Absolute profit`: Profit made in stake currency.
- `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
- `Trades per day`: Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
- `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
- `Total trade volume`: Volume generated on the exchange to reach the above profit.
- `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Cum Profit %`.
- `Best Trade` / `Worst Trade`: Biggest winning trade and biggest losing trade
- `Best Trade` / `Worst Trade`: Biggest single winning trade and biggest single losing trade.
- `Best day` / `Worst day`: Best and worst day based on daily profit.
- `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
- `Max Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
- `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
- `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
- `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
- `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
- `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
@ -335,6 +477,5 @@ Detailed output for all strategies one after the other will be available, so mak
## Next step
Great, your strategy is profitable. What if the bot can give your the
optimal parameters to use for your strategy?
Great, your strategy is profitable. What if the bot can give your the optimal parameters to use for your strategy?
Your next step is to learn [how to find optimal parameters with Hyperopt](hyperopt.md)

20
docs/bot-basics.md
View File

@ -4,13 +4,14 @@ This page provides you some basic concepts on how Freqtrade works and operates.
## Freqtrade terminology
* Trade: Open position.
* Open Order: Order which is currently placed on the exchange, and is not yet complete.
* Pair: Tradable pair, usually in the format of Quote/Base (e.g. XRP/USDT).
* Timeframe: Candle length to use (e.g. `"5m"`, `"1h"`, ...).
* Indicators: Technical indicators (SMA, EMA, RSI, ...).
* Limit order: Limit orders which execute at the defined limit price or better.
* Market order: Guaranteed to fill, may move price depending on the order size.
* **Strategy**: Your trading strategy, telling the bot what to do.
* **Trade**: Open position.
* **Open Order**: Order which is currently placed on the exchange, and is not yet complete.
* **Pair**: Tradable pair, usually in the format of Quote/Base (e.g. XRP/USDT).
* **Timeframe**: Candle length to use (e.g. `"5m"`, `"1h"`, ...).
* **Indicators**: Technical indicators (SMA, EMA, RSI, ...).
* **Limit order**: Limit orders which execute at the defined limit price or better.
* **Market order**: Guaranteed to fill, may move price depending on the order size.
## Fee handling
@ -49,8 +50,9 @@ This loop will be repeated again and again until the bot is stopped.
[backtesting](backtesting.md) or [hyperopt](hyperopt.md) do only part of the above logic, since most of the trading operations are fully simulated.
* Load historic data for configured pairlist.
* Calculate indicators (calls `populate_indicators()`).
* Calls `populate_buy_trend()` and `populate_sell_trend()`
* Calls `bot_loop_start()` once.
* Calculate indicators (calls `populate_indicators()` once per pair).
* Calculate buy / sell signals (calls `populate_buy_trend()` and `populate_sell_trend()` once per pair)
* Loops per candle simulating entry and exit points.
* Generate backtest report output

256
docs/bot-usage.md
View File

@ -56,6 +56,7 @@ optional arguments:
usage: freqtrade trade [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[--db-url PATH] [--sd-notify] [--dry-run]
[--dry-run-wallet DRY_RUN_WALLET]
optional arguments:
-h, --help show this help message and exit
@ -66,6 +67,9 @@ optional arguments:
--sd-notify Notify systemd service manager.
--dry-run Enforce dry-run for trading (removes Exchange secrets
and simulates trades).
--dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
Starting balance, used for backtesting / hyperopt and
dry-runs.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
@ -205,258 +209,6 @@ in production mode. Example command:
freqtrade trade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
```
## Backtesting commands
Backtesting also uses the config specified via `-c/--config`.
```
usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-i TIMEFRAME]
[--timerange TIMERANGE]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[--eps] [--dmmp] [--enable-protections]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
[--export EXPORT] [--export-filename PATH]
optional arguments:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--data-format-ohlcv {json,jsongz,hdf5}
Storage format for downloaded candle (OHLCV) data.
(default: `None`).
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--stake-amount STAKE_AMOUNT
Override the value of the `stake_amount` configuration
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
number).
--enable-protections, --enableprotections
Enable protections for backtesting.Will slow
backtesting down by a considerable amount, but will
include configured protections
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
Provide a space-separated list of strategies to
backtest. Please note that ticker-interval needs to be
set either in config or via command line. When using
this together with `--export trades`, the strategy-
name is injected into the filename (so `backtest-
data.json` becomes `backtest-data-
DefaultStrategy.json`
--export EXPORT Export backtest results, argument are: trades.
Example: `--export=trades`
--export-filename PATH
Save backtest results to the file with this filename.
Requires `--export` to be set as well. Example:
`--export-filename=user_data/backtest_results/backtest
_today.json`
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
Strategy arguments:
-s NAME, --strategy NAME
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
### Getting historic data for backtesting
The first time your run Backtesting, you will need to download some historic data first.
This can be accomplished by using `freqtrade download-data`.
Check the corresponding [Data Downloading](data-download.md) section for more details
## Hyperopt commands
To optimize your strategy, you can use hyperopt parameter hyperoptimization
to find optimal parameter values for your strategy.
```
usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[-i TIMEFRAME] [--timerange TIMERANGE]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[--hyperopt NAME] [--hyperopt-path PATH] [--eps]
[--dmmp] [--enable-protections] [-e INT]
[--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]]
[--print-all] [--no-color] [--print-json] [-j JOBS]
[--random-state INT] [--min-trades INT]
[--hyperopt-loss NAME]
optional arguments:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--data-format-ohlcv {json,jsongz,hdf5}
Storage format for downloaded candle (OHLCV) data.
(default: `None`).
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--stake-amount STAKE_AMOUNT
Override the value of the `stake_amount` configuration
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
--hyperopt NAME Specify hyperopt class name which will be used by the
bot.
--hyperopt-path PATH Specify additional lookup path for Hyperopt and
Hyperopt Loss functions.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
number).
--enable-protections, --enableprotections
Enable protections for backtesting.Will slow
backtesting down by a considerable amount, but will
include configured protections
-e INT, --epochs INT Specify number of epochs (default: 100).
--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]
Specify which parameters to hyperopt. Space-separated
list.
--print-all Print all results, not only the best ones.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--print-json Print output in JSON format.
-j JOBS, --job-workers JOBS
The number of concurrently running jobs for
hyperoptimization (hyperopt worker processes). If -1
(default), all CPUs are used, for -2, all CPUs but one
are used, etc. If 1 is given, no parallel computing
code is used at all.
--random-state INT Set random state to some positive integer for
reproducible hyperopt results.
--min-trades INT Set minimal desired number of trades for evaluations
in the hyperopt optimization path (default: 1).
--hyperopt-loss NAME Specify the class name of the hyperopt loss function
class (IHyperOptLoss). Different functions can
generate completely different results, since the
target for optimization is different. Built-in
Hyperopt-loss-functions are:
ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
SharpeHyperOptLoss, SharpeHyperOptLossDaily,
SortinoHyperOptLoss, SortinoHyperOptLossDaily
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
Strategy arguments:
-s NAME, --strategy NAME
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
## Edge commands
To know your trade expectancy and winrate against historical data, you can use Edge.
```
usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[-i TIMEFRAME] [--timerange TIMERANGE]
[--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
[--fee FLOAT] [--stoplosses STOPLOSS_RANGE]
optional arguments:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--stake-amount STAKE_AMOUNT
Override the value of the `stake_amount` configuration
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
--stoplosses STOPLOSS_RANGE
Defines a range of stoploss values against which edge
will assess the strategy. The format is "min,max,step"
(without any space). Example:
`--stoplosses=-0.01,-0.1,-0.001`
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
Strategy arguments:
-s NAME, --strategy NAME
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
To understand edge and how to read the results, please read the [edge documentation](edge.md).
## Next step
The optimal strategy of the bot will change with time depending of the market trends. The next step is to

295
docs/configuration.md
View File

@ -16,8 +16,7 @@ In some advanced use cases, multiple configuration files can be specified and us
If you used the [Quick start](installation.md/#quick-start) method for installing
the bot, the installation script should have already created the default configuration file (`config.json`) for you.
If default configuration file is not created we recommend you to copy and use the `config.json.example` as a template
for your bot configuration.
If default configuration file is not created we recommend you to use `freqtrade new-config --config config.json` to generate a basic configuration file.
The Freqtrade configuration file is to be written in the JSON format.
@ -41,8 +40,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| Parameter | Description |
|------------|-------------|
| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation which can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
| `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Positive float or `"unlimited"`.
| `stake_currency` | **Required.** Crypto-currency used for trading. <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). <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)
@ -50,7 +49,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `timeframe` | The timeframe (former ticker interval) to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in the Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
| `cancel_open_orders_on_exit` | Cancel open orders when the `/stop` RPC command is issued, `Ctrl+C` is pressed or the bot dies unexpectedly. When set to `true`, this allows you to use `/stop` to cancel unfilled and partially filled orders in the event of a market crash. It does not impact open positions. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `minimal_roi` | **Required.** Set the threshold as ratio the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
@ -59,21 +58,25 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-custom-positive-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `fee` | Fee used during backtesting / dry-runs. Should normally not be configured, which has freqtrade fall back to the exchange default fee. Set as ratio (e.g. 0.001 = 0.1%). Fee is applied twice for each trade, once when buying, once when selling. <br> **Datatype:** Float (as ratio)
| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook-enabled).
| `bid_strategy.ask_last_balance` | **Required.** Interpolate the bidding price. More information [below](#buy-price-without-orderbook-enabled).
| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.* <br> **Datatype:** Float (as ratio)
| `ask_strategy.price_side` | Select the side of the spread the bot should look at to get the sell rate. [More information below](#sell-price-side).<br> *Defaults to `ask`.* <br> **Datatype:** String (either `ask` or `bid`).
| `ask_strategy.bid_last_balance` | Interpolate the selling price. More information [below](#sell-price-without-orderbook-enabled).
| `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.sell_profit_only` | Wait until the bot reaches `ask_strategy.sell_profit_offset` before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `ask_strategy.sell_profit_offset` | Sell-signal is only active above this value. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0`.* <br> **Datatype:** Float (as ratio)
| `ask_strategy.ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `ask_strategy.ignore_buying_expired_candle_after` | Specifies the number of seconds until a buy signal is no longer used. <br> **Datatype:** Integer
| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
@ -81,20 +84,22 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#pairlists-and-pairlist-handlers)). <br> **Datatype:** List
| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#pairlists-and-pairlist-handlers)). <br> **Datatype:** List
| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Supports regex pairs as `.*/BTC`. Not used by VolumePairList. [More information](plugins.md#pairlists-and-pairlist-handlers). <br> **Datatype:** List
| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting. [More information](plugins.md#pairlists-and-pairlist-handlers). <br> **Datatype:** List
| `exchange.ccxt_config` | Additional CCXT parameters passed to both ccxt instances (sync and async). This is usually the correct place for ccxt configurations. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
| `exchange.ccxt_sync_config` | Additional CCXT parameters passed to the regular (sync) ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
| `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
| `exchange.skip_pair_validation` | Skip pairlist validation on startup.<br>*Defaults to `false`<br> **Datatype:** Boolean
| `exchange.skip_open_order_update` | Skips open order updates on startup should the exchange cause problems. Only relevant in live conditions.<br>*Defaults to `false`<br> **Datatype:** Boolean
| `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `pairlists` | Define one or more pairlists to be used. [More information below](#pairlists-and-pairlist-handlers). <br>*Defaults to `StaticPairList`.* <br> **Datatype:** List of Dicts
| `protections` | Define one or more protections to be used. [More information below](#protections). <br> **Datatype:** List of Dicts
| `pairlists` | Define one or more pairlists to be used. [More information](plugins.md#pairlists-and-pairlist-handlers). <br>*Defaults to `StaticPairList`.* <br> **Datatype:** List of Dicts
| `protections` | Define one or more protections to be used. [More information](plugins.md#protections). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** List of Dicts
| `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `telegram.balance_dust_level` | Dust-level (in stake currency) - currencies with a balance below this will not be shown by `/balance`. <br> **Datatype:** float
| `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
@ -108,13 +113,14 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `api_server.verbosity` | Logging verbosity. `info` will print all RPC Calls, while "error" will only display errors. <br>**Datatype:** Enum, either `info` or `error`. Defaults to `info`.
| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
| `bot_name` | Name of the bot. Passed via API to a client - can be shown to distinguish / name bots.<br> *Defaults to `freqtrade`*<br> **Datatype:** String
| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
| `initial_state` | Defines the initial application state. More information below. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
| `initial_state` | Defines the initial application state. If set to stopped, then the bot has to be explicitly started via `/start` RPC command. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> **Datatype:** Boolean
| `disable_dataframe_checks` | Disable checking the OHLCV dataframe returned from the strategy methods for correctness. Only use when intentionally changing the dataframe and understand what you are doing. [Strategy Override](#parameters-in-the-strategy).<br> *Defaults to `False`*. <br> **Datatype:** Boolean
| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
| `internals.process_throttle_secs` | Set the process throttle, or minimum loop duration for one bot iteration loop. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
@ -134,21 +140,40 @@ Values set in the configuration file always overwrite values set in the strategy
* `trailing_stop_positive`
* `trailing_stop_positive_offset`
* `trailing_only_offset_is_reached`
* `use_custom_stoploss`
* `process_only_new_candles`
* `order_types`
* `order_time_in_force`
* `stake_currency`
* `stake_amount`
* `unfilledtimeout`
* `disable_dataframe_checks`
* `protections`
* `use_sell_signal` (ask_strategy)
* `sell_profit_only` (ask_strategy)
* `sell_profit_offset` (ask_strategy)
* `ignore_roi_if_buy_signal` (ask_strategy)
* `ignore_buying_expired_candle_after` (ask_strategy)
### Configuring amount per trade
There are several methods to configure how much of the stake currency the bot will use to enter a trade. All methods respect the [available balance configuration](#available-balance) as explained below.
#### Minimum trade stake
The minimum stake amount will depend by exchange and pair, and is usually listed in the exchange support pages.
Assuming the minimum tradable amount for XRP/USD is 20 XRP (given by the exchange), and the price is 0.4$.
The minimum stake amount to buy this pair is therefore `20 * 0.6 ~= 12`.
This exchange has also a limit on USD - where all orders must be > 10$ - which however does not apply in this case.
To guarantee safe execution, freqtrade will not allow buying with a stake-amount of 10.1$, instead, it'll make sure that there's enough space to place a stoploss below the pair (+ an offset, defined by `amount_reserve_percent`, which defaults to 5%).
With a stoploss of 10% - we'd therefore end up with a value of ~13.8$ (`12 * (1 + 0.05 + 0.1)`).
To limit this calculation in case of large stoploss values, the calculated minimum stake-limit will never be more than 50% above the real limit.
!!! Warning
Since the limits on exchanges are usually stable and are not updated often, some pairs can show pretty high minimum limits, simply because the price increased a lot since the last limit adjustment by the exchange.
#### Available balance
By default, the bot assumes that the `complete amount - 1%` is at it's disposal, and when using [dynamic stake amount](#dynamic-stake-amount), it will split the complete balance into `max_open_trades` buckets per trade.
@ -211,11 +236,14 @@ To allow the bot to trade all the available `stake_currency` in your account (mi
"tradable_balance_ratio": 0.99,
```
!!! Note
This configuration will allow increasing / decreasing stakes depending on the performance of the bot (lower stake if bot is loosing, higher stakes if the bot has a winning record, since higher balances are available).
!!! Tip "Compounding profits"
This configuration will allow increasing / decreasing stakes depending on the performance of the bot (lower stake if bot is loosing, higher stakes if the bot has a winning record, since higher balances are available), and will result in profit compounding.
!!! Note "When using Dry-Run Mode"
When using `"stake_amount" : "unlimited",` in combination with Dry-Run, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve over time. It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
When using `"stake_amount" : "unlimited",` in combination with Dry-Run, Backtesting or Hyperopt, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve over time.
It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
--8<-- "includes/pricing.md"
### Understand minimal_roi
@ -240,41 +268,35 @@ If it is not set in either Strategy or Configuration, a default of 1000% `{"0":
!!! Note "Special case to forcesell after a specific time"
A special case presents using `"<N>": -1` as ROI. This forces the bot to sell a trade after N Minutes, no matter if it's positive or negative, so represents a time-limited force-sell.
### Understand stoploss
Go to the [stoploss documentation](stoploss.md) for more details.
### Understand trailing stoploss
Go to the [trailing stoploss Documentation](stoploss.md#trailing-stop-loss) for details on trailing stoploss.
### Understand initial_state
The `initial_state` configuration parameter is an optional field that defines the initial application state.
Possible values are `running` or `stopped`. (default=`running`)
If the value is `stopped` the bot has to be started with `/start` first.
### Understand forcebuy_enable
The `forcebuy_enable` configuration parameter enables the usage of forcebuy commands via Telegram.
This is disabled for security reasons by default, and will show a warning message on startup if enabled.
For example, you can send `/forcebuy ETH/BTC` Telegram command when this feature if enabled to the bot,
who then buys the pair and holds it until a regular sell-signal (ROI, stoploss, /forcesell) appears.
The `forcebuy_enable` configuration parameter enables the usage of forcebuy commands via Telegram and REST API.
For security reasons, it's disabled by default, and freqtrade will show a warning message on startup if enabled.
For example, you can send `/forcebuy ETH/BTC` to the bot, which will result in freqtrade buying the pair and holds it until a regular sell-signal (ROI, stoploss, /forcesell) appears.
This can be dangerous with some strategies, so use with care.
See [the telegram documentation](telegram-usage.md) for details on usage.
### Understand process_throttle_secs
### Ignoring expired candles
The `process_throttle_secs` configuration parameter is an optional field that defines in seconds how long the bot should wait
before asking the strategy if we should buy or a sell an asset. After each wait period, the strategy is asked again for
every opened trade wether or not we should sell, and for all the remaining pairs (either the dynamic list of pairs or
the static list of pairs) if we should buy.
When working with larger timeframes (for example 1h or more) and using a low `max_open_trades` value, the last candle can be processed as soon as a trade slot becomes available. When processing the last candle, this can lead to a situation where it may not be desirable to use the buy signal on that candle. For example, when using a condition in your strategy where you use a cross-over, that point may have passed too long ago for you to start a trade on it.
In these situations, you can enable the functionality to ignore candles that are beyond a specified period by setting `ask_strategy.ignore_buying_expired_candle_after` to a positive number, indicating the number of seconds after which the buy signal becomes expired.
For example, if your strategy is using a 1h timeframe, and you only want to buy within the first 5 minutes when a new candle comes in, you can add the following configuration to your strategy:
``` json
"ask_strategy":{
"ignore_buying_expired_candle_after": 300,
"price_side": "bid",
// ...
},
```
### Understand order_types
The `order_types` configuration parameter maps actions (`buy`, `sell`, `stoploss`, `emergencysell`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
The `order_types` configuration parameter maps actions (`buy`, `sell`, `stoploss`, `emergencysell`, `forcesell`, `forcebuy`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
This allows to buy using limit orders, sell using
limit-orders, and create stoplosses using market orders. It also allows to set the
@ -286,7 +308,7 @@ the buy order is fulfilled.
If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
`stoploss_on_exchange`) need to be present, otherwise the bot will fail to start.
For information on (`emergencysell`,`stoploss_on_exchange`,`stoploss_on_exchange_interval`,`stoploss_on_exchange_limit_ratio`) please see stop loss documentation [stop loss on exchange](stoploss.md)
For information on (`emergencysell`,`forcesell`, `forcebuy`, `stoploss_on_exchange`,`stoploss_on_exchange_interval`,`stoploss_on_exchange_limit_ratio`) please see stop loss documentation [stop loss on exchange](stoploss.md)
Syntax for Strategy:
@ -295,6 +317,8 @@ order_types = {
"buy": "limit",
"sell": "limit",
"emergencysell": "market",
"forcebuy": "market",
"forcesell": "market",
"stoploss": "market",
"stoploss_on_exchange": False,
"stoploss_on_exchange_interval": 60,
@ -309,6 +333,8 @@ Configuration:
"buy": "limit",
"sell": "limit",
"emergencysell": "market",
"forcebuy": "market",
"forcesell": "market",
"stoploss": "market",
"stoploss_on_exchange": false,
"stoploss_on_exchange_interval": 60
@ -409,26 +435,6 @@ This configuration enables binance, as well as rate limiting to avoid bans from
Optimal settings for rate limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that `"enableRateLimit"` is enabled and increase the `"rateLimit"` parameter step by step.
#### Advanced Freqtrade Exchange configuration
Advanced options can be configured using the `_ft_has_params` setting, which will override Defaults and exchange-specific behaviours.
Available options are listed in the exchange-class as `_ft_has_default`.
For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
```json
"exchange": {
"name": "kraken",
"_ft_has_params": {
"order_time_in_force": ["gtc", "fok"],
"ohlcv_candle_limit": 200
}
```
!!! Warning
Please make sure to fully understand the impacts of these settings before modifying them.
### What values can be used for fiat_display_currency?
The `fiat_display_currency` configuration parameter sets the base currency to use for the
@ -448,137 +454,7 @@ The valid values are:
"BTC", "ETH", "XRP", "LTC", "BCH", "USDT"
```
## Prices used for orders
Prices for regular orders can be controlled via the parameter structures `bid_strategy` for buying and `ask_strategy` for selling.
Prices are always retrieved right before an order is placed, either by querying the exchange tickers or by using the orderbook data.
!!! Note
Orderbook data used by Freqtrade are the data retrieved from exchange by the ccxt's function `fetch_order_book()`, i.e. are usually data from the L2-aggregated orderbook, while the ticker data are the structures returned by the ccxt's `fetch_ticker()`/`fetch_tickers()` functions. Refer to the ccxt library [documentation](https://github.com/ccxt/ccxt/wiki/Manual#market-data) for more details.
!!! Warning "Using market orders"
Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
### Buy price
#### Check depth of market
When check depth of market is enabled (`bid_strategy.check_depth_of_market.enabled=True`), the buy signals are filtered based on the orderbook depth (sum of all amounts) for each orderbook side.
Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) side depth and the resulting delta is compared to the value of the `bid_strategy.check_depth_of_market.bids_to_ask_delta` parameter. The buy order is only executed if the orderbook delta is greater than or equal to the configured delta value.
!!! Note
A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
#### Buy price side
The configuration setting `bid_strategy.price_side` defines the side of the spread the bot looks for when buying.
The following displays an orderbook.
``` explanation
...
103
102
101 # ask
-------------Current spread
99 # bid
98
97
...
```
If `bid_strategy.price_side` is set to `"bid"`, then the bot will use 99 as buying price.
In line with that, if `bid_strategy.price_side` is set to `"ask"`, then the bot will use 101 as buying price.
Using `ask` price often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
Taker fees instead of maker fees will most likely apply even when using limit buy orders.
Also, prices at the "ask" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
#### Buy price with Orderbook enabled
When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the configured side (`bid_strategy.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
#### Buy price without Orderbook enabled
The following section uses `side` as the configured `bid_strategy.price_side`.
When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
### Sell price
#### Sell price side
The configuration setting `ask_strategy.price_side` defines the side of the spread the bot looks for when selling.
The following displays an orderbook:
``` explanation
...
103
102
101 # ask
-------------Current spread
99 # bid
98
97
...
```
If `ask_strategy.price_side` is set to `"ask"`, then the bot will use 101 as selling price.
In line with that, if `ask_strategy.price_side` is set to `"bid"`, then the bot will use 99 as selling price.
#### Sell price with Orderbook enabled
When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the configured orderbook side are validated for a profitable sell-possibility based on the strategy configuration (`minimal_roi` conditions) and the sell order is placed at the first profitable spot.
!!! Note
Using `order_book_max` higher than `order_book_min` only makes sense when ask_strategy.price_side is set to `"ask"`.
The idea here is to place the sell order early, to be ahead in the queue.
A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting `ask_strategy.order_book_min` and `ask_strategy.order_book_max` to the same number.
!!! Warning "Order_book_max > 1 - increased risks for stoplosses!"
Using `ask_strategy.order_book_max` higher than 1 will increase the risk the stoploss on exchange is cancelled too early, since an eventual [stoploss on exchange](#understand-order_types) will be cancelled as soon as the order is placed.
Also, the sell order will remain on the exchange for `unfilledtimeout.sell` (or until it's filled) - which can lead to missed stoplosses (with or without using stoploss on exchange).
!!! Warning "Order_book_max > 1 in dry-run"
Using `ask_strategy.order_book_max` higher than 1 will result in improper dry-run results (significantly better than real orders executed on exchange), since dry-run assumes orders to be filled almost instantly.
It is therefore advised to not use this setting for dry-runs.
#### Sell price without Orderbook enabled
When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
### Market order pricing
When using market orders, prices should be configured to use the "correct" side of the orderbook to allow realistic pricing detection.
Assuming both buy and sell are using market orders, a configuration similar to the following might be used
``` jsonc
"order_types": {
"buy": "market",
"sell": "market"
// ...
},
"bid_strategy": {
"price_side": "ask",
// ...
},
"ask_strategy":{
"price_side": "bid",
// ...
},
```
Obviously, if only one side is using limit orders, different pricing combinations can be used.
--8<-- "includes/pairlists.md"
--8<-- "includes/protections.md"
## Switch to Dry-run mode
## Using Dry-run mode
We recommend starting the bot in the Dry-run mode to see how your bot will
behave and what is the performance of your strategy. In the Dry-run mode the
@ -611,9 +487,10 @@ Once you will be happy with your bot performance running in the Dry-run mode, yo
### Considerations for dry-run
* API-keys may or may not be provided. Only Read-Only operations (i.e. operations that do not alter account state) on the exchange are performed in the dry-run mode.
* Wallets (`/balance`) are simulated.
* API-keys may or may not be provided. Only Read-Only operations (i.e. operations that do not alter account state) on the exchange are performed in dry-run mode.
* Wallets (`/balance`) are simulated based on `dry_run_wallet`.
* Orders are simulated, and will not be posted to the exchange.
* Orders are assumed to fill immediately, and will never time out.
* In combination with `stoploss_on_exchange`, the stop_loss price is assumed to be filled.
* Open orders (not trades, which are stored in the database) are reset on bot restart.
@ -671,32 +548,6 @@ export HTTPS_PROXY="http://addr:port"
freqtrade
```
## Embedding Strategies
Freqtrade provides you with with an easy way to embed the strategy into your configuration file.
This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
in your chosen config file.
### Encoding a string as BASE64
This is a quick example, how to generate the BASE64 string in python
```python
from base64 import urlsafe_b64encode
with open(file, 'r') as f:
content = f.read()
content = urlsafe_b64encode(content.encode('utf-8'))
```
The variable 'content', will contain the strategy file in a BASE64 encoded form. Which can now be set in your configurations file as following
```json
"strategy": "NameOfStrategy:BASE64String"
```
Please ensure that 'NameOfStrategy' is identical to the strategy name!
## Next step
Now you have configured your config.json, the next step is to [start your bot](bot-usage.md).

19
docs/data-download.md
View File

@ -264,7 +264,19 @@ If you are using Binance for example:
```bash
mkdir -p user_data/data/binance
cp freqtrade/tests/testdata/pairs.json user_data/data/binance
cp tests/testdata/pairs.json user_data/data/binance
```
If you your configuration directory `user_data` was made by docker, you may get the following error:
```
cp: cannot create regular file 'user_data/data/binance/pairs.json': Permission denied
```
You can fix the permissions of your user-data directory as follows:
```
sudo chown -R $UID:$GID user_data
```
The format of the `pairs.json` file is a simple json list.
@ -308,10 +320,13 @@ Since this data is large by default, the files use gzip by default. They are sto
To use this mode, simply add `--dl-trades` to your call. This will swap the download method to download trades, and resamples the data locally.
!!! Warning "do not use"
You should not use this unless you're a kraken user. Most other exchanges provide OHLCV data with sufficient history.
Example call:
```bash
freqtrade download-data --exchange binance --pairs XRP/ETH ETH/BTC --days 20 --dl-trades
freqtrade download-data --exchange kraken --pairs XRP/EUR ETH/EUR --days 20 --dl-trades
```
!!! Note

7
docs/developer.md
View File

@ -2,7 +2,7 @@
This page is intended for developers of Freqtrade, people who want to contribute to the Freqtrade codebase or documentation, or people who want to understand the source code of the application they're running.
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel on [discord](https://discord.gg/MA9v74M) or [slack](https://join.slack.com/t/highfrequencybot/shared_invite/zt-jaut7r4m-Y17k4x5mcQES9a9swKuxbg) where you can ask questions.
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel on [discord](https://discord.gg/MA9v74M) or [slack](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw) where you can ask questions.
## Documentation
@ -177,7 +177,7 @@ In `VolumePairList`, this implements different methods of sorting, does early va
### Protections
Best read the [Protection documentation](configuration.md#protections) to understand protections.
Best read the [Protection documentation](plugins.md#protections) to understand protections.
This Guide is directed towards Developers who want to develop a new protection.
No protection should use datetime directly, but use the provided `date_now` variable for date calculations. This preserves the ability to backtest protections.
@ -242,6 +242,9 @@ The `IProtection` parent class provides a helper method for this in `calculate_l
Most exchanges supported by CCXT should work out of the box.
To quickly test the public endpoints of an exchange, add a configuration for your exchange to `test_ccxt_compat.py` and run these tests with `pytest --longrun tests/exchange/test_ccxt_compat.py`.
Completing these tests successfully a good basis point (it's a requirement, actually), however these won't guarantee correct exchange functioning, as this only tests public endpoints, but no private endpoint (like generate order or similar).
### Stoploss On Exchange
Check if the new exchange supports Stoploss on Exchange orders through their API.

201
docs/docker.md
View File

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

41
docs/docker_quickstart.md
View File

@ -1,5 +1,7 @@
# Using Freqtrade with Docker
This page explains how to run the bot with Docker. It is not meant to work out of the box. You'll still need to read through the documentation and understand how to properly configure it.
## Install Docker
Start by downloading and installing Docker CE for your platform:
@ -8,9 +10,7 @@ Start by downloading and installing Docker CE for your platform:
* [Windows](https://docs.docker.com/docker-for-windows/install/)
* [Linux](https://docs.docker.com/install/)
Optionally, [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
To simplify running freqtrade, please install [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the below [docker quick start guide](#docker-quick-start).
## Freqtrade with docker-compose
@ -71,19 +71,20 @@ The last 2 steps in the snippet create the directory with `user_data`, as well a
!!! Question "How to edit the bot configuration?"
You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
You can also change the both Strategy and commands by editing the `docker-compose.yml` file.
You can also change the both Strategy and commands by editing the command section of your `docker-compose.yml` file.
#### Adding a custom strategy
1. The configuration is now available as `user_data/config.json`
2. Copy a custom strategy to the directory `user_data/strategies/`
3. add the Strategy' class name to the `docker-compose.yml` file
3. Add the Strategy' class name to the `docker-compose.yml` file
The `SampleStrategy` is run by default.
!!! Warning "`SampleStrategy` is just a demo!"
The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
Please always backtest the strategy and use dry-run for some time before risking real money!
Please always backtest your strategy and use dry-run for some time before risking real money!
You will find more information about Strategy development in the [Strategy documentation](strategy-customization.md).
Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
@ -91,18 +92,26 @@ Once this is done, you're ready to launch the bot in trading mode (Dry-run or Li
docker-compose up -d
```
!!! Warning "Default configuration"
While the configuration generated will be mostly functional, you will still need to verify that all options correspond to what you want (like Pricing, pairlist, ...) before starting the bot.
#### Monitoring the bot
You can check for running instances with `docker-compose ps`.
This should list the service `freqtrade` as `running`. If that's not the case, best check the logs (see next point).
#### Docker-compose logs
Logs will be located at: `user_data/logs/freqtrade.log`.
You can check the latest log with the command `docker-compose logs -f`.
Logs will be written to: `user_data/logs/freqtrade.log`.
You can also check the latest log with the command `docker-compose logs -f`.
#### Database
The database will be at: `user_data/tradesv3.sqlite`
The database will be located at: `user_data/tradesv3.sqlite`
#### Updating freqtrade with docker-compose
To update freqtrade when using `docker-compose` is as simple as running the following 2 commands:
Updating freqtrade when using `docker-compose` is as simple as running the following 2 commands:
``` bash
# Download the latest image
@ -120,10 +129,10 @@ This will first pull the latest image, and will then restart the container with
Advanced users may edit the docker-compose file further to include all possible options or arguments.
All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
All freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
!!! Note "`docker-compose run --rm`"
Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
Including `--rm` will remove the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
#### Example: Download data with docker-compose
@ -172,19 +181,19 @@ docker-compose run --rm freqtrade plot-dataframe --strategy AwesomeStrategy -p B
The output will be stored in the `user_data/plot` directory, and can be opened with any modern browser.
## Data analayis using docker compose
## Data analysis using docker compose
Freqtrade provides a docker-compose file which starts up a jupyter lab server.
You can run this server using the following command:
``` bash
docker-compose --rm -f docker/docker-compose-jupyter.yml up
docker-compose -f docker/docker-compose-jupyter.yml up
```
This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
This will create a docker-container running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
Please use the link that's printed in the console after startup for simplified login.
Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) uptodate.
Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) up-to-date.
``` bash
docker-compose -f docker/docker-compose-jupyter.yml build --no-cache

61
docs/edge.md
View File

@ -1,6 +1,6 @@
# Edge positioning
The `Edge Positioning` module uses probability to calculate your win rate and risk reward ration. It will use these statistics to control your strategy trade entry points, position side and, stoploss.
The `Edge Positioning` module uses probability to calculate your win rate and risk reward ratio. It will use these statistics to control your strategy trade entry points, position size and, stoploss.
!!! Warning
`Edge positioning` is not compatible with dynamic (volume-based) whitelist.
@ -9,6 +9,7 @@ The `Edge Positioning` module uses probability to calculate your win rate and ri
`Edge Positioning` only considers *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
`Edge Positioning` improves the performance of some trading strategies and *decreases* the performance of others.
## Introduction
Trading strategies are not perfect. They are frameworks that are susceptible to the market and its indicators. Because the market is not at all predictable, sometimes a strategy will win and sometimes the same strategy will lose.
@ -55,7 +56,7 @@ Similarly, we can discover the set of losing trades $T_{lose}$ as follows:
$$ T_{lose} = \{o \in O | o \leq 0\} $$
!!! Example
In a section where a strategy made three transactions $O = \{3.5, -1, 15, 0\}$:<br>
In a section where a strategy made four transactions $O = \{3.5, -1, 15, 0\}$:<br>
$T_{win} = \{3.5, 15\}$<br>
$T_{lose} = \{-1, 0\}$<br>
@ -206,7 +207,61 @@ Let's say the stake currency is **ETH** and there is $10$ **ETH** on the wallet.
- The strategy detects a sell signal in the **XLM/ETH** market. The bot exits **Trade 1** for a profit of $1$ **ETH**. The total capital in the wallet becomes $11$ **ETH** and the available capital for trading becomes $5.5$ **ETH**.
- **Trade 4** The strategy detects a new buy signal int the **XLM/ETH** market. `Edge Positioning` calculates the stoploss of $2%$, and the position size of $0.055 / 0.02 = 2.75$ **ETH**.
- **Trade 4** The strategy detects a new buy signal int the **XLM/ETH** market. `Edge Positioning` calculates the stoploss of $2\%$, and the position size of $0.055 / 0.02 = 2.75$ **ETH**.
## Edge command reference
```
usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[-i TIMEFRAME] [--timerange TIMERANGE]
[--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
[--fee FLOAT] [--stoplosses STOPLOSS_RANGE]
optional arguments:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--stake-amount STAKE_AMOUNT
Override the value of the `stake_amount` configuration
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
--stoplosses STOPLOSS_RANGE
Defines a range of stoploss values against which edge
will assess the strategy. The format is "min,max,step"
(without any space). Example:
`--stoplosses=-0.01,-0.1,-0.001`
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
Strategy arguments:
-s NAME, --strategy NAME
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
## Configurations

27
docs/exchanges.md
View File

@ -40,6 +40,10 @@ Due to the heavy rate-limiting applied by Kraken, the following configuration se
},
```
!!! Warning "Downloading data from kraken"
Downloading kraken data will require significantly more memory (RAM) than any other exchange, as the trades-data needs to be converted into candles on your machine.
It will also take a long time, as freqtrade will need to download every single trade that happened on the exchange for the pair / timerange combination, therefore please be patient.
## Bittrex
### Order types
@ -92,9 +96,6 @@ To use subaccounts with FTX, you need to edit the configuration and add the foll
}
```
!!! Note
Older versions of freqtrade may require this key to be added to `"ccxt_async_config"` as well.
## All exchanges
Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
@ -117,3 +118,23 @@ Whether your exchange returns incomplete candles or not can be checked using [th
Due to the danger of repainting, Freqtrade does not allow you to use this incomplete candle.
However, if it is based on the need for the latest price for your strategy - then this requirement can be acquired using the [data provider](strategy-customization.md#possible-options-for-dataprovider) from within the strategy.
### Advanced Freqtrade Exchange configuration
Advanced options can be configured using the `_ft_has_params` setting, which will override Defaults and exchange-specific behavior.
Available options are listed in the exchange-class as `_ft_has_default`.
For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
```json
"exchange": {
"name": "kraken",
"_ft_has_params": {
"order_time_in_force": ["gtc", "fok"],
"ohlcv_candle_limit": 200
}
```
!!! Warning
Please make sure to fully understand the impacts of these settings before modifying them.

7
docs/faq.md
View File

@ -38,12 +38,11 @@ you can't say much from few trades.
### I’d like to make changes to the config. Can I do that without having to kill the bot?
Yes. You can edit your config, use the `/stop` command in Telegram, followed by `/reload_config` and the bot will run with the new config.
Yes. You can edit your config and use the `/reload_config` command to reload the configuration. The bot will stop, reload the configuration and strategy and will restart with the new configuration and strategy.
### I want to improve the bot with a new strategy
That's great. We have a nice backtesting and hyperoptimization setup. See
the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-commands).
That's great. We have a nice backtesting and hyperoptimization setup. See the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-commands).
### Is there a setting to only SELL the coins being held and not perform anymore BUYS?
@ -143,7 +142,7 @@ freqtrade hyperopt --hyperopt SampleHyperopt --hyperopt-loss SharpeHyperOptLossD
### Why does it take a long time to run hyperopt?
* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/zt-jaut7r4m-Y17k4x5mcQES9a9swKuxbg) - or the Freqtrade [discord community](https://discord.gg/X89cVG). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw) - or the Freqtrade [discord community](https://discord.gg/X89cVG). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
* If you wonder why it can take from 20 minutes to days to do 1000 epochs here are some answers:

107
docs/hyperopt.md
View File

@ -32,6 +32,111 @@ source .env/bin/activate
pip install -r requirements-hyperopt.txt
```
## Hyperopt command reference
```
usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[-i TIMEFRAME] [--timerange TIMERANGE]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[--hyperopt NAME] [--hyperopt-path PATH] [--eps]
[--dmmp] [--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET] [-e INT]
[--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]]
[--print-all] [--no-color] [--print-json] [-j JOBS]
[--random-state INT] [--min-trades INT]
[--hyperopt-loss NAME]
optional arguments:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--data-format-ohlcv {json,jsongz,hdf5}
Storage format for downloaded candle (OHLCV) data.
(default: `None`).
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--stake-amount STAKE_AMOUNT
Override the value of the `stake_amount` configuration
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
--hyperopt NAME Specify hyperopt class name which will be used by the
bot.
--hyperopt-path PATH Specify additional lookup path for Hyperopt and
Hyperopt Loss functions.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
number).
--enable-protections, --enableprotections
Enable protections for backtesting.Will slow
backtesting down by a considerable amount, but will
include configured protections
--dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
Starting balance, used for backtesting / hyperopt and
dry-runs.
-e INT, --epochs INT Specify number of epochs (default: 100).
--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]
Specify which parameters to hyperopt. Space-separated
list.
--print-all Print all results, not only the best ones.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--print-json Print output in JSON format.
-j JOBS, --job-workers JOBS
The number of concurrently running jobs for
hyperoptimization (hyperopt worker processes). If -1
(default), all CPUs are used, for -2, all CPUs but one
are used, etc. If 1 is given, no parallel computing
code is used at all.
--random-state INT Set random state to some positive integer for
reproducible hyperopt results.
--min-trades INT Set minimal desired number of trades for evaluations
in the hyperopt optimization path (default: 1).
--hyperopt-loss NAME Specify the class name of the hyperopt loss function
class (IHyperOptLoss). Different functions can
generate completely different results, since the
target for optimization is different. Built-in
Hyperopt-loss-functions are:
ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
SharpeHyperOptLoss, SharpeHyperOptLossDaily,
SortinoHyperOptLoss, SortinoHyperOptLossDaily
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
Strategy arguments:
-s NAME, --strategy NAME
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
## Prepare Hyperopting
Before we start digging into Hyperopt, we recommend you to take a look at
@ -178,7 +283,7 @@ So let's write the buy strategy using these values:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
if 'adx-enabled' in params and params['adx-enabled']:

BIN
docs/images/logo.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 12 KiB

After

Width:  |  Height:  |  Size: 11 KiB

10
docs/includes/pairlists.md
View File

@ -10,6 +10,14 @@ If multiple Pairlist Handlers are used, they are chained and a combination of al
Inactive markets are always removed from the resulting pairlist. Explicitly blacklisted pairs (those in the `pair_blacklist` configuration setting) are also always removed from the resulting pairlist.
### Pair blacklist
The pair blacklist (configured via `exchange.pair_blacklist` in the configuration) disallows certain pairs from trading.
This can be as simple as excluding `DOGE/BTC` - which will remove exactly this pair.
The pair-blacklist does also support wildcards (in regex-style) - so `BNB/.*` will exclude ALL pairs that start with BNB.
You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged tokens (check Pair naming conventions for your exchange!)
### Available Pairlist Handlers
* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
@ -27,7 +35,7 @@ Inactive markets are always removed from the resulting pairlist. Explicitly blac
#### Static Pair List
By default, the `StaticPairList` method is used, which uses a statically defined pair whitelist from the configuration.
By default, the `StaticPairList` method is used, which uses a statically defined pair whitelist from the configuration. The pairlist also supports wildcards (in regex-style) - so `.*/BTC` will include all pairs with BTC as a stake.
It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.

131
docs/includes/pricing.md Normal file
View File

@ -0,0 +1,131 @@
## Prices used for orders
Prices for regular orders can be controlled via the parameter structures `bid_strategy` for buying and `ask_strategy` for selling.
Prices are always retrieved right before an order is placed, either by querying the exchange tickers or by using the orderbook data.
!!! Note
Orderbook data used by Freqtrade are the data retrieved from exchange by the ccxt's function `fetch_order_book()`, i.e. are usually data from the L2-aggregated orderbook, while the ticker data are the structures returned by the ccxt's `fetch_ticker()`/`fetch_tickers()` functions. Refer to the ccxt library [documentation](https://github.com/ccxt/ccxt/wiki/Manual#market-data) for more details.
!!! Warning "Using market orders"
Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
### Buy price
#### Check depth of market
When check depth of market is enabled (`bid_strategy.check_depth_of_market.enabled=True`), the buy signals are filtered based on the orderbook depth (sum of all amounts) for each orderbook side.
Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) side depth and the resulting delta is compared to the value of the `bid_strategy.check_depth_of_market.bids_to_ask_delta` parameter. The buy order is only executed if the orderbook delta is greater than or equal to the configured delta value.
!!! Note
A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
#### Buy price side
The configuration setting `bid_strategy.price_side` defines the side of the spread the bot looks for when buying.
The following displays an orderbook.
``` explanation
...
103
102
101 # ask
-------------Current spread
99 # bid
98
97
...
```
If `bid_strategy.price_side` is set to `"bid"`, then the bot will use 99 as buying price.
In line with that, if `bid_strategy.price_side` is set to `"ask"`, then the bot will use 101 as buying price.
Using `ask` price often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
Taker fees instead of maker fees will most likely apply even when using limit buy orders.
Also, prices at the "ask" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
#### Buy price with Orderbook enabled
When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the configured side (`bid_strategy.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
#### Buy price without Orderbook enabled
The following section uses `side` as the configured `bid_strategy.price_side`.
When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
### Sell price
#### Sell price side
The configuration setting `ask_strategy.price_side` defines the side of the spread the bot looks for when selling.
The following displays an orderbook:
``` explanation
...
103
102
101 # ask
-------------Current spread
99 # bid
98
97
...
```
If `ask_strategy.price_side` is set to `"ask"`, then the bot will use 101 as selling price.
In line with that, if `ask_strategy.price_side` is set to `"bid"`, then the bot will use 99 as selling price.
#### Sell price with Orderbook enabled
When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the configured orderbook side are validated for a profitable sell-possibility based on the strategy configuration (`minimal_roi` conditions) and the sell order is placed at the first profitable spot.
!!! Note
Using `order_book_max` higher than `order_book_min` only makes sense when ask_strategy.price_side is set to `"ask"`.
The idea here is to place the sell order early, to be ahead in the queue.
A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting `ask_strategy.order_book_min` and `ask_strategy.order_book_max` to the same number.
!!! Warning "Order_book_max > 1 - increased risks for stoplosses!"
Using `ask_strategy.order_book_max` higher than 1 will increase the risk the stoploss on exchange is cancelled too early, since an eventual [stoploss on exchange](#understand-order_types) will be cancelled as soon as the order is placed.
Also, the sell order will remain on the exchange for `unfilledtimeout.sell` (or until it's filled) - which can lead to missed stoplosses (with or without using stoploss on exchange).
!!! Warning "Order_book_max > 1 in dry-run"
Using `ask_strategy.order_book_max` higher than 1 will result in improper dry-run results (significantly better than real orders executed on exchange), since dry-run assumes orders to be filled almost instantly.
It is therefore advised to not use this setting for dry-runs.
#### Sell price without Orderbook enabled
When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
When not using orderbook (`ask_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
The `ask_strategy.bid_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the last price and values between those interpolate between `side` and last price.
### Market order pricing
When using market orders, prices should be configured to use the "correct" side of the orderbook to allow realistic pricing detection.
Assuming both buy and sell are using market orders, a configuration similar to the following might be used
``` jsonc
"order_types": {
"buy": "market",
"sell": "market"
// ...
},
"bid_strategy": {
"price_side": "ask",
// ...
},
"ask_strategy":{
"price_side": "bid",
// ...
},
```
Obviously, if only one side is using limit orders, different pricing combinations can be used.

64
docs/includes/protections.md
View File

@ -7,7 +7,8 @@ Protections will protect your strategy from unexpected events and market conditi
All protection end times are rounded up to the next candle to avoid sudden, unexpected intra-candle buys.
!!! Note
Not all Protections will work for all strategies, and parameters will need to be tuned for your strategy to improve performance.
Not all Protections will work for all strategies, and parameters will need to be tuned for your strategy to improve performance.
To align your protection with your strategy, you can define protections in the strategy.
!!! Tip
Each Protection can be configured multiple times with different parameters, to allow different levels of protection (short-term / long-term).
@ -39,7 +40,9 @@ All protection end times are rounded up to the next candle to avoid sudden, unex
#### Stoploss Guard
`StoplossGuard` selects all trades within `lookback_period`, and determines if the amount of trades that resulted in stoploss are above `trade_limit` - in which case trading will stop for `stop_duration`.
`StoplossGuard` selects all trades within `lookback_period` in minutes (or in candles when using `lookback_period_candles`).
If `trade_limit` or more trades resulted in stoploss, trading will stop for `stop_duration` in minutes (or in candles when using `stop_duration_candles`).
This applies across all pairs, unless `only_per_pair` is set to true, which will then only look at one pair at a time.
The below example stops trading for all pairs for 4 candles after the last trade if the bot hit stoploss 4 times within the last 24 candles.
@ -57,14 +60,14 @@ The below example stops trading for all pairs for 4 candles after the last trade
```
!!! Note
`StoplossGuard` considers all trades with the results `"stop_loss"` and `"trailing_stop_loss"` if the resulting profit was negative.
`StoplossGuard` considers all trades with the results `"stop_loss"`, `"stoploss_on_exchange"` and `"trailing_stop_loss"` if the resulting profit was negative.
`trade_limit` and `lookback_period` will need to be tuned for your strategy.
#### MaxDrawdown
`MaxDrawdown` uses all trades within `lookback_period` (in minutes) to determine the maximum drawdown. If the drawdown is below `max_allowed_drawdown`, trading will stop for `stop_duration` (in minutes) after the last trade - assuming that the bot needs some time to let markets recover.
`MaxDrawdown` uses all trades within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the maximum drawdown. If the drawdown is below `max_allowed_drawdown`, trading will stop for `stop_duration` in minutes (or in candles when using `stop_duration_candles`) after the last trade - assuming that the bot needs some time to let markets recover.
The below sample stops trading for 12 candles if max-drawdown is > 20% considering all trades within the last 48 candles.
The below sample stops trading for 12 candles if max-drawdown is > 20% considering all pairs - with a minimum of `trade_limit` trades - within the last 48 candles. If desired, `lookback_period` and/or `stop_duration` can be used.
```json
"protections": [
@ -76,13 +79,12 @@ The below sample stops trading for 12 candles if max-drawdown is > 20% consideri
"max_allowed_drawdown": 0.2
},
],
```
#### Low Profit Pairs
`LowProfitPairs` uses all trades for a pair within `lookback_period` (in minutes) to determine the overall profit ratio.
If that ratio is below `required_profit`, that pair will be locked for `stop_duration` (in minutes).
`LowProfitPairs` uses all trades for a pair within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the overall profit ratio.
If that ratio is below `required_profit`, that pair will be locked for `stop_duration` in minutes (or in candles when using `stop_duration_candles`).
The below example will stop trading a pair for 60 minutes if the pair does not have a required profit of 2% (and a minimum of 2 trades) within the last 6 candles.
@ -100,7 +102,7 @@ The below example will stop trading a pair for 60 minutes if the pair does not h
#### Cooldown Period
`CooldownPeriod` locks a pair for `stop_duration` (in minutes) after selling, avoiding a re-entry for this pair for `stop_duration` minutes.
`CooldownPeriod` locks a pair for `stop_duration` in minutes (or in candles when using `stop_duration_candles`) after selling, avoiding a re-entry for this pair for `stop_duration` minutes.
The below example will stop trading a pair for 2 candles after closing a trade, allowing this pair to "cool down".
@ -167,3 +169,47 @@ The below example assumes a timeframe of 1 hour:
}
],
```
You can use the same in your strategy, the syntax is only slightly different:
``` python
from freqtrade.strategy import IStrategy
class AwesomeStrategy(IStrategy)
timeframe = '1h'
protections = [
{
"method": "CooldownPeriod",
"stop_duration_candles": 5
},
{
"method": "MaxDrawdown",
"lookback_period_candles": 48,
"trade_limit": 20,
"stop_duration_candles": 4,
"max_allowed_drawdown": 0.2
},
{
"method": "StoplossGuard",
"lookback_period_candles": 24,
"trade_limit": 4,
"stop_duration_candles": 2,
"only_per_pair": False
},
{
"method": "LowProfitPairs",
"lookback_period_candles": 6,
"trade_limit": 2,
"stop_duration_candles": 60,
"required_profit": 0.02
},
{
"method": "LowProfitPairs",
"lookback_period_candles": 24,
"trade_limit": 4,
"stop_duration_candles": 2,
"required_profit": 0.01
}
]
# ...
```

22
docs/index.md
View File

@ -5,12 +5,8 @@
<!-- Place this tag where you want the button to render. -->
<a class="github-button" href="https://github.com/freqtrade/freqtrade" data-icon="octicon-star" data-size="large" aria-label="Star freqtrade/freqtrade on GitHub">Star</a>
<!-- Place this tag where you want the button to render. -->
<a class="github-button" href="https://github.com/freqtrade/freqtrade/fork" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork freqtrade/freqtrade on GitHub">Fork</a>
<!-- Place this tag where you want the button to render. -->
<a class="github-button" href="https://github.com/freqtrade/freqtrade/archive/stable.zip" data-icon="octicon-cloud-download" data-size="large" aria-label="Download freqtrade/freqtrade on GitHub">Download</a>
<!-- Place this tag where you want the button to render. -->
<a class="github-button" href="https://github.com/freqtrade" data-size="large" aria-label="Follow @freqtrade on GitHub">Follow @freqtrade</a>
## Introduction
@ -35,6 +31,22 @@ Freqtrade is a crypto-currency algorithmic trading software developed in python
- Control/Monitor: Use Telegram or a REST API (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
- Analyse: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
## Supported exchange marketplaces
Please read the [exchange specific notes](exchanges.md) to learn about eventual, special configurations needed for each exchange.
- [X] [Binance](https://www.binance.com/) ([*Note for binance users](exchanges.md#blacklists))
- [X] [Bittrex](https://bittrex.com/)
- [X] [FTX](https://ftx.com)
- [X] [Kraken](https://kraken.com/)
- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
### Community tested
Exchanges confirmed working by the community:
- [X] [Bitvavo](https://bitvavo.com/)
## Requirements
### Hardware requirements
@ -65,7 +77,7 @@ For any questions not covered by the documentation or for further information ab
Please check out our [discord server](https://discord.gg/MA9v74M).
You can also join our [Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/zt-jaut7r4m-Y17k4x5mcQES9a9swKuxbg).
You can also join our [Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw).
## Ready to try?

442
docs/installation.md
View File

@ -2,93 +2,49 @@
This page explains how to prepare your environment for running the bot.
Please consider using the prebuilt [docker images](docker.md) to get started quickly while trying out freqtrade evaluating how it operates.
The freqtrade documentation describes various ways to install freqtrade
## Prerequisite
* [Docker images](docker_quickstart.md) (separate page)
* [Script Installation](#script-installation)
* [Manual Installation](#manual-installation)
* [Installation with Conda](#installation-with-conda)
### Requirements
Please consider using the prebuilt [docker images](docker_quickstart.md) to get started quickly while evaluating how freqtrade works.
Click each one for install guide:
------
## Information
For Windows installation, please use the [windows installation guide](windows_installation.md).
The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the `./setup.sh` script, if it's available for your platform.
!!! Note "Version considerations"
When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests).
The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
!!! Note
Python3.7 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.
Also, python headers (`python<yourversion>-dev` / `python<yourversion>-devel`) must be available for the installation to complete successfully.
!!! Warning "Up-to-date clock"
The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
------
## Requirements
These requirements apply to both [Script Installation](#script-installation) and [Manual Installation](#manual-installation).
### Install guide
* [Python >= 3.7.x](http://docs.python-guide.org/en/latest/starting/installation/)
* [pip](https://pip.pypa.io/en/stable/installing/)
* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
* [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions [below](#install-ta-lib))
We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
!!! Warning "Up-to-date clock"
The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
## Quick start
Freqtrade provides the Linux/MacOS Easy Installation script to install all dependencies and help you configure the bot.
!!! Note
Windows installation is explained [here](#windows).
The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the Easy Installation script, if it's available for your platform.
!!! Note "Version considerations"
When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
!!! Note
Python3.7 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.
This can be achieved with the following commands:
```bash
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
# git checkout stable # Optional, see (1)
./setup.sh --install
```
(1) This command switches the cloned repository to the use of the `stable` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout stable`/`git checkout develop` commands.
## Easy Installation Script (Linux/MacOS)
If you are on Debian, Ubuntu or MacOS Freqtrade provides the script to install, update, configure and reset the codebase of your bot.
```bash
$ ./setup.sh
usage:
-i,--install Install freqtrade from scratch
-u,--update Command git pull to update.
-r,--reset Hard reset your develop/stable branch.
-c,--config Easy config generator (Will override your existing file).
```
** --install **
With this option, the script will install the bot and most dependencies:
You will need to have git and python3.7+ installed beforehand for this to work.
* Mandatory software as: `ta-lib`
* Setup your virtualenv under `.env/`
This option is a combination of installation tasks, `--reset` and `--config`.
** --update **
This option will pull the last version of your current branch and update your virtualenv. Run the script with this option periodically to update your bot.
** --reset **
This option will hard reset your branch (only if you are on either `stable` or `develop`) and recreate your virtualenv.
** --config **
DEPRECATED - use `freqtrade new-config -c config.json` instead.
### Activate your virtual environment
Each time you open a new terminal, you must run `source .env/bin/activate`.
------
## Custom Installation
### Install code
We've included/collected install instructions for Ubuntu, MacOS, and Windows. These are guidelines and your success may vary with other distros.
OS Specific steps are listed first, the [Common](#common) section below is necessary for all systems.
@ -96,12 +52,15 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
!!! Note
Python3.7 or higher and the corresponding pip are assumed to be available.
=== "Ubuntu/Debian"
=== "Debian/Ubuntu"
#### Install necessary dependencies
```bash
# update repository
sudo apt-get update
sudo apt-get install build-essential git
# install packages
sudo apt install -y python3-pip python3-venv python3-pandas python3-pip git
```
=== "RaspberryPi/Raspbian"
@ -109,9 +68,9 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
``` bash
```bash
sudo apt-get install python3-venv libatlas-base-dev cmake
# Use pywheels.org to speed up installation
sudo echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > tee /etc/pip.conf
@ -124,17 +83,106 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
!!! Note "Installation duration"
Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
Due to this, we recommend to use the prebuild docker-image for Raspberry, by following the [Docker quickstart documentation](docker_quickstart.md)
Due to this, we recommend to use the pre-build docker-image for Raspberry, by following the [Docker quickstart documentation](docker_quickstart.md)
!!! Note
The above does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
We do not advise to run hyperopt on a Raspberry Pi, since this is a very resource-heavy operation, which should be done on powerful machine.
### Common
------
#### 1. Install TA-Lib
## Freqtrade repository
Use the provided ta-lib installation script
Freqtrade is an open source crypto-currency trading bot, whose code is hosted on `github.com`
```bash
# Download `develop` branch of freqtrade repository
git clone https://github.com/freqtrade/freqtrade.git
# Enter downloaded directory
cd freqtrade
# your choice (1): novice user
git checkout stable
# your choice (2): advanced user
git checkout develop
```
(1) This command switches the cloned repository to the use of the `stable` branch. It's not needed, if you wish to stay on the (2) `develop` branch.
You may later switch between branches at any time with the `git checkout stable`/`git checkout develop` commands.
------
## Script Installation
First of the ways to install Freqtrade, is to use provided the Linux/MacOS `./setup.sh` script, which install all dependencies and help you configure the bot.
Make sure you fulfill the [Requirements](#requirements) and have downloaded the [Freqtrade repository](#freqtrade-repository).
### Use /setup.sh -install (Linux/MacOS)
If you are on Debian, Ubuntu or MacOS, freqtrade provides the script to install freqtrade.
```bash
# --install, Install freqtrade from scratch
./setup.sh -i
```
### Activate your virtual environment
Each time you open a new terminal, you must run `source .env/bin/activate` to activate your virtual environment.
```bash
# then activate your .env
source ./.env/bin/activate
```
### Congratulations
[You are ready](#you-are-ready), and run the bot
### Other options of /setup.sh script
You can as well update, configure and reset the codebase of your bot with `./script.sh`
```bash
# --update, Command git pull to update.
./setup.sh -u
# --reset, Hard reset your develop/stable branch.
./setup.sh -r
```
```
** --install **
With this option, the script will install the bot and most dependencies:
You will need to have git and python3.7+ installed beforehand for this to work.
* Mandatory software as: `ta-lib`
* Setup your virtualenv under `.env/`
This option is a combination of installation tasks and `--reset`
** --update **
This option will pull the last version of your current branch and update your virtualenv. Run the script with this option periodically to update your bot.
** --reset **
This option will hard reset your branch (only if you are on either `stable` or `develop`) and recreate your virtualenv.
```
-----
## Manual Installation
Make sure you fulfill the [Requirements](#requirements) and have downloaded the [Freqtrade repository](#freqtrade-repository).
### Install TA-Lib
#### TA-Lib script installation
```bash
sudo ./build_helpers/install_ta-lib.sh
@ -159,77 +207,193 @@ cd ..
rm -rf ./ta-lib*
```
!!! Note
An already downloaded version of ta-lib is included in the repository, as the sourceforge.net source seems to have problems frequently.
#### Setup Python virtual environment (virtualenv)
#### 2. Setup your Python virtual environment (virtualenv)
!!! Note
This step is optional but strongly recommended to keep your system organized
You will run freqtrade in separated `virtual environment`
```bash
# create virtualenv in directory /freqtrade/.env
python3 -m venv .env
# run virtualenv
source .env/bin/activate
```
#### 3. Install Freqtrade
Clone the git repository:
#### Install python dependencies
```bash
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
git checkout stable
```
#### 4. Install python dependencies
``` bash
python3 -m pip install --upgrade pip
python3 -m pip install -e .
```
#### 5. Initialize the configuration
### Congratulations
```bash
# Initialize the user_directory
freqtrade create-userdir --userdir user_data/
[You are ready](#you-are-ready), and run the bot
# Create a new configuration file
freqtrade new-config --config config.json
```
#### (Optional) Post-installation Tasks
> *To edit the config please refer to [Bot Configuration](configuration.md).*
!!! Note
If you run the bot on a server, you should consider using [Docker](docker_quickstart.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
#### 6. Run the Bot
If this is the first time you run the bot, ensure you are running it in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
```bash
freqtrade trade -c config.json
```
*Note*: If you run the bot on a server, you should consider using [Docker](docker.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
#### 7. (Optional) Post-installation Tasks
On Linux, as an optional post-installation task, you may wish to setup the bot to run as a `systemd` service or configure it to send the log messages to the `syslog`/`rsyslog` or `journald` daemons. See [Advanced Logging](advanced-setup.md#advanced-logging) for details.
On Linux with software suite `systemd`, as an optional post-installation task, you may wish to setup the bot to run as a `systemd service` or configure it to send the log messages to the `syslog`/`rsyslog` or `journald` daemons. See [Advanced Logging](advanced-setup.md#advanced-logging) for details.
------
### Anaconda
## Installation with Conda
Freqtrade can also be installed using Anaconda (or Miniconda).
Freqtrade can also be installed with Miniconda or Anaconda. We recommend using Miniconda as it's installation footprint is smaller. Conda will automatically prepare and manage the extensive library-dependencies of the Freqtrade program.
!!! Note
This requires the [ta-lib](#1-install-ta-lib) C-library to be installed first. See below.
### What is Conda?
``` bash
conda env create -f environment.yml
Conda is a package, dependency and environment manager for multiple programming languages: [conda docs](https://docs.conda.io/projects/conda/en/latest/index.html)
### Installation with conda
#### Install Conda
[Installing on linux](https://conda.io/projects/conda/en/latest/user-guide/install/linux.html#install-linux-silent)
[Installing on windows](https://conda.io/projects/conda/en/latest/user-guide/install/windows.html)
Answer all questions. After installation, it is mandatory to turn your terminal OFF and ON again.
#### Freqtrade download
Download and install freqtrade.
```bash
# download freqtrade
git clone https://github.com/freqtrade/freqtrade.git
# enter downloaded directory 'freqtrade'
cd freqtrade
```
#### Freqtrade instal: Conda Environment
Prepare conda-freqtrade environment, using file `environment.yml`, which exist in main freqtrade directory
```bash
conda env create -n freqtrade-conda -f environment.yml
```
!!! Note "Creating Conda Environment"
The conda command `create -n` automatically installs all nested dependencies for the selected libraries, general structure of installation command is:
```bash
# choose your own packages
conda env create -n [name of the environment] [python version] [packages]
# point to file with packages
conda env create -n [name of the environment] -f [file]
```
#### Enter/exit freqtrade-conda environment
To check available environments, type
```bash
conda env list
```
Enter installed environment
```bash
# enter conda environment
conda activate freqtrade-conda
# exit conda environment - don't do it now
conda deactivate
```
Install last python dependencies with pip
```bash
python3 -m pip install --upgrade pip
python3 -m pip install -e .
```
### Congratulations
[You are ready](#you-are-ready), and run the bot
### Important shortcuts
```bash
# list installed conda environments
conda env list
# activate base environment
conda activate
# activate freqtrade-conda environment
conda activate freqtrade-conda
#deactivate any conda environments
conda deactivate
```
### Further info on anaconda
!!! Info "New heavy packages"
It may happen that creating a new Conda environment, populated with selected packages at the moment of creation takes less time than installing a large, heavy library or application, into previously set environment.
!!! Warning "pip install within conda"
The documentation of conda says that pip should NOT be used within conda, because internal problems can occur.
However, they are rare. [Anaconda Blogpost](https://www.anaconda.com/blog/using-pip-in-a-conda-environment)
Nevertheless, that is why, the `conda-forge` channel is preferred:
* more libraries are available (less need for `pip`)
* `conda-forge` works better with `pip`
* the libraries are newer
Happy trading!
-----
## Troubleshooting
## You are ready
You've made it this far, so you have successfully installed freqtrade.
### Initialize the configuration
```bash
# Step 1 - Initialize user folder
freqtrade create-userdir --userdir user_data
# Step 2 - Create a new configuration file
freqtrade new-config --config config.json
```
You are ready to run, read [Bot Configuration](configuration.md), remember to start with `dry_run: True` and verify that everything is working.
To learn how to setup your configuration, please refer to the [Bot Configuration](configuration.md) documentation page.
### Start the Bot
```bash
freqtrade trade --config config.json --strategy SampleStrategy
```
!!! Warning
You should read through the rest of the documentation, backtest the strategy you're going to use, and use dry-run before enabling trading with real money.
-----
## Troubleshooting
### Common problem: "command not found"
If you used (1)`Script` or (2)`Manual` installation, you need to run the bot in virtual environment. If you get error as below, make sure venv is active.
```bash
# if:
bash: freqtrade: command not found
# then activate your .env
source ./.env/bin/activate
```
### MacOS installation error
@ -238,13 +402,21 @@ Newer versions of MacOS may have installation failed with errors like `error: co
This error will require explicit installation of the SDK Headers, which are not installed by default in this version of MacOS.
For MacOS 10.14, this can be accomplished with the below command.
``` bash
```bash
open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg
```
If this file is inexistent, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
-----
### MacOS installation error with python 3.9
Now you have an environment ready, the next step is
[Bot Configuration](configuration.md).
When using python 3.9 on macOS, it's currently necessary to install some os-level modules to allow dependencies to compile.
The errors you'll see happen during installation and are related to the installation of `tables` or `blosc`.
You can install the necessary libraries with the following command:
```bash
brew install hdf5 c-blosc
```
After this, please run the installation (script) again.

114
docs/partials/header.html
View File

@ -1,54 +1,72 @@
{#-
This file was automatically generated - do not edit
-#}
{% set site_url = config.site_url | d(nav.homepage.url, true) | url %}
{% if not config.use_directory_urls and site_url[0] == site_url[-1] == "." %}
{% set site_url = site_url ~ "/index.html" %}
{% endif %}
<header class="md-header" data-md-component="header">
<nav class="md-header-nav md-grid">
<div class="md-flex">
<div class="md-flex__cell md-flex__cell--shrink">
<a href="{{ config.site_url | default(nav.homepage.url, true) | url }}" title="{{ config.site_name }}"
class="md-header-nav__button md-logo">
{% if config.theme.logo.icon %}
<i class="md-icon">{{ config.theme.logo.icon }}</i>
{% else %}
<img src="{{ config.theme.logo | url }}" width="24" height="24">
{% endif %}
</a>
</div>
<div class="md-flex__cell md-flex__cell--shrink">
<label class="md-icon md-icon--menu md-header-nav__button" for="__drawer"></label>
</div>
<div class="md-flex__cell md-flex__cell--stretch">
<div class="md-flex__ellipsis md-header-nav__title" data-md-component="title">
{% block site_name %}
{% if config.site_name == page.title %}
{{ config.site_name }}
{% else %}
<span class="md-header-nav__topic">
{{ config.site_name }}
</span>
<span class="md-header-nav__topic">
{{ page.title }}
</span>
{% endif %}
{% endblock %}
</div>
</div>
<div class="md-flex__cell md-flex__cell--shrink">
{% block search_box %}
{% if "search" in config["plugins"] %}
<label class="md-icon md-icon--search md-header-nav__button" for="__search"></label>
{% include "partials/search.html" %}
{% endif %}
{% endblock %}
</div>
{% if config.repo_url %}
<div class="md-flex__cell md-flex__cell--shrink">
<div class="md-header-nav__source">
{% include "partials/source.html" %}
</div>
</div>
{% endif %}
<nav class="md-header__inner md-grid" aria-label="{{ lang.t('header.title') }}">
<a href="{{ site_url }}" title="{{ config.site_name | e }}" class="md-header__button md-logo"
aria-label="{{ config.site_name }}">
{% include "partials/logo.html" %}
</a>
<label class="md-header__button md-icon" for="__drawer">
{% include ".icons/material/menu" ~ ".svg" %}
</label>
<div class="md-header__title" data-md-component="header-title">
<div class="md-header__ellipsis">
<div class="md-header__topic">
<span class="md-ellipsis">
{{ config.site_name }}
</span>
</div>
</nav>
<div class="md-header__topic" data-md-component="header-topic">
<span class="md-ellipsis">
{% if page and page.meta and page.meta.title %}
{{ page.meta.title }}
{% else %}
{{ page.title }}
{% endif %}
</span>
</div>
</div>
</div>
<div class="md-header__options">
{% if config.extra.alternate %}
<div class="md-select">
{% set icon = config.theme.icon.alternate or "material/translate" %}
<span class="md-header__button md-icon">
{% include ".icons/" ~ icon ~ ".svg" %}
</span>
<div class="md-select__inner">
<ul class="md-select__list">
{% for alt in config.extra.alternate %}
<li class="md-select__item">
<a href="{{ alt.link | url }}" class="md-select__link">
{{ alt.name }}
</a>
</li>
{% endfor %}
</ul>
</div>
</div>
{% endif %}
</div>
{% if "search" in config["plugins"] %}
<label class="md-header__button md-icon" for="__search">
{% include ".icons/material/magnify.svg" %}
</label>
{% include "partials/search.html" %}
{% endif %}
{% if config.repo_url %}
<div class="md-header__source">
{% include "partials/source.html" %}
</div>
{% endif %}
</nav>
<!-- Place this tag in your head or just before your close body tag. -->
<script async defer src="https://buttons.github.io/buttons.js"></script>
<script src="https://code.jquery.com/jquery-3.4.1.min.js"
integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
</header>

3
docs/plotting.md
View File

@ -188,7 +188,7 @@ Sample configuration with inline comments explaining the process:
'senkou_a': {
'color': 'green', #optional
'fill_to': 'senkou_b',
'fill_label': 'Ichimoku Cloud' #optional,
'fill_label': 'Ichimoku Cloud', #optional
'fill_color': 'rgba(255,76,46,0.2)', #optional
},
# plot senkou_b, too. Not only the area to it.
@ -208,6 +208,7 @@ Sample configuration with inline comments explaining the process:
}
```
!!! Note
The above configuration assumes that `ema10`, `ema50`, `senkou_a`, `senkou_b`,
`macd`, `macdsignal`, `macdhist` and `rsi` are columns in the DataFrame created by the strategy.

4
docs/requirements-docs.txt
View File

@ -1,3 +1,3 @@
mkdocs-material==6.1.7
mkdocs-material==7.0.6
mdx_truly_sane_lists==1.2
pymdown-extensions==8.0.1
pymdown-extensions==8.1.1

83
docs/rest-api.md
View File

@ -1,4 +1,19 @@
# REST API Usage
# REST API & FreqUI
## FreqUI
Freqtrade provides a builtin webserver, which can serve [FreqUI](https://github.com/freqtrade/frequi), the freqtrade UI.
By default, the UI is not included in the installation (except for docker images), and must be installed explicitly with `freqtrade install-ui`.
This same command can also be used to update freqUI, should there be a new release.
Once the bot is started in trade / dry-run mode (with `freqtrade trade`) - the UI will be available under the configured port below (usually `http://127.0.0.1:8080`).
!!! info "Alpha release"
FreqUI is still considered an alpha release - if you encounter bugs or inconsistencies please open a [FreqUI issue](https://github.com/freqtrade/frequi/issues/new/choose).
!!! Note "developers"
Developers should not use this method, but instead use the method described in the [freqUI repository](https://github.com/freqtrade/frequi) to get the source-code of freqUI.
## Configuration
@ -11,7 +26,8 @@ Sample configuration:
"enabled": true,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"verbosity": "info",
"verbosity": "error",
"enable_openapi": false,
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "Freqtrader",
@ -22,9 +38,6 @@ Sample configuration:
!!! Danger "Security warning"
By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
!!! Danger "Password selection"
Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
You can then access the API by going to `http://127.0.0.1:8080/api/v1/ping` in a browser to check if the API is running correctly.
This should return the response:
@ -34,16 +47,22 @@ This should return the response:
All other endpoints return sensitive info and require authentication and are therefore not available through a web browser.
To generate a secure password, either use a password manager, or use the below code snipped.
### Security
To generate a secure password, best use a password manager, or use the below code.
``` python
import secrets
secrets.token_hex()
```
!!! Hint
!!! Hint "JWT token"
Use the same method to also generate a JWT secret key (`jwt_secret_key`).
!!! Danger "Password selection"
Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
Also change `jwt_secret_key` to something random (no need to remember this, but it'll be used to encrypt your session, so it better be something unique!).
### Configuration with docker
If you run your bot using docker, you'll need to have the bot listen to incoming connections. The security is then handled by docker.
@ -56,28 +75,20 @@ If you run your bot using docker, you'll need to have the bot listen to incoming
},
```
Add the following to your docker command:
Uncomment the following from your docker-compose file:
``` bash
-p 127.0.0.1:8080:8080
```
A complete sample-command may then look as follows:
```bash
docker run -d \
--name freqtrade \
-v ~/.freqtrade/config.json:/freqtrade/config.json \
-v ~/.freqtrade/user_data/:/freqtrade/user_data \
-v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-p 127.0.0.1:8080:8080 \
freqtrade trade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
```yml
ports:
- "127.0.0.1:8080:8080"
```
!!! Danger "Security warning"
By using `-p 8080:8080` the API is available to everyone connecting to the server under the correct port, so others may be able to control your bot.
By using `8080:8080` in the docker port mapping, the API will be available to everyone connecting to the server under the correct port, so others may be able to control your bot.
## Consuming the API
## Rest API
### Consuming the API
You can consume the API by using the script `scripts/rest_client.py`.
The client script only requires the `requests` module, so Freqtrade does not need to be installed on the system.
@ -88,7 +99,7 @@ python3 scripts/rest_client.py <command> [optional parameters]
By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be used, however you can specify a configuration file to override this behaviour.
### Minimalistic client config
#### Minimalistic client config
``` json
{
@ -104,7 +115,7 @@ By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be use
python3 scripts/rest_client.py --config rest_config.json <command> [optional parameters]
```
## Available endpoints
### Available endpoints
| Command | Description |
|----------|-------------|
@ -120,6 +131,7 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
| `status` | Lists all open trades.
| `count` | Displays number of trades used and available.
| `locks` | Displays currently locked pairs.
| `delete_lock <lock_id>` | Deletes (disables) the lock by id.
| `profit` | Display a summary of your profit/loss from close trades and some stats about your performance.
| `forcesell <trade_id>` | Instantly sells the given trade (Ignoring `minimum_roi`).
| `forcesell all` | Instantly sells all open trades (Ignoring `minimum_roi`).
@ -171,6 +183,11 @@ count
daily
Return the amount of open trades.
delete_lock
Delete (disable) lock from the database.
:param lock_id: ID for the lock to delete
delete_trade
Delete trade from the database.
Tries to close open orders. Requires manual handling of this asset on the exchange.
@ -191,6 +208,9 @@ forcesell
:param tradeid: Id of the trade (can be received via status command)
locks
Return current locks
logs
Show latest logs.
@ -263,7 +283,12 @@ whitelist
```
## Advanced API usage using JWT tokens
### OpenAPI interface
To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration.
This will enable the Swagger UI at the `/docs` endpoint. By default, that's running at http://localhost:8080/docs/ - but it'll depend on your settings.
### Advanced API usage using JWT tokens
!!! Note
The below should be done in an application (a Freqtrade REST API client, which fetches info via API), and is not intended to be used on a regular basis.
@ -288,9 +313,9 @@ Since the access token has a short timeout (15 min) - the `token/refresh` reques
{"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk5NzQsIm5iZiI6MTU4OTExOTk3NCwianRpIjoiMDBjNTlhMWUtMjBmYS00ZTk0LTliZjAtNWQwNTg2MTdiZDIyIiwiZXhwIjoxNTg5MTIwODc0LCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.1seHlII3WprjjclY6DpRhen0rqdF4j6jbvxIhUFaSbs"}
```
## CORS
### CORS
All web-based frontends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
All web-based front-ends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.

4
docs/sandbox-testing.md
View File

@ -6,6 +6,10 @@ With some configuration, freqtrade (in combination with ccxt) provides access to
This document is an overview to configure Freqtrade to be used with sandboxes.
This can be useful to developers and trader alike.
!!! Warning
Sandboxes usually have very low volume, and either a very wide spread, or no orders available at all.
Therefore, sandboxes will usually not do a good job of showing you how a strategy would work in real trading.
## Exchanges known to have a sandbox / testnet
* [binance](https://testnet.binance.vision/)

9
docs/stoploss.md
View File

@ -51,6 +51,14 @@ The bot cannot do these every 5 seconds (at each iteration), otherwise it would
So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
### forcesell
`forcesell` is an optional value, which defaults to the same value as `sell` and is used when sending a `/forcesell` command from Telegram or from the Rest API.
### forcebuy
`forcebuy` is an optional value, which defaults to the same value as `buy` and is used when sending a `/forcebuy` command from Telegram or from the Rest API.
### emergencysell
`emergencysell` is an optional value, which defaults to `market` and is used when creating stop loss on exchange orders fails.
@ -78,6 +86,7 @@ At this stage the bot contains the following stoploss support modes:
2. Trailing stop loss.
3. Trailing stop loss, custom positive loss.
4. Trailing stop loss only once the trade has reached a certain offset.
5. [Custom stoploss function](strategy-advanced.md#custom-stoploss)
### Static Stop Loss

363
docs/strategy-advanced.md
View File

@ -8,11 +8,303 @@ If you're just getting started, please be familiar with the methods described in
!!! Note
All callback methods described below should only be implemented in a strategy if they are actually used.
!!! Tip
You can get a strategy template containing all below methods by running `freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced`
## Storing information
Storing information can be accomplished by creating a new dictionary within the strategy class.
The name of the variable can be chosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
```python
class AwesomeStrategy(IStrategy):
# Create custom dictionary
custom_info = {}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Check if the entry already exists
if not metadata["pair"] in self.custom_info:
# Create empty entry for this pair
self.custom_info[metadata["pair"]] = {}
if "crosstime" in self.custom_info[metadata["pair"]]:
self.custom_info[metadata["pair"]]["crosstime"] += 1
else:
self.custom_info[metadata["pair"]]["crosstime"] = 1
```
!!! Warning
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
!!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
***
### Storing custom information using DatetimeIndex from `dataframe`
Imagine you need to store an indicator like `ATR` or `RSI` into `custom_info`. To use this in a meaningful way, you will not only need the raw data of the indicator, but probably also need to keep the right timestamps.
```python
import talib.abstract as ta
class AwesomeStrategy(IStrategy):
# Create custom dictionary
custom_info = {}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# using "ATR" here as example
dataframe['atr'] = ta.ATR(dataframe)
if self.dp.runmode.value in ('backtest', 'hyperopt'):
# add indicator mapped to correct DatetimeIndex to custom_info
self.custom_info[metadata['pair']] = dataframe[['date', 'atr']].copy().set_index('date')
return dataframe
```
!!! Warning
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
!!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
See `custom_stoploss` examples below on how to access the saved dataframe columns
## Custom stoploss
The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss.
The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
The method must return a stoploss value (float / number) as a percentage of the current price.
E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
``` python
# additional imports required
from datetime import datetime
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
"""
Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
e.g. returning -0.05 would create a stoploss 5% below current_rate.
The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns the initial stoploss value
Only called when use_custom_stoploss is set to True.
:param pair: Pair that's currently analyzed
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New stoploss value, relative to the currentrate
"""
return -0.04
```
Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchange-freqtrade)).
!!! Note "Use of dates"
All time-based calculations should be done based on `current_time` - using `datetime.now()` or `datetime.utcnow()` is discouraged, as this will break backtesting support.
!!! Tip "Trailing stoploss"
It's recommended to disable `trailing_stop` when using custom stoploss values. Both can work in tandem, but you might encounter the trailing stop to move the price higher while your custom function would not want this, causing conflicting behavior.
### Custom stoploss examples
The next section will show some examples on what's possible with the custom stoploss function.
Of course, many more things are possible, and all examples can be combined at will.
#### Time based trailing stop
Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
``` python
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
if current_time - timedelta(minutes=120) > trade.open_date_utc:
return -0.05
elif current_time - timedelta(minutes=60) > trade.open_date_utc:
return -0.10
return 1
```
#### Different stoploss per pair
Use a different stoploss depending on the pair.
In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
if pair in ('ETH/BTC', 'XRP/BTC'):
return -0.10
elif pair in ('LTC/BTC'):
return -0.05
return -0.15
```
#### Trailing stoploss with positive offset
Use the initial stoploss until the profit is above 4%, then use a trailing stoploss of 50% of the current profit with a minimum of 2.5% and a maximum of 5%.
Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
``` python
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
if current_profit < 0.04:
return -1 # return a value bigger than the inital stoploss to keep using the inital stoploss
# After reaching the desired offset, allow the stoploss to trail by half the profit
desired_stoploss = current_profit / 2
# Use a minimum of 2.5% and a maximum of 5%
return max(min(desired_stoploss, 0.05), 0.025)
```
#### Calculating stoploss relative to open price
Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss relative to the *open* price, we need to use `current_profit` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
The helper function [`stoploss_from_open()`](strategy-customization.md#stoploss_from_open) can be used to convert from an open price relative stop, to a current price relative stop which can be returned from `custom_stoploss()`.
#### Stepped stoploss
Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
* Use the regular stoploss until 20% profit is reached
* Once profit is > 20% - set stoploss to 7% above open price.
* Once profit is > 25% - set stoploss to 15% above open price.
* Once profit is > 40% - set stoploss to 25% above open price.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# evaluate highest to lowest, so that highest possible stop is used
if current_profit > 0.40:
return stoploss_from_open(0.25, current_profit)
elif current_profit > 0.25:
return stoploss_from_open(0.15, current_profit)
elif current_profit > 0.20:
return stoploss_from_open(0.07, current_profit)
# return maximum stoploss value, keeping current stoploss price unchanged
return 1
```
#### Custom stoploss using an indicator from dataframe example
Imagine you want to use `custom_stoploss()` to use a trailing indicator like e.g. "ATR"
See: "Storing custom information using DatetimeIndex from `dataframe`" example above) on how to store the indicator into `custom_info`
!!! Warning
only use .iat[-1] in live mode, not in backtesting/hyperopt
otherwise you will look into the future
see [Common mistakes when developing strategies](strategy-customization.md#common-mistakes-when-developing-strategies) for more info.
``` python
from freqtrade.persistence import Trade
from freqtrade.state import RunMode
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
result = 1
if self.custom_info and pair in self.custom_info and trade:
# using current_time directly (like below) will only work in backtesting.
# so check "runmode" to make sure that it's only used in backtesting/hyperopt
if self.dp and self.dp.runmode.value in ('backtest', 'hyperopt'):
relative_sl = self.custom_info[pair].loc[current_time]['atr']
# in live / dry-run, it'll be really the current time
else:
# but we can just use the last entry from an already analyzed dataframe instead
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
timeframe=self.timeframe)
# WARNING
# only use .iat[-1] in live mode, not in backtesting/hyperopt
# otherwise you will look into the future
# see: https://www.freqtrade.io/en/latest/strategy-customization/#common-mistakes-when-developing-strategies
relative_sl = dataframe['atr'].iat[-1]
if (relative_sl is not None):
# new stoploss relative to current_rate
new_stoploss = (current_rate-relative_sl)/current_rate
# turn into relative negative offset required by `custom_stoploss` return implementation
result = new_stoploss - 1
return result
```
---
## Custom order timeout rules
Simple, timebased order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
Simple, time-based order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
However, freqtrade also offers a custom callback for both ordertypes, which allows you to decide based on custom criteria if a order did time out or not.
However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if a order did time out or not.
!!! Note
Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
@ -25,10 +317,10 @@ It applies a tight timeout for higher priced assets, while allowing more time to
The function must return either `True` (cancel order) or `False` (keep order alive).
``` python
from datetime import datetime, timedelta
from datetime import datetime, timedelta, timezone
from freqtrade.persistence import Trade
class Awesomestrategy(IStrategy):
class AwesomeStrategy(IStrategy):
# ... populate_* methods
@ -39,21 +331,21 @@ class Awesomestrategy(IStrategy):
}
def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
return True
return False
def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
return True
return False
```
@ -67,7 +359,7 @@ class Awesomestrategy(IStrategy):
from datetime import datetime
from freqtrade.persistence import Trade
class Awesomestrategy(IStrategy):
class AwesomeStrategy(IStrategy):
# ... populate_* methods
@ -95,6 +387,8 @@ class Awesomestrategy(IStrategy):
return False
```
---
## Bot loop start callback
A simple callback which is called once at the start of every bot throttling iteration.
@ -103,7 +397,7 @@ This can be used to perform calculations which are pair independent (apply to al
``` python
import requests
class Awesomestrategy(IStrategy):
class AwesomeStrategy(IStrategy):
# ... populate_* methods
@ -128,7 +422,7 @@ class Awesomestrategy(IStrategy):
`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
``` python
class Awesomestrategy(IStrategy):
class AwesomeStrategy(IStrategy):
# ... populate_* methods
@ -164,7 +458,7 @@ class Awesomestrategy(IStrategy):
from freqtrade.persistence import Trade
class Awesomestrategy(IStrategy):
class AwesomeStrategy(IStrategy):
# ... populate_* methods
@ -200,6 +494,8 @@ class Awesomestrategy(IStrategy):
```
---
## Derived strategies
The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
@ -219,4 +515,41 @@ class MyAwesomeStrategy2(MyAwesomeStrategy):
trailing_stop = True
```
Both attributes and methods may be overriden, altering behavior of the original strategy in a way you need.
Both attributes and methods may be overridden, altering behavior of the original strategy in a way you need.
!!! Note "Parent-strategy in different files"
If you have the parent-strategy in a different file, you'll need to add the following to the top of your "child"-file to ensure proper loading, otherwise freqtrade may not be able to load the parent strategy correctly.
``` python
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent))
from myawesomestrategy import MyAwesomeStrategy
```
## Embedding Strategies
Freqtrade provides you with with an easy way to embed the strategy into your configuration file.
This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
in your chosen config file.
### Encoding a string as BASE64
This is a quick example, how to generate the BASE64 string in python
```python
from base64 import urlsafe_b64encode
with open(file, 'r') as f:
content = f.read()
content = urlsafe_b64encode(content.encode('utf-8'))
```
The variable 'content', will contain the strategy file in a BASE64 encoded form. Which can now be set in your configurations file as following
```json
"strategy": "NameOfStrategy:BASE64String"
```
Please ensure that 'NameOfStrategy' is identical to the strategy name!

115
docs/strategy-customization.md
View File

@ -300,38 +300,7 @@ The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `p
Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
The Metadata-dict should not be modified and does not persist information across multiple calls.
Instead, have a look at the section [Storing information](#Storing-information)
### Storing information
Storing information can be accomplished by creating a new dictionary within the strategy class.
The name of the variable can be chosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
```python
class Awesomestrategy(IStrategy):
# Create custom dictionary
cust_info = {}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Check if the entry already exists
if not metadata["pair"] in self._cust_info:
# Create empty entry for this pair
self._cust_info[metadata["pair"]] = {}
if "crosstime" in self.cust_info[metadata["pair"]:
self.cust_info[metadata["pair"]]["crosstime"] += 1
else:
self.cust_info[metadata["pair"]]["crosstime"] = 1
```
!!! Warning
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
!!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
***
Instead, have a look at the section [Storing information](strategy-advanced.md#Storing-information)
## Additional data (informative_pairs)
@ -399,7 +368,7 @@ if self.dp:
### *current_whitelist()*
Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume.
Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume.
The strategy might look something like this:
@ -418,7 +387,7 @@ This is where calling `self.dp.current_whitelist()` comes in handy.
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1d') for pair in pairs]
return informative_pairs
return informative_pairs
```
### *get_pair_dataframe(pair, timeframe)*
@ -444,14 +413,19 @@ It can also be used in specific callbacks to get the signal that caused the acti
``` python
# fetch current dataframe
if self.dp:
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
timeframe=self.timeframe)
if self.dp.runmode.value in ('live', 'dry_run'):
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
timeframe=self.timeframe)
```
!!! Note "No data available"
Returns an empty dataframe if the requested pair was not cached.
This should not happen when using whitelisted pairs.
!!! Warning "Warning about backtesting"
This method will return an empty dataframe during backtesting.
### *orderbook(pair, maximum)*
``` python
@ -462,8 +436,28 @@ if self.dp:
dataframe['best_ask'] = ob['asks'][0][0]
```
!!! Warning
The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used.
The orderbook structure is aligned with the order structure from [ccxt](https://github.com/ccxt/ccxt/wiki/Manual#order-book-structure), so the result will look as follows:
``` js
{
'bids': [
[ price, amount ], // [ float, float ]
[ price, amount ],
...
],
'asks': [
[ price, amount ],
[ price, amount ],
//...
],
//...
}
```
Therefore, using `ob['bids'][0][0]` as demonstrated above will result in using the best bid price. `ob['bids'][0][1]` would look at the amount at this orderbook position.
!!! Warning "Warning about backtesting"
The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return uptodate values.
### *ticker(pair)*
@ -578,7 +572,7 @@ All columns of the informative dataframe will be available on the returning data
``` python
'date', 'open', 'high', 'low', 'close', 'rsi' # from the original dataframe
'date_1h', 'open_1h', 'high_1h', 'low_1h', 'close_1h', 'rsi_1h' # from the informative dataframe
'date_1h', 'open_1h', 'high_1h', 'low_1h', 'close_1h', 'rsi_1h' # from the informative dataframe
```
??? Example "Custom implementation"
@ -613,6 +607,43 @@ All columns of the informative dataframe will be available on the returning data
***
### *stoploss_from_open()*
Stoploss values returned from `custom_stoploss` must specify a percentage relative to `current_rate`, but sometimes you may want to specify a stoploss relative to the open price instead. `stoploss_from_open()` is a helper function to calculate a stoploss value that can be returned from `custom_stoploss` which will be equivalent to the desired percentage above the open price.
??? Example "Returning a stoploss relative to the open price from the custom stoploss function"
Say the open price was $100, and `current_price` is $121 (`current_profit` will be `0.21`).
If we want a stop price at 7% above the open price we can call `stoploss_from_open(0.07, current_profit)` which will return `0.1157024793`. 11.57% below $121 is $107, which is the same as 7% above $100.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy, stoploss_from_open
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# once the profit has risin above 10%, keep the stoploss at 7% above the open price
if current_profit > 0.10:
return stoploss_from_open(0.07, current_profit)
return 1
```
Full examples can be found in the [Custom stoploss](strategy-advanced.md#custom-stoploss) section of the Documentation.
## Additional data (Wallets)
The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
@ -653,7 +684,7 @@ The following example queries for the current pair and trades from today, howeve
if self.config['runmode'].value in ('live', 'dry_run'):
trades = Trade.get_trades([Trade.pair == metadata['pair'],
Trade.open_date > datetime.utcnow() - timedelta(days=1),
Trade.is_open == False,
Trade.is_open.is_(False),
]).order_by(Trade.close_date).all()
# Summarize profit for this pair.
curdayprofit = sum(trade.close_profit for trade in trades)
@ -704,7 +735,7 @@ To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
Locked pairs will always be rounded up to the next candle. So assuming a `5m` timeframe, a lock with `until` set to 10:18 will lock the pair until the candle from 10:15-10:20 will be finished.
!!! Warning
Locking pairs is not available during backtesting.
Manually locking pairs is not available during backtesting, only locks via Protections are allowed.
#### Pair locking example
@ -719,7 +750,7 @@ if self.config['runmode'].value in ('live', 'dry_run'):
# fetch closed trades for the last 2 days
trades = Trade.get_trades([Trade.pair == metadata['pair'],
Trade.open_date > datetime.utcnow() - timedelta(days=2),
Trade.is_open == False,
Trade.is_open.is_(False),
]).all()
# Analyze the conditions you'd like to lock the pair .... will probably be different for every strategy
sumprofit = sum(trade.close_profit for trade in trades)

6
docs/strategy_analysis_example.md
View File

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

7
docs/telegram-usage.md
View File

@ -83,10 +83,13 @@ Example configuration showing the different settings:
"sell": "on",
"buy_cancel": "silent",
"sell_cancel": "on"
}
},
"balance_dust_level": 0.01
},
```
`balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown.
## Create a custom keyboard (command shortcut buttons)
Telegram allows us to create a custom keyboard with buttons for commands.
@ -137,11 +140,13 @@ official commands. You can ask at any moment for help with `/help`.
| `/show_config` | Shows part of the current configuration with relevant settings to operation
| `/logs [limit]` | Show last log messages.
| `/status` | Lists all open trades
| `/status <trade_id>` | Lists one or more specific trade. Separate multiple <trade_id> with a blank space.
| `/status table` | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
| `/trades [limit]` | List all recently closed trades in a table format.
| `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `/count` | Displays number of trades used and available
| `/locks` | Show currently locked pairs.
| `/unlock <pair or lock_id>` | Remove the lock for this pair (or for this lock id).
| `/profit` | Display a summary of your profit/loss from close trades and some stats about your performance
| `/forcesell <trade_id>` | Instantly sells the given trade (Ignoring `minimum_roi`).
| `/forcesell all` | Instantly sells all open trades (Ignoring `minimum_roi`).

4
docs/utils.md
View File

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

15
docs/webhook-config.md
View File

@ -40,6 +40,21 @@ Sample configuration (tested using IFTTT).
The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert our event and key to the url.
You can set the POST body format to Form-Encoded (default) or JSON-Encoded. Use `"format": "form"` or `"format": "json"` respectively. Example configuration for Mattermost Cloud integration:
```json
"webhook": {
"enabled": true,
"url": "https://<YOURSUBDOMAIN>.cloud.mattermost.com/hooks/<YOURHOOK>",
"format": "json",
"webhookstatus": {
"text": "Status: {status}"
}
},
```
The result would be POST request with e.g. `{"text":"Status: running"}` body and `Content-Type: application/json` header which results `Status: running` message in the Mattermost channel.
Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
### Webhookbuy

4
docs/windows_installation.md
View File

@ -1,4 +1,4 @@
We **strongly** recommend that Windows users use [Docker](docker.md) as this will work much easier and smoother (also more secure).
We **strongly** recommend that Windows users use [Docker](docker_quickstart.md) as this will work much easier and smoother (also more secure).
If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
Otherwise, try the instructions below.
@ -52,6 +52,6 @@ error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++
Unfortunately, many packages requiring compilation don't provide a pre-built wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building C code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or [docker](docker.md) first.
The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building C code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or [docker compose](docker_quickstart.md) first.
---

113
environment.yml
View File

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

4
freqtrade/commands/__init__.py
View File

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

20
freqtrade/commands/arguments.py
View File

@ -14,18 +14,18 @@ ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_dat
ARGS_STRATEGY = ["strategy", "strategy_path"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run", "dry_run_wallet", "fee"]
ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
"max_open_trades", "stake_amount", "fee"]
ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
"enable_protections",
"enable_protections", "dry_run_wallet",
"strategy_list", "export", "exportfilename"]
ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
"position_stacking", "use_max_market_positions",
"enable_protections",
"enable_protections", "dry_run_wallet",
"epochs", "spaces", "print_all",
"print_colorized", "print_json", "hyperopt_jobs",
"hyperopt_random_state", "hyperopt_min_trades",
@ -70,6 +70,8 @@ ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
"trade_source", "timeframe"]
ARGS_INSTALL_UI = ["erase_ui_only"]
ARGS_SHOW_TRADES = ["db_url", "trade_ids", "print_json"]
ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
@ -167,8 +169,8 @@ class Arguments:
from freqtrade.commands import (start_backtesting, start_convert_data, start_create_userdir,
start_download_data, start_edge, start_hyperopt,
start_hyperopt_list, start_hyperopt_show, start_list_data,
start_list_exchanges, start_list_hyperopts,
start_hyperopt_list, start_hyperopt_show, start_install_ui,
start_list_data, start_list_exchanges, start_list_hyperopts,
start_list_markets, start_list_strategies,
start_list_timeframes, start_new_config, start_new_hyperopt,
start_new_strategy, start_plot_dataframe, start_plot_profit,
@ -355,6 +357,14 @@ class Arguments:
test_pairlist_cmd.set_defaults(func=start_test_pairlist)
self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_cmd)
# Add install-ui subcommand
install_ui_cmd = subparsers.add_parser(
'install-ui',
help='Install FreqUI',
)
install_ui_cmd.set_defaults(func=start_install_ui)
self._build_args(optionlist=ARGS_INSTALL_UI, parser=install_ui_cmd)
# Add Plotting subcommand
plot_dataframe_cmd = subparsers.add_parser(
'plot-dataframe',

5
freqtrade/commands/build_config_commands.py
View File

@ -93,10 +93,10 @@ def ask_user_config() -> Dict[str, Any]:
"message": "Select exchange",
"choices": [
"binance",
"binanceje",
"binanceus",
"bittrex",
"kraken",
"ftx",
Separator(),
"other",
],
@ -173,6 +173,9 @@ def deploy_new_config(config_path: Path, selections: Dict[str, Any]) -> None:
arguments=selections)
logger.info(f"Writing config to `{config_path}`.")
logger.info(
"Please make sure to check the configuration contents and adjust settings to your needs.")
config_path.write_text(config_text)

12
freqtrade/commands/cli_options.py
View File

@ -110,6 +110,11 @@ AVAILABLE_CLI_OPTIONS = {
help='Enforce dry-run for trading (removes Exchange secrets and simulates trades).',
action='store_true',
),
"dry_run_wallet": Arg(
'--dry-run-wallet', '--starting-balance',
help='Starting balance, used for backtesting / hyperopt and dry-runs.',
type=float,
),
# Optimize common
"timeframe": Arg(
'-i', '--timeframe', '--ticker-interval',
@ -128,7 +133,6 @@ AVAILABLE_CLI_OPTIONS = {
"stake_amount": Arg(
'--stake-amount',
help='Override the value of the `stake_amount` configuration setting.',
type=float,
),
# Backtesting
"position_stacking": Arg(
@ -387,6 +391,12 @@ AVAILABLE_CLI_OPTIONS = {
help='Clean all existing data for the selected exchange/pairs/timeframes.',
action='store_true',
),
"erase_ui_only": Arg(
'--erase',
help="Clean UI folder, don't download new version.",
action='store_true',
default=False,
),
# Templating options
"template": Arg(
'--template',

17
freqtrade/commands/data_commands.py
View File

@ -10,6 +10,7 @@ from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_oh
refresh_backtest_trades_data)
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.resolvers import ExchangeResolver
from freqtrade.state import RunMode
@ -42,15 +43,17 @@ def start_download_data(args: Dict[str, Any]) -> None:
"Downloading data requires a list of pairs. "
"Please check the documentation on how to configure this.")
logger.info(f"About to download pairs: {config['pairs']}, "
f"intervals: {config['timeframes']} to {config['datadir']}")
pairs_not_available: List[str] = []
# Init exchange
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
# Manual validations of relevant settings
exchange.validate_pairs(config['pairs'])
expanded_pairs = expand_pairlist(config['pairs'], list(exchange.markets))
logger.info(f"About to download pairs: {expanded_pairs}, "
f"intervals: {config['timeframes']} to {config['datadir']}")
for timeframe in config['timeframes']:
exchange.validate_timeframes(timeframe)
@ -58,20 +61,20 @@ def start_download_data(args: Dict[str, Any]) -> None:
if config.get('download_trades'):
pairs_not_available = refresh_backtest_trades_data(
exchange, pairs=config['pairs'], datadir=config['datadir'],
exchange, pairs=expanded_pairs, datadir=config['datadir'],
timerange=timerange, erase=bool(config.get('erase')),
data_format=config['dataformat_trades'])
# Convert downloaded trade data to different timeframes
convert_trades_to_ohlcv(
pairs=config['pairs'], timeframes=config['timeframes'],
pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
data_format_ohlcv=config['dataformat_ohlcv'],
data_format_trades=config['dataformat_trades'],
)
)
else:
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=config['pairs'], timeframes=config['timeframes'],
exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
data_format=config['dataformat_ohlcv'])

88
freqtrade/commands/deploy_commands.py
View File

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

21
freqtrade/commands/hyperopt_commands.py
View File

@ -17,7 +17,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
"""
List hyperopt epochs previously evaluated
"""
from freqtrade.optimize.hyperopt import Hyperopt
from freqtrade.optimize.hyperopt_tools import HyperoptTools
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
@ -47,7 +47,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
config.get('hyperoptexportfilename'))
# Previous evaluations
epochs = Hyperopt.load_previous_results(results_file)
epochs = HyperoptTools.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
@ -57,18 +57,19 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
if not export_csv:
try:
print(Hyperopt.get_result_table(config, epochs, total_epochs,
not filteroptions['only_best'], print_colorized, 0))
print(HyperoptTools.get_result_table(config, epochs, total_epochs,
not filteroptions['only_best'],
print_colorized, 0))
except KeyboardInterrupt:
print('User interrupted..')
if epochs and not no_details:
sorted_epochs = sorted(epochs, key=itemgetter('loss'))
results = sorted_epochs[0]
Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
HyperoptTools.print_epoch_details(results, total_epochs, print_json, no_header)
if epochs and export_csv:
Hyperopt.export_csv_file(
HyperoptTools.export_csv_file(
config, epochs, total_epochs, not filteroptions['only_best'], export_csv
)
@ -77,7 +78,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
"""
Show details of a hyperopt epoch previously evaluated
"""
from freqtrade.optimize.hyperopt import Hyperopt
from freqtrade.optimize.hyperopt_tools import HyperoptTools
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
@ -105,7 +106,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
}
# Previous evaluations
epochs = Hyperopt.load_previous_results(results_file)
epochs = HyperoptTools.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
@ -124,8 +125,8 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
if epochs:
val = epochs[n]
Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,
header_str="Epoch details")
HyperoptTools.print_epoch_details(val, total_epochs, print_json, no_header,
header_str="Epoch details")
def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:

15
freqtrade/commands/optimize_commands.py
View File

@ -3,7 +3,8 @@ from typing import Any, Dict
from freqtrade import constants
from freqtrade.configuration import setup_utils_configuration
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_coin_value
from freqtrade.state import RunMode
@ -22,11 +23,13 @@ def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[
RunMode.BACKTEST: 'backtesting',
RunMode.HYPEROPT: 'hyperoptimization',
}
if (method in no_unlimited_runmodes.keys() and
config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT):
raise DependencyException(
f'The value of `stake_amount` cannot be set as "{constants.UNLIMITED_STAKE_AMOUNT}" '
f'for {no_unlimited_runmodes[method]}')
if method in no_unlimited_runmodes.keys():
if (config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT
and config['stake_amount'] > config['dry_run_wallet']):
wallet = round_coin_value(config['dry_run_wallet'], config['stake_currency'])
stake = round_coin_value(config['stake_amount'], config['stake_currency'])
raise OperationalException(f"Starting balance ({wallet}) "
f"is smaller than stake_amount {stake}.")
return config

18
freqtrade/configuration/config_validation.py
View File

@ -47,6 +47,8 @@ def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
conf_schema = deepcopy(constants.CONF_SCHEMA)
if conf.get('runmode', RunMode.OTHER) in (RunMode.DRY_RUN, RunMode.LIVE):
conf_schema['required'] = constants.SCHEMA_TRADE_REQUIRED
elif conf.get('runmode', RunMode.OTHER) in (RunMode.BACKTEST, RunMode.HYPEROPT):
conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED
else:
conf_schema['required'] = constants.SCHEMA_MINIMAL_REQUIRED
try:
@ -54,7 +56,7 @@ def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
return conf
except ValidationError as e:
logger.critical(
f"Invalid configuration. See config.json.example. Reason: {e}"
f"Invalid configuration. Reason: {e}"
)
raise ValidationError(
best_match(Draft4Validator(conf_schema).iter_errors(conf)).message
@ -72,6 +74,7 @@ def validate_config_consistency(conf: Dict[str, Any]) -> None:
# validating trailing stoploss
_validate_trailing_stoploss(conf)
_validate_price_config(conf)
_validate_edge(conf)
_validate_whitelist(conf)
_validate_protections(conf)
@ -93,6 +96,19 @@ def _validate_unlimited_amount(conf: Dict[str, Any]) -> None:
raise OperationalException("`max_open_trades` and `stake_amount` cannot both be unlimited.")
def _validate_price_config(conf: Dict[str, Any]) -> None:
"""
When using market orders, price sides must be using the "other" side of the price
"""
if (conf.get('order_types', {}).get('buy') == 'market'
and conf.get('bid_strategy', {}).get('price_side') != 'ask'):
raise OperationalException('Market buy orders require bid_strategy.price_side = "ask".')
if (conf.get('order_types', {}).get('sell') == 'market'
and conf.get('ask_strategy', {}).get('price_side') != 'bid'):
raise OperationalException('Market sell orders require ask_strategy.price_side = "bid".')
def _validate_trailing_stoploss(conf: Dict[str, Any]) -> None:
if conf.get('stoploss') == 0.0:

17
freqtrade/configuration/configuration.py
View File

@ -214,9 +214,6 @@ class Configuration:
self._args_to_config(
config, argname='enable_protections',
logstring='Parameter --enable-protections detected, enabling Protections. ...')
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
if 'use_max_market_positions' in self.args and not self.args["use_max_market_positions"]:
config.update({'use_max_market_positions': False})
@ -228,11 +225,23 @@ class Configuration:
'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
elif config['runmode'] in NON_UTIL_MODES:
logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
if self.args.get('stake_amount', None):
# Convert explicitly to float to support CLI argument for both unlimited and value
try:
self.args['stake_amount'] = float(self.args['stake_amount'])
except ValueError:
pass
self._args_to_config(config, argname='stake_amount',
logstring='Parameter --stake-amount detected, '
'overriding stake_amount to: {} ...')
self._args_to_config(config, argname='dry_run_wallet',
logstring='Parameter --dry-run-wallet detected, '
'overriding dry_run_wallet to: {} ...')
self._args_to_config(config, argname='fee',
logstring='Parameter --fee detected, '
'setting fee to: {} ...')

7
freqtrade/configuration/timerange.py
View File

@ -7,6 +7,8 @@ from typing import Optional
import arrow
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
@ -103,5 +105,8 @@ class TimeRange:
stop = int(stops) // 1000
else:
stop = int(stops)
if start > stop > 0:
raise OperationalException(
f'Start date is after stop date for timerange "{text}"')
return TimeRange(stype[0], stype[1], start, stop)
raise Exception('Incorrect syntax for timerange "%s"' % text)
raise OperationalException(f'Incorrect syntax for timerange "{text}"')

38
freqtrade/constants.py
View File

@ -45,6 +45,21 @@ USERPATH_NOTEBOOKS = 'notebooks'
TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
# Define decimals per coin for outputs
# Only used for outputs.
DECIMAL_PER_COIN_FALLBACK = 3 # Should be low to avoid listing all possible FIAT's
DECIMALS_PER_COIN = {
'BTC': 8,
'ETH': 5,
}
DUST_PER_COIN = {
'BTC': 0.0001,
'ETH': 0.01
}
# Soure files with destination directories within user-directory
USER_DATA_FILES = {
'sample_strategy.py': USERPATH_STRATEGIES,
@ -116,6 +131,7 @@ CONF_SCHEMA = {
'trailing_stop_positive': {'type': 'number', 'minimum': 0, 'maximum': 1},
'trailing_stop_positive_offset': {'type': 'number', 'minimum': 0, 'maximum': 1},
'trailing_only_offset_is_reached': {'type': 'boolean'},
'bot_name': {'type': 'string'},
'unfilledtimeout': {
'type': 'object',
'properties': {
@ -149,11 +165,18 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'ask'},
'bid_last_balance': {
'type': 'number',
'minimum': 0,
'maximum': 1,
'exclusiveMaximum': False,
},
'use_order_book': {'type': 'boolean'},
'order_book_min': {'type': 'integer', 'minimum': 1},
'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
'use_sell_signal': {'type': 'boolean'},
'sell_profit_only': {'type': 'boolean'},
'sell_profit_offset': {'type': 'number', 'minimum': 0.0},
'ignore_roi_if_buy_signal': {'type': 'boolean'}
}
},
@ -162,6 +185,8 @@ CONF_SCHEMA = {
'properties': {
'buy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'sell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'forcesell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'forcebuy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'emergencysell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss_on_exchange': {'type': 'boolean'},
@ -218,6 +243,7 @@ CONF_SCHEMA = {
'enabled': {'type': 'boolean'},
'token': {'type': 'string'},
'chat_id': {'type': 'string'},
'balance_dust_level': {'type': 'number', 'minimum': 0.0},
'notification_settings': {
'type': 'object',
'properties': {
@ -231,7 +257,7 @@ CONF_SCHEMA = {
}
}
},
'required': ['enabled', 'token', 'chat_id']
'required': ['enabled', 'token', 'chat_id'],
},
'webhook': {
'type': 'object',
@ -358,6 +384,16 @@ SCHEMA_TRADE_REQUIRED = [
'dataformat_trades',
]
SCHEMA_BACKTEST_REQUIRED = [
'exchange',
'max_open_trades',
'stake_currency',
'stake_amount',
'dry_run_wallet',
'dataformat_ohlcv',
'dataformat_trades',
]
SCHEMA_MINIMAL_REQUIRED = [
'exchange',
'dry_run',

114
freqtrade/data/btanalysis.py
View File

@ -2,23 +2,35 @@
Helpers when analyzing backtest data
"""
import logging
from datetime import timezone
from pathlib import Path
from typing import Any, Dict, Optional, Tuple, Union
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.misc import json_load
from freqtrade.persistence import Trade, init_db
from freqtrade.persistence import LocalTrade, Trade, init_db
logger = logging.getLogger(__name__)
# must align with columns in backtest.py
BT_DATA_COLUMNS = ["pair", "profit_percent", "open_date", "close_date", "index", "trade_duration",
"open_rate", "close_rate", "open_at_end", "sell_reason"]
# Old format - maybe remove?
BT_DATA_COLUMNS_OLD = ["pair", "profit_percent", "open_date", "close_date", "index",
"trade_duration", "open_rate", "close_rate", "open_at_end", "sell_reason"]
# Mid-term format, crated by BacktestResult Named Tuple
BT_DATA_COLUMNS_MID = ['pair', 'profit_percent', 'open_date', 'close_date', 'trade_duration',
'open_rate', 'close_rate', 'open_at_end', 'sell_reason', 'fee_open',
'fee_close', 'amount', 'profit_abs', 'profit_ratio']
# Newest format
BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
'open_rate', 'close_rate',
'fee_open', 'fee_close', 'trade_duration',
'profit_ratio', 'profit_abs', 'sell_reason',
'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', ]
def get_latest_optimize_filename(directory: Union[Path, str], variant: str) -> str:
@ -154,7 +166,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
)
else:
# old format - only with lists.
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS_OLD)
df['open_date'] = pd.to_datetime(df['open_date'],
unit='s',
@ -166,7 +178,10 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
utc=True,
infer_datetime_format=True
)
# Create compatibility with new format
df['profit_abs'] = df['close_rate'] - df['open_rate']
if 'profit_ratio' not in df.columns:
df['profit_ratio'] = df['profit_percent']
df = df.sort_values("open_date").reset_index(drop=True)
return df
@ -233,6 +248,20 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
return df_final[df_final['open_trades'] > max_open_trades]
def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
"""
Convert list of Trade objects to pandas Dataframe
:param trades: List of trade objects
:return: Dataframe with BT_DATA_COLUMNS
"""
df = pd.DataFrame.from_records([t.to_json() for t in trades], columns=BT_DATA_COLUMNS)
if len(df) > 0:
df.loc[:, 'close_date'] = pd.to_datetime(df['close_date'], utc=True)
df.loc[:, 'open_date'] = pd.to_datetime(df['open_date'], utc=True)
df.loc[:, 'close_rate'] = df['close_rate'].astype('float64')
return df
def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataFrame:
"""
Load trades from a DB (using dburl)
@ -243,36 +272,10 @@ def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataF
"""
init_db(db_url, clean_open_orders=False)
columns = ["pair", "open_date", "close_date", "profit", "profit_percent",
"open_rate", "close_rate", "amount", "trade_duration", "sell_reason",
"fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
"stake_amount", "max_rate", "min_rate", "id", "exchange",
"stop_loss", "initial_stop_loss", "strategy", "timeframe"]
filters = []
if strategy:
filters.append(Trade.strategy == strategy)
trades = pd.DataFrame([(t.pair,
t.open_date.replace(tzinfo=timezone.utc),
t.close_date.replace(tzinfo=timezone.utc) if t.close_date else None,
t.calc_profit(), t.calc_profit_ratio(),
t.open_rate, t.close_rate, t.amount,
(round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
if t.close_date else None),
t.sell_reason,
t.fee_open, t.fee_close,
t.open_rate_requested,
t.close_rate_requested,
t.stake_amount,
t.max_rate,
t.min_rate,
t.id, t.exchange,
t.stop_loss, t.initial_stop_loss,
t.strategy, t.timeframe
)
for t in Trade.get_trades(filters).all()],
columns=columns)
trades = trade_list_to_dataframe(Trade.get_trades(filters).all())
return trades
@ -333,7 +336,7 @@ def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close"
end = df[column].dropna().iloc[-1]
tmp_means.append((end - start) / start)
return np.mean(tmp_means)
return float(np.mean(tmp_means))
def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
@ -358,7 +361,7 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
"""
Adds a column `col_name` with the cumulative profit for the given trades array.
:param df: DataFrame with date index
:param trades: DataFrame containing trades (requires columns close_date and profit_percent)
:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
:param col_name: Column name that will be assigned the results
:param timeframe: Timeframe used during the operations
:return: Returns df with one additional column, col_name, containing the cumulative profit.
@ -370,8 +373,8 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to timeframe to make sure trades match candles
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date'
)[['profit_percent']].sum()
df.loc[:, col_name] = _trades_sum.cumsum()
)[['profit_ratio']].sum()
df.loc[:, col_name] = _trades_sum['profit_ratio'].cumsum()
# Set first value to 0
df.loc[df.iloc[0].name, col_name] = 0
# FFill to get continuous
@ -380,14 +383,15 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_percent'
) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
value_col: str = 'profit_ratio'
) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
"""
Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_date and profit_percent)
:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_percent')
:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
high and low time and high and low value.
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
@ -403,7 +407,29 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
raise ValueError("No losing trade, therefore no drawdown.")
high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
low_date = profit_results.loc[idxmin, date_col]
return abs(min(max_drawdown_df['drawdown'])), high_date, low_date
high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
['high_value'].idxmax(), 'cumulative']
low_val = max_drawdown_df.loc[idxmin, 'cumulative']
return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:
"""
Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
:param trades: DataFrame containing trades (requires columns close_date and profit_percent)
:param starting_balance: Add starting balance to results, to show the wallets high / low points
:return: Tuple (float, float) with cumsum of profit_abs
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
csum_df = pd.DataFrame()
csum_df['sum'] = trades['profit_abs'].cumsum()
csum_min = csum_df['sum'].min() + starting_balance
csum_max = csum_df['sum'].max() + starting_balance
return csum_min, csum_max
def calculate_outstanding_balance(results: pd.DataFrame, timeframe: str,

8
freqtrade/data/history/jsondatahandler.py
View File

@ -86,8 +86,12 @@ class JsonDataHandler(IDataHandler):
filename = self._pair_data_filename(self._datadir, pair, timeframe)
if not filename.exists():
return DataFrame(columns=self._columns)
pairdata = read_json(filename, orient='values')
pairdata.columns = self._columns
try:
pairdata = read_json(filename, orient='values')
pairdata.columns = self._columns
except ValueError:
logger.error(f"Could not load data for {pair}.")
return DataFrame(columns=self._columns)
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
pairdata['date'] = to_datetime(pairdata['date'],

11
freqtrade/edge/edge_positioning.py
View File

@ -12,6 +12,7 @@ from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.exceptions import OperationalException
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.strategy.interface import SellType
@ -80,10 +81,12 @@ class Edge:
if config.get('fee'):
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(symbol=self.config['exchange']['pair_whitelist'][0])
self.fee = self.exchange.get_fee(symbol=expand_pairlist(
self.config['exchange']['pair_whitelist'], list(self.exchange.markets))[0])
def calculate(self) -> bool:
pairs = self.config['exchange']['pair_whitelist']
pairs = expand_pairlist(self.config['exchange']['pair_whitelist'],
list(self.exchange.markets))
heartbeat = self.edge_config.get('process_throttle_secs')
if (self._last_updated > 0) and (
@ -101,6 +104,7 @@ class Edge:
exchange=self.exchange,
timeframe=self.strategy.timeframe,
timerange=self._timerange,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
data = load_data(
@ -156,7 +160,8 @@ class Edge:
available_capital = (total_capital + capital_in_trade) * self._capital_ratio
allowed_capital_at_risk = available_capital * self._allowed_risk
max_position_size = abs(allowed_capital_at_risk / stoploss)
position_size = min(max_position_size, free_capital)
# Position size must be below available capital.
position_size = min(min(max_position_size, free_capital), available_capital)
if pair in self._cached_pairs:
logger.info(
'winrate: %s, expectancy: %s, position size: %s, pair: %s,'

6
freqtrade/exchange/bittrex.py
View File

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

1
freqtrade/exchange/common.py
View File

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

114
freqtrade/exchange/exchange.py
View File

@ -3,6 +3,7 @@
Cryptocurrency Exchanges support
"""
import asyncio
import http
import inspect
import logging
from copy import deepcopy
@ -17,7 +18,7 @@ from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE, TRU
decimal_to_precision)
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.constants import DEFAULT_AMOUNT_RESERVE_PERCENT, ListPairsWithTimeframes
from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
InvalidOrderException, OperationalException, RetryableOrderError,
@ -25,6 +26,7 @@ from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFun
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES, retrier,
retrier_async)
from freqtrade.misc import deep_merge_dicts, safe_value_fallback2
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
CcxtModuleType = Any
@ -33,6 +35,12 @@ CcxtModuleType = Any
logger = logging.getLogger(__name__)
# Workaround for adding samesite support to pre 3.8 python
# Only applies to python3.7, and only on certain exchanges (kraken)
# Replicates the fix from starlette (which is actually causing this problem)
http.cookies.Morsel._reserved["samesite"] = "SameSite" # type: ignore
class Exchange:
_config: Dict = {}
@ -65,6 +73,7 @@ class Exchange:
"""
self._api: ccxt.Exchange = None
self._api_async: ccxt_async.Exchange = None
self._markets: Dict = {}
self._config.update(config)
@ -92,7 +101,6 @@ class Exchange:
logger.info("Overriding exchange._ft_has with config params, result: %s", self._ft_has)
# Assign this directly for easy access
self._ohlcv_candle_limit = self._ft_has['ohlcv_candle_limit']
self._ohlcv_partial_candle = self._ft_has['ohlcv_partial_candle']
self._trades_pagination = self._ft_has['trades_pagination']
@ -128,7 +136,8 @@ class Exchange:
self.validate_pairs(config['exchange']['pair_whitelist'])
self.validate_ordertypes(config.get('order_types', {}))
self.validate_order_time_in_force(config.get('order_time_in_force', {}))
self.validate_required_startup_candles(config.get('startup_candle_count', 0))
self.validate_required_startup_candles(config.get('startup_candle_count', 0),
config.get('timeframe', ''))
# Converts the interval provided in minutes in config to seconds
self.markets_refresh_interval: int = exchange_config.get(
@ -138,6 +147,9 @@ class Exchange:
"""
Destructor - clean up async stuff
"""
self.close()
def close(self):
logger.debug("Exchange object destroyed, closing async loop")
if self._api_async and inspect.iscoroutinefunction(self._api_async.close):
asyncio.get_event_loop().run_until_complete(self._api_async.close())
@ -189,26 +201,32 @@ class Exchange:
def timeframes(self) -> List[str]:
return list((self._api.timeframes or {}).keys())
@property
def ohlcv_candle_limit(self) -> int:
"""exchange ohlcv candle limit"""
return int(self._ohlcv_candle_limit)
@property
def markets(self) -> Dict:
"""exchange ccxt markets"""
if not self._api.markets:
if not self._markets:
logger.info("Markets were not loaded. Loading them now..")
self._load_markets()
return self._api.markets
return self._markets
@property
def precisionMode(self) -> str:
"""exchange ccxt precisionMode"""
return self._api.precisionMode
def ohlcv_candle_limit(self, timeframe: str) -> int:
"""
Exchange ohlcv candle limit
Uses ohlcv_candle_limit_per_timeframe if the exchange has different limts
per timeframe (e.g. bittrex), otherwise falls back to ohlcv_candle_limit
:param timeframe: Timeframe to check
:return: Candle limit as integer
"""
return int(self._ft_has.get('ohlcv_candle_limit_per_timeframe', {}).get(
timeframe, self._ft_has.get('ohlcv_candle_limit')))
def get_markets(self, base_currencies: List[str] = None, quote_currencies: List[str] = None,
pairs_only: bool = False, active_only: bool = False) -> Dict:
pairs_only: bool = False, active_only: bool = False) -> Dict[str, Any]:
"""
Return exchange ccxt markets, filtered out by base currency and quote currency
if this was requested in parameters.
@ -290,11 +308,11 @@ class Exchange:
def _load_markets(self) -> None:
""" Initialize markets both sync and async """
try:
self._api.load_markets()
self._markets = self._api.load_markets()
self._load_async_markets()
self._last_markets_refresh = arrow.utcnow().int_timestamp
except ccxt.BaseError as e:
logger.warning('Unable to initialize markets. Reason: %s', e)
except ccxt.BaseError:
logger.exception('Unable to initialize markets.')
def reload_markets(self) -> None:
"""Reload markets both sync and async if refresh interval has passed """
@ -305,7 +323,7 @@ class Exchange:
return None
logger.debug("Performing scheduled market reload..")
try:
self._api.load_markets(reload=True)
self._markets = self._api.load_markets(reload=True)
# Also reload async markets to avoid issues with newly listed pairs
self._load_async_markets(reload=True)
self._last_markets_refresh = arrow.utcnow().int_timestamp
@ -335,8 +353,9 @@ class Exchange:
if not self.markets:
logger.warning('Unable to validate pairs (assuming they are correct).')
return
extended_pairs = expand_pairlist(pairs, list(self.markets), keep_invalid=True)
invalid_pairs = []
for pair in pairs:
for pair in extended_pairs:
# Note: ccxt has BaseCurrency/QuoteCurrency format for pairs
# TODO: add a support for having coins in BTC/USDT format
if self.markets and pair not in self.markets:
@ -418,15 +437,16 @@ class Exchange:
raise OperationalException(
f'Time in force policies are not supported for {self.name} yet.')
def validate_required_startup_candles(self, startup_candles: int) -> None:
def validate_required_startup_candles(self, startup_candles: int, timeframe: str) -> None:
"""
Checks if required startup_candles is more than ohlcv_candle_limit.
Checks if required startup_candles is more than ohlcv_candle_limit().
Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
"""
if startup_candles + 5 > self._ft_has['ohlcv_candle_limit']:
candle_limit = self.ohlcv_candle_limit(timeframe)
if startup_candles + 5 > candle_limit:
raise OperationalException(
f"This strategy requires {startup_candles} candles to start. "
f"{self.name} only provides {self._ft_has['ohlcv_candle_limit']}.")
f"{self.name} only provides {candle_limit} for {timeframe}.")
def exchange_has(self, endpoint: str) -> bool:
"""
@ -487,6 +507,41 @@ class Exchange:
else:
return 1 / pow(10, precision)
def get_min_pair_stake_amount(self, pair: str, price: float,
stoploss: float) -> Optional[float]:
try:
market = self.markets[pair]
except KeyError:
raise ValueError(f"Can't get market information for symbol {pair}")
if 'limits' not in market:
return None
min_stake_amounts = []
limits = market['limits']
if ('cost' in limits and 'min' in limits['cost']
and limits['cost']['min'] is not None):
min_stake_amounts.append(limits['cost']['min'])
if ('amount' in limits and 'min' in limits['amount']
and limits['amount']['min'] is not None):
min_stake_amounts.append(limits['amount']['min'] * price)
if not min_stake_amounts:
return None
# reserve some percent defined in config (5% default) + stoploss
amount_reserve_percent = 1.0 + self._config.get('amount_reserve_percent',
DEFAULT_AMOUNT_RESERVE_PERCENT)
amount_reserve_percent += abs(stoploss)
# it should not be more than 50%
amount_reserve_percent = max(min(amount_reserve_percent, 1.5), 1)
# The value returned should satisfy both limits: for amount (base currency) and
# for cost (quote, stake currency), so max() is used here.
# See also #2575 at github.
return max(min_stake_amounts) * amount_reserve_percent
def dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
rate: float, params: Dict = {}) -> Dict[str, Any]:
order_id = f'dry_run_{side}_{datetime.now().timestamp()}'
@ -658,8 +713,8 @@ class Exchange:
@retrier
def fetch_ticker(self, pair: str) -> dict:
try:
if (pair not in self._api.markets or
self._api.markets[pair].get('active', False) is False):
if (pair not in self.markets or
self.markets[pair].get('active', False) is False):
raise ExchangeError(f"Pair {pair} not available")
data = self._api.fetch_ticker(pair)
return data
@ -676,7 +731,7 @@ class Exchange:
"""
Get candle history using asyncio and returns the list of candles.
Handles all async work for this.
Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
Async over one pair, assuming we get `self.ohlcv_candle_limit()` candles per call.
:param pair: Pair to download
:param timeframe: Timeframe to get data for
:param since_ms: Timestamp in milliseconds to get history from
@ -706,7 +761,7 @@ class Exchange:
Download historic ohlcv
"""
one_call = timeframe_to_msecs(timeframe) * self._ohlcv_candle_limit
one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(timeframe)
logger.debug(
"one_call: %s msecs (%s)",
one_call,
@ -808,7 +863,7 @@ class Exchange:
data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
since=since_ms,
limit=self._ohlcv_candle_limit)
limit=self.ohlcv_candle_limit(timeframe))
# Some exchanges sort OHLCV in ASC order and others in DESC.
# Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
@ -936,7 +991,7 @@ class Exchange:
while True:
t = await self._async_fetch_trades(pair, since=since)
if len(t):
since = t[-1][1]
since = t[-1][0]
trades.extend(t)
# Reached the end of the defined-download period
if until and t[-1][0] > until:
@ -981,7 +1036,7 @@ class Exchange:
"""
Get trade history data using asyncio.
Handles all async work and returns the list of candles.
Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
Async over one pair, assuming we get `self.ohlcv_candle_limit()` candles per call.
:param pair: Pair to download
:param since: Timestamp in milliseconds to get history from
:param until: Timestamp in milliseconds. Defaults to current timestamp if not defined.
@ -1001,7 +1056,8 @@ class Exchange:
:param order: Order dict as returned from fetch_order()
:return: True if order has been cancelled without being filled, False otherwise.
"""
return order.get('status') in ('closed', 'canceled') and order.get('filled') == 0.0
return (order.get('status') in ('closed', 'canceled', 'cancelled')
and order.get('filled') == 0.0)
@retrier
def cancel_order(self, order_id: str, pair: str) -> Dict:
@ -1176,6 +1232,8 @@ class Exchange:
def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
price: float = 1, taker_or_maker: str = 'maker') -> float:
try:
if self._config['dry_run'] and self._config.get('fee', None) is not None:
return self._config['fee']
# validate that markets are loaded before trying to get fee
if self._api.markets is None or len(self._api.markets) == 0:
self._api.load_markets()

2
freqtrade/exchange/kraken.py
View File

@ -48,7 +48,7 @@ class Kraken(Exchange):
orders = self._api.fetch_open_orders()
order_list = [(x["symbol"].split("/")[0 if x["side"] == "sell" else 1],
x["remaining"],
x["remaining"] if x["side"] == "sell" else x["remaining"] * x["price"],
# Don't remove the below comment, this can be important for debuggung
# x["side"], x["amount"],
) for x in orders]

167
freqtrade/freqtradebot.py
View File

@ -179,6 +179,7 @@ class FreqtradeBot(LoggingMixin):
# Without this, freqtrade my try to recreate stoploss_on_exchange orders
# while selling is in process, since telegram messages arrive in an different thread.
with self._sell_lock:
trades = Trade.get_open_trades()
# First process current opened trades (positions)
self.exit_positions(trades)
@ -200,7 +201,7 @@ class FreqtradeBot(LoggingMixin):
Notify the user when the bot is stopped
and there are still open trades active.
"""
open_trades = Trade.get_trades([Trade.is_open == 1]).all()
open_trades = Trade.get_trades([Trade.is_open.is_(True)]).all()
if len(open_trades) != 0:
msg = {
@ -233,7 +234,7 @@ class FreqtradeBot(LoggingMixin):
_whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
return _whitelist
def get_free_open_trades(self):
def get_free_open_trades(self) -> int:
"""
Return the number of free open trades slots or 0 if
max number of open trades reached
@ -246,6 +247,10 @@ class FreqtradeBot(LoggingMixin):
Updates open orders based on order list kept in the database.
Mainly updates the state of orders - but may also close trades
"""
if self.config['dry_run'] or self.config['exchange'].get('skip_open_order_update', False):
# Updating open orders in dry-run does not make sense and will fail.
return
orders = Order.get_open_orders()
logger.info(f"Updating {len(orders)} open orders.")
for order in orders:
@ -256,6 +261,7 @@ class FreqtradeBot(LoggingMixin):
self.update_trade_state(order.trade, order.order_id, fo)
except ExchangeError as e:
logger.warning(f"Error updating Order {order.order_id} due to {e}")
def update_closed_trades_without_assigned_fees(self):
@ -263,6 +269,10 @@ class FreqtradeBot(LoggingMixin):
Update closed trades without close fees assigned.
Only acts when Orders are in the database, otherwise the last orderid is unknown.
"""
if self.config['dry_run']:
# Updating open orders in dry-run does not make sense and will fail.
return
trades: List[Trade] = Trade.get_sold_trades_without_assigned_fees()
for trade in trades:
@ -422,7 +432,7 @@ class FreqtradeBot(LoggingMixin):
ticker = self.exchange.fetch_ticker(pair)
ticker_rate = ticker[bid_strategy['price_side']]
if ticker['last'] and ticker_rate > ticker['last']:
balance = self.config['bid_strategy']['ask_last_balance']
balance = bid_strategy['ask_last_balance']
ticker_rate = ticker_rate + balance * (ticker['last'] - ticker_rate)
used_rate = ticker_rate
@ -430,117 +440,6 @@ class FreqtradeBot(LoggingMixin):
return used_rate
def get_trade_stake_amount(self, pair: str) -> float:
"""
Calculate stake amount for the trade
:return: float: Stake amount
:raise: DependencyException if the available stake amount is too low
"""
stake_amount: float
# Ensure wallets are uptodate.
self.wallets.update()
if self.edge:
stake_amount = self.edge.stake_amount(
pair,
self.wallets.get_free(self.config['stake_currency']),
self.wallets.get_total(self.config['stake_currency']),
Trade.total_open_trades_stakes()
)
else:
stake_amount = self.config['stake_amount']
if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
stake_amount = self._calculate_unlimited_stake_amount()
return self._check_available_stake_amount(stake_amount)
def _get_available_stake_amount(self) -> float:
"""
Return the total currently available balance in stake currency,
respecting tradable_balance_ratio.
Calculated as
<open_trade stakes> + free amount ) * tradable_balance_ratio - <open_trade stakes>
"""
val_tied_up = Trade.total_open_trades_stakes()
# Ensure <tradable_balance_ratio>% is used from the overall balance
# Otherwise we'd risk lowering stakes with each open trade.
# (tied up + current free) * ratio) - tied up
available_amount = ((val_tied_up + self.wallets.get_free(self.config['stake_currency'])) *
self.config['tradable_balance_ratio']) - val_tied_up
return available_amount
def _calculate_unlimited_stake_amount(self) -> float:
"""
Calculate stake amount for "unlimited" stake amount
:return: 0 if max number of trades reached, else stake_amount to use.
"""
free_open_trades = self.get_free_open_trades()
if not free_open_trades:
return 0
available_amount = self._get_available_stake_amount()
return available_amount / free_open_trades
def _check_available_stake_amount(self, stake_amount: float) -> float:
"""
Check if stake amount can be fulfilled with the available balance
for the stake currency
:return: float: Stake amount
"""
available_amount = self._get_available_stake_amount()
if self.config['amend_last_stake_amount']:
# Remaining amount needs to be at least stake_amount * last_stake_amount_min_ratio
# Otherwise the remaining amount is too low to trade.
if available_amount > (stake_amount * self.config['last_stake_amount_min_ratio']):
stake_amount = min(stake_amount, available_amount)
else:
stake_amount = 0
if available_amount < stake_amount:
raise DependencyException(
f"Available balance ({available_amount} {self.config['stake_currency']}) is "
f"lower than stake amount ({stake_amount} {self.config['stake_currency']})"
)
return stake_amount
def _get_min_pair_stake_amount(self, pair: str, price: float) -> Optional[float]:
try:
market = self.exchange.markets[pair]
except KeyError:
raise ValueError(f"Can't get market information for symbol {pair}")
if 'limits' not in market:
return None
min_stake_amounts = []
limits = market['limits']
if ('cost' in limits and 'min' in limits['cost']
and limits['cost']['min'] is not None):
min_stake_amounts.append(limits['cost']['min'])
if ('amount' in limits and 'min' in limits['amount']
and limits['amount']['min'] is not None):
min_stake_amounts.append(limits['amount']['min'] * price)
if not min_stake_amounts:
return None
# reserve some percent defined in config (5% default) + stoploss
amount_reserve_percent = 1.0 - self.config.get('amount_reserve_percent',
constants.DEFAULT_AMOUNT_RESERVE_PERCENT)
amount_reserve_percent += self.strategy.stoploss
# it should not be more than 50%
amount_reserve_percent = max(amount_reserve_percent, 0.5)
# The value returned should satisfy both limits: for amount (base currency) and
# for cost (quote, stake currency), so max() is used here.
# See also #2575 at github.
return max(min_stake_amounts) / amount_reserve_percent
def create_trade(self, pair: str) -> bool:
"""
Check the implemented trading strategy for buy signals.
@ -574,7 +473,8 @@ class FreqtradeBot(LoggingMixin):
(buy, sell) = self.strategy.get_signal(pair, self.strategy.timeframe, analyzed_df)
if buy and not sell:
stake_amount = self.get_trade_stake_amount(pair)
stake_amount = self.wallets.get_trade_stake_amount(pair, self.get_free_open_trades(),
self.edge)
if not stake_amount:
logger.debug(f"Stake amount is 0, ignoring possible trade for {pair}.")
return False
@ -620,7 +520,8 @@ class FreqtradeBot(LoggingMixin):
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:
def execute_buy(self, pair: str, stake_amount: float, price: Optional[float] = None,
forcebuy: bool = False) -> bool:
"""
Executes a limit buy for the given pair
:param pair: pair for which we want to create a LIMIT_BUY
@ -637,7 +538,8 @@ class FreqtradeBot(LoggingMixin):
if not buy_limit_requested:
raise PricingError('Could not determine buy price.')
min_stake_amount = self._get_min_pair_stake_amount(pair, buy_limit_requested)
min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, buy_limit_requested,
self.strategy.stoploss)
if min_stake_amount is not None and min_stake_amount > stake_amount:
logger.warning(
f"Can't open a new trade for {pair}: stake amount "
@ -647,6 +549,10 @@ class FreqtradeBot(LoggingMixin):
amount = stake_amount / buy_limit_requested
order_type = self.strategy.order_types['buy']
if forcebuy:
# Forcebuy can define a different ordertype
order_type = self.strategy.order_types.get('forcebuy', order_type)
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force):
@ -839,7 +745,13 @@ class FreqtradeBot(LoggingMixin):
logger.warning("Sell Price at location from orderbook could not be determined.")
raise PricingError from e
else:
rate = self.exchange.fetch_ticker(pair)[ask_strategy['price_side']]
ticker = self.exchange.fetch_ticker(pair)
ticker_rate = ticker[ask_strategy['price_side']]
if ticker['last'] and ticker_rate < ticker['last']:
balance = ask_strategy.get('bid_last_balance', 0.0)
ticker_rate = ticker_rate - balance * (ticker_rate - ticker['last'])
rate = ticker_rate
if rate is None:
raise PricingError(f"Sell-Rate for {pair} was empty.")
self._sell_rate_cache[pair] = rate
@ -989,7 +901,8 @@ class FreqtradeBot(LoggingMixin):
logger.warning('Stoploss order was cancelled, but unable to recreate one.')
# Finally we check if stoploss on exchange should be moved up because of trailing.
if stoploss_order and self.config.get('trailing_stop', False):
if stoploss_order and (self.config.get('trailing_stop', False)
or self.config.get('use_custom_stoploss', False)):
# if trailing stoploss is enabled we check if stoploss value has changed
# in which case we cancel stoploss order and put another one with new
# value immediately
@ -1030,7 +943,7 @@ class FreqtradeBot(LoggingMixin):
Check and execute sell
"""
should_sell = self.strategy.should_sell(
trade, sell_rate, datetime.utcnow(), buy, sell,
trade, sell_rate, datetime.now(timezone.utc), buy, sell,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
)
@ -1116,13 +1029,13 @@ class FreqtradeBot(LoggingMixin):
was_trade_fully_canceled = False
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in ('canceled', 'closed'):
if order['status'] not in ('cancelled', 'canceled', 'closed'):
corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount)
# Avoid race condition where the order could not be cancelled coz its already filled.
# Simply bailing here is the only safe way - as this order will then be
# handled in the next iteration.
if corder.get('status') not in ('canceled', 'closed'):
if corder.get('status') not in ('cancelled', 'canceled', 'closed'):
logger.warning(f"Order {trade.open_order_id} for {trade.pair} not cancelled.")
return False
else:
@ -1169,7 +1082,9 @@ class FreqtradeBot(LoggingMixin):
if not self.exchange.check_order_canceled_empty(order):
try:
# if trade is not partially completed, just delete the order
self.exchange.cancel_order(trade.open_order_id, trade.pair)
co = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount)
trade.update_order(co)
except InvalidOrderException:
logger.exception(f"Could not cancel sell order {trade.open_order_id}")
return 'error cancelling order'
@ -1177,6 +1092,7 @@ class FreqtradeBot(LoggingMixin):
else:
reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
logger.info('Sell order %s for %s.', reason, trade)
trade.update_order(order)
trade.close_rate = None
trade.close_rate_requested = None
@ -1251,6 +1167,10 @@ class FreqtradeBot(LoggingMixin):
if sell_reason == SellType.EMERGENCY_SELL:
# Emergency sells (default to market!)
order_type = self.strategy.order_types.get("emergencysell", "market")
if sell_reason == SellType.FORCE_SELL:
# Force sells (default to the sell_type defined in the strategy,
# but we allow this value to be changed)
order_type = self.strategy.order_types.get("forcesell", order_type)
amount = self._safe_sell_amount(trade.pair, trade.amount)
time_in_force = self.strategy.order_time_in_force['sell']
@ -1279,6 +1199,7 @@ class FreqtradeBot(LoggingMixin):
trade.orders.append(order_obj)
trade.open_order_id = order['id']
trade.sell_order_status = ''
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.value
# In case of market sell orders the order can be closed immediately

40
freqtrade/misc.py
View File

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

18
contrib/loss_functions/SortinoLossBalance.py → freqtrade/optimize/SortinoLossBalance.py
View File

@ -56,22 +56,22 @@ class SortinoLossBalance(IHyperOptLoss):
# index becomes open_time
pair_trades = (
results.loc[results["pair"].values == pair]
.set_index("open_time")
.set_index("open_date")
.resample(timeframe)
.asfreq()
.reindex(date_index)
)
open_rate = pair_trades["open_rate"].fillna(0).values
open_time = pair_trades.index.values
close_time = pair_trades["close_time"].values
open_date = pair_trades.index.values
close_date = pair_trades["close_date"].values
close = pair_candles["close"].values
profits = pair_trades["profit_percent"].values - slippage
profits = pair_trades["profit_ratio"].values - slippage
# at the open_time candle, the balance is matched to the close of the candle
pair_balance = np.where(
# only the rows with actual trades
(open_rate > 0)
# only if the trade is not also closed on the same candle
& (open_time != close_time),
& (open_date != close_date),
1 - open_rate / close - slippage,
# or initialize to 0
0,
@ -81,16 +81,16 @@ class SortinoLossBalance(IHyperOptLoss):
# only rows with actual trades
(open_rate > 0)
# the rows where a close happens
& (open_time == close_time),
& (open_date == close_date),
# use to profits
profits,
# otherwise leave unchanged
pair_balance,
)
# how much time each trade was open, close - open time
periods = close_time - open_time
# how many candles each trade was open, set as a counter at each trade open_time index
# how much time each trade was open, close - open date
periods = close_date - open_date
# how many candles each trade was open, set as a counter at each trade open_date index
hops = np.nan_to_num(periods / timedelta).astype(int)
# each loop update one timeframe forward, the balance on each timeframe

283
freqtrade/optimize/backtesting.py
View File

@ -6,26 +6,29 @@ This module contains the backtesting logic
import logging
from collections import defaultdict
from copy import deepcopy
from datetime import datetime, timedelta
from typing import Any, Dict, List, NamedTuple, Optional, Tuple
from datetime import datetime, timedelta, timezone
from typing import Any, Dict, List, Optional, Tuple
from pandas import DataFrame
from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history
from freqtrade.data.btanalysis import trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import OperationalException
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.mixins import LoggingMixin
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
store_backtest_stats)
from freqtrade.persistence import PairLocks, Trade
from freqtrade.persistence import LocalTrade, PairLocks, Trade
from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
@ -40,25 +43,6 @@ LOW_IDX = 5
HIGH_IDX = 6
class BacktestResult(NamedTuple):
"""
NamedTuple Defining BacktestResults inputs.
"""
pair: str
profit_percent: float
profit_abs: float
open_date: datetime
open_rate: float
open_fee: float
close_date: datetime
close_rate: float
close_fee: float
amount: float
trade_duration: float
open_at_end: bool
sell_reason: SellType
class Backtesting:
"""
Backtesting class, this class contains all the logic to run a backtest
@ -76,6 +60,8 @@ class Backtesting:
# Reset keys for backtesting
remove_credentials(self.config)
self.strategylist: List[IStrategy] = []
self.all_results: Dict[str, Dict] = {}
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
dataprovider = DataProvider(self.config, self.exchange)
@ -129,6 +115,8 @@ class Backtesting:
if self.config.get('enable_protections', False):
self.protections = ProtectionManager(self.config)
self.wallets = Wallets(self.config, self.exchange, log=False)
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
# Load one (first) strategy
@ -139,7 +127,7 @@ class Backtesting:
PairLocks.use_db = True
Trade.use_db = True
def _set_strategy(self, strategy):
def _set_strategy(self, strategy: IStrategy):
"""
Load strategy into backtesting
"""
@ -150,6 +138,10 @@ class Backtesting:
self.strategy.order_types['stoploss_on_exchange'] = False
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
"""
Loads backtest data and returns the data combined with the timerange
as tuple.
"""
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
@ -180,11 +172,10 @@ class Backtesting:
Backtesting setup method - called once for every call to "backtest()".
"""
PairLocks.use_db = False
PairLocks.timeframe = self.config['timeframe']
Trade.use_db = False
if enable_protections:
# Reset persisted data - used for protections only
PairLocks.reset_locks()
Trade.reset_trades()
PairLocks.reset_locks()
Trade.reset_trades()
def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
"""
@ -213,10 +204,10 @@ class Backtesting:
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
data[pair] = [x for x in df_analyzed.itertuples(index=False, name=None)]
data[pair] = df_analyzed.values.tolist()
return data
def _get_close_rate(self, sell_row: Tuple, trade: Trade, sell: SellCheckTuple,
def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple,
trade_dur: int) -> float:
"""
Get close rate for backtesting result
@ -256,37 +247,48 @@ class Backtesting:
else:
return sell_row[OPEN_IDX]
def _get_sell_trade_entry(self, trade: Trade, sell_row: Tuple) -> Optional[BacktestResult]:
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], sell_row[DATE_IDX],
sell_row[BUY_IDX], sell_row[SELL_IDX],
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
sell_row[DATE_IDX], sell_row[BUY_IDX], sell_row[SELL_IDX],
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade_dur = int((sell_row[DATE_IDX] - trade.open_date).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
trade.close_date = sell_row[DATE_IDX]
trade.sell_reason = sell.sell_type
trade.sell_reason = sell.sell_type.value
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
trade.close(closerate, show_msg=False)
return trade
return BacktestResult(pair=trade.pair,
profit_percent=trade.calc_profit_ratio(rate=closerate),
profit_abs=trade.calc_profit(rate=closerate),
open_date=trade.open_date,
open_rate=trade.open_rate,
open_fee=self.fee,
close_date=sell_row[DATE_IDX],
close_rate=closerate,
close_fee=self.fee,
amount=trade.amount,
trade_duration=trade_dur,
open_at_end=False,
sell_reason=sell.sell_type
)
return None
def handle_left_open(self, open_trades: Dict[str, List[Trade]],
data: Dict[str, List[Tuple]]) -> List[BacktestResult]:
def _enter_trade(self, pair: str, row: List, max_open_trades: int,
open_trade_count: int) -> Optional[LocalTrade]:
try:
stake_amount = self.wallets.get_trade_stake_amount(
pair, max_open_trades - open_trade_count, None)
except DependencyException:
return None
min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, row[OPEN_IDX], -0.05)
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
# Enter trade
trade = LocalTrade(
pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX],
stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
exchange='backtesting',
)
return trade
return None
def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
"""
Handling of left open trades at the end of backtesting
"""
@ -296,27 +298,17 @@ class Backtesting:
for trade in open_trades[pair]:
sell_row = data[pair][-1]
trade_entry = BacktestResult(pair=trade.pair,
profit_percent=trade.calc_profit_ratio(
rate=sell_row[OPEN_IDX]),
profit_abs=trade.calc_profit(sell_row[OPEN_IDX]),
open_date=trade.open_date,
open_rate=trade.open_rate,
open_fee=self.fee,
close_date=sell_row[DATE_IDX],
close_rate=sell_row[OPEN_IDX],
close_fee=self.fee,
amount=trade.amount,
trade_duration=int((
sell_row[DATE_IDX] - trade.open_date
).total_seconds() // 60),
open_at_end=True,
sell_reason=SellType.FORCE_SELL
)
trades.append(trade_entry)
trade.close_date = sell_row[DATE_IDX]
trade.sell_reason = SellType.FORCE_SELL.value
trade.close(sell_row[OPEN_IDX], show_msg=False)
LocalTrade.close_bt_trade(trade)
# Deepcopy object to have wallets update correctly
trade1 = deepcopy(trade)
trade1.is_open = True
trades.append(trade1)
return trades
def backtest(self, processed: Dict, stake_amount: float,
def backtest(self, processed: Dict,
start_date: datetime, end_date: datetime,
max_open_trades: int = 0, position_stacking: bool = False,
enable_protections: bool = False) -> DataFrame:
@ -328,7 +320,6 @@ class Backtesting:
Avoid extensive logging in this method and functions it calls.
:param processed: a processed dictionary with format {pair, data}
:param stake_amount: amount to use for each trade
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
@ -336,11 +327,7 @@ class Backtesting:
:param enable_protections: Should protections be enabled?
:return: DataFrame with trades (results of backtesting)
"""
logger.debug(f"Run backtest, stake_amount: {stake_amount}, "
f"start_date: {start_date}, end_date: {end_date}, "
f"max_open_trades: {max_open_trades}, position_stacking: {position_stacking}"
)
trades = []
trades: List[LocalTrade] = []
self.prepare_backtest(enable_protections)
# Use dict of lists with data for performance
@ -351,7 +338,7 @@ class Backtesting:
indexes: Dict = {}
tmp = start_date + timedelta(minutes=self.timeframe_min)
open_trades: Dict[str, List] = defaultdict(list)
open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
open_trade_count = 0
# Loop timerange and get candle for each pair at that point in time
@ -382,28 +369,18 @@ class Backtesting:
and tmp != end_date
and row[BUY_IDX] == 1 and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
# Enter trade
trade = Trade(
pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX],
stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
)
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behaviour - not sure if this is correct
# Prevents buying if the trade-slot was freed in this candle
open_trade_count_start += 1
open_trade_count += 1
# logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
open_trades[pair].append(trade)
Trade.trades.append(trade)
trade = self._enter_trade(pair, row, max_open_trades, open_trade_count_start)
if trade:
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behaviour - not sure if this is correct
# Prevents buying if the trade-slot was freed in this candle
open_trade_count_start += 1
open_trade_count += 1
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
open_trades[pair].append(trade)
LocalTrade.add_bt_trade(trade)
for trade in open_trades[pair]:
# since indexes has been incremented before, we need to go one step back to
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(trade, row)
# Sell occured
@ -411,6 +388,8 @@ class Backtesting:
# logger.debug(f"{pair} - Backtesting sell {trade}")
open_trade_count -= 1
open_trades[pair].remove(trade)
LocalTrade.close_bt_trade(trade)
trades.append(trade_entry)
if enable_protections:
self.protections.stop_per_pair(pair, row[DATE_IDX])
@ -420,8 +399,56 @@ class Backtesting:
tmp += timedelta(minutes=self.timeframe_min)
trades += self.handle_left_open(open_trades, data=data)
self.wallets.update()
return DataFrame.from_records(trades, columns=BacktestResult._fields)
return trade_list_to_dataframe(trades)
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
backtest_start_time = datetime.now(timezone.utc)
self._set_strategy(strat)
strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
max_open_trades = self.strategy.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
# need to reprocess data every time to populate signals
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = history.get_timerange(preprocessed)
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Execute backtest and store results
results = self.backtest(
processed=preprocessed,
start_date=min_date.datetime,
end_date=max_date.datetime,
max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
self.all_results[self.strategy.get_strategy_name()] = {
'results': results,
'config': self.strategy.config,
'locks': PairLocks.get_all_locks(),
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
}
return min_date, max_date
def start(self) -> None:
"""
@ -430,58 +457,16 @@ class Backtesting:
"""
data: Dict[str, Any] = {}
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
position_stacking = self.config.get('position_stacking', False)
data, timerange = self.load_bt_data()
all_results = {}
for strat in self.strategylist:
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
self._set_strategy(strat)
min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
if len(self.strategylist) > 0:
stats = generate_backtest_stats(data, self.all_results,
min_date=min_date, max_date=max_date)
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
max_open_trades = self.strategy.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
if self.config.get('export', False):
store_backtest_stats(self.config['exportfilename'], stats)
# need to reprocess data every time to populate signals
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = history.get_timerange(preprocessed)
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Execute backtest and print results
results = self.backtest(
processed=preprocessed,
stake_amount=self.config['stake_amount'],
start_date=min_date.datetime,
end_date=max_date.datetime,
max_open_trades=max_open_trades,
position_stacking=position_stacking,
enable_protections=self.config.get('enable_protections', False),
)
all_results[self.strategy.get_strategy_name()] = {
'results': results,
'config': self.strategy.config,
'locks': PairLocks.locks,
}
stats = generate_backtest_stats(data, all_results, min_date=min_date, max_date=max_date)
if self.config.get('export', False):
store_backtest_stats(self.config['exportfilename'], stats)
# Show backtest results
show_backtest_results(self.config, stats)
# Show backtest results
show_backtest_results(self.config, stats)

2
freqtrade/optimize/default_hyperopt_loss.py
View File

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

325
freqtrade/optimize/hyperopt.py
View File

@ -4,36 +4,31 @@
This module contains the hyperopt logic
"""
import io
import locale
import logging
import random
import warnings
from collections import OrderedDict
from datetime import datetime
from math import ceil
from operator import itemgetter
from pathlib import Path
from pprint import pformat
from typing import Any, Dict, List, Optional
import progressbar
import rapidjson
import tabulate
from colorama import Fore, Style
from colorama import init as colorama_init
from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects
from pandas import DataFrame, isna, json_normalize
from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import get_timerange
from freqtrade.exceptions import OperationalException
from freqtrade.misc import file_dump_json, plural, round_dict
from freqtrade.misc import file_dump_json, plural
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.optimize.hyperopt_tools import HyperoptTools
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
from freqtrade.strategy import IStrategy
@ -73,12 +68,15 @@ class Hyperopt:
self.backtesting = Backtesting(self.config)
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
self.custom_hyperopt.__class__.strategy = self.backtesting.strategy
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
strategy = str(self.config['strategy'])
self.results_file = (self.config['user_data_dir'] /
'hyperopt_results' / f'hyperopt_results_{time_now}.pickle')
'hyperopt_results' /
f'strategy_{strategy}_hyperopt_results_{time_now}.pickle')
self.data_pickle_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_tickerdata.pkl')
self.total_epochs = config.get('epochs', 0)
@ -166,15 +164,6 @@ class Hyperopt:
file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
log=False)
@staticmethod
def _read_results(results_file: Path) -> List:
"""
Read hyperopt results from file
"""
logger.info("Reading epochs from '%s'", results_file)
data = load(results_file)
return data
def _get_params_details(self, params: Dict) -> Dict:
"""
Return the params for each space
@ -197,102 +186,16 @@ class Hyperopt:
return result
@staticmethod
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
"""
params = results.get('params_details', {})
# Default header string
if header_str is None:
header_str = "Best result"
if not no_header:
explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
print(f"\n{header_str}:\n\n{explanation_str}\n")
if print_json:
result_dict: Dict = {}
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
Hyperopt._params_update_for_json(result_dict, params, s)
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else:
Hyperopt._params_pretty_print(params, 'buy', "Buy hyperspace params:")
Hyperopt._params_pretty_print(params, 'sell', "Sell hyperspace params:")
Hyperopt._params_pretty_print(params, 'roi', "ROI table:")
Hyperopt._params_pretty_print(params, 'stoploss', "Stoploss:")
Hyperopt._params_pretty_print(params, 'trailing', "Trailing stop:")
@staticmethod
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']:
result_dict.setdefault('params', {}).update(space_params)
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
# OrderedDict is used to keep the numeric order of the items
# in the dict.
result_dict['minimal_roi'] = OrderedDict(
(str(k), v) for k, v in space_params.items()
)
else: # 'stoploss', 'trailing'
result_dict.update(space_params)
@staticmethod
def _params_pretty_print(params, space: str, header: str) -> None:
if space in params:
space_params = Hyperopt._space_params(params, space, 5)
params_result = f"\n# {header}\n"
if space == 'stoploss':
params_result += f"stoploss = {space_params.get('stoploss')}"
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
minimal_roi_result = rapidjson.dumps(
OrderedDict(
(str(k), v) for k, v in space_params.items()
),
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
params_result += f"minimal_roi = {minimal_roi_result}"
elif space == 'trailing':
for k, v in space_params.items():
params_result += f'{k} = {v}\n'
else:
params_result += f"{space}_params = {pformat(space_params, indent=4)}"
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
params_result = params_result.replace("\n", "\n ")
print(params_result)
@staticmethod
def _space_params(params, space: str, r: int = None) -> Dict:
d = params[space]
# Round floats to `r` digits after the decimal point if requested
return round_dict(d, r) if r else d
@staticmethod
def is_best_loss(results, current_best_loss: float) -> bool:
return results['loss'] < current_best_loss
def print_results(self, results) -> None:
"""
Log results if it is better than any previous evaluation
TODO: this should be moved to HyperoptTools too
"""
is_best = results['is_best']
if self.print_all or is_best:
print(
self.get_result_table(
HyperoptTools.get_result_table(
self.config, results, self.total_epochs,
self.print_all, self.print_colorized,
self.hyperopt_table_header
@ -300,164 +203,6 @@ class Hyperopt:
)
self.hyperopt_table_header = 2
@staticmethod
def _format_explanation_string(results, total_epochs) -> str:
return (("*" if results['is_initial_point'] else " ") +
f"{results['current_epoch']:5d}/{total_epochs}: " +
f"{results['results_explanation']} " +
f"Objective: {results['loss']:.5f}")
@staticmethod
def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> str:
"""
Log result table
"""
if not results:
return ''
tabulate.PRESERVE_WHITESPACE = True
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
trials['Epoch'] = trials['Epoch'].apply(
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
)
trials['Profit'] = trials.apply(
lambda x: '{:,.8f} {} {}'.format(
x['Total profit'], config['stake_currency'],
'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
).rjust(25+len(config['stake_currency']))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
axis=1
)
trials = trials.drop(columns=['Total profit'])
if print_colorized:
for i in range(len(trials)):
if trials.loc[i]['is_profit']:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
str(trials.loc[i][j]), Fore.RESET)
if trials.loc[i]['is_best'] and highlight_best:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
str(trials.loc[i][j]), Style.RESET_ALL)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
if remove_header > 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='orgtbl',
headers='keys', stralign="right"
)
table = table.split("\n", remove_header)[remove_header]
elif remove_header < 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
table = "\n".join(table.split("\n")[0:remove_header])
else:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
return table
@staticmethod
def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
csv_file: str) -> None:
"""
Log result to csv-file
"""
if not results:
return
# Verification for overwrite
if Path(csv_file).is_file():
logger.error(f"CSV file already exists: {csv_file}")
return
try:
io.open(csv_file, 'w+').close()
except IOError:
logger.error(f"Failed to create CSV file: {csv_file}")
return
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
trials = trials[base_metrics + param_metrics]
base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
param_columns = list(results[0]['params_dict'].keys())
trials.columns = base_columns + param_columns
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str)
trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
)
trials['Profit'] = trials['Profit'].apply(
lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
logger.info(f"CSV file created: {csv_file}")
def has_space(self, space: str) -> bool:
"""
Tell if the space value is contained in the configuration
@ -537,7 +282,6 @@ class Hyperopt:
backtesting_results = self.backtesting.backtest(
processed=processed,
stake_amount=self.config['stake_amount'],
start_date=min_date.datetime,
end_date=max_date.datetime,
max_open_trades=self.max_open_trades,
@ -546,10 +290,11 @@ class Hyperopt:
)
return self._get_results_dict(backtesting_results, min_date, max_date,
params_dict, params_details)
params_dict, params_details,
processed=processed)
def _get_results_dict(self, backtesting_results, min_date, max_date,
params_dict, params_details):
params_dict, params_details, processed: Dict[str, DataFrame]):
results_metrics = self._calculate_results_metrics(backtesting_results)
results_explanation = self._format_results_explanation_string(results_metrics)
@ -563,7 +308,8 @@ class Hyperopt:
loss: float = MAX_LOSS
if trade_count >= self.config['hyperopt_min_trades']:
loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
min_date=min_date.datetime, max_date=max_date.datetime)
min_date=min_date.datetime, max_date=max_date.datetime,
config=self.config, processed=processed)
return {
'loss': loss,
'params_dict': params_dict,
@ -574,20 +320,20 @@ class Hyperopt:
}
def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
wins = len(backtesting_results[backtesting_results.profit_percent > 0])
draws = len(backtesting_results[backtesting_results.profit_percent == 0])
losses = len(backtesting_results[backtesting_results.profit_percent < 0])
wins = len(backtesting_results[backtesting_results['profit_ratio'] > 0])
draws = len(backtesting_results[backtesting_results['profit_ratio'] == 0])
losses = len(backtesting_results[backtesting_results['profit_ratio'] < 0])
return {
'trade_count': len(backtesting_results.index),
'wins': wins,
'draws': draws,
'losses': losses,
'winsdrawslosses': f"{wins:>4} {draws:>4} {losses:>4}",
'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
'median_profit': backtesting_results.profit_percent.median() * 100.0,
'total_profit': backtesting_results.profit_abs.sum(),
'profit': backtesting_results.profit_percent.sum() * 100.0,
'duration': backtesting_results.trade_duration.mean(),
'avg_profit': backtesting_results['profit_ratio'].mean() * 100.0,
'median_profit': backtesting_results['profit_ratio'].median() * 100.0,
'total_profit': backtesting_results['profit_abs'].sum(),
'profit': backtesting_results['profit_ratio'].sum() * 100.0,
'duration': backtesting_results['trade_duration'].mean(),
}
def _format_results_explanation_string(self, results_metrics: Dict) -> str:
@ -620,22 +366,6 @@ class Hyperopt:
return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
@staticmethod
def load_previous_results(results_file: Path) -> List:
"""
Load data for epochs from the file if we have one
"""
epochs: List = []
if results_file.is_file() and results_file.stat().st_size > 0:
epochs = Hyperopt._read_results(results_file)
# Detection of some old format, without 'is_best' field saved
if epochs[0].get('is_best') is None:
raise OperationalException(
"The file with Hyperopt results is incompatible with this version "
"of Freqtrade and cannot be loaded.")
logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
return epochs
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
@ -650,7 +380,7 @@ class Hyperopt:
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = get_timerange(data)
min_date, max_date = get_timerange(preprocessed)
logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
@ -659,7 +389,10 @@ class Hyperopt:
dump(preprocessed, self.data_pickle_file)
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange = None # type: ignore
self.backtesting.exchange.close()
self.backtesting.exchange._api = None # type: ignore
self.backtesting.exchange._api_async = None # type: ignore
# self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore
self.backtesting.strategy.dp = None # type: ignore
IStrategy.dp = None # type: ignore
@ -725,7 +458,7 @@ class Hyperopt:
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = self.is_best_loss(val, self.current_best_loss)
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
@ -753,7 +486,7 @@ class Hyperopt:
if self.epochs:
sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
best_epoch = sorted_epochs[0]
self.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
HyperoptTools.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
else:
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.

2
freqtrade/optimize/hyperopt_interface.py
View File

@ -12,6 +12,7 @@ from skopt.space import Categorical, Dimension, Integer, Real
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import round_dict
from freqtrade.strategy import IStrategy
logger = logging.getLogger(__name__)
@ -34,6 +35,7 @@ class IHyperOpt(ABC):
"""
ticker_interval: str # DEPRECATED
timeframe: str
strategy: IStrategy
def __init__(self, config: dict) -> None:
self.config = config

5
freqtrade/optimize/hyperopt_loss_interface.py
View File

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

2
freqtrade/optimize/hyperopt_loss_onlyprofit.py
View File

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

2
freqtrade/optimize/hyperopt_loss_sharpe.py
View File

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

10
freqtrade/optimize/hyperopt_loss_sharpe_daily.py
View File

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

4
freqtrade/optimize/hyperopt_loss_sortino.py
View File

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

12
freqtrade/optimize/hyperopt_loss_sortino_daily.py
View File

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

294
freqtrade/optimize/hyperopt_tools.py Normal file
View File

@ -0,0 +1,294 @@
import io
import logging
from collections import OrderedDict
from pathlib import Path
from pprint import pformat
from typing import Dict, List
import rapidjson
import tabulate
from colorama import Fore, Style
from joblib import load
from pandas import isna, json_normalize
from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_dict
logger = logging.getLogger(__name__)
class HyperoptTools():
@staticmethod
def _read_results(results_file: Path) -> List:
"""
Read hyperopt results from file
"""
logger.info("Reading epochs from '%s'", results_file)
data = load(results_file)
return data
@staticmethod
def load_previous_results(results_file: Path) -> List:
"""
Load data for epochs from the file if we have one
"""
epochs: List = []
if results_file.is_file() and results_file.stat().st_size > 0:
epochs = HyperoptTools._read_results(results_file)
# Detection of some old format, without 'is_best' field saved
if epochs[0].get('is_best') is None:
raise OperationalException(
"The file with HyperoptTools results is incompatible with this version "
"of Freqtrade and cannot be loaded.")
logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
return epochs
@staticmethod
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
"""
params = results.get('params_details', {})
# Default header string
if header_str is None:
header_str = "Best result"
if not no_header:
explanation_str = HyperoptTools._format_explanation_string(results, total_epochs)
print(f"\n{header_str}:\n\n{explanation_str}\n")
if print_json:
result_dict: Dict = {}
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
HyperoptTools._params_update_for_json(result_dict, params, s)
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else:
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:")
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:")
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:")
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:")
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:")
@staticmethod
def _params_update_for_json(result_dict, params, space: str) -> None:
if space in params:
space_params = HyperoptTools._space_params(params, space)
if space in ['buy', 'sell']:
result_dict.setdefault('params', {}).update(space_params)
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
# OrderedDict is used to keep the numeric order of the items
# in the dict.
result_dict['minimal_roi'] = OrderedDict(
(str(k), v) for k, v in space_params.items()
)
else: # 'stoploss', 'trailing'
result_dict.update(space_params)
@staticmethod
def _params_pretty_print(params, space: str, header: str) -> None:
if space in params:
space_params = HyperoptTools._space_params(params, space, 5)
params_result = f"\n# {header}\n"
if space == 'stoploss':
params_result += f"stoploss = {space_params.get('stoploss')}"
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
minimal_roi_result = rapidjson.dumps(
OrderedDict(
(str(k), v) for k, v in space_params.items()
),
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
params_result += f"minimal_roi = {minimal_roi_result}"
elif space == 'trailing':
for k, v in space_params.items():
params_result += f'{k} = {v}\n'
else:
params_result += f"{space}_params = {pformat(space_params, indent=4)}"
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
params_result = params_result.replace("\n", "\n ")
print(params_result)
@staticmethod
def _space_params(params, space: str, r: int = None) -> Dict:
d = params[space]
# Round floats to `r` digits after the decimal point if requested
return round_dict(d, r) if r else d
@staticmethod
def is_best_loss(results, current_best_loss: float) -> bool:
return results['loss'] < current_best_loss
@staticmethod
def _format_explanation_string(results, total_epochs) -> str:
return (("*" if results['is_initial_point'] else " ") +
f"{results['current_epoch']:5d}/{total_epochs}: " +
f"{results['results_explanation']} " +
f"Objective: {results['loss']:.5f}")
@staticmethod
def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> str:
"""
Log result table
"""
if not results:
return ''
tabulate.PRESERVE_WHITESPACE = True
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
trials['Epoch'] = trials['Epoch'].apply(
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
)
trials['Profit'] = trials.apply(
lambda x: '{:,.8f} {} {}'.format(
x['Total profit'], config['stake_currency'],
'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
).rjust(25+len(config['stake_currency']))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
axis=1
)
trials = trials.drop(columns=['Total profit'])
if print_colorized:
for i in range(len(trials)):
if trials.loc[i]['is_profit']:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
str(trials.loc[i][j]), Fore.RESET)
if trials.loc[i]['is_best'] and highlight_best:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
str(trials.loc[i][j]), Style.RESET_ALL)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
if remove_header > 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='orgtbl',
headers='keys', stralign="right"
)
table = table.split("\n", remove_header)[remove_header]
elif remove_header < 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
table = "\n".join(table.split("\n")[0:remove_header])
else:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
return table
@staticmethod
def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
csv_file: str) -> None:
"""
Log result to csv-file
"""
if not results:
return
# Verification for overwrite
if Path(csv_file).is_file():
logger.error(f"CSV file already exists: {csv_file}")
return
try:
io.open(csv_file, 'w+').close()
except IOError:
logger.error(f"Failed to create CSV file: {csv_file}")
return
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.median_profit',
'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
trials = trials[base_metrics + param_metrics]
base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Median profit', 'Total profit',
'Stake currency', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best']
param_columns = list(results[0]['params_dict'].keys())
trials.columns = base_columns + param_columns
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str)
trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
)
trials['Profit'] = trials['Profit'].apply(
lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
logger.info(f"CSV file created: {csv_file}")

175
freqtrade/optimize/optimize_reports.py
View File

@ -8,9 +8,10 @@ from numpy import int64
from pandas import DataFrame
from tabulate import tabulate
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import calculate_market_change, calculate_max_drawdown
from freqtrade.misc import file_dump_json
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
from freqtrade.data.btanalysis import (calculate_csum, calculate_market_change,
calculate_max_drawdown)
from freqtrade.misc import decimals_per_coin, file_dump_json, round_coin_value
logger = logging.getLogger(__name__)
@ -38,11 +39,12 @@ def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> N
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
def _get_line_floatfmt() -> List[str]:
def _get_line_floatfmt(stake_currency: str) -> List[str]:
"""
Generate floatformat (goes in line with _generate_result_line())
"""
return ['s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', 'd', 'd', 'd']
return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f',
'.2f', 'd', 'd', 'd', 'd']
def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
@ -54,18 +56,19 @@ def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
'Wins', 'Draws', 'Losses']
def _generate_result_line(result: DataFrame, max_open_trades: int, first_column: str) -> Dict:
def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
"""
Generate one result dict, with "first_column" as key.
"""
profit_sum = result['profit_percent'].sum()
profit_total = profit_sum / max_open_trades
profit_sum = result['profit_ratio'].sum()
# (end-capital - starting capital) / starting capital
profit_total = result['profit_abs'].sum() / starting_balance
return {
'key': first_column,
'trades': len(result),
'profit_mean': result['profit_percent'].mean() if len(result) > 0 else 0.0,
'profit_mean_pct': result['profit_percent'].mean() * 100.0 if len(result) > 0 else 0.0,
'profit_mean': result['profit_ratio'].mean() if len(result) > 0 else 0.0,
'profit_mean_pct': result['profit_ratio'].mean() * 100.0 if len(result) > 0 else 0.0,
'profit_sum': profit_sum,
'profit_sum_pct': round(profit_sum * 100.0, 2),
'profit_total_abs': result['profit_abs'].sum(),
@ -86,13 +89,13 @@ def _generate_result_line(result: DataFrame, max_open_trades: int, first_column:
}
def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_balance: int,
results: DataFrame, skip_nan: bool = False) -> List[Dict]:
"""
Generates and returns a list for the given backtest data and the results dataframe
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades
:param starting_balance: Starting balance
:param results: Dataframe containing the backtest results
:param skip_nan: Print "left open" open trades
:return: List of Dicts containing the metrics per pair
@ -105,10 +108,10 @@ def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_t
if skip_nan and result['profit_abs'].isnull().all():
continue
tabular_data.append(_generate_result_line(result, max_open_trades, pair))
tabular_data.append(_generate_result_line(result, starting_balance, pair))
# Append Total
tabular_data.append(_generate_result_line(results, max_open_trades, 'TOTAL'))
tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
return tabular_data
@ -124,13 +127,13 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
for reason, count in results['sell_reason'].value_counts().iteritems():
result = results.loc[results['sell_reason'] == reason]
profit_mean = result['profit_percent'].mean()
profit_sum = result['profit_percent'].sum()
profit_mean = result['profit_ratio'].mean()
profit_sum = result['profit_ratio'].sum()
profit_total = profit_sum / max_open_trades
tabular_data.append(
{
'sell_reason': reason.value,
'sell_reason': reason,
'trades': count,
'wins': len(result[result['profit_abs'] > 0]),
'draws': len(result[result['profit_abs'] == 0]),
@ -150,14 +153,14 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
:return: List of Dicts containing the metrics per Strategy
"""
tabular_data = []
for strategy, results in all_results.items():
tabular_data.append(_generate_result_line(
results['results'], results['config']['max_open_trades'], strategy)
results['results'], results['config']['dry_run_wallet'], strategy)
)
return tabular_data
@ -193,25 +196,32 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
return {
'backtest_best_day': 0,
'backtest_worst_day': 0,
'backtest_best_day_abs': 0,
'backtest_worst_day_abs': 0,
'winning_days': 0,
'draw_days': 0,
'losing_days': 0,
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
daily_profit = results.resample('1d', on='close_date')['profit_percent'].sum()
daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
worst_rel = min(daily_profit_rel)
best_rel = max(daily_profit_rel)
worst = min(daily_profit)
best = max(daily_profit)
winning_days = sum(daily_profit > 0)
draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0)
winning_trades = results.loc[results['profit_percent'] > 0]
losing_trades = results.loc[results['profit_percent'] < 0]
winning_trades = results.loc[results['profit_ratio'] > 0]
losing_trades = results.loc[results['profit_ratio'] < 0]
return {
'backtest_best_day': best,
'backtest_worst_day': worst,
'backtest_best_day': best_rel,
'backtest_worst_day': worst_rel,
'backtest_best_day_abs': best,
'backtest_worst_day_abs': worst,
'winning_days': winning_days,
'draw_days': draw_days,
'losing_days': losing_days,
@ -243,17 +253,18 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
if not isinstance(results, DataFrame):
continue
config = content['config']
max_open_trades = config['max_open_trades']
max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
starting_balance = config['dry_run_wallet']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
starting_balance=starting_balance,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
results=results.loc[results['open_at_end']],
starting_balance=starting_balance,
results=results.loc[results['is_open']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
@ -273,8 +284,10 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
'profit_mean': results['profit_percent'].mean() if len(results) > 0 else 0,
'profit_total': results['profit_percent'].sum(),
'total_volume': float(results['stake_amount'].sum()),
'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
'profit_total': results['profit_abs'].sum() / starting_balance,
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.datetime,
'backtest_start_ts': min_date.int_timestamp * 1000,
@ -282,45 +295,76 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
'backtest_end_ts': max_date.int_timestamp * 1000,
'backtest_days': backtest_days,
'backtest_run_start_ts': content['backtest_start_time'],
'backtest_run_end_ts': content['backtest_end_time'],
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'market_change': market_change,
'pairlist': list(btdata.keys()),
'stake_amount': config['stake_amount'],
'stake_currency': config['stake_currency'],
'max_open_trades': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
'starting_balance': starting_balance,
'dry_run_wallet': starting_balance,
'final_balance': content['final_balance'],
'max_open_trades': max_open_trades,
'max_open_trades_setting': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe': config['timeframe'],
'timerange': config.get('timerange', ''),
'enable_protections': config.get('enable_protections', False),
'strategy_name': strategy,
# Parameters relevant for backtesting
'stoploss': config['stoploss'],
'trailing_stop': config.get('trailing_stop', False),
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
'use_custom_stoploss': config.get('use_custom_stoploss', False),
'minimal_roi': config['minimal_roi'],
'use_sell_signal': config['ask_strategy']['use_sell_signal'],
'sell_profit_only': config['ask_strategy']['sell_profit_only'],
'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
**daily_stats,
}
result['strategy'][strategy] = strat_stats
try:
max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
results, value_col='profit_percent')
max_drawdown, _, _, _, _ = calculate_max_drawdown(
results, value_col='profit_ratio')
drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
results, value_col='profit_abs')
strat_stats.update({
'max_drawdown': max_drawdown,
'max_drawdown_abs': drawdown_abs,
'drawdown_start': drawdown_start,
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end,
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
'max_drawdown_low': low_val,
'max_drawdown_high': high_val,
})
csum_min, csum_max = calculate_csum(results, starting_balance)
strat_stats.update({
'csum_min': csum_min,
'csum_max': csum_max
})
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'max_drawdown_abs': 0.0,
'max_drawdown_low': 0.0,
'max_drawdown_high': 0.0,
'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_start_ts': 0,
'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_end_ts': 0,
'csum_min': 0,
'csum_max': 0
})
strategy_results = generate_strategy_metrics(all_results=all_results)
@ -343,7 +387,7 @@ def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: st
"""
headers = _get_line_header('Pair', stake_currency)
floatfmt = _get_line_floatfmt()
floatfmt = _get_line_floatfmt(stake_currency)
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
@ -374,7 +418,9 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
output = [[
t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_total_pct'],
t['profit_mean_pct'], t['profit_sum_pct'],
round_coin_value(t['profit_total_abs'], stake_currency, False),
t['profit_total_pct'],
] for t in sell_reason_stats]
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
@ -384,10 +430,10 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
Generate summary table per strategy
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades used for backtest
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
:return: pretty printed table with tabulate as string
"""
floatfmt = _get_line_floatfmt()
floatfmt = _get_line_floatfmt(stake_currency)
headers = _get_line_header('Strategy', stake_currency)
output = [[
@ -401,33 +447,58 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
best_trade = max(strat_results['trades'], key=lambda x: x['profit_percent'])
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_percent'])
best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])
metrics = [
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
('Max open trades', strat_results['max_open_trades']),
('', ''), # Empty line to improve readability
('Total trades', strat_results['total_trades']),
('Total Profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Starting balance', round_coin_value(strat_results['starting_balance'],
strat_results['stake_currency'])),
('Final balance', round_coin_value(strat_results['final_balance'],
strat_results['stake_currency'])),
('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
strat_results['stake_currency'])),
('Total profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Trades per day', strat_results['trades_per_day']),
('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
strat_results['stake_currency'])),
('Total trade volume', round_coin_value(strat_results['total_volume'],
strat_results['stake_currency'])),
('', ''), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} "
f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
('Worst Pair', f"{strat_results['worst_pair']['key']} "
f"{round(strat_results['worst_pair']['profit_sum_pct'], 2)}%"),
('Best trade', f"{best_trade['pair']} {round(best_trade['profit_percent'] * 100, 2)}%"),
('Best trade', f"{best_trade['pair']} {round(best_trade['profit_ratio'] * 100, 2)}%"),
('Worst trade', f"{worst_trade['pair']} "
f"{round(worst_trade['profit_percent'] * 100, 2)}%"),
f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
('Best day', round_coin_value(strat_results['backtest_best_day_abs'],
strat_results['stake_currency'])),
('Worst day', round_coin_value(strat_results['backtest_worst_day_abs'],
strat_results['stake_currency'])),
('Days win/draw/lose', f"{strat_results['winning_days']} / "
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
('', ''), # Empty line to improve readability
('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Min balance', round_coin_value(strat_results['csum_min'],
strat_results['stake_currency'])),
('Max balance', round_coin_value(strat_results['csum_max'],
strat_results['stake_currency'])),
('Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
strat_results['stake_currency'])),
('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
strat_results['stake_currency'])),
('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
strat_results['stake_currency'])),
('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
@ -435,7 +506,17 @@ def text_table_add_metrics(strat_results: Dict) -> str:
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
else:
return ''
start_balance = round_coin_value(strat_results['starting_balance'],
strat_results['stake_currency'])
stake_amount = round_coin_value(
strat_results['stake_amount'], strat_results['stake_currency']
) if strat_results['stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'
message = ("No trades made. "
f"Your starting balance was {start_balance}, "
f"and your stake was {stake_amount}."
)
return message
def show_backtest_results(config: Dict, backtest_stats: Dict):

3
freqtrade/persistence/__init__.py
View File

@ -1,4 +1,5 @@
# flake8: noqa: F401
from freqtrade.persistence.models import Order, Trade, clean_dry_run_db, cleanup_db, init_db
from freqtrade.persistence.models import (LocalTrade, Order, Trade, clean_dry_run_db, cleanup_db,
init_db)
from freqtrade.persistence.pairlock_middleware import PairLocks

2
freqtrade/persistence/migrations.py
View File

@ -141,7 +141,7 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
inspector = inspect(engine)
cols = inspector.get_columns('trades')
if 'orders' not in previous_tables:
if 'orders' not in previous_tables and 'trades' in previous_tables:
logger.info('Moving open orders to Orders table.')
migrate_open_orders_to_trades(engine)
else:

312
freqtrade/persistence/models.py
View File

@ -171,6 +171,10 @@ class Order(_DECL_BASE):
"""
Get all non-closed orders - useful when trying to batch-update orders
"""
if not isinstance(order, dict):
logger.warning(f"{order} is not a valid response object.")
return
filtered_orders = [o for o in orders if o.order_id == order.get('id')]
if filtered_orders:
oobj = filtered_orders[0]
@ -195,67 +199,69 @@ class Order(_DECL_BASE):
return Order.query.filter(Order.ft_is_open.is_(True)).all()
class Trade(_DECL_BASE):
class LocalTrade():
"""
Trade database model.
Also handles updating and querying trades
Used in backtesting - must be aligned to Trade model!
"""
__tablename__ = 'trades'
use_db: bool = True
use_db: bool = False
# Trades container for backtesting
trades: List['Trade'] = []
trades: List['LocalTrade'] = []
trades_open: List['LocalTrade'] = []
total_profit: float = 0
id = Column(Integer, primary_key=True)
id: int = 0
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
orders: List[Order] = []
exchange = Column(String, nullable=False)
pair = Column(String, nullable=False, index=True)
is_open = Column(Boolean, nullable=False, default=True, index=True)
fee_open = Column(Float, nullable=False, default=0.0)
fee_open_cost = Column(Float, nullable=True)
fee_open_currency = Column(String, nullable=True)
fee_close = Column(Float, nullable=False, default=0.0)
fee_close_cost = Column(Float, nullable=True)
fee_close_currency = Column(String, nullable=True)
open_rate = Column(Float)
open_rate_requested = Column(Float)
exchange: str = ''
pair: str = ''
is_open: bool = True
fee_open: float = 0.0
fee_open_cost: Optional[float] = None
fee_open_currency: str = ''
fee_close: float = 0.0
fee_close_cost: Optional[float] = None
fee_close_currency: str = ''
open_rate: float = 0.0
open_rate_requested: Optional[float] = None
# open_trade_value - calculated via _calc_open_trade_value
open_trade_value = Column(Float)
close_rate = Column(Float)
close_rate_requested = Column(Float)
close_profit = Column(Float)
close_profit_abs = Column(Float)
stake_amount = Column(Float, nullable=False)
amount = Column(Float)
amount_requested = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
close_date = Column(DateTime)
open_order_id = Column(String)
open_trade_value: float = 0.0
close_rate: Optional[float] = None
close_rate_requested: Optional[float] = None
close_profit: Optional[float] = None
close_profit_abs: Optional[float] = None
stake_amount: float = 0.0
amount: float = 0.0
amount_requested: Optional[float] = None
open_date: datetime
close_date: Optional[datetime] = None
open_order_id: Optional[str] = None
# absolute value of the stop loss
stop_loss = Column(Float, nullable=True, default=0.0)
stop_loss: float = 0.0
# percentage value of the stop loss
stop_loss_pct = Column(Float, nullable=True)
stop_loss_pct: float = 0.0
# absolute value of the initial stop loss
initial_stop_loss = Column(Float, nullable=True, default=0.0)
initial_stop_loss: float = 0.0
# percentage value of the initial stop loss
initial_stop_loss_pct = Column(Float, nullable=True)
initial_stop_loss_pct: float = 0.0
# stoploss order id which is on exchange
stoploss_order_id = Column(String, nullable=True, index=True)
stoploss_order_id: Optional[str] = None
# last update time of the stoploss order on exchange
stoploss_last_update = Column(DateTime, nullable=True)
stoploss_last_update: Optional[datetime] = None
# absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0)
max_rate: float = 0.0
# Lowest price reached
min_rate = Column(Float, nullable=True)
sell_reason = Column(String, nullable=True)
sell_order_status = Column(String, nullable=True)
strategy = Column(String, nullable=True)
timeframe = Column(Integer, nullable=True)
min_rate: float = 0.0
sell_reason: str = ''
sell_order_status: str = ''
strategy: str = ''
timeframe: Optional[int] = None
def __init__(self, **kwargs):
super().__init__(**kwargs)
for key in kwargs:
setattr(self, key, kwargs[key])
self.recalc_open_trade_value()
def __repr__(self):
@ -264,6 +270,14 @@ class Trade(_DECL_BASE):
return (f'Trade(id={self.id}, pair={self.pair}, amount={self.amount:.8f}, '
f'open_rate={self.open_rate:.8f}, open_since={open_since})')
@property
def open_date_utc(self):
return self.open_date.replace(tzinfo=timezone.utc)
@property
def close_date_utc(self):
return self.close_date.replace(tzinfo=timezone.utc)
def to_json(self) -> Dict[str, Any]:
return {
'trade_id': self.id,
@ -302,6 +316,11 @@ class Trade(_DECL_BASE):
'close_profit_pct': round(self.close_profit * 100, 2) if self.close_profit else None,
'close_profit_abs': self.close_profit_abs, # Deprecated
'trade_duration_s': (int((self.close_date_utc - self.open_date_utc).total_seconds())
if self.close_date else None),
'trade_duration': (int((self.close_date_utc - self.open_date_utc).total_seconds() // 60)
if self.close_date else None),
'profit_ratio': self.close_profit,
'profit_pct': round(self.close_profit * 100, 2) if self.close_profit else None,
'profit_abs': self.close_profit_abs,
@ -332,8 +351,9 @@ class Trade(_DECL_BASE):
"""
Resets all trades. Only active for backtesting mode.
"""
if not Trade.use_db:
Trade.trades = []
LocalTrade.trades = []
LocalTrade.trades_open = []
LocalTrade.total_profit = 0
def adjust_min_max_rates(self, current_price: float) -> None:
"""
@ -342,6 +362,12 @@ class Trade(_DECL_BASE):
self.max_rate = max(current_price, self.max_rate or self.open_rate)
self.min_rate = min(current_price, self.min_rate or self.open_rate)
def _set_new_stoploss(self, new_loss: float, stoploss: float):
"""Assign new stop value"""
self.stop_loss = new_loss
self.stop_loss_pct = -1 * abs(stoploss)
self.stoploss_last_update = datetime.utcnow()
def adjust_stop_loss(self, current_price: float, stoploss: float,
initial: bool = False) -> None:
"""
@ -360,19 +386,15 @@ class Trade(_DECL_BASE):
# no stop loss assigned yet
if not self.stop_loss:
logger.debug(f"{self.pair} - Assigning new stoploss...")
self.stop_loss = new_loss
self.stop_loss_pct = -1 * abs(stoploss)
self._set_new_stoploss(new_loss, stoploss)
self.initial_stop_loss = new_loss
self.initial_stop_loss_pct = -1 * abs(stoploss)
self.stoploss_last_update = datetime.utcnow()
# evaluate if the stop loss needs to be updated
else:
if new_loss > self.stop_loss: # stop losses only walk up, never down!
logger.debug(f"{self.pair} - Adjusting stoploss...")
self.stop_loss = new_loss
self.stop_loss_pct = -1 * abs(stoploss)
self.stoploss_last_update = datetime.utcnow()
self._set_new_stoploss(new_loss, stoploss)
else:
logger.debug(f"{self.pair} - Keeping current stoploss...")
@ -399,8 +421,8 @@ class Trade(_DECL_BASE):
if order_type in ('market', 'limit') and order['side'] == 'buy':
# Update open rate and actual amount
self.open_rate = Decimal(safe_value_fallback(order, 'average', 'price'))
self.amount = Decimal(safe_value_fallback(order, 'filled', 'amount'))
self.open_rate = float(safe_value_fallback(order, 'average', 'price'))
self.amount = float(safe_value_fallback(order, 'filled', 'amount'))
self.recalc_open_trade_value()
if self.is_open:
logger.info(f'{order_type.upper()}_BUY has been fulfilled for {self}.')
@ -414,7 +436,7 @@ class Trade(_DECL_BASE):
self.close_rate_requested = self.stop_loss
if self.is_open:
logger.info(f'{order_type.upper()} is hit for {self}.')
self.close(order['average'])
self.close(safe_value_fallback(order, 'average', 'price'))
else:
raise ValueError(f'Unknown order type: {order_type}')
cleanup_db()
@ -424,7 +446,7 @@ class Trade(_DECL_BASE):
Sets close_rate to the given rate, calculates total profit
and marks trade as closed
"""
self.close_rate = Decimal(rate)
self.close_rate = rate
self.close_profit = self.calc_profit_ratio()
self.close_profit_abs = self.calc_profit()
self.close_date = self.close_date or datetime.utcnow()
@ -469,14 +491,6 @@ class Trade(_DECL_BASE):
def update_order(self, order: Dict) -> None:
Order.update_orders(self.orders, order)
def delete(self) -> None:
for order in self.orders:
Order.session.delete(order)
Trade.session.delete(self)
Trade.session.flush()
def _calc_open_trade_value(self) -> float:
"""
Calculate the open_rate including open_fee.
@ -506,7 +520,7 @@ class Trade(_DECL_BASE):
if rate is None and not self.close_rate:
return 0.0
sell_trade = Decimal(self.amount) * Decimal(rate or self.close_rate)
sell_trade = Decimal(self.amount) * Decimal(rate or self.close_rate) # type: ignore
fees = sell_trade * Decimal(fee or self.fee_close)
return float(sell_trade - fees)
@ -578,7 +592,7 @@ class Trade(_DECL_BASE):
@staticmethod
def get_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,
) -> List['Trade']:
) -> List['LocalTrade']:
"""
Helper function to query Trades.
Returns a List of trades, filtered on the parameters given.
@ -587,30 +601,40 @@ class Trade(_DECL_BASE):
:return: unsorted List[Trade]
"""
if Trade.use_db:
trade_filter = []
if pair:
trade_filter.append(Trade.pair == pair)
if open_date:
trade_filter.append(Trade.open_date > open_date)
if close_date:
trade_filter.append(Trade.close_date > close_date)
if is_open is not None:
trade_filter.append(Trade.is_open.is_(is_open))
return Trade.get_trades(trade_filter).all()
# Offline mode - without database
if is_open is not None:
if is_open:
sel_trades = LocalTrade.trades_open
else:
sel_trades = LocalTrade.trades
else:
# Offline mode - without database
sel_trades = [trade for trade in Trade.trades]
if pair:
sel_trades = [trade for trade in sel_trades if trade.pair == pair]
if open_date:
sel_trades = [trade for trade in sel_trades if trade.open_date > open_date]
if close_date:
sel_trades = [trade for trade in sel_trades if trade.close_date
and trade.close_date > close_date]
if is_open is not None:
sel_trades = [trade for trade in sel_trades if trade.is_open == is_open]
return sel_trades
# Not used during backtesting, but might be used by a strategy
sel_trades = [trade for trade in LocalTrade.trades + LocalTrade.trades_open]
if pair:
sel_trades = [trade for trade in sel_trades if trade.pair == pair]
if open_date:
sel_trades = [trade for trade in sel_trades if trade.open_date > open_date]
if close_date:
sel_trades = [trade for trade in sel_trades if trade.close_date
and trade.close_date > close_date]
return sel_trades
@staticmethod
def close_bt_trade(trade):
LocalTrade.trades_open.remove(trade)
LocalTrade.trades.append(trade)
LocalTrade.total_profit += trade.close_profit_abs
@staticmethod
def add_bt_trade(trade):
if trade.is_open:
LocalTrade.trades_open.append(trade)
else:
LocalTrade.trades.append(trade)
@staticmethod
def get_open_trades() -> List[Any]:
@ -652,9 +676,12 @@ class Trade(_DECL_BASE):
Calculates total invested amount in open trades
in stake currency
"""
total_open_stake_amount = Trade.session.query(func.sum(Trade.stake_amount))\
.filter(Trade.is_open.is_(True))\
.scalar()
if Trade.use_db:
total_open_stake_amount = Trade.session.query(
func.sum(Trade.stake_amount)).filter(Trade.is_open.is_(True)).scalar()
else:
total_open_stake_amount = sum(
t.stake_amount for t in Trade.get_trades_proxy(is_open=True))
return total_open_stake_amount or 0
@staticmethod
@ -712,6 +739,108 @@ class Trade(_DECL_BASE):
logger.info(f"New stoploss: {trade.stop_loss}.")
class Trade(_DECL_BASE, LocalTrade):
"""
Trade database model.
Also handles updating and querying trades
Note: Fields must be aligned with LocalTrade class
"""
__tablename__ = 'trades'
use_db: bool = True
id = Column(Integer, primary_key=True)
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
exchange = Column(String, nullable=False)
pair = Column(String, nullable=False, index=True)
is_open = Column(Boolean, nullable=False, default=True, index=True)
fee_open = Column(Float, nullable=False, default=0.0)
fee_open_cost = Column(Float, nullable=True)
fee_open_currency = Column(String, nullable=True)
fee_close = Column(Float, nullable=False, default=0.0)
fee_close_cost = Column(Float, nullable=True)
fee_close_currency = Column(String, nullable=True)
open_rate = Column(Float)
open_rate_requested = Column(Float)
# open_trade_value - calculated via _calc_open_trade_value
open_trade_value = Column(Float)
close_rate = Column(Float)
close_rate_requested = Column(Float)
close_profit = Column(Float)
close_profit_abs = Column(Float)
stake_amount = Column(Float, nullable=False)
amount = Column(Float)
amount_requested = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
close_date = Column(DateTime)
open_order_id = Column(String)
# absolute value of the stop loss
stop_loss = Column(Float, nullable=True, default=0.0)
# percentage value of the stop loss
stop_loss_pct = Column(Float, nullable=True)
# absolute value of the initial stop loss
initial_stop_loss = Column(Float, nullable=True, default=0.0)
# percentage value of the initial stop loss
initial_stop_loss_pct = Column(Float, nullable=True)
# stoploss order id which is on exchange
stoploss_order_id = Column(String, nullable=True, index=True)
# last update time of the stoploss order on exchange
stoploss_last_update = Column(DateTime, nullable=True)
# absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0)
# Lowest price reached
min_rate = Column(Float, nullable=True)
sell_reason = Column(String, nullable=True)
sell_order_status = Column(String, nullable=True)
strategy = Column(String, nullable=True)
timeframe = Column(Integer, nullable=True)
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.recalc_open_trade_value()
def delete(self) -> None:
for order in self.orders:
Order.session.delete(order)
Trade.session.delete(self)
Trade.session.flush()
@staticmethod
def get_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,
) -> List['LocalTrade']:
"""
Helper function to query Trades.
Returns a List of trades, filtered on the parameters given.
In live mode, converts the filter to a database query and returns all rows
In Backtest mode, uses filters on Trade.trades to get the result.
:return: unsorted List[Trade]
"""
if Trade.use_db:
trade_filter = []
if pair:
trade_filter.append(Trade.pair == pair)
if open_date:
trade_filter.append(Trade.open_date > open_date)
if close_date:
trade_filter.append(Trade.close_date > close_date)
if is_open is not None:
trade_filter.append(Trade.is_open.is_(is_open))
return Trade.get_trades(trade_filter).all()
else:
return LocalTrade.get_trades_proxy(
pair=pair, is_open=is_open,
open_date=open_date,
close_date=close_date
)
class PairLock(_DECL_BASE):
"""
Pair Locks database model.
@ -754,6 +883,7 @@ class PairLock(_DECL_BASE):
def to_json(self) -> Dict[str, Any]:
return {
'id': self.id,
'pair': self.pair,
'lock_time': self.lock_time.strftime(DATETIME_PRINT_FORMAT),
'lock_timestamp': int(self.lock_time.replace(tzinfo=timezone.utc).timestamp() * 1000),

8
freqtrade/persistence/pairlock_middleware.py
View File

@ -123,3 +123,11 @@ class PairLocks():
now = datetime.now(timezone.utc)
return len(PairLocks.get_pair_locks(pair, now)) > 0 or PairLocks.is_global_lock(now)
@staticmethod
def get_all_locks() -> List[PairLock]:
if PairLocks.use_db:
return PairLock.query.all()
else:
return PairLocks.locks

52
freqtrade/plot/plotting.py
View File

@ -13,6 +13,7 @@ from freqtrade.data.history import get_timerange, load_data
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_seconds
from freqtrade.misc import pair_to_filename
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.strategy import IStrategy
@ -29,16 +30,16 @@ except ImportError:
exit(1)
def init_plotscript(config, startup_candles: int = 0):
def init_plotscript(config, markets: List, startup_candles: int = 0):
"""
Initialize objects needed for plotting
:return: Dict with candle (OHLCV) data, trades and pairs
"""
if "pairs" in config:
pairs = config['pairs']
pairs = expand_pairlist(config['pairs'], markets)
else:
pairs = config['exchange']['pair_whitelist']
pairs = expand_pairlist(config['exchange']['pair_whitelist'], markets)
# Set timerange to use
timerange = TimeRange.parse_timerange(config.get('timerange'))
@ -52,7 +53,7 @@ def init_plotscript(config, startup_candles: int = 0):
data_format=config.get('dataformat_ohlcv', 'json'),
)
if startup_candles:
if startup_candles and data:
min_date, max_date = get_timerange(data)
logger.info(f"Loading data from {min_date} to {max_date}")
timerange.adjust_start_if_necessary(timeframe_to_seconds(config.get('timeframe', '5m')),
@ -66,14 +67,16 @@ def init_plotscript(config, startup_candles: int = 0):
if not filename.is_dir() and not filename.is_file():
logger.warning("Backtest file is missing skipping trades.")
no_trades = True
trades = load_trades(
config['trade_source'],
db_url=config.get('db_url'),
exportfilename=filename,
no_trades=no_trades,
strategy=config.get('strategy'),
)
try:
trades = load_trades(
config['trade_source'],
db_url=config.get('db_url'),
exportfilename=filename,
no_trades=no_trades,
strategy=config.get('strategy'),
)
except ValueError as e:
raise OperationalException(e) from e
trades = trim_dataframe(trades, timerange, 'open_date')
return {"ohlcv": data,
@ -142,7 +145,7 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
Add scatter points indicating max drawdown
"""
try:
max_drawdown, highdate, lowdate = calculate_max_drawdown(trades)
max_drawdown, highdate, lowdate, _, _ = calculate_max_drawdown(trades)
drawdown = go.Scatter(
x=[highdate, lowdate],
@ -174,10 +177,10 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
# Trades can be empty
if trades is not None and len(trades) > 0:
# Create description for sell summarizing the trade
trades['desc'] = trades.apply(lambda row: f"{round(row['profit_percent'] * 100, 1)}%, "
trades['desc'] = trades.apply(lambda row: f"{round(row['profit_ratio'] * 100, 1)}%, "
f"{row['sell_reason']}, "
f"{row['trade_duration']} min",
axis=1)
axis=1)
trade_buys = go.Scatter(
x=trades["open_date"],
y=trades["open_rate"],
@ -194,9 +197,9 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
)
trade_sells = go.Scatter(
x=trades.loc[trades['profit_percent'] > 0, "close_date"],
y=trades.loc[trades['profit_percent'] > 0, "close_rate"],
text=trades.loc[trades['profit_percent'] > 0, "desc"],
x=trades.loc[trades['profit_ratio'] > 0, "close_date"],
y=trades.loc[trades['profit_ratio'] > 0, "close_rate"],
text=trades.loc[trades['profit_ratio'] > 0, "desc"],
mode='markers',
name='Sell - Profit',
marker=dict(
@ -207,9 +210,9 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
)
)
trade_sells_loss = go.Scatter(
x=trades.loc[trades['profit_percent'] <= 0, "close_date"],
y=trades.loc[trades['profit_percent'] <= 0, "close_rate"],
text=trades.loc[trades['profit_percent'] <= 0, "desc"],
x=trades.loc[trades['profit_ratio'] <= 0, "close_date"],
y=trades.loc[trades['profit_ratio'] <= 0, "close_rate"],
text=trades.loc[trades['profit_ratio'] <= 0, "desc"],
mode='markers',
name='Sell - Loss',
marker=dict(
@ -444,6 +447,8 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
# Trim trades to available OHLCV data
trades = extract_trades_of_period(df_comb, trades, date_index=True)
if len(trades) == 0:
raise OperationalException('No trades found in selected timerange.')
# Add combined cumulative profit
df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
@ -525,7 +530,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
IStrategy.dp = DataProvider(config, exchange)
plot_elements = init_plotscript(config, strategy.startup_candle_count)
plot_elements = init_plotscript(config, list(exchange.markets), strategy.startup_candle_count)
timerange = plot_elements['timerange']
trades = plot_elements['trades']
pair_counter = 0
@ -560,7 +565,8 @@ def plot_profit(config: Dict[str, Any]) -> None:
But should be somewhat proportional, and therefor useful
in helping out to find a good algorithm.
"""
plot_elements = init_plotscript(config)
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
plot_elements = init_plotscript(config, list(exchange.markets))
trades = plot_elements['trades']
# Filter trades to relevant pairs
# Remove open pairs - we don't know the profit yet so can't calculate profit for these.

4
freqtrade/plugins/pairlist/AgeFilter.py
View File

@ -30,10 +30,10 @@ class AgeFilter(IPairList):
if self._min_days_listed < 1:
raise OperationalException("AgeFilter requires min_days_listed to be >= 1")
if self._min_days_listed > exchange.ohlcv_candle_limit:
if self._min_days_listed > exchange.ohlcv_candle_limit('1d'):
raise OperationalException("AgeFilter requires min_days_listed to not exceed "
"exchange max request size "
f"({exchange.ohlcv_candle_limit})")
f"({exchange.ohlcv_candle_limit('1d')})")
@property
def needstickers(self) -> bool:

15
freqtrade/plugins/pairlist/IPairList.py
View File

@ -124,10 +124,21 @@ class IPairList(LoggingMixin, ABC):
"""
return self._pairlistmanager.verify_blacklist(pairlist, logmethod)
def verify_whitelist(self, pairlist: List[str], logmethod,
keep_invalid: bool = False) -> List[str]:
"""
Proxy method to verify_whitelist for easy access for child classes.
:param pairlist: Pairlist to validate
:param logmethod: Function that'll be called, `logger.info` or `logger.warning`
:param keep_invalid: If sets to True, drops invalid pairs silently while expanding regexes.
:return: pairlist - whitelisted pairs
"""
return self._pairlistmanager.verify_whitelist(pairlist, logmethod, keep_invalid)
def _whitelist_for_active_markets(self, pairlist: List[str]) -> List[str]:
"""
Check available markets and remove pair from whitelist if necessary
:param whitelist: the sorted list of pairs the user might want to trade
:param pairlist: the sorted list of pairs the user might want to trade
:return: the list of pairs the user wants to trade without those unavailable or
black_listed
"""
@ -157,7 +168,7 @@ class IPairList(LoggingMixin, ABC):
# Check if market is active
market = markets[pair]
if not market_is_active(market):
logger.info(f"Ignoring {pair} from whitelist. Market is not active.")
self.log_once(f"Ignoring {pair} from whitelist. Market is not active.", logger.info)
continue
if pair not in sanitized_whitelist:
sanitized_whitelist.append(pair)

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

@ -64,7 +64,7 @@ class PriceFilter(IPairList):
:param ticker: ticker dict as returned from ccxt.load_markets()
:return: True if the pair can stay, false if it should be removed
"""
if ticker['last'] is None or ticker['last'] == 0:
if ticker.get('last', None) is None or ticker.get('last') == 0:
self.log_once(f"Removed {pair} from whitelist, because "
"ticker['last'] is empty (Usually no trade in the last 24h).",
logger.info)

4
freqtrade/plugins/pairlist/SpreadFilter.py
View File

@ -43,7 +43,7 @@ class SpreadFilter(IPairList):
:param ticker: ticker dict as returned from ccxt.load_markets()
:return: True if the pair can stay, false if it should be removed
"""
if 'bid' in ticker and 'ask' in ticker:
if 'bid' in ticker and 'ask' in ticker and ticker['ask']:
spread = 1 - ticker['bid'] / ticker['ask']
if spread > self._max_spread_ratio:
self.log_once(f"Removed {pair} from whitelist, because spread "
@ -52,4 +52,6 @@ class SpreadFilter(IPairList):
return False
else:
return True
self.log_once(f"Removed {pair} from whitelist due to invalid ticker data: {ticker}",
logger.info)
return False

7
freqtrade/plugins/pairlist/StaticPairList.py
View File

@ -50,9 +50,12 @@ class StaticPairList(IPairList):
:return: List of pairs
"""
if self._allow_inactive:
return self._config['exchange']['pair_whitelist']
return self.verify_whitelist(
self._config['exchange']['pair_whitelist'], logger.info, keep_invalid=True
)
else:
return self._whitelist_for_active_markets(self._config['exchange']['pair_whitelist'])
return self._whitelist_for_active_markets(
self.verify_whitelist(self._config['exchange']['pair_whitelist'], logger.info))
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""

42
freqtrade/plugins/pairlist/pairlist_helpers.py Normal file
View File

@ -0,0 +1,42 @@
import re
from typing import List
def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
keep_invalid: bool = False) -> List[str]:
"""
Expand pairlist potentially containing wildcards based on available markets.
This will implicitly filter all pairs in the wildcard-list which are not in available_pairs.
:param wildcardpl: List of Pairlists, which may contain regex
:param available_pairs: List of all available pairs (`exchange.get_markets().keys()`)
:param keep_invalid: If sets to True, drops invalid pairs silently while expanding regexes
:return expanded pairlist, with Regexes from wildcardpl applied to match all available pairs.
:raises: ValueError if a wildcard is invalid (like '*/BTC' - which should be `.*/BTC`)
"""
result = []
if keep_invalid:
for pair_wc in wildcardpl:
try:
comp = re.compile(pair_wc)
result_partial = [
pair for pair in available_pairs if re.fullmatch(comp, pair)
]
# Add all matching pairs.
# If there are no matching pairs (Pair not on exchange) keep it.
result += result_partial or [pair_wc]
except re.error as err:
raise ValueError(f"Wildcard error in {pair_wc}, {err}")
for element in result:
if not re.fullmatch(r'^[A-Za-z0-9/-]+$', element):
result.remove(element)
else:
for pair_wc in wildcardpl:
try:
comp = re.compile(pair_wc)
result += [
pair for pair in available_pairs if re.fullmatch(comp, pair)
]
except re.error as err:
raise ValueError(f"Wildcard error in {pair_wc}, {err}")
return result

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

@ -28,14 +28,14 @@ class RangeStabilityFilter(IPairList):
self._min_rate_of_change = pairlistconfig.get('min_rate_of_change', 0.01)
self._refresh_period = pairlistconfig.get('refresh_period', 1440)
self._pair_cache: TTLCache = TTLCache(maxsize=100, ttl=self._refresh_period)
self._pair_cache: TTLCache = TTLCache(maxsize=1000, ttl=self._refresh_period)
if self._days < 1:
raise OperationalException("RangeStabilityFilter requires lookback_days to be >= 1")
if self._days > exchange.ohlcv_candle_limit:
if self._days > exchange.ohlcv_candle_limit('1d'):
raise OperationalException("RangeStabilityFilter requires lookback_days to not "
"exceed exchange max request size "
f"({exchange.ohlcv_candle_limit})")
f"({exchange.ohlcv_candle_limit('1d')})")
@property
def needstickers(self) -> bool:

51
freqtrade/plugins/pairlistmanager.py
View File

@ -10,6 +10,7 @@ from cachetools import TTLCache, cached
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.exceptions import OperationalException
from freqtrade.plugins.pairlist.IPairList import IPairList
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.resolvers import PairListResolver
@ -42,30 +43,29 @@ class PairListManager():
@property
def whitelist(self) -> List[str]:
"""
Has the current whitelist
"""
"""The current whitelist"""
return self._whitelist
@property
def blacklist(self) -> List[str]:
"""
Has the current blacklist
The current blacklist
-> no need to overwrite in subclasses
"""
return self._blacklist
@property
def expanded_blacklist(self) -> List[str]:
"""The expanded blacklist (including wildcard expansion)"""
return expand_pairlist(self._blacklist, self._exchange.get_markets().keys())
@property
def name_list(self) -> List[str]:
"""
Get list of loaded Pairlist Handler names
"""
"""Get list of loaded Pairlist Handler names"""
return [p.name for p in self._pairlist_handlers]
def short_desc(self) -> List[Dict]:
"""
List of short_desc for each Pairlist Handler
"""
"""List of short_desc for each Pairlist Handler"""
return [{p.name: p.short_desc()} for p in self._pairlist_handlers]
@cached(TTLCache(maxsize=1, ttl=1800))
@ -73,9 +73,7 @@ class PairListManager():
return self._exchange.get_tickers()
def refresh_pairlist(self) -> None:
"""
Run pairlist through all configured Pairlist Handlers.
"""
"""Run pairlist through all configured Pairlist Handlers."""
# Tickers should be cached to avoid calling the exchange on each call.
tickers: Dict = {}
if self._tickers_needed:
@ -120,12 +118,37 @@ class PairListManager():
:param logmethod: Function that'll be called, `logger.info` or `logger.warning`.
:return: pairlist - blacklisted pairs
"""
try:
blacklist = self.expanded_blacklist
except ValueError as err:
logger.error(f"Pair blacklist contains an invalid Wildcard: {err}")
return []
for pair in deepcopy(pairlist):
if pair in self._blacklist:
if pair in blacklist:
logmethod(f"Pair {pair} in your blacklist. Removing it from whitelist...")
pairlist.remove(pair)
return pairlist
def verify_whitelist(self, pairlist: List[str], logmethod,
keep_invalid: bool = False) -> List[str]:
"""
Verify and remove items from pairlist - returning a filtered pairlist.
Logs a warning or info depending on `aswarning`.
Pairlist Handlers explicitly using this method shall use
`logmethod=logger.info` to avoid spamming with warning messages
:param pairlist: Pairlist to validate
:param logmethod: Function that'll be called, `logger.info` or `logger.warning`
:param keep_invalid: If sets to True, drops invalid pairs silently while expanding regexes.
:return: pairlist - whitelisted pairs
"""
try:
whitelist = expand_pairlist(pairlist, self._exchange.get_markets().keys(), keep_invalid)
except ValueError as err:
logger.error(f"Pair whitelist contains an invalid Wildcard: {err}")
return []
return whitelist
def create_pair_list(self, pairs: List[str], timeframe: str = None) -> ListPairsWithTimeframes:
"""
Create list of pair tuples with (pair, timeframe)

3
freqtrade/plugins/protections/cooldown_period.py
View File

@ -44,7 +44,8 @@ class CooldownPeriod(IProtection):
trades = Trade.get_trades_proxy(pair=pair, is_open=False, close_date=look_back_until)
if trades:
# Get latest trade
trade = sorted(trades, key=lambda t: t.close_date)[-1]
# Ignore type error as we know we only get closed trades.
trade = sorted(trades, key=lambda t: t.close_date)[-1] # type: ignore
self.log_once(f"Cooldown for {pair} for {self.stop_duration_str}.", logger.info)
until = self.calculate_lock_end([trade], self._stop_duration)

6
freqtrade/plugins/protections/iprotection.py
View File

@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional, Tuple
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import plural
from freqtrade.mixins import LoggingMixin
from freqtrade.persistence import Trade
from freqtrade.persistence import LocalTrade
logger = logging.getLogger(__name__)
@ -93,11 +93,11 @@ class IProtection(LoggingMixin, ABC):
"""
@staticmethod
def calculate_lock_end(trades: List[Trade], stop_minutes: int) -> datetime:
def calculate_lock_end(trades: List[LocalTrade], stop_minutes: int) -> datetime:
"""
Get lock end time
"""
max_date: datetime = max([trade.close_date for trade in trades])
max_date: datetime = max([trade.close_date for trade in trades if trade.close_date])
# comming from Database, tzinfo is not set.
if max_date.tzinfo is None:
max_date = max_date.replace(tzinfo=timezone.utc)

2
freqtrade/plugins/protections/low_profit_pairs.py
View File

@ -53,7 +53,7 @@ class LowProfitPairs(IProtection):
# Not enough trades in the relevant period
return False, None, None
profit = sum(trade.close_profit for trade in trades)
profit = sum(trade.close_profit for trade in trades if trade.close_profit)
if profit < self._required_profit:
self.log_once(
f"Trading for {pair} stopped due to {profit:.2f} < {self._required_profit} "

2
freqtrade/plugins/protections/max_drawdown_protection.py
View File

@ -55,7 +55,7 @@ class MaxDrawdown(IProtection):
# Drawdown is always positive
try:
drawdown, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
drawdown, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
except ValueError:
return False, None, None

Some files were not shown because too many files have changed in this diff Show More