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Merge pull request #1438 from freqtrade/release_1804

Browse Source 
Release last version for 2018
This commit is contained in:
Matthias 2018-12-27 12:47:12 +01:00 committed by GitHub
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32
.pyup.yml
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@ -1,4 +1,32 @@
# autogenerated pyup.io config file
# autogenerated pyup.io config file
# see https://pyup.io/docs/configuration/ for all available options
schedule: every day
# configure updates globally
# default: all
# allowed: all, insecure, False
update: all
# configure dependency pinning globally
# default: True
# allowed: True, False
pin: True
schedule: "every day"
search: False
# Specify requirement files by hand, default is empty
# default: empty
# allowed: list
requirements:
- requirements.txt
- requirements-dev.txt
# configure the branch prefix the bot is using
# default: pyup-
branch_prefix: pyup/
# allow to close stale PRs
# default: True
close_prs: True

26
.travis.yml
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@ -1,9 +1,15 @@
sudo: true
os:
- linux
dist: trusty
language: python
python:
- 3.6
services:
- docker
env:
global:
- IMAGE_NAME=freqtradeorg/freqtrade
addons:
apt:
packages:
@ -11,24 +17,38 @@ addons:
- libdw-dev
- binutils-dev
install:
- ./install_ta-lib.sh
- ./build_helpers/install_ta-lib.sh
- export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
- pip install --upgrade flake8 coveralls pytest-random-order pytest-asyncio mypy
- pip install -r requirements.txt
- pip install -r requirements-dev.txt
- pip install -e .
jobs:
include:
- script:
- stage: tests
script:
- pytest --cov=freqtrade --cov-config=.coveragerc freqtrade/tests/
- coveralls
name: pytest
- script:
- cp config.json.example config.json
- python freqtrade/main.py --datadir freqtrade/tests/testdata backtesting
name: backtest
- script:
- cp config.json.example config.json
- python freqtrade/main.py --datadir freqtrade/tests/testdata hyperopt -e 5
name: hyperopt
- script: flake8 freqtrade
name: flake8
- script: mypy freqtrade
name: mypy
- stage: docker
if: branch in (master, develop, feat/improve_travis) AND (type in (push, cron))
script:
- build_helpers/publish_docker.sh
name: "Build and test and push docker image"
notifications:
slack:
secure: 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

103
CONTRIBUTING.md
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@ -1,43 +1,54 @@
# Contribute to freqtrade
# Contributing
Feel like our bot is missing a feature? We welcome your pull requests! Few pointers for contributions:
## Contribute to freqtrade
Feel like our bot is missing a feature? We welcome your pull requests!
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.
Few pointers for contributions:
- Create your PR against the `develop` branch, not `master`.
- New features need to contain unit tests and must be PEP8
conformant (max-line-length = 100).
- New features need to contain unit tests and must be PEP8 conformant (max-line-length = 100).
If you are unsure, discuss the feature on our [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE)
or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
## Before sending the PR:
**Before sending the PR:**
## 1. Run unit tests
### 1. Run unit tests
All unit tests must pass. If a unit test is broken, change your code to
make it pass. It means you have introduced a regression.
**Test the whole project**
#### Test the whole project
```bash
pytest freqtrade
```
**Test only one file**
#### Test only one file
```bash
pytest freqtrade/tests/test_<file_name>.py
```
**Test only one method from one file**
#### Test only one method from one file
```bash
pytest freqtrade/tests/test_<file_name>.py::test_<method_name>
```
## 2. Test if your code is PEP8 compliant
**Install packages** (If not already installed)
### 2. Test if your code is PEP8 compliant
#### Install packages
```bash
pip3.6 install flake8 coveralls
```
**Run Flake8**
```
#### Run Flake8
```bash
flake8 freqtrade
```
@ -47,16 +58,74 @@ To help with that, we encourage you to install the git pre-commit
hook that will warn you when you try to commit code that fails these checks.
Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using-hooks.html).
## 3. Test if all type-hints are correct
### 3. Test if all type-hints are correct
**Install packages** (If not already installed)
#### Install packages
``` bash
pip3.6 install mypy
```
**Run mypy**
#### Run mypy
``` bash
mypy freqtrade
```
## Getting started
Best start by reading the [documentation](https://github.com/freqtrade/freqtrade/blob/develop/docs/index.md) to get a feel for what is possible with the bot, or head straight to the [Developer-documentation](https://github.com/freqtrade/freqtrade/blob/develop/docs/developer.md) (WIP) which should help you getting started.
## (Core)-Committer Guide
### Process: Pull Requests
How to prioritize pull requests, from most to least important:
1. Fixes for broken tests. Broken means broken on any supported platform or Python version.
1. Extra tests to cover corner cases.
1. Minor edits to docs.
1. Bug fixes.
1. Major edits to docs.
1. Features.
Ensure that each pull request meets all requirements in the Contributing document.
### Process: Issues
If an issue is a bug that needs an urgent fix, mark it for the next patch release.
Then either fix it or mark as please-help.
For other issues: encourage friendly discussion, moderate debate, offer your thoughts.
### Process: Your own code changes
All code changes, regardless of who does them, need to be reviewed and merged by someone else.
This rule applies to all the core committers.
Exceptions:
- Minor corrections and fixes to pull requests submitted by others.
- While making a formal release, the release manager can make necessary, appropriate changes.
- Small documentation changes that reinforce existing subject matter. Most commonly being, but not limited to spelling and grammar corrections.
### Responsibilities
- Ensure cross-platform compatibility for every change that's accepted. Windows, Mac & Linux.
- Ensure no malicious code is introduced into the core code.
- Create issues for any major changes and enhancements that you wish to make. Discuss things transparently and get community feedback.
- Keep feature versions as small as possible, preferably one new feature per version.
- Be welcoming to newcomers and encourage diverse new contributors from all backgrounds. See the Python Community Code of Conduct (https://www.python.org/psf/codeofconduct/).
### Becoming a Committer
Contributors may be given commit privileges. Preference will be given to those with:
1. Past contributions to FreqTrade and other related open-source projects. Contributions to FreqTrade include both code (both accepted and pending) and friendly participation in the issue tracker and Pull request reviews. Quantity and quality are considered.
1. A coding style that the other core committers find simple, minimal, and clean.
1. Access to resources for cross-platform development and testing.
1. Time to devote to the project regularly.
Beeing a Committer does not grant write permission on `develop` or `master` for security reasons (Users trust FreqTrade with their Exchange API keys).
After beeing Committer for some time, a Committer may be named Core Committer and given full repository access.

19
Dockerfile
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@ -1,19 +1,20 @@
FROM python:3.7.0-slim-stretch
# Install TA-lib
RUN apt-get update && apt-get -y install curl build-essential && apt-get clean
RUN curl -L http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz | \
tar xzvf - && \
cd ta-lib && \
sed -i "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h && \
./configure && make && make install && \
cd .. && rm -rf ta-lib
ENV LD_LIBRARY_PATH /usr/local/lib
RUN apt-get update \
&& apt-get -y install curl build-essential \
&& apt-get clean \
&& pip install --upgrade pip
# Prepare environment
RUN mkdir /freqtrade
WORKDIR /freqtrade
# Install TA-lib
COPY build_helpers/* /tmp/
RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
ENV LD_LIBRARY_PATH /usr/local/lib
# Install dependencies
COPY requirements.txt /freqtrade/
RUN pip install numpy --no-cache-dir \

9
Dockerfile.develop Normal file
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@ -0,0 +1,9 @@
FROM freqtradeorg/freqtrade:develop
# Install dependencies
COPY requirements-dev.txt /freqtrade/
RUN pip install numpy --no-cache-dir \
&& pip install -r requirements-dev.txt --no-cache-dir
# Empty the ENTRYPOINT to allow all commands
ENTRYPOINT []

6
Dockerfile.technical Normal file
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@ -0,0 +1,6 @@
FROM freqtradeorg/freqtrade:develop
RUN apt-get update \
&& apt-get -y install git \
&& apt-get clean \
&& pip install git+https://github.com/berlinguyinca/technical

14
README.md
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@ -34,13 +34,15 @@ hesitate to read the source code and understand the mechanism of this bot.
- [x] **Dry-run**: Run the bot without playing 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] **Whitelist crypto-currencies**: Select which crypto-currency you want to trade.
- [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://github.com/freqtrade/freqtrade/blob/develop/docs/edge.md)
- [x] **Whitelist crypto-currencies**: Select which crypto-currency you want to trade or use dynamic whitelists.
- [x] **Blacklist crypto-currencies**: Select which crypto-currency you want to avoid.
- [x] **Manageable via Telegram**: Manage the bot with Telegram
- [x] **Display profit/loss in fiat**: Display your profit/loss in 33 fiat.
- [x] **Daily summary of profit/loss**: Provide a daily summary of your profit/loss.
- [x] **Performance status report**: Provide a performance status of your current trades.
## Table of Contents
- [Quick start](#quick-start)
@ -51,6 +53,7 @@ hesitate to read the source code and understand the mechanism of this bot.
- [Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.md)
- [Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)
- [Sandbox Testing](https://github.com/freqtrade/freqtrade/blob/develop/docs/sandbox-testing.md)
- [Edge](https://github.com/freqtrade/freqtrade/blob/develop/docs/edge.md)
- [Basic Usage](#basic-usage)
- [Bot commands](#bot-commands)
- [Telegram RPC commands](#telegram-rpc-commands)
@ -62,6 +65,8 @@ hesitate to read the source code and understand the mechanism of this bot.
- [Requirements](#requirements)
- [Min hardware required](#min-hardware-required)
- [Software requirements](#software-requirements)
- [Wanna help?](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
- [Dev - getting started](https://github.com/freqtrade/freqtrade/blob/develop/docs/developer.md) (WIP)
## Quick start
@ -189,11 +194,15 @@ in the bug reports.
### [Pull Requests](https://github.com/freqtrade/freqtrade/pulls)
Feel like our bot is missing a feature? We welcome your pull requests!
Feel like our bot is missing a feature? We welcome your pull requests!
Please read our
[Contributing document](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
to understand the requirements before sending your pull-requests.
Coding is not a neccessity 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 [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE). 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 `master`.
@ -218,3 +227,4 @@ To run this bot we recommend you a cloud instance with a minimum of:
- [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)
- [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
- [Docker](https://www.docker.com/products/docker) (Recommended)

13
build_helpers/install_ta-lib.sh Executable file
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@ -0,0 +1,13 @@
if [ ! -f "ta-lib/CHANGELOG.TXT" ]; then
tar zxvf ta-lib-0.4.0-src.tar.gz
cd ta-lib \
&& sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \
&& ./configure \
&& make \
&& which sudo && sudo make install || make install \
&& cd ..
else
echo "TA-lib already installed, skipping download and build."
cd ta-lib && sudo make install && cd ..
fi

60
build_helpers/publish_docker.sh Executable file
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@ -0,0 +1,60 @@
#!/bin/sh
# - export TAG=`if [ "$TRAVIS_BRANCH" == "develop" ]; then echo "latest"; else echo $TRAVIS_BRANCH ; fi`
# Replace / with _ to create a valid tag
TAG=$(echo "${TRAVIS_BRANCH}" | sed -e "s/\//_/")
# Add commit and commit_message to docker container
echo "${TRAVIS_COMMIT} ${TRAVIS_COMMIT_MESSAGE}" > freqtrade_commit
if [ "${TRAVIS_EVENT_TYPE}" = "cron" ]; then
echo "event ${TRAVIS_EVENT_TYPE}: full rebuild - skipping cache"
docker build -t freqtrade:${TAG} .
else
echo "event ${TRAVIS_EVENT_TYPE}: building with cache"
# Pull last build to avoid rebuilding the whole image
docker pull ${REPO}:${TAG}
docker build --cache-from ${IMAGE_NAME}:${TAG} -t freqtrade:${TAG} .
fi
if [ $? -ne 0 ]; then
echo "failed building image"
return 1
fi
# Run backtest
docker run --rm -it -v $(pwd)/config.json.example:/freqtrade/config.json:ro freqtrade:${TAG} --datadir freqtrade/tests/testdata backtesting
if [ $? -ne 0 ]; then
echo "failed running backtest"
return 1
fi
# Tag image for upload
docker tag freqtrade:$TAG ${IMAGE_NAME}:$TAG
if [ $? -ne 0 ]; then
echo "failed tagging image"
return 1
fi
# Tag as latest for develop builds
if [ "${TRAVIS_BRANCH}" = "develop" ]; then
docker tag freqtrade:$TAG ${IMAGE_NAME}:latest
fi
# Login
echo "$DOCKER_PASS" | docker login -u $DOCKER_USER --password-stdin
if [ $? -ne 0 ]; then
echo "failed login"
return 1
fi
# Show all available images
docker images
docker push ${IMAGE_NAME}
if [ $? -ne 0 ]; then
echo "failed pushing repo"
return 1
fi

0
ta-lib-0.4.0-src.tar.gz → build_helpers/ta-lib-0.4.0-src.tar.gz
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21
config.json.example
View File

@ -28,7 +28,10 @@
"name": "bittrex",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"ccxt_rate_limit": true,
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": false
},
"pair_whitelist": [
"ETH/BTC",
"LTC/BTC",
@ -50,12 +53,28 @@
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false
},
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"capital_available_percentage": 0.5,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": true,
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
},
"initial_state": "running",
"forcebuy_enable": false,
"internals": {
"process_throttle_secs": 5
}

83
config_binance.json.example Normal file
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@ -0,0 +1,83 @@
{
"max_open_trades": 3,
"stake_currency": "BTC",
"stake_amount": 0.05,
"fiat_display_currency": "USD",
"ticker_interval" : "5m",
"dry_run": true,
"trailing_stop": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30
},
"bid_strategy": {
"ask_last_balance": 0.0,
"use_order_book": false,
"order_book_top": 1,
"check_depth_of_market": {
"enabled": false,
"bids_to_ask_delta": 1
}
},
"ask_strategy":{
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 9
},
"exchange": {
"name": "binance",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": false
},
"pair_whitelist": [
"AST/BTC",
"ETC/BTC",
"ETH/BTC",
"EOS/BTC",
"IOTA/BTC",
"LTC/BTC",
"MTH/BTC",
"NCASH/BTC",
"TNT/BTC",
"XMR/BTC",
"XLM/BTC",
"XRP/BTC"
],
"pair_blacklist": [
"BNB/BTC"
]
},
"experimental": {
"use_sell_signal": false,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false
},
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"capital_available_percentage": 0.5,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
},
"initial_state": "running",
"forcebuy_enable": false,
"internals": {
"process_throttle_secs": 5
}
}

39
config_full.json.example
View File

@ -33,11 +33,32 @@
"order_book_min": 1,
"order_book_max": 9
},
"order_types": {
"buy": "limit",
"sell": "limit",
"stoploss": "market",
"stoploss_on_exchange": "false"
},
"order_time_in_force": {
"buy": "gtc",
"sell": "gtc",
},
"pairlist": {
"method": "VolumePairList",
"config": {
"number_assets": 20,
"sort_key": "quoteVolume"
}
},
"exchange": {
"name": "bittrex",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"ccxt_rate_limit": true,
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": false,
"aiohttp_trust_env": false
},
"pair_whitelist": [
"ETH/BTC",
"LTC/BTC",
@ -55,6 +76,21 @@
],
"outdated_offset": 5
},
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"capital_available_percentage": 0.5,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"experimental": {
"use_sell_signal": false,
"sell_profit_only": false,
@ -67,6 +103,7 @@
},
"db_url": "sqlite:///tradesv3.sqlite",
"initial_state": "running",
"forcebuy_enable": false,
"internals": {
"process_throttle_secs": 5
},

218
docs/bot-optimization.md
View File

@ -7,7 +7,18 @@ indicators.
- [Install a custom strategy file](#install-a-custom-strategy-file)
- [Customize your strategy](#change-your-strategy)
- [Add more Indicator](#add-more-indicator)
- [Anatomy of a strategy](#anatomy-of-a-strategy)
- [Customize indicators](#customize-indicators)
- [Buy signal rules](#buy-signal-rules)
- [Sell signal rules](#sell-signal-rules)
- [Minimal ROI](#minimal-roi)
- [Stoploss](#stoploss)
- [Ticker interval](#ticker-interval)
- [Metadata dict](#metadata-dict)
- [Where is the default strategy](#where-is-the-default-strategy)
- [Specify custom strategy location](#specify-custom-strategy-location)
- [Further strategy ideas](#further-strategy-ideas)
- [Where is the default strategy](#where-is-the-default-strategy)
Since the version `0.16.0` the bot allows using custom strategy file.
@ -33,12 +44,18 @@ use your own file to not have to lose your parameters every time the default
strategy file will be updated on Github. Put your custom strategy file
into the folder `user_data/strategies`.
Best copy the test-strategy and modify this copy to avoid having bot-updates override your changes.
`cp user_data/strategies/test_strategy.py user_data/strategies/awesome-strategy.py`
### Anatomy of a strategy
A strategy file contains all the information needed to build a good strategy:
- Indicators
- Buy strategy rules
- Sell strategy rules
- Minimal ROI recommended
- Stoploss recommended
- Stoploss strongly recommended
The bot also include a sample strategy called `TestStrategy` you can update: `user_data/strategies/test_strategy.py`.
You can test it with the parameter: `--strategy TestStrategy`
@ -47,73 +64,17 @@ You can test it with the parameter: `--strategy TestStrategy`
python3 ./freqtrade/main.py --strategy AwesomeStrategy
```
### Specify custom strategy location
If you want to use a strategy from a different folder you can pass `--strategy-path`
```bash
python3 ./freqtrade/main.py --strategy AwesomeStrategy --strategy-path /some/folder
```
**For the following section we will use the [user_data/strategies/test_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py)
file as reference.**
### Buy strategy
### Customize Indicators
Edit the method `populate_buy_trend()` into your strategy file to update your buy strategy.
Sample from `user_data/strategies/test_strategy.py`:
```python
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['adx'] > 30) &
(dataframe['tema'] <= dataframe['bb_middleband']) &
(dataframe['tema'] > dataframe['tema'].shift(1))
),
'buy'] = 1
return dataframe
```
### Sell strategy
Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
Please note that the sell-signal is only used if `use_sell_signal` is set to true in the configuration.
Sample from `user_data/strategies/test_strategy.py`:
```python
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['adx'] > 70) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
),
'sell'] = 1
return dataframe
```
## Add more Indicators
As you have seen, buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
Buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
You should only add the indicators used in either `populate_buy_trend()`, `populate_sell_trend()`, or to populate another indicator, otherwise performance may suffer.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
Sample:
```python
@ -157,21 +118,144 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
return dataframe
```
#### Want more indicator examples
Look into the [user_data/strategies/test_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py).
Then uncomment indicators you need.
### Buy signal rules
Edit the method `populate_buy_trend()` in your strategy file to update your buy strategy.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
This will method will also define a new column, `"buy"`, which needs to contain 1 for buys, and 0 for "no action".
Sample from `user_data/strategies/test_strategy.py`:
```python
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['adx'] > 30) &
(dataframe['tema'] <= dataframe['bb_middleband']) &
(dataframe['tema'] > dataframe['tema'].shift(1))
),
'buy'] = 1
return dataframe
```
### Sell signal rules
Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
Please note that the sell-signal is only used if `use_sell_signal` is set to true in the configuration.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
This will method will also define a new column, `"sell"`, which needs to contain 1 for sells, and 0 for "no action".
Sample from `user_data/strategies/test_strategy.py`:
```python
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['adx'] > 70) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
),
'sell'] = 1
return dataframe
```
### Minimal ROI
This dict defines the minimal Return On Investment (ROI) a trade should reach before selling, independent from the sell signal.
It is of the following format, with the dict key (left side of the colon) being the minutes passed since the trade opened, and the value (right side of the colon) being the percentage.
```python
minimal_roi = {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
}
```
The above configuration would therefore mean:
- Sell whenever 4% profit was reached
- Sell after 20 minutes when 2% profit was reached
- Sell after 20 minutes when 2% profit was reached
- Sell after 30 minutes when 1% profit was reached
- Sell after 40 minutes when the trade is non-loosing (no profit)
The calculation does include fees.
To disable ROI completely, set it to an insanely high number:
```python
minimal_roi = {
"0": 100
}
```
While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
### Stoploss
Setting a stoploss is highly recommended to protect your capital from strong moves against you.
Sample:
``` python
stoploss = -0.10
```
This would signify a stoploss of -10%.
If your exchange supports it, it's recommended to also set `"stoploss_on_exchange"` in the order dict, so your stoploss is on the exchange and cannot be missed for network-problems (or other problems).
For more information on order_types please look [here](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md#understand-order_types).
### Ticker interval
This is the set of candles the bot should download and use for the analysis.
Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
Please note that the same buy/sell signals may work with one interval, but not the other.
### Metadata dict
The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `populate_indicators`) contains additional information.
Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
### Want more indicator examples
Look into the [user_data/strategies/test_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py).
Then uncomment indicators you need.
### Where is the default strategy?
The default buy strategy is located in the file
[freqtrade/default_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/strategy/default_strategy.py).
### Specify custom strategy location
If you want to use a strategy from a different folder you can pass `--strategy-path`
```bash
python3 ./freqtrade/main.py --strategy AwesomeStrategy --strategy-path /some/folder
```
### Further strategy ideas
To get additional Ideas for strategies, head over to our [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.

71
docs/bot-usage.md
View File

@ -36,7 +36,7 @@ optional arguments:
--strategy-path PATH specify additional strategy lookup path
--dynamic-whitelist [INT]
dynamically generate and update whitelist based on 24h
BaseVolume (default: 20)
BaseVolume (default: 20) DEPRECATED
--db-url PATH Override trades database URL, this is useful if
dry_run is enabled or in custom deployments (default:
sqlite:///tradesv3.sqlite)
@ -44,11 +44,11 @@ optional arguments:
### How to use a different config file?
The bot allows you to select which config file you want to use. Per
The bot allows you to select which config file you want to use. Per
default, the bot will load the file `./config.json`
```bash
python3 ./freqtrade/main.py -c path/far/far/away/config.json
python3 ./freqtrade/main.py -c path/far/far/away/config.json
```
### How to use --strategy?
@ -61,7 +61,7 @@ The bot will search your strategy file within `user_data/strategies` and `freqtr
To load a strategy, simply pass the class name (e.g.: `CustomStrategy`) in this parameter.
**Example:**
**Example:**
In `user_data/strategies` you have a file `my_awesome_strategy.py` which has
a strategy class called `AwesomeStrategy` to load it:
@ -69,7 +69,7 @@ a strategy class called `AwesomeStrategy` to load it:
python3 ./freqtrade/main.py --strategy AwesomeStrategy
```
If the bot does not find your strategy file, it will display in an error
If the bot does not find your strategy file, it will display in an error
message the reason (File not found, or errors in your code).
Learn more about strategy file in [optimize your bot](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md).
@ -84,37 +84,39 @@ python3 ./freqtrade/main.py --strategy AwesomeStrategy --strategy-path /some/fol
#### How to install a strategy?
This is very simple. Copy paste your strategy file into the folder
This is very simple. Copy paste your strategy file into the folder
`user_data/strategies` or use `--strategy-path`. And voila, the bot is ready to use it.
### How to use --dynamic-whitelist?
Per default `--dynamic-whitelist` will retrieve the 20 currencies based
> Dynamic-whitelist is deprecated. Please move your configurations to the configuration as outlined [here](docs/configuration.md#Dynamic-Pairlists)
Per default `--dynamic-whitelist` will retrieve the 20 currencies based
on BaseVolume. This value can be changed when you run the script.
**By Default**
Get the 20 currencies based on BaseVolume.
**By Default**
Get the 20 currencies based on BaseVolume.
```bash
python3 ./freqtrade/main.py --dynamic-whitelist
```
**Customize the number of currencies to retrieve**
Get the 30 currencies based on BaseVolume.
**Customize the number of currencies to retrieve**
Get the 30 currencies based on BaseVolume.
```bash
python3 ./freqtrade/main.py --dynamic-whitelist 30
```
**Exception**
**Exception**
`--dynamic-whitelist` must be greater than 0. If you enter 0 or a
negative value (e.g -2), `--dynamic-whitelist` will use the default
value (20).
### How to use --db-url?
When you run the bot in Dry-run mode, per default no transactions are
stored in a database. If you want to store your bot actions in a DB
When you run the bot in Dry-run mode, per default no transactions are
stored in a database. If you want to store your bot actions in a DB
using `--db-url`. This can also be used to specify a custom database
in production mode. Example command:
@ -170,15 +172,15 @@ optional arguments:
### How to use --refresh-pairs-cached parameter?
The first time your run Backtesting, it will take the pairs you have
set in your config file and download data from Bittrex.
The first time your run Backtesting, it will take the pairs you have
set in your config file and download data from Bittrex.
If for any reason you want to update your data set, you use
`--refresh-pairs-cached` to force Backtesting to update the data it has.
If for any reason you want to update your data set, you use
`--refresh-pairs-cached` to force Backtesting to update the data it has.
**Use it only if you want to update your data set. You will not be able
to come back to the previous version.**
To test your strategy with latest data, we recommend continuing using
To test your strategy with latest data, we recommend continuing using
the parameter `-l` or `--live`.
## Hyperopt commands
@ -204,6 +206,8 @@ optional arguments:
number)
--timerange TIMERANGE
specify what timerange of data to use.
--hyperopt PATH specify hyperopt file (default:
freqtrade/optimize/default_hyperopt.py)
-e INT, --epochs INT specify number of epochs (default: 100)
-s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...], --spaces {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]
Specify which parameters to hyperopt. Space separate
@ -211,6 +215,33 @@ optional arguments:
```
## Edge commands
To know your trade expectacny and winrate against historical data, you can use Edge.
```
usage: main.py edge [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE] [-r]
[--stoplosses STOPLOSS_RANGE]
optional arguments:
-h, --help show this help message and exit
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
specify ticker interval (1m, 5m, 30m, 1h, 1d)
--timerange TIMERANGE
specify what timerange of data to use.
-r, --refresh-pairs-cached
refresh the pairs files in tests/testdata with the
latest data from the exchange. Use it if you want to
run your edge with up-to-date data.
--stoplosses STOPLOSS_RANGE
defines a range of stoploss against which edge will
assess the strategythe format is "min,max,step"
(without any space).example:
--stoplosses=-0.01,-0.1,-0.001
```
To understand edge and how to read the results, please read the [edge documentation](edge.md).
## A parameter missing in the configuration?
All parameters for `main.py`, `backtesting`, `hyperopt` are referenced
@ -218,5 +249,5 @@ in [misc.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/misc.
## Next step
The optimal strategy of the bot will change with time depending of the market trends. The next step is to
The optimal strategy of the bot will change with time depending of the market trends. The next step is to
[optimize your bot](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md).

136
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@ -13,19 +13,19 @@ This page explains how to configure your `config.json` file.
We recommend to copy and use the `config.json.example` as a template
for your bot configuration.
The table below will list all configuration parameters.
The table below will list all configuration parameters.
| Command | Default | Mandatory | Description |
|----------|---------|----------|-------------|
| `max_open_trades` | 3 | Yes | Number of trades open your bot will have.
| `max_open_trades` | 3 | Yes | Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades)
| `stake_currency` | BTC | Yes | Crypto-currency used for trading.
| `stake_amount` | 0.05 | Yes | Amount of crypto-currency your bot will use for each trade. Per default, the bot will use (0.05 BTC x 3) = 0.15 BTC in total will be always engaged. Set it to 'unlimited' to allow the bot to use all avaliable balance.
| `ticker_interval` | [1m, 5m, 30m, 1h, 1d] | No | The ticker interval to use (1min, 5 min, 30 min, 1 hour or 1 day). Default is 5 minutes
| `fiat_display_currency` | USD | Yes | Fiat currency used to show your profits. More information below.
| `fiat_display_currency` | USD | Yes | Fiat currency used to show your profits. More information below.
| `dry_run` | true | Yes | Define if the bot must be in Dry-run or production mode.
| `process_only_new_candles` | false | No | If set to true indicators are processed only once a new candle arrives. 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. Can be set either in Configuration or in the strategy.
| `minimal_roi` | See below | No | Set the threshold in percent the bot will use to sell a trade. More information below. If set, this parameter will override `minimal_roi` from your strategy file.
| `stoploss` | -0.10 | No | Value of the stoploss in percent used by the bot. More information below. If set, this parameter will override `stoploss` from your strategy file.
| `process_only_new_candles` | false | No | If set to true indicators are processed only once a new candle arrives. 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. Can be set either in Configuration or in the strategy.
| `minimal_roi` | See below | No | Set the threshold in percent the bot will use to sell a trade. More information below. If set, this parameter will override `minimal_roi` from your strategy file.
| `stoploss` | -0.10 | No | Value of the stoploss in percent used by the bot. More information below. If set, this parameter will override `stoploss` from your strategy file.
| `trailing_stop` | false | No | Enables trailing stop-loss (based on `stoploss` in either configuration or strategy file).
| `trailing_stop_positve` | 0 | No | Changes stop-loss once profit has been reached.
| `trailing_stop_positve_offset` | 0 | No | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive.
@ -34,30 +34,38 @@ The table below will list all configuration parameters.
| `bid_strategy.ask_last_balance` | 0.0 | Yes | Set the bidding price. More information below.
| `bid_strategy.use_order_book` | false | No | Allows buying of pair using the rates in Order Book Bids.
| `bid_strategy.order_book_top` | 0 | No | Bot will use the top N rate in Order Book Bids. Ie. a value of 2 will allow the bot to pick the 2nd bid rate in Order Book Bids.
| `bid_strategy.check_depth_of_market.enabled` | false | No | Does not buy if the % difference of buy orders and sell orders is met in Order Book.
| `bid_strategy.check_depth_of_market.bids_to_ask_delta` | 0 | No | The % difference of buy orders and sell orders found in Order Book. A value lesser than 1 means sell orders is greater, while value greater than 1 means buy orders is higher.
| `bid_strategy. check_depth_of_market.enabled` | false | No | Does not buy if the % difference of buy orders and sell orders is met in Order Book.
| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | 0 | No | The % difference of buy orders and sell orders found in Order Book. A value lesser than 1 means sell orders is greater, while value greater than 1 means buy orders is higher.
| `ask_strategy.use_order_book` | false | No | Allows selling of open traded pair using the rates in Order Book Asks.
| `ask_strategy.order_book_min` | 0 | No | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
| `ask_strategy.order_book_max` | 0 | No | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
| `order_types` | None | No | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types).
| `order_time_in_force` | None | No | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force).
| `exchange.name` | bittrex | Yes | Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename).
| `exchange.key` | key | No | API key to use for the exchange. Only required when you are in production mode.
| `exchange.secret` | secret | No | API secret to use for the exchange. Only required when you are in production mode.
| `exchange.pair_whitelist` | [] | No | List of currency to use by the bot. Can be overrided with `--dynamic-whitelist` param.
| `exchange.pair_blacklist` | [] | No | List of currency the bot must avoid. Useful when using `--dynamic-whitelist` param.
| `exchange.ccxt_rate_limit` | True | No | Have CCXT handle Exchange rate limits. Depending on the exchange, having this to false can lead to temporary bans from the exchange.
| `exchange.ccxt_rate_limit` | True | No | DEPRECATED!! Have CCXT handle Exchange rate limits. Depending on the exchange, having this to false can lead to temporary bans from the exchange.
| `exchange.ccxt_config` | None | No | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation)
| `exchange.ccxt_async_config` | None | No | 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)
| `edge` | false | No | Please refer to [edge configuration document](edge.md) for detailed explanation.
| `experimental.use_sell_signal` | false | No | Use your sell strategy in addition of the `minimal_roi`.
| `experimental.sell_profit_only` | false | No | waits until you have made a positive profit before taking a sell decision.
| `experimental.ignore_roi_if_buy_signal` | false | No | Does not sell if the buy-signal is still active. Takes preference over `minimal_roi` and `use_sell_signal`
| `pairlist.method` | StaticPairList | No | Use Static whitelist. [More information below](#dynamic-pairlists).
| `pairlist.config` | None | No | Additional configuration for dynamic pairlists. [More information below](#dynamic-pairlists).
| `telegram.enabled` | true | Yes | Enable or not the usage of Telegram.
| `telegram.token` | token | No | Your Telegram bot token. Only required if `telegram.enabled` is `true`.
| `telegram.chat_id` | chat_id | No | Your personal Telegram account id. Only required if `telegram.enabled` is `true`.
| `webhook.enabled` | false | No | Enable useage of Webhook notifications
| `webhook.enabled` | false | No | Enable usage of Webhook notifications
| `webhook.url` | false | No | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details.
| `webhook.webhookbuy` | false | No | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
| `webhook.webhooksell` | false | No | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
| `webhook.webhookstatus` | false | No | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
| `db_url` | `sqlite:///tradesv3.sqlite` | No | Declares database URL to use. NOTE: This defaults to `sqlite://` if `dry_run` is `True`.
| `initial_state` | running | No | Defines the initial application state. More information below.
| `forcebuy_enable` | false | No | Enables the RPC Commands to force a buy. More information below.
| `strategy` | DefaultStrategy | No | Defines Strategy class to use.
| `strategy_path` | null | No | Adds an additional strategy lookup path (must be a folder).
| `internals.process_throttle_secs` | 5 | Yes | Set the process throttle. Value in second.
@ -67,7 +75,7 @@ The definition of each config parameters is in [misc.py](https://github.com/freq
### Understand stake_amount
`stake_amount` is an amount of crypto-currency your bot will use for each trade.
The minimal value is 0.0005. If there is not enough crypto-currency in
The minimal value is 0.0005. If there is not enough crypto-currency in
the account an exception is generated.
To allow the bot to trade all the avaliable `stake_currency` in your account set `stake_amount` = `unlimited`.
In this case a trade amount is calclulated as `currency_balanse / (max_open_trades - current_open_trades)`.
@ -111,6 +119,15 @@ Go to the [trailing stoploss Documentation](stoploss.md) for details on trailing
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
`forcebuy_enable` 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.
You send `/forcebuy ETH/BTC` to the bot, who buys the pair and holds it until a regular sell-signal appears (ROI, stoploss, /forcesell).
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
`process_throttle_secs` is an optional field that defines in seconds how long the bot should wait
@ -125,6 +142,45 @@ use the `last` price and values between those interpolate between ask and last
price. Using `ask` price will guarantee quick success in bid, but bot will also
end up paying more then would probably have been necessary.
### Understand order_types
`order_types` contains a dict mapping order-types to market-types as well as stoploss on or off exchange type. This allows to buy using limit orders, sell using limit-orders, and create stoploss orders using market. It also allows to set the stoploss "on exchange" which means stoploss order would be placed immediately once the buy order is fulfilled.
This can be set in the configuration or in the strategy. Configuration overwrites strategy configurations.
If this is configured, all 4 values (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`) need to be present, otherwise the bot warn about it and will fail to start.
The below is the default which is used if this is not configured in either Strategy or configuration.
``` python
"order_types": {
"buy": "limit",
"sell": "limit",
"stoploss": "market",
"stoploss_on_exchange": False
},
```
**NOTE**: Not all exchanges support "market" orders.
The following message will be shown if your exchange does not support market orders: `"Exchange <yourexchange> does not support market orders."`
### Understand order_time_in_force
Order time in force defines the policy by which the order is executed on the exchange. Three commonly used time in force are:<br/>
**GTC (Goog Till Canceled):**
This is most of the time the default time in force. It means the order will remain on exchange till it is canceled by user. It can be fully or partially fulfilled. If partially fulfilled, the remaining will stay on the exchange till cancelled.<br/>
**FOK (Full Or Kill):**
It means if the order is not executed immediately AND fully then it is canceled by the exchange.<br/>
**IOC (Immediate Or Canceled):**
It is the same as FOK (above) except it can be partially fulfilled. The remaining part is automatically cancelled by the exchange.
<br/>
`order_time_in_force` contains a dict buy and sell time in force policy. This can be set in the configuration or in the strategy. Configuration overwrites strategy configurations.<br/>
possible values are: `gtc` (default), `fok` or `ioc`.<br/>
``` python
"order_time_in_force": {
"buy": "gtc",
"sell": "gtc"
},
```
**NOTE**: This is an ongoing work. For now it is supported only for binance and only for buy orders. Please don't change the default value unless you know what you are doing.<br/>
### What values for exchange.name?
Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports 115 cryptocurrency
@ -174,16 +230,41 @@ creating trades.
}
```
Once you will be happy with your bot performance, you can switch it to
Once you will be happy with your bot performance, you can switch it to
production mode.
### Dynamic Pairlists
Dynamic pairlists select pairs for you based on the logic configured.
The bot runs against all pairs (with that stake) on the exchange, and a number of assets (`number_assets`) is selected based on the selected criteria.
By *default*, a Static Pairlist is used (configured as `"pair_whitelist"` under the `"exchange"` section of this configuration).
#### Available Pairlist methods
* `"StaticPairList"`
* uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`
* `"VolumePairList"`
* Formerly available as `--dynamic-whitelist [<number_assets>]`
* Selects `number_assets` top pairs based on `sort_key`, which can be one of `askVolume`, `bidVolume` and `quoteVolume`, defaults to `quoteVolume`.
```json
"pairlist": {
"method": "VolumePairList",
"config": {
"number_assets": 20,
"sort_key": "quoteVolume"
}
},
```
## Switch to production mode
In production mode, the bot will engage your money. Be careful a wrong
strategy can lose all your money. Be aware of what you are doing when
In production mode, the bot will engage your money. Be careful a wrong
strategy can lose all your money. Be aware of what you are doing when
you run it in production mode.
### To switch your bot in production mode:
### To switch your bot in production mode
1. Edit your `config.json` file
@ -204,16 +285,37 @@ you run it in production mode.
}
```
If you have not your Bittrex API key yet, [see our tutorial](https://github.com/freqtrade/freqtrade/blob/develop/docs/pre-requisite.md).
### Using proxy with FreqTrade
To use a proxy with freqtrade, add the kwarg `"aiohttp_trust_env"=true` to the `"ccxt_async_kwargs"` dict in the exchange section of the configuration.
An example for this can be found in `config_full.json.example`
``` json
"ccxt_async_config": {
"aiohttp_trust_env": true
}
```
Then, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values
``` bash
export HTTP_PROXY="http://addr:port"
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.
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
#### Encoding a string as BASE64
This is a quick example, how to generate the BASE64 string in python

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@ -0,0 +1,70 @@
# Development Help
This page is intended for developers of FreqTrade, people who want to contribute to the FreqTrade codebase or documentation, or people who want to understand the source code of the application they're running.
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel in [slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE) where you can ask questions.
## Module
### Dynamic Pairlist
You have a great idea for a new pair selection algorithm you would like to try out? Great.
Hopefully you also want to contribute this back upstream.
Whatever your motivations are - This should get you off the ground in trying to develop a new Pairlist provider.
First of all, have a look at the [VolumePairList](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/pairlist/VolumePairList.py) provider, and best copy this file with a name of your new Pairlist Provider.
This is a simple provider, which however serves as a good example on how to start developing.
Next, modify the classname of the provider (ideally align this with the Filename).
The base-class provides the an instance of the bot (`self._freqtrade`), as well as the configuration (`self._config`), and initiates both `_blacklist` and `_whitelist`.
```python
self._freqtrade = freqtrade
self._config = config
self._whitelist = self._config['exchange']['pair_whitelist']
self._blacklist = self._config['exchange'].get('pair_blacklist', [])
```
Now, let's step through the methods which require actions:
#### configuration
Configuration for PairListProvider is done in the bot configuration file in the element `"pairlist"`.
This Pairlist-object may contain a `"config"` dict with additional configurations for the configured pairlist.
By convention, `"number_assets"` is used to specify the maximum number of pairs to keep in the whitelist. Please follow this to ensure a consistent user experience.
Additional elements can be configured as needed. `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successfull and dynamic.
#### short_desc
Returns a description used for Telegram messages.
This should contain the name of the Provider, as well as a short description containing the number of assets. Please follow the format `"PairlistName - top/bottom X pairs"`.
#### refresh_pairlist
Override this method and run all calculations needed in this method.
This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
Assign the resulting whiteslist to `self._whitelist` and `self._blacklist` respectively. These will then be used to run the bot in this iteration. Pairs with open trades will be added to the whitelist to have the sell-methods run correctly.
Please also run `self._validate_whitelist(pairs)` and to check and remove pairs with inactive markets. This function is available in the Parent class (`StaticPairList`) and should ideally not be overwritten.
##### sample
``` python
def refresh_pairlist(self) -> None:
# Generate dynamic whitelist
pairs = self._gen_pair_whitelist(self._config['stake_currency'], self._sort_key)
# Validate whitelist to only have active market pairs
self._whitelist = self._validate_whitelist(pairs)[:self._number_pairs]
```
#### _gen_pair_whitelist
This is a simple method used by `VolumePairList` - however serves as a good example.
It implements caching (`@cached(TTLCache(maxsize=1, ttl=1800))`) as well as a configuration option to allow different (but similar) strategies to work with the same PairListProvider.

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@ -0,0 +1,216 @@
# Edge positioning
This page explains how to use Edge Positioning module in your bot in order to enter into a trade only if the trade has a reasonable win rate and risk reward ratio, and consequently adjust your position size and stoploss.
**NOTICE:** Edge positioning is not compatible with dynamic whitelist. it overrides dynamic whitelist.
**NOTICE2:** Edge won't consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else will be ignored in its calculation.
## Table of Contents
- [Introduction](#introduction)
- [How does it work?](#how-does-it-work?)
- [Configurations](#configurations)
- [Running Edge independently](#running-edge-independently)
## Introduction
Trading is all about probability. No one can claim that he has a strategy working all the time. You have to assume that sometimes you lose.<br/><br/>
But it doesn't mean there is no rule, it only means rules should work "most of the time". Let's play a game: we toss a coin, heads: I give you 10$, tails: You give me 10$. Is it an interesting game ? no, it is quite boring, isn't it?<br/><br/>
But let's say the probability that we have heads is 80%, and the probability that we have tails is 20%. Now it is becoming interesting ...
That means 10$ x 80% versus 10$ x 20%. 8$ versus 2$. That means over time you will win 8$ risking only 2$ on each toss of coin.<br/><br/>
Let's complicate it more: you win 80% of the time but only 2$, I win 20% of the time but 8$. The calculation is: 80% * 2$ versus 20% * 8$. It is becoming boring again because overtime you win $1.6$ (80% x 2$) and me $1.6 (20% * 8$) too.<br/><br/>
The question is: How do you calculate that? how do you know if you wanna play?
The answer comes to two factors:
- Win Rate
- Risk Reward Ratio
### Win Rate
Means over X trades what is the percentage of winning trades to total number of trades (note that we don't consider how much you gained but only If you won or not).
`W = (Number of winning trades) / (Number of losing trades)`
### Risk Reward Ratio
Risk Reward Ratio is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose:
`R = Profit / Loss`
Over time, on many trades, you can calculate your risk reward by dividing your average profit on winning trades by your average loss on losing trades:
`Average profit = (Sum of profits) / (Number of winning trades)`
`Average loss = (Sum of losses) / (Number of losing trades)`
`R = (Average profit) / (Average loss)`
### Expectancy
At this point we can combine W and R to create an expectancy ratio. This is a simple process of multiplying the risk reward ratio by the percentage of winning trades, and subtracting the percentage of losing trades, which is calculated as follows:
Expectancy Ratio = (Risk Reward Ratio x Win Rate) – Loss Rate
So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
`Expectancy = (5 * 0.28) - 0.72 = 0.68`
Superficially, this means that on average you expect this strategy’s trades to return .68 times the size of your losers. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
It is important to remember that any system with an expectancy greater than 0 is profitable using past data. The key is finding one that will be profitable in the future.
You can also use this number to evaluate the effectiveness of modifications to this system.
**NOTICE:** It's important to keep in mind that Edge is testing your expectancy using historical data , there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology, but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
## How does it work?
If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over X trades for each stoploss. Here is an example:
| Pair | Stoploss | Win Rate | Risk Reward Ratio | Expectancy |
|----------|:-------------:|-------------:|------------------:|-----------:|
| XZC/ETH | -0.03 | 0.52 |1.359670 | 0.228 |
| XZC/ETH | -0.01 | 0.50 |1.176384 | 0.088 |
| XZC/ETH | -0.02 | 0.51 |1.115941 | 0.079 |
The goal here is to find the best stoploss for the strategy in order to have the maximum expectancy. In the above example stoploss at 3% leads to the maximum expectancy according to historical data.
Edge then forces stoploss to your strategy dynamically.
### Position size
Edge dictates the stake amount for each trade to the bot according to the following factors:
- Allowed capital at risk
- Stoploss
Allowed capital at risk is calculated as follows:
**allowed capital at risk** = **capital_available_percentage** X **allowed risk per trade**
**Stoploss** is calculated as described above against historical data.
Your position size then will be:
**position size** = **allowed capital at risk** / **stoploss**
Example:<br/>
Let's say the stake currency is ETH and you have 10 ETH on the exchange, your **capital_available_percentage** is 50% and you would allow 1% of risk for each trade. thus your available capital for trading is **10 x 0.5 = 5 ETH** and allowed capital at risk would be **5 x 0.01 = 0.05 ETH**. <br/>
Let's assume Edge has calculated that for **XLM/ETH** market your stoploss should be at 2%. So your position size will be **0.05 / 0.02 = 2.5ETH**.<br/>
Bot takes a position of 2.5ETH on XLM/ETH (call it trade 1). Up next, you receive another buy signal while trade 1 is still open. This time on BTC/ETH market. Edge calculated stoploss for this market at 4%. So your position size would be 0.05 / 0.04 = 1.25ETH (call it trade 2).<br/>
Note that available capital for trading didn’t change for trade 2 even if you had already trade 1. The available capital doesn’t mean the free amount on your wallet.<br/>
Now you have two trades open. The Bot receives yet another buy signal for another market: **ADA/ETH**. This time the stoploss is calculated at 1%. So your position size is **0.05 / 0.01 = 5ETH**. But there are already 4ETH blocked in two previous trades. So the position size for this third trade would be 1ETH.<br/>
Available capital doesn’t change before a position is sold. Let’s assume that trade 1 receives a sell signal and it is sold with a profit of 1ETH. Your total capital on exchange would be 11 ETH and the available capital for trading becomes 5.5ETH. <br/>
So the Bot receives another buy signal for trade 4 with a stoploss at 2% then your position size would be **0.055 / 0.02 = 2.75**.
## Configurations
Edge has following configurations:
#### enabled
If true, then Edge will run periodically.<br/>
(default to false)
#### process_throttle_secs
How often should Edge run in seconds? <br/>
(default to 3600 so one hour)
#### calculate_since_number_of_days
Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy
Note that it downloads historical data so increasing this number would lead to slowing down the bot.<br/>
(default to 7)
#### capital_available_percentage
This is the percentage of the total capital on exchange in stake currency. <br/>
As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital.<br/>
(default to 0.5)
#### allowed_risk
Percentage of allowed risk per trade.<br/>
(default to 0.01 [1%])
#### stoploss_range_min
Minimum stoploss.<br/>
(default to -0.01)
#### stoploss_range_max
Maximum stoploss.<br/>
(default to -0.10)
#### stoploss_range_step
As an example if this is set to -0.01 then Edge will test the strategy for [-0.01, -0,02, -0,03 ..., -0.09, -0.10] ranges.
Note than having a smaller step means having a bigger range which could lead to slow calculation. <br/>
if you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br/>
(default to -0.01)
#### minimum_winrate
It filters pairs which don't have at least minimum_winrate.
This comes handy if you want to be conservative and don't comprise win rate in favor of risk reward ratio.<br/>
(default to 0.60)
#### minimum_expectancy
It filters paris which have an expectancy lower than this number .
Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return.<br/>
(default to 0.20)
#### min_trade_number
When calculating W and R and E (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br/>
(default to 10, it is highly recommended not to decrease this number)
#### max_trade_duration_minute
Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br/>
**NOTICE:** While configuring this value, you should take into consideration your ticker interval. as an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. default value is set assuming your strategy interval is relatively small (1m or 5m, etc).<br/>
(default to 1 day, 1440 = 60 * 24)
#### remove_pumps
Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br/>
(default to false)
## Running Edge independently
You can run Edge independently in order to see in details the result. Here is an example:
```bash
python3 ./freqtrade/main.py edge
```
An example of its output:
| pair | stoploss | win rate | risk reward ratio | required risk reward | expectancy | total number of trades | average duration (min) |
|:----------|-----------:|-----------:|--------------------:|-----------------------:|-------------:|-------------------------:|-------------------------:|
| AGI/BTC | -0.02 | 0.64 | 5.86 | 0.56 | 3.41 | 14 | 54 |
| NXS/BTC | -0.03 | 0.64 | 2.99 | 0.57 | 1.54 | 11 | 26 |
| LEND/BTC | -0.02 | 0.82 | 2.05 | 0.22 | 1.50 | 11 | 36 |
| VIA/BTC | -0.01 | 0.55 | 3.01 | 0.83 | 1.19 | 11 | 48 |
| MTH/BTC | -0.09 | 0.56 | 2.82 | 0.80 | 1.12 | 18 | 52 |
| ARDR/BTC | -0.04 | 0.42 | 3.14 | 1.40 | 0.73 | 12 | 42 |
| BCPT/BTC | -0.01 | 0.71 | 1.34 | 0.40 | 0.67 | 14 | 30 |
| WINGS/BTC | -0.02 | 0.56 | 1.97 | 0.80 | 0.65 | 27 | 42 |
| VIBE/BTC | -0.02 | 0.83 | 0.91 | 0.20 | 0.59 | 12 | 35 |
| MCO/BTC | -0.02 | 0.79 | 0.97 | 0.27 | 0.55 | 14 | 31 |
| GNT/BTC | -0.02 | 0.50 | 2.06 | 1.00 | 0.53 | 18 | 24 |
| HOT/BTC | -0.01 | 0.17 | 7.72 | 4.81 | 0.50 | 209 | 7 |
| SNM/BTC | -0.03 | 0.71 | 1.06 | 0.42 | 0.45 | 17 | 38 |
| APPC/BTC | -0.02 | 0.44 | 2.28 | 1.27 | 0.44 | 25 | 43 |
| NEBL/BTC | -0.03 | 0.63 | 1.29 | 0.58 | 0.44 | 19 | 59 |
### Update cached pairs with the latest data
```bash
python3 ./freqtrade/main.py edge --refresh-pairs-cached
```
### Precising stoploss range
```bash
python3 ./freqtrade/main.py edge --stoplosses=-0.01,-0.1,-0.001 #min,max,step
```
### Advanced use of timerange
```bash
python3 ./freqtrade/main.py edge --timerange=20181110-20181113
```
Doing --timerange=-200 will get the last 200 timeframes from your inputdata. You can also specify specific dates, or a range span indexed by start and stop.
The full timerange specification:
* Use last 123 tickframes of data: --timerange=-123
* Use first 123 tickframes of data: --timerange=123-
* Use tickframes from line 123 through 456: --timerange=123-456
* 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

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@ -1,4 +1,5 @@
# Hyperopt
This page explains how to tune your strategy by finding the optimal
parameters, a process called hyperparameter optimization. The bot uses several
algorithms included in the `scikit-optimize` package to accomplish this. The
@ -8,25 +9,37 @@ and still take a long time.
*Note:* Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
## Table of Contents
- [Prepare your Hyperopt](#prepare-hyperopt)
- [Configure your Guards and Triggers](#configure-your-guards-and-triggers)
- [Solving a Mystery](#solving-a-mystery)
- [Adding New Indicators](#adding-new-indicators)
- [Execute Hyperopt](#execute-hyperopt)
- [Understand the hyperopts result](#understand-the-backtesting-result)
- [Understand the hyperopt result](#understand-the-hyperopt-result)
## Prepare Hyperopting
We recommend you start by taking a look at `hyperopt.py` file located in [freqtrade/optimize](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py)
### Configure your Guards and Triggers
There are two places you need to change to add a new buy strategy for testing:
- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L278-L294).
- Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L218-L229)
and the associated methods `indicator_space`, `roi_space`, `stoploss_space`.
There you have two different type of indicators: 1. `guards` and 2. `triggers`.
1. Guards are conditions like "never buy if ADX < 10", or "never buy if
current price is over EMA10".
Before we start digging in Hyperopt, we recommend you to take a look at
an example hyperopt file located into [user_data/hyperopts/](https://github.com/gcarq/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py)
### 1. Install a Custom Hyperopt File
This is very simple. Put your hyperopt file into the folder
`user_data/hyperopts`.
Let assume you want a hyperopt file `awesome_hyperopt.py`:
1. Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts/awesome_hyperopt.py`
### 2. Configure your Guards and Triggers
There are two places you need to change in your hyperopt file to add a
new buy hyperopt for testing:
- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L230-L251).
- Inside [indicator_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L207-L223).
There you have two different types of indicators: 1. `guards` and 2. `triggers`.
1. Guards are conditions like "never buy if ADX < 10", or never buy if
current price is over EMA10.
2. Triggers are ones that actually trigger buy in specific moment, like
"buy when EMA5 crosses over EMA10" or "buy when close price touches lower
bollinger band".
@ -113,33 +126,40 @@ When you want to test an indicator that isn't used by the bot currently, remembe
add it to the `populate_indicators()` method in `hyperopt.py`.
## Execute Hyperopt
Once you have updated your hyperopt configuration you can run it.
Because hyperopt tries a lot of combination to find the best parameters
it will take time you will have the result (more than 30 mins).
We strongly recommend to use `screen` to prevent any connection loss.
Once you have updated your hyperopt configuration you can run it.
Because hyperopt tries a lot of combinations to find the best parameters it will take time you will have the result (more than 30 mins).
We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
```bash
python3 ./freqtrade/main.py -c config.json hyperopt -e 5000
python3 ./freqtrade/main.py -s <strategyname> --hyperopt <hyperoptname> -c config.json hyperopt -e 5000
```
Use `<strategyname>` and `<hyperoptname>` as the names of the custom strategy
(only required for generating sells) and the custom hyperopt used.
The `-e` flag will set how many evaluations hyperopt will do. We recommend
running at least several thousand evaluations.
### Execute Hyperopt with Different Ticker-Data Source
If you would like to hyperopt parameters using an alternate ticker data that
you have on-disk, use the `--datadir PATH` option. Default hyperopt will
use data from directory `user_data/data`.
### Running Hyperopt with Smaller Testset
Use the `--timeperiod` argument to change how much of the testset
Use the `--timerange` argument to change how much of the testset
you want to use. The last N ticks/timeframes will be used.
Example:
```bash
python3 ./freqtrade/main.py hyperopt --timeperiod -200
python3 ./freqtrade/main.py hyperopt --timerange -200
```
### Running Hyperopt with Smaller Search Space
Use the `--spaces` argument to limit the search space used by hyperopt.
Letting Hyperopt optimize everything is a huuuuge search space. Often it
might make more sense to start by just searching for initial buy algorithm.
@ -154,7 +174,8 @@ Legal values are:
- `stoploss`: search for the best stoploss value
- space-separated list of any of the above values for example `--spaces roi stoploss`
## Understand the Hyperopts Result
## Understand the Hyperopt Result
Once Hyperopt is completed you can use the result to create a new strategy.
Given the following result from hyperopt:
@ -166,22 +187,24 @@ with values:
```
You should understand this result like:
- The buy trigger that worked best was `bb_lower`.
- You should not use ADX because `adx-enabled: False`)
- You should **consider** using the RSI indicator (`rsi-enabled: True` and the best value is `29.0` (`rsi-value: 29.0`)
You have to look inside your strategy file into `buy_strategy_generator()`
method, what those values match to.
method, what those values match to.
So for example you had `rsi-value: 29.0` so we would look
at `rsi`-block, that translates to the following code block:
So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block:
```
(dataframe['rsi'] < 29.0)
```
Translating your whole hyperopt result as the new buy-signal
would then look like:
```
```python
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
dataframe.loc[
(
@ -192,6 +215,39 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
return dataframe
```
### Understand Hyperopt ROI results
If you are optimizing ROI, you're result will look as follows and include a ROI table.
```
Best result:
135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
with values:
{'adx-value': 44, 'rsi-value': 29, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'bb_lower', 'roi_t1': 40, 'roi_t2': 57, 'roi_t3': 21, 'roi_p1': 0.03634636907306948, 'roi_p2': 0.055237357937802885, 'roi_p3': 0.015163796015548354, 'stoploss': -0.37996664668703606}
ROI table:
{0: 0.10674752302642071, 21: 0.09158372701087236, 78: 0.03634636907306948, 118: 0}
```
This would translate to the following ROI table:
``` python
minimal_roi = {
"118": 0,
"78": 0.0363463,
"21": 0.0915,
"0": 0.106
}
```
### Validate backtest result
Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
To archive the same results (number of trades, ...) than during hyperopt, please use the command line flag `--disable-max-market-positions`.
This setting is the default for hyperopt for speed reasons. You can overwrite this in the configuration by setting `"position_stacking"=false` or by changing the relevant line in your hyperopt file [here](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L283).
Dry/live runs will **NOT** use position stacking - therefore it does make sense to also validate the strategy without this as it's closer to reality.
## Next Step
Now you have a perfect bot and want to control it from Telegram. Your
next step is to learn the [Telegram usage](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md).

9
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@ -1,8 +1,8 @@
# freqtrade documentation
Welcome to freqtrade documentation. Please feel free to contribute to
this documentation if you see it became outdated by sending us a
Pull-request. Do not hesitate to reach us on
this documentation if you see it became outdated by sending us a
Pull-request. Do not hesitate to reach us on
[Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE)
if you do not find the answer to your questions.
@ -21,10 +21,12 @@ Pull-request. Do not hesitate to reach us on
- [Bot commands](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md#bot-commands)
- [Backtesting commands](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md#backtesting-commands)
- [Hyperopt commands](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md#hyperopt-commands)
- [Edge commands](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md#edge-commands)
- [Bot Optimization](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md)
- [Change your strategy](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md#change-your-strategy)
- [Add more Indicator](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md#add-more-indicator)
- [Test your strategy with Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.md)
- [Edge positioning](https://github.com/freqtrade/freqtrade/blob/develop/docs/edge.md)
- [Find optimal parameters with Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)
- [Control the bot with telegram](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md)
- [Receive notifications via webhook](https://github.com/freqtrade/freqtrade/blob/develop/docs/webhook-config.md)
@ -33,4 +35,5 @@ Pull-request. Do not hesitate to reach us on
- [Run tests & Check PEP8 compliance](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
- [FAQ](https://github.com/freqtrade/freqtrade/blob/develop/docs/faq.md)
- [SQL cheatsheet](https://github.com/freqtrade/freqtrade/blob/develop/docs/sql_cheatsheet.md)
- [Sandbox Testing](https://github.com/freqtrade/freqtrade/blob/develop/docs/sandbox-testing.md))
- [Sandbox Testing](https://github.com/freqtrade/freqtrade/blob/develop/docs/sandbox-testing.md)
- [Developer Docs](https://github.com/freqtrade/freqtrade/blob/develop/docs/developer.md)

47
docs/installation.md
View File

@ -109,13 +109,37 @@ Dry-Run
touch tradesv3.dryrun.sqlite
```
### 2. Build the Docker image
### 2. Download or build the docker image
Either use the prebuilt image from docker hub - or build the image yourself if you would like more control on which version is used.
Branches / tags available can be checked out on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
#### 2.1. Download the docker image
Pull the image from docker hub and (optionally) change the name of the image
```bash
docker pull freqtradeorg/freqtrade:develop
# Optionally tag the repository so the run-commands remain shorter
docker tag freqtradeorg/freqtrade:develop freqtrade
```
To update the image, simply run the above commands again and restart your running container.
#### 2.2. Build the Docker image
```bash
cd freqtrade
docker build -t freqtrade .
```
If you are developing using Docker, use `Dockerfile.develop` to build a dev Docker image, which will also set up develop dependencies:
```bash
docker build -f ./Dockerfile.develop -t freqtrade-dev .
```
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 the "5. Run a restartable docker image" section) to keep it between updates.
### 3. Verify the Docker image
@ -236,22 +260,27 @@ sudo apt-get install python3.6 python3.6-venv python3.6-dev build-essential auto
Before installing FreqTrade on a Raspberry Pi running the official Raspbian Image, make sure you have at least Python 3.6 installed. The default image only provides Python 3.5. Probably the easiest way to get a recent version of python is [miniconda](https://repo.continuum.io/miniconda/).
The following assumes that miniconda3 is installed and available in your environment, and is installed.
It's recommended to use (mini)conda for this as installation/compilation of `scipy` and `pandas` takes a long time.
The following assumes that miniconda3 is installed and available in your environment. Last miniconda3 installation file use python 3.4, we will update to python 3.6 on this installation.
It's recommended to use (mini)conda for this as installation/compilation of `numpy`, `scipy` and `pandas` takes a long time.
If you have installed it from (mini)conda, you can remove `numpy`, `scipy`, and `pandas` from `requirements.txt` before you install it with `pip`.
Additional package to install on your Raspbian, `libffi-dev` required by cryptography (from python-telegram-bot).
``` bash
conda config --add channels rpi
conda install python=3.6
conda create -n freqtrade python=3.6
conda install scipy pandas
conda activate freqtrade
conda install scipy pandas numpy
pip install -r requirements.txt
pip install -e .
sudo apt install libffi-dev
python3 -m pip install -r requirements.txt
python3 -m pip install -e .
```
### MacOS
#### Install Python 3.6, git, wget and ta-lib
#### Install Python 3.6, git and wget
```bash
brew install python3 git wget
@ -268,9 +297,9 @@ wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar xvzf ta-lib-0.4.0-src.tar.gz
cd ta-lib
sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h
./configure --prefix=/usr
./configure --prefix=/usr/local
make
make install
sudo make install
cd ..
rm -rf ./ta-lib*
```

2
docs/stoploss.md
View File

@ -48,4 +48,4 @@ Both values can be configured in the main configuration file and requires `"trai
The 0.01 would translate to a 1% stop loss, once you hit 1.1% profit.
You should also make sure to have this value higher than your minimal ROI, otherwise minimal ROI will apply first and sell your trade.
You should also make sure to have this value (`trailing_stop_positive_offset`) lower than your minimal ROI, otherwise minimal ROI will apply first and sell your trade.

24
docs/telegram-usage.md
View File

@ -23,6 +23,7 @@ official commands. You can ask at any moment for help with `/help`.
| `/profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
| `/forcesell <trade_id>` | | Instantly sells the given trade (Ignoring `minimum_roi`).
| `/forcesell all` | | Instantly sells all open trades (Ignoring `minimum_roi`).
| `/forcebuy <pair> [rate]` | | Instantly buys the given pair. Rate is optional. (`forcebuy_enable` must be set to True)
| `/performance` | | Show performance of each finished trade grouped by pair
| `/balance` | | Show account balance per currency
| `/daily <n>` | 7 | Shows profit or loss per day, over the last n days
@ -30,16 +31,20 @@ official commands. You can ask at any moment for help with `/help`.
| `/version` | | Show version
## Telegram commands in action
Below, example of Telegram message you will receive for each command.
### /start
> **Status:** `running`
### /stop
> `Stopping trader ...`
> **Status:** `stopped`
## /status
For each open trade, the bot will send you the following message.
> **Trade ID:** `123`
@ -54,6 +59,7 @@ For each open trade, the bot will send you the following message.
> **Open Order:** `None`
## /status table
Return the status of all open trades in a table format.
```
ID Pair Since Profit
@ -63,6 +69,7 @@ Return the status of all open trades in a table format.
```
## /count
Return the number of trades used and available.
```
current max
@ -71,6 +78,7 @@ current max
```
## /profit
Return a summary of your profit/loss and performance.
> **ROI:** Close trades
@ -90,7 +98,14 @@ Return a summary of your profit/loss and performance.
> **BITTREX:** Selling BTC/LTC with limit `0.01650000 (profit: ~-4.07%, -0.00008168)`
## /forcebuy <pair>
> **BITTREX**: Buying ETH/BTC with limit `0.03400000` (`1.000000 ETH`, `225.290 USD`)
Note that for this to work, `forcebuy_enable` needs to be set to true.
## /performance
Return the performance of each crypto-currency the bot has sold.
> Performance:
> 1. `RCN/BTC 57.77%`
@ -101,6 +116,7 @@ Return the performance of each crypto-currency the bot has sold.
> ...
## /balance
Return the balance of all crypto-currency your have on the exchange.
> **Currency:** BTC
@ -114,6 +130,7 @@ Return the balance of all crypto-currency your have on the exchange.
> **Pending:** 0.0
## /daily <n>
Per default `/daily` will return the 7 last days.
The example below if for `/daily 3`:
@ -127,11 +144,6 @@ Day Profit BTC Profit USD
```
## /version
> **Version:** `0.14.3`
### using proxy with telegram
```
$ export HTTP_PROXY="http://addr:port"
$ export HTTPS_PROXY="http://addr:port"
$ freqtrade
```

1
docs/webhook-config.md
View File

@ -66,6 +66,7 @@ Possible parameters are:
* profit_fiat
* stake_currency
* fiat_currency
* sell_reason
### Webhookstatus

2
freqtrade/__init__.py
View File

@ -1,5 +1,5 @@
""" FreqTrade bot """
__version__ = '0.17.1'
__version__ = '0.18.0'
class DependencyException(BaseException):

95
freqtrade/arguments.py
View File

@ -104,10 +104,19 @@ class Arguments(object):
type=str,
metavar='PATH',
)
self.parser.add_argument(
'--customhyperopt',
help='specify hyperopt class name (default: %(default)s)',
dest='hyperopt',
default=constants.DEFAULT_HYPEROPT,
type=str,
metavar='NAME',
)
self.parser.add_argument(
'--dynamic-whitelist',
help='dynamically generate and update whitelist'
' based on 24h BaseVolume (default: %(const)s)',
' based on 24h BaseVolume (default: %(const)s)'
' DEPRECATED.',
dest='dynamic_whitelist',
const=constants.DYNAMIC_WHITELIST,
type=int,
@ -128,6 +137,22 @@ class Arguments(object):
"""
Parses given arguments for Backtesting scripts.
"""
parser.add_argument(
'--eps', '--enable-position-stacking',
help='Allow buying the same pair multiple times (position stacking)',
action='store_true',
dest='position_stacking',
default=False
)
parser.add_argument(
'--dmmp', '--disable-max-market-positions',
help='Disable applying `max_open_trades` during backtest '
'(same as setting `max_open_trades` to a very high number)',
action='store_false',
dest='use_max_market_positions',
default=True
)
parser.add_argument(
'-l', '--live',
help='using live data',
@ -171,6 +196,27 @@ class Arguments(object):
metavar='PATH',
)
@staticmethod
def edge_options(parser: argparse.ArgumentParser) -> None:
"""
Parses given arguments for Backtesting scripts.
"""
parser.add_argument(
'-r', '--refresh-pairs-cached',
help='refresh the pairs files in tests/testdata with the latest data from the '
'exchange. Use it if you want to run your edge with up-to-date data.',
action='store_true',
dest='refresh_pairs',
)
parser.add_argument(
'--stoplosses',
help='defines a range of stoploss against which edge will assess the strategy '
'the format is "min,max,step" (without any space).'
'example: --stoplosses=-0.01,-0.1,-0.001',
type=str,
dest='stoploss_range',
)
@staticmethod
def optimizer_shared_options(parser: argparse.ArgumentParser) -> None:
"""
@ -184,6 +230,20 @@ class Arguments(object):
dest='ticker_interval',
type=str,
)
parser.add_argument(
'--timerange',
help='specify what timerange of data to use.',
default=None,
type=str,
dest='timerange',
)
@staticmethod
def hyperopt_options(parser: argparse.ArgumentParser) -> None:
"""
Parses given arguments for Hyperopt scripts.
"""
parser.add_argument(
'--eps', '--enable-position-stacking',
help='Allow buying the same pair multiple times (position stacking)',
@ -200,20 +260,6 @@ class Arguments(object):
dest='use_max_market_positions',
default=True
)
parser.add_argument(
'--timerange',
help='specify what timerange of data to use.',
default=None,
type=str,
dest='timerange',
)
@staticmethod
def hyperopt_options(parser: argparse.ArgumentParser) -> None:
"""
Parses given arguments for Hyperopt scripts.
"""
parser.add_argument(
'-e', '--epochs',
help='specify number of epochs (default: %(default)d)',
@ -237,7 +283,7 @@ class Arguments(object):
Builds and attaches all subcommands
:return: None
"""
from freqtrade.optimize import backtesting, hyperopt
from freqtrade.optimize import backtesting, hyperopt, edge_cli
subparsers = self.parser.add_subparsers(dest='subparser')
@ -247,6 +293,12 @@ class Arguments(object):
self.optimizer_shared_options(backtesting_cmd)
self.backtesting_options(backtesting_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='edge module')
edge_cmd.set_defaults(func=edge_cli.start)
self.optimizer_shared_options(edge_cmd)
self.edge_options(edge_cmd)
# Add hyperopt subcommand
hyperopt_cmd = subparsers.add_parser('hyperopt', help='hyperopt module')
hyperopt_cmd.set_defaults(func=hyperopt.start)
@ -326,6 +378,15 @@ class Arguments(object):
metavar='PATH',
)
self.parser.add_argument(
'-c', '--config',
help='specify configuration file, used for additional exchange parameters',
dest='config',
default=None,
type=str,
metavar='PATH',
)
self.parser.add_argument(
'--days',
help='Download data for number of days',
@ -337,7 +398,7 @@ class Arguments(object):
self.parser.add_argument(
'--exchange',
help='Exchange name (default: %(default)s)',
help='Exchange name (default: %(default)s). Only valid if no config is provided',
dest='exchange',
type=str,
default='bittrex'

59
freqtrade/configuration.py
View File

@ -33,6 +33,7 @@ class Configuration(object):
Class to read and init the bot configuration
Reuse this class for the bot, backtesting, hyperopt and every script that required configuration
"""
def __init__(self, args: Namespace) -> None:
self.args = args
self.config: Optional[Dict[str, Any]] = None
@ -52,12 +53,18 @@ class Configuration(object):
if self.args.strategy_path:
config.update({'strategy_path': self.args.strategy_path})
# Add the hyperopt file to use
config.update({'hyperopt': self.args.hyperopt})
# Load Common configuration
config = self._load_common_config(config)
# Load Backtesting
config = self._load_backtesting_config(config)
# Load Edge
config = self._load_edge_config(config)
# Load Hyperopt
config = self._load_hyperopt_config(config)
@ -103,10 +110,14 @@ class Configuration(object):
# Add dynamic_whitelist if found
if 'dynamic_whitelist' in self.args and self.args.dynamic_whitelist:
config.update({'dynamic_whitelist': self.args.dynamic_whitelist})
logger.info(
'Parameter --dynamic-whitelist detected. '
'Using dynamically generated whitelist. '
# Update to volumePairList (the previous default)
config['pairlist'] = {'method': 'VolumePairList',
'config': {'number_assets': self.args.dynamic_whitelist}
}
logger.warning(
'Parameter --dynamic-whitelist has been deprecated, '
'and will be completely replaced by the whitelist dict in the future. '
'For now: using dynamically generated whitelist based on VolumePairList. '
'(not applicable with Backtesting and Hyperopt)'
)
@ -127,6 +138,13 @@ class Configuration(object):
config['db_url'] = constants.DEFAULT_DB_PROD_URL
logger.info('Dry run is disabled')
if config.get('forcebuy_enable', False):
logger.warning('`forcebuy` RPC message enabled.')
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
logger.info(f'Using DB: "{config["db_url"]}"')
# Check if the exchange set by the user is supported
@ -210,6 +228,32 @@ class Configuration(object):
return config
def _load_edge_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
"""
Extract information for sys.argv and load Edge configuration
:return: configuration as dictionary
"""
# If --timerange is used we add it to the configuration
if 'timerange' in self.args and self.args.timerange:
config.update({'timerange': self.args.timerange})
logger.info('Parameter --timerange detected: %s ...', self.args.timerange)
# If --timerange is used we add it to the configuration
if 'stoploss_range' in self.args and self.args.stoploss_range:
txt_range = eval(self.args.stoploss_range)
config['edge'].update({'stoploss_range_min': txt_range[0]})
config['edge'].update({'stoploss_range_max': txt_range[1]})
config['edge'].update({'stoploss_range_step': txt_range[2]})
logger.info('Parameter --stoplosses detected: %s ...', self.args.stoploss_range)
# If -r/--refresh-pairs-cached is used we add it to the configuration
if 'refresh_pairs' in self.args and self.args.refresh_pairs:
config.update({'refresh_pairs': True})
logger.info('Parameter -r/--refresh-pairs-cached detected ...')
return config
def _load_hyperopt_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
"""
Extract information for sys.argv and load Hyperopt configuration
@ -235,7 +279,7 @@ class Configuration(object):
:return: Returns the config if valid, otherwise throw an exception
"""
try:
validate(conf, constants.CONF_SCHEMA)
validate(conf, constants.CONF_SCHEMA, Draft4Validator)
return conf
except ValidationError as exception:
logger.critical(
@ -271,6 +315,11 @@ class Configuration(object):
raise OperationalException(
exception_msg
)
# Depreciation warning
if 'ccxt_rate_limit' in config.get('exchange', {}):
logger.warning("`ccxt_rate_limit` has been deprecated in favor of "
"`ccxt_config` and `ccxt_async_config` and will be removed "
"in a future version.")
logger.debug('Exchange "%s" supported', exchange)
return True

62
freqtrade/constants.py
View File

@ -9,10 +9,15 @@ TICKER_INTERVAL = 5 # min
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
DEFAULT_STRATEGY = 'DefaultStrategy'
DEFAULT_HYPEROPT = 'DefaultHyperOpts'
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite://'
UNLIMITED_STAKE_AMOUNT = 'unlimited'
REQUIRED_ORDERTIF = ['buy', 'sell']
REQUIRED_ORDERTYPES = ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList']
TICKER_INTERVAL_MINUTES = {
'1m': 1,
@ -37,13 +42,13 @@ SUPPORTED_FIAT = [
"KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN",
"RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD",
"BTC", "XBT", "ETH", "XRP", "LTC", "BCH", "USDT"
]
]
# Required json-schema for user specified config
CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': 'integer', 'minimum': 0},
'max_open_trades': {'type': 'integer', 'minimum': -1},
'ticker_interval': {'type': 'string', 'enum': list(TICKER_INTERVAL_MINUTES.keys())},
'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
'stake_amount': {
@ -101,7 +106,26 @@ CONF_SCHEMA = {
'order_book_max': {'type': 'number', 'minimum': 1, 'maximum': 50}
}
},
'order_types': {
'type': 'object',
'properties': {
'buy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'sell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss_on_exchange': {'type': 'boolean'}
},
'required': ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
},
'order_time_in_force': {
'type': 'object',
'properties': {
'buy': {'type': 'string', 'enum': ORDERTIF_POSSIBILITIES},
'sell': {'type': 'string', 'enum': ORDERTIF_POSSIBILITIES}
},
'required': ['buy', 'sell']
},
'exchange': {'$ref': '#/definitions/exchange'},
'edge': {'$ref': '#/definitions/edge'},
'experimental': {
'type': 'object',
'properties': {
@ -110,6 +134,14 @@ CONF_SCHEMA = {
'ignore_roi_if_buy_signal_true': {'type': 'boolean'}
}
},
'pairlist': {
'type': 'object',
'properties': {
'method': {'type': 'string', 'enum': AVAILABLE_PAIRLISTS},
'config': {'type': 'object'}
},
'required': ['method']
},
'telegram': {
'type': 'object',
'properties': {
@ -130,6 +162,7 @@ CONF_SCHEMA = {
},
'db_url': {'type': 'string'},
'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
'forcebuy_enable': {'type': 'boolean'},
'internals': {
'type': 'object',
'properties': {
@ -164,9 +197,30 @@ CONF_SCHEMA = {
},
'uniqueItems': True
},
'outdated_offset': {'type': 'integer', 'minimum': 1}
'outdated_offset': {'type': 'integer', 'minimum': 1},
'ccxt_config': {'type': 'object'},
'ccxt_async_config': {'type': 'object'}
},
'required': ['name', 'key', 'secret', 'pair_whitelist']
},
'edge': {
'type': 'object',
'properties': {
"enabled": {'type': 'boolean'},
"process_throttle_secs": {'type': 'integer', 'minimum': 600},
"calculate_since_number_of_days": {'type': 'integer'},
"allowed_risk": {'type': 'number'},
"capital_available_percentage": {'type': 'number'},
"stoploss_range_min": {'type': 'number'},
"stoploss_range_max": {'type': 'number'},
"stoploss_range_step": {'type': 'number'},
"minimum_winrate": {'type': 'number'},
"minimum_expectancy": {'type': 'number'},
"min_trade_number": {'type': 'number'},
"max_trade_duration_minute": {'type': 'integer'},
"remove_pumps": {'type': 'boolean'}
},
'required': ['process_throttle_secs', 'allowed_risk', 'capital_available_percentage']
}
},
'anyOf': [

8
freqtrade/data/__init__.py Normal file
View File

@ -0,0 +1,8 @@
"""
Module to handle data operations for freqtrade
"""
# limit what's imported when using `from freqtrad.data import *``
__all__ = [
'converter'
]

8
freqtrade/exchange/exchange_helpers.py → freqtrade/data/converter.py
View File

@ -1,5 +1,5 @@
"""
Functions to analyze ticker data with indicators and produce buy and sell signals
Functions to convert data from one format to another
"""
import logging
import pandas as pd
@ -10,10 +10,11 @@ logger = logging.getLogger(__name__)
def parse_ticker_dataframe(ticker: list) -> DataFrame:
"""
Analyses the trend for the given ticker history
:param ticker: See exchange.get_candle_history
Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
:param ticker: ticker list, as returned by exchange.async_get_candle_history
:return: DataFrame
"""
logger.debug("Parsing tickerlist to dataframe")
cols = ['date', 'open', 'high', 'low', 'close', 'volume']
frame = DataFrame(ticker, columns=cols)
@ -31,6 +32,7 @@ def parse_ticker_dataframe(ticker: list) -> DataFrame:
'volume': 'max',
})
frame.drop(frame.tail(1).index, inplace=True) # eliminate partial candle
logger.debug('Dropping last candle')
return frame

251
freqtrade/data/history.py Normal file
View File

@ -0,0 +1,251 @@
"""
Handle historic data (ohlcv).
includes:
* load data for a pair (or a list of pairs) from disk
* download data from exchange and store to disk
"""
import gzip
import logging
from pathlib import Path
from typing import Optional, List, Dict, Tuple, Any
import arrow
from pandas import DataFrame
import ujson
from freqtrade import misc, constants, OperationalException
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.exchange import Exchange
from freqtrade.arguments import TimeRange
logger = logging.getLogger(__name__)
def json_load(data):
"""
load data with ujson
Use this to have a consistent experience,
otherwise "precise_float" needs to be passed to all load operations
"""
return ujson.load(data, precise_float=True)
def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]:
"""
Trim tickerlist based on given timerange
"""
if not tickerlist:
return tickerlist
start_index = 0
stop_index = len(tickerlist)
if timerange.starttype == 'line':
stop_index = timerange.startts
if timerange.starttype == 'index':
start_index = timerange.startts
elif timerange.starttype == 'date':
while (start_index < len(tickerlist) and
tickerlist[start_index][0] < timerange.startts * 1000):
start_index += 1
if timerange.stoptype == 'line':
start_index = len(tickerlist) + timerange.stopts
if timerange.stoptype == 'index':
stop_index = timerange.stopts
elif timerange.stoptype == 'date':
while (stop_index > 0 and
tickerlist[stop_index-1][0] > timerange.stopts * 1000):
stop_index -= 1
if start_index > stop_index:
raise ValueError(f'The timerange [{timerange.startts},{timerange.stopts}] is incorrect')
return tickerlist[start_index:stop_index]
def load_tickerdata_file(
datadir: Optional[Path], pair: str,
ticker_interval: str,
timerange: Optional[TimeRange] = None) -> Optional[list]:
"""
Load a pair from file, either .json.gz or .json
:return tickerlist or None if unsuccesful
"""
path = make_testdata_path(datadir)
pair_s = pair.replace('/', '_')
file = path.joinpath(f'{pair_s}-{ticker_interval}.json')
gzipfile = file.with_suffix(file.suffix + '.gz')
# Try gzip file first, otherwise regular json file.
if gzipfile.is_file():
logger.debug('Loading ticker data from file %s', gzipfile)
with gzip.open(gzipfile) as tickerdata:
pairdata = json_load(tickerdata)
elif file.is_file():
logger.debug('Loading ticker data from file %s', file)
with open(file) as tickerdata:
pairdata = json_load(tickerdata)
else:
return None
if timerange:
pairdata = trim_tickerlist(pairdata, timerange)
return pairdata
def load_pair_history(pair: str,
ticker_interval: str,
datadir: Optional[Path],
timerange: TimeRange = TimeRange(None, None, 0, 0),
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
) -> DataFrame:
"""
Loads cached ticker history for the given pair.
:return: DataFrame with ohlcv data
"""
pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
# If the user force the refresh of pairs
if refresh_pairs:
if not exchange:
raise OperationalException("Exchange needs to be initialized when "
"calling load_data with refresh_pairs=True")
logger.info('Download data for all pairs and store them in %s', datadir)
download_pair_history(datadir=datadir,
exchange=exchange,
pair=pair,
tick_interval=ticker_interval,
timerange=timerange)
if pairdata:
if timerange.starttype == 'date' and pairdata[0][0] > timerange.startts * 1000:
logger.warning('Missing data at start for pair %s, data starts at %s',
pair, arrow.get(pairdata[0][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
if timerange.stoptype == 'date' and pairdata[-1][0] < timerange.stopts * 1000:
logger.warning('Missing data at end for pair %s, data ends at %s',
pair,
arrow.get(pairdata[-1][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
return parse_ticker_dataframe(pairdata)
else:
logger.warning('No data for pair: "%s", Interval: %s. '
'Use --refresh-pairs-cached to download the data',
pair, ticker_interval)
return None
def load_data(datadir: Optional[Path],
ticker_interval: str,
pairs: List[str],
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
timerange: TimeRange = TimeRange(None, None, 0, 0)) -> Dict[str, DataFrame]:
"""
Loads ticker history data for a list of pairs the given parameters
:return: dict(<pair>:<tickerlist>)
"""
result = {}
for pair in pairs:
hist = load_pair_history(pair=pair, ticker_interval=ticker_interval,
datadir=datadir, timerange=timerange,
refresh_pairs=refresh_pairs,
exchange=exchange)
if hist is not None:
result[pair] = hist
return result
def make_testdata_path(datadir: Optional[Path]) -> Path:
"""Return the path where testdata files are stored"""
return datadir or (Path(__file__).parent.parent / "tests" / "testdata").resolve()
def load_cached_data_for_updating(filename: Path, tick_interval: str,
timerange: Optional[TimeRange]) -> Tuple[List[Any],
Optional[int]]:
"""
Load cached data and choose what part of the data should be updated
"""
since_ms = None
# user sets timerange, so find the start time
if timerange:
if timerange.starttype == 'date':
since_ms = timerange.startts * 1000
elif timerange.stoptype == 'line':
num_minutes = timerange.stopts * constants.TICKER_INTERVAL_MINUTES[tick_interval]
since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
# read the cached file
if filename.is_file():
with open(filename, "rt") as file:
data = json_load(file)
# remove the last item, could be incomplete candle
if data:
data.pop()
else:
data = []
if data:
if since_ms and since_ms < data[0][0]:
# Earlier data than existing data requested, redownload all
data = []
else:
# a part of the data was already downloaded, so download unexist data only
since_ms = data[-1][0] + 1
return (data, since_ms)
def download_pair_history(datadir: Optional[Path],
exchange: Exchange,
pair: str,
tick_interval: str = '5m',
timerange: Optional[TimeRange] = None) -> bool:
"""
Download the latest ticker intervals from the exchange for the pair passed in parameters
The data is downloaded starting from the last correct ticker interval data that
exists in a cache. If timerange starts earlier than the data in the cache,
the full data will be redownloaded
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pair: pair to download
:param tick_interval: ticker interval
:param timerange: range of time to download
:return: bool with success state
"""
try:
path = make_testdata_path(datadir)
filepair = pair.replace("/", "_")
filename = path.joinpath(f'{filepair}-{tick_interval}.json')
logger.info('Download the pair: "%s", Interval: %s', pair, tick_interval)
data, since_ms = load_cached_data_for_updating(filename, tick_interval, timerange)
logger.debug("Current Start: %s", misc.format_ms_time(data[1][0]) if data else 'None')
logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
# Default since_ms to 30 days if nothing is given
new_data = exchange.get_history(pair=pair, tick_interval=tick_interval,
since_ms=since_ms if since_ms
else
int(arrow.utcnow().shift(days=-30).float_timestamp) * 1000)
data.extend(new_data)
logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
logger.debug("New End: %s", misc.format_ms_time(data[-1][0]))
misc.file_dump_json(filename, data)
return True
except BaseException:
logger.info('Failed to download the pair: "%s", Interval: %s',
pair, tick_interval)
return False

434
freqtrade/edge/__init__.py Normal file
View File

@ -0,0 +1,434 @@
# pragma pylint: disable=W0603
""" Edge positioning package """
import logging
from pathlib import Path
from typing import Any, Dict, NamedTuple
import arrow
import numpy as np
import utils_find_1st as utf1st
from pandas import DataFrame
from freqtrade import constants, OperationalException
from freqtrade.arguments import Arguments
from freqtrade.arguments import TimeRange
from freqtrade.data import history
from freqtrade.optimize import get_timeframe
from freqtrade.strategy.interface import SellType
logger = logging.getLogger(__name__)
class PairInfo(NamedTuple):
stoploss: float
winrate: float
risk_reward_ratio: float
required_risk_reward: float
expectancy: float
nb_trades: int
avg_trade_duration: float
class Edge():
"""
Calculates Win Rate, Risk Reward Ratio, Expectancy
against historical data for a give set of markets and a strategy
it then adjusts stoploss and position size accordingly
and force it into the strategy
Author: https://github.com/mishaker
"""
config: Dict = {}
_cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
self.config = config
self.exchange = exchange
self.strategy = strategy
self.ticker_interval = self.strategy.ticker_interval
self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe
self.get_timeframe = get_timeframe
self.advise_sell = self.strategy.advise_sell
self.advise_buy = self.strategy.advise_buy
self.edge_config = self.config.get('edge', {})
self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
self._final_pairs: list = []
# checking max_open_trades. it should be -1 as with Edge
# the number of trades is determined by position size
if self.config['max_open_trades'] != -1:
logger.critical('max_open_trades should be -1 in config !')
if self.config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT:
raise OperationalException('Edge works only with unlimited stake amount')
self._capital_percentage: float = self.edge_config.get('capital_available_percentage')
self._allowed_risk: float = self.edge_config.get('allowed_risk')
self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14)
self._last_updated: int = 0 # Timestamp of pairs last updated time
self._refresh_pairs = True
self._stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
self._stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
self._stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
# calculating stoploss range
self._stoploss_range = np.arange(
self._stoploss_range_min,
self._stoploss_range_max,
self._stoploss_range_step
)
self._timerange: TimeRange = Arguments.parse_timerange("%s-" % arrow.now().shift(
days=-1 * self._since_number_of_days).format('YYYYMMDD'))
self.fee = self.exchange.get_fee()
def calculate(self) -> bool:
pairs = self.config['exchange']['pair_whitelist']
heartbeat = self.edge_config.get('process_throttle_secs')
if (self._last_updated > 0) and (
self._last_updated + heartbeat > arrow.utcnow().timestamp):
return False
data: Dict[str, Any] = {}
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using local backtesting data (using whitelist in given config) ...')
data = history.load_data(
datadir=Path(self.config['datadir']) if self.config.get('datadir') else None,
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self._refresh_pairs,
exchange=self.exchange,
timerange=self._timerange
)
if not data:
# Reinitializing cached pairs
self._cached_pairs = {}
logger.critical("No data found. Edge is stopped ...")
return False
preprocessed = self.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = self.get_timeframe(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days) ...',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
trades: list = []
for pair, pair_data in preprocessed.items():
# Sorting dataframe by date and reset index
pair_data = pair_data.sort_values(by=['date'])
pair_data = pair_data.reset_index(drop=True)
ticker_data = self.advise_sell(
self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
trades += self._find_trades_for_stoploss_range(ticker_data, pair, self._stoploss_range)
# If no trade found then exit
if len(trades) == 0:
return False
# Fill missing, calculable columns, profit, duration , abs etc.
trades_df = self._fill_calculable_fields(DataFrame(trades))
self._cached_pairs = self._process_expectancy(trades_df)
self._last_updated = arrow.utcnow().timestamp
return True
def stake_amount(self, pair: str, free_capital: float,
total_capital: float, capital_in_trade: float) -> float:
stoploss = self.stoploss(pair)
available_capital = (total_capital + capital_in_trade) * self._capital_percentage
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)
if pair in self._cached_pairs:
logger.info(
'winrate: %s, expectancy: %s, position size: %s, pair: %s,'
' capital in trade: %s, free capital: %s, total capital: %s,'
' stoploss: %s, available capital: %s.',
self._cached_pairs[pair].winrate,
self._cached_pairs[pair].expectancy,
position_size, pair,
capital_in_trade, free_capital, total_capital,
stoploss, available_capital
)
return round(position_size, 15)
def stoploss(self, pair: str) -> float:
if pair in self._cached_pairs:
return self._cached_pairs[pair].stoploss
else:
logger.warning('tried to access stoploss of a non-existing pair, '
'strategy stoploss is returned instead.')
return self.strategy.stoploss
def adjust(self, pairs) -> list:
"""
Filters out and sorts "pairs" according to Edge calculated pairs
"""
final = []
for pair, info in self._cached_pairs.items():
if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)) and \
pair in pairs:
final.append(pair)
if self._final_pairs != final:
self._final_pairs = final
if self._final_pairs:
logger.info('Edge validated only %s', self._final_pairs)
else:
logger.info('Edge removed all pairs as no pair with minimum expectancy was found !')
return self._final_pairs
def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
"""
The result frame contains a number of columns that are calculable
from other columns. These are left blank till all rows are added,
to be populated in single vector calls.
Columns to be populated are:
- Profit
- trade duration
- profit abs
:param result Dataframe
:return: result Dataframe
"""
# stake and fees
# stake = 0.015
# 0.05% is 0.0005
# fee = 0.001
# we set stake amount to an arbitrary amount.
# as it doesn't change the calculation.
# all returned values are relative. they are percentages.
stake = 0.015
fee = self.fee
open_fee = fee / 2
close_fee = fee / 2
result['trade_duration'] = result['close_time'] - result['open_time']
result['trade_duration'] = result['trade_duration'].map(
lambda x: int(x.total_seconds() / 60))
# Spends, Takes, Profit, Absolute Profit
# Buy Price
result['buy_vol'] = stake / result['open_rate'] # How many target are we buying
result['buy_fee'] = stake * open_fee
result['buy_spend'] = stake + result['buy_fee'] # How much we're spending
# Sell price
result['sell_sum'] = result['buy_vol'] * result['close_rate']
result['sell_fee'] = result['sell_sum'] * close_fee
result['sell_take'] = result['sell_sum'] - result['sell_fee']
# profit_percent
result['profit_percent'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
# Absolute profit
result['profit_abs'] = result['sell_take'] - result['buy_spend']
return result
def _process_expectancy(self, results: DataFrame) -> Dict[str, Any]:
"""
This calculates WinRate, Required Risk Reward, Risk Reward and Expectancy of all pairs
The calulation will be done per pair and per strategy.
"""
# Removing pairs having less than min_trades_number
min_trades_number = self.edge_config.get('min_trade_number', 10)
results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
###################################
# Removing outliers (Only Pumps) from the dataset
# The method to detect outliers is to calculate standard deviation
# Then every value more than (standard deviation + 2*average) is out (pump)
#
# Removing Pumps
if self.edge_config.get('remove_pumps', False):
results = results.groupby(['pair', 'stoploss']).apply(
lambda x: x[x['profit_abs'] < 2 * x['profit_abs'].std() + x['profit_abs'].mean()])
##########################################################################
# Removing trades having a duration more than X minutes (set in config)
max_trade_duration = self.edge_config.get('max_trade_duration_minute', 1440)
results = results[results.trade_duration < max_trade_duration]
#######################################################################
if results.empty:
return {}
groupby_aggregator = {
'profit_abs': [
('nb_trades', 'count'), # number of all trades
('profit_sum', lambda x: x[x > 0].sum()), # cumulative profit of all winning trades
('loss_sum', lambda x: abs(x[x < 0].sum())), # cumulative loss of all losing trades
('nb_win_trades', lambda x: x[x > 0].count()) # number of winning trades
],
'trade_duration': [('avg_trade_duration', 'mean')]
}
# Group by (pair and stoploss) by applying above aggregator
df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
groupby_aggregator).reset_index(col_level=1)
# Dropping level 0 as we don't need it
df.columns = df.columns.droplevel(0)
# Calculating number of losing trades, average win and average loss
df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
df['average_win'] = df['profit_sum'] / df['nb_win_trades']
df['average_loss'] = df['loss_sum'] / df['nb_loss_trades']
# Win rate = number of profitable trades / number of trades
df['winrate'] = df['nb_win_trades'] / df['nb_trades']
# risk_reward_ratio = average win / average loss
df['risk_reward_ratio'] = df['average_win'] / df['average_loss']
# required_risk_reward = (1 / winrate) - 1
df['required_risk_reward'] = (1 / df['winrate']) - 1
# expectancy = (risk_reward_ratio * winrate) - (lossrate)
df['expectancy'] = (df['risk_reward_ratio'] * df['winrate']) - (1 - df['winrate'])
# sort by expectancy and stoploss
df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
final = {}
for x in df.itertuples():
final[x.pair] = PairInfo(
x.stoploss,
x.winrate,
x.risk_reward_ratio,
x.required_risk_reward,
x.expectancy,
x.nb_trades,
x.avg_trade_duration
)
# Returning a list of pairs in order of "expectancy"
return final
def _find_trades_for_stoploss_range(self, ticker_data, pair, stoploss_range):
buy_column = ticker_data['buy'].values
sell_column = ticker_data['sell'].values
date_column = ticker_data['date'].values
ohlc_columns = ticker_data[['open', 'high', 'low', 'close']].values
result: list = []
for stoploss in stoploss_range:
result += self._detect_next_stop_or_sell_point(
buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
)
return result
def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
ohlc_columns, stoploss, pair, start_point=0):
"""
Iterate through ohlc_columns recursively in order to find the next trade
Next trade opens from the first buy signal noticed to
The sell or stoploss signal after it.
It then calls itself cutting OHLC, buy_column, sell_colum and date_column
Cut from (the exit trade index) + 1
Author: https://github.com/mishaker
"""
result: list = []
open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal)
# return empty if we don't find trade entry (i.e. buy==1) or
# we find a buy but at the of array
if open_trade_index == -1 or open_trade_index == len(buy_column) - 1:
return []
else:
open_trade_index += 1 # when a buy signal is seen,
# trade opens in reality on the next candle
stop_price_percentage = stoploss + 1
open_price = ohlc_columns[open_trade_index, 0]
stop_price = (open_price * stop_price_percentage)
# Searching for the index where stoploss is hit
stop_index = utf1st.find_1st(
ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller)
# If we don't find it then we assume stop_index will be far in future (infinite number)
if stop_index == -1:
stop_index = float('inf')
# Searching for the index where sell is hit
sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal)
# If we don't find it then we assume sell_index will be far in future (infinite number)
if sell_index == -1:
sell_index = float('inf')
# Check if we don't find any stop or sell point (in that case trade remains open)
# It is not interesting for Edge to consider it so we simply ignore the trade
# And stop iterating there is no more entry
if stop_index == sell_index == float('inf'):
return []
if stop_index <= sell_index:
exit_index = open_trade_index + stop_index
exit_type = SellType.STOP_LOSS
exit_price = stop_price
elif stop_index > sell_index:
# if exit is SELL then we exit at the next candle
exit_index = open_trade_index + sell_index + 1
# check if we have the next candle
if len(ohlc_columns) - 1 < exit_index:
return []
exit_type = SellType.SELL_SIGNAL
exit_price = ohlc_columns[exit_index, 0]
trade = {'pair': pair,
'stoploss': stoploss,
'profit_percent': '',
'profit_abs': '',
'open_time': date_column[open_trade_index],
'close_time': date_column[exit_index],
'open_index': start_point + open_trade_index,
'close_index': start_point + exit_index,
'trade_duration': '',
'open_rate': round(open_price, 15),
'close_rate': round(exit_price, 15),
'exit_type': exit_type
}
result.append(trade)
# Calling again the same function recursively but giving
# it a view of exit_index till the end of array
return result + self._detect_next_stop_or_sell_point(
buy_column[exit_index:],
sell_column[exit_index:],
date_column[exit_index:],
ohlc_columns[exit_index:],
stoploss,
pair,
(start_point + exit_index)
)

267
freqtrade/exchange/__init__.py
View File

@ -7,12 +7,14 @@ from typing import List, Dict, Tuple, Any, Optional
from datetime import datetime
from math import floor, ceil
import arrow
import asyncio
import ccxt
import ccxt.async_support as ccxt_async
import arrow
from pandas import DataFrame
from freqtrade import constants, OperationalException, DependencyException, TemporaryError
from freqtrade.data.converter import parse_ticker_dataframe
logger = logging.getLogger(__name__)
@ -64,14 +66,8 @@ def retrier(f):
class Exchange(object):
# Current selected exchange
_api: ccxt.Exchange = None
_api_async: ccxt_async.Exchange = None
_conf: Dict = {}
# Holds all open sell orders for dry_run
_dry_run_open_orders: Dict[str, Any] = {}
def __init__(self, config: dict) -> None:
"""
Initializes this module with the given config,
@ -87,21 +83,27 @@ class Exchange(object):
self._pairs_last_refresh_time: Dict[str, int] = {}
# Holds candles
self.klines: Dict[str, Any] = {}
self._klines: Dict[str, DataFrame] = {}
# Holds all open sell orders for dry_run
self._dry_run_open_orders: Dict[str, Any] = {}
if config['dry_run']:
logger.info('Instance is running with dry_run enabled')
exchange_config = config['exchange']
self._api = self._init_ccxt(exchange_config)
self._api_async = self._init_ccxt(exchange_config, ccxt_async)
self._api: ccxt.Exchange = self._init_ccxt(
exchange_config, ccxt_kwargs=exchange_config.get('ccxt_config'))
self._api_async: ccxt_async.Exchange = self._init_ccxt(
exchange_config, ccxt_async, ccxt_kwargs=exchange_config.get('ccxt_async_config'))
logger.info('Using Exchange "%s"', self.name)
self.markets = self._load_markets()
# Check if all pairs are available
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', {}))
if config.get('ticker_interval'):
# Check if timeframe is available
self.validate_timeframes(config['ticker_interval'])
@ -114,7 +116,8 @@ class Exchange(object):
if self._api_async and inspect.iscoroutinefunction(self._api_async.close):
asyncio.get_event_loop().run_until_complete(self._api_async.close())
def _init_ccxt(self, exchange_config: dict, ccxt_module=ccxt) -> ccxt.Exchange:
def _init_ccxt(self, exchange_config: dict, ccxt_module=ccxt,
ccxt_kwargs: dict = None) -> ccxt.Exchange:
"""
Initialize ccxt with given config and return valid
ccxt instance.
@ -124,14 +127,20 @@ class Exchange(object):
if name not in ccxt_module.exchanges:
raise OperationalException(f'Exchange {name} is not supported')
ex_config = {
'apiKey': exchange_config.get('key'),
'secret': exchange_config.get('secret'),
'password': exchange_config.get('password'),
'uid': exchange_config.get('uid', ''),
'enableRateLimit': exchange_config.get('ccxt_rate_limit', True)
}
if ccxt_kwargs:
logger.info('Applying additional ccxt config: %s', ccxt_kwargs)
ex_config.update(ccxt_kwargs)
try:
api = getattr(ccxt_module, name.lower())({
'apiKey': exchange_config.get('key'),
'secret': exchange_config.get('secret'),
'password': exchange_config.get('password'),
'uid': exchange_config.get('uid', ''),
'enableRateLimit': exchange_config.get('ccxt_rate_limit', True)
})
api = getattr(ccxt_module, name.lower())(ex_config)
except (KeyError, AttributeError):
raise OperationalException(f'Exchange {name} is not supported')
@ -149,6 +158,12 @@ class Exchange(object):
"""exchange ccxt id"""
return self._api.id
def klines(self, pair: str, copy=True) -> DataFrame:
if pair in self._klines:
return self._klines[pair].copy() if copy else self._klines[pair]
else:
return None
def set_sandbox(self, api, exchange_config: dict, name: str):
if exchange_config.get('sandbox'):
if api.urls.get('test'):
@ -199,7 +214,8 @@ class Exchange(object):
f'Pair {pair} not compatible with stake_currency: {stake_cur}')
if self.markets and pair not in self.markets:
raise OperationalException(
f'Pair {pair} is not available at {self.name}')
f'Pair {pair} is not available at {self.name}'
f'Please remove {pair} from your whitelist.')
def validate_timeframes(self, timeframe: List[str]) -> None:
"""
@ -210,6 +226,30 @@ class Exchange(object):
raise OperationalException(
f'Invalid ticker {timeframe}, this Exchange supports {timeframes}')
def validate_ordertypes(self, order_types: Dict) -> None:
"""
Checks if order-types configured in strategy/config are supported
"""
if any(v == 'market' for k, v in order_types.items()):
if not self.exchange_has('createMarketOrder'):
raise OperationalException(
f'Exchange {self.name} does not support market orders.')
if order_types.get('stoploss_on_exchange'):
if self.name is not 'Binance':
raise OperationalException(
'On exchange stoploss is not supported for %s.' % self.name
)
def validate_order_time_in_force(self, order_time_in_force: Dict) -> None:
"""
Checks if order time in force configured in strategy/config are supported
"""
if any(v != 'gtc' for k, v in order_time_in_force.items()):
if self.name is not 'Binance':
raise OperationalException(
f'Time in force policies are not supporetd for {self.name} yet.')
def exchange_has(self, endpoint: str) -> bool:
"""
Checks if exchange implements a specific API endpoint.
@ -241,14 +281,15 @@ class Exchange(object):
price = ceil(big_price) / pow(10, symbol_prec)
return price
def buy(self, pair: str, rate: float, amount: float) -> Dict:
def buy(self, pair: str, ordertype: str, amount: float,
rate: float, time_in_force) -> Dict:
if self._conf['dry_run']:
order_id = f'dry_run_buy_{randint(0, 10**6)}'
self._dry_run_open_orders[order_id] = {
'pair': pair,
'price': rate,
'amount': amount,
'type': 'limit',
'type': ordertype,
'side': 'buy',
'remaining': 0.0,
'datetime': arrow.utcnow().isoformat(),
@ -260,9 +301,14 @@ class Exchange(object):
try:
# Set the precision for amount and price(rate) as accepted by the exchange
amount = self.symbol_amount_prec(pair, amount)
rate = self.symbol_price_prec(pair, rate)
rate = self.symbol_price_prec(pair, rate) if ordertype != 'market' else None
if time_in_force == 'gtc':
return self._api.create_order(pair, ordertype, 'buy', amount, rate)
else:
return self._api.create_order(pair, ordertype, 'buy',
amount, rate, {'timeInForce': time_in_force})
return self._api.create_limit_buy_order(pair, amount, rate)
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to create limit buy order on market {pair}.'
@ -279,14 +325,15 @@ class Exchange(object):
except ccxt.BaseError as e:
raise OperationalException(e)
def sell(self, pair: str, rate: float, amount: float) -> Dict:
def sell(self, pair: str, ordertype: str, amount: float,
rate: float, time_in_force='gtc') -> Dict:
if self._conf['dry_run']:
order_id = f'dry_run_sell_{randint(0, 10**6)}'
self._dry_run_open_orders[order_id] = {
'pair': pair,
'price': rate,
'amount': amount,
'type': 'limit',
'type': ordertype,
'side': 'sell',
'remaining': 0.0,
'datetime': arrow.utcnow().isoformat(),
@ -297,9 +344,14 @@ class Exchange(object):
try:
# Set the precision for amount and price(rate) as accepted by the exchange
amount = self.symbol_amount_prec(pair, amount)
rate = self.symbol_price_prec(pair, rate)
rate = self.symbol_price_prec(pair, rate) if ordertype != 'market' else None
if time_in_force == 'gtc':
return self._api.create_order(pair, ordertype, 'sell', amount, rate)
else:
return self._api.create_order(pair, ordertype, 'sell',
amount, rate, {'timeInForce': time_in_force})
return self._api.create_limit_sell_order(pair, amount, rate)
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to create limit sell order on market {pair}.'
@ -316,6 +368,61 @@ class Exchange(object):
except ccxt.BaseError as e:
raise OperationalException(e)
def stoploss_limit(self, pair: str, amount: float, stop_price: float, rate: float) -> Dict:
"""
creates a stoploss limit order.
NOTICE: it is not supported by all exchanges. only binance is tested for now.
"""
# Set the precision for amount and price(rate) as accepted by the exchange
amount = self.symbol_amount_prec(pair, amount)
rate = self.symbol_price_prec(pair, rate)
stop_price = self.symbol_price_prec(pair, stop_price)
# Ensure rate is less than stop price
if stop_price <= rate:
raise OperationalException(
'In stoploss limit order, stop price should be more than limit price')
if self._conf['dry_run']:
order_id = f'dry_run_buy_{randint(0, 10**6)}'
self._dry_run_open_orders[order_id] = {
'info': {},
'id': order_id,
'pair': pair,
'price': stop_price,
'amount': amount,
'type': 'stop_loss_limit',
'side': 'sell',
'remaining': amount,
'datetime': arrow.utcnow().isoformat(),
'status': 'open',
'fee': None
}
return self._dry_run_open_orders[order_id]
try:
return self._api.create_order(pair, 'stop_loss_limit', 'sell',
amount, rate, {'stopPrice': stop_price})
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to place stoploss limit order on market {pair}. '
f'Tried to put a stoploss amount {amount} with '
f'stop {stop_price} and limit {rate} (total {rate*amount}).'
f'Message: {e}')
except ccxt.InvalidOrder as e:
raise DependencyException(
f'Could not place stoploss limit order on market {pair}.'
f'Tried to place stoploss amount {amount} with '
f'stop {stop_price} and limit {rate} (total {rate*amount}).'
f'Message: {e}')
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place stoploss limit order due to {e.__class__.__name__}. Message: {e}')
except ccxt.BaseError as e:
raise OperationalException(e)
@retrier
def get_balance(self, currency: str) -> float:
if self._conf['dry_run']:
@ -367,6 +474,8 @@ class Exchange(object):
def get_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
if refresh or pair not in self._cached_ticker.keys():
try:
if pair not in self._api.markets:
raise DependencyException(f"Pair {pair} not available")
data = self._api.fetch_ticker(pair)
try:
self._cached_ticker[pair] = {
@ -410,9 +519,9 @@ class Exchange(object):
# Combine tickers
data: List = []
for tick in tickers:
if tick[0] == pair:
data.extend(tick[1])
for p, ticker in tickers:
if p == pair:
data.extend(ticker)
# Sort data again after extending the result - above calls return in "async order" order
data = sorted(data, key=lambda x: x[0])
logger.info("downloaded %s with length %s.", pair, len(data))
@ -420,7 +529,7 @@ class Exchange(object):
def refresh_tickers(self, pair_list: List[str], ticker_interval: str) -> None:
"""
Refresh tickers asyncronously and return the result.
Refresh tickers asyncronously and set `_klines` of this object with the result
"""
logger.debug("Refreshing klines for %d pairs", len(pair_list))
asyncio.get_event_loop().run_until_complete(
@ -429,9 +538,27 @@ class Exchange(object):
async def async_get_candles_history(self, pairs: List[str],
tick_interval: str) -> List[Tuple[str, List]]:
"""Download ohlcv history for pair-list asyncronously """
input_coroutines = [self._async_get_candle_history(
symbol, tick_interval) for symbol in pairs]
# Calculating ticker interval in second
interval_in_sec = constants.TICKER_INTERVAL_MINUTES[tick_interval] * 60
input_coroutines = []
# Gather corotines to run
for pair in pairs:
if not (self._pairs_last_refresh_time.get(pair, 0) + interval_in_sec >=
arrow.utcnow().timestamp and pair in self._klines):
input_coroutines.append(self._async_get_candle_history(pair, tick_interval))
else:
logger.debug("Using cached klines data for %s ...", pair)
tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
# handle caching
for pair, ticks in tickers:
# keeping last candle time as last refreshed time of the pair
if ticks:
self._pairs_last_refresh_time[pair] = ticks[-1][0] // 1000
# keeping parsed dataframe in cache
self._klines[pair] = parse_ticker_dataframe(ticks)
return tickers
@retrier_async
@ -441,32 +568,15 @@ class Exchange(object):
# fetch ohlcv asynchronously
logger.debug("fetching %s since %s ...", pair, since_ms)
# Calculating ticker interval in second
interval_in_sec = constants.TICKER_INTERVAL_MINUTES[tick_interval] * 60
# If (last update time) + (interval in second) is greater or equal than now
# that means we don't have to hit the API as there is no new candle
# so we fetch it from local cache
if (not since_ms and
self._pairs_last_refresh_time.get(pair, 0) + interval_in_sec >=
arrow.utcnow().timestamp):
data = self.klines[pair]
logger.debug("Using cached klines data for %s ...", pair)
else:
data = await self._api_async.fetch_ohlcv(pair, timeframe=tick_interval,
since=since_ms)
data = await self._api_async.fetch_ohlcv(pair, timeframe=tick_interval,
since=since_ms)
# Because some exchange sort Tickers ASC and other DESC.
# Ex: Bittrex returns a list of tickers ASC (oldest first, newest last)
# when GDAX returns a list of tickers DESC (newest first, oldest last)
data = sorted(data, key=lambda x: x[0])
# keeping last candle time as last refreshed time of the pair
if data:
self._pairs_last_refresh_time[pair] = data[-1][0] // 1000
# keeping candles in cache
self.klines[pair] = data
# Only sort if necessary to save computing time
if data and data[0][0] > data[-1][0]:
data = sorted(data, key=lambda x: x[0])
logger.debug("done fetching %s ...", pair)
return pair, data
@ -481,51 +591,6 @@ class Exchange(object):
except ccxt.BaseError as e:
raise OperationalException(f'Could not fetch ticker data. Msg: {e}')
@retrier
def get_candle_history(self, pair: str, tick_interval: str,
since_ms: Optional[int] = None) -> List[Dict]:
try:
# last item should be in the time interval [now - tick_interval, now]
till_time_ms = arrow.utcnow().shift(
minutes=-constants.TICKER_INTERVAL_MINUTES[tick_interval]
).timestamp * 1000
# it looks as if some exchanges return cached data
# and they update it one in several minute, so 10 mins interval
# is necessary to skeep downloading of an empty array when all
# chached data was already downloaded
till_time_ms = min(till_time_ms, arrow.utcnow().shift(minutes=-10).timestamp * 1000)
data: List[Dict[Any, Any]] = []
while not since_ms or since_ms < till_time_ms:
data_part = self._api.fetch_ohlcv(pair, timeframe=tick_interval, since=since_ms)
# Because some exchange sort Tickers ASC and other DESC.
# Ex: Bittrex returns a list of tickers ASC (oldest first, newest last)
# when GDAX returns a list of tickers DESC (newest first, oldest last)
data_part = sorted(data_part, key=lambda x: x[0])
if not data_part:
break
logger.debug('Downloaded data for %s time range [%s, %s]',
pair,
arrow.get(data_part[0][0] / 1000).format(),
arrow.get(data_part[-1][0] / 1000).format())
data.extend(data_part)
since_ms = data[-1][0] + 1
return data
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching historical candlestick data.'
f'Message: {e}')
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load ticker history due to {e.__class__.__name__}. Message: {e}')
except ccxt.BaseError as e:
raise OperationalException(f'Could not fetch ticker data. Msg: {e}')
@retrier
def cancel_order(self, order_id: str, pair: str) -> None:
if self._conf['dry_run']:

354
freqtrade/freqtradebot.py
View File

@ -12,17 +12,18 @@ from typing import Any, Callable, Dict, List, Optional
import arrow
from requests.exceptions import RequestException
from cachetools import TTLCache, cached
from freqtrade import (DependencyException, OperationalException,
TemporaryError, __version__, constants, persistence)
from freqtrade.data.converter import order_book_to_dataframe
from freqtrade.edge import Edge
from freqtrade.exchange import Exchange
from freqtrade.persistence import Trade
from freqtrade.rpc import RPCManager, RPCMessageType
from freqtrade.resolvers import StrategyResolver, PairListResolver
from freqtrade.state import State
from freqtrade.strategy.interface import SellType
from freqtrade.strategy.resolver import IStrategy, StrategyResolver
from freqtrade.exchange.exchange_helpers import order_book_to_dataframe
from freqtrade.strategy.interface import SellType, IStrategy
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
@ -51,9 +52,19 @@ class FreqtradeBot(object):
# Init objects
self.config = config
self.strategy: IStrategy = StrategyResolver(self.config).strategy
self.rpc: RPCManager = RPCManager(self)
self.persistence = None
self.exchange = Exchange(self.config)
self.wallets = Wallets(self.exchange)
pairlistname = self.config.get('pairlist', {}).get('method', 'StaticPairList')
self.pairlists = PairListResolver(pairlistname, self, self.config).pairlist
# Initializing Edge only if enabled
self.edge = Edge(self.config, self.exchange, self.strategy) if \
self.config.get('edge', {}).get('enabled', False) else None
self.active_pair_whitelist: List[str] = self.config['exchange']['pair_whitelist']
self._init_modules()
def _init_modules(self) -> None:
@ -97,7 +108,7 @@ class FreqtradeBot(object):
})
logger.info('Changing state to: %s', state.name)
if state == State.RUNNING:
self._startup_messages()
self.rpc.startup_messages(self.config, self.pairlists)
if state == State.STOPPED:
time.sleep(1)
@ -107,45 +118,10 @@ class FreqtradeBot(object):
constants.PROCESS_THROTTLE_SECS
)
nb_assets = self.config.get('dynamic_whitelist', None)
self._throttle(func=self._process,
min_secs=min_secs,
nb_assets=nb_assets)
min_secs=min_secs)
return state
def _startup_messages(self) -> None:
if self.config.get('dry_run', False):
self.rpc.send_msg({
'type': RPCMessageType.WARNING_NOTIFICATION,
'status': 'Dry run is enabled. All trades are simulated.'
})
stake_currency = self.config['stake_currency']
stake_amount = self.config['stake_amount']
minimal_roi = self.config['minimal_roi']
ticker_interval = self.config['ticker_interval']
exchange_name = self.config['exchange']['name']
strategy_name = self.config.get('strategy', '')
self.rpc.send_msg({
'type': RPCMessageType.CUSTOM_NOTIFICATION,
'status': f'*Exchange:* `{exchange_name}`\n'
f'*Stake per trade:* `{stake_amount} {stake_currency}`\n'
f'*Minimum ROI:* `{minimal_roi}`\n'
f'*Ticker Interval:* `{ticker_interval}`\n'
f'*Strategy:* `{strategy_name}`'
})
if self.config.get('dynamic_whitelist', False):
top_pairs = 'top ' + str(self.config.get('dynamic_whitelist', 20))
specific_pairs = ''
else:
top_pairs = 'whitelisted'
specific_pairs = '\n' + ', '.join(self.config['exchange'].get('pair_whitelist', ''))
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'Searching for {top_pairs} {stake_currency} pairs to buy and sell...'
f'{specific_pairs}'
})
def _throttle(self, func: Callable[..., Any], min_secs: float, *args, **kwargs) -> Any:
"""
Throttles the given callable that it
@ -162,32 +138,37 @@ class FreqtradeBot(object):
time.sleep(duration)
return result
def _process(self, nb_assets: Optional[int] = 0) -> bool:
def _process(self) -> bool:
"""
Queries the persistence layer for open trades and handles them,
otherwise a new trade is created.
:param: nb_assets: the maximum number of pairs to be traded at the same time
:return: True if one or more trades has been created or closed, False otherwise
"""
state_changed = False
try:
# Refresh whitelist based on wallet maintenance
sanitized_list = self._refresh_whitelist(
self._gen_pair_whitelist(
self.config['stake_currency']
) if nb_assets else self.config['exchange']['pair_whitelist']
)
# Refresh whitelist
self.pairlists.refresh_pairlist()
self.active_pair_whitelist = self.pairlists.whitelist
# Keep only the subsets of pairs wanted (up to nb_assets)
final_list = sanitized_list[:nb_assets] if nb_assets else sanitized_list
self.config['exchange']['pair_whitelist'] = final_list
# Refreshing candles
self.exchange.refresh_tickers(final_list, self.strategy.ticker_interval)
# Calculating Edge positiong
# Should be called before refresh_tickers
# Otherwise it will override cached klines in exchange
# with delta value (klines only from last refresh_pairs)
if self.edge:
self.edge.calculate()
self.active_pair_whitelist = self.edge.adjust(self.active_pair_whitelist)
# Query trades from persistence layer
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
# Extend active-pair whitelist with pairs from open trades
# ensures that tickers are downloaded for open trades
self.active_pair_whitelist.extend([trade.pair for trade in trades
if trade.pair not in self.active_pair_whitelist])
# Refreshing candles
self.exchange.refresh_tickers(self.active_pair_whitelist, self.strategy.ticker_interval)
# First process current opened trades
for trade in trades:
state_changed |= self.process_maybe_execute_sell(trade)
@ -215,63 +196,6 @@ class FreqtradeBot(object):
self.state = State.STOPPED
return state_changed
@cached(TTLCache(maxsize=1, ttl=1800))
def _gen_pair_whitelist(self, base_currency: str, key: str = 'quoteVolume') -> List[str]:
"""
Updates the whitelist with with a dynamically generated list
:param base_currency: base currency as str
:param key: sort key (defaults to 'quoteVolume')
:return: List of pairs
"""
if not self.exchange.exchange_has('fetchTickers'):
raise OperationalException(
'Exchange does not support dynamic whitelist.'
'Please edit your config and restart the bot'
)
tickers = self.exchange.get_tickers()
# check length so that we make sure that '/' is actually in the string
tickers = [v for k, v in tickers.items()
if len(k.split('/')) == 2 and k.split('/')[1] == base_currency]
sorted_tickers = sorted(tickers, reverse=True, key=lambda t: t[key])
pairs = [s['symbol'] for s in sorted_tickers]
return pairs
def _refresh_whitelist(self, whitelist: List[str]) -> List[str]:
"""
Check available markets and remove pair from whitelist if necessary
:param whitelist: the sorted list (based on BaseVolume) of pairs the user might want to
trade
:return: the list of pairs the user wants to trade without the one unavailable or
black_listed
"""
sanitized_whitelist = whitelist
markets = self.exchange.get_markets()
markets = [m for m in markets if m['quote'] == self.config['stake_currency']]
known_pairs = set()
for market in markets:
pair = market['symbol']
# pair is not int the generated dynamic market, or in the blacklist ... ignore it
if pair not in whitelist or pair in self.config['exchange'].get('pair_blacklist', []):
continue
# else the pair is valid
known_pairs.add(pair)
# Market is not active
if not market['active']:
sanitized_whitelist.remove(pair)
logger.info(
'Ignoring %s from whitelist. Market is not active.',
pair
)
# We need to remove pairs that are unknown
final_list = [x for x in sanitized_whitelist if x in known_pairs]
return final_list
def get_target_bid(self, pair: str, ticker: Dict[str, float]) -> float:
"""
Calculates bid target between current ask price and last price
@ -307,14 +231,23 @@ class FreqtradeBot(object):
return used_rate
def _get_trade_stake_amount(self) -> Optional[float]:
def _get_trade_stake_amount(self, pair) -> Optional[float]:
"""
Check if stake amount can be fulfilled with the available balance
for the stake currency
:return: float: Stake Amount
"""
stake_amount = self.config['stake_amount']
avaliable_amount = self.exchange.get_balance(self.config['stake_currency'])
if self.edge:
return 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']
avaliable_amount = self.wallets.get_free(self.config['stake_currency'])
if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
open_trades = len(Trade.query.filter(Trade.is_open.is_(True)).all())
@ -371,16 +304,7 @@ class FreqtradeBot(object):
:return: True if a trade object has been created and persisted, False otherwise
"""
interval = self.strategy.ticker_interval
stake_amount = self._get_trade_stake_amount()
if not stake_amount:
return False
logger.info(
'Checking buy signals to create a new trade with stake_amount: %f ...',
stake_amount
)
whitelist = copy.deepcopy(self.config['exchange']['pair_whitelist'])
whitelist = copy.deepcopy(self.active_pair_whitelist)
# Remove currently opened and latest pairs from whitelist
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
@ -392,10 +316,18 @@ class FreqtradeBot(object):
raise DependencyException('No currency pairs in whitelist')
# running get_signal on historical data fetched
# to find buy signals
for _pair in whitelist:
(buy, sell) = self.strategy.get_signal(_pair, interval, self.exchange.klines.get(_pair))
(buy, sell) = self.strategy.get_signal(_pair, interval, self.exchange.klines(_pair))
if buy and not sell:
stake_amount = self._get_trade_stake_amount(_pair)
if not stake_amount:
return False
logger.info(
'Buy signal found: about create a new trade with stake_amount: %f ...',
stake_amount
)
bidstrat_check_depth_of_market = self.config.get('bid_strategy', {}).\
get('check_depth_of_market', {})
if (bidstrat_check_depth_of_market.get('enabled', False)) and\
@ -425,7 +357,7 @@ class FreqtradeBot(object):
return True
return False
def execute_buy(self, pair: str, stake_amount: float) -> bool:
def execute_buy(self, pair: str, stake_amount: float, price: Optional[float] = None) -> bool:
"""
Executes a limit buy for the given pair
:param pair: pair for which we want to create a LIMIT_BUY
@ -435,11 +367,15 @@ class FreqtradeBot(object):
pair_url = self.exchange.get_pair_detail_url(pair)
stake_currency = self.config['stake_currency']
fiat_currency = self.config.get('fiat_display_currency', None)
time_in_force = self.strategy.order_time_in_force['buy']
# Calculate amount
buy_limit = self.get_target_bid(pair, self.exchange.get_ticker(pair))
if price:
buy_limit_requested = price
else:
# Calculate amount
buy_limit_requested = self.get_target_bid(pair, self.exchange.get_ticker(pair))
min_stake_amount = self._get_min_pair_stake_amount(pair_s, buy_limit)
min_stake_amount = self._get_min_pair_stake_amount(pair_s, buy_limit_requested)
if min_stake_amount is not None and min_stake_amount > stake_amount:
logger.warning(
f'Can\'t open a new trade for {pair_s}: stake amount'
@ -447,20 +383,59 @@ class FreqtradeBot(object):
)
return False
amount = stake_amount / buy_limit
amount = stake_amount / buy_limit_requested
order_id = self.exchange.buy(pair, buy_limit, amount)['id']
order = self.exchange.buy(pair=pair, ordertype=self.strategy.order_types['buy'],
amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force)
order_id = order['id']
order_status = order.get('status', None)
# we assume the order is executed at the price requested
buy_limit_filled_price = buy_limit_requested
if order_status == 'expired' or order_status == 'rejected':
order_type = self.strategy.order_types['buy']
order_tif = self.strategy.order_time_in_force['buy']
# return false if the order is not filled
if float(order['filled']) == 0:
logger.warning('Buy %s order with time in force %s for %s is %s by %s.'
' zero amount is fulfilled.',
order_tif, order_type, pair_s, order_status, self.exchange.name)
return False
else:
# the order is partially fulfilled
# in case of IOC orders we can check immediately
# if the order is fulfilled fully or partially
logger.warning('Buy %s order with time in force %s for %s is %s by %s.'
' %s amount fulfilled out of %s (%s remaining which is canceled).',
order_tif, order_type, pair_s, order_status, self.exchange.name,
order['filled'], order['amount'], order['remaining']
)
stake_amount = order['cost']
amount = order['amount']
buy_limit_filled_price = order['price']
order_id = None
# in case of FOK the order may be filled immediately and fully
elif order_status == 'closed':
stake_amount = order['cost']
amount = order['amount']
buy_limit_filled_price = order['price']
order_id = None
self.rpc.send_msg({
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': pair_s,
'market_url': pair_url,
'limit': buy_limit,
'limit': buy_limit_filled_price,
'stake_amount': stake_amount,
'stake_currency': stake_currency,
'fiat_currency': fiat_currency
})
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
trade = Trade(
@ -469,16 +444,21 @@ class FreqtradeBot(object):
amount=amount,
fee_open=fee,
fee_close=fee,
open_rate=buy_limit,
open_rate_requested=buy_limit,
open_rate=buy_limit_filled_price,
open_rate_requested=buy_limit_requested,
open_date=datetime.utcnow(),
exchange=self.exchange.id,
open_order_id=order_id,
strategy=self.strategy.get_strategy_name(),
ticker_interval=constants.TICKER_INTERVAL_MINUTES[self.config['ticker_interval']]
)
Trade.session.add(trade)
Trade.session.flush()
# Updating wallets
self.wallets.update()
return True
def process_maybe_execute_buy(self) -> bool:
@ -521,9 +501,22 @@ class FreqtradeBot(object):
trade.update(order)
if self.strategy.order_types.get('stoploss_on_exchange') and trade.is_open:
result = self.handle_stoploss_on_exchange(trade)
if result:
self.wallets.update()
return result
if trade.is_open and trade.open_order_id is None:
# Check if we can sell our current pair
return self.handle_trade(trade)
result = self.handle_trade(trade)
# Updating wallets if any trade occured
if result:
self.wallets.update()
return result
except DependencyException as exception:
logger.warning('Unable to sell trade: %s', exception)
return False
@ -585,9 +578,8 @@ class FreqtradeBot(object):
(buy, sell) = (False, False)
experimental = self.config.get('experimental', {})
if experimental.get('use_sell_signal') or experimental.get('ignore_roi_if_buy_signal'):
ticker = self.exchange.klines.get(trade.pair)
(buy, sell) = self.strategy.get_signal(trade.pair, self.strategy.ticker_interval,
ticker)
self.exchange.klines(trade.pair))
config_ask_strategy = self.config.get('ask_strategy', {})
if config_ask_strategy.get('use_order_book', False):
@ -611,18 +603,65 @@ class FreqtradeBot(object):
return True
break
else:
logger.info('checking sell')
logger.debug('checking sell')
if self.check_sell(trade, sell_rate, buy, sell):
return True
logger.info('Found no sell signals for whitelisted currencies. Trying again..')
logger.debug('Found no sell signal for %s.', trade)
return False
def handle_stoploss_on_exchange(self, trade: Trade) -> bool:
"""
Check if trade is fulfilled in which case the stoploss
on exchange should be added immediately if stoploss on exchnage
is enabled.
"""
result = False
# If trade is open and the buy order is fulfilled but there is no stoploss,
# then we add a stoploss on exchange
if not trade.open_order_id and not trade.stoploss_order_id:
if self.edge:
stoploss = self.edge.stoploss(pair=trade.pair)
else:
stoploss = self.strategy.stoploss
stop_price = trade.open_rate * (1 + stoploss)
# limit price should be less than stop price.
# 0.98 is arbitrary here.
limit_price = stop_price * 0.98
stoploss_order_id = self.exchange.stoploss_limit(
pair=trade.pair, amount=trade.amount, stop_price=stop_price, rate=limit_price
)['id']
trade.stoploss_order_id = str(stoploss_order_id)
# Or the trade open and there is already a stoploss on exchange.
# so we check if it is hit ...
elif trade.stoploss_order_id:
logger.debug('Handling stoploss on exchange %s ...', trade)
order = self.exchange.get_order(trade.stoploss_order_id, trade.pair)
if order['status'] == 'closed':
trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
trade.update(order)
result = True
else:
result = False
return result
def check_sell(self, trade: Trade, sell_rate: float, buy: bool, sell: bool) -> bool:
should_sell = self.strategy.should_sell(trade, sell_rate, datetime.utcnow(), buy, sell)
if self.edge:
stoploss = self.edge.stoploss(trade.pair)
should_sell = self.strategy.should_sell(
trade, sell_rate, datetime.utcnow(), buy, sell, force_stoploss=stoploss)
else:
should_sell = self.strategy.should_sell(trade, sell_rate, datetime.utcnow(), buy, sell)
if should_sell.sell_flag:
self.execute_sell(trade, sell_rate, should_sell.sell_type)
logger.info('excuted sell')
logger.info('executed sell, reason: %s', should_sell.sell_type)
return True
return False
@ -655,15 +694,18 @@ class FreqtradeBot(object):
ordertime = arrow.get(order['datetime']).datetime
# Check if trade is still actually open
if int(order['remaining']) == 0:
if float(order['remaining']) == 0.0:
self.wallets.update()
continue
# Check if trade is still actually open
if order['status'] == 'open':
if order['side'] == 'buy' and ordertime < buy_timeoutthreashold:
self.handle_timedout_limit_buy(trade, order)
self.wallets.update()
elif order['side'] == 'sell' and ordertime < sell_timeoutthreashold:
self.handle_timedout_limit_sell(trade, order)
self.wallets.update()
# FIX: 20180110, why is cancel.order unconditionally here, whereas
# it is conditionally called in the
@ -730,8 +772,27 @@ class FreqtradeBot(object):
:param sellreason: Reason the sell was triggered
:return: None
"""
sell_type = 'sell'
if sell_reason in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
sell_type = 'stoploss'
# if stoploss is on exchange and we are on dry_run mode,
# we consider the sell price stop price
if self.config.get('dry_run', False) and sell_type == 'stoploss' \
and self.strategy.order_types['stoploss_on_exchange']:
limit = trade.stop_loss
# First cancelling stoploss on exchange ...
if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
self.exchange.cancel_order(trade.stoploss_order_id, trade.pair)
# Execute sell and update trade record
order_id = self.exchange.sell(str(trade.pair), limit, trade.amount)['id']
order_id = self.exchange.sell(pair=str(trade.pair),
ordertype=self.strategy.order_types[sell_type],
amount=trade.amount, rate=limit,
time_in_force=self.strategy.order_time_in_force['sell']
)['id']
trade.open_order_id = order_id
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.value
@ -754,6 +815,7 @@ class FreqtradeBot(object):
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_percent': profit_percent,
'sell_reason': sell_reason.value
}
# For regular case, when the configuration exists

266
freqtrade/optimize/__init__.py
View File

@ -1,245 +1,49 @@
# pragma pylint: disable=missing-docstring
import gzip
try:
import ujson as json
_UJSON = True
except ImportError:
# see mypy/issues/1153
import json # type: ignore
_UJSON = False
import logging
import os
from typing import Optional, List, Dict, Tuple, Any
import arrow
from datetime import datetime
from typing import Dict, Tuple
import operator
from freqtrade import misc, constants, OperationalException
from freqtrade.exchange import Exchange
from freqtrade.arguments import TimeRange
import arrow
from pandas import DataFrame
from freqtrade.optimize.default_hyperopt import DefaultHyperOpts # noqa: F401
logger = logging.getLogger(__name__)
def json_load(data):
"""Try to load data with ujson"""
if _UJSON:
return json.load(data, precise_float=True)
else:
return json.load(data)
def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]:
if not tickerlist:
return tickerlist
start_index = 0
stop_index = len(tickerlist)
if timerange.starttype == 'line':
stop_index = timerange.startts
if timerange.starttype == 'index':
start_index = timerange.startts
elif timerange.starttype == 'date':
while (start_index < len(tickerlist) and
tickerlist[start_index][0] < timerange.startts * 1000):
start_index += 1
if timerange.stoptype == 'line':
start_index = len(tickerlist) + timerange.stopts
if timerange.stoptype == 'index':
stop_index = timerange.stopts
elif timerange.stoptype == 'date':
while (stop_index > 0 and
tickerlist[stop_index-1][0] > timerange.stopts * 1000):
stop_index -= 1
if start_index > stop_index:
raise ValueError(f'The timerange [{timerange.startts},{timerange.stopts}] is incorrect')
return tickerlist[start_index:stop_index]
def load_tickerdata_file(
datadir: str, pair: str,
ticker_interval: str,
timerange: Optional[TimeRange] = None) -> Optional[List[Dict]]:
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
"""
Load a pair from file,
:return dict OR empty if unsuccesful
Get the maximum timeframe for the given backtest data
:param data: dictionary with preprocessed backtesting data
:return: tuple containing min_date, max_date
"""
path = make_testdata_path(datadir)
pair_s = pair.replace('/', '_')
file = os.path.join(path, f'{pair_s}-{ticker_interval}.json')
gzipfile = file + '.gz'
# If the file does not exist we download it when None is returned.
# If file exists, read the file, load the json
if os.path.isfile(gzipfile):
logger.debug('Loading ticker data from file %s', gzipfile)
with gzip.open(gzipfile) as tickerdata:
pairdata = json.load(tickerdata)
elif os.path.isfile(file):
logger.debug('Loading ticker data from file %s', file)
with open(file) as tickerdata:
pairdata = json.load(tickerdata)
else:
return None
if timerange:
pairdata = trim_tickerlist(pairdata, timerange)
return pairdata
timeframe = [
(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
for frame in data.values()
]
return min(timeframe, key=operator.itemgetter(0))[0], \
max(timeframe, key=operator.itemgetter(1))[1]
def load_data(datadir: str,
ticker_interval: str,
pairs: List[str],
refresh_pairs: Optional[bool] = False,
exchange: Optional[Exchange] = None,
timerange: TimeRange = TimeRange(None, None, 0, 0)) -> Dict[str, List]:
def validate_backtest_data(data: Dict[str, DataFrame], min_date: datetime,
max_date: datetime, ticker_interval_mins: int) -> bool:
"""
Loads ticker history data for the given parameters
:return: dict
Validates preprocessed backtesting data for missing values and shows warnings about it that.
:param data: dictionary with preprocessed backtesting data
:param min_date: start-date of the data
:param max_date: end-date of the data
:param ticker_interval_mins: ticker interval in minutes
"""
result = {}
# If the user force the refresh of pairs
if refresh_pairs:
logger.info('Download data for all pairs and store them in %s', datadir)
if not exchange:
raise OperationalException("Exchange needs to be initialized when "
"calling load_data with refresh_pairs=True")
download_pairs(datadir, exchange, pairs, ticker_interval, timerange=timerange)
for pair in pairs:
pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
if pairdata:
result[pair] = pairdata
else:
logger.warning(
'No data for pair: "%s", Interval: %s. '
'Use --refresh-pairs-cached to download the data',
pair,
ticker_interval
)
return result
def make_testdata_path(datadir: str) -> str:
"""Return the path where testdata files are stored"""
return datadir or os.path.abspath(
os.path.join(
os.path.dirname(__file__), '..', 'tests', 'testdata'
)
)
def download_pairs(datadir, exchange: Exchange, pairs: List[str],
ticker_interval: str,
timerange: TimeRange = TimeRange(None, None, 0, 0)) -> bool:
"""For each pairs passed in parameters, download the ticker intervals"""
for pair in pairs:
try:
download_backtesting_testdata(datadir,
exchange=exchange,
pair=pair,
tick_interval=ticker_interval,
timerange=timerange)
except BaseException:
logger.info(
'Failed to download the pair: "%s", Interval: %s',
pair,
ticker_interval
)
return False
return True
def load_cached_data_for_updating(filename: str,
tick_interval: str,
timerange: Optional[TimeRange]) -> Tuple[
List[Any],
Optional[int]]:
"""
Load cached data and choose what part of the data should be updated
"""
since_ms = None
# user sets timerange, so find the start time
if timerange:
if timerange.starttype == 'date':
since_ms = timerange.startts * 1000
elif timerange.stoptype == 'line':
num_minutes = timerange.stopts * constants.TICKER_INTERVAL_MINUTES[tick_interval]
since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
# read the cached file
if os.path.isfile(filename):
with open(filename, "rt") as file:
data = json_load(file)
# remove the last item, because we are not sure if it is correct
# it could be fetched when the candle was incompleted
if data:
data.pop()
else:
data = []
if data:
if since_ms and since_ms < data[0][0]:
# the data is requested for earlier period than the cache has
# so fully redownload all the data
data = []
else:
# a part of the data was already downloaded, so
# download unexist data only
since_ms = data[-1][0] + 1
return (data, since_ms)
def download_backtesting_testdata(datadir: str,
exchange: Exchange,
pair: str,
tick_interval: str = '5m',
timerange: Optional[TimeRange] = None) -> None:
"""
Download the latest ticker intervals from the exchange for the pair passed in parameters
The data is downloaded starting from the last correct ticker interval data that
exists in a cache. If timerange starts earlier than the data in the cache,
the full data will be redownloaded
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pair: pair to download
:param tick_interval: ticker interval
:param timerange: range of time to download
:return: None
"""
path = make_testdata_path(datadir)
filepair = pair.replace("/", "_")
filename = os.path.join(path, f'{filepair}-{tick_interval}.json')
logger.info(
'Download the pair: "%s", Interval: %s',
pair,
tick_interval
)
data, since_ms = load_cached_data_for_updating(filename, tick_interval, timerange)
logger.debug("Current Start: %s", misc.format_ms_time(data[1][0]) if data else 'None')
logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
# Default since_ms to 30 days if nothing is given
new_data = exchange.get_history(pair=pair, tick_interval=tick_interval,
since_ms=since_ms if since_ms
else
int(arrow.utcnow().shift(days=-30).float_timestamp) * 1000)
data.extend(new_data)
logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
logger.debug("New End: %s", misc.format_ms_time(data[-1][0]))
misc.file_dump_json(filename, data)
# total difference in minutes / interval-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // ticker_interval_mins)
found_missing = False
for pair, df in data.items():
dflen = len(df)
if dflen < expected_frames:
found_missing = True
logger.warning("%s has missing frames: expected %s, got %s, that's %s missing values",
pair, expected_frames, dflen, expected_frames - dflen)
return found_missing

105
freqtrade/optimize/backtesting.py
View File

@ -4,14 +4,12 @@
This module contains the backtesting logic
"""
import logging
import operator
from argparse import Namespace
from copy import deepcopy
from datetime import datetime, timedelta
from pathlib import Path
from typing import Any, Dict, List, NamedTuple, Optional, Tuple
from typing import Any, Dict, List, NamedTuple, Optional
import arrow
from pandas import DataFrame
from tabulate import tabulate
@ -20,10 +18,11 @@ from freqtrade import DependencyException, constants
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.exchange import Exchange
from freqtrade.data import history
from freqtrade.misc import file_dump_json
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import SellType
from freqtrade.strategy.resolver import IStrategy, StrategyResolver
from freqtrade.resolvers import StrategyResolver
from freqtrade.strategy.interface import SellType, IStrategy
logger = logging.getLogger(__name__)
@ -68,6 +67,7 @@ class Backtesting(object):
if self.config.get('strategy_list', None):
# Force one interval
self.ticker_interval = str(self.config.get('ticker_interval'))
self.ticker_interval_mins = constants.TICKER_INTERVAL_MINUTES[self.ticker_interval]
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
@ -88,24 +88,11 @@ class Backtesting(object):
"""
self.strategy = strategy
self.ticker_interval = self.config.get('ticker_interval')
self.ticker_interval_mins = constants.TICKER_INTERVAL_MINUTES[self.ticker_interval]
self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe
self.advise_buy = strategy.advise_buy
self.advise_sell = strategy.advise_sell
@staticmethod
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
"""
Get the maximum timeframe for the given backtest data
:param data: dictionary with preprocessed backtesting data
:return: tuple containing min_date, max_date
"""
timeframe = [
(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
for frame in data.values()
]
return min(timeframe, key=operator.itemgetter(0))[0], \
max(timeframe, key=operator.itemgetter(1))[1]
def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame,
skip_nan: bool = False) -> str:
"""
@ -223,21 +210,37 @@ class Backtesting(object):
buy_signal = sell_row.buy
sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
sell_row.sell)
sell_row.sell, low=sell_row.low, high=sell_row.high)
if sell.sell_flag:
trade_dur = int((sell_row.date - buy_row.date).total_seconds() // 60)
# Special handling if high or low hit STOP_LOSS or ROI
if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
# Set close_rate to stoploss
closerate = trade.stop_loss
elif sell.sell_type == (SellType.ROI):
# get entry in min_roi >= to trade duration
roi_entry = max(list(filter(lambda x: trade_dur >= x,
self.strategy.minimal_roi.keys())))
roi = self.strategy.minimal_roi[roi_entry]
# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
closerate = - (trade.open_rate * roi + trade.open_rate *
(1 + trade.fee_open)) / (trade.fee_close - 1)
else:
closerate = sell_row.open
return BacktestResult(pair=pair,
profit_percent=trade.calc_profit_percent(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
profit_percent=trade.calc_profit_percent(rate=closerate),
profit_abs=trade.calc_profit(rate=closerate),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=int((
sell_row.date - buy_row.date).total_seconds() // 60),
trade_duration=trade_dur,
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=False,
open_rate=buy_row.open,
close_rate=sell_row.open,
close_rate=closerate,
sell_reason=sell.sell_type
)
if partial_ticker:
@ -277,12 +280,17 @@ class Backtesting(object):
position_stacking: do we allow position stacking? (default: False)
:return: DataFrame
"""
headers = ['date', 'buy', 'open', 'close', 'sell']
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
position_stacking = args.get('position_stacking', False)
start_date = args['start_date']
end_date = args['end_date']
trades = []
trade_count_lock: Dict = {}
ticker: Dict = {}
pairs = []
# Create ticker dict
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
@ -297,15 +305,28 @@ class Backtesting(object):
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker = [x for x in ticker_data.itertuples()]
ticker[pair] = [x for x in ticker_data.itertuples()]
pairs.append(pair)
lock_pair_until: Dict = {}
tmp = start_date + timedelta(minutes=self.ticker_interval_mins)
index = 0
# Loop timerange and test per pair
while tmp < end_date:
# print(f"time: {tmp}")
for i, pair in enumerate(ticker):
try:
row = ticker[pair][index]
except IndexError:
# missing Data for one pair ...
# Warnings for this are shown by `validate_backtest_data`
continue
lock_pair_until = None
for index, row in enumerate(ticker):
if row.buy == 0 or row.sell == 1:
continue # skip rows where no buy signal or that would immediately sell off
if not position_stacking:
if lock_pair_until is not None and row.date <= lock_pair_until:
if pair in lock_pair_until and row.date <= lock_pair_until[pair]:
continue
if max_open_trades > 0:
# Check if max_open_trades has already been reached for the given date
@ -314,17 +335,19 @@ class Backtesting(object):
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][index + 1:],
trade_count_lock, args)
if trade_entry:
lock_pair_until = trade_entry.close_time
lock_pair_until[pair] = trade_entry.close_time
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
# This happens only if the buy-signal was with the last candle
lock_pair_until = ticker_data.iloc[-1].date
lock_pair_until[pair] = end_date
tmp += timedelta(minutes=self.ticker_interval_mins)
index += 1
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
@ -340,14 +363,14 @@ class Backtesting(object):
if self.config.get('live'):
logger.info('Downloading data for all pairs in whitelist ...')
self.exchange.refresh_tickers(pairs, self.ticker_interval)
data = self.exchange.klines
data = self.exchange._klines
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = optimize.load_data(
self.config['datadir'],
data = history.load_data(
datadir=Path(self.config['datadir']) if self.config.get('datadir') else None,
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get('refresh_pairs', False),
@ -371,10 +394,12 @@ class Backtesting(object):
self._set_strategy(strat)
# need to reprocess data every time to populate signals
preprocessed = self.tickerdata_to_dataframe(data)
preprocessed = self.strategy.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = self.get_timeframe(preprocessed)
min_date, max_date = optimize.get_timeframe(preprocessed)
# Validate dataframe for missing values
optimize.validate_backtest_data(preprocessed, min_date, max_date,
constants.TICKER_INTERVAL_MINUTES[self.ticker_interval])
logger.info(
'Measuring data from %s up to %s (%s days)..',
min_date.isoformat(),
@ -389,6 +414,8 @@ class Backtesting(object):
'processed': preprocessed,
'max_open_trades': max_open_trades,
'position_stacking': self.config.get('position_stacking', False),
'start_date': min_date,
'end_date': max_date,
}
)

130
freqtrade/optimize/default_hyperopt.py Normal file
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@ -0,0 +1,130 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import talib.abstract as ta
from pandas import DataFrame
from typing import Dict, Any, Callable, List
from functools import reduce
from skopt.space import Categorical, Dimension, Integer, Real
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class_name = 'DefaultHyperOpts'
class DefaultHyperOpts(IHyperOpt):
"""
Default hyperopt provided by freqtrade bot.
You can override it with your own hyperopt
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
@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:
"""
Buy strategy Hyperopt will build and use
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Generate the ROI table that will be used by Hyperopt
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
@staticmethod
def stoploss_space() -> List[Dimension]:
"""
Stoploss Value to search
"""
return [
Real(-0.5, -0.02, name='stoploss'),
]
@staticmethod
def roi_space() -> List[Dimension]:
"""
Values to search for each ROI steps
"""
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
Real(0.01, 0.04, name='roi_p1'),
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
]

106
freqtrade/optimize/edge_cli.py Normal file
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@ -0,0 +1,106 @@
# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
"""
This module contains the edge backtesting interface
"""
import logging
from argparse import Namespace
from typing import Dict, Any
from tabulate import tabulate
from freqtrade.edge import Edge
from freqtrade.configuration import Configuration
from freqtrade.arguments import Arguments
from freqtrade.exchange import Exchange
from freqtrade.resolvers import StrategyResolver
logger = logging.getLogger(__name__)
class EdgeCli(object):
"""
EdgeCli class, this class contains all the logic to run edge backtesting
To run a edge backtest:
edge = EdgeCli(config)
edge.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
# Reset keys for edge
self.config['exchange']['key'] = ''
self.config['exchange']['secret'] = ''
self.config['exchange']['password'] = ''
self.config['exchange']['uid'] = ''
self.config['dry_run'] = True
self.exchange = Exchange(self.config)
self.strategy = StrategyResolver(self.config).strategy
self.edge = Edge(config, self.exchange, self.strategy)
self.edge._refresh_pairs = self.config.get('refresh_pairs', False)
self.timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
self.edge._timerange = self.timerange
def _generate_edge_table(self, results: dict) -> str:
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', '.d')
tabular_data = []
headers = ['pair', 'stoploss', 'win rate', 'risk reward ratio',
'required risk reward', 'expectancy', 'total number of trades',
'average duration (min)']
for result in results.items():
if result[1].nb_trades > 0:
tabular_data.append([
result[0],
result[1].stoploss,
result[1].winrate,
result[1].risk_reward_ratio,
result[1].required_risk_reward,
result[1].expectancy,
result[1].nb_trades,
round(result[1].avg_trade_duration)
])
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
def start(self) -> None:
self.edge.calculate()
print('') # blank like for readability
print(self._generate_edge_table(self.edge._cached_pairs))
def setup_configuration(args: Namespace) -> Dict[str, Any]:
"""
Prepare the configuration for edge backtesting
:param args: Cli args from Arguments()
:return: Configuration
"""
configuration = Configuration(args)
config = configuration.get_config()
# Ensure we do not use Exchange credentials
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
return config
def start(args: Namespace) -> None:
"""
Start Edge script
:param args: Cli args from Arguments()
:return: None
"""
# Initialize configuration
config = setup_configuration(args)
logger.info('Starting freqtrade in Edge mode')
# Initialize Edge object
edge_cli = EdgeCli(config)
edge_cli.start()

162
freqtrade/optimize/hyperopt.py
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@ -5,26 +5,27 @@ This module contains the hyperopt logic
"""
import logging
import multiprocessing
from argparse import Namespace
import os
import sys
from argparse import Namespace
from functools import reduce
from pathlib import Path
from math import exp
import multiprocessing
from operator import itemgetter
from typing import Any, Callable, Dict, List
from typing import Any, Dict, List
import talib.abstract as ta
from pandas import DataFrame
from sklearn.externals.joblib import Parallel, delayed, dump, load
from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects
from skopt import Optimizer
from skopt.space import Categorical, Dimension, Integer, Real
from skopt.space import Dimension
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.optimize import load_data
from freqtrade.data.history import load_data
from freqtrade.optimize import get_timeframe
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.resolvers import HyperOptResolver
logger = logging.getLogger(__name__)
@ -42,6 +43,9 @@ class Hyperopt(Backtesting):
"""
def __init__(self, config: Dict[str, Any]) -> None:
super().__init__(config)
self.config = config
self.custom_hyperopt = HyperOptResolver(self.config).hyperopt
# set TARGET_TRADES to suit your number concurrent trades so its realistic
# to the number of days
self.target_trades = 600
@ -74,24 +78,6 @@ class Hyperopt(Backtesting):
arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
return arg_dict
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
def save_trials(self) -> None:
"""
Save hyperopt trials to file
@ -121,7 +107,8 @@ class Hyperopt(Backtesting):
best_result['params']
)
if 'roi_t1' in best_result['params']:
logger.info('ROI table:\n%s', self.generate_roi_table(best_result['params']))
logger.info('ROI table:\n%s',
self.custom_hyperopt.generate_roi_table(best_result['params']))
def log_results(self, results) -> None:
"""
@ -149,59 +136,6 @@ class Hyperopt(Backtesting):
result = trade_loss + profit_loss + duration_loss
return result
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Generate the ROI table that will be used by Hyperopt
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
@staticmethod
def roi_space() -> List[Dimension]:
"""
Values to search for each ROI steps
"""
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
Real(0.01, 0.04, name='roi_p1'),
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
]
@staticmethod
def stoploss_space() -> List[Dimension]:
"""
Stoploss search space
"""
return [
Real(-0.5, -0.02, name='stoploss'),
]
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
def has_space(self, space: str) -> bool:
"""
Tell if a space value is contained in the configuration
@ -216,71 +150,33 @@ class Hyperopt(Backtesting):
"""
spaces: List[Dimension] = []
if self.has_space('buy'):
spaces += Hyperopt.indicator_space()
spaces += self.custom_hyperopt.indicator_space()
if self.has_space('roi'):
spaces += Hyperopt.roi_space()
spaces += self.custom_hyperopt.roi_space()
if self.has_space('stoploss'):
spaces += Hyperopt.stoploss_space()
spaces += self.custom_hyperopt.stoploss_space()
return spaces
@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:
"""
Buy strategy Hyperopt will build and use
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
def generate_optimizer(self, _params) -> Dict:
def generate_optimizer(self, _params: Dict) -> Dict:
params = self.get_args(_params)
if self.has_space('roi'):
self.strategy.minimal_roi = self.generate_roi_table(params)
self.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params)
if self.has_space('buy'):
self.advise_buy = self.buy_strategy_generator(params)
self.advise_buy = self.custom_hyperopt.buy_strategy_generator(params)
if self.has_space('stoploss'):
self.strategy.stoploss = params['stoploss']
processed = load(TICKERDATA_PICKLE)
min_date, max_date = get_timeframe(processed)
results = self.backtest(
{
'stake_amount': self.config['stake_amount'],
'processed': processed,
'position_stacking': self.config.get('position_stacking', True),
'start_date': min_date,
'end_date': max_date,
}
)
result_explanation = self.format_results(results)
@ -329,7 +225,8 @@ class Hyperopt(Backtesting):
)
def run_optimizer_parallel(self, parallel, asked) -> List:
return parallel(delayed(self.generate_optimizer)(v) for v in asked)
return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v) for v in asked)
def load_previous_results(self):
""" read trials file if we have one """
@ -344,15 +241,16 @@ class Hyperopt(Backtesting):
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = load_data(
datadir=str(self.config.get('datadir')),
datadir=Path(self.config['datadir']) if self.config.get('datadir') else None,
pairs=self.config['exchange']['pair_whitelist'],
ticker_interval=self.ticker_interval,
timerange=timerange
)
if self.has_space('buy'):
self.strategy.advise_indicators = Hyperopt.populate_indicators # type: ignore
dump(self.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
self.strategy.advise_indicators = \
self.custom_hyperopt.populate_indicators # type: ignore
dump(self.strategy.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
self.exchange = None # type: ignore
self.load_previous_results()

66
freqtrade/optimize/hyperopt_interface.py Normal file
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@ -0,0 +1,66 @@
"""
IHyperOpt interface
This module defines the interface to apply for hyperopts
"""
from abc import ABC, abstractmethod
from typing import Dict, Any, Callable, List
from pandas import DataFrame
from skopt.space import Dimension
class IHyperOpt(ABC):
"""
Interface for freqtrade hyperopts
Defines the mandatory structure must follow any custom strategies
Attributes you can use:
minimal_roi -> Dict: Minimal ROI designed for the strategy
stoploss -> float: optimal stoploss designed for the strategy
ticker_interval -> int: value of the ticker interval to use for the strategy
"""
@staticmethod
@abstractmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:return: a Dataframe with all mandatory indicators for the strategies
"""
@staticmethod
@abstractmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Create a buy strategy generator
"""
@staticmethod
@abstractmethod
def indicator_space() -> List[Dimension]:
"""
Create an indicator space
"""
@staticmethod
@abstractmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Create an roi table
"""
@staticmethod
@abstractmethod
def stoploss_space() -> List[Dimension]:
"""
Create a stoploss space
"""
@staticmethod
@abstractmethod
def roi_space() -> List[Dimension]:
"""
Create a roi space
"""

91
freqtrade/pairlist/IPairList.py Normal file
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@ -0,0 +1,91 @@
"""
Static List provider
Provides lists as configured in config.json
"""
import logging
from abc import ABC, abstractmethod
from typing import List
logger = logging.getLogger(__name__)
class IPairList(ABC):
def __init__(self, freqtrade, config: dict) -> None:
self._freqtrade = freqtrade
self._config = config
self._whitelist = self._config['exchange']['pair_whitelist']
self._blacklist = self._config['exchange'].get('pair_blacklist', [])
@property
def name(self) -> str:
"""
Gets name of the class
-> no need to overwrite in subclasses
"""
return self.__class__.__name__
@property
def whitelist(self) -> List[str]:
"""
Has the current whitelist
-> no need to overwrite in subclasses
"""
return self._whitelist
@property
def blacklist(self) -> List[str]:
"""
Has the current blacklist
-> no need to overwrite in subclasses
"""
return self._blacklist
@abstractmethod
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
-> Please overwrite in subclasses
"""
@abstractmethod
def refresh_pairlist(self) -> None:
"""
Refreshes pairlists and assigns them to self._whitelist and self._blacklist respectively
-> Please overwrite in subclasses
"""
def _validate_whitelist(self, whitelist: List[str]) -> List[str]:
"""
Check available markets and remove pair from whitelist if necessary
:param whitelist: the sorted list (based on BaseVolume) of pairs the user might want to
trade
:return: the list of pairs the user wants to trade without the one unavailable or
black_listed
"""
sanitized_whitelist = whitelist
markets = self._freqtrade.exchange.get_markets()
# Filter to markets in stake currency
markets = [m for m in markets if m['quote'] == self._config['stake_currency']]
known_pairs = set()
for market in markets:
pair = market['symbol']
# pair is not int the generated dynamic market, or in the blacklist ... ignore it
if pair not in whitelist or pair in self.blacklist:
continue
# else the pair is valid
known_pairs.add(pair)
# Market is not active
if not market['active']:
sanitized_whitelist.remove(pair)
logger.info(
'Ignoring %s from whitelist. Market is not active.',
pair
)
# We need to remove pairs that are unknown
return [x for x in sanitized_whitelist if x in known_pairs]

30
freqtrade/pairlist/StaticPairList.py Normal file
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@ -0,0 +1,30 @@
"""
Static List provider
Provides lists as configured in config.json
"""
import logging
from freqtrade.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
class StaticPairList(IPairList):
def __init__(self, freqtrade, config: dict) -> None:
super().__init__(freqtrade, config)
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
-> Please overwrite in subclasses
"""
return f"{self.name}: {self.whitelist}"
def refresh_pairlist(self) -> None:
"""
Refreshes pairlists and assigns them to self._whitelist and self._blacklist respectively
"""
self._whitelist = self._validate_whitelist(self._config['exchange']['pair_whitelist'])

75
freqtrade/pairlist/VolumePairList.py Normal file
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@ -0,0 +1,75 @@
"""
Static List provider
Provides lists as configured in config.json
"""
import logging
from typing import List
from cachetools import TTLCache, cached
from freqtrade.pairlist.IPairList import IPairList
from freqtrade import OperationalException
logger = logging.getLogger(__name__)
SORT_VALUES = ['askVolume', 'bidVolume', 'quoteVolume']
class VolumePairList(IPairList):
def __init__(self, freqtrade, config: dict) -> None:
super().__init__(freqtrade, config)
self._whitelistconf = self._config.get('pairlist', {}).get('config')
if 'number_assets' not in self._whitelistconf:
raise OperationalException(
f'`number_assets` not specified. Please check your configuration '
'for "pairlist.config.number_assets"')
self._number_pairs = self._whitelistconf['number_assets']
self._sort_key = self._whitelistconf.get('sort_key', 'quoteVolume')
if not self._freqtrade.exchange.exchange_has('fetchTickers'):
raise OperationalException(
'Exchange does not support dynamic whitelist.'
'Please edit your config and restart the bot'
)
if not self._validate_keys(self._sort_key):
raise OperationalException(
f'key {self._sort_key} not in {SORT_VALUES}')
def _validate_keys(self, key):
return key in SORT_VALUES
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
-> Please overwrite in subclasses
"""
return f"{self.name} - top {self._whitelistconf['number_assets']} volume pairs."
def refresh_pairlist(self) -> None:
"""
Refreshes pairlists and assigns them to self._whitelist and self._blacklist respectively
-> Please overwrite in subclasses
"""
# Generate dynamic whitelist
pairs = self._gen_pair_whitelist(self._config['stake_currency'], self._sort_key)
# Validate whitelist to only have active market pairs
self._whitelist = self._validate_whitelist(pairs)[:self._number_pairs]
@cached(TTLCache(maxsize=1, ttl=1800))
def _gen_pair_whitelist(self, base_currency: str, key: str) -> List[str]:
"""
Updates the whitelist with with a dynamically generated list
:param base_currency: base currency as str
:param key: sort key (defaults to 'quoteVolume')
:return: List of pairs
"""
tickers = self._freqtrade.exchange.get_tickers()
# check length so that we make sure that '/' is actually in the string
tickers = [v for k, v in tickers.items()
if len(k.split('/')) == 2 and k.split('/')[1] == base_currency]
sorted_tickers = sorted(tickers, reverse=True, key=lambda t: t[key])
pairs = [s['symbol'] for s in sorted_tickers]
return pairs

0
freqtrade/pairlist/__init__.py Normal file
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54
freqtrade/persistence.py
View File

@ -4,7 +4,7 @@ This module contains the class to persist trades into SQLite
import logging
from datetime import datetime
from decimal import Decimal, getcontext
from decimal import Decimal
from typing import Any, Dict, Optional
import arrow
@ -14,6 +14,7 @@ from sqlalchemy.exc import NoSuchModuleError
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm.scoping import scoped_session
from sqlalchemy.orm.session import sessionmaker
from sqlalchemy import func
from sqlalchemy.pool import StaticPool
from freqtrade import OperationalException
@ -82,7 +83,7 @@ def check_migrate(engine) -> None:
logger.debug(f'trying {table_back_name}')
# Check for latest column
if not has_column(cols, 'ticker_interval'):
if not has_column(cols, 'stoploss_order_id'):
logger.info(f'Running database migration - backup available as {table_back_name}')
fee_open = get_column_def(cols, 'fee_open', 'fee')
@ -91,6 +92,7 @@ def check_migrate(engine) -> None:
close_rate_requested = get_column_def(cols, 'close_rate_requested', 'null')
stop_loss = get_column_def(cols, 'stop_loss', '0.0')
initial_stop_loss = get_column_def(cols, 'initial_stop_loss', '0.0')
stoploss_order_id = get_column_def(cols, 'stoploss_order_id', 'null')
max_rate = get_column_def(cols, 'max_rate', '0.0')
sell_reason = get_column_def(cols, 'sell_reason', 'null')
strategy = get_column_def(cols, 'strategy', 'null')
@ -98,6 +100,9 @@ def check_migrate(engine) -> None:
# Schema migration necessary
engine.execute(f"alter table trades rename to {table_back_name}")
# drop indexes on backup table
for index in inspector.get_indexes(table_back_name):
engine.execute(f"drop index {index['name']}")
# let SQLAlchemy create the schema as required
_DECL_BASE.metadata.create_all(engine)
@ -106,7 +111,7 @@ def check_migrate(engine) -> None:
(id, exchange, pair, is_open, fee_open, fee_close, open_rate,
open_rate_requested, close_rate, close_rate_requested, close_profit,
stake_amount, amount, open_date, close_date, open_order_id,
stop_loss, initial_stop_loss, max_rate, sell_reason, strategy,
stop_loss, initial_stop_loss, stoploss_order_id, max_rate, sell_reason, strategy,
ticker_interval
)
select id, lower(exchange),
@ -122,7 +127,8 @@ def check_migrate(engine) -> None:
{close_rate_requested} close_rate_requested, close_profit,
stake_amount, amount, open_date, close_date, open_order_id,
{stop_loss} stop_loss, {initial_stop_loss} initial_stop_loss,
{max_rate} max_rate, {sell_reason} sell_reason, {strategy} strategy,
{stoploss_order_id} stoploss_order_id, {max_rate} max_rate,
{sell_reason} sell_reason, {strategy} strategy,
{ticker_interval} ticker_interval
from {table_back_name}
""")
@ -177,6 +183,8 @@ class Trade(_DECL_BASE):
stop_loss = Column(Float, nullable=True, default=0.0)
# absolute value of the initial stop loss
initial_stop_loss = Column(Float, nullable=True, default=0.0)
# stoploss order id which is on exchange
stoploss_order_id = Column(String, nullable=True, index=True)
# absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0)
sell_reason = Column(String, nullable=True)
@ -239,17 +247,21 @@ class Trade(_DECL_BASE):
if order['status'] == 'open' or order['price'] is None:
return
logger.info('Updating trade (id=%d) ...', self.id)
logger.info('Updating trade (id=%s) ...', self.id)
getcontext().prec = 8 # Bittrex do not go above 8 decimal
if order_type == 'limit' and order['side'] == 'buy':
if order_type in ('market', 'limit') and order['side'] == 'buy':
# Update open rate and actual amount
self.open_rate = Decimal(order['price'])
self.amount = Decimal(order['amount'])
logger.info('LIMIT_BUY has been fulfilled for %s.', self)
logger.info('%s_BUY has been fulfilled for %s.', order_type.upper(), self)
self.open_order_id = None
elif order_type == 'limit' and order['side'] == 'sell':
elif order_type in ('market', 'limit') and order['side'] == 'sell':
self.close(order['price'])
logger.info('%s_SELL has been fulfilled for %s.', order_type.upper(), self)
elif order_type == 'stop_loss_limit':
self.stoploss_order_id = None
logger.info('STOP_LOSS_LIMIT is hit for %s.', self)
self.close(order['average'])
else:
raise ValueError(f'Unknown order type: {order_type}')
cleanup()
@ -273,12 +285,11 @@ class Trade(_DECL_BASE):
self,
fee: Optional[float] = None) -> float:
"""
Calculate the open_rate in BTC
Calculate the open_rate including fee.
:param fee: fee to use on the open rate (optional).
If rate is not set self.fee will be used
:return: Price in BTC of the open trade
:return: Price in of the open trade incl. Fees
"""
getcontext().prec = 8
buy_trade = (Decimal(self.amount) * Decimal(self.open_rate))
fees = buy_trade * Decimal(fee or self.fee_open)
@ -289,14 +300,13 @@ class Trade(_DECL_BASE):
rate: Optional[float] = None,
fee: Optional[float] = None) -> float:
"""
Calculate the close_rate in BTC
Calculate the close_rate including fee
:param fee: fee to use on the close rate (optional).
If rate is not set self.fee will be used
:param rate: rate to compare with (optional).
If rate is not set self.close_rate will be used
:return: Price in BTC of the open trade
"""
getcontext().prec = 8
if rate is None and not self.close_rate:
return 0.0
@ -310,12 +320,12 @@ class Trade(_DECL_BASE):
rate: Optional[float] = None,
fee: Optional[float] = None) -> float:
"""
Calculate the profit in BTC between Close and Open trade
Calculate the absolute profit in stake currency between Close and Open trade
:param fee: fee to use on the close rate (optional).
If rate is not set self.fee will be used
:param rate: close rate to compare with (optional).
If rate is not set self.close_rate will be used
:return: profit in BTC as float
:return: profit in stake currency as float
"""
open_trade_price = self.calc_open_trade_price()
close_trade_price = self.calc_close_trade_price(
@ -336,7 +346,6 @@ class Trade(_DECL_BASE):
:param fee: fee to use on the close rate (optional).
:return: profit in percentage as float
"""
getcontext().prec = 8
open_trade_price = self.calc_open_trade_price()
close_trade_price = self.calc_close_trade_price(
@ -345,3 +354,14 @@ class Trade(_DECL_BASE):
)
profit_percent = (close_trade_price / open_trade_price) - 1
return float(f"{profit_percent:.8f}")
@staticmethod
def total_open_trades_stakes() -> float:
"""
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()
return total_open_stake_amount or 0

4
freqtrade/resolvers/__init__.py Normal file
View File

@ -0,0 +1,4 @@
from freqtrade.resolvers.iresolver import IResolver # noqa: F401
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver # noqa: F401
from freqtrade.resolvers.pairlist_resolver import PairListResolver # noqa: F401
from freqtrade.resolvers.strategy_resolver import StrategyResolver # noqa: F401

67
freqtrade/resolvers/hyperopt_resolver.py Normal file
View File

@ -0,0 +1,67 @@
# pragma pylint: disable=attribute-defined-outside-init
"""
This module load custom hyperopts
"""
import logging
from pathlib import Path
from typing import Optional, Dict
from freqtrade.constants import DEFAULT_HYPEROPT
from freqtrade.optimize.hyperopt_interface import IHyperOpt
from freqtrade.resolvers import IResolver
logger = logging.getLogger(__name__)
class HyperOptResolver(IResolver):
"""
This class contains all the logic to load custom hyperopt class
"""
__slots__ = ['hyperopt']
def __init__(self, config: Optional[Dict] = None) -> None:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
"""
config = config or {}
# Verify the hyperopt is in the configuration, otherwise fallback to the default hyperopt
hyperopt_name = config.get('hyperopt') or DEFAULT_HYPEROPT
self.hyperopt = self._load_hyperopt(hyperopt_name, extra_dir=config.get('hyperopt_path'))
def _load_hyperopt(
self, hyperopt_name: str, extra_dir: Optional[str] = None) -> IHyperOpt:
"""
Search and loads the specified hyperopt.
:param hyperopt_name: name of the module to import
:param extra_dir: additional directory to search for the given hyperopt
:return: HyperOpt instance or None
"""
current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
abs_paths = [
current_path.parent.parent.joinpath('user_data/hyperopts'),
current_path,
]
if extra_dir:
# Add extra hyperopt directory on top of search paths
abs_paths.insert(0, Path(extra_dir))
for _path in abs_paths:
try:
hyperopt = self._search_object(directory=_path, object_type=IHyperOpt,
object_name=hyperopt_name)
if hyperopt:
logger.info('Using resolved hyperopt %s from \'%s\'', hyperopt_name, _path)
return hyperopt
except FileNotFoundError:
logger.warning('Path "%s" does not exist', _path.relative_to(Path.cwd()))
raise ImportError(
"Impossible to load Hyperopt '{}'. This class does not exist"
" or contains Python code errors".format(hyperopt_name)
)

61
freqtrade/resolvers/iresolver.py Normal file
View File

@ -0,0 +1,61 @@
# pragma pylint: disable=attribute-defined-outside-init
"""
This module load custom objects
"""
import importlib.util
import inspect
import logging
from pathlib import Path
from typing import Optional, Type, Any
logger = logging.getLogger(__name__)
class IResolver(object):
"""
This class contains all the logic to load custom classes
"""
@staticmethod
def _get_valid_object(object_type, module_path: Path,
object_name: str) -> Optional[Type[Any]]:
"""
Returns the first object with matching object_type and object_name in the path given.
:param object_type: object_type (class)
:param module_path: absolute path to the module
:param object_name: Class name of the object
:return: class or None
"""
# Generate spec based on absolute path
spec = importlib.util.spec_from_file_location('unknown', str(module_path))
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module) # type: ignore # importlib does not use typehints
valid_objects_gen = (
obj for name, obj in inspect.getmembers(module, inspect.isclass)
if object_name == name and object_type in obj.__bases__
)
return next(valid_objects_gen, None)
@staticmethod
def _search_object(directory: Path, object_type, object_name: str,
kwargs: dict = {}) -> Optional[Any]:
"""
Search for the objectname in the given directory
:param directory: relative or absolute directory path
:return: object instance
"""
logger.debug('Searching for %s %s in \'%s\'', object_type.__name__, object_name, directory)
for entry in directory.iterdir():
# Only consider python files
if not str(entry).endswith('.py'):
logger.debug('Ignoring %s', entry)
continue
obj = IResolver._get_valid_object(
object_type, Path.resolve(directory.joinpath(entry)), object_name
)
if obj:
return obj(**kwargs)
return None

59
freqtrade/resolvers/pairlist_resolver.py Normal file
View File

@ -0,0 +1,59 @@
# pragma pylint: disable=attribute-defined-outside-init
"""
This module load custom hyperopts
"""
import logging
from pathlib import Path
from freqtrade.pairlist.IPairList import IPairList
from freqtrade.resolvers import IResolver
logger = logging.getLogger(__name__)
class PairListResolver(IResolver):
"""
This class contains all the logic to load custom hyperopt class
"""
__slots__ = ['pairlist']
def __init__(self, pairlist_name: str, freqtrade, config: dict) -> None:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
"""
self.pairlist = self._load_pairlist(pairlist_name, kwargs={'freqtrade': freqtrade,
'config': config})
def _load_pairlist(
self, pairlist_name: str, kwargs: dict) -> IPairList:
"""
Search and loads the specified pairlist.
:param pairlist_name: name of the module to import
:param extra_dir: additional directory to search for the given pairlist
:return: PairList instance or None
"""
current_path = Path(__file__).parent.parent.joinpath('pairlist').resolve()
abs_paths = [
current_path.parent.parent.joinpath('user_data/pairlist'),
current_path,
]
for _path in abs_paths:
try:
pairlist = self._search_object(directory=_path, object_type=IPairList,
object_name=pairlist_name,
kwargs=kwargs)
if pairlist:
logger.info('Using resolved pairlist %s from \'%s\'', pairlist_name, _path)
return pairlist
except FileNotFoundError:
logger.warning('Path "%s" does not exist', _path.relative_to(Path.cwd()))
raise ImportError(
"Impossible to load Pairlist '{}'. This class does not exist"
" or contains Python code errors".format(pairlist_name)
)

93
freqtrade/strategy/resolver.py → freqtrade/resolvers/strategy_resolver.py
View File

@ -3,24 +3,23 @@
"""
This module load custom strategies
"""
import importlib.util
import inspect
import logging
import os
import tempfile
from base64 import urlsafe_b64decode
from collections import OrderedDict
from pathlib import Path
from typing import Dict, Optional, Type
from typing import Dict, Optional
from freqtrade import constants
from freqtrade.resolvers import IResolver
from freqtrade.strategy import import_strategy
from freqtrade.strategy.interface import IStrategy
logger = logging.getLogger(__name__)
class StrategyResolver(object):
class StrategyResolver(IResolver):
"""
This class contains all the logic to load custom strategy class
"""
@ -75,6 +74,32 @@ class StrategyResolver(object):
else:
config['process_only_new_candles'] = self.strategy.process_only_new_candles
if 'order_types' in config:
self.strategy.order_types = config['order_types']
logger.info(
"Override strategy 'order_types' with value in config file: %s.",
config['order_types']
)
else:
config['order_types'] = self.strategy.order_types
if 'order_time_in_force' in config:
self.strategy.order_time_in_force = config['order_time_in_force']
logger.info(
"Override strategy 'order_time_in_force' with value in config file: %s.",
config['order_time_in_force']
)
else:
config['order_time_in_force'] = self.strategy.order_time_in_force
if not all(k in self.strategy.order_types for k in constants.REQUIRED_ORDERTYPES):
raise ImportError(f"Impossible to load Strategy '{self.strategy.__class__.__name__}'. "
f"Order-types mapping is incomplete.")
if not all(k in self.strategy.order_time_in_force for k in constants.REQUIRED_ORDERTIF):
raise ImportError(f"Impossible to load Strategy '{self.strategy.__class__.__name__}'. "
f"Order-time-in-force mapping is incomplete.")
# Sort and apply type conversions
self.strategy.minimal_roi = OrderedDict(sorted(
{int(key): value for (key, value) in self.strategy.minimal_roi.items()}.items(),
@ -90,15 +115,16 @@ class StrategyResolver(object):
:param extra_dir: additional directory to search for the given strategy
:return: Strategy instance or None
"""
current_path = os.path.dirname(os.path.realpath(__file__))
current_path = Path(__file__).parent.parent.joinpath('strategy').resolve()
abs_paths = [
os.path.join(os.getcwd(), 'user_data', 'strategies'),
Path.cwd().joinpath('user_data/strategies'),
current_path,
]
if extra_dir:
# Add extra strategy directory on top of search paths
abs_paths.insert(0, extra_dir)
abs_paths.insert(0, Path(extra_dir).resolve())
if ":" in strategy_name:
logger.info("loading base64 endocded strategy")
@ -111,16 +137,17 @@ class StrategyResolver(object):
temp.joinpath(name).write_text(urlsafe_b64decode(strat[1]).decode('utf-8'))
temp.joinpath("__init__.py").touch()
strategy_name = os.path.splitext(name)[0]
strategy_name = strat[0]
# register temp path with the bot
abs_paths.insert(0, str(temp.resolve()))
abs_paths.insert(0, temp.resolve())
for path in abs_paths:
for _path in abs_paths:
try:
strategy = self._search_strategy(path, strategy_name=strategy_name, config=config)
strategy = self._search_object(directory=_path, object_type=IStrategy,
object_name=strategy_name, kwargs={'config': config})
if strategy:
logger.info('Using resolved strategy %s from \'%s\'', strategy_name, path)
logger.info('Using resolved strategy %s from \'%s\'', strategy_name, _path)
strategy._populate_fun_len = len(
inspect.getfullargspec(strategy.populate_indicators).args)
strategy._buy_fun_len = len(
@ -130,49 +157,9 @@ class StrategyResolver(object):
return import_strategy(strategy, config=config)
except FileNotFoundError:
logger.warning('Path "%s" does not exist', path)
logger.warning('Path "%s" does not exist', _path.relative_to(Path.cwd()))
raise ImportError(
"Impossible to load Strategy '{}'. This class does not exist"
" or contains Python code errors".format(strategy_name)
)
@staticmethod
def _get_valid_strategies(module_path: str, strategy_name: str) -> Optional[Type[IStrategy]]:
"""
Returns a list of all possible strategies for the given module_path
:param module_path: absolute path to the module
:param strategy_name: Class name of the strategy
:return: Tuple with (name, class) or None
"""
# Generate spec based on absolute path
spec = importlib.util.spec_from_file_location('unknown', module_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module) # type: ignore # importlib does not use typehints
valid_strategies_gen = (
obj for name, obj in inspect.getmembers(module, inspect.isclass)
if strategy_name == name and IStrategy in obj.__bases__
)
return next(valid_strategies_gen, None)
@staticmethod
def _search_strategy(directory: str, strategy_name: str, config: dict) -> Optional[IStrategy]:
"""
Search for the strategy_name in the given directory
:param directory: relative or absolute directory path
:return: name of the strategy class
"""
logger.debug('Searching for strategy %s in \'%s\'', strategy_name, directory)
for entry in os.listdir(directory):
# Only consider python files
if not entry.endswith('.py'):
logger.debug('Ignoring %s', entry)
continue
strategy = StrategyResolver._get_valid_strategies(
os.path.abspath(os.path.join(directory, entry)), strategy_name
)
if strategy:
return strategy(config)
return None

0
freqtrade/fiat_convert.py → freqtrade/rpc/fiat_convert.py
View File

76
freqtrade/rpc/rpc.py
View File

@ -10,13 +10,13 @@ from typing import Dict, Any, List, Optional
import arrow
import sqlalchemy as sql
from numpy import mean, nan_to_num
from numpy import mean, nan_to_num, NAN
from pandas import DataFrame
from freqtrade import TemporaryError
from freqtrade.fiat_convert import CryptoToFiatConverter
from freqtrade import TemporaryError, DependencyException
from freqtrade.misc import shorten_date
from freqtrade.persistence import Trade
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
from freqtrade.state import State
from freqtrade.strategy.interface import SellType
@ -84,9 +84,7 @@ class RPC(object):
"""
# Fetch open trade
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
elif not trades:
if not trades:
raise RPCException('no active trade')
else:
results = []
@ -95,7 +93,10 @@ class RPC(object):
if trade.open_order_id:
order = self._freqtrade.exchange.get_order(trade.open_order_id, trade.pair)
# calculate profit and send message to user
current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
try:
current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
except DependencyException:
current_rate = NAN
current_profit = trade.calc_profit_percent(current_rate)
fmt_close_profit = (f'{round(trade.close_profit * 100, 2):.2f}%'
if trade.close_profit else None)
@ -118,15 +119,16 @@ class RPC(object):
def _rpc_status_table(self) -> DataFrame:
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
elif not trades:
if not trades:
raise RPCException('no active order')
else:
trades_list = []
for trade in trades:
# calculate profit and send message to user
current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
try:
current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
except DependencyException:
current_rate = NAN
trade_perc = (100 * trade.calc_profit_percent(current_rate))
trades_list.append([
trade.id,
@ -211,7 +213,10 @@ class RPC(object):
profit_closed_percent.append(profit_percent)
else:
# Get current rate
current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
try:
current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
except DependencyException:
current_rate = NAN
profit_percent = trade.calc_profit_percent(rate=current_rate)
profit_all_coin.append(
@ -279,7 +284,7 @@ class RPC(object):
rate = 1.0 / self._freqtrade.exchange.get_ticker('BTC/USDT', False)['bid']
else:
rate = self._freqtrade.exchange.get_ticker(coin + '/BTC', False)['bid']
except TemporaryError:
except (TemporaryError, DependencyException):
continue
est_btc: float = rate * balance['total']
total = total + est_btc
@ -363,6 +368,7 @@ class RPC(object):
# Execute sell for all open orders
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
_exec_forcesell(trade)
Trade.session.flush()
return
# Query for trade
@ -379,13 +385,45 @@ class RPC(object):
_exec_forcesell(trade)
Trade.session.flush()
def _rpc_forcebuy(self, pair: str, price: Optional[float]) -> Optional[Trade]:
"""
Handler for forcebuy <asset> <price>
Buys a pair trade at the given or current price
"""
if not self._freqtrade.config.get('forcebuy_enable', False):
raise RPCException('Forcebuy not enabled.')
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
# Check pair is in stake currency
stake_currency = self._freqtrade.config.get('stake_currency')
if not pair.endswith(stake_currency):
raise RPCException(
f'Wrong pair selected. Please pairs with stake {stake_currency} pairs only')
# check if valid pair
# check if pair already has an open pair
trade = Trade.query.filter(Trade.is_open.is_(True)).filter(Trade.pair.is_(pair)).first()
if trade:
raise RPCException(f'position for {pair} already open - id: {trade.id}')
# gen stake amount
stakeamount = self._freqtrade._get_trade_stake_amount(pair)
# execute buy
if self._freqtrade.execute_buy(pair, stakeamount, price):
trade = Trade.query.filter(Trade.is_open.is_(True)).filter(Trade.pair.is_(pair)).first()
return trade
else:
return None
def _rpc_performance(self) -> List[Dict]:
"""
Handler for performance.
Shows a performance statistic from finished trades
"""
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
pair_rates = Trade.session.query(Trade.pair,
sql.func.sum(Trade.close_profit).label('profit_sum'),
@ -405,3 +443,11 @@ class RPC(object):
raise RPCException('trader is not running')
return Trade.query.filter(Trade.is_open.is_(True)).all()
def _rpc_whitelist(self) -> Dict:
""" Returns the currently active whitelist"""
res = {'method': self._freqtrade.pairlists.name,
'length': len(self._freqtrade.pairlists.whitelist),
'whitelist': self._freqtrade.active_pair_whitelist
}
return res

28
freqtrade/rpc/rpc_manager.py
View File

@ -4,7 +4,7 @@ This module contains class to manage RPC communications (Telegram, Slack, ...)
import logging
from typing import List, Dict, Any
from freqtrade.rpc import RPC
from freqtrade.rpc import RPC, RPCMessageType
logger = logging.getLogger(__name__)
@ -51,3 +51,29 @@ class RPCManager(object):
for mod in self.registered_modules:
logger.debug('Forwarding message to rpc.%s', mod.name)
mod.send_msg(msg)
def startup_messages(self, config, pairlist) -> None:
if config.get('dry_run', False):
self.send_msg({
'type': RPCMessageType.WARNING_NOTIFICATION,
'status': 'Dry run is enabled. All trades are simulated.'
})
stake_currency = config['stake_currency']
stake_amount = config['stake_amount']
minimal_roi = config['minimal_roi']
ticker_interval = config['ticker_interval']
exchange_name = config['exchange']['name']
strategy_name = config.get('strategy', '')
self.send_msg({
'type': RPCMessageType.CUSTOM_NOTIFICATION,
'status': f'*Exchange:* `{exchange_name}`\n'
f'*Stake per trade:* `{stake_amount} {stake_currency}`\n'
f'*Minimum ROI:* `{minimal_roi}`\n'
f'*Ticker Interval:* `{ticker_interval}`\n'
f'*Strategy:* `{strategy_name}`'
})
self.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'Searching for {stake_currency} pairs to buy and sell '
f'based on {pairlist.short_desc()}'
})

77
freqtrade/rpc/telegram.py
View File

@ -12,8 +12,8 @@ from telegram.error import NetworkError, TelegramError
from telegram.ext import CommandHandler, Updater
from freqtrade.__init__ import __version__
from freqtrade.fiat_convert import CryptoToFiatConverter
from freqtrade.rpc import RPC, RPCException, RPCMessageType
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
logger = logging.getLogger(__name__)
@ -86,10 +86,12 @@ class Telegram(RPC):
CommandHandler('start', self._start),
CommandHandler('stop', self._stop),
CommandHandler('forcesell', self._forcesell),
CommandHandler('forcebuy', self._forcebuy),
CommandHandler('performance', self._performance),
CommandHandler('daily', self._daily),
CommandHandler('count', self._count),
CommandHandler('reload_conf', self._reload_conf),
CommandHandler('whitelist', self._whitelist),
CommandHandler('help', self._help),
CommandHandler('version', self._version),
]
@ -123,9 +125,9 @@ class Telegram(RPC):
else:
msg['stake_amount_fiat'] = 0
message = "*{exchange}:* Buying [{pair}]({market_url})\n" \
"with limit `{limit:.8f}\n" \
"({stake_amount:.6f} {stake_currency}".format(**msg)
message = ("*{exchange}:* Buying [{pair}]({market_url})\n"
"with limit `{limit:.8f}\n"
"({stake_amount:.6f} {stake_currency}").format(**msg)
if msg.get('fiat_currency', None):
message += ",{stake_amount_fiat:.3f} {fiat_currency}".format(**msg)
@ -135,12 +137,13 @@ class Telegram(RPC):
msg['amount'] = round(msg['amount'], 8)
msg['profit_percent'] = round(msg['profit_percent'] * 100, 2)
message = "*{exchange}:* Selling [{pair}]({market_url})\n" \
"*Limit:* `{limit:.8f}`\n" \
"*Amount:* `{amount:.8f}`\n" \
"*Open Rate:* `{open_rate:.8f}`\n" \
"*Current Rate:* `{current_rate:.8f}`\n" \
"*Profit:* `{profit_percent:.2f}%`".format(**msg)
message = ("*{exchange}:* Selling [{pair}]({market_url})\n"
"*Limit:* `{limit:.8f}`\n"
"*Amount:* `{amount:.8f}`\n"
"*Open Rate:* `{open_rate:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
"*Sell Reason:* `{sell_reason}`\n"
"*Profit:* `{profit_percent:.2f}%`").format(**msg)
# Check if all sell properties are available.
# This might not be the case if the message origin is triggered by /forcesell
@ -148,8 +151,8 @@ class Telegram(RPC):
and self._fiat_converter):
msg['profit_fiat'] = self._fiat_converter.convert_amount(
msg['profit_amount'], msg['stake_currency'], msg['fiat_currency'])
message += '` ({gain}: {profit_amount:.8f} {stake_currency}`' \
'` / {profit_fiat:.3f} {fiat_currency})`'.format(**msg)
message += ('` ({gain}: {profit_amount:.8f} {stake_currency}`'
'` / {profit_fiat:.3f} {fiat_currency})`').format(**msg)
elif msg['type'] == RPCMessageType.STATUS_NOTIFICATION:
message = '*Status:* `{status}`'.format(**msg)
@ -307,11 +310,14 @@ class Telegram(RPC):
result = self._rpc_balance(self._config.get('fiat_display_currency', ''))
output = ''
for currency in result['currencies']:
output += "*{currency}:*\n" \
"\t`Available: {available: .8f}`\n" \
"\t`Balance: {balance: .8f}`\n" \
"\t`Pending: {pending: .8f}`\n" \
"\t`Est. BTC: {est_btc: .8f}`\n".format(**currency)
if currency['est_btc'] > 0.0001:
output += "*{currency}:*\n" \
"\t`Available: {available: .8f}`\n" \
"\t`Balance: {balance: .8f}`\n" \
"\t`Pending: {pending: .8f}`\n" \
"\t`Est. BTC: {est_btc: .8f}`\n".format(**currency)
else:
output += "*{currency}:* not showing <1$ amount \n".format(**currency)
output += "\n*Estimated Value*:\n" \
"\t`BTC: {total: .8f}`\n" \
@ -372,6 +378,24 @@ class Telegram(RPC):
except RPCException as e:
self._send_msg(str(e), bot=bot)
@authorized_only
def _forcebuy(self, bot: Bot, update: Update) -> None:
"""
Handler for /forcebuy <asset> <price>.
Buys a pair trade at the given or current price
:param bot: telegram bot
:param update: message update
:return: None
"""
message = update.message.text.replace('/forcebuy', '').strip().split()
pair = message[0]
price = float(message[1]) if len(message) > 1 else None
try:
self._rpc_forcebuy(pair, price)
except RPCException as e:
self._send_msg(str(e), bot=bot)
@authorized_only
def _performance(self, bot: Bot, update: Update) -> None:
"""
@ -416,6 +440,23 @@ class Telegram(RPC):
except RPCException as e:
self._send_msg(str(e), bot=bot)
@authorized_only
def _whitelist(self, bot: Bot, update: Update) -> None:
"""
Handler for /whitelist
Shows the currently active whitelist
"""
try:
whitelist = self._rpc_whitelist()
message = f"Using whitelist `{whitelist['method']}` with {whitelist['length']} pairs\n"
message += f"`{', '.join(whitelist['whitelist'])}`"
logger.debug(message)
self._send_msg(message)
except RPCException as e:
self._send_msg(str(e), bot=bot)
@authorized_only
def _help(self, bot: Bot, update: Update) -> None:
"""
@ -437,6 +478,8 @@ class Telegram(RPC):
"*/count:* `Show number of trades running compared to allowed number of trades`" \
"\n" \
"*/balance:* `Show account balance per currency`\n" \
"*/reload_conf:* `Reload configuration file` \n" \
"*/whitelist:* `Show current whitelist` \n" \
"*/help:* `This help message`\n" \
"*/version:* `Show version`"

22
freqtrade/strategy/default_strategy.py
View File

@ -16,10 +16,10 @@ class DefaultStrategy(IStrategy):
# Minimal ROI designed for the strategy
minimal_roi = {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy
@ -28,6 +28,20 @@ class DefaultStrategy(IStrategy):
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': False
}
# Optional time in force for orders
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc',
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame

59
freqtrade/strategy/interface.py
View File

@ -6,14 +6,13 @@ import logging
from abc import ABC, abstractmethod
from datetime import datetime
from enum import Enum
from typing import Dict, List, NamedTuple, Optional, Tuple
from typing import Dict, List, NamedTuple, Tuple
import warnings
import arrow
from pandas import DataFrame
from freqtrade import constants
from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
@ -33,6 +32,7 @@ class SellType(Enum):
"""
ROI = "roi"
STOP_LOSS = "stop_loss"
STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange"
TRAILING_STOP_LOSS = "trailing_stop_loss"
SELL_SIGNAL = "sell_signal"
FORCE_SELL = "force_sell"
@ -70,6 +70,20 @@ class IStrategy(ABC):
# associated ticker interval
ticker_interval: str
# Optional order types
order_types: Dict = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': False
}
# Optional time in force
order_time_in_force: Dict = {
'buy': 'gtc',
'sell': 'gtc',
}
# run "populate_indicators" only for new candle
process_only_new_candles: bool = False
@ -113,19 +127,17 @@ class IStrategy(ABC):
"""
return self.__class__.__name__
def analyze_ticker(self, ticker_history: List[Dict], metadata: dict) -> DataFrame:
def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
dataframe = parse_ticker_dataframe(ticker_history)
pair = str(metadata.get('pair'))
# Test if seen this pair and last candle before.
# always run if process_only_new_candles is set to true
# always run if process_only_new_candles is set to false
if (not self.process_only_new_candles or
self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
# Defs that only make change on new candle data.
@ -146,19 +158,20 @@ class IStrategy(ABC):
return dataframe
def get_signal(self, pair: str, interval: str,
ticker_hist: Optional[List[Dict]]) -> Tuple[bool, bool]:
dataframe: DataFrame) -> Tuple[bool, bool]:
"""
Calculates current signal based several technical analysis indicators
:param pair: pair in format ANT/BTC
:param interval: Interval to use (in min)
:param dataframe: Dataframe to analyze
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
if not ticker_hist:
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty ticker history for pair %s', pair)
return False, False
try:
dataframe = self.analyze_ticker(ticker_hist, {'pair': pair})
dataframe = self.analyze_ticker(dataframe, {'pair': pair})
except ValueError as error:
logger.warning(
'Unable to analyze ticker for pair %s: %s',
@ -203,18 +216,31 @@ class IStrategy(ABC):
return buy, sell
def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
sell: bool) -> SellCheckTuple:
sell: bool, low: float = None, high: float = None,
force_stoploss: float = 0) -> SellCheckTuple:
"""
This function evaluate if on the condition required to trigger a sell has been reached
if the threshold is reached and updates the trade record.
:return: True if trade should be sold, False otherwise
"""
current_profit = trade.calc_profit_percent(rate)
stoplossflag = self.stop_loss_reached(current_rate=rate, trade=trade, current_time=date,
current_profit=current_profit)
# Set current rate to low for backtesting sell
current_rate = low or rate
current_profit = trade.calc_profit_percent(current_rate)
if self.order_types.get('stoploss_on_exchange'):
stoplossflag = SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
else:
stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
current_time=date, current_profit=current_profit,
force_stoploss=force_stoploss)
if stoplossflag.sell_flag:
return stoplossflag
# Set current rate to low for backtesting sell
current_rate = high or rate
current_profit = trade.calc_profit_percent(current_rate)
experimental = self.config.get('experimental', {})
if buy and experimental.get('ignore_roi_if_buy_signal', False):
@ -237,7 +263,7 @@ class IStrategy(ABC):
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: datetime,
current_profit: float) -> SellCheckTuple:
current_profit: float, force_stoploss: float) -> SellCheckTuple:
"""
Based on current profit of the trade and configured (trailing) stoploss,
decides to sell or not
@ -246,7 +272,8 @@ class IStrategy(ABC):
trailing_stop = self.config.get('trailing_stop', False)
trade.adjust_stop_loss(trade.open_rate, self.stoploss, initial=True)
trade.adjust_stop_loss(trade.open_rate, force_stoploss if force_stoploss
else self.stoploss, initial=True)
# evaluate if the stoploss was hit
if self.stoploss is not None and trade.stop_loss >= current_rate:
@ -304,7 +331,7 @@ class IStrategy(ABC):
"""
Creates a dataframe and populates indicators for given ticker data
"""
return {pair: self.advise_indicators(parse_ticker_dataframe(pair_data), {'pair': pair})
return {pair: self.advise_indicators(pair_data, {'pair': pair})
for pair, pair_data in tickerdata.items()}
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

107
freqtrade/tests/conftest.py
View File

@ -10,8 +10,10 @@ import arrow
import pytest
from telegram import Chat, Message, Update
from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
from freqtrade import constants
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.exchange import Exchange
from freqtrade.edge import Edge, PairInfo
from freqtrade.freqtradebot import FreqtradeBot
logging.getLogger('').setLevel(logging.INFO)
@ -25,24 +27,54 @@ def log_has(line, logs):
False)
def patch_exchange(mocker, api_mock=None) -> None:
def patch_exchange(mocker, api_mock=None, id='bittrex') -> None:
mocker.patch('freqtrade.exchange.Exchange._load_markets', MagicMock(return_value={}))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value="Bittrex"))
mocker.patch('freqtrade.exchange.Exchange.id', PropertyMock(return_value="bittrex"))
mocker.patch('freqtrade.exchange.Exchange.validate_ordertypes', MagicMock())
mocker.patch('freqtrade.exchange.Exchange.id', PropertyMock(return_value=id))
mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value=id.title()))
if api_mock:
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
else:
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock())
def get_patched_exchange(mocker, config, api_mock=None) -> Exchange:
patch_exchange(mocker, api_mock)
def get_patched_exchange(mocker, config, api_mock=None, id='bittrex') -> Exchange:
patch_exchange(mocker, api_mock, id)
exchange = Exchange(config)
return exchange
def patch_wallet(mocker, free=999.9) -> None:
mocker.patch('freqtrade.wallets.Wallets.get_free', MagicMock(
return_value=free
))
def patch_edge(mocker) -> None:
# "ETH/BTC",
# "LTC/BTC",
# "XRP/BTC",
# "NEO/BTC"
mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
return_value={
'NEO/BTC': PairInfo(-0.20, 0.66, 3.71, 0.50, 1.71, 10, 25),
'LTC/BTC': PairInfo(-0.21, 0.66, 3.71, 0.50, 1.71, 11, 20),
}
))
mocker.patch('freqtrade.edge.Edge.calculate', MagicMock(return_value=True))
def get_patched_edge(mocker, config) -> Edge:
patch_edge(mocker)
edge = Edge(config)
return edge
# Functions for recurrent object patching
def get_patched_freqtradebot(mocker, config) -> FreqtradeBot:
"""
This function patch _init_modules() to not call dependencies
@ -50,7 +82,6 @@ def get_patched_freqtradebot(mocker, config) -> FreqtradeBot:
:param config: Config to pass to the bot
:return: None
"""
# mocker.patch('freqtrade.fiat_convert.Market', {'price_usd': 12345.0})
patch_coinmarketcap(mocker, {'price_usd': 12345.0})
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
mocker.patch('freqtrade.freqtradebot.persistence.init', MagicMock())
@ -75,7 +106,7 @@ def patch_coinmarketcap(mocker, value: Optional[Dict[str, float]] = None) -> Non
'website_slug': 'ethereum'}
]})
mocker.patch.multiple(
'freqtrade.fiat_convert.Market',
'freqtrade.rpc.fiat_convert.Market',
ticker=tickermock,
listings=listmock,
@ -356,6 +387,36 @@ def limit_buy_order():
}
@pytest.fixture(scope='function')
def market_buy_order():
return {
'id': 'mocked_market_buy',
'type': 'market',
'side': 'buy',
'pair': 'mocked',
'datetime': arrow.utcnow().isoformat(),
'price': 0.00004099,
'amount': 91.99181073,
'remaining': 0.0,
'status': 'closed'
}
@pytest.fixture
def market_sell_order():
return {
'id': 'mocked_limit_sell',
'type': 'market',
'side': 'sell',
'pair': 'mocked',
'datetime': arrow.utcnow().isoformat(),
'price': 0.00004173,
'amount': 91.99181073,
'remaining': 0.0,
'status': 'closed'
}
@pytest.fixture
def limit_buy_order_old():
return {
@ -450,7 +511,7 @@ def order_book_l2():
@pytest.fixture
def ticker_history():
def ticker_history_list():
return [
[
1511686200000, # unix timestamp ms
@ -479,6 +540,11 @@ def ticker_history():
]
@pytest.fixture
def ticker_history(ticker_history_list):
return parse_ticker_dataframe(ticker_history_list)
@pytest.fixture
def tickers():
return MagicMock(return_value={
@ -752,3 +818,26 @@ def buy_order_fee():
'status': 'closed',
'fee': None
}
@pytest.fixture(scope="function")
def edge_conf(default_conf):
default_conf['max_open_trades'] = -1
default_conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
default_conf['edge'] = {
"enabled": True,
"process_throttle_secs": 1800,
"calculate_since_number_of_days": 14,
"capital_available_percentage": 0.5,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"maximum_winrate": 0.80,
"minimum_expectancy": 0.20,
"min_trade_number": 15,
"max_trade_duration_minute": 1440,
"remove_pumps": False
}
return default_conf

0
freqtrade/tests/data/__init__.py Normal file
View File

8
freqtrade/tests/exchange/test_exchange_helpers.py → freqtrade/tests/data/test_converter.py
View File

@ -1,6 +1,8 @@
# pragma pylint: disable=missing-docstring, C0103
import logging
from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.tests.conftest import log_has
def test_dataframe_correct_length(result):
@ -13,9 +15,11 @@ def test_dataframe_correct_columns(result):
['date', 'open', 'high', 'low', 'close', 'volume']
def test_parse_ticker_dataframe(ticker_history):
def test_parse_ticker_dataframe(ticker_history, caplog):
columns = ['date', 'open', 'high', 'low', 'close', 'volume']
caplog.set_level(logging.DEBUG)
# Test file with BV data
dataframe = parse_ticker_dataframe(ticker_history)
assert dataframe.columns.tolist() == columns
assert log_has('Parsing tickerlist to dataframe', caplog.record_tuples)

475
freqtrade/tests/data/test_history.py Normal file
View File

@ -0,0 +1,475 @@
# pragma pylint: disable=missing-docstring, protected-access, C0103
import json
import os
from pathlib import Path
import uuid
from shutil import copyfile
import arrow
from pandas import DataFrame
import pytest
from freqtrade import OperationalException
from freqtrade.arguments import TimeRange
from freqtrade.data import history
from freqtrade.data.history import (download_pair_history,
load_cached_data_for_updating,
load_tickerdata_file,
make_testdata_path,
trim_tickerlist)
from freqtrade.misc import file_dump_json
from freqtrade.tests.conftest import get_patched_exchange, log_has
# Change this if modifying UNITTEST/BTC testdatafile
_BTC_UNITTEST_LENGTH = 13681
def _backup_file(file: str, copy_file: bool = False) -> None:
"""
Backup existing file to avoid deleting the user file
:param file: complete path to the file
:param touch_file: create an empty file in replacement
:return: None
"""
file_swp = file + '.swp'
if os.path.isfile(file):
os.rename(file, file_swp)
if copy_file:
copyfile(file_swp, file)
def _clean_test_file(file: str) -> None:
"""
Backup existing file to avoid deleting the user file
:param file: complete path to the file
:return: None
"""
file_swp = file + '.swp'
# 1. Delete file from the test
if os.path.isfile(file):
os.remove(file)
# 2. Rollback to the initial file
if os.path.isfile(file_swp):
os.rename(file_swp, file)
def test_load_data_30min_ticker(mocker, caplog, default_conf) -> None:
ld = history.load_pair_history(pair='UNITTEST/BTC', ticker_interval='30m', datadir=None)
assert isinstance(ld, DataFrame)
assert not log_has('Download the pair: "UNITTEST/BTC", Interval: 30m', caplog.record_tuples)
def test_load_data_7min_ticker(mocker, caplog, default_conf) -> None:
ld = history.load_pair_history(pair='UNITTEST/BTC', ticker_interval='7m', datadir=None)
assert not isinstance(ld, DataFrame)
assert ld is None
assert log_has(
'No data for pair: "UNITTEST/BTC", Interval: 7m. '
'Use --refresh-pairs-cached to download the data', caplog.record_tuples)
def test_load_data_1min_ticker(ticker_history, mocker, caplog) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'UNITTEST_BTC-1m.json')
_backup_file(file, copy_file=True)
history.load_data(datadir=None, ticker_interval='1m', pairs=['UNITTEST/BTC'])
assert os.path.isfile(file) is True
assert not log_has('Download the pair: "UNITTEST/BTC", Interval: 1m', caplog.record_tuples)
_clean_test_file(file)
def test_load_data_with_new_pair_1min(ticker_history_list, mocker, caplog, default_conf) -> None:
"""
Test load_pair_history() with 1 min ticker
"""
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history_list)
exchange = get_patched_exchange(mocker, default_conf)
file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-1m.json')
_backup_file(file)
# do not download a new pair if refresh_pairs isn't set
history.load_pair_history(datadir=None,
ticker_interval='1m',
refresh_pairs=False,
pair='MEME/BTC')
assert os.path.isfile(file) is False
assert log_has('No data for pair: "MEME/BTC", Interval: 1m. '
'Use --refresh-pairs-cached to download the data',
caplog.record_tuples)
# download a new pair if refresh_pairs is set
history.load_pair_history(datadir=None,
ticker_interval='1m',
refresh_pairs=True,
exchange=exchange,
pair='MEME/BTC')
assert os.path.isfile(file) is True
assert log_has('Download the pair: "MEME/BTC", Interval: 1m', caplog.record_tuples)
with pytest.raises(OperationalException, match=r'Exchange needs to be initialized when.*'):
history.load_pair_history(datadir=None,
ticker_interval='1m',
refresh_pairs=True,
exchange=None,
pair='MEME/BTC')
_clean_test_file(file)
def test_testdata_path() -> None:
assert str(Path('freqtrade') / 'tests' / 'testdata') in str(make_testdata_path(None))
def test_load_cached_data_for_updating(mocker) -> None:
datadir = Path(__file__).parent.parent.joinpath('testdata')
test_data = None
test_filename = datadir.joinpath('UNITTEST_BTC-1m.json')
with open(test_filename, "rt") as file:
test_data = json.load(file)
# change now time to test 'line' cases
# now = last cached item + 1 hour
now_ts = test_data[-1][0] / 1000 + 60 * 60
mocker.patch('arrow.utcnow', return_value=arrow.get(now_ts))
# timeframe starts earlier than the cached data
# should fully update data
timerange = TimeRange('date', None, test_data[0][0] / 1000 - 1, 0)
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
timerange)
assert data == []
assert start_ts == test_data[0][0] - 1000
# same with 'line' timeframe
num_lines = (test_data[-1][0] - test_data[1][0]) / 1000 / 60 + 120
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
TimeRange(None, 'line', 0, -num_lines))
assert data == []
assert start_ts < test_data[0][0] - 1
# timeframe starts in the center of the cached data
# should return the chached data w/o the last item
timerange = TimeRange('date', None, test_data[0][0] / 1000 + 1, 0)
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
timerange)
assert data == test_data[:-1]
assert test_data[-2][0] < start_ts < test_data[-1][0]
# same with 'line' timeframe
num_lines = (test_data[-1][0] - test_data[1][0]) / 1000 / 60 + 30
timerange = TimeRange(None, 'line', 0, -num_lines)
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
timerange)
assert data == test_data[:-1]
assert test_data[-2][0] < start_ts < test_data[-1][0]
# timeframe starts after the chached data
# should return the chached data w/o the last item
timerange = TimeRange('date', None, test_data[-1][0] / 1000 + 1, 0)
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
timerange)
assert data == test_data[:-1]
assert test_data[-2][0] < start_ts < test_data[-1][0]
# same with 'line' timeframe
num_lines = 30
timerange = TimeRange(None, 'line', 0, -num_lines)
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
timerange)
assert data == test_data[:-1]
assert test_data[-2][0] < start_ts < test_data[-1][0]
# no timeframe is set
# should return the chached data w/o the last item
num_lines = 30
timerange = TimeRange(None, 'line', 0, -num_lines)
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
timerange)
assert data == test_data[:-1]
assert test_data[-2][0] < start_ts < test_data[-1][0]
# no datafile exist
# should return timestamp start time
timerange = TimeRange('date', None, now_ts - 10000, 0)
data, start_ts = load_cached_data_for_updating(test_filename.with_name('unexist'),
'1m',
timerange)
assert data == []
assert start_ts == (now_ts - 10000) * 1000
# same with 'line' timeframe
num_lines = 30
timerange = TimeRange(None, 'line', 0, -num_lines)
data, start_ts = load_cached_data_for_updating(test_filename.with_name('unexist'),
'1m',
timerange)
assert data == []
assert start_ts == (now_ts - num_lines * 60) * 1000
# no datafile exist, no timeframe is set
# should return an empty array and None
data, start_ts = load_cached_data_for_updating(test_filename.with_name('unexist'),
'1m',
None)
assert data == []
assert start_ts is None
def test_download_pair_history(ticker_history_list, mocker, default_conf) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history_list)
exchange = get_patched_exchange(mocker, default_conf)
file1_1 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-1m.json')
file1_5 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-5m.json')
file2_1 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'CFI_BTC-1m.json')
file2_5 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'CFI_BTC-5m.json')
_backup_file(file1_1)
_backup_file(file1_5)
_backup_file(file2_1)
_backup_file(file2_5)
assert os.path.isfile(file1_1) is False
assert os.path.isfile(file2_1) is False
assert download_pair_history(datadir=None, exchange=exchange,
pair='MEME/BTC',
tick_interval='1m')
assert download_pair_history(datadir=None, exchange=exchange,
pair='CFI/BTC',
tick_interval='1m')
assert not exchange._pairs_last_refresh_time
assert os.path.isfile(file1_1) is True
assert os.path.isfile(file2_1) is True
# clean files freshly downloaded
_clean_test_file(file1_1)
_clean_test_file(file2_1)
assert os.path.isfile(file1_5) is False
assert os.path.isfile(file2_5) is False
assert download_pair_history(datadir=None, exchange=exchange,
pair='MEME/BTC',
tick_interval='5m')
assert download_pair_history(datadir=None, exchange=exchange,
pair='CFI/BTC',
tick_interval='5m')
assert not exchange._pairs_last_refresh_time
assert os.path.isfile(file1_5) is True
assert os.path.isfile(file2_5) is True
# clean files freshly downloaded
_clean_test_file(file1_5)
_clean_test_file(file2_5)
def test_download_pair_history2(mocker, default_conf) -> None:
tick = [
[1509836520000, 0.00162008, 0.00162008, 0.00162008, 0.00162008, 108.14853839],
[1509836580000, 0.00161, 0.00161, 0.00161, 0.00161, 82.390199]
]
json_dump_mock = mocker.patch('freqtrade.misc.file_dump_json', return_value=None)
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=tick)
exchange = get_patched_exchange(mocker, default_conf)
download_pair_history(None, exchange, pair="UNITTEST/BTC", tick_interval='1m')
download_pair_history(None, exchange, pair="UNITTEST/BTC", tick_interval='3m')
assert json_dump_mock.call_count == 2
def test_download_backtesting_data_exception(ticker_history, mocker, caplog, default_conf) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_history',
side_effect=BaseException('File Error'))
exchange = get_patched_exchange(mocker, default_conf)
file1_1 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-1m.json')
file1_5 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-5m.json')
_backup_file(file1_1)
_backup_file(file1_5)
assert not download_pair_history(datadir=None, exchange=exchange,
pair='MEME/BTC',
tick_interval='1m')
# clean files freshly downloaded
_clean_test_file(file1_1)
_clean_test_file(file1_5)
assert log_has('Failed to download the pair: "MEME/BTC", Interval: 1m', caplog.record_tuples)
def test_load_tickerdata_file() -> None:
# 7 does not exist in either format.
assert not load_tickerdata_file(None, 'UNITTEST/BTC', '7m')
# 1 exists only as a .json
tickerdata = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
assert _BTC_UNITTEST_LENGTH == len(tickerdata)
# 8 .json is empty and will fail if it's loaded. .json.gz is a copy of 1.json
tickerdata = load_tickerdata_file(None, 'UNITTEST/BTC', '8m')
assert _BTC_UNITTEST_LENGTH == len(tickerdata)
def test_load_partial_missing(caplog) -> None:
# Make sure we start fresh - test missing data at start
start = arrow.get('2018-01-01T00:00:00')
end = arrow.get('2018-01-11T00:00:00')
tickerdata = history.load_data(None, '5m', ['UNITTEST/BTC'],
refresh_pairs=False,
timerange=TimeRange('date', 'date',
start.timestamp, end.timestamp))
# timedifference in 5 minutes
td = ((end - start).total_seconds() // 60 // 5) + 1
assert td != len(tickerdata['UNITTEST/BTC'])
start_real = tickerdata['UNITTEST/BTC'].iloc[0, 0]
assert log_has(f'Missing data at start for pair '
f'UNITTEST/BTC, data starts at {start_real.strftime("%Y-%m-%d %H:%M:%S")}',
caplog.record_tuples)
# Make sure we start fresh - test missing data at end
caplog.clear()
start = arrow.get('2018-01-10T00:00:00')
end = arrow.get('2018-02-20T00:00:00')
tickerdata = history.load_data(datadir=None, ticker_interval='5m',
pairs=['UNITTEST/BTC'], refresh_pairs=False,
timerange=TimeRange('date', 'date',
start.timestamp, end.timestamp))
# timedifference in 5 minutes
td = ((end - start).total_seconds() // 60 // 5) + 1
assert td != len(tickerdata['UNITTEST/BTC'])
# Shift endtime with +5 - as last candle is dropped (partial candle)
end_real = arrow.get(tickerdata['UNITTEST/BTC'].iloc[-1, 0]).shift(minutes=5)
assert log_has(f'Missing data at end for pair '
f'UNITTEST/BTC, data ends at {end_real.strftime("%Y-%m-%d %H:%M:%S")}',
caplog.record_tuples)
def test_init(default_conf, mocker) -> None:
exchange = get_patched_exchange(mocker, default_conf)
assert {} == history.load_data(
datadir='',
exchange=exchange,
pairs=[],
refresh_pairs=True,
ticker_interval=default_conf['ticker_interval']
)
def test_trim_tickerlist() -> None:
file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'UNITTEST_BTC-1m.json')
with open(file) as data_file:
ticker_list = json.load(data_file)
ticker_list_len = len(ticker_list)
# Test the pattern ^(-\d+)$
# This pattern uses the latest N elements
timerange = TimeRange(None, 'line', 0, -5)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == 5
assert ticker_list[0] is not ticker[0] # The first element should be different
assert ticker_list[-1] is ticker[-1] # The last element must be the same
# Test the pattern ^(\d+)-$
# This pattern keep X element from the end
timerange = TimeRange('line', None, 5, 0)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == 5
assert ticker_list[0] is ticker[0] # The first element must be the same
assert ticker_list[-1] is not ticker[-1] # The last element should be different
# Test the pattern ^(\d+)-(\d+)$
# This pattern extract a window
timerange = TimeRange('index', 'index', 5, 10)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == 5
assert ticker_list[0] is not ticker[0] # The first element should be different
assert ticker_list[5] is ticker[0] # The list starts at the index 5
assert ticker_list[9] is ticker[-1] # The list ends at the index 9 (5 elements)
# Test the pattern ^(\d{8})-(\d{8})$
# This pattern extract a window between the dates
timerange = TimeRange('date', 'date', ticker_list[5][0] / 1000, ticker_list[10][0] / 1000 - 1)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == 5
assert ticker_list[0] is not ticker[0] # The first element should be different
assert ticker_list[5] is ticker[0] # The list starts at the index 5
assert ticker_list[9] is ticker[-1] # The list ends at the index 9 (5 elements)
# Test the pattern ^-(\d{8})$
# This pattern extracts elements from the start to the date
timerange = TimeRange(None, 'date', 0, ticker_list[10][0] / 1000 - 1)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == 10
assert ticker_list[0] is ticker[0] # The start of the list is included
assert ticker_list[9] is ticker[-1] # The element 10 is not included
# Test the pattern ^(\d{8})-$
# This pattern extracts elements from the date to now
timerange = TimeRange('date', None, ticker_list[10][0] / 1000 - 1, None)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == ticker_list_len - 10
assert ticker_list[10] is ticker[0] # The first element is element #10
assert ticker_list[-1] is ticker[-1] # The last element is the same
# Test a wrong pattern
# This pattern must return the list unchanged
timerange = TimeRange(None, None, None, 5)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_list_len == ticker_len
# Test invalid timerange (start after stop)
timerange = TimeRange('index', 'index', 10, 5)
with pytest.raises(ValueError, match=r'The timerange .* is incorrect'):
trim_tickerlist(ticker_list, timerange)
assert ticker_list_len == ticker_len
# passing empty list
timerange = TimeRange(None, None, None, 5)
ticker = trim_tickerlist([], timerange)
assert 0 == len(ticker)
assert not ticker
def test_file_dump_json() -> None:
file = os.path.join(os.path.dirname(__file__), '..', 'testdata',
'test_{id}.json'.format(id=str(uuid.uuid4())))
data = {'bar': 'foo'}
# check the file we will create does not exist
assert os.path.isfile(file) is False
# Create the Json file
file_dump_json(file, data)
# Check the file was create
assert os.path.isfile(file) is True
# Open the Json file created and test the data is in it
with open(file) as data_file:
json_from_file = json.load(data_file)
assert 'bar' in json_from_file
assert json_from_file['bar'] == 'foo'
# Remove the file
_clean_test_file(file)

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# pragma pylint: disable=missing-docstring, C0103, C0330
# pragma pylint: disable=protected-access, too-many-lines, invalid-name, too-many-arguments
import logging
import math
from unittest.mock import MagicMock
import arrow
import numpy as np
import pytest
from pandas import DataFrame, to_datetime
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.edge import Edge, PairInfo
from freqtrade.strategy.interface import SellType
from freqtrade.tests.conftest import get_patched_freqtradebot
from freqtrade.tests.optimize import (BTContainer, BTrade,
_build_backtest_dataframe,
_get_frame_time_from_offset)
# Cases to be tested:
# 1) Open trade should be removed from the end
# 2) Two complete trades within dataframe (with sell hit for all)
# 3) Entered, sl 1%, candle drops 8% => Trade closed, 1% loss
# 4) Entered, sl 3%, candle drops 4%, recovers to 1% => Trade closed, 3% loss
# 5) Stoploss and sell are hit. should sell on stoploss
####################################################################
ticker_start_time = arrow.get(2018, 10, 3)
ticker_interval_in_minute = 60
_ohlc = {'date': 0, 'buy': 1, 'open': 2, 'high': 3, 'low': 4, 'close': 5, 'sell': 6, 'volume': 7}
# Open trade should be removed from the end
tc0 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 1]], # enter trade (signal on last candle)
stop_loss=-0.99, roi=float('inf'), profit_perc=0.00,
trades=[]
)
# Two complete trades within dataframe(with sell hit for all)
tc1 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 1], # enter trade (signal on last candle)
[2, 5000, 5025, 4975, 4987, 6172, 0, 0], # exit at open
[3, 5000, 5025, 4975, 4987, 6172, 1, 0], # no action
[4, 5000, 5025, 4975, 4987, 6172, 0, 0], # should enter the trade
[5, 5000, 5025, 4975, 4987, 6172, 0, 1], # no action
[6, 5000, 5025, 4975, 4987, 6172, 0, 0], # should sell
],
stop_loss=-0.99, roi=float('inf'), profit_perc=0.00,
trades=[BTrade(sell_reason=SellType.SELL_SIGNAL, open_tick=1, close_tick=2),
BTrade(sell_reason=SellType.SELL_SIGNAL, open_tick=4, close_tick=6)]
)
# 3) Entered, sl 1%, candle drops 8% => Trade closed, 1% loss
tc2 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4600, 4987, 6172, 0, 0], # enter trade, stoploss hit
[2, 5000, 5025, 4975, 4987, 6172, 0, 0],
],
stop_loss=-0.01, roi=float('inf'), profit_perc=-0.01,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=1)]
)
# 4) Entered, sl 3 %, candle drops 4%, recovers to 1 % = > Trade closed, 3 % loss
tc3 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4800, 4987, 6172, 0, 0], # enter trade, stoploss hit
[2, 5000, 5025, 4975, 4987, 6172, 0, 0],
],
stop_loss=-0.03, roi=float('inf'), profit_perc=-0.03,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=1)]
)
# 5) Stoploss and sell are hit. should sell on stoploss
tc4 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4800, 4987, 6172, 0, 1], # enter trade, stoploss hit, sell signal
[2, 5000, 5025, 4975, 4987, 6172, 0, 0],
],
stop_loss=-0.03, roi=float('inf'), profit_perc=-0.03,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=1)]
)
TESTS = [
tc0,
tc1,
tc2,
tc3,
tc4
]
@pytest.mark.parametrize("data", TESTS)
def test_edge_results(edge_conf, mocker, caplog, data) -> None:
"""
run functional tests
"""
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
frame = _build_backtest_dataframe(data.data)
caplog.set_level(logging.DEBUG)
edge.fee = 0
trades = edge._find_trades_for_stoploss_range(frame, 'TEST/BTC', [data.stop_loss])
results = edge._fill_calculable_fields(DataFrame(trades)) if trades else DataFrame()
print(results)
assert len(trades) == len(data.trades)
if not results.empty:
assert round(results["profit_percent"].sum(), 3) == round(data.profit_perc, 3)
for c, trade in enumerate(data.trades):
res = results.iloc[c]
assert res.exit_type == trade.sell_reason
assert res.open_time == _get_frame_time_from_offset(trade.open_tick)
assert res.close_time == _get_frame_time_from_offset(trade.close_tick)
def test_adjust(mocker, edge_conf):
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
return_value={
'E/F': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
'C/D': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
'N/O': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60)
}
))
pairs = ['A/B', 'C/D', 'E/F', 'G/H']
assert(edge.adjust(pairs) == ['E/F', 'C/D'])
def test_stoploss(mocker, edge_conf):
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
return_value={
'E/F': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
'C/D': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
'N/O': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60)
}
))
assert edge.stoploss('E/F') == -0.01
def test_nonexisting_stoploss(mocker, edge_conf):
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
return_value={
'E/F': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
}
))
assert edge.stoploss('N/O') == -0.1
def test_stake_amount(mocker, edge_conf):
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
return_value={
'E/F': PairInfo(-0.02, 0.66, 3.71, 0.50, 1.71, 10, 60),
}
))
free = 100
total = 100
in_trade = 25
assert edge.stake_amount('E/F', free, total, in_trade) == 31.25
free = 20
total = 100
in_trade = 25
assert edge.stake_amount('E/F', free, total, in_trade) == 20
free = 0
total = 100
in_trade = 25
assert edge.stake_amount('E/F', free, total, in_trade) == 0
def test_nonexisting_stake_amount(mocker, edge_conf):
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
return_value={
'E/F': PairInfo(-0.11, 0.66, 3.71, 0.50, 1.71, 10, 60),
}
))
# should use strategy stoploss
assert edge.stake_amount('N/O', 1, 2, 1) == 0.15
def _validate_ohlc(buy_ohlc_sell_matrice):
for index, ohlc in enumerate(buy_ohlc_sell_matrice):
# if not high < open < low or not high < close < low
if not ohlc[3] >= ohlc[2] >= ohlc[4] or not ohlc[3] >= ohlc[5] >= ohlc[4]:
raise Exception('Line ' + str(index + 1) + ' of ohlc has invalid values!')
return True
def _build_dataframe(buy_ohlc_sell_matrice):
_validate_ohlc(buy_ohlc_sell_matrice)
tickers = []
for ohlc in buy_ohlc_sell_matrice:
ticker = {
'date': ticker_start_time.shift(
minutes=(
ohlc[0] *
ticker_interval_in_minute)).timestamp *
1000,
'buy': ohlc[1],
'open': ohlc[2],
'high': ohlc[3],
'low': ohlc[4],
'close': ohlc[5],
'sell': ohlc[6]}
tickers.append(ticker)
frame = DataFrame(tickers)
frame['date'] = to_datetime(frame['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
return frame
def _time_on_candle(number):
return np.datetime64(ticker_start_time.shift(
minutes=(number * ticker_interval_in_minute)).timestamp * 1000, 'ms')
def test_edge_heartbeat_calculate(mocker, edge_conf):
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
heartbeat = edge_conf['edge']['process_throttle_secs']
# should not recalculate if heartbeat not reached
edge._last_updated = arrow.utcnow().timestamp - heartbeat + 1
assert edge.calculate() is False
def mocked_load_data(datadir, pairs=[], ticker_interval='0m', refresh_pairs=False,
timerange=None, exchange=None):
hz = 0.1
base = 0.001
ETHBTC = [
[
ticker_start_time.shift(minutes=(x * ticker_interval_in_minute)).timestamp * 1000,
math.sin(x * hz) / 1000 + base,
math.sin(x * hz) / 1000 + base + 0.0001,
math.sin(x * hz) / 1000 + base - 0.0001,
math.sin(x * hz) / 1000 + base,
123.45
] for x in range(0, 500)]
hz = 0.2
base = 0.002
LTCBTC = [
[
ticker_start_time.shift(minutes=(x * ticker_interval_in_minute)).timestamp * 1000,
math.sin(x * hz) / 1000 + base,
math.sin(x * hz) / 1000 + base + 0.0001,
math.sin(x * hz) / 1000 + base - 0.0001,
math.sin(x * hz) / 1000 + base,
123.45
] for x in range(0, 500)]
pairdata = {'NEO/BTC': parse_ticker_dataframe(ETHBTC),
'LTC/BTC': parse_ticker_dataframe(LTCBTC)}
return pairdata
def test_edge_process_downloaded_data(mocker, edge_conf):
edge_conf['datadir'] = None
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.001))
mocker.patch('freqtrade.data.history.load_data', mocked_load_data)
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
assert edge.calculate()
assert len(edge._cached_pairs) == 2
assert edge._last_updated <= arrow.utcnow().timestamp + 2
def test_process_expectancy(mocker, edge_conf):
edge_conf['edge']['min_trade_number'] = 2
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
def get_fee():
return 0.001
freqtrade.exchange.get_fee = get_fee
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
trades = [
{'pair': 'TEST/BTC',
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:05:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:10:00.000000000'),
'open_index': 1,
'close_index': 1,
'trade_duration': '',
'open_rate': 17,
'close_rate': 17,
'exit_type': 'sell_signal'},
{'pair': 'TEST/BTC',
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4,
'close_index': 4,
'trade_duration': '',
'open_rate': 20,
'close_rate': 20,
'exit_type': 'sell_signal'},
{'pair': 'TEST/BTC',
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:30:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:40:00.000000000'),
'open_index': 6,
'close_index': 7,
'trade_duration': '',
'open_rate': 26,
'close_rate': 34,
'exit_type': 'sell_signal'}
]
trades_df = DataFrame(trades)
trades_df = edge._fill_calculable_fields(trades_df)
final = edge._process_expectancy(trades_df)
assert len(final) == 1
assert 'TEST/BTC' in final
assert final['TEST/BTC'].stoploss == -0.9
assert round(final['TEST/BTC'].winrate, 10) == 0.3333333333
assert round(final['TEST/BTC'].risk_reward_ratio, 10) == 306.5384615384
assert round(final['TEST/BTC'].required_risk_reward, 10) == 2.0
assert round(final['TEST/BTC'].expectancy, 10) == 101.5128205128

0
freqtrade/tests/exchange/__init__.py Normal file
View File

412
freqtrade/tests/exchange/test_exchange.py
View File

@ -1,5 +1,6 @@
# pragma pylint: disable=missing-docstring, C0103, bad-continuation, global-statement
# pragma pylint: disable=protected-access
import copy
import logging
from datetime import datetime
from random import randint
@ -8,6 +9,7 @@ from unittest.mock import Mock, MagicMock, PropertyMock
import arrow
import ccxt
import pytest
from pandas import DataFrame
from freqtrade import DependencyException, OperationalException, TemporaryError
from freqtrade.exchange import API_RETRY_COUNT, Exchange
@ -56,6 +58,32 @@ def test_init(default_conf, mocker, caplog):
assert log_has('Instance is running with dry_run enabled', caplog.record_tuples)
def test_init_ccxt_kwargs(default_conf, mocker, caplog):
mocker.patch('freqtrade.exchange.Exchange._load_markets', MagicMock(return_value={}))
caplog.set_level(logging.INFO)
conf = copy.deepcopy(default_conf)
conf['exchange']['ccxt_async_config'] = {'aiohttp_trust_env': True}
ex = Exchange(conf)
assert log_has("Applying additional ccxt config: {'aiohttp_trust_env': True}",
caplog.record_tuples)
assert ex._api_async.aiohttp_trust_env
assert not ex._api.aiohttp_trust_env
# Reset logging and config
caplog.clear()
conf = copy.deepcopy(default_conf)
conf['exchange']['ccxt_config'] = {'TestKWARG': 11}
ex = Exchange(conf)
assert not log_has("Applying additional ccxt config: {'aiohttp_trust_env': True}",
caplog.record_tuples)
assert not ex._api_async.aiohttp_trust_env
assert hasattr(ex._api, 'TestKWARG')
assert ex._api.TestKWARG == 11
assert not hasattr(ex._api_async, 'TestKWARG')
assert log_has("Applying additional ccxt config: {'TestKWARG': 11}",
caplog.record_tuples)
def test_destroy(default_conf, mocker, caplog):
caplog.set_level(logging.DEBUG)
get_patched_exchange(mocker, default_conf)
@ -328,6 +356,59 @@ def test_validate_timeframes_not_in_config(default_conf, mocker):
Exchange(default_conf)
def test_validate_order_types(default_conf, mocker):
api_mock = MagicMock()
type(api_mock).has = PropertyMock(return_value={'createMarketOrder': True})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange._load_markets', MagicMock(return_value={}))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
mocker.patch('freqtrade.exchange.Exchange.name', 'Bittrex')
default_conf['order_types'] = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
Exchange(default_conf)
type(api_mock).has = PropertyMock(return_value={'createMarketOrder': False})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
default_conf['order_types'] = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': 'false'
}
with pytest.raises(OperationalException,
match=r'Exchange .* does not support market orders.'):
Exchange(default_conf)
default_conf['order_types'] = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': True
}
with pytest.raises(OperationalException,
match=r'On exchange stoploss is not supported for .*'):
Exchange(default_conf)
def test_validate_order_types_not_in_config(default_conf, mocker):
api_mock = MagicMock()
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange._load_markets', MagicMock(return_value={}))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
conf = copy.deepcopy(default_conf)
Exchange(conf)
def test_exchange_has(default_conf, mocker):
exchange = get_patched_exchange(mocker, default_conf)
assert not exchange.exchange_has('ASDFASDF')
@ -346,7 +427,8 @@ def test_buy_dry_run(default_conf, mocker):
default_conf['dry_run'] = True
exchange = get_patched_exchange(mocker, default_conf)
order = exchange.buy(pair='ETH/BTC', rate=200, amount=1)
order = exchange.buy(pair='ETH/BTC', ordertype='limit',
amount=1, rate=200, time_in_force='gtc')
assert 'id' in order
assert 'dry_run_buy_' in order['id']
@ -354,47 +436,106 @@ def test_buy_dry_run(default_conf, mocker):
def test_buy_prod(default_conf, mocker):
api_mock = MagicMock()
order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
api_mock.create_limit_buy_order = MagicMock(return_value={
order_type = 'market'
time_in_force = 'gtc'
api_mock.create_order = MagicMock(return_value={
'id': order_id,
'info': {
'foo': 'bar'
}
})
default_conf['dry_run'] = False
mocker.patch('freqtrade.exchange.Exchange.symbol_amount_prec', lambda s, x, y: y)
mocker.patch('freqtrade.exchange.Exchange.symbol_price_prec', lambda s, x, y: y)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
order = exchange.buy(pair='ETH/BTC', rate=200, amount=1)
order = exchange.buy(pair='ETH/BTC', ordertype=order_type,
amount=1, rate=200, time_in_force=time_in_force)
assert 'id' in order
assert 'info' in order
assert order['id'] == order_id
assert api_mock.create_order.call_args[0][0] == 'ETH/BTC'
assert api_mock.create_order.call_args[0][1] == order_type
assert api_mock.create_order.call_args[0][2] == 'buy'
assert api_mock.create_order.call_args[0][3] == 1
assert api_mock.create_order.call_args[0][4] is None
api_mock.create_order.reset_mock()
order_type = 'limit'
order = exchange.buy(
pair='ETH/BTC',
ordertype=order_type,
amount=1,
rate=200,
time_in_force=time_in_force)
assert api_mock.create_order.call_args[0][0] == 'ETH/BTC'
assert api_mock.create_order.call_args[0][1] == order_type
assert api_mock.create_order.call_args[0][2] == 'buy'
assert api_mock.create_order.call_args[0][3] == 1
assert api_mock.create_order.call_args[0][4] == 200
# test exception handling
with pytest.raises(DependencyException):
api_mock.create_limit_buy_order = MagicMock(side_effect=ccxt.InsufficientFunds)
api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.buy(pair='ETH/BTC', rate=200, amount=1)
exchange.buy(pair='ETH/BTC', ordertype=order_type,
amount=1, rate=200, time_in_force=time_in_force)
with pytest.raises(DependencyException):
api_mock.create_limit_buy_order = MagicMock(side_effect=ccxt.InvalidOrder)
api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.buy(pair='ETH/BTC', rate=200, amount=1)
exchange.buy(pair='ETH/BTC', ordertype=order_type,
amount=1, rate=200, time_in_force=time_in_force)
with pytest.raises(TemporaryError):
api_mock.create_limit_buy_order = MagicMock(side_effect=ccxt.NetworkError)
api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.buy(pair='ETH/BTC', rate=200, amount=1)
exchange.buy(pair='ETH/BTC', ordertype=order_type,
amount=1, rate=200, time_in_force=time_in_force)
with pytest.raises(OperationalException):
api_mock.create_limit_buy_order = MagicMock(side_effect=ccxt.BaseError)
api_mock.create_order = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.buy(pair='ETH/BTC', rate=200, amount=1)
exchange.buy(pair='ETH/BTC', ordertype=order_type,
amount=1, rate=200, time_in_force=time_in_force)
def test_buy_considers_time_in_force(default_conf, mocker):
api_mock = MagicMock()
order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
order_type = 'market'
time_in_force = 'ioc'
api_mock.create_order = MagicMock(return_value={
'id': order_id,
'info': {
'foo': 'bar'
}
})
default_conf['dry_run'] = False
mocker.patch('freqtrade.exchange.Exchange.symbol_amount_prec', lambda s, x, y: y)
mocker.patch('freqtrade.exchange.Exchange.symbol_price_prec', lambda s, x, y: y)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
order = exchange.buy(pair='ETH/BTC', ordertype=order_type,
amount=1, rate=200, time_in_force=time_in_force)
assert 'id' in order
assert 'info' in order
assert order['id'] == order_id
assert api_mock.create_order.call_args[0][0] == 'ETH/BTC'
assert api_mock.create_order.call_args[0][1] == order_type
assert api_mock.create_order.call_args[0][2] == 'buy'
assert api_mock.create_order.call_args[0][3] == 1
assert api_mock.create_order.call_args[0][4] is None
assert api_mock.create_order.call_args[0][5] == {'timeInForce': 'ioc'}
def test_sell_dry_run(default_conf, mocker):
default_conf['dry_run'] = True
exchange = get_patched_exchange(mocker, default_conf)
order = exchange.sell(pair='ETH/BTC', rate=200, amount=1)
order = exchange.sell(pair='ETH/BTC', ordertype='limit', amount=1, rate=200)
assert 'id' in order
assert 'dry_run_sell_' in order['id']
@ -402,7 +543,8 @@ def test_sell_dry_run(default_conf, mocker):
def test_sell_prod(default_conf, mocker):
api_mock = MagicMock()
order_id = 'test_prod_sell_{}'.format(randint(0, 10 ** 6))
api_mock.create_limit_sell_order = MagicMock(return_value={
order_type = 'market'
api_mock.create_order = MagicMock(return_value={
'id': order_id,
'info': {
'foo': 'bar'
@ -411,32 +553,48 @@ def test_sell_prod(default_conf, mocker):
default_conf['dry_run'] = False
exchange = get_patched_exchange(mocker, default_conf, api_mock)
mocker.patch('freqtrade.exchange.Exchange.symbol_amount_prec', lambda s, x, y: y)
mocker.patch('freqtrade.exchange.Exchange.symbol_price_prec', lambda s, x, y: y)
order = exchange.sell(pair='ETH/BTC', rate=200, amount=1)
order = exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
assert 'id' in order
assert 'info' in order
assert order['id'] == order_id
assert api_mock.create_order.call_args[0][0] == 'ETH/BTC'
assert api_mock.create_order.call_args[0][1] == order_type
assert api_mock.create_order.call_args[0][2] == 'sell'
assert api_mock.create_order.call_args[0][3] == 1
assert api_mock.create_order.call_args[0][4] is None
api_mock.create_order.reset_mock()
order_type = 'limit'
order = exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
assert api_mock.create_order.call_args[0][0] == 'ETH/BTC'
assert api_mock.create_order.call_args[0][1] == order_type
assert api_mock.create_order.call_args[0][2] == 'sell'
assert api_mock.create_order.call_args[0][3] == 1
assert api_mock.create_order.call_args[0][4] == 200
# test exception handling
with pytest.raises(DependencyException):
api_mock.create_limit_sell_order = MagicMock(side_effect=ccxt.InsufficientFunds)
api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.sell(pair='ETH/BTC', rate=200, amount=1)
exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
with pytest.raises(DependencyException):
api_mock.create_limit_sell_order = MagicMock(side_effect=ccxt.InvalidOrder)
api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.sell(pair='ETH/BTC', rate=200, amount=1)
exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
with pytest.raises(TemporaryError):
api_mock.create_limit_sell_order = MagicMock(side_effect=ccxt.NetworkError)
api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.sell(pair='ETH/BTC', rate=200, amount=1)
exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
with pytest.raises(OperationalException):
api_mock.create_limit_sell_order = MagicMock(side_effect=ccxt.BaseError)
api_mock.create_order = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.sell(pair='ETH/BTC', rate=200, amount=1)
exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
def test_get_balance_dry_run(default_conf, mocker):
@ -545,6 +703,7 @@ def test_get_ticker(default_conf, mocker):
'last': 0.0001,
}
api_mock.fetch_ticker = MagicMock(return_value=tick)
api_mock.markets = {'ETH/BTC': {}}
exchange = get_patched_exchange(mocker, default_conf, api_mock)
# retrieve original ticker
ticker = exchange.get_ticker(pair='ETH/BTC')
@ -587,6 +746,9 @@ def test_get_ticker(default_conf, mocker):
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_ticker(pair='ETH/BTC', refresh=True)
with pytest.raises(DependencyException, match=r'Pair XRP/ETH not available'):
exchange.get_ticker(pair='XRP/ETH', refresh=True)
def test_get_history(default_conf, mocker, caplog):
exchange = get_patched_exchange(mocker, default_conf)
@ -619,12 +781,20 @@ def test_get_history(default_conf, mocker, caplog):
def test_refresh_tickers(mocker, default_conf, caplog) -> None:
tick = [
[
1511686200000, # unix timestamp ms
(arrow.utcnow().timestamp - 1) * 1000, # unix timestamp ms
1, # open
2, # high
3, # low
4, # close
5, # volume (in quote currency)
],
[
arrow.utcnow().timestamp * 1000, # unix timestamp ms
3, # open
1, # high
4, # low
6, # close
5, # volume (in quote currency)
]
]
@ -634,13 +804,28 @@ def test_refresh_tickers(mocker, default_conf, caplog) -> None:
pairs = ['IOTA/ETH', 'XRP/ETH']
# empty dicts
assert not exchange.klines
assert not exchange._klines
exchange.refresh_tickers(['IOTA/ETH', 'XRP/ETH'], '5m')
assert log_has(f'Refreshing klines for {len(pairs)} pairs', caplog.record_tuples)
assert exchange.klines
assert exchange._klines
assert exchange._api_async.fetch_ohlcv.call_count == 2
for pair in pairs:
assert exchange.klines[pair]
assert isinstance(exchange.klines(pair), DataFrame)
assert len(exchange.klines(pair)) > 0
# klines function should return a different object on each call
# if copy is "True"
assert exchange.klines(pair) is not exchange.klines(pair)
assert exchange.klines(pair) is not exchange.klines(pair, copy=True)
assert exchange.klines(pair, copy=True) is not exchange.klines(pair, copy=True)
assert exchange.klines(pair, copy=False) is exchange.klines(pair, copy=False)
# test caching
exchange.refresh_tickers(['IOTA/ETH', 'XRP/ETH'], '5m')
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert log_has(f"Using cached klines data for {pairs[0]} ...", caplog.record_tuples)
@pytest.mark.asyncio
@ -670,10 +855,6 @@ async def test__async_get_candle_history(default_conf, mocker, caplog):
assert res[1] == tick
assert exchange._api_async.fetch_ohlcv.call_count == 1
assert not log_has(f"Using cached klines data for {pair} ...", caplog.record_tuples)
# test caching
res = await exchange._async_get_candle_history(pair, "5m")
assert exchange._api_async.fetch_ohlcv.call_count == 1
assert log_has(f"Using cached klines data for {pair} ...", caplog.record_tuples)
# exchange = Exchange(default_conf)
await async_ccxt_exception(mocker, default_conf, MagicMock(),
@ -780,65 +961,10 @@ def make_fetch_ohlcv_mock(data):
return fetch_ohlcv_mock
def test_get_candle_history(default_conf, mocker):
api_mock = MagicMock()
tick = [
[
1511686200000, # unix timestamp ms
1, # open
2, # high
3, # low
4, # close
5, # volume (in quote currency)
]
]
type(api_mock).has = PropertyMock(return_value={'fetchOHLCV': True})
api_mock.fetch_ohlcv = MagicMock(side_effect=make_fetch_ohlcv_mock(tick))
exchange = get_patched_exchange(mocker, default_conf, api_mock)
# retrieve original ticker
ticks = exchange.get_candle_history('ETH/BTC', default_conf['ticker_interval'])
assert ticks[0][0] == 1511686200000
assert ticks[0][1] == 1
assert ticks[0][2] == 2
assert ticks[0][3] == 3
assert ticks[0][4] == 4
assert ticks[0][5] == 5
# change ticker and ensure tick changes
new_tick = [
[
1511686210000, # unix timestamp ms
6, # open
7, # high
8, # low
9, # close
10, # volume (in quote currency)
]
]
api_mock.fetch_ohlcv = MagicMock(side_effect=make_fetch_ohlcv_mock(new_tick))
exchange = get_patched_exchange(mocker, default_conf, api_mock)
ticks = exchange.get_candle_history('ETH/BTC', default_conf['ticker_interval'])
assert ticks[0][0] == 1511686210000
assert ticks[0][1] == 6
assert ticks[0][2] == 7
assert ticks[0][3] == 8
assert ticks[0][4] == 9
assert ticks[0][5] == 10
ccxt_exceptionhandlers(mocker, default_conf, api_mock,
"get_candle_history", "fetch_ohlcv",
pair='ABCD/BTC', tick_interval=default_conf['ticker_interval'])
with pytest.raises(OperationalException, match=r'Exchange .* does not support.*'):
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.NotSupported)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_candle_history(pair='ABCD/BTC', tick_interval=default_conf['ticker_interval'])
def test_get_candle_history_sort(default_conf, mocker):
api_mock = MagicMock()
@pytest.mark.asyncio
async def test___async_get_candle_history_sort(default_conf, mocker):
def sort_data(data, key):
return sorted(data, key=key)
# GDAX use-case (real data from GDAX)
# This ticker history is ordered DESC (newest first, oldest last)
@ -854,13 +980,15 @@ def test_get_candle_history_sort(default_conf, mocker):
[1527830700000, 0.07652, 0.07652, 0.07651, 0.07652, 10.04822687],
[1527830400000, 0.07649, 0.07651, 0.07649, 0.07651, 2.5734867]
]
type(api_mock).has = PropertyMock(return_value={'fetchOHLCV': True})
api_mock.fetch_ohlcv = MagicMock(side_effect=make_fetch_ohlcv_mock(tick))
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange = get_patched_exchange(mocker, default_conf)
exchange._api_async.fetch_ohlcv = get_mock_coro(tick)
sort_mock = mocker.patch('freqtrade.exchange.sorted', MagicMock(side_effect=sort_data))
# Test the ticker history sort
ticks = exchange.get_candle_history('ETH/BTC', default_conf['ticker_interval'])
res = await exchange._async_get_candle_history('ETH/BTC', default_conf['ticker_interval'])
assert res[0] == 'ETH/BTC'
ticks = res[1]
assert sort_mock.call_count == 1
assert ticks[0][0] == 1527830400000
assert ticks[0][1] == 0.07649
assert ticks[0][2] == 0.07651
@ -889,11 +1017,15 @@ def test_get_candle_history_sort(default_conf, mocker):
[1527830100000, 0.076695, 0.07671, 0.07624171, 0.07671, 1.80689244],
[1527830400000, 0.07671, 0.07674399, 0.07629216, 0.07655213, 2.31452783]
]
type(api_mock).has = PropertyMock(return_value={'fetchOHLCV': True})
api_mock.fetch_ohlcv = MagicMock(side_effect=make_fetch_ohlcv_mock(tick))
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange._api_async.fetch_ohlcv = get_mock_coro(tick)
# Reset sort mock
sort_mock = mocker.patch('freqtrade.exchange.sorted', MagicMock(side_effect=sort_data))
# Test the ticker history sort
ticks = exchange.get_candle_history('ETH/BTC', default_conf['ticker_interval'])
res = await exchange._async_get_candle_history('ETH/BTC', default_conf['ticker_interval'])
assert res[0] == 'ETH/BTC'
ticks = res[1]
# Sorted not called again - data is already in order
assert sort_mock.call_count == 0
assert ticks[0][0] == 1527827700000
assert ticks[0][1] == 0.07659999
assert ticks[0][2] == 0.0766
@ -1076,3 +1208,85 @@ def test_get_fee(default_conf, mocker):
ccxt_exceptionhandlers(mocker, default_conf, api_mock,
'get_fee', 'calculate_fee')
def test_stoploss_limit_order(default_conf, mocker):
api_mock = MagicMock()
order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
order_type = 'stop_loss_limit'
api_mock.create_order = MagicMock(return_value={
'id': order_id,
'info': {
'foo': 'bar'
}
})
default_conf['dry_run'] = False
mocker.patch('freqtrade.exchange.Exchange.symbol_amount_prec', lambda s, x, y: y)
mocker.patch('freqtrade.exchange.Exchange.symbol_price_prec', lambda s, x, y: y)
exchange = get_patched_exchange(mocker, default_conf, api_mock, 'binance')
with pytest.raises(OperationalException):
order = exchange.stoploss_limit(pair='ETH/BTC', amount=1, stop_price=190, rate=200)
api_mock.create_order.reset_mock()
order = exchange.stoploss_limit(pair='ETH/BTC', amount=1, stop_price=220, rate=200)
assert 'id' in order
assert 'info' in order
assert order['id'] == order_id
assert api_mock.create_order.call_args[0][0] == 'ETH/BTC'
assert api_mock.create_order.call_args[0][1] == order_type
assert api_mock.create_order.call_args[0][2] == 'sell'
assert api_mock.create_order.call_args[0][3] == 1
assert api_mock.create_order.call_args[0][4] == 200
assert api_mock.create_order.call_args[0][5] == {'stopPrice': 220}
# test exception handling
with pytest.raises(DependencyException):
api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.stoploss_limit(pair='ETH/BTC', amount=1, stop_price=220, rate=200)
with pytest.raises(DependencyException):
api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.stoploss_limit(pair='ETH/BTC', amount=1, stop_price=220, rate=200)
with pytest.raises(TemporaryError):
api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.stoploss_limit(pair='ETH/BTC', amount=1, stop_price=220, rate=200)
with pytest.raises(OperationalException):
api_mock.create_order = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.stoploss_limit(pair='ETH/BTC', amount=1, stop_price=220, rate=200)
def test_stoploss_limit_order_dry_run(default_conf, mocker):
api_mock = MagicMock()
order_type = 'stop_loss_limit'
default_conf['dry_run'] = True
mocker.patch('freqtrade.exchange.Exchange.symbol_amount_prec', lambda s, x, y: y)
mocker.patch('freqtrade.exchange.Exchange.symbol_price_prec', lambda s, x, y: y)
exchange = get_patched_exchange(mocker, default_conf, api_mock, 'binance')
with pytest.raises(OperationalException):
order = exchange.stoploss_limit(pair='ETH/BTC', amount=1, stop_price=190, rate=200)
api_mock.create_order.reset_mock()
order = exchange.stoploss_limit(pair='ETH/BTC', amount=1, stop_price=220, rate=200)
assert 'id' in order
assert 'info' in order
assert 'type' in order
assert order['type'] == order_type
assert order['price'] == 220
assert order['amount'] == 1

46
freqtrade/tests/optimize/__init__.py Normal file
View File

@ -0,0 +1,46 @@
from typing import NamedTuple, List
import arrow
from pandas import DataFrame
from freqtrade.strategy.interface import SellType
from freqtrade.constants import TICKER_INTERVAL_MINUTES
ticker_start_time = arrow.get(2018, 10, 3)
tests_ticker_interval = "1h"
class BTrade(NamedTuple):
"""
Minimalistic Trade result used for functional backtesting
"""
sell_reason: SellType
open_tick: int
close_tick: int
class BTContainer(NamedTuple):
"""
Minimal BacktestContainer defining Backtest inputs and results.
"""
data: List[float]
stop_loss: float
roi: float
trades: List[BTrade]
profit_perc: float
def _get_frame_time_from_offset(offset):
return ticker_start_time.shift(minutes=(offset * TICKER_INTERVAL_MINUTES[tests_ticker_interval])
).datetime.replace(tzinfo=None)
def _build_backtest_dataframe(ticker_with_signals):
columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell']
frame = DataFrame.from_records(ticker_with_signals, columns=columns)
frame['date'] = frame['date'].apply(_get_frame_time_from_offset)
# Ensure floats are in place
for column in ['open', 'high', 'low', 'close', 'volume']:
frame[column] = frame[column].astype('float64')
return frame

182
freqtrade/tests/optimize/test_backtest_detail.py Normal file
View File

@ -0,0 +1,182 @@
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, C0330, unused-argument
import logging
from unittest.mock import MagicMock
from pandas import DataFrame
import pytest
from freqtrade.optimize import get_timeframe
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.strategy.interface import SellType
from freqtrade.tests.optimize import (BTrade, BTContainer, _build_backtest_dataframe,
_get_frame_time_from_offset, tests_ticker_interval)
from freqtrade.tests.conftest import patch_exchange
# Test 0 Minus 8% Close
# Test with Stop-loss at 1%
# TC1: Stop-Loss Triggered 1% loss
tc0 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5012, 4600, 4600, 6172, 0, 0], # exit with stoploss hit
[3, 4975, 5000, 4980, 4977, 6172, 0, 0],
[4, 4977, 4987, 4977, 4995, 6172, 0, 0],
[5, 4995, 4995, 4995, 4950, 6172, 0, 0]],
stop_loss=-0.01, roi=1, profit_perc=-0.01,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
)
# Test 1 Minus 4% Low, minus 1% close
# Test with Stop-Loss at 3%
# TC2: Stop-Loss Triggered 3% Loss
tc1 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5012, 4962, 4975, 6172, 0, 0],
[3, 4975, 5000, 4800, 4962, 6172, 0, 0], # exit with stoploss hit
[4, 4962, 4987, 4937, 4950, 6172, 0, 0],
[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
stop_loss=-0.03, roi=1, profit_perc=-0.03,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=3)]
)
# Test 3 Candle drops 4%, Recovers 1%.
# Entry Criteria Met
# Candle drops 20%
# Candle Data for test 3
# Test with Stop-Loss at 2%
# TC3: Trade-A: Stop-Loss Triggered 2% Loss
# Trade-B: Stop-Loss Triggered 2% Loss
tc2 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5012, 4800, 4975, 6172, 0, 0], # exit with stoploss hit
[3, 4975, 5000, 4950, 4962, 6172, 1, 0],
[4, 4975, 5000, 4950, 4962, 6172, 0, 0], # enter trade 2 (signal on last candle)
[5, 4962, 4987, 4000, 4000, 6172, 0, 0], # exit with stoploss hit
[6, 4950, 4975, 4975, 4950, 6172, 0, 0]],
stop_loss=-0.02, roi=1, profit_perc=-0.04,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2),
BTrade(sell_reason=SellType.STOP_LOSS, open_tick=4, close_tick=5)]
)
# Test 4 Minus 3% / recovery +15%
# Candle Data for test 3 – Candle drops 3% Closed 15% up
# Test with Stop-loss at 2% ROI 6%
# TC4: Stop-Loss Triggered 2% Loss
tc3 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5750, 4850, 5750, 6172, 0, 0], # Exit with stoploss hit
[3, 4975, 5000, 4950, 4962, 6172, 0, 0],
[4, 4962, 4987, 4937, 4950, 6172, 0, 0],
[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
stop_loss=-0.02, roi=0.06, profit_perc=-0.02,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
)
# Test 4 / Drops 0.5% Closes +20%
# Set stop-loss at 1% ROI 3%
# TC5: ROI triggers 3% Gain
tc4 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4980, 4987, 6172, 1, 0],
[1, 5000, 5025, 4980, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5025, 4975, 4987, 6172, 0, 0],
[3, 4975, 6000, 4975, 6000, 6172, 0, 0], # ROI
[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
stop_loss=-0.01, roi=0.03, profit_perc=0.03,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=3)]
)
# Test 6 / Drops 3% / Recovers 6% Positive / Closes 1% positve
# Candle Data for test 6
# Set stop-loss at 2% ROI at 5%
# TC6: Stop-Loss triggers 2% Loss
tc5 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5300, 4850, 5050, 6172, 0, 0], # Exit with stoploss
[3, 4975, 5000, 4950, 4962, 6172, 0, 0],
[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
stop_loss=-0.02, roi=0.05, profit_perc=-0.02,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
)
# Test 7 - 6% Positive / 1% Negative / Close 1% Positve
# Candle Data for test 7
# Set stop-loss at 2% ROI at 3%
# TC7: ROI Triggers 3% Gain
tc6 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0],
[2, 4987, 5300, 4950, 5050, 6172, 0, 0],
[3, 4975, 5000, 4950, 4962, 6172, 0, 0],
[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
stop_loss=-0.02, roi=0.03, profit_perc=0.03,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=2)]
)
TESTS = [
tc0,
tc1,
tc2,
tc3,
tc4,
tc5,
tc6,
]
@pytest.mark.parametrize("data", TESTS)
def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
"""
run functional tests
"""
default_conf["stoploss"] = data.stop_loss
default_conf["minimal_roi"] = {"0": data.roi}
default_conf['ticker_interval'] = tests_ticker_interval
mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.0))
patch_exchange(mocker)
frame = _build_backtest_dataframe(data.data)
backtesting = Backtesting(default_conf)
backtesting.advise_buy = lambda a, m: frame
backtesting.advise_sell = lambda a, m: frame
caplog.set_level(logging.DEBUG)
pair = 'UNITTEST/BTC'
# Dummy data as we mock the analyze functions
data_processed = {pair: DataFrame()}
min_date, max_date = get_timeframe({pair: frame})
results = backtesting.backtest(
{
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 10,
'start_date': min_date,
'end_date': max_date,
}
)
print(results.T)
assert len(results) == len(data.trades)
assert round(results["profit_percent"].sum(), 3) == round(data.profit_perc, 3)
for c, trade in enumerate(data.trades):
res = results.iloc[c]
assert res.sell_reason == trade.sell_reason
assert res.open_time == _get_frame_time_from_offset(trade.open_tick)
assert res.close_time == _get_frame_time_from_offset(trade.close_tick)

256
freqtrade/tests/optimize/test_backtesting.py
View File

@ -11,13 +11,16 @@ import pandas as pd
import pytest
from arrow import Arrow
from freqtrade import DependencyException, constants, optimize
from freqtrade import DependencyException, constants
from freqtrade.arguments import Arguments, TimeRange
from freqtrade.data import history
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.optimize import get_timeframe
from freqtrade.optimize.backtesting import (Backtesting, setup_configuration,
start)
from freqtrade.tests.conftest import log_has, patch_exchange
from freqtrade.strategy.interface import SellType
from freqtrade.strategy.default_strategy import DefaultStrategy
from freqtrade.strategy.interface import SellType
from freqtrade.tests.conftest import log_has, patch_exchange
def get_args(args) -> List[str]:
@ -33,22 +36,13 @@ def trim_dictlist(dict_list, num):
def load_data_test(what):
timerange = TimeRange(None, 'line', 0, -101)
data = optimize.load_data(None, ticker_interval='1m',
pairs=['UNITTEST/BTC'], timerange=timerange)
pair = data['UNITTEST/BTC']
pair = history.load_tickerdata_file(None, ticker_interval='1m',
pair='UNITTEST/BTC', timerange=timerange)
datalen = len(pair)
# Depending on the what parameter we now adjust the
# loaded data looks:
# pair :: [[ 1509836520000, unix timestamp in ms
# 0.00162008, open
# 0.00162008, high
# 0.00162008, low
# 0.00162008, close
# 108.14853839 base volume
# ]]
base = 0.001
if what == 'raise':
return {'UNITTEST/BTC': [
data = [
[
pair[x][0], # Keep old dates
x * base, # But replace O,H,L,C
@ -57,9 +51,9 @@ def load_data_test(what):
x * base,
pair[x][5], # Keep old volume
] for x in range(0, datalen)
]}
]
if what == 'lower':
return {'UNITTEST/BTC': [
data = [
[
pair[x][0], # Keep old dates
1 - x * base, # But replace O,H,L,C
@ -68,10 +62,10 @@ def load_data_test(what):
1 - x * base,
pair[x][5] # Keep old volume
] for x in range(0, datalen)
]}
]
if what == 'sine':
hz = 0.1 # frequency
return {'UNITTEST/BTC': [
data = [
[
pair[x][0], # Keep old dates
math.sin(x * hz) / 1000 + base, # But replace O,H,L,C
@ -80,23 +74,27 @@ def load_data_test(what):
math.sin(x * hz) / 1000 + base,
pair[x][5] # Keep old volume
] for x in range(0, datalen)
]}
return data
]
return {'UNITTEST/BTC': parse_ticker_dataframe(data)}
def simple_backtest(config, contour, num_results, mocker) -> None:
patch_exchange(mocker)
config['ticker_interval'] = '1m'
backtesting = Backtesting(config)
data = load_data_test(contour)
processed = backtesting.tickerdata_to_dataframe(data)
processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(processed)
assert isinstance(processed, dict)
results = backtesting.backtest(
{
'stake_amount': config['stake_amount'],
'processed': processed,
'max_open_trades': 1,
'position_stacking': False
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
)
# results :: <class 'pandas.core.frame.DataFrame'>
@ -105,30 +103,34 @@ def simple_backtest(config, contour, num_results, mocker) -> None:
def mocked_load_data(datadir, pairs=[], ticker_interval='0m', refresh_pairs=False,
timerange=None, exchange=None):
tickerdata = optimize.load_tickerdata_file(datadir, 'UNITTEST/BTC', '1m', timerange=timerange)
pairdata = {'UNITTEST/BTC': tickerdata}
tickerdata = history.load_tickerdata_file(datadir, 'UNITTEST/BTC', '1m', timerange=timerange)
pairdata = {'UNITTEST/BTC': parse_ticker_dataframe(tickerdata)}
return pairdata
# use for mock ccxt.fetch_ohlvc'
def _load_pair_as_ticks(pair, tickfreq):
ticks = optimize.load_data(None, ticker_interval=tickfreq, pairs=[pair])
ticks = trim_dictlist(ticks, -201)
return ticks[pair]
ticks = history.load_tickerdata_file(None, ticker_interval=tickfreq, pair=pair)
ticks = ticks[-201:]
return ticks
# FIX: fixturize this?
def _make_backtest_conf(mocker, conf=None, pair='UNITTEST/BTC', record=None):
data = optimize.load_data(None, ticker_interval='8m', pairs=[pair])
data = history.load_data(datadir=None, ticker_interval='1m', pairs=[pair])
data = trim_dictlist(data, -201)
patch_exchange(mocker)
backtesting = Backtesting(conf)
processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(processed)
return {
'stake_amount': conf['stake_amount'],
'processed': backtesting.tickerdata_to_dataframe(data),
'processed': processed,
'max_open_trades': 10,
'position_stacking': False,
'record': record
'record': record,
'start_date': min_date,
'end_date': max_date,
}
@ -313,7 +315,7 @@ def test_backtesting_init(mocker, default_conf) -> None:
backtesting = Backtesting(default_conf)
assert backtesting.config == default_conf
assert backtesting.ticker_interval == '5m'
assert callable(backtesting.tickerdata_to_dataframe)
assert callable(backtesting.strategy.tickerdata_to_dataframe)
assert callable(backtesting.advise_buy)
assert callable(backtesting.advise_sell)
get_fee.assert_called()
@ -323,11 +325,11 @@ def test_backtesting_init(mocker, default_conf) -> None:
def test_tickerdata_to_dataframe(default_conf, mocker) -> None:
patch_exchange(mocker)
timerange = TimeRange(None, 'line', 0, -100)
tick = optimize.load_tickerdata_file(None, 'UNITTEST/BTC', '1m', timerange=timerange)
tickerlist = {'UNITTEST/BTC': tick}
tick = history.load_tickerdata_file(None, 'UNITTEST/BTC', '1m', timerange=timerange)
tickerlist = {'UNITTEST/BTC': parse_ticker_dataframe(tick)}
backtesting = Backtesting(default_conf)
data = backtesting.tickerdata_to_dataframe(tickerlist)
data = backtesting.strategy.tickerdata_to_dataframe(tickerlist)
assert len(data['UNITTEST/BTC']) == 99
# Load strategy to compare the result between Backtesting function and strategy are the same
@ -336,22 +338,6 @@ def test_tickerdata_to_dataframe(default_conf, mocker) -> None:
assert data['UNITTEST/BTC'].equals(data2['UNITTEST/BTC'])
def test_get_timeframe(default_conf, mocker) -> None:
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
data = backtesting.tickerdata_to_dataframe(
optimize.load_data(
None,
ticker_interval='1m',
pairs=['UNITTEST/BTC']
)
)
min_date, max_date = backtesting.get_timeframe(data)
assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
assert max_date.isoformat() == '2017-11-14T22:58:00+00:00'
def test_generate_text_table(default_conf, mocker):
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
@ -451,21 +437,21 @@ def test_generate_text_table_strategyn(default_conf, mocker):
def test_backtesting_start(default_conf, mocker, caplog) -> None:
def get_timeframe(input1, input2):
def get_timeframe(input1):
return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)
mocker.patch('freqtrade.optimize.load_data', mocked_load_data)
mocker.patch('freqtrade.data.history.load_data', mocked_load_data)
mocker.patch('freqtrade.optimize.get_timeframe', get_timeframe)
mocker.patch('freqtrade.exchange.Exchange.refresh_tickers', MagicMock())
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.optimize.backtesting.Backtesting',
backtest=MagicMock(),
_generate_text_table=MagicMock(return_value='1'),
get_timeframe=get_timeframe,
)
default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
default_conf['ticker_interval'] = 1
default_conf['ticker_interval'] = '1m'
default_conf['live'] = False
default_conf['datadir'] = None
default_conf['export'] = None
@ -486,17 +472,17 @@ def test_backtesting_start(default_conf, mocker, caplog) -> None:
def test_backtesting_start_no_data(default_conf, mocker, caplog) -> None:
def get_timeframe(input1, input2):
def get_timeframe(input1):
return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)
mocker.patch('freqtrade.optimize.load_data', MagicMock(return_value={}))
mocker.patch('freqtrade.data.history.load_data', MagicMock(return_value={}))
mocker.patch('freqtrade.optimize.get_timeframe', get_timeframe)
mocker.patch('freqtrade.exchange.Exchange.refresh_tickers', MagicMock())
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.optimize.backtesting.Backtesting',
backtest=MagicMock(),
_generate_text_table=MagicMock(return_value='1'),
get_timeframe=get_timeframe,
)
default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
@ -518,15 +504,19 @@ def test_backtest(default_conf, fee, mocker) -> None:
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
pair = 'UNITTEST/BTC'
data = optimize.load_data(None, ticker_interval='5m', pairs=['UNITTEST/BTC'])
data = trim_dictlist(data, -200)
data_processed = backtesting.tickerdata_to_dataframe(data)
timerange = TimeRange(None, 'line', 0, -201)
data = history.load_data(datadir=None, ticker_interval='5m', pairs=['UNITTEST/BTC'],
timerange=timerange)
data_processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(data_processed)
results = backtesting.backtest(
{
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 10,
'position_stacking': False
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
)
assert not results.empty
@ -534,18 +524,18 @@ def test_backtest(default_conf, fee, mocker) -> None:
expected = pd.DataFrame(
{'pair': [pair, pair],
'profit_percent': [0.00029975, 0.00056708],
'profit_abs': [1.49e-06, 7.6e-07],
'profit_percent': [0.0, 0.0],
'profit_abs': [0.0, 0.0],
'open_time': [Arrow(2018, 1, 29, 18, 40, 0).datetime,
Arrow(2018, 1, 30, 3, 30, 0).datetime],
'close_time': [Arrow(2018, 1, 29, 22, 40, 0).datetime,
Arrow(2018, 1, 30, 4, 20, 0).datetime],
'open_index': [77, 183],
'close_time': [Arrow(2018, 1, 29, 22, 35, 0).datetime,
Arrow(2018, 1, 30, 4, 15, 0).datetime],
'open_index': [78, 184],
'close_index': [125, 193],
'trade_duration': [240, 50],
'trade_duration': [235, 45],
'open_at_end': [False, False],
'open_rate': [0.104445, 0.10302485],
'close_rate': [0.105, 0.10359999],
'close_rate': [0.104969, 0.103541],
'sell_reason': [SellType.ROI, SellType.ROI]
})
pd.testing.assert_frame_equal(results, expected)
@ -555,9 +545,11 @@ def test_backtest(default_conf, fee, mocker) -> None:
# Check open trade rate alignes to open rate
assert ln is not None
assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
# check close trade rate alignes to close rate
# check close trade rate alignes to close rate or is between high and low
ln = data_pair.loc[data_pair["date"] == t["close_time"]]
assert round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6)
assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
round(ln.iloc[0]["low"], 6) < round(
t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))
def test_backtest_1min_ticker_interval(default_conf, fee, mocker) -> None:
@ -565,15 +557,20 @@ def test_backtest_1min_ticker_interval(default_conf, fee, mocker) -> None:
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
# Run a backtesting for an exiting 5min ticker_interval
data = optimize.load_data(None, ticker_interval='1m', pairs=['UNITTEST/BTC'])
data = trim_dictlist(data, -200)
# Run a backtesting for an exiting 1min ticker_interval
timerange = TimeRange(None, 'line', 0, -200)
data = history.load_data(datadir=None, ticker_interval='1m', pairs=['UNITTEST/BTC'],
timerange=timerange)
processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(processed)
results = backtesting.backtest(
{
'stake_amount': default_conf['stake_amount'],
'processed': backtesting.tickerdata_to_dataframe(data),
'processed': processed,
'max_open_trades': 1,
'position_stacking': False
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
)
assert not results.empty
@ -585,7 +582,7 @@ def test_processed(default_conf, mocker) -> None:
backtesting = Backtesting(default_conf)
dict_of_tickerrows = load_data_test('raise')
dataframes = backtesting.tickerdata_to_dataframe(dict_of_tickerrows)
dataframes = backtesting.strategy.tickerdata_to_dataframe(dict_of_tickerrows)
dataframe = dataframes['UNITTEST/BTC']
cols = dataframe.columns
# assert the dataframe got some of the indicator columns
@ -596,26 +593,14 @@ def test_processed(default_conf, mocker) -> None:
def test_backtest_pricecontours(default_conf, fee, mocker) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
tests = [['raise', 18], ['lower', 0], ['sine', 16]]
tests = [['raise', 18], ['lower', 0], ['sine', 19]]
# We need to enable sell-signal - otherwise it sells on ROI!!
default_conf['experimental'] = {"use_sell_signal": True}
for [contour, numres] in tests:
simple_backtest(default_conf, contour, numres, mocker)
# Test backtest using offline data (testdata directory)
def test_backtest_ticks(default_conf, fee, mocker):
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
ticks = [1, 5]
fun = Backtesting(default_conf).advise_buy
for _ in ticks:
backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
backtesting = Backtesting(default_conf)
backtesting.advise_buy = fun # Override
backtesting.advise_sell = fun # Override
results = backtesting.backtest(backtest_conf)
assert not results.empty
def test_backtest_clash_buy_sell(mocker, default_conf):
# Override the default buy trend function in our default_strategy
def fun(dataframe=None, pair=None):
@ -648,15 +633,94 @@ def test_backtest_only_sell(mocker, default_conf):
def test_backtest_alternate_buy_sell(default_conf, fee, mocker):
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
mocker.patch('freqtrade.optimize.backtesting.file_dump_json', MagicMock())
backtest_conf = _make_backtest_conf(mocker, conf=default_conf, pair='UNITTEST/BTC')
# We need to enable sell-signal - otherwise it sells on ROI!!
default_conf['experimental'] = {"use_sell_signal": True}
default_conf['ticker_interval'] = '1m'
backtesting = Backtesting(default_conf)
backtesting.advise_buy = _trend_alternate # Override
backtesting.advise_sell = _trend_alternate # Override
results = backtesting.backtest(backtest_conf)
backtesting._store_backtest_result("test_.json", results)
assert len(results) == 4
# 200 candles in backtest data
# won't buy on first (shifted by 1)
# 100 buys signals
assert len(results) == 100
# One trade was force-closed at the end
assert len(results.loc[results.open_at_end]) == 1
assert len(results.loc[results.open_at_end]) == 0
def test_backtest_multi_pair(default_conf, fee, mocker):
def evaluate_result_multi(results, freq, max_open_trades):
# Find overlapping trades by expanding each trade once per period
# and then counting overlaps
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
for row in results[['open_time', 'close_time']].iterrows()]
deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date')
df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
df2 = df2.astype(dtype={"open_time": "datetime64", "close_time": "datetime64"})
df2 = pd.concat([dates, df2], axis=1)
df2 = df2.set_index('date')
df_final = df2.resample(freq)[['pair']].count()
return df_final[df_final['pair'] > max_open_trades]
def _trend_alternate_hold(dataframe=None, metadata=None):
"""
Buy every 8th candle - sell every other 8th -2 (hold on to pairs a bit)
"""
multi = 8
dataframe['buy'] = np.where(dataframe.index % multi == 0, 1, 0)
dataframe['sell'] = np.where((dataframe.index + multi - 2) % multi == 0, 1, 0)
if metadata['pair'] in('ETH/BTC', 'LTC/BTC'):
dataframe['buy'] = dataframe['buy'].shift(-4)
dataframe['sell'] = dataframe['sell'].shift(-4)
return dataframe
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
pairs = ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC', 'NXT/BTC']
data = history.load_data(datadir=None, ticker_interval='5m', pairs=pairs)
data = trim_dictlist(data, -500)
# We need to enable sell-signal - otherwise it sells on ROI!!
default_conf['experimental'] = {"use_sell_signal": True}
default_conf['ticker_interval'] = '5m'
backtesting = Backtesting(default_conf)
backtesting.advise_buy = _trend_alternate_hold # Override
backtesting.advise_sell = _trend_alternate_hold # Override
data_processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(data_processed)
backtest_conf = {
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 3,
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
results = backtesting.backtest(backtest_conf)
# Make sure we have parallel trades
assert len(evaluate_result_multi(results, '5min', 2)) > 0
# make sure we don't have trades with more than configured max_open_trades
assert len(evaluate_result_multi(results, '5min', 3)) == 0
backtest_conf = {
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 1,
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
results = backtesting.backtest(backtest_conf)
assert len(evaluate_result_multi(results, '5min', 1)) == 0
def test_backtest_record(default_conf, fee, mocker):

131
freqtrade/tests/optimize/test_edge_cli.py Normal file
View File

@ -0,0 +1,131 @@
# pragma pylint: disable=missing-docstring, C0103, C0330
# pragma pylint: disable=protected-access, too-many-lines, invalid-name, too-many-arguments
from unittest.mock import MagicMock
import json
from typing import List
from freqtrade.edge import PairInfo
from freqtrade.arguments import Arguments
from freqtrade.optimize.edge_cli import (EdgeCli, setup_configuration, start)
from freqtrade.tests.conftest import log_has, patch_exchange
def get_args(args) -> List[str]:
return Arguments(args, '').get_parsed_arg()
def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
'edge'
]
config = setup_configuration(get_args(args))
assert 'max_open_trades' in config
assert 'stake_currency' in config
assert 'stake_amount' in config
assert 'exchange' in config
assert 'pair_whitelist' in config['exchange']
assert 'datadir' in config
assert log_has(
'Using data folder: {} ...'.format(config['datadir']),
caplog.record_tuples
)
assert 'ticker_interval' in config
assert not log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
assert 'refresh_pairs' not in config
assert not log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
assert 'timerange' not in config
assert 'stoploss_range' not in config
def test_setup_configuration_with_arguments(mocker, edge_conf, caplog) -> None:
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(edge_conf)
))
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
'--datadir', '/foo/bar',
'edge',
'--ticker-interval', '1m',
'--refresh-pairs-cached',
'--timerange', ':100',
'--stoplosses=-0.01,-0.10,-0.001'
]
config = setup_configuration(get_args(args))
assert 'max_open_trades' in config
assert 'stake_currency' in config
assert 'stake_amount' in config
assert 'exchange' in config
assert 'pair_whitelist' in config['exchange']
assert 'datadir' in config
assert log_has(
'Using data folder: {} ...'.format(config['datadir']),
caplog.record_tuples
)
assert 'ticker_interval' in config
assert log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
assert log_has(
'Using ticker_interval: 1m ...',
caplog.record_tuples
)
assert 'refresh_pairs' in config
assert log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
assert 'timerange' in config
assert log_has(
'Parameter --timerange detected: {} ...'.format(config['timerange']),
caplog.record_tuples
)
def test_start(mocker, fee, edge_conf, caplog) -> None:
start_mock = MagicMock()
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
mocker.patch('freqtrade.optimize.edge_cli.EdgeCli.start', start_mock)
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(edge_conf)
))
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
'edge'
]
args = get_args(args)
start(args)
assert log_has(
'Starting freqtrade in Edge mode',
caplog.record_tuples
)
assert start_mock.call_count == 1
def test_edge_init(mocker, edge_conf) -> None:
patch_exchange(mocker)
edge_cli = EdgeCli(edge_conf)
assert edge_cli.config == edge_conf
assert callable(edge_cli.edge.calculate)
def test_generate_edge_table(edge_conf, mocker):
patch_exchange(mocker)
edge_cli = EdgeCli(edge_conf)
results = {}
results['ETH/BTC'] = PairInfo(-0.01, 0.60, 2, 1, 3, 10, 60)
assert edge_cli._generate_edge_table(results).count(':|') == 7
assert edge_cli._generate_edge_table(results).count('| ETH/BTC |') == 1
assert edge_cli._generate_edge_table(results).count(
'| risk reward ratio | required risk reward | expectancy |') == 1

30
freqtrade/tests/optimize/test_hyperopt.py
View File

@ -1,13 +1,15 @@
# pragma pylint: disable=missing-docstring,W0212,C0103
from datetime import datetime
import os
from unittest.mock import MagicMock
import pandas as pd
import pytest
from freqtrade.optimize.__init__ import load_tickerdata_file
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.history import load_tickerdata_file
from freqtrade.optimize.hyperopt import Hyperopt, start
from freqtrade.strategy.resolver import StrategyResolver
from freqtrade.resolvers import StrategyResolver
from freqtrade.tests.conftest import log_has, patch_exchange
from freqtrade.tests.optimize.test_backtesting import get_args
@ -175,7 +177,7 @@ def test_roi_table_generation(hyperopt) -> None:
'roi_p3': 3,
}
assert hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
assert hyperopt.custom_hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
@ -194,7 +196,7 @@ def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
default_conf.update({'spaces': 'all'})
hyperopt = Hyperopt(default_conf)
hyperopt.tickerdata_to_dataframe = MagicMock()
hyperopt.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.start()
parallel.assert_called_once()
@ -241,9 +243,10 @@ def test_has_space(hyperopt):
def test_populate_indicators(hyperopt) -> None:
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
tickerlist = {'UNITTEST/BTC': tick}
dataframes = hyperopt.tickerdata_to_dataframe(tickerlist)
dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
tickerlist = {'UNITTEST/BTC': parse_ticker_dataframe(tick)}
dataframes = hyperopt.strategy.tickerdata_to_dataframe(tickerlist)
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
# Check if some indicators are generated. We will not test all of them
assert 'adx' in dataframe
@ -253,11 +256,12 @@ def test_populate_indicators(hyperopt) -> None:
def test_buy_strategy_generator(hyperopt) -> None:
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
tickerlist = {'UNITTEST/BTC': tick}
dataframes = hyperopt.tickerdata_to_dataframe(tickerlist)
dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
tickerlist = {'UNITTEST/BTC': parse_ticker_dataframe(tick)}
dataframes = hyperopt.strategy.tickerdata_to_dataframe(tickerlist)
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
populate_buy_trend = hyperopt.buy_strategy_generator(
populate_buy_trend = hyperopt.custom_hyperopt.buy_strategy_generator(
{
'adx-value': 20,
'fastd-value': 20,
@ -291,6 +295,10 @@ def test_generate_optimizer(mocker, default_conf) -> None:
'freqtrade.optimize.hyperopt.Hyperopt.backtest',
MagicMock(return_value=backtest_result)
)
mocker.patch(
'freqtrade.optimize.hyperopt.get_timeframe',
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
)
patch_exchange(mocker)
mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock())

473
freqtrade/tests/optimize/test_optimize.py
View File

@ -1,435 +1,64 @@
# pragma pylint: disable=missing-docstring, protected-access, C0103
import json
import os
import uuid
from shutil import copyfile
import arrow
from freqtrade import optimize
from freqtrade import optimize, constants
from freqtrade.arguments import TimeRange
from freqtrade.misc import file_dump_json
from freqtrade.optimize.__init__ import (download_backtesting_testdata,
download_pairs,
load_cached_data_for_updating,
load_tickerdata_file,
make_testdata_path, trim_tickerlist)
from freqtrade.tests.conftest import get_patched_exchange, log_has
# Change this if modifying UNITTEST/BTC testdatafile
_BTC_UNITTEST_LENGTH = 13681
from freqtrade.data import history
from freqtrade.strategy.default_strategy import DefaultStrategy
from freqtrade.tests.conftest import log_has, patch_exchange
def _backup_file(file: str, copy_file: bool = False) -> None:
"""
Backup existing file to avoid deleting the user file
:param file: complete path to the file
:param touch_file: create an empty file in replacement
:return: None
"""
file_swp = file + '.swp'
if os.path.isfile(file):
os.rename(file, file_swp)
def test_get_timeframe(default_conf, mocker) -> None:
patch_exchange(mocker)
strategy = DefaultStrategy(default_conf)
if copy_file:
copyfile(file_swp, file)
data = strategy.tickerdata_to_dataframe(
history.load_data(
datadir=None,
ticker_interval='1m',
pairs=['UNITTEST/BTC']
)
)
min_date, max_date = optimize.get_timeframe(data)
assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
assert max_date.isoformat() == '2017-11-14T22:58:00+00:00'
def _clean_test_file(file: str) -> None:
"""
Backup existing file to avoid deleting the user file
:param file: complete path to the file
:return: None
"""
file_swp = file + '.swp'
# 1. Delete file from the test
if os.path.isfile(file):
os.remove(file)
def test_validate_backtest_data_warn(default_conf, mocker, caplog) -> None:
patch_exchange(mocker)
strategy = DefaultStrategy(default_conf)
# 2. Rollback to the initial file
if os.path.isfile(file_swp):
os.rename(file_swp, file)
data = strategy.tickerdata_to_dataframe(
history.load_data(
datadir=None,
ticker_interval='1m',
pairs=['UNITTEST/BTC']
)
)
min_date, max_date = optimize.get_timeframe(data)
caplog.clear()
assert optimize.validate_backtest_data(data, min_date, max_date,
constants.TICKER_INTERVAL_MINUTES["1m"])
assert len(caplog.record_tuples) == 1
assert log_has(
"UNITTEST/BTC has missing frames: expected 14396, got 13680, that's 716 missing values",
caplog.record_tuples)
def test_load_data_30min_ticker(ticker_history, mocker, caplog, default_conf) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'UNITTEST_BTC-30m.json')
_backup_file(file, copy_file=True)
optimize.load_data(None, pairs=['UNITTEST/BTC'], ticker_interval='30m')
assert os.path.isfile(file) is True
assert not log_has('Download the pair: "UNITTEST/BTC", Interval: 30m', caplog.record_tuples)
_clean_test_file(file)
def test_validate_backtest_data(default_conf, mocker, caplog) -> None:
patch_exchange(mocker)
strategy = DefaultStrategy(default_conf)
def test_load_data_5min_ticker(ticker_history, mocker, caplog, default_conf) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'UNITTEST_BTC-5m.json')
_backup_file(file, copy_file=True)
optimize.load_data(None, pairs=['UNITTEST/BTC'], ticker_interval='5m')
assert os.path.isfile(file) is True
assert not log_has('Download the pair: "UNITTEST/BTC", Interval: 5m', caplog.record_tuples)
_clean_test_file(file)
def test_load_data_1min_ticker(ticker_history, mocker, caplog) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'UNITTEST_BTC-1m.json')
_backup_file(file, copy_file=True)
optimize.load_data(None, ticker_interval='1m', pairs=['UNITTEST/BTC'])
assert os.path.isfile(file) is True
assert not log_has('Download the pair: "UNITTEST/BTC", Interval: 1m', caplog.record_tuples)
_clean_test_file(file)
def test_load_data_with_new_pair_1min(ticker_history, mocker, caplog, default_conf) -> None:
"""
Test load_data() with 1 min ticker
"""
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
exchange = get_patched_exchange(mocker, default_conf)
file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-1m.json')
_backup_file(file)
# do not download a new pair if refresh_pairs isn't set
optimize.load_data(None,
ticker_interval='1m',
refresh_pairs=False,
pairs=['MEME/BTC'])
assert os.path.isfile(file) is False
assert log_has('No data for pair: "MEME/BTC", Interval: 1m. '
'Use --refresh-pairs-cached to download the data',
caplog.record_tuples)
# download a new pair if refresh_pairs is set
optimize.load_data(None,
ticker_interval='1m',
refresh_pairs=True,
exchange=exchange,
pairs=['MEME/BTC'])
assert os.path.isfile(file) is True
assert log_has('Download the pair: "MEME/BTC", Interval: 1m', caplog.record_tuples)
_clean_test_file(file)
def test_testdata_path() -> None:
assert os.path.join('freqtrade', 'tests', 'testdata') in make_testdata_path(None)
def test_download_pairs(ticker_history, mocker, default_conf) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
exchange = get_patched_exchange(mocker, default_conf)
file1_1 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-1m.json')
file1_5 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-5m.json')
file2_1 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'CFI_BTC-1m.json')
file2_5 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'CFI_BTC-5m.json')
_backup_file(file1_1)
_backup_file(file1_5)
_backup_file(file2_1)
_backup_file(file2_5)
assert os.path.isfile(file1_1) is False
assert os.path.isfile(file2_1) is False
assert download_pairs(None, exchange,
pairs=['MEME/BTC', 'CFI/BTC'], ticker_interval='1m') is True
assert os.path.isfile(file1_1) is True
assert os.path.isfile(file2_1) is True
# clean files freshly downloaded
_clean_test_file(file1_1)
_clean_test_file(file2_1)
assert os.path.isfile(file1_5) is False
assert os.path.isfile(file2_5) is False
assert download_pairs(None, exchange,
pairs=['MEME/BTC', 'CFI/BTC'], ticker_interval='5m') is True
assert os.path.isfile(file1_5) is True
assert os.path.isfile(file2_5) is True
# clean files freshly downloaded
_clean_test_file(file1_5)
_clean_test_file(file2_5)
def test_load_cached_data_for_updating(mocker) -> None:
datadir = os.path.join(os.path.dirname(__file__), '..', 'testdata')
test_data = None
test_filename = os.path.join(datadir, 'UNITTEST_BTC-1m.json')
with open(test_filename, "rt") as file:
test_data = json.load(file)
# change now time to test 'line' cases
# now = last cached item + 1 hour
now_ts = test_data[-1][0] / 1000 + 60 * 60
mocker.patch('arrow.utcnow', return_value=arrow.get(now_ts))
# timeframe starts earlier than the cached data
# should fully update data
timerange = TimeRange('date', None, test_data[0][0] / 1000 - 1, 0)
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
timerange)
assert data == []
assert start_ts == test_data[0][0] - 1000
# same with 'line' timeframe
num_lines = (test_data[-1][0] - test_data[1][0]) / 1000 / 60 + 120
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
TimeRange(None, 'line', 0, -num_lines))
assert data == []
assert start_ts < test_data[0][0] - 1
# timeframe starts in the center of the cached data
# should return the chached data w/o the last item
timerange = TimeRange('date', None, test_data[0][0] / 1000 + 1, 0)
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
timerange)
assert data == test_data[:-1]
assert test_data[-2][0] < start_ts < test_data[-1][0]
# same with 'line' timeframe
num_lines = (test_data[-1][0] - test_data[1][0]) / 1000 / 60 + 30
timerange = TimeRange(None, 'line', 0, -num_lines)
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
timerange)
assert data == test_data[:-1]
assert test_data[-2][0] < start_ts < test_data[-1][0]
# timeframe starts after the chached data
# should return the chached data w/o the last item
timerange = TimeRange('date', None, test_data[-1][0] / 1000 + 1, 0)
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
timerange)
assert data == test_data[:-1]
assert test_data[-2][0] < start_ts < test_data[-1][0]
# same with 'line' timeframe
num_lines = 30
timerange = TimeRange(None, 'line', 0, -num_lines)
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
timerange)
assert data == test_data[:-1]
assert test_data[-2][0] < start_ts < test_data[-1][0]
# no timeframe is set
# should return the chached data w/o the last item
num_lines = 30
timerange = TimeRange(None, 'line', 0, -num_lines)
data, start_ts = load_cached_data_for_updating(test_filename,
'1m',
timerange)
assert data == test_data[:-1]
assert test_data[-2][0] < start_ts < test_data[-1][0]
# no datafile exist
# should return timestamp start time
timerange = TimeRange('date', None, now_ts - 10000, 0)
data, start_ts = load_cached_data_for_updating(test_filename + 'unexist',
'1m',
timerange)
assert data == []
assert start_ts == (now_ts - 10000) * 1000
# same with 'line' timeframe
num_lines = 30
timerange = TimeRange(None, 'line', 0, -num_lines)
data, start_ts = load_cached_data_for_updating(test_filename + 'unexist',
'1m',
timerange)
assert data == []
assert start_ts == (now_ts - num_lines * 60) * 1000
# no datafile exist, no timeframe is set
# should return an empty array and None
data, start_ts = load_cached_data_for_updating(test_filename + 'unexist',
'1m',
None)
assert data == []
assert start_ts is None
def test_download_pairs_exception(ticker_history, mocker, caplog, default_conf) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
mocker.patch('freqtrade.optimize.__init__.download_backtesting_testdata',
side_effect=BaseException('File Error'))
exchange = get_patched_exchange(mocker, default_conf)
file1_1 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-1m.json')
file1_5 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'MEME_BTC-5m.json')
_backup_file(file1_1)
_backup_file(file1_5)
download_pairs(None, exchange, pairs=['MEME/BTC'], ticker_interval='1m')
# clean files freshly downloaded
_clean_test_file(file1_1)
_clean_test_file(file1_5)
assert log_has('Failed to download the pair: "MEME/BTC", Interval: 1m', caplog.record_tuples)
def test_download_backtesting_testdata(ticker_history, mocker, default_conf) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=ticker_history)
exchange = get_patched_exchange(mocker, default_conf)
# Download a 1 min ticker file
file1 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'XEL_BTC-1m.json')
_backup_file(file1)
download_backtesting_testdata(None, exchange, pair="XEL/BTC", tick_interval='1m')
assert os.path.isfile(file1) is True
_clean_test_file(file1)
# Download a 5 min ticker file
file2 = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'STORJ_BTC-5m.json')
_backup_file(file2)
download_backtesting_testdata(None, exchange, pair="STORJ/BTC", tick_interval='5m')
assert os.path.isfile(file2) is True
_clean_test_file(file2)
def test_download_backtesting_testdata2(mocker, default_conf) -> None:
tick = [
[1509836520000, 0.00162008, 0.00162008, 0.00162008, 0.00162008, 108.14853839],
[1509836580000, 0.00161, 0.00161, 0.00161, 0.00161, 82.390199]
]
json_dump_mock = mocker.patch('freqtrade.misc.file_dump_json', return_value=None)
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=tick)
exchange = get_patched_exchange(mocker, default_conf)
download_backtesting_testdata(None, exchange, pair="UNITTEST/BTC", tick_interval='1m')
download_backtesting_testdata(None, exchange, pair="UNITTEST/BTC", tick_interval='3m')
assert json_dump_mock.call_count == 2
def test_load_tickerdata_file() -> None:
# 7 does not exist in either format.
assert not load_tickerdata_file(None, 'UNITTEST/BTC', '7m')
# 1 exists only as a .json
tickerdata = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
assert _BTC_UNITTEST_LENGTH == len(tickerdata)
# 8 .json is empty and will fail if it's loaded. .json.gz is a copy of 1.json
tickerdata = load_tickerdata_file(None, 'UNITTEST/BTC', '8m')
assert _BTC_UNITTEST_LENGTH == len(tickerdata)
def test_init(default_conf, mocker) -> None:
exchange = get_patched_exchange(mocker, default_conf)
assert {} == optimize.load_data(
'',
exchange=exchange,
pairs=[],
refresh_pairs=True,
ticker_interval=default_conf['ticker_interval']
timerange = TimeRange('index', 'index', 200, 250)
data = strategy.tickerdata_to_dataframe(
history.load_data(
datadir=None,
ticker_interval='5m',
pairs=['UNITTEST/BTC'],
timerange=timerange
)
)
def test_trim_tickerlist() -> None:
file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'UNITTEST_BTC-1m.json')
with open(file) as data_file:
ticker_list = json.load(data_file)
ticker_list_len = len(ticker_list)
# Test the pattern ^(-\d+)$
# This pattern uses the latest N elements
timerange = TimeRange(None, 'line', 0, -5)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == 5
assert ticker_list[0] is not ticker[0] # The first element should be different
assert ticker_list[-1] is ticker[-1] # The last element must be the same
# Test the pattern ^(\d+)-$
# This pattern keep X element from the end
timerange = TimeRange('line', None, 5, 0)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == 5
assert ticker_list[0] is ticker[0] # The first element must be the same
assert ticker_list[-1] is not ticker[-1] # The last element should be different
# Test the pattern ^(\d+)-(\d+)$
# This pattern extract a window
timerange = TimeRange('index', 'index', 5, 10)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == 5
assert ticker_list[0] is not ticker[0] # The first element should be different
assert ticker_list[5] is ticker[0] # The list starts at the index 5
assert ticker_list[9] is ticker[-1] # The list ends at the index 9 (5 elements)
# Test the pattern ^(\d{8})-(\d{8})$
# This pattern extract a window between the dates
timerange = TimeRange('date', 'date', ticker_list[5][0] / 1000, ticker_list[10][0] / 1000 - 1)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == 5
assert ticker_list[0] is not ticker[0] # The first element should be different
assert ticker_list[5] is ticker[0] # The list starts at the index 5
assert ticker_list[9] is ticker[-1] # The list ends at the index 9 (5 elements)
# Test the pattern ^-(\d{8})$
# This pattern extracts elements from the start to the date
timerange = TimeRange(None, 'date', 0, ticker_list[10][0] / 1000 - 1)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == 10
assert ticker_list[0] is ticker[0] # The start of the list is included
assert ticker_list[9] is ticker[-1] # The element 10 is not included
# Test the pattern ^(\d{8})-$
# This pattern extracts elements from the date to now
timerange = TimeRange('date', None, ticker_list[10][0] / 1000 - 1, None)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == ticker_list_len - 10
assert ticker_list[10] is ticker[0] # The first element is element #10
assert ticker_list[-1] is ticker[-1] # The last element is the same
# Test a wrong pattern
# This pattern must return the list unchanged
timerange = TimeRange(None, None, None, 5)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_list_len == ticker_len
def test_file_dump_json() -> None:
file = os.path.join(os.path.dirname(__file__), '..', 'testdata',
'test_{id}.json'.format(id=str(uuid.uuid4())))
data = {'bar': 'foo'}
# check the file we will create does not exist
assert os.path.isfile(file) is False
# Create the Json file
file_dump_json(file, data)
# Check the file was create
assert os.path.isfile(file) is True
# Open the Json file created and test the data is in it
with open(file) as data_file:
json_from_file = json.load(data_file)
assert 'bar' in json_from_file
assert json_from_file['bar'] == 'foo'
# Remove the file
_clean_test_file(file)
min_date, max_date = optimize.get_timeframe(data)
caplog.clear()
assert not optimize.validate_backtest_data(data, min_date, max_date,
constants.TICKER_INTERVAL_MINUTES["5m"])
assert len(caplog.record_tuples) == 0

0
freqtrade/tests/pairlist/__init__.py Normal file
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170
freqtrade/tests/pairlist/test_pairlist.py Normal file
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@ -0,0 +1,170 @@
# pragma pylint: disable=missing-docstring,C0103,protected-access
from unittest.mock import MagicMock
from freqtrade import OperationalException
from freqtrade.constants import AVAILABLE_PAIRLISTS
from freqtrade.resolvers import PairListResolver
from freqtrade.tests.conftest import get_patched_freqtradebot
import pytest
# whitelist, blacklist
@pytest.fixture(scope="function")
def whitelist_conf(default_conf):
default_conf['stake_currency'] = 'BTC'
default_conf['exchange']['pair_whitelist'] = [
'ETH/BTC',
'TKN/BTC',
'TRST/BTC',
'SWT/BTC',
'BCC/BTC'
]
default_conf['exchange']['pair_blacklist'] = [
'BLK/BTC'
]
default_conf['pairlist'] = {'method': 'StaticPairList',
'config': {'number_assets': 3}
}
return default_conf
def test_load_pairlist_noexist(mocker, markets, default_conf):
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
mocker.patch('freqtrade.exchange.Exchange.get_markets', markets)
with pytest.raises(ImportError,
match=r"Impossible to load Pairlist 'NonexistingPairList'."
r" This class does not exist or contains Python code errors"):
PairListResolver('NonexistingPairList', freqtradebot, default_conf).pairlist
def test_refresh_market_pair_not_in_whitelist(mocker, markets, whitelist_conf):
freqtradebot = get_patched_freqtradebot(mocker, whitelist_conf)
mocker.patch('freqtrade.exchange.Exchange.get_markets', markets)
freqtradebot.pairlists.refresh_pairlist()
# List ordered by BaseVolume
whitelist = ['ETH/BTC', 'TKN/BTC']
# Ensure all except those in whitelist are removed
assert set(whitelist) == set(freqtradebot.pairlists.whitelist)
# Ensure config dict hasn't been changed
assert (whitelist_conf['exchange']['pair_whitelist'] ==
freqtradebot.config['exchange']['pair_whitelist'])
def test_refresh_pairlists(mocker, markets, whitelist_conf):
freqtradebot = get_patched_freqtradebot(mocker, whitelist_conf)
mocker.patch('freqtrade.exchange.Exchange.get_markets', markets)
freqtradebot.pairlists.refresh_pairlist()
# List ordered by BaseVolume
whitelist = ['ETH/BTC', 'TKN/BTC']
# Ensure all except those in whitelist are removed
assert set(whitelist) == set(freqtradebot.pairlists.whitelist)
assert whitelist_conf['exchange']['pair_blacklist'] == freqtradebot.pairlists.blacklist
def test_refresh_pairlist_dynamic(mocker, markets, tickers, whitelist_conf):
whitelist_conf['pairlist'] = {'method': 'VolumePairList',
'config': {'number_assets': 5}
}
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_markets=markets,
get_tickers=tickers,
exchange_has=MagicMock(return_value=True)
)
freqtradebot = get_patched_freqtradebot(mocker, whitelist_conf)
# argument: use the whitelist dynamically by exchange-volume
whitelist = ['ETH/BTC', 'TKN/BTC']
freqtradebot.pairlists.refresh_pairlist()
assert whitelist == freqtradebot.pairlists.whitelist
whitelist_conf['pairlist'] = {'method': 'VolumePairList',
'config': {}
}
with pytest.raises(OperationalException,
match=r'`number_assets` not specified. Please check your configuration '
r'for "pairlist.config.number_assets"'):
PairListResolver('VolumePairList', freqtradebot, whitelist_conf).pairlist
def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
freqtradebot = get_patched_freqtradebot(mocker, whitelist_conf)
mocker.patch('freqtrade.exchange.Exchange.get_markets', markets_empty)
# argument: use the whitelist dynamically by exchange-volume
whitelist = []
whitelist_conf['exchange']['pair_whitelist'] = []
freqtradebot.pairlists.refresh_pairlist()
pairslist = whitelist_conf['exchange']['pair_whitelist']
assert set(whitelist) == set(pairslist)
def test_VolumePairList_whitelist_gen(mocker, whitelist_conf, markets, tickers) -> None:
whitelist_conf['pairlist']['method'] = 'VolumePairList'
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
mocker.patch('freqtrade.exchange.Exchange.get_markets', markets)
mocker.patch('freqtrade.exchange.Exchange.get_tickers', tickers)
# Test to retrieved BTC sorted on quoteVolume (default)
whitelist = freqtrade.pairlists._gen_pair_whitelist(base_currency='BTC', key='quoteVolume')
assert whitelist == ['ETH/BTC', 'TKN/BTC', 'BLK/BTC', 'LTC/BTC']
# Test to retrieve BTC sorted on bidVolume
whitelist = freqtrade.pairlists._gen_pair_whitelist(base_currency='BTC', key='bidVolume')
assert whitelist == ['LTC/BTC', 'TKN/BTC', 'ETH/BTC', 'BLK/BTC']
# Test with USDT sorted on quoteVolume (default)
whitelist = freqtrade.pairlists._gen_pair_whitelist(base_currency='USDT', key='quoteVolume')
assert whitelist == ['TKN/USDT', 'ETH/USDT', 'LTC/USDT', 'BLK/USDT']
# Test with ETH (our fixture does not have ETH, so result should be empty)
whitelist = freqtrade.pairlists._gen_pair_whitelist(base_currency='ETH', key='quoteVolume')
assert whitelist == []
def test_gen_pair_whitelist_not_supported(mocker, default_conf, tickers) -> None:
default_conf['pairlist'] = {'method': 'VolumePairList',
'config': {'number_assets': 10}
}
mocker.patch('freqtrade.exchange.Exchange.get_tickers', tickers)
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=False))
with pytest.raises(OperationalException):
get_patched_freqtradebot(mocker, default_conf)
@pytest.mark.parametrize("pairlist", AVAILABLE_PAIRLISTS)
def test_pairlist_class(mocker, whitelist_conf, markets, pairlist):
whitelist_conf['pairlist']['method'] = pairlist
mocker.patch('freqtrade.exchange.Exchange.get_markets', markets)
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
assert freqtrade.pairlists.name == pairlist
assert pairlist in freqtrade.pairlists.short_desc()
assert isinstance(freqtrade.pairlists.whitelist, list)
assert isinstance(freqtrade.pairlists.blacklist, list)
whitelist = ['ETH/BTC', 'TKN/BTC']
new_whitelist = freqtrade.pairlists._validate_whitelist(whitelist)
assert set(whitelist) == set(new_whitelist)
whitelist = ['ETH/BTC', 'TKN/BTC', 'TRX/ETH']
new_whitelist = freqtrade.pairlists._validate_whitelist(whitelist)
# TRX/ETH was removed
assert set(['ETH/BTC', 'TKN/BTC']) == set(new_whitelist)
whitelist = ['ETH/BTC', 'TKN/BTC', 'BLK/BTC']
new_whitelist = freqtrade.pairlists._validate_whitelist(whitelist)
# BLK/BTC is in blacklist ...
assert set(['ETH/BTC', 'TKN/BTC']) == set(new_whitelist)

0
freqtrade/tests/rpc/__init__.py Normal file
View File

19
freqtrade/tests/test_fiat_convert.py → freqtrade/tests/rpc/test_fiat_convert.py
View File

@ -7,7 +7,7 @@ from unittest.mock import MagicMock
import pytest
from requests.exceptions import RequestException
from freqtrade.fiat_convert import CryptoFiat, CryptoToFiatConverter
from freqtrade.rpc.fiat_convert import CryptoFiat, CryptoToFiatConverter
from freqtrade.tests.conftest import log_has, patch_coinmarketcap
@ -81,16 +81,18 @@ def test_fiat_convert_find_price(mocker):
assert fiat_convert.get_price(crypto_symbol='XRP', fiat_symbol='USD') == 0.0
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=12345.0)
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price',
return_value=12345.0)
assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='USD') == 12345.0
assert fiat_convert.get_price(crypto_symbol='btc', fiat_symbol='usd') == 12345.0
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=13000.2)
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price',
return_value=13000.2)
assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='EUR') == 13000.2
def test_fiat_convert_unsupported_crypto(mocker, caplog):
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._cryptomap', return_value=[])
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._cryptomap', return_value=[])
patch_coinmarketcap(mocker)
fiat_convert = CryptoToFiatConverter()
assert fiat_convert._find_price(crypto_symbol='CRYPTO_123', fiat_symbol='EUR') == 0.0
@ -100,7 +102,8 @@ def test_fiat_convert_unsupported_crypto(mocker, caplog):
def test_fiat_convert_get_price(mocker):
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=28000.0)
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price',
return_value=28000.0)
fiat_convert = CryptoToFiatConverter()
@ -157,7 +160,7 @@ def test_fiat_init_network_exception(mocker):
# Because CryptoToFiatConverter is a Singleton we reset the listings
listmock = MagicMock(side_effect=RequestException)
mocker.patch.multiple(
'freqtrade.fiat_convert.Market',
'freqtrade.rpc.fiat_convert.Market',
listings=listmock,
)
# with pytest.raises(RequestEsxception):
@ -187,7 +190,7 @@ def test_fiat_invalid_response(mocker, caplog):
# Because CryptoToFiatConverter is a Singleton we reset the listings
listmock = MagicMock(return_value="{'novalidjson':DEADBEEFf}")
mocker.patch.multiple(
'freqtrade.fiat_convert.Market',
'freqtrade.rpc.fiat_convert.Market',
listings=listmock,
)
# with pytest.raises(RequestEsxception):
@ -203,7 +206,7 @@ def test_fiat_invalid_response(mocker, caplog):
def test_convert_amount(mocker):
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter.get_price', return_value=12345.0)
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter.get_price', return_value=12345.0)
fiat_convert = CryptoToFiatConverter()
result = fiat_convert.convert_amount(

171
freqtrade/tests/rpc/test_rpc.py
View File

@ -5,12 +5,13 @@ from datetime import datetime
from unittest.mock import MagicMock, ANY
import pytest
from numpy import isnan
from freqtrade import TemporaryError
from freqtrade.fiat_convert import CryptoToFiatConverter
from freqtrade import TemporaryError, DependencyException
from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.persistence import Trade
from freqtrade.rpc import RPC, RPCException
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
from freqtrade.state import State
from freqtrade.tests.test_freqtradebot import patch_get_signal
from freqtrade.tests.conftest import patch_coinmarketcap, patch_exchange
@ -40,10 +41,6 @@ def test_rpc_trade_status(default_conf, ticker, fee, markets, mocker) -> None:
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
freqtradebot.state = State.STOPPED
with pytest.raises(RPCException, match=r'.*trader is not running*'):
rpc._rpc_trade_status()
freqtradebot.state = State.RUNNING
with pytest.raises(RPCException, match=r'.*no active trade*'):
rpc._rpc_trade_status()
@ -65,6 +62,27 @@ def test_rpc_trade_status(default_conf, ticker, fee, markets, mocker) -> None:
'open_order': '(limit buy rem=0.00000000)'
} == results[0]
mocker.patch('freqtrade.exchange.Exchange.get_ticker',
MagicMock(side_effect=DependencyException(f"Pair 'ETH/BTC' not available")))
# invalidate ticker cache
rpc._freqtrade.exchange._cached_ticker = {}
results = rpc._rpc_trade_status()
assert isnan(results[0]['current_profit'])
assert isnan(results[0]['current_rate'])
assert {
'trade_id': 1,
'pair': 'ETH/BTC',
'market_url': 'https://bittrex.com/Market/Index?MarketName=BTC-ETH',
'date': ANY,
'open_rate': 1.099e-05,
'close_rate': None,
'current_rate': ANY,
'amount': 90.99181074,
'close_profit': None,
'current_profit': ANY,
'open_order': '(limit buy rem=0.00000000)'
} == results[0]
def test_rpc_status_table(default_conf, ticker, fee, markets, mocker) -> None:
patch_coinmarketcap(mocker)
@ -81,10 +99,6 @@ def test_rpc_status_table(default_conf, ticker, fee, markets, mocker) -> None:
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
freqtradebot.state = State.STOPPED
with pytest.raises(RPCException, match=r'.*trader is not running*'):
rpc._rpc_status_table()
freqtradebot.state = State.RUNNING
with pytest.raises(RPCException, match=r'.*no active order*'):
rpc._rpc_status_table()
@ -95,6 +109,15 @@ def test_rpc_status_table(default_conf, ticker, fee, markets, mocker) -> None:
assert 'ETH/BTC' in result['Pair'].all()
assert '-0.59%' in result['Profit'].all()
mocker.patch('freqtrade.exchange.Exchange.get_ticker',
MagicMock(side_effect=DependencyException(f"Pair 'ETH/BTC' not available")))
# invalidate ticker cache
rpc._freqtrade.exchange._cached_ticker = {}
result = rpc._rpc_status_table()
assert 'just now' in result['Since'].all()
assert 'ETH/BTC' in result['Pair'].all()
assert 'nan%' in result['Profit'].all()
def test_rpc_daily_profit(default_conf, update, ticker, fee,
limit_buy_order, limit_sell_order, markets, mocker) -> None:
@ -148,7 +171,7 @@ def test_rpc_daily_profit(default_conf, update, ticker, fee,
def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
limit_buy_order, limit_sell_order, markets, mocker) -> None:
mocker.patch.multiple(
'freqtrade.fiat_convert.Market',
'freqtrade.rpc.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
)
patch_coinmarketcap(mocker)
@ -216,6 +239,20 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
assert stats['best_pair'] == 'ETH/BTC'
assert prec_satoshi(stats['best_rate'], 6.2)
# Test non-available pair
mocker.patch('freqtrade.exchange.Exchange.get_ticker',
MagicMock(side_effect=DependencyException(f"Pair 'ETH/BTC' not available")))
# invalidate ticker cache
rpc._freqtrade.exchange._cached_ticker = {}
stats = rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
assert stats['trade_count'] == 2
assert stats['first_trade_date'] == 'just now'
assert stats['latest_trade_date'] == 'just now'
assert stats['avg_duration'] == '0:00:00'
assert stats['best_pair'] == 'ETH/BTC'
assert prec_satoshi(stats['best_rate'], 6.2)
assert isnan(stats['profit_all_coin'])
# Test that rpc_trade_statistics can handle trades that lacks
# trade.open_rate (it is set to None)
@ -223,10 +260,11 @@ def test_rpc_trade_statistics_closed(mocker, default_conf, ticker, fee, markets,
ticker_sell_up, limit_buy_order, limit_sell_order):
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.fiat_convert.Market',
'freqtrade.rpc.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
)
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price',
return_value=15000.0)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
@ -291,7 +329,7 @@ def test_rpc_balance_handle(default_conf, mocker):
# ETH will be skipped due to mocked Error below
mocker.patch.multiple(
'freqtrade.fiat_convert.Market',
'freqtrade.rpc.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
)
patch_coinmarketcap(mocker)
@ -532,3 +570,108 @@ def test_rpc_count(mocker, default_conf, ticker, fee, markets) -> None:
trades = rpc._rpc_count()
nb_trades = len(trades)
assert nb_trades == 1
def test_rpcforcebuy(mocker, default_conf, ticker, fee, markets, limit_buy_order) -> None:
default_conf['forcebuy_enable'] = True
patch_coinmarketcap(mocker)
patch_exchange(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
buy_mm = MagicMock(return_value={'id': limit_buy_order['id']})
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value=ticker),
get_ticker=ticker,
get_fee=fee,
get_markets=markets,
buy=buy_mm
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
pair = 'ETH/BTC'
trade = rpc._rpc_forcebuy(pair, None)
assert isinstance(trade, Trade)
assert trade.pair == pair
assert trade.open_rate == ticker()['ask']
# Test buy duplicate
with pytest.raises(RPCException, match=r'position for ETH/BTC already open - id: 1'):
rpc._rpc_forcebuy(pair, 0.0001)
pair = 'XRP/BTC'
trade = rpc._rpc_forcebuy(pair, 0.0001)
assert isinstance(trade, Trade)
assert trade.pair == pair
assert trade.open_rate == 0.0001
# Test buy pair not with stakes
with pytest.raises(RPCException, match=r'Wrong pair selected. Please pairs with stake.*'):
rpc._rpc_forcebuy('XRP/ETH', 0.0001)
pair = 'XRP/BTC'
# Test not buying
default_conf['stake_amount'] = 0.0000001
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
pair = 'TKN/BTC'
trade = rpc._rpc_forcebuy(pair, None)
assert trade is None
def test_rpcforcebuy_stopped(mocker, default_conf) -> None:
default_conf['forcebuy_enable'] = True
default_conf['initial_state'] = 'stopped'
patch_coinmarketcap(mocker)
patch_exchange(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
pair = 'ETH/BTC'
with pytest.raises(RPCException, match=r'trader is not running'):
rpc._rpc_forcebuy(pair, None)
def test_rpcforcebuy_disabled(mocker, default_conf) -> None:
patch_coinmarketcap(mocker)
patch_exchange(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
pair = 'ETH/BTC'
with pytest.raises(RPCException, match=r'Forcebuy not enabled.'):
rpc._rpc_forcebuy(pair, None)
def test_rpc_whitelist(mocker, default_conf) -> None:
patch_coinmarketcap(mocker)
patch_exchange(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
freqtradebot = FreqtradeBot(default_conf)
rpc = RPC(freqtradebot)
ret = rpc._rpc_whitelist()
assert ret['method'] == 'StaticPairList'
assert ret['whitelist'] == default_conf['exchange']['pair_whitelist']
def test_rpc_whitelist_dynamic(mocker, default_conf) -> None:
patch_coinmarketcap(mocker)
patch_exchange(mocker)
default_conf['pairlist'] = {'method': 'VolumePairList',
'config': {'number_assets': 4}
}
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
freqtradebot = FreqtradeBot(default_conf)
rpc = RPC(freqtradebot)
ret = rpc._rpc_whitelist()
assert ret['method'] == 'VolumePairList'
assert ret['length'] == 4
assert ret['whitelist'] == default_conf['exchange']['pair_whitelist']

22
freqtrade/tests/rpc/test_rpc_manager.py
View File

@ -113,3 +113,25 @@ def test_init_webhook_enabled(mocker, default_conf, caplog) -> None:
assert log_has('Enabling rpc.webhook ...', caplog.record_tuples)
assert len(rpc_manager.registered_modules) == 1
assert 'webhook' in [mod.name for mod in rpc_manager.registered_modules]
def test_startupmessages_telegram_enabled(mocker, default_conf, caplog) -> None:
telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock())
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
rpc_manager = RPCManager(freqtradebot)
rpc_manager.startup_messages(default_conf, freqtradebot.pairlists)
assert telegram_mock.call_count == 3
assert "*Exchange:* `bittrex`" in telegram_mock.call_args_list[1][0][0]['status']
telegram_mock.reset_mock()
default_conf['dry_run'] = True
default_conf['whitelist'] = {'method': 'VolumePairList',
'config': {'number_assets': 20}
}
rpc_manager.startup_messages(default_conf, freqtradebot.pairlists)
assert telegram_mock.call_count == 3
assert "Dry run is enabled." in telegram_mock.call_args_list[0][0][0]['status']

202
freqtrade/tests/rpc/test_rpc_telegram.py
View File

@ -17,6 +17,7 @@ from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.persistence import Trade
from freqtrade.rpc import RPCMessageType
from freqtrade.rpc.telegram import Telegram, authorized_only
from freqtrade.strategy.interface import SellType
from freqtrade.state import State
from freqtrade.tests.conftest import (get_patched_freqtradebot, log_has,
patch_exchange)
@ -71,8 +72,9 @@ def test_init(default_conf, mocker, caplog) -> None:
assert start_polling.start_polling.call_count == 1
message_str = "rpc.telegram is listening for following commands: [['status'], ['profit'], " \
"['balance'], ['start'], ['stop'], ['forcesell'], ['performance'], ['daily'], " \
"['count'], ['reload_conf'], ['help'], ['version']]"
"['balance'], ['start'], ['stop'], ['forcesell'], ['forcebuy'], " \
"['performance'], ['daily'], ['count'], ['reload_conf'], " \
"['whitelist'], ['help'], ['version']]"
assert log_has(message_str, caplog.record_tuples)
@ -250,9 +252,10 @@ def test_status_handle(default_conf, update, ticker, fee, markets, mocker) -> No
telegram = Telegram(freqtradebot)
freqtradebot.state = State.STOPPED
# Status is also enabled when stopped
telegram._status(bot=MagicMock(), update=update)
assert msg_mock.call_count == 1
assert 'trader is not running' in msg_mock.call_args_list[0][0][0]
assert 'no active trade' in msg_mock.call_args_list[0][0][0]
msg_mock.reset_mock()
freqtradebot.state = State.RUNNING
@ -295,9 +298,10 @@ def test_status_table_handle(default_conf, update, ticker, fee, markets, mocker)
telegram = Telegram(freqtradebot)
freqtradebot.state = State.STOPPED
# Status table is also enabled when stopped
telegram._status_table(bot=MagicMock(), update=update)
assert msg_mock.call_count == 1
assert 'trader is not running' in msg_mock.call_args_list[0][0][0]
assert 'no active order' in msg_mock.call_args_list[0][0][0]
msg_mock.reset_mock()
freqtradebot.state = State.RUNNING
@ -507,7 +511,12 @@ def test_telegram_balance_handle(default_conf, update, mocker) -> None:
'total': 10.0,
'free': 10.0,
'used': 0.0
}
},
'XRP': {
'total': 1.0,
'free': 1.0,
'used': 0.0
}
}
def mock_ticker(symbol, refresh):
@ -517,7 +526,12 @@ def test_telegram_balance_handle(default_conf, update, mocker) -> None:
'ask': 10000.00,
'last': 10000.00,
}
elif symbol == 'XRP/BTC':
return {
'bid': 0.00001,
'ask': 0.00001,
'last': 0.00001,
}
return {
'bid': 0.1,
'ask': 0.1,
@ -548,7 +562,8 @@ def test_telegram_balance_handle(default_conf, update, mocker) -> None:
assert '*USDT:*' in result
assert 'Balance:' in result
assert 'Est. BTC:' in result
assert 'BTC: 14.00000000' in result
assert 'BTC: 12.00000000' in result
assert '*XRP:* not showing <1$ amount' in result
def test_balance_handle_empty_response(default_conf, update, mocker) -> None:
@ -712,16 +727,18 @@ def test_forcesell_handle(default_conf, update, ticker, fee,
'open_rate': 1.099e-05,
'current_rate': 1.172e-05,
'profit_amount': 6.126e-05,
'profit_percent': 0.06110514,
'profit_percent': 0.0611052,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.FORCE_SELL.value
} == last_msg
def test_forcesell_down_handle(default_conf, update, ticker, fee,
ticker_sell_down, markets, mocker) -> None:
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price',
return_value=15000.0)
rpc_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock())
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
mocker.patch.multiple(
@ -765,16 +782,18 @@ def test_forcesell_down_handle(default_conf, update, ticker, fee,
'open_rate': 1.099e-05,
'current_rate': 1.044e-05,
'profit_amount': -5.492e-05,
'profit_percent': -0.05478343,
'profit_percent': -0.05478342,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.FORCE_SELL.value
} == last_msg
def test_forcesell_all_handle(default_conf, update, ticker, fee, markets, mocker) -> None:
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
patch_exchange(mocker)
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price',
return_value=15000.0)
rpc_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock())
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
mocker.patch('freqtrade.exchange.Exchange.get_pair_detail_url', MagicMock())
@ -810,15 +829,17 @@ def test_forcesell_all_handle(default_conf, update, ticker, fee, markets, mocker
'open_rate': 1.099e-05,
'current_rate': 1.098e-05,
'profit_amount': -5.91e-06,
'profit_percent': -0.00589292,
'profit_percent': -0.00589291,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.FORCE_SELL.value
} == msg
def test_forcesell_handle_invalid(default_conf, update, mocker) -> None:
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price',
return_value=15000.0)
msg_mock = MagicMock()
mocker.patch.multiple(
'freqtrade.rpc.telegram.Telegram',
@ -855,6 +876,63 @@ def test_forcesell_handle_invalid(default_conf, update, mocker) -> None:
assert 'invalid argument' in msg_mock.call_args_list[0][0][0]
def test_forcebuy_handle(default_conf, update, markets, mocker) -> None:
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.telegram.Telegram._send_msg', MagicMock())
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
_load_markets=MagicMock(return_value={}),
get_markets=markets
)
fbuy_mock = MagicMock(return_value=None)
mocker.patch('freqtrade.rpc.RPC._rpc_forcebuy', fbuy_mock)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
update.message.text = '/forcebuy ETH/BTC'
telegram._forcebuy(bot=MagicMock(), update=update)
assert fbuy_mock.call_count == 1
assert fbuy_mock.call_args_list[0][0][0] == 'ETH/BTC'
assert fbuy_mock.call_args_list[0][0][1] is None
# Reset and retry with specified price
fbuy_mock = MagicMock(return_value=None)
mocker.patch('freqtrade.rpc.RPC._rpc_forcebuy', fbuy_mock)
update.message.text = '/forcebuy ETH/BTC 0.055'
telegram._forcebuy(bot=MagicMock(), update=update)
assert fbuy_mock.call_count == 1
assert fbuy_mock.call_args_list[0][0][0] == 'ETH/BTC'
assert isinstance(fbuy_mock.call_args_list[0][0][1], float)
assert fbuy_mock.call_args_list[0][0][1] == 0.055
def test_forcebuy_handle_exception(default_conf, update, markets, mocker) -> None:
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
rpc_mock = mocker.patch('freqtrade.rpc.telegram.Telegram._send_msg', MagicMock())
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
_load_markets=MagicMock(return_value={}),
get_markets=markets
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
update.message.text = '/forcebuy ETH/Nonepair'
telegram._forcebuy(bot=MagicMock(), update=update)
assert rpc_mock.call_count == 1
assert rpc_mock.call_args_list[0][0][0] == 'Forcebuy not enabled.'
def test_performance_handle(default_conf, update, ticker, fee,
limit_buy_order, limit_sell_order, markets, mocker) -> None:
patch_coinmarketcap(mocker)
@ -895,26 +973,6 @@ def test_performance_handle(default_conf, update, ticker, fee,
assert '<code>ETH/BTC\t6.20% (1)</code>' in msg_mock.call_args_list[0][0][0]
def test_performance_handle_invalid(default_conf, update, mocker) -> None:
patch_coinmarketcap(mocker)
patch_exchange(mocker)
msg_mock = MagicMock()
mocker.patch.multiple(
'freqtrade.rpc.telegram.Telegram',
_init=MagicMock(),
_send_msg=msg_mock
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
# Trader is not running
freqtradebot.state = State.STOPPED
telegram._performance(bot=MagicMock(), update=update)
assert msg_mock.call_count == 1
assert 'not running' in msg_mock.call_args_list[0][0][0]
def test_count_handle(default_conf, update, ticker, fee, markets, mocker) -> None:
patch_coinmarketcap(mocker)
patch_exchange(mocker)
@ -956,6 +1014,46 @@ def test_count_handle(default_conf, update, ticker, fee, markets, mocker) -> Non
assert msg in msg_mock.call_args_list[0][0][0]
def test_whitelist_static(default_conf, update, mocker) -> None:
patch_coinmarketcap(mocker)
msg_mock = MagicMock()
mocker.patch.multiple(
'freqtrade.rpc.telegram.Telegram',
_init=MagicMock(),
_send_msg=msg_mock
)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
telegram = Telegram(freqtradebot)
telegram._whitelist(bot=MagicMock(), update=update)
assert msg_mock.call_count == 1
assert ('Using whitelist `StaticPairList` with 4 pairs\n`ETH/BTC, LTC/BTC, XRP/BTC, NEO/BTC`'
in msg_mock.call_args_list[0][0][0])
def test_whitelist_dynamic(default_conf, update, mocker) -> None:
patch_coinmarketcap(mocker)
msg_mock = MagicMock()
mocker.patch.multiple(
'freqtrade.rpc.telegram.Telegram',
_init=MagicMock(),
_send_msg=msg_mock
)
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
default_conf['pairlist'] = {'method': 'VolumePairList',
'config': {'number_assets': 4}
}
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
telegram = Telegram(freqtradebot)
telegram._whitelist(bot=MagicMock(), update=update)
assert msg_mock.call_count == 1
assert ('Using whitelist `VolumePairList` with 4 pairs\n`ETH/BTC, LTC/BTC, XRP/BTC, NEO/BTC`'
in msg_mock.call_args_list[0][0][0])
def test_help_handle(default_conf, update, mocker) -> None:
patch_coinmarketcap(mocker)
msg_mock = MagicMock()
@ -1039,16 +1137,18 @@ def test_send_msg_sell_notification(default_conf, mocker) -> None:
'profit_amount': -0.05746268,
'profit_percent': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD'
'fiat_currency': 'USD',
'sell_reason': SellType.STOP_LOSS.value
})
assert msg_mock.call_args[0][0] \
== '*Binance:* Selling [KEY/ETH]' \
'(https://www.binance.com/tradeDetail.html?symbol=KEY_ETH)\n' \
'*Limit:* `0.00003201`\n' \
'*Amount:* `1333.33333333`\n' \
'*Open Rate:* `0.00007500`\n' \
'*Current Rate:* `0.00003201`\n' \
'*Profit:* `-57.41%`` (loss: -0.05746268 ETH`` / -24.812 USD)`'
== ('*Binance:* Selling [KEY/ETH]'
'(https://www.binance.com/tradeDetail.html?symbol=KEY_ETH)\n'
'*Limit:* `0.00003201`\n'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Current Rate:* `0.00003201`\n'
'*Sell Reason:* `stop_loss`\n'
'*Profit:* `-57.41%`` (loss: -0.05746268 ETH`` / -24.812 USD)`')
msg_mock.reset_mock()
telegram.send_msg({
@ -1064,15 +1164,17 @@ def test_send_msg_sell_notification(default_conf, mocker) -> None:
'profit_amount': -0.05746268,
'profit_percent': -0.57405275,
'stake_currency': 'ETH',
'sell_reason': SellType.STOP_LOSS.value
})
assert msg_mock.call_args[0][0] \
== '*Binance:* Selling [KEY/ETH]' \
'(https://www.binance.com/tradeDetail.html?symbol=KEY_ETH)\n' \
'*Limit:* `0.00003201`\n' \
'*Amount:* `1333.33333333`\n' \
'*Open Rate:* `0.00007500`\n' \
'*Current Rate:* `0.00003201`\n' \
'*Profit:* `-57.41%`'
== ('*Binance:* Selling [KEY/ETH]'
'(https://www.binance.com/tradeDetail.html?symbol=KEY_ETH)\n'
'*Limit:* `0.00003201`\n'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Current Rate:* `0.00003201`\n'
'*Sell Reason:* `stop_loss`\n'
'*Profit:* `-57.41%`')
# Reset singleton function to avoid random breaks
telegram._fiat_converter.convert_amount = old_convamount
@ -1190,7 +1292,8 @@ def test_send_msg_sell_notification_no_fiat(default_conf, mocker) -> None:
'profit_amount': -0.05746268,
'profit_percent': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD'
'fiat_currency': 'USD',
'sell_reason': SellType.STOP_LOSS.value
})
assert msg_mock.call_args[0][0] \
== '*Binance:* Selling [KEY/ETH]' \
@ -1199,6 +1302,7 @@ def test_send_msg_sell_notification_no_fiat(default_conf, mocker) -> None:
'*Amount:* `1333.33333333`\n' \
'*Open Rate:* `0.00007500`\n' \
'*Current Rate:* `0.00003201`\n' \
'*Sell Reason:* `stop_loss`\n' \
'*Profit:* `-57.41%`'

2
freqtrade/tests/rpc/test_rpc_webhook.py
View File

@ -7,6 +7,7 @@ from requests import RequestException
from freqtrade.rpc import RPCMessageType
from freqtrade.rpc.webhook import Webhook
from freqtrade.strategy.interface import SellType
from freqtrade.tests.conftest import get_patched_freqtradebot, log_has
@ -80,6 +81,7 @@ def test_send_msg(default_conf, mocker):
'profit_amount': 0.001,
'profit_percent': 0.20,
'stake_currency': 'BTC',
'sell_reason': SellType.STOP_LOSS.value
}
webhook.send_msg(msg=msg)
assert msg_mock.call_count == 1

0
freqtrade/tests/strategy/__init__.py Normal file
View File

2
freqtrade/tests/strategy/test_default_strategy.py
View File

@ -3,7 +3,7 @@ import json
import pytest
from pandas import DataFrame
from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.strategy.default_strategy import DefaultStrategy

47
freqtrade/tests/strategy/test_interface.py
View File

@ -7,7 +7,8 @@ import arrow
from pandas import DataFrame
from freqtrade.arguments import TimeRange
from freqtrade.optimize.__init__ import load_tickerdata_file
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.history import load_tickerdata_file
from freqtrade.persistence import Trade
from freqtrade.tests.conftest import get_patched_exchange, log_has
from freqtrade.strategy.default_strategy import DefaultStrategy
@ -16,62 +17,69 @@ from freqtrade.strategy.default_strategy import DefaultStrategy
_STRATEGY = DefaultStrategy(config={})
def test_returns_latest_buy_signal(mocker, default_conf):
def test_returns_latest_buy_signal(mocker, default_conf, ticker_history):
mocker.patch.object(
_STRATEGY, 'analyze_ticker',
return_value=DataFrame([{'buy': 1, 'sell': 0, 'date': arrow.utcnow()}])
)
assert _STRATEGY.get_signal('ETH/BTC', '5m', MagicMock()) == (True, False)
assert _STRATEGY.get_signal('ETH/BTC', '5m', ticker_history) == (True, False)
mocker.patch.object(
_STRATEGY, 'analyze_ticker',
return_value=DataFrame([{'buy': 0, 'sell': 1, 'date': arrow.utcnow()}])
)
assert _STRATEGY.get_signal('ETH/BTC', '5m', MagicMock()) == (False, True)
assert _STRATEGY.get_signal('ETH/BTC', '5m', ticker_history) == (False, True)
def test_returns_latest_sell_signal(mocker, default_conf):
def test_returns_latest_sell_signal(mocker, default_conf, ticker_history):
mocker.patch.object(
_STRATEGY, 'analyze_ticker',
return_value=DataFrame([{'sell': 1, 'buy': 0, 'date': arrow.utcnow()}])
)
assert _STRATEGY.get_signal('ETH/BTC', '5m', MagicMock()) == (False, True)
assert _STRATEGY.get_signal('ETH/BTC', '5m', ticker_history) == (False, True)
mocker.patch.object(
_STRATEGY, 'analyze_ticker',
return_value=DataFrame([{'sell': 0, 'buy': 1, 'date': arrow.utcnow()}])
)
assert _STRATEGY.get_signal('ETH/BTC', '5m', MagicMock()) == (True, False)
assert _STRATEGY.get_signal('ETH/BTC', '5m', ticker_history) == (True, False)
def test_get_signal_empty(default_conf, mocker, caplog):
assert (False, False) == _STRATEGY.get_signal('foo', default_conf['ticker_interval'],
None)
DataFrame())
assert log_has('Empty ticker history for pair foo', caplog.record_tuples)
caplog.clear()
assert (False, False) == _STRATEGY.get_signal('bar', default_conf['ticker_interval'],
[])
assert log_has('Empty ticker history for pair bar', caplog.record_tuples)
def test_get_signal_exception_valueerror(default_conf, mocker, caplog):
def test_get_signal_exception_valueerror(default_conf, mocker, caplog, ticker_history):
caplog.set_level(logging.INFO)
mocker.patch.object(
_STRATEGY, 'analyze_ticker',
side_effect=ValueError('xyz')
)
assert (False, False) == _STRATEGY.get_signal('foo', default_conf['ticker_interval'], 1)
assert (False, False) == _STRATEGY.get_signal('foo', default_conf['ticker_interval'],
ticker_history)
assert log_has('Unable to analyze ticker for pair foo: xyz', caplog.record_tuples)
def test_get_signal_empty_dataframe(default_conf, mocker, caplog):
def test_get_signal_empty_dataframe(default_conf, mocker, caplog, ticker_history):
caplog.set_level(logging.INFO)
mocker.patch.object(
_STRATEGY, 'analyze_ticker',
return_value=DataFrame([])
)
assert (False, False) == _STRATEGY.get_signal('xyz', default_conf['ticker_interval'], 1)
assert (False, False) == _STRATEGY.get_signal('xyz', default_conf['ticker_interval'],
ticker_history)
assert log_has('Empty dataframe for pair xyz', caplog.record_tuples)
def test_get_signal_old_dataframe(default_conf, mocker, caplog):
def test_get_signal_old_dataframe(default_conf, mocker, caplog, ticker_history):
caplog.set_level(logging.INFO)
# default_conf defines a 5m interval. we check interval * 2 + 5m
# this is necessary as the last candle is removed (partial candles) by default
@ -81,7 +89,8 @@ def test_get_signal_old_dataframe(default_conf, mocker, caplog):
_STRATEGY, 'analyze_ticker',
return_value=DataFrame(ticks)
)
assert (False, False) == _STRATEGY.get_signal('xyz', default_conf['ticker_interval'], 1)
assert (False, False) == _STRATEGY.get_signal('xyz', default_conf['ticker_interval'],
ticker_history)
assert log_has(
'Outdated history for pair xyz. Last tick is 16 minutes old',
caplog.record_tuples
@ -102,7 +111,7 @@ def test_tickerdata_to_dataframe(default_conf) -> None:
timerange = TimeRange(None, 'line', 0, -100)
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m', timerange=timerange)
tickerlist = {'UNITTEST/BTC': tick}
tickerlist = {'UNITTEST/BTC': parse_ticker_dataframe(tick)}
data = strategy.tickerdata_to_dataframe(tickerlist)
assert len(data['UNITTEST/BTC']) == 99 # partial candle was removed
@ -179,6 +188,10 @@ def test_analyze_ticker_skip_analyze(ticker_history, mocker, caplog) -> None:
strategy.process_only_new_candles = True
ret = strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
assert 'high' in ret.columns
assert 'low' in ret.columns
assert 'close' in ret.columns
assert isinstance(ret, DataFrame)
assert ind_mock.call_count == 1
assert buy_mock.call_count == 1
assert buy_mock.call_count == 1
@ -193,8 +206,8 @@ def test_analyze_ticker_skip_analyze(ticker_history, mocker, caplog) -> None:
assert buy_mock.call_count == 1
assert buy_mock.call_count == 1
# only skipped analyze adds buy and sell columns, otherwise it's all mocked
assert 'buy' in ret
assert 'sell' in ret
assert 'buy' in ret.columns
assert 'sell' in ret.columns
assert ret['buy'].sum() == 0
assert ret['sell'].sum() == 0
assert not log_has('TA Analysis Launched', caplog.record_tuples)

118
freqtrade/tests/strategy/test_strategy.py
View File

@ -2,6 +2,7 @@
import logging
from base64 import urlsafe_b64encode
from os import path
from pathlib import Path
import warnings
import pytest
@ -10,7 +11,7 @@ from pandas import DataFrame
from freqtrade.strategy import import_strategy
from freqtrade.strategy.default_strategy import DefaultStrategy
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.resolver import StrategyResolver
from freqtrade.resolvers import StrategyResolver
def test_import_strategy(caplog):
@ -40,21 +41,21 @@ def test_import_strategy(caplog):
def test_search_strategy():
default_config = {}
default_location = path.join(path.dirname(
path.realpath(__file__)), '..', '..', 'strategy'
)
default_location = Path(__file__).parent.parent.joinpath('strategy').resolve()
assert isinstance(
StrategyResolver._search_strategy(
default_location,
config=default_config,
strategy_name='DefaultStrategy'
StrategyResolver._search_object(
directory=default_location,
object_type=IStrategy,
kwargs={'config': default_config},
object_name='DefaultStrategy'
),
IStrategy
)
assert StrategyResolver._search_strategy(
default_location,
config=default_config,
strategy_name='NotFoundStrategy'
assert StrategyResolver._search_object(
directory=default_location,
object_type=IStrategy,
kwargs={'config': default_config},
object_name='NotFoundStrategy'
) is None
@ -77,7 +78,7 @@ def test_load_strategy_invalid_directory(result, caplog):
resolver._load_strategy('TestStrategy', config={}, extra_dir=extra_dir)
assert (
'freqtrade.strategy.resolver',
'freqtrade.resolvers.strategy_resolver',
logging.WARNING,
'Path "{}" does not exist'.format(extra_dir),
) in caplog.record_tuples
@ -88,8 +89,8 @@ def test_load_strategy_invalid_directory(result, caplog):
def test_load_not_found_strategy():
strategy = StrategyResolver()
with pytest.raises(ImportError,
match=r'Impossible to load Strategy \'NotFoundStrategy\'.'
r' This class does not exist or contains Python code errors'):
match=r"Impossible to load Strategy 'NotFoundStrategy'."
r" This class does not exist or contains Python code errors"):
strategy._load_strategy(strategy_name='NotFoundStrategy', config={})
@ -128,7 +129,7 @@ def test_strategy_override_minimal_roi(caplog):
resolver = StrategyResolver(config)
assert resolver.strategy.minimal_roi[0] == 0.5
assert ('freqtrade.strategy.resolver',
assert ('freqtrade.resolvers.strategy_resolver',
logging.INFO,
"Override strategy 'minimal_roi' with value in config file: {'0': 0.5}."
) in caplog.record_tuples
@ -143,7 +144,7 @@ def test_strategy_override_stoploss(caplog):
resolver = StrategyResolver(config)
assert resolver.strategy.stoploss == -0.5
assert ('freqtrade.strategy.resolver',
assert ('freqtrade.resolvers.strategy_resolver',
logging.INFO,
"Override strategy 'stoploss' with value in config file: -0.5."
) in caplog.record_tuples
@ -159,7 +160,7 @@ def test_strategy_override_ticker_interval(caplog):
resolver = StrategyResolver(config)
assert resolver.strategy.ticker_interval == 60
assert ('freqtrade.strategy.resolver',
assert ('freqtrade.resolvers.strategy_resolver',
logging.INFO,
"Override strategy 'ticker_interval' with value in config file: 60."
) in caplog.record_tuples
@ -175,13 +176,86 @@ def test_strategy_override_process_only_new_candles(caplog):
resolver = StrategyResolver(config)
assert resolver.strategy.process_only_new_candles
assert ('freqtrade.strategy.resolver',
assert ('freqtrade.resolvers.strategy_resolver',
logging.INFO,
"Override process_only_new_candles 'process_only_new_candles' "
"with value in config file: True."
) in caplog.record_tuples
def test_strategy_override_order_types(caplog):
caplog.set_level(logging.INFO)
order_types = {
'buy': 'market',
'sell': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': True,
}
config = {
'strategy': 'DefaultStrategy',
'order_types': order_types
}
resolver = StrategyResolver(config)
assert resolver.strategy.order_types
for method in ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']:
assert resolver.strategy.order_types[method] == order_types[method]
assert ('freqtrade.resolvers.strategy_resolver',
logging.INFO,
"Override strategy 'order_types' with value in config file:"
" {'buy': 'market', 'sell': 'limit', 'stoploss': 'limit',"
" 'stoploss_on_exchange': True}."
) in caplog.record_tuples
config = {
'strategy': 'DefaultStrategy',
'order_types': {'buy': 'market'}
}
# Raise error for invalid configuration
with pytest.raises(ImportError,
match=r"Impossible to load Strategy 'DefaultStrategy'. "
r"Order-types mapping is incomplete."):
StrategyResolver(config)
def test_strategy_override_order_tif(caplog):
caplog.set_level(logging.INFO)
order_time_in_force = {
'buy': 'fok',
'sell': 'gtc',
}
config = {
'strategy': 'DefaultStrategy',
'order_time_in_force': order_time_in_force
}
resolver = StrategyResolver(config)
assert resolver.strategy.order_time_in_force
for method in ['buy', 'sell']:
assert resolver.strategy.order_time_in_force[method] == order_time_in_force[method]
assert ('freqtrade.resolvers.strategy_resolver',
logging.INFO,
"Override strategy 'order_time_in_force' with value in config file:"
" {'buy': 'fok', 'sell': 'gtc'}."
) in caplog.record_tuples
config = {
'strategy': 'DefaultStrategy',
'order_time_in_force': {'buy': 'fok'}
}
# Raise error for invalid configuration
with pytest.raises(ImportError,
match=r"Impossible to load Strategy 'DefaultStrategy'. "
r"Order-time-in-force mapping is incomplete."):
StrategyResolver(config)
def test_deprecate_populate_indicators(result):
default_location = path.join(path.dirname(path.realpath(__file__)))
resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
@ -226,13 +300,13 @@ def test_call_deprecated_function(result, monkeypatch):
assert resolver.strategy._sell_fun_len == 2
indicator_df = resolver.strategy.advise_indicators(result, metadata=metadata)
assert type(indicator_df) is DataFrame
assert isinstance(indicator_df, DataFrame)
assert 'adx' in indicator_df.columns
buydf = resolver.strategy.advise_buy(result, metadata=metadata)
assert type(buydf) is DataFrame
assert isinstance(buydf, DataFrame)
assert 'buy' in buydf.columns
selldf = resolver.strategy.advise_sell(result, metadata=metadata)
assert type(selldf) is DataFrame
assert isinstance(selldf, DataFrame)
assert 'sell' in selldf

87
freqtrade/tests/test_acl_pair.py
View File

@ -1,87 +0,0 @@
# pragma pylint: disable=missing-docstring,C0103,protected-access
from unittest.mock import MagicMock
from freqtrade.tests.conftest import get_patched_freqtradebot
import pytest
# whitelist, blacklist, filtering, all of that will
# eventually become some rules to run on a generic ACL engine
# perhaps try to anticipate that by using some python package
@pytest.fixture(scope="function")
def whitelist_conf(default_conf):
default_conf['stake_currency'] = 'BTC'
default_conf['exchange']['pair_whitelist'] = [
'ETH/BTC',
'TKN/BTC',
'TRST/BTC',
'SWT/BTC',
'BCC/BTC'
]
default_conf['exchange']['pair_blacklist'] = [
'BLK/BTC'
]
return default_conf
def test_refresh_market_pair_not_in_whitelist(mocker, markets, whitelist_conf):
freqtradebot = get_patched_freqtradebot(mocker, whitelist_conf)
mocker.patch('freqtrade.exchange.Exchange.get_markets', markets)
refreshedwhitelist = freqtradebot._refresh_whitelist(
whitelist_conf['exchange']['pair_whitelist'] + ['XXX/BTC']
)
# List ordered by BaseVolume
whitelist = ['ETH/BTC', 'TKN/BTC']
# Ensure all except those in whitelist are removed
assert whitelist == refreshedwhitelist
def test_refresh_whitelist(mocker, markets, whitelist_conf):
freqtradebot = get_patched_freqtradebot(mocker, whitelist_conf)
mocker.patch('freqtrade.exchange.Exchange.get_markets', markets)
refreshedwhitelist = freqtradebot._refresh_whitelist(
whitelist_conf['exchange']['pair_whitelist'])
# List ordered by BaseVolume
whitelist = ['ETH/BTC', 'TKN/BTC']
# Ensure all except those in whitelist are removed
assert whitelist == refreshedwhitelist
def test_refresh_whitelist_dynamic(mocker, markets, tickers, whitelist_conf):
freqtradebot = get_patched_freqtradebot(mocker, whitelist_conf)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_markets=markets,
get_tickers=tickers,
exchange_has=MagicMock(return_value=True)
)
# argument: use the whitelist dynamically by exchange-volume
whitelist = ['ETH/BTC', 'TKN/BTC']
refreshedwhitelist = freqtradebot._refresh_whitelist(
freqtradebot._gen_pair_whitelist(whitelist_conf['stake_currency'])
)
assert whitelist == refreshedwhitelist
def test_refresh_whitelist_dynamic_empty(mocker, markets_empty, whitelist_conf):
freqtradebot = get_patched_freqtradebot(mocker, whitelist_conf)
mocker.patch('freqtrade.exchange.Exchange.get_markets', markets_empty)
# argument: use the whitelist dynamically by exchange-volume
whitelist = []
whitelist_conf['exchange']['pair_whitelist'] = []
freqtradebot._refresh_whitelist(whitelist)
pairslist = whitelist_conf['exchange']['pair_whitelist']
assert set(whitelist) == set(pairslist)

3
freqtrade/tests/test_arguments.py
View File

@ -17,7 +17,8 @@ def test_parse_args_none() -> None:
def test_parse_args_defaults() -> None:
args = Arguments([], '').get_parsed_arg()
assert args.config == 'config.json'
assert args.dynamic_whitelist is None
assert args.strategy_path is None
assert args.datadir is None
assert args.loglevel == 0

57
freqtrade/tests/test_configuration.py
View File

@ -6,7 +6,7 @@ import logging
from unittest.mock import MagicMock
import pytest
from jsonschema import validate, ValidationError
from jsonschema import validate, ValidationError, Draft4Validator
from freqtrade import constants
from freqtrade import OperationalException
@ -64,6 +64,22 @@ def test_load_config_max_open_trades_zero(default_conf, mocker, caplog) -> None:
assert log_has('Validating configuration ...', caplog.record_tuples)
def test_load_config_max_open_trades_minus_one(default_conf, mocker, caplog) -> None:
default_conf['max_open_trades'] = -1
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = Arguments([], '').get_parsed_arg()
configuration = Configuration(args)
validated_conf = configuration.load_config()
print(validated_conf)
assert validated_conf['max_open_trades'] > 999999999
assert validated_conf['max_open_trades'] == float('inf')
assert log_has('Validating configuration ...', caplog.record_tuples)
def test_load_config_file_exception(mocker) -> None:
mocker.patch(
'freqtrade.configuration.open',
@ -86,7 +102,7 @@ def test_load_config(default_conf, mocker) -> None:
assert validated_conf.get('strategy') == 'DefaultStrategy'
assert validated_conf.get('strategy_path') is None
assert 'dynamic_whitelist' not in validated_conf
assert 'edge' not in validated_conf
def test_load_config_with_params(default_conf, mocker) -> None:
@ -103,7 +119,8 @@ def test_load_config_with_params(default_conf, mocker) -> None:
configuration = Configuration(args)
validated_conf = configuration.load_config()
assert validated_conf.get('dynamic_whitelist') == 10
assert validated_conf.get('pairlist', {}).get('method') == 'VolumePairList'
assert validated_conf.get('pairlist', {}).get('config').get('number_assets') == 10
assert validated_conf.get('strategy') == 'TestStrategy'
assert validated_conf.get('strategy_path') == '/some/path'
assert validated_conf.get('db_url') == 'sqlite:///someurl'
@ -116,7 +133,6 @@ def test_load_config_with_params(default_conf, mocker) -> None:
))
arglist = [
'--dynamic-whitelist', '10',
'--strategy', 'TestStrategy',
'--strategy-path', '/some/path'
]
@ -135,7 +151,6 @@ def test_load_config_with_params(default_conf, mocker) -> None:
))
arglist = [
'--dynamic-whitelist', '10',
'--strategy', 'TestStrategy',
'--strategy-path', '/some/path'
]
@ -178,8 +193,9 @@ def test_show_info(default_conf, mocker, caplog) -> None:
configuration.get_config()
assert log_has(
'Parameter --dynamic-whitelist detected. '
'Using dynamically generated whitelist. '
'Parameter --dynamic-whitelist has been deprecated, '
'and will be completely replaced by the whitelist dict in the future. '
'For now: using dynamically generated whitelist based on VolumePairList. '
'(not applicable with Backtesting and Hyperopt)',
caplog.record_tuples
)
@ -371,7 +387,7 @@ def test_hyperopt_with_arguments(mocker, default_conf, caplog) -> None:
assert log_has('Parameter -s/--spaces detected: [\'all\']', caplog.record_tuples)
def test_check_exchange(default_conf) -> None:
def test_check_exchange(default_conf, caplog) -> None:
configuration = Configuration(Namespace())
# Test a valid exchange
@ -392,6 +408,15 @@ def test_check_exchange(default_conf) -> None:
):
configuration.check_exchange(default_conf)
# Test ccxt_rate_limit depreciation
default_conf.get('exchange').update({'name': 'binance'})
default_conf['exchange']['ccxt_rate_limit'] = True
configuration.check_exchange(default_conf)
assert log_has("`ccxt_rate_limit` has been deprecated in favor of "
"`ccxt_config` and `ccxt_async_config` and will be removed "
"in a future version.",
caplog.record_tuples)
def test_cli_verbose_with_params(default_conf, mocker, caplog) -> None:
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
@ -446,5 +471,19 @@ def test_set_loggers() -> None:
assert logging.getLogger('telegram').level is logging.INFO
def test_load_config_warn_forcebuy(default_conf, mocker, caplog) -> None:
default_conf['forcebuy_enable'] = True
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = Arguments([], '').get_parsed_arg()
configuration = Configuration(args)
validated_conf = configuration.load_config()
assert validated_conf.get('forcebuy_enable')
assert log_has('`forcebuy` RPC message enabled.', caplog.record_tuples)
def test_validate_default_conf(default_conf) -> None:
validate(default_conf, constants.CONF_SCHEMA)
validate(default_conf, constants.CONF_SCHEMA, Draft4Validator)

32
freqtrade/tests/test_dataframe.py
View File

@ -1,32 +0,0 @@
# pragma pylint: disable=missing-docstring, C0103
import pandas
from freqtrade.optimize import load_data
from freqtrade.strategy.resolver import StrategyResolver
_pairs = ['ETH/BTC']
def load_dataframe_pair(pairs, strategy):
ld = load_data(None, ticker_interval='5m', pairs=pairs)
assert isinstance(ld, dict)
assert isinstance(pairs[0], str)
dataframe = ld[pairs[0]]
dataframe = strategy.analyze_ticker(dataframe, {'pair': pairs[0]})
return dataframe
def test_dataframe_load():
strategy = StrategyResolver({'strategy': 'DefaultStrategy'}).strategy
dataframe = load_dataframe_pair(_pairs, strategy)
assert isinstance(dataframe, pandas.core.frame.DataFrame)
def test_dataframe_columns_exists():
strategy = StrategyResolver({'strategy': 'DefaultStrategy'}).strategy
dataframe = load_dataframe_pair(_pairs, strategy)
assert 'high' in dataframe.columns
assert 'low' in dataframe.columns
assert 'close' in dataframe.columns

670
freqtrade/tests/test_freqtradebot.py
View File

@ -18,7 +18,7 @@ from freqtrade.persistence import Trade
from freqtrade.rpc import RPCMessageType
from freqtrade.state import State
from freqtrade.strategy.interface import SellType, SellCheckTuple
from freqtrade.tests.conftest import log_has, patch_exchange
from freqtrade.tests.conftest import log_has, patch_exchange, patch_edge, patch_wallet
# Functions for recurrent object patching
@ -136,42 +136,6 @@ def test_throttle_with_assets(mocker, default_conf) -> None:
assert result == -1
def test_gen_pair_whitelist(mocker, default_conf, tickers) -> None:
freqtrade = get_patched_freqtradebot(mocker, default_conf)
mocker.patch('freqtrade.exchange.Exchange.get_tickers', tickers)
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
# Test to retrieved BTC sorted on quoteVolume (default)
whitelist = freqtrade._gen_pair_whitelist(base_currency='BTC')
assert whitelist == ['ETH/BTC', 'TKN/BTC', 'BLK/BTC', 'LTC/BTC']
# Test to retrieve BTC sorted on bidVolume
whitelist = freqtrade._gen_pair_whitelist(base_currency='BTC', key='bidVolume')
assert whitelist == ['LTC/BTC', 'TKN/BTC', 'ETH/BTC', 'BLK/BTC']
# Test with USDT sorted on quoteVolume (default)
whitelist = freqtrade._gen_pair_whitelist(base_currency='USDT')
assert whitelist == ['TKN/USDT', 'ETH/USDT', 'LTC/USDT', 'BLK/USDT']
# Test with ETH (our fixture does not have ETH, so result should be empty)
whitelist = freqtrade._gen_pair_whitelist(base_currency='ETH')
assert whitelist == []
def test_gen_pair_whitelist_not_supported(mocker, default_conf, tickers) -> None:
freqtrade = get_patched_freqtradebot(mocker, default_conf)
mocker.patch('freqtrade.exchange.Exchange.get_tickers', tickers)
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=False))
with pytest.raises(OperationalException):
freqtrade._gen_pair_whitelist(base_currency='BTC')
@pytest.mark.skip(reason="Test not implemented")
def test_refresh_whitelist() -> None:
pass
def test_get_trade_stake_amount(default_conf, ticker, limit_buy_order, fee, mocker) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
@ -182,25 +146,18 @@ def test_get_trade_stake_amount(default_conf, ticker, limit_buy_order, fee, mock
freqtrade = FreqtradeBot(default_conf)
result = freqtrade._get_trade_stake_amount()
result = freqtrade._get_trade_stake_amount('ETH/BTC')
assert result == default_conf['stake_amount']
def test_get_trade_stake_amount_no_stake_amount(default_conf,
ticker,
limit_buy_order,
fee,
mocker) -> None:
def test_get_trade_stake_amount_no_stake_amount(default_conf, mocker) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balance=MagicMock(return_value=default_conf['stake_amount'] * 0.5)
)
patch_wallet(mocker, free=default_conf['stake_amount'] * 0.5)
freqtrade = FreqtradeBot(default_conf)
with pytest.raises(DependencyException, match=r'.*stake amount.*'):
freqtrade._get_trade_stake_amount()
freqtrade._get_trade_stake_amount('ETH/BTC')
def test_get_trade_stake_amount_unlimited_amount(default_conf,
@ -211,12 +168,12 @@ def test_get_trade_stake_amount_unlimited_amount(default_conf,
mocker) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
patch_wallet(mocker, free=default_conf['stake_amount'])
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
buy=MagicMock(return_value={'id': limit_buy_order['id']}),
get_balance=MagicMock(return_value=default_conf['stake_amount']),
get_fee=fee,
get_markets=markets
)
@ -229,28 +186,164 @@ def test_get_trade_stake_amount_unlimited_amount(default_conf,
patch_get_signal(freqtrade)
# no open trades, order amount should be 'balance / max_open_trades'
result = freqtrade._get_trade_stake_amount()
result = freqtrade._get_trade_stake_amount('ETH/BTC')
assert result == default_conf['stake_amount'] / conf['max_open_trades']
# create one trade, order amount should be 'balance / (max_open_trades - num_open_trades)'
freqtrade.create_trade()
result = freqtrade._get_trade_stake_amount()
result = freqtrade._get_trade_stake_amount('LTC/BTC')
assert result == default_conf['stake_amount'] / (conf['max_open_trades'] - 1)
# create 2 trades, order amount should be None
freqtrade.create_trade()
result = freqtrade._get_trade_stake_amount()
result = freqtrade._get_trade_stake_amount('XRP/BTC')
assert result is None
# set max_open_trades = None, so do not trade
conf['max_open_trades'] = 0
freqtrade = FreqtradeBot(conf)
result = freqtrade._get_trade_stake_amount()
result = freqtrade._get_trade_stake_amount('NEO/BTC')
assert result is None
def test_edge_called_in_process(mocker, edge_conf) -> None:
patch_RPCManager(mocker)
patch_edge(mocker)
def _refresh_whitelist(list):
return ['ETH/BTC', 'LTC/BTC', 'XRP/BTC', 'NEO/BTC']
patch_exchange(mocker)
freqtrade = FreqtradeBot(edge_conf)
freqtrade.pairlists._validate_whitelist = _refresh_whitelist
patch_get_signal(freqtrade)
freqtrade._process()
assert freqtrade.active_pair_whitelist == ['NEO/BTC', 'LTC/BTC']
def test_edge_overrides_stake_amount(mocker, edge_conf) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
patch_edge(mocker)
freqtrade = FreqtradeBot(edge_conf)
assert freqtrade._get_trade_stake_amount('NEO/BTC') == (999.9 * 0.5 * 0.01) / 0.20
assert freqtrade._get_trade_stake_amount('LTC/BTC') == (999.9 * 0.5 * 0.01) / 0.21
def test_edge_overrides_stoploss(limit_buy_order, fee, markets, caplog, mocker, edge_conf) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
patch_edge(mocker)
# Strategy stoploss is -0.1 but Edge imposes a stoploss at -0.2
# Thus, if price falls 21%, stoploss should be triggered
#
# mocking the ticker: price is falling ...
buy_price = limit_buy_order['price']
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_ticker=MagicMock(return_value={
'bid': buy_price * 0.79,
'ask': buy_price * 0.79,
'last': buy_price * 0.79
}),
buy=MagicMock(return_value={'id': limit_buy_order['id']}),
get_fee=fee,
get_markets=markets,
)
#############################################
# Create a trade with "limit_buy_order" price
freqtrade = FreqtradeBot(edge_conf)
freqtrade.active_pair_whitelist = ['NEO/BTC']
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
freqtrade.create_trade()
trade = Trade.query.first()
trade.update(limit_buy_order)
#############################################
# stoploss shoud be hit
assert freqtrade.handle_trade(trade) is True
assert log_has('executed sell, reason: SellType.STOP_LOSS', caplog.record_tuples)
assert trade.sell_reason == SellType.STOP_LOSS.value
def test_edge_should_ignore_strategy_stoploss(limit_buy_order, fee, markets,
mocker, edge_conf) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
patch_edge(mocker)
# Strategy stoploss is -0.1 but Edge imposes a stoploss at -0.2
# Thus, if price falls 15%, stoploss should not be triggered
#
# mocking the ticker: price is falling ...
buy_price = limit_buy_order['price']
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_ticker=MagicMock(return_value={
'bid': buy_price * 0.85,
'ask': buy_price * 0.85,
'last': buy_price * 0.85
}),
buy=MagicMock(return_value={'id': limit_buy_order['id']}),
get_fee=fee,
get_markets=markets,
)
#############################################
# Create a trade with "limit_buy_order" price
freqtrade = FreqtradeBot(edge_conf)
freqtrade.active_pair_whitelist = ['NEO/BTC']
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
freqtrade.create_trade()
trade = Trade.query.first()
trade.update(limit_buy_order)
#############################################
# stoploss shoud not be hit
assert freqtrade.handle_trade(trade) is False
def test_total_open_trades_stakes(mocker, default_conf, ticker,
limit_buy_order, fee, markets) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
default_conf['stake_amount'] = 0.0000098751
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_ticker=ticker,
buy=MagicMock(return_value={'id': limit_buy_order['id']}),
get_fee=fee,
get_markets=markets
)
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
freqtrade.create_trade()
trade = Trade.query.first()
assert trade is not None
assert trade.stake_amount == 0.0000098751
assert trade.is_open
assert trade.open_date is not None
freqtrade.create_trade()
trade = Trade.query.order_by(Trade.id.desc()).first()
assert trade is not None
assert trade.stake_amount == 0.0000098751
assert trade.is_open
assert trade.open_date is not None
assert Trade.total_open_trades_stakes() == 1.97502e-05
def test_get_min_pair_stake_amount(mocker, default_conf) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
@ -423,11 +516,11 @@ def test_create_trade_no_stake_amount(default_conf, ticker, limit_buy_order,
fee, markets, mocker) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
patch_wallet(mocker, free=default_conf['stake_amount'] * 0.5)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_ticker=ticker,
buy=MagicMock(return_value={'id': limit_buy_order['id']}),
get_balance=MagicMock(return_value=default_conf['stake_amount'] * 0.5),
get_fee=fee,
get_markets=markets
)
@ -455,7 +548,7 @@ def test_create_trade_minimal_amount(default_conf, ticker, limit_buy_order,
patch_get_signal(freqtrade)
freqtrade.create_trade()
rate, amount = buy_mock.call_args[0][1], buy_mock.call_args[0][2]
rate, amount = buy_mock.call_args[1]['rate'], buy_mock.call_args[1]['amount']
assert rate * amount >= default_conf['stake_amount']
@ -499,7 +592,7 @@ def test_create_trade_limit_reached(default_conf, ticker, limit_buy_order,
patch_get_signal(freqtrade)
assert freqtrade.create_trade() is False
assert freqtrade._get_trade_stake_amount() is None
assert freqtrade._get_trade_stake_amount('ETH/BTC') is None
def test_create_trade_no_pairs(default_conf, ticker, limit_buy_order, fee, markets, mocker) -> None:
@ -598,7 +691,7 @@ def test_process_trade_creation(default_conf, ticker, limit_buy_order,
assert trade.amount == 90.99181073703367
assert log_has(
'Checking buy signals to create a new trade with stake_amount: 0.001000 ...',
'Buy signal found: about create a new trade with stake_amount: 0.001000 ...',
caplog.record_tuples
)
@ -668,6 +761,52 @@ def test_process_trade_handling(
assert result is False
def test_process_trade_no_whitelist_pair(
default_conf, ticker, limit_buy_order, markets, fee, mocker) -> None:
""" Test _process with trade not in pair list """
patch_RPCManager(mocker)
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_ticker=ticker,
get_markets=markets,
buy=MagicMock(return_value={'id': limit_buy_order['id']}),
get_order=MagicMock(return_value=limit_buy_order),
get_fee=fee,
)
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
pair = 'NOCLUE/BTC'
# create open trade not in whitelist
Trade.session.add(Trade(
pair=pair,
stake_amount=0.001,
fee_open=fee.return_value,
fee_close=fee.return_value,
is_open=True,
amount=20,
open_rate=0.01,
exchange='bittrex',
))
Trade.session.add(Trade(
pair='ETH/BTC',
stake_amount=0.001,
fee_open=fee.return_value,
fee_close=fee.return_value,
is_open=True,
amount=12,
open_rate=0.001,
exchange='bittrex',
))
assert pair not in freqtrade.active_pair_whitelist
result = freqtrade._process()
assert pair in freqtrade.active_pair_whitelist
# Make sure each pair is only in the list once
assert len(freqtrade.active_pair_whitelist) == len(set(freqtrade.active_pair_whitelist))
assert result is True
def test_balance_fully_ask_side(mocker, default_conf) -> None:
default_conf['bid_strategy']['ask_last_balance'] = 0.0
freqtrade = get_patched_freqtradebot(mocker, default_conf)
@ -689,6 +828,192 @@ def test_balance_bigger_last_ask(mocker, default_conf) -> None:
assert freqtrade.get_target_bid('ETH/BTC', {'ask': 5, 'last': 10}) == 5
def test_execute_buy(mocker, default_conf, fee, markets, limit_buy_order) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
freqtrade = FreqtradeBot(default_conf)
stake_amount = 2
bid = 0.11
get_bid = MagicMock(return_value=bid)
mocker.patch.multiple(
'freqtrade.freqtradebot.FreqtradeBot',
get_target_bid=get_bid,
_get_min_pair_stake_amount=MagicMock(return_value=1)
)
buy_mm = MagicMock(return_value={'id': limit_buy_order['id']})
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_ticker=MagicMock(return_value={
'bid': 0.00001172,
'ask': 0.00001173,
'last': 0.00001172
}),
buy=buy_mm,
get_fee=fee,
get_markets=markets
)
pair = 'ETH/BTC'
print(buy_mm.call_args_list)
assert freqtrade.execute_buy(pair, stake_amount)
assert get_bid.call_count == 1
assert buy_mm.call_count == 1
call_args = buy_mm.call_args_list[0][1]
assert call_args['pair'] == pair
assert call_args['rate'] == bid
assert call_args['amount'] == stake_amount / bid
# Should create an open trade with an open order id
# As the order is not fulfilled yet
trade = Trade.query.first()
assert trade
assert trade.is_open is True
assert trade.open_order_id == limit_buy_order['id']
# Test calling with price
fix_price = 0.06
assert freqtrade.execute_buy(pair, stake_amount, fix_price)
# Make sure get_target_bid wasn't called again
assert get_bid.call_count == 1
assert buy_mm.call_count == 2
call_args = buy_mm.call_args_list[1][1]
assert call_args['pair'] == pair
assert call_args['rate'] == fix_price
assert call_args['amount'] == stake_amount / fix_price
# In case of closed order
limit_buy_order['status'] = 'closed'
limit_buy_order['price'] = 10
limit_buy_order['cost'] = 100
mocker.patch('freqtrade.exchange.Exchange.buy', MagicMock(return_value=limit_buy_order))
assert freqtrade.execute_buy(pair, stake_amount)
trade = Trade.query.all()[2]
assert trade
assert trade.open_order_id is None
assert trade.open_rate == 10
assert trade.stake_amount == 100
# In case of rejected or expired order and partially filled
limit_buy_order['status'] = 'expired'
limit_buy_order['amount'] = 90.99181073
limit_buy_order['filled'] = 80.99181073
limit_buy_order['remaining'] = 10.00
limit_buy_order['price'] = 0.5
limit_buy_order['cost'] = 40.495905365
mocker.patch('freqtrade.exchange.Exchange.buy', MagicMock(return_value=limit_buy_order))
assert freqtrade.execute_buy(pair, stake_amount)
trade = Trade.query.all()[3]
assert trade
assert trade.open_order_id is None
assert trade.open_rate == 0.5
assert trade.stake_amount == 40.495905365
# In case of the order is rejected and not filled at all
limit_buy_order['status'] = 'rejected'
limit_buy_order['amount'] = 90.99181073
limit_buy_order['filled'] = 0.0
limit_buy_order['remaining'] = 90.99181073
limit_buy_order['price'] = 0.5
limit_buy_order['cost'] = 0.0
mocker.patch('freqtrade.exchange.Exchange.buy', MagicMock(return_value=limit_buy_order))
assert not freqtrade.execute_buy(pair, stake_amount)
def test_add_stoploss_on_exchange(mocker, default_conf, limit_buy_order) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.handle_trade', MagicMock(return_value=True))
mocker.patch('freqtrade.exchange.Exchange.get_order', return_value=limit_buy_order)
mocker.patch('freqtrade.exchange.Exchange.get_trades_for_order', return_value=[])
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount',
return_value=limit_buy_order['amount'])
stoploss_limit = MagicMock(return_value={'id': 13434334})
mocker.patch('freqtrade.exchange.Exchange.stoploss_limit', stoploss_limit)
freqtrade = FreqtradeBot(default_conf)
freqtrade.strategy.order_types['stoploss_on_exchange'] = True
trade = MagicMock()
trade.open_order_id = None
trade.stoploss_order_id = None
trade.is_open = True
freqtrade.process_maybe_execute_sell(trade)
assert trade.stoploss_order_id == '13434334'
assert stoploss_limit.call_count == 1
assert trade.is_open is True
def test_handle_stoploss_on_exchange(mocker, default_conf, fee, caplog,
markets, limit_buy_order, limit_sell_order) -> None:
stoploss_limit = MagicMock(return_value={'id': 13434334})
patch_RPCManager(mocker)
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_ticker=MagicMock(return_value={
'bid': 0.00001172,
'ask': 0.00001173,
'last': 0.00001172
}),
buy=MagicMock(return_value={'id': limit_buy_order['id']}),
sell=MagicMock(return_value={'id': limit_sell_order['id']}),
get_fee=fee,
get_markets=markets,
stoploss_limit=stoploss_limit
)
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
# First case: when stoploss is not yet set but the order is open
# should get the stoploss order id immediately
# and should return false as no trade actually happened
trade = MagicMock()
trade.is_open = True
trade.open_order_id = None
trade.stoploss_order_id = None
assert freqtrade.handle_stoploss_on_exchange(trade) is False
assert stoploss_limit.call_count == 1
assert trade.stoploss_order_id == "13434334"
# Second case: when stoploss is set but it is not yet hit
# should do nothing and return false
trade.is_open = True
trade.open_order_id = None
trade.stoploss_order_id = 100
hanging_stoploss_order = MagicMock(return_value={'status': 'open'})
mocker.patch('freqtrade.exchange.Exchange.get_order', hanging_stoploss_order)
assert freqtrade.handle_stoploss_on_exchange(trade) is False
assert trade.stoploss_order_id == 100
# Third case: when stoploss is set and it is hit
# should unset stoploss_order_id and return true
# as a trade actually happened
freqtrade.create_trade()
trade = Trade.query.first()
trade.is_open = True
trade.open_order_id = None
trade.stoploss_order_id = 100
assert trade
stoploss_order_hit = MagicMock(return_value={
'status': 'closed',
'type': 'stop_loss_limit',
'price': 3,
'average': 2
})
mocker.patch('freqtrade.exchange.Exchange.get_order', stoploss_order_hit)
assert freqtrade.handle_stoploss_on_exchange(trade) is True
assert log_has('STOP_LOSS_LIMIT is hit for {}.'.format(trade), caplog.record_tuples)
assert trade.stoploss_order_id is None
assert trade.is_open is False
def test_process_maybe_execute_buy(mocker, default_conf) -> None:
freqtrade = get_patched_freqtradebot(mocker, default_conf)
@ -804,7 +1129,7 @@ def test_handle_trade(default_conf, limit_buy_order, limit_sell_order,
trade.update(limit_sell_order)
assert trade.close_rate == 0.00001173
assert trade.close_profit == 0.06201057
assert trade.close_profit == 0.06201058
assert trade.calc_profit() == 0.00006217
assert trade.close_date is not None
@ -825,7 +1150,7 @@ def test_handle_overlpapping_signals(default_conf, ticker, limit_buy_order,
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade, value=(True, True))
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: False
freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
freqtrade.create_trade()
@ -881,7 +1206,7 @@ def test_handle_trade_roi(default_conf, ticker, limit_buy_order,
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade, value=(True, False))
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: True
freqtrade.strategy.min_roi_reached = MagicMock(return_value=True)
freqtrade.create_trade()
@ -914,7 +1239,7 @@ def test_handle_trade_experimental(
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: False
freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
freqtrade.create_trade()
trade = Trade.query.first()
@ -1231,9 +1556,10 @@ def test_execute_sell_up(default_conf, ticker, fee, ticker_sell_up, markets, moc
'open_rate': 1.099e-05,
'current_rate': 1.172e-05,
'profit_amount': 6.126e-05,
'profit_percent': 0.06110514,
'profit_percent': 0.0611052,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.ROI.value
} == last_msg
@ -1277,12 +1603,192 @@ def test_execute_sell_down(default_conf, ticker, fee, ticker_sell_down, markets,
'open_rate': 1.099e-05,
'current_rate': 1.044e-05,
'profit_amount': -5.492e-05,
'profit_percent': -0.05478343,
'profit_percent': -0.05478342,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.STOP_LOSS.value
} == last_msg
def test_execute_sell_down_stoploss_on_exchange_dry_run(default_conf, ticker, fee,
ticker_sell_down,
markets, mocker) -> None:
rpc_mock = patch_RPCManager(mocker)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
_load_markets=MagicMock(return_value={}),
get_ticker=ticker,
get_fee=fee,
get_markets=markets
)
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
# Create some test data
freqtrade.create_trade()
trade = Trade.query.first()
assert trade
# Decrease the price and sell it
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_ticker=ticker_sell_down
)
default_conf['dry_run'] = True
freqtrade.strategy.order_types['stoploss_on_exchange'] = True
# Setting trade stoploss to 0.01
trade.stop_loss = 0.00001099 * 0.99
freqtrade.execute_sell(trade=trade, limit=ticker_sell_down()['bid'],
sell_reason=SellType.STOP_LOSS)
assert rpc_mock.call_count == 2
last_msg = rpc_mock.call_args_list[-1][0][0]
assert {
'type': RPCMessageType.SELL_NOTIFICATION,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'gain': 'loss',
'market_url': 'https://bittrex.com/Market/Index?MarketName=BTC-ETH',
'limit': 1.08801e-05,
'amount': 90.99181073703367,
'open_rate': 1.099e-05,
'current_rate': 1.044e-05,
'profit_amount': -1.498e-05,
'profit_percent': -0.01493766,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.STOP_LOSS.value
} == last_msg
def test_execute_sell_with_stoploss_on_exchange(default_conf,
ticker, fee, ticker_sell_up,
markets, mocker) -> None:
default_conf['exchange']['name'] = 'binance'
rpc_mock = patch_RPCManager(mocker)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
_load_markets=MagicMock(return_value={}),
get_ticker=ticker,
get_fee=fee,
get_markets=markets
)
stoploss_limit = MagicMock(return_value={
'id': 123,
'info': {
'foo': 'bar'
}
})
cancel_order = MagicMock(return_value=True)
mocker.patch('freqtrade.exchange.Exchange.symbol_amount_prec', lambda s, x, y: y)
mocker.patch('freqtrade.exchange.Exchange.symbol_price_prec', lambda s, x, y: y)
mocker.patch('freqtrade.exchange.Exchange.stoploss_limit', stoploss_limit)
mocker.patch('freqtrade.exchange.Exchange.cancel_order', cancel_order)
freqtrade = FreqtradeBot(default_conf)
freqtrade.strategy.order_types['stoploss_on_exchange'] = True
patch_get_signal(freqtrade)
# Create some test data
freqtrade.create_trade()
trade = Trade.query.first()
assert trade
freqtrade.process_maybe_execute_sell(trade)
# Increase the price and sell it
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_ticker=ticker_sell_up
)
freqtrade.execute_sell(trade=trade, limit=ticker_sell_up()['bid'],
sell_reason=SellType.SELL_SIGNAL)
trade = Trade.query.first()
assert trade
assert cancel_order.call_count == 1
assert rpc_mock.call_count == 2
def test_may_execute_sell_after_stoploss_on_exchange_hit(default_conf,
ticker, fee,
limit_buy_order,
markets, mocker) -> None:
default_conf['exchange']['name'] = 'binance'
rpc_mock = patch_RPCManager(mocker)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
_load_markets=MagicMock(return_value={}),
get_ticker=ticker,
get_fee=fee,
get_markets=markets
)
stoploss_limit = MagicMock(return_value={
'id': 123,
'info': {
'foo': 'bar'
}
})
mocker.patch('freqtrade.exchange.Exchange.symbol_amount_prec', lambda s, x, y: y)
mocker.patch('freqtrade.exchange.Exchange.symbol_price_prec', lambda s, x, y: y)
mocker.patch('freqtrade.exchange.Exchange.stoploss_limit', stoploss_limit)
freqtrade = FreqtradeBot(default_conf)
freqtrade.strategy.order_types['stoploss_on_exchange'] = True
patch_get_signal(freqtrade)
# Create some test data
freqtrade.create_trade()
trade = Trade.query.first()
freqtrade.process_maybe_execute_sell(trade)
assert trade
assert trade.stoploss_order_id == '123'
assert trade.open_order_id is None
# Assuming stoploss on exchnage is hit
# stoploss_order_id should become None
# and trade should be sold at the price of stoploss
stoploss_limit_executed = MagicMock(return_value={
"id": "123",
"timestamp": 1542707426845,
"datetime": "2018-11-20T09:50:26.845Z",
"lastTradeTimestamp": None,
"symbol": "BTC/USDT",
"type": "stop_loss_limit",
"side": "sell",
"price": 1.08801,
"amount": 90.99181074,
"cost": 99.0000000032274,
"average": 1.08801,
"filled": 90.99181074,
"remaining": 0.0,
"status": "closed",
"fee": None,
"trades": None
})
mocker.patch('freqtrade.exchange.Exchange.get_order', stoploss_limit_executed)
freqtrade.process_maybe_execute_sell(trade)
assert trade.stoploss_order_id is None
assert trade.is_open is False
print(trade.sell_reason)
assert trade.sell_reason == SellType.STOPLOSS_ON_EXCHANGE.value
assert rpc_mock.call_count == 1
def test_execute_sell_without_conf_sell_up(default_conf, ticker, fee,
ticker_sell_up, markets, mocker) -> None:
rpc_mock = patch_RPCManager(mocker)
@ -1324,7 +1830,9 @@ def test_execute_sell_without_conf_sell_up(default_conf, ticker, fee,
'open_rate': 1.099e-05,
'current_rate': 1.172e-05,
'profit_amount': 6.126e-05,
'profit_percent': 0.06110514,
'profit_percent': 0.0611052,
'sell_reason': SellType.ROI.value
} == last_msg
@ -1370,7 +1878,8 @@ def test_execute_sell_without_conf_sell_down(default_conf, ticker, fee,
'open_rate': 1.099e-05,
'current_rate': 1.044e-05,
'profit_amount': -5.492e-05,
'profit_percent': -0.05478343,
'profit_percent': -0.05478342,
'sell_reason': SellType.STOP_LOSS.value
} == last_msg
@ -1395,7 +1904,7 @@ def test_sell_profit_only_enable_profit(default_conf, limit_buy_order,
}
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: False
freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
freqtrade.create_trade()
@ -1427,7 +1936,7 @@ def test_sell_profit_only_disable_profit(default_conf, limit_buy_order,
}
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: False
freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
freqtrade.create_trade()
trade = Trade.query.first()
@ -1458,7 +1967,7 @@ def test_sell_profit_only_enable_loss(default_conf, limit_buy_order, fee, market
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
freqtrade.strategy.stop_loss_reached = \
lambda current_rate, trade, current_time, current_profit: SellCheckTuple(
lambda current_rate, trade, current_time, force_stoploss, current_profit: SellCheckTuple(
sell_flag=False, sell_type=SellType.NONE)
freqtrade.create_trade()
@ -1489,7 +1998,7 @@ def test_sell_profit_only_disable_loss(default_conf, limit_buy_order, fee, marke
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: False
freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
freqtrade.create_trade()
@ -1519,7 +2028,7 @@ def test_ignore_roi_if_buy_signal(default_conf, limit_buy_order, fee, markets, m
}
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: True
freqtrade.strategy.min_roi_reached = MagicMock(return_value=True)
freqtrade.create_trade()
@ -1551,7 +2060,7 @@ def test_trailing_stop_loss(default_conf, limit_buy_order, fee, markets, caplog,
default_conf['trailing_stop'] = True
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: False
freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
freqtrade.create_trade()
@ -1586,7 +2095,7 @@ def test_trailing_stop_loss_positive(default_conf, limit_buy_order, fee, markets
default_conf['trailing_stop_positive'] = 0.01
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: False
freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
freqtrade.create_trade()
trade = Trade.query.first()
@ -1646,7 +2155,7 @@ def test_trailing_stop_loss_offset(default_conf, limit_buy_order, fee,
default_conf['trailing_stop_positive_offset'] = 0.011
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: False
freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
freqtrade.create_trade()
trade = Trade.query.first()
@ -1705,7 +2214,7 @@ def test_disable_ignore_roi_if_buy_signal(default_conf, limit_buy_order,
}
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: True
freqtrade.strategy.min_roi_reached = MagicMock(return_value=True)
freqtrade.create_trade()
@ -1732,7 +2241,7 @@ def test_get_real_amount_quote(default_conf, trades_for_order, buy_order_fee, ca
exchange='binance',
open_rate=0.245441,
open_order_id="123456"
)
)
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
@ -2008,9 +2517,9 @@ def test_order_book_bid_strategy2(mocker, default_conf, order_book_l2, markets)
"""
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_markets=markets,
get_order_book=order_book_l2
'freqtrade.exchange.Exchange',
get_markets=markets,
get_order_book=order_book_l2
)
default_conf['exchange']['name'] = 'binance'
default_conf['bid_strategy']['use_order_book'] = True
@ -2107,6 +2616,9 @@ def test_order_book_ask_strategy(default_conf, limit_buy_order, limit_sell_order
def test_startup_messages(default_conf, mocker):
default_conf['dynamic_whitelist'] = 20
default_conf['pairlist'] = {'method': 'VolumePairList',
'config': {'number_assets': 20}
}
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
freqtrade = get_patched_freqtradebot(mocker, default_conf)
assert freqtrade.state is State.RUNNING

10
freqtrade/tests/test_misc.py
View File

@ -3,10 +3,10 @@
import datetime
from unittest.mock import MagicMock
from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.misc import (common_datearray, datesarray_to_datetimearray,
file_dump_json, format_ms_time, shorten_date)
from freqtrade.optimize.__init__ import load_tickerdata_file
from freqtrade.data.history import load_tickerdata_file
from freqtrade.strategy.default_strategy import DefaultStrategy
@ -16,8 +16,8 @@ def test_shorten_date() -> None:
assert shorten_date(str_data) == str_shorten_data
def test_datesarray_to_datetimearray(ticker_history):
dataframes = parse_ticker_dataframe(ticker_history)
def test_datesarray_to_datetimearray(ticker_history_list):
dataframes = parse_ticker_dataframe(ticker_history_list)
dates = datesarray_to_datetimearray(dataframes['date'])
assert isinstance(dates[0], datetime.datetime)
@ -34,7 +34,7 @@ def test_datesarray_to_datetimearray(ticker_history):
def test_common_datearray(default_conf) -> None:
strategy = DefaultStrategy(default_conf)
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
tickerlist = {'UNITTEST/BTC': tick}
tickerlist = {'UNITTEST/BTC': parse_ticker_dataframe(tick)}
dataframes = strategy.tickerdata_to_dataframe(tickerlist)
dates = common_datearray(dataframes)

73
freqtrade/tests/test_persistence.py
View File

@ -62,7 +62,7 @@ def test_init_dryrun_db(default_conf, mocker):
@pytest.mark.usefixtures("init_persistence")
def test_update_with_bittrex(limit_buy_order, limit_sell_order, fee):
def test_update_with_bittrex(limit_buy_order, limit_sell_order, fee, caplog):
"""
On this test we will buy and sell a crypto currency.
@ -91,6 +91,7 @@ def test_update_with_bittrex(limit_buy_order, limit_sell_order, fee):
"""
trade = Trade(
id=2,
pair='ETH/BTC',
stake_amount=0.001,
fee_open=fee.return_value,
@ -108,13 +109,53 @@ def test_update_with_bittrex(limit_buy_order, limit_sell_order, fee):
assert trade.open_rate == 0.00001099
assert trade.close_profit is None
assert trade.close_date is None
assert log_has("LIMIT_BUY has been fulfilled for Trade(id=2, "
"pair=ETH/BTC, amount=90.99181073, open_rate=0.00001099, open_since=closed).",
caplog.record_tuples)
caplog.clear()
trade.open_order_id = 'something'
trade.update(limit_sell_order)
assert trade.open_order_id is None
assert trade.close_rate == 0.00001173
assert trade.close_profit == 0.06201057
assert trade.close_profit == 0.06201058
assert trade.close_date is not None
assert log_has("LIMIT_SELL has been fulfilled for Trade(id=2, "
"pair=ETH/BTC, amount=90.99181073, open_rate=0.00001099, open_since=closed).",
caplog.record_tuples)
@pytest.mark.usefixtures("init_persistence")
def test_update_market_order(market_buy_order, market_sell_order, fee, caplog):
trade = Trade(
id=1,
pair='ETH/BTC',
stake_amount=0.001,
fee_open=fee.return_value,
fee_close=fee.return_value,
exchange='bittrex',
)
trade.open_order_id = 'something'
trade.update(market_buy_order)
assert trade.open_order_id is None
assert trade.open_rate == 0.00004099
assert trade.close_profit is None
assert trade.close_date is None
assert log_has("MARKET_BUY has been fulfilled for Trade(id=1, "
"pair=ETH/BTC, amount=91.99181073, open_rate=0.00004099, open_since=closed).",
caplog.record_tuples)
caplog.clear()
trade.open_order_id = 'something'
trade.update(market_sell_order)
assert trade.open_order_id is None
assert trade.close_rate == 0.00004173
assert trade.close_profit == 0.01297561
assert trade.close_date is not None
assert log_has("MARKET_SELL has been fulfilled for Trade(id=1, "
"pair=ETH/BTC, amount=91.99181073, open_rate=0.00004099, open_since=closed).",
caplog.record_tuples)
@pytest.mark.usefixtures("init_persistence")
@ -129,16 +170,16 @@ def test_calc_open_close_trade_price(limit_buy_order, limit_sell_order, fee):
trade.open_order_id = 'something'
trade.update(limit_buy_order)
assert trade.calc_open_trade_price() == 0.001002500
assert trade.calc_open_trade_price() == 0.0010024999999225068
trade.update(limit_sell_order)
assert trade.calc_close_trade_price() == 0.0010646656
assert trade.calc_close_trade_price() == 0.0010646656050132426
# Profit in BTC
assert trade.calc_profit() == 0.00006217
# Profit in percent
assert trade.calc_profit_percent() == 0.06201057
assert trade.calc_profit_percent() == 0.06201058
@pytest.mark.usefixtures("init_persistence")
@ -207,10 +248,10 @@ def test_calc_open_trade_price(limit_buy_order, fee):
trade.update(limit_buy_order) # Buy @ 0.00001099
# Get the open rate price with the standard fee rate
assert trade.calc_open_trade_price() == 0.001002500
assert trade.calc_open_trade_price() == 0.0010024999999225068
# Get the open rate price with a custom fee rate
assert trade.calc_open_trade_price(fee=0.003) == 0.001003000
assert trade.calc_open_trade_price(fee=0.003) == 0.001002999999922468
@pytest.mark.usefixtures("init_persistence")
@ -226,14 +267,14 @@ def test_calc_close_trade_price(limit_buy_order, limit_sell_order, fee):
trade.update(limit_buy_order) # Buy @ 0.00001099
# Get the close rate price with a custom close rate and a regular fee rate
assert trade.calc_close_trade_price(rate=0.00001234) == 0.0011200318
assert trade.calc_close_trade_price(rate=0.00001234) == 0.0011200318470471794
# Get the close rate price with a custom close rate and a custom fee rate
assert trade.calc_close_trade_price(rate=0.00001234, fee=0.003) == 0.0011194704
assert trade.calc_close_trade_price(rate=0.00001234, fee=0.003) == 0.0011194704275749754
# Test when we apply a Sell order, and ask price with a custom fee rate
trade.update(limit_sell_order)
assert trade.calc_close_trade_price(fee=0.005) == 0.0010619972
assert trade.calc_close_trade_price(fee=0.005) == 0.0010619972701635854
@pytest.mark.usefixtures("init_persistence")
@ -281,17 +322,17 @@ def test_calc_profit_percent(limit_buy_order, limit_sell_order, fee):
trade.update(limit_buy_order) # Buy @ 0.00001099
# Get percent of profit with a custom rate (Higher than open rate)
assert trade.calc_profit_percent(rate=0.00001234) == 0.1172387
assert trade.calc_profit_percent(rate=0.00001234) == 0.11723875
# Get percent of profit with a custom rate (Lower than open rate)
assert trade.calc_profit_percent(rate=0.00000123) == -0.88863827
assert trade.calc_profit_percent(rate=0.00000123) == -0.88863828
# Test when we apply a Sell order. Sell higher than open rate @ 0.00001173
trade.update(limit_sell_order)
assert trade.calc_profit_percent() == 0.06201057
assert trade.calc_profit_percent() == 0.06201058
# Test with a custom fee rate on the close trade
assert trade.calc_profit_percent(fee=0.003) == 0.0614782
assert trade.calc_profit_percent(fee=0.003) == 0.06147824
def test_clean_dry_run_db(default_conf, fee):
@ -426,6 +467,7 @@ def test_migrate_new(mocker, default_conf, fee, caplog):
max_rate FLOAT,
sell_reason VARCHAR,
strategy VARCHAR,
ticker_interval INTEGER,
PRIMARY KEY (id),
CHECK (is_open IN (0, 1))
);"""
@ -445,6 +487,8 @@ def test_migrate_new(mocker, default_conf, fee, caplog):
# Create table using the old format
engine.execute(create_table_old)
engine.execute("create index ix_trades_is_open on trades(is_open)")
engine.execute("create index ix_trades_pair on trades(pair)")
engine.execute(insert_table_old)
# fake previous backup
@ -471,6 +515,7 @@ def test_migrate_new(mocker, default_conf, fee, caplog):
assert trade.sell_reason is None
assert trade.strategy is None
assert trade.ticker_interval is None
assert trade.stoploss_order_id is None
assert log_has("trying trades_bak1", caplog.record_tuples)
assert log_has("trying trades_bak2", caplog.record_tuples)
assert log_has("Running database migration - backup available as trades_bak2",

91
freqtrade/tests/test_wallets.py Normal file
View File

@ -0,0 +1,91 @@
# pragma pylint: disable=missing-docstring
from freqtrade.tests.conftest import get_patched_freqtradebot
from unittest.mock import MagicMock
def test_sync_wallet_at_boot(mocker, default_conf):
default_conf['dry_run'] = False
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value={
"BNT": {
"free": 1.0,
"used": 2.0,
"total": 3.0
},
"GAS": {
"free": 0.260739,
"used": 0.0,
"total": 0.260739
},
})
)
freqtrade = get_patched_freqtradebot(mocker, default_conf)
assert len(freqtrade.wallets.wallets) == 2
assert freqtrade.wallets.wallets['BNT'].free == 1.0
assert freqtrade.wallets.wallets['BNT'].used == 2.0
assert freqtrade.wallets.wallets['BNT'].total == 3.0
assert freqtrade.wallets.wallets['GAS'].free == 0.260739
assert freqtrade.wallets.wallets['GAS'].used == 0.0
assert freqtrade.wallets.wallets['GAS'].total == 0.260739
assert freqtrade.wallets.get_free('BNT') == 1.0
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value={
"BNT": {
"free": 1.2,
"used": 1.9,
"total": 3.5
},
"GAS": {
"free": 0.270739,
"used": 0.1,
"total": 0.260439
},
})
)
freqtrade.wallets.update()
assert len(freqtrade.wallets.wallets) == 2
assert freqtrade.wallets.wallets['BNT'].free == 1.2
assert freqtrade.wallets.wallets['BNT'].used == 1.9
assert freqtrade.wallets.wallets['BNT'].total == 3.5
assert freqtrade.wallets.wallets['GAS'].free == 0.270739
assert freqtrade.wallets.wallets['GAS'].used == 0.1
assert freqtrade.wallets.wallets['GAS'].total == 0.260439
assert freqtrade.wallets.get_free('GAS') == 0.270739
assert freqtrade.wallets.get_used('GAS') == 0.1
assert freqtrade.wallets.get_total('GAS') == 0.260439
def test_sync_wallet_missing_data(mocker, default_conf):
default_conf['dry_run'] = False
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value={
"BNT": {
"free": 1.0,
"used": 2.0,
"total": 3.0
},
"GAS": {
"free": 0.260739,
"total": 0.260739
},
})
)
freqtrade = get_patched_freqtradebot(mocker, default_conf)
assert len(freqtrade.wallets.wallets) == 2
assert freqtrade.wallets.wallets['BNT'].free == 1.0
assert freqtrade.wallets.wallets['BNT'].used == 2.0
assert freqtrade.wallets.wallets['BNT'].total == 3.0
assert freqtrade.wallets.wallets['GAS'].free == 0.260739
assert freqtrade.wallets.wallets['GAS'].used is None
assert freqtrade.wallets.wallets['GAS'].total == 0.260739
assert freqtrade.wallets.get_free('GAS') == 0.260739

77
freqtrade/wallets.py Normal file
View File

@ -0,0 +1,77 @@
# pragma pylint: disable=W0603
""" Wallet """
import logging
from typing import Dict, Any, NamedTuple
from collections import namedtuple
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Wallet(NamedTuple):
exchange: str
currency: str
free: float = 0
used: float = 0
total: float = 0
class Wallets(object):
# wallet data structure
wallet = namedtuple(
'wallet',
['exchange', 'currency', 'free', 'used', 'total']
)
def __init__(self, exchange: Exchange) -> None:
self.exchange = exchange
self.wallets: Dict[str, Any] = {}
self.update()
def get_free(self, currency) -> float:
if self.exchange._conf['dry_run']:
return 999.9
balance = self.wallets.get(currency)
if balance and balance.free:
return balance.free
else:
return 0
def get_used(self, currency) -> float:
if self.exchange._conf['dry_run']:
return 999.9
balance = self.wallets.get(currency)
if balance and balance.used:
return balance.used
else:
return 0
def get_total(self, currency) -> float:
if self.exchange._conf['dry_run']:
return 999.9
balance = self.wallets.get(currency)
if balance and balance.total:
return balance.total
else:
return 0
def update(self) -> None:
balances = self.exchange.get_balances()
for currency in balances:
self.wallets[currency] = Wallet(
self.exchange.id,
currency,
balances[currency].get('free', None),
balances[currency].get('used', None),
balances[currency].get('total', None)
)
logger.info('Wallets synced ...')

7
install_ta-lib.sh
View File

@ -1,7 +0,0 @@
if [ ! -f "ta-lib/CHANGELOG.TXT" ]; then
tar zxvf ta-lib-0.4.0-src.tar.gz
cd ta-lib && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h && ./configure && make && sudo make install && cd ..
else
echo "TA-lib already installed, skipping download and build."
cd ta-lib && sudo make install && cd ..
fi

8
requirements-dev.txt Normal file
View File

@ -0,0 +1,8 @@
# Include all requirements to run the bot.
-r requirements.txt
flake8==3.6.0
pytest==4.0.2
pytest-mock==1.10.0
pytest-asyncio==0.9.0
pytest-cov==2.6.0

28
requirements.txt
View File

@ -1,26 +1,26 @@
ccxt==1.17.363
SQLAlchemy==1.2.12
ccxt==1.18.71
SQLAlchemy==1.2.15
python-telegram-bot==11.1.0
arrow==0.12.1
cachetools==2.1.0
requests==2.19.1
urllib3==1.22
cachetools==3.0.0
requests==2.21.0
urllib3==1.24.1
wrapt==1.10.11
pandas==0.23.4
scikit-learn==0.20.0
scipy==1.1.0
scikit-learn==0.20.2
joblib==0.13.0
scipy==1.2.0
jsonschema==2.6.0
numpy==1.15.2
numpy==1.15.4
TA-Lib==0.4.17
pytest==3.8.1
pytest-mock==1.10.0
pytest-asyncio==0.9.0
pytest-cov==2.6.0
tabulate==0.8.2
coinmarketcap==5.0.3
# Required for hyperopt
scikit-optimize==0.5.2
# Required for plotting data
#plotly==3.1.1
# find first, C search in arrays
py_find_1st==1.1.3
#Load ticker files 30% faster
ujson==1.35

200
scripts/convert_backtestdata.py
View File

@ -1,200 +0,0 @@
#!/usr/bin/env python3
"""
Script to display when the bot will buy a specific pair
Mandatory Cli parameters:
-p / --pair: pair to examine
Optional Cli parameters
-d / --datadir: path to pair backtest data
--timerange: specify what timerange of data to use.
-l / --live: Live, to download the latest ticker for the pair
"""
import logging
import sys
from argparse import Namespace
from os import path
import glob
import json
import re
from typing import List, Dict
import gzip
from freqtrade.arguments import Arguments
from freqtrade import misc, constants
from pandas import DataFrame
import dateutil.parser
logger = logging.getLogger('freqtrade')
def load_old_file(filename) -> (List[Dict], bool):
if not path.isfile(filename):
logger.warning("filename %s does not exist", filename)
return (None, False)
logger.debug('Loading ticker data from file %s', filename)
pairdata = None
if filename.endswith('.gz'):
logger.debug('Loading ticker data from file %s', filename)
is_zip = True
with gzip.open(filename) as tickerdata:
pairdata = json.load(tickerdata)
else:
is_zip = False
with open(filename) as tickerdata:
pairdata = json.load(tickerdata)
return (pairdata, is_zip)
def parse_old_backtest_data(ticker) -> DataFrame:
"""
Reads old backtest data
Format: "O": 8.794e-05,
"H": 8.948e-05,
"L": 8.794e-05,
"C": 8.88e-05,
"V": 991.09056638,
"T": "2017-11-26T08:50:00",
"BV": 0.0877869
"""
columns = {'C': 'close', 'V': 'volume', 'O': 'open',
'H': 'high', 'L': 'low', 'T': 'date'}
frame = DataFrame(ticker) \
.rename(columns=columns)
if 'BV' in frame:
frame.drop('BV', 1, inplace=True)
if 'date' not in frame:
logger.warning("Date not in frame - probably not a Ticker file")
return None
frame.sort_values('date', inplace=True)
return frame
def convert_dataframe(frame: DataFrame):
"""Convert dataframe to new format"""
# reorder columns:
cols = ['date', 'open', 'high', 'low', 'close', 'volume']
frame = frame[cols]
# Make sure parsing/printing data is assumed to be UTC
frame['date'] = frame['date'].apply(
lambda d: int(dateutil.parser.parse(d+'+00:00').timestamp()) * 1000)
frame['date'] = frame['date'].astype('int64')
# Convert columns one by one to preserve type.
by_column = [frame[x].values.tolist() for x in frame.columns]
return list(list(x) for x in zip(*by_column))
def convert_file(filename: str, filename_new: str) -> None:
"""Converts a file from old format to ccxt format"""
(pairdata, is_zip) = load_old_file(filename)
if pairdata and type(pairdata) is list:
if type(pairdata[0]) is list:
logger.error("pairdata for %s already in new format", filename)
return
frame = parse_old_backtest_data(pairdata)
# Convert frame to new format
if frame is not None:
frame1 = convert_dataframe(frame)
misc.file_dump_json(filename_new, frame1, is_zip)
def convert_main(args: Namespace) -> None:
"""
converts a folder given in --datadir from old to new format to support ccxt
"""
workdir = path.join(args.datadir, "")
logger.info("Workdir: %s", workdir)
for filename in glob.glob(workdir + "*.json"):
# swap currency names
ret = re.search(r'[A-Z_]{7,}', path.basename(filename))
if args.norename:
filename_new = filename
else:
if not ret:
logger.warning("file %s could not be converted, could not extract currencies",
filename)
continue
pair = ret.group(0)
currencies = pair.split("_")
if len(currencies) != 2:
logger.warning("file %s could not be converted, could not extract currencies",
filename)
continue
ret_integer = re.search(r'\d+(?=\.json)', path.basename(filename))
ret_string = re.search(r'(\d+[mhdw])(?=\.json)', path.basename(filename))
if ret_integer:
minutes = int(ret_integer.group(0))
# default to adding 'm' to end of minutes for new interval name
interval = str(minutes) + 'm'
# but check if there is a mapping between int and string also
for str_interval, minutes_interval in constants.TICKER_INTERVAL_MINUTES.items():
if minutes_interval == minutes:
interval = str_interval
break
# change order on pairs if old ticker interval found
filename_new = path.join(path.dirname(filename),
f"{currencies[1]}_{currencies[0]}-{interval}.json")
elif ret_string:
interval = ret_string.group(0)
filename_new = path.join(path.dirname(filename),
f"{currencies[0]}_{currencies[1]}-{interval}.json")
else:
logger.warning("file %s could not be converted, interval not found", filename)
continue
logger.debug("Converting and renaming %s to %s", filename, filename_new)
convert_file(filename, filename_new)
def convert_parse_args(args: List[str]) -> Namespace:
"""
Parse args passed to the script
:param args: Cli arguments
:return: args: Array with all arguments
"""
arguments = Arguments(args, 'Convert datafiles')
arguments.parser.add_argument(
'-d', '--datadir',
help='path to backtest data (default: %(default)s',
dest='datadir',
default=path.join('freqtrade', 'tests', 'testdata'),
type=str,
metavar='PATH',
)
arguments.parser.add_argument(
'-n', '--norename',
help='don''t rename files from BTC_<PAIR> to <PAIR>_BTC - '
'Note that not renaming will overwrite source files',
dest='norename',
default=False,
action='store_true'
)
return arguments.parse_args()
def main(sysargv: List[str]) -> None:
"""
This function will initiate the bot and start the trading loop.
:return: None
"""
logger.info('Starting Dataframe conversation')
convert_main(convert_parse_args(sysargv))
if __name__ == '__main__':
main(sys.argv[1:])

53
scripts/download_backtest_data.py
View File

@ -9,8 +9,15 @@ import arrow
from freqtrade import arguments
from freqtrade.arguments import TimeRange
from freqtrade.exchange import Exchange
from freqtrade.optimize import download_backtesting_testdata
from freqtrade.data.history import download_pair_history
from freqtrade.configuration import Configuration, set_loggers
import logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
set_loggers(0)
DEFAULT_DL_PATH = 'user_data/data'
@ -20,7 +27,29 @@ args = arguments.parse_args()
timeframes = args.timeframes
dl_path = Path(DEFAULT_DL_PATH).joinpath(args.exchange)
if args.config:
configuration = Configuration(args)
config = configuration._load_config_file(args.config)
# Ensure we do not use Exchange credentials
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
else:
config = {'stake_currency': '',
'dry_run': True,
'exchange': {
'name': args.exchange,
'key': '',
'secret': '',
'pair_whitelist': [],
'ccxt_async_config': {
"enableRateLimit": False
}
}
}
dl_path = Path(DEFAULT_DL_PATH).joinpath(config['exchange']['name'])
if args.export:
dl_path = Path(args.export)
@ -45,18 +74,8 @@ if args.days:
print(f'About to download pairs: {PAIRS} to {dl_path}')
# Init exchange
exchange = Exchange({'key': '',
'secret': '',
'stake_currency': '',
'dry_run': True,
'exchange': {
'name': args.exchange,
'pair_whitelist': [],
'ccxt_rate_limit': False
}
})
exchange = Exchange(config)
pairs_not_available = []
for pair in PAIRS:
@ -73,10 +92,10 @@ for pair in PAIRS:
dl_file.unlink()
print(f'downloading pair {pair}, interval {tick_interval}')
download_backtesting_testdata(str(dl_path), exchange=exchange,
pair=pair,
tick_interval=tick_interval,
timerange=timerange)
download_pair_history(datadir=dl_path, exchange=exchange,
pair=pair,
tick_interval=tick_interval,
timerange=timerange)
if pairs_not_available:

10
scripts/plot_dataframe.py
View File

@ -38,13 +38,13 @@ import pytz
from plotly import tools
from plotly.offline import plot
import freqtrade.optimize as optimize
from freqtrade import persistence
from freqtrade.arguments import Arguments, TimeRange
from freqtrade.data import history
from freqtrade.exchange import Exchange
from freqtrade.optimize.backtesting import setup_configuration
from freqtrade.persistence import Trade
from freqtrade.strategy.resolver import StrategyResolver
from freqtrade.resolvers import StrategyResolver
logger = logging.getLogger(__name__)
_CONF: Dict[str, Any] = {}
@ -139,10 +139,10 @@ def plot_analyzed_dataframe(args: Namespace) -> None:
if args.live:
logger.info('Downloading pair.')
exchange.refresh_tickers([pair], tick_interval)
tickers[pair] = exchange.klines[pair]
tickers[pair] = exchange.klines(pair)
else:
tickers = optimize.load_data(
datadir=_CONF.get("datadir"),
tickers = history.load_data(
datadir=Path(_CONF.get("datadir")),
pairs=[pair],
ticker_interval=tick_interval,
refresh_pairs=_CONF.get('refresh_pairs', False),

12
scripts/plot_profit.py
View File

@ -13,10 +13,10 @@ Optional Cli parameters
--export-filename: Specify where the backtest export is located.
"""
import logging
import os
import sys
import json
from argparse import Namespace
from pathlib import Path
from typing import List, Optional
import numpy as np
@ -27,8 +27,8 @@ import plotly.graph_objs as go
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade import constants
from freqtrade.strategy.resolver import StrategyResolver
import freqtrade.optimize as optimize
from freqtrade.data import history
from freqtrade.resolvers import StrategyResolver
import freqtrade.misc as misc
@ -120,8 +120,8 @@ def plot_profit(args: Namespace) -> None:
pairs = list(set(pairs) & set(filter_pairs))
logger.info('Filter, keep pairs %s' % pairs)
tickers = optimize.load_data(
datadir=config.get('datadir'),
tickers = history.load_data(
datadir=Path(config.get('datadir')),
pairs=pairs,
ticker_interval=tick_interval,
refresh_pairs=False,
@ -187,7 +187,7 @@ def plot_profit(args: Namespace) -> None:
)
fig.append_trace(pair_profit, 3, 1)
plot(fig, filename=os.path.join('user_data', 'freqtrade-profit-plot.html'))
plot(fig, filename=str(Path('user_data').joinpath('freqtrade-profit-plot.html')))
def define_index(min_date: int, max_date: int, interval: str) -> int:

3
setup.py
View File

@ -31,12 +31,15 @@ setup(name='freqtrade',
'pandas',
'scikit-learn',
'scipy',
'joblib',
'jsonschema',
'TA-Lib',
'tabulate',
'cachetools',
'coinmarketcap',
'scikit-optimize',
'ujson',
'py_find_1st'
],
include_package_data=True,
zip_safe=False,

23
setup.sh
View File

@ -28,6 +28,16 @@ function updateenv () {
pip3 install --quiet --upgrade pip
pip3 install --quiet -r requirements.txt --upgrade
pip3 install --quiet -r requirements.txt
read -p "Do you want to install dependencies for dev [Y/N]? "
if [[ $REPLY =~ ^[Yy]$ ]]
then
pip3 install --quiet -r requirements-dev.txt --upgrade
pip3 install --quiet -r requirements-dev.txt
else
echo "Dev dependencies ignored."
fi
pip3 install --quiet -e .
echo "pip3 install completed"
echo
@ -35,10 +45,13 @@ function updateenv () {
# Install tab lib
function install_talib () {
curl -O -L http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar zxvf ta-lib-0.4.0-src.tar.gz
cd ta-lib && ./configure --prefix=/usr && make && sudo make install
cd .. && rm -rf ./ta-lib*
cd ta-lib
sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h
./configure --prefix=/usr/local
make
sudo make install
cd .. && rm -rf ./ta-lib/
}
# Install bot MacOS
@ -50,8 +63,8 @@ function install_macos () {
echo "-------------------------"
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
fi
brew install python3 wget ta-lib
brew install python3 wget
install_talib
test_and_fix_python_on_mac
}

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