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6
Dockerfile
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@ -1,10 +1,11 @@
FROM python:3.6.5-slim-stretch
FROM python:3.6.6-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
@ -15,7 +16,8 @@ WORKDIR /freqtrade
# Install dependencies
COPY requirements.txt /freqtrade/
RUN pip install -r requirements.txt
RUN pip install numpy \
&& pip install -r requirements.txt
# Install and execute
COPY . /freqtrade/

87
README.md
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@ -4,13 +4,12 @@
[![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
[![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
Simple High frequency trading bot for crypto currencies designed to
support multi exchanges and be controlled via Telegram.
Simple High frequency trading bot for crypto currencies designed to support multi exchanges and be controlled via Telegram.
![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade-screenshot.png)
## Disclaimer
This software is for educational purposes only. Do not risk money which
you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS
AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
@ -23,18 +22,18 @@ We strongly recommend you to have coding and Python knowledge. Do not
hesitate to read the source code and understand the mechanism of this bot.
## Exchange marketplaces supported
- [X] [Bittrex](https://bittrex.com/)
- [X] [Binance](https://www.binance.com/)
- [X] [Binance](https://www.binance.com/) ([*Note for binance users](#a-note-on-binance))
- [ ] [113 others to tests](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
## Features
- [x] **Based on Python 3.6+**: For botting on any operating system -
Windows, macOS and Linux
- [x] **Based on Python 3.6+**: For botting on any operating system - Windows, macOS and Linux
- [x] **Persistence**: Persistence is achieved through sqlite
- [x] **Dry-run**: Run the bot without playing money.
- [x] **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] **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] **Blacklist crypto-currencies**: Select which crypto-currency you want to avoid.
- [x] **Manageable via Telegram**: Manage the bot with Telegram
@ -43,38 +42,45 @@ strategy parameters with real exchange data.
- [x] **Performance status report**: Provide a performance status of your current trades.
## Table of Contents
- [Quick start](#quick-start)
- [Documentations](https://github.com/freqtrade/freqtrade/blob/develop/docs/index.md)
- [Installation](https://github.com/freqtrade/freqtrade/blob/develop/docs/installation.md)
- [Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md)
- [Strategy Optimization](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md)
- [Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.md)
- [Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)
- [Installation](https://github.com/freqtrade/freqtrade/blob/develop/docs/installation.md)
- [Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md)
- [Strategy Optimization](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md)
- [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)
- [Basic Usage](#basic-usage)
- [Bot commands](#bot-commands)
- [Telegram RPC commands](#telegram-rpc-commands)
- [Support](#support)
- [Help](#help--slack)
- [Bugs](#bugs--issues)
- [Feature Requests](#feature-requests)
- [Pull Requests](#pull-requests)
- [Help](#help--slack)
- [Bugs](#bugs--issues)
- [Feature Requests](#feature-requests)
- [Pull Requests](#pull-requests)
- [Requirements](#requirements)
- [Min hardware required](#min-hardware-required)
- [Software requirements](#software-requirements)
- [Min hardware required](#min-hardware-required)
- [Software requirements](#software-requirements)
## Quick start
Freqtrade provides a Linux/macOS script to install all dependencies and help you to configure the bot.
```bash
git clone git@github.com:freqtrade/freqtrade.git
git checkout develop
cd freqtrade
git checkout develop
./setup.sh --install
```
_Windows installation is explained in [Installation doc](https://github.com/freqtrade/freqtrade/blob/develop/docs/installation.md)_
## Documentation
We invite you to read the bot documentation to ensure you understand how the bot is working.
- [Index](https://github.com/freqtrade/freqtrade/blob/develop/docs/index.md)
- [Installation](https://github.com/freqtrade/freqtrade/blob/develop/docs/installation.md)
- [Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md)
@ -86,7 +92,6 @@ We invite you to read the bot documentation to ensure you understand how the bot
- [Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.md)
- [Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)
## Basic Usage
### Bot commands
@ -125,17 +130,15 @@ optional arguments:
```
### Telegram RPC commands
Telegram is not mandatory. However, this is a great way to control your
bot. More details on our
[documentation](https://github.com/freqtrade/freqtrade/blob/develop/docs/index.md)
Telegram is not mandatory. However, this is a great way to control your bot. More details on our [documentation](https://github.com/freqtrade/freqtrade/blob/develop/docs/index.md)
- `/start`: Starts the trader
- `/stop`: Stops the trader
- `/status [table]`: Lists all open trades
- `/count`: Displays number of open trades
- `/profit`: Lists cumulative profit from all finished trades
- `/forcesell <trade_id>|all`: Instantly sells the given trade
(Ignoring `minimum_roi`).
- `/forcesell <trade_id>|all`: Instantly sells the given trade (Ignoring `minimum_roi`).
- `/performance`: Show performance of each finished trade grouped by pair
- `/balance`: Show account balance per currency
- `/daily <n>`: Shows profit or loss per day, over the last n days
@ -144,20 +147,30 @@ bot. More details on our
## Development branches
The project is currently setup in two main branches:
- `develop` - This branch has often new features, but might also cause
breaking changes.
- `master` - This branch contains the latest stable release. The bot
'should' be stable on this branch, and is generally well tested.
The project is currently setup in two main branches:
- `develop` - This branch has often new features, but might also cause breaking changes.
- `master` - This branch contains the latest stable release. The bot 'should' be stable on this branch, and is generally well tested.
- `feat/*` - These are feature branches, which are beeing worked on heavily. Please don't use these unless you want to test a specific feature.
## A note on Binance
For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB order unsellable as the expected amount is not there anymore.
## Support
### Help / Slack
For any questions not covered by the documentation or for further
information about the bot, we encourage you to join our slack channel.
- [Click here to join Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE).
### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
If you discover a bug in the bot, please
[search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
first. If it hasn't been reported, please
@ -166,6 +179,7 @@ ensure you follow the template guide so that our team can assist you as
quickly as possible.
### [Feature Requests](https://github.com/freqtrade/freqtrade/labels/enhancement)
Have you a great idea to improve the bot you want to share? Please,
first search if this feature was not [already discussed](https://github.com/freqtrade/freqtrade/labels/enhancement).
If it hasn't been requested, please
@ -174,6 +188,7 @@ and ensure you follow the template guide so that it does not get lost
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!
Please read our
[Contributing document](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
@ -181,16 +196,18 @@ to understand the requirements before sending your pull-requests.
**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`.
**Important:** Always create your PR against the `develop` branch, not `master`.
## Requirements
### Min hardware required
To run this bot we recommend you a cloud instance with a minimum of:
* Minimal (advised) system requirements: 2GB RAM, 1GB disk space, 2vCPU
- Minimal (advised) system requirements: 2GB RAM, 1GB disk space, 2vCPU
### Software requirements
- [Python 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/)
- [pip](https://pip.pypa.io/en/stable/installing/)
- [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)

7
config.json.example
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@ -5,7 +5,11 @@
"fiat_display_currency": "USD",
"ticker_interval" : "5m",
"dry_run": false,
"unfilledtimeout": 600,
"trailing_stop": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30
},
"bid_strategy": {
"ask_last_balance": 0.0
},
@ -13,6 +17,7 @@
"name": "bittrex",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"ccxt_rate_limit": true,
"pair_whitelist": [
"ETH/BTC",
"LTC/BTC",

9
config_full.json.example
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@ -5,6 +5,9 @@
"fiat_display_currency": "USD",
"dry_run": false,
"ticker_interval": "5m",
"trailing_stop": false,
"trailing_stop_positive": 0.005,
"trailing_stop_positive_offset": 0.0051,
"minimal_roi": {
"40": 0.0,
"30": 0.01,
@ -12,7 +15,10 @@
"0": 0.04
},
"stoploss": -0.10,
"unfilledtimeout": 600,
"unfilledtimeout": {
"buy": 10,
"sell": 30
},
"bid_strategy": {
"ask_last_balance": 0.0
},
@ -20,6 +26,7 @@
"name": "bittrex",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"ccxt_rate_limit": true,
"pair_whitelist": [
"ETH/BTC",
"LTC/BTC",

65
docs/backtesting.md
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@ -29,25 +29,25 @@ The backtesting is very easy with freqtrade.
#### With 5 min tickers (Per default)
```bash
python3 ./freqtrade/main.py backtesting --realistic-simulation
python3 ./freqtrade/main.py backtesting
```
#### With 1 min tickers
```bash
python3 ./freqtrade/main.py backtesting --realistic-simulation --ticker-interval 1m
python3 ./freqtrade/main.py backtesting --ticker-interval 1m
```
#### Update cached pairs with the latest data
```bash
python3 ./freqtrade/main.py backtesting --realistic-simulation --refresh-pairs-cached
python3 ./freqtrade/main.py backtesting --refresh-pairs-cached
```
#### With live data (do not alter your testdata files)
```bash
python3 ./freqtrade/main.py backtesting --realistic-simulation --live
python3 ./freqtrade/main.py backtesting --live
```
#### Using a different on-disk ticker-data source
@ -70,6 +70,36 @@ Where `-s TestStrategy` refers to the class name within the strategy file `test_
python3 ./freqtrade/main.py backtesting --export trades
```
The exported trades can be read using the following code for manual analysis, or can be used by the plotting script `plot_dataframe.py` in the scripts folder.
``` python
import json
from pathlib import Path
import pandas as pd
filename=Path('user_data/backtest_data/backtest-result.json')
with filename.open() as file:
data = json.load(file)
columns = ["pair", "profit", "opents", "closets", "index", "duration",
"open_rate", "close_rate", "open_at_end", "sell_reason"]
df = pd.DataFrame(data, columns=columns)
df['opents'] = pd.to_datetime(df['opents'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['closets'] = pd.to_datetime(df['closets'],
unit='s',
utc=True,
infer_datetime_format=True
)
```
If you have some ideas for interesting / helpful backtest data analysis, feel free to submit a PR so the community can benefit from it.
#### Exporting trades to file specifying a custom filename
```bash
@ -121,7 +151,7 @@ cp freqtrade/tests/testdata/pairs.json user_data/data/binance
Then run:
```bash
python scripts/download_backtest_data --exchange binance
python scripts/download_backtest_data.py --exchange binance
```
This will download ticker data for all the currency pairs you defined in `pairs.json`.
@ -208,6 +238,31 @@ On the other hand, if you set a too high `minimal_roi` like `"0": 0.55`
profit. Hence, keep in mind that your performance is a mix of your
strategies, your configuration, and the crypto-currency you have set up.
## Backtesting multiple strategies
To backtest multiple strategies, a list of Strategies can be provided.
This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple
strategies you'd like to compare, this should give a nice runtime boost.
All listed Strategies need to be in the same folder.
``` bash
freqtrade backtesting --timerange 20180401-20180410 --ticker-interval 5m --strategy-list Strategy001 Strategy002 --export trades
```
This will save the results to `user_data/backtest_data/backtest-result-<strategy>.json`, injecting the strategy-name into the target filename.
There will be an additional table comparing win/losses of the different strategies (identical to the "Total" row in the first table).
Detailed output for all strategies one after the other will be available, so make sure to scroll up.
```
=================================================== Strategy Summary ====================================================
| Strategy | buy count | avg profit % | cum profit % | total profit ETH | avg duration | profit | loss |
|:-----------|------------:|---------------:|---------------:|-------------------:|:----------------|---------:|-------:|
| Strategy1 | 19 | -0.76 | -14.39 | -0.01440287 | 15:48:00 | 15 | 4 |
| Strategy2 | 6 | -2.73 | -16.40 | -0.01641299 | 1 day, 14:12:00 | 3 | 3 |
```
## Next step
Great, your strategy is profitable. What if the bot can give your the

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docs/bot-optimization.md
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@ -39,7 +39,6 @@ A strategy file contains all the information needed to build a good strategy:
- Sell strategy rules
- Minimal ROI recommended
- Stoploss recommended
- Hyperopt parameter
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`
@ -61,22 +60,22 @@ file as reference.**
### Buy strategy
Edit the method `populate_buy_trend()` into your strategy file to
update your buy strategy.
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) -> DataFrame:
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
: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['blower']) &
(dataframe['tema'] <= dataframe['bb_middleband']) &
(dataframe['tema'] > dataframe['tema'].shift(1))
),
'buy'] = 1
@ -87,38 +86,47 @@ def populate_buy_trend(self, dataframe: DataFrame) -> 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) -> DataFrame:
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
: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['blower']) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
),
'sell'] = 1
return dataframe
```
## Add more Indicator
## 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.
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.
You should only add the indicators used in either `populate_buy_trend()`, `populate_sell_trend()`, or to populate another indicator, otherwise performance may suffer.
Sample:
```python
def populate_indicators(dataframe: DataFrame) -> DataFrame:
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
dataframe['sar'] = ta.SAR(dataframe)
dataframe['adx'] = ta.ADX(dataframe)
@ -149,6 +157,11 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
return dataframe
```
### 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).

76
docs/bot-usage.md
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@ -1,13 +1,15 @@
# Bot usage
This page explains the difference parameters of the bot and how to run
it.
This page explains the difference parameters of the bot and how to run it.
## Table of Contents
- [Bot commands](#bot-commands)
- [Backtesting commands](#backtesting-commands)
- [Hyperopt commands](#hyperopt-commands)
## Bot commands
```
usage: freqtrade [-h] [-v] [--version] [-c PATH] [-d PATH] [-s NAME]
[--strategy-path PATH] [--dynamic-whitelist [INT]]
@ -41,6 +43,7 @@ optional arguments:
```
### How to use a different config file?
The bot allows you to select which config file you want to use. Per
default, the bot will load the file `./config.json`
@ -49,6 +52,7 @@ python3 ./freqtrade/main.py -c path/far/far/away/config.json
```
### How to use --strategy?
This parameter will allow you to load your custom strategy class.
Per default without `--strategy` or `-s` the bot will load the
`DefaultStrategy` included with the bot (`freqtrade/strategy/default_strategy.py`).
@ -60,6 +64,7 @@ To load a strategy, simply pass the class name (e.g.: `CustomStrategy`) in this
**Example:**
In `user_data/strategies` you have a file `my_awesome_strategy.py` which has
a strategy class called `AwesomeStrategy` to load it:
```bash
python3 ./freqtrade/main.py --strategy AwesomeStrategy
```
@ -70,6 +75,7 @@ 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).
### How to use --strategy-path?
This parameter allows you to add an additional strategy lookup path, which gets
checked before the default locations (The passed path must be a folder!):
```bash
@ -77,21 +83,25 @@ 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
`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
on BaseVolume. This value can be changed when you run the script.
**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.
```bash
python3 ./freqtrade/main.py --dynamic-whitelist 30
```
@ -102,6 +112,7 @@ 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
using `--db-url`. This can also be used to specify a custom database
@ -111,24 +122,27 @@ in production mode. Example command:
python3 ./freqtrade/main.py -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
```
## Backtesting commands
Backtesting also uses the config specified via `-c/--config`.
```
usage: main.py backtesting [-h] [-i TICKER_INTERVAL] [--realistic-simulation]
[--timerange TIMERANGE] [-l] [-r] [--export EXPORT]
[--export-filename EXPORTFILENAME]
usage: freqtrade backtesting [-h] [-i TICKER_INTERVAL] [--eps] [--dmmp]
[--timerange TIMERANGE] [-l] [-r]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
[--export EXPORT] [--export-filename PATH]
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)
--realistic-simulation
uses max_open_trades from config to simulate real
world limitations
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking)
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
number)
--timerange TIMERANGE
specify what timerange of data to use.
-l, --live using live data
@ -136,16 +150,26 @@ optional arguments:
refresh the pairs files in tests/testdata with the
latest data from the exchange. Use it if you want to
run your backtesting with up-to-date data.
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
Provide a commaseparated list of strategies to
backtest Please note that ticker-interval needs to be
set either in config or via command line. When using
this together with --export trades, the strategy-name
is injected into the filename (so backtest-data.json
becomes backtest-data-DefaultStrategy.json
--export EXPORT export backtest results, argument are: trades Example
--export=trades
--export-filename EXPORTFILENAME
--export-filename PATH
Save backtest results to this filename requires
--export to be set as well Example --export-
filename=backtest_today.json (default: backtest-
result.json
filename=user_data/backtest_data/backtest_today.json
(default: user_data/backtest_data/backtest-
result.json)
```
### 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.
@ -157,36 +181,42 @@ to come back to the previous version.**
To test your strategy with latest data, we recommend continuing using
the parameter `-l` or `--live`.
## Hyperopt commands
To optimize your strategy, you can use hyperopt parameter hyperoptimization
to find optimal parameter values for your stategy.
```
usage: main.py hyperopt [-h] [-i TICKER_INTERVAL] [--realistic-simulation]
[--timerange TIMERANGE] [-e INT]
[-s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]]
usage: freqtrade hyperopt [-h] [-i TICKER_INTERVAL] [--eps] [--dmmp]
[--timerange TIMERANGE] [-e INT]
[-s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]]
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)
--realistic-simulation
uses max_open_trades from config to simulate real
world limitations
--timerange TIMERANGE specify what timerange of data to use.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking)
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
number)
--timerange TIMERANGE
specify what timerange of data to use.
-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
list. Default: all
```
## A parameter missing in the configuration?
All parameters for `main.py`, `backtesting`, `hyperopt` are referenced
in [misc.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/misc.py#L84)
## 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).

76
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View File

@ -1,12 +1,15 @@
# Configure the bot
This page explains how to configure your `config.json` file.
## Table of Contents
- [Bot commands](#bot-commands)
- [Backtesting commands](#backtesting-commands)
- [Hyperopt commands](#hyperopt-commands)
## Setup config.json
We recommend to copy and use the `config.json.example` as a template
for your bot configuration.
@ -22,7 +25,11 @@ The table below will list all configuration parameters.
| `dry_run` | true | Yes | Define if the bot must be in Dry-run or production mode.
| `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.
| `unfilledtimeout` | 0 | No | How long (in minutes) the bot will wait for an unfilled order to complete, after which the order will be cancelled.
| `trailing_stoploss` | false | No | Enables trailing stop-loss (based on `stoploss` in either configuration or strategy file).
| `trailing_stoploss_positve` | 0 | No | Changes stop-loss once profit has been reached.
| `trailing_stoploss_positve_offset` | 0 | No | Offset on when to apply `trailing_stoploss_positive`. Percentage value which should be positive.
| `unfilledtimeout.buy` | 10 | Yes | How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled.
| `unfilledtimeout.sell` | 10 | Yes | How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled.
| `bid_strategy.ask_last_balance` | 0.0 | Yes | Set the bidding price. More information below.
| `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.
@ -35,16 +42,21 @@ The table below will list all configuration parameters.
| `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.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.
| `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.
The definition of each config parameters is in
[misc.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/misc.py#L205).
The definition of each config parameters is in [misc.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/misc.py#L205).
### 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 account an exception is generated.
@ -52,9 +64,11 @@ To allow the bot to trade all the avaliable `stake_currency` in your account set
In this case a trade amount is calclulated as `currency_balanse / (max_open_trades - current_open_trades)`.
### Understand minimal_roi
`minimal_roi` is a JSON object where the key is a duration
in minutes and the value is the minimum ROI in percent.
See the example below:
```
"minimal_roi": {
"40": 0.0, # Sell after 40 minutes if the profit is not negative
@ -69,6 +83,7 @@ value. This parameter is optional. If you use it, it will take over the
`minimal_roi` value from the strategy file.
### Understand stoploss
`stoploss` is loss in percentage that should trigger a sale.
For example value `-0.10` will cause immediate sell if the
profit dips below -10% for a given trade. This parameter is optional.
@ -77,82 +92,100 @@ Most of the strategy files already include the optimal `stoploss`
value. This parameter is optional. If you use it, it will take over the
`stoploss` value from the strategy file.
### Understand trailing stoploss
Go to the [trailing stoploss Documentation](stoploss.md) for details on trailing stoploss.
### Understand initial_state
`initial_state` is an optional field that defines the initial application state.
Possible values are `running` or `stopped`. (default=`running`)
If the value is `stopped` the bot has to be started with `/start` first.
### Understand process_throttle_secs
`process_throttle_secs` is an optional field that defines in seconds how long the bot should wait
before asking the strategy if we should buy or a sell an asset. After each wait period, the strategy is asked again for
every opened trade wether or not we should sell, and for all the remaining pairs (either the dynamic list of pairs or
the static list of pairs) if we should buy.
### Understand ask_last_balance
`ask_last_balance` sets the bidding price. Value `0.0` will use `ask` price, `1.0` will
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.
### What values for exchange.name?
Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports 115 cryptocurrency
exchange markets and trading APIs. The complete up-to-date list can be found in the
[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python). However, the bot was tested
with only Bittrex and Binance.
The bot was tested with the following exchanges:
- [Bittrex](https://bittrex.com/): "bittrex"
- [Binance](https://www.binance.com/): "binance"
Feel free to test other exchanges and submit your PR to improve the bot.
### What values for fiat_display_currency?
`fiat_display_currency` set the base currency to use for the conversion from coin to fiat in Telegram.
The valid values are: "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK", "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY", "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN", "RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD".
In addition to central bank currencies, a range of cryto currencies are supported.
The valid values are: "BTC", "ETH", "XRP", "LTC", "BCH", "USDT".
## Switch to dry-run mode
We recommend starting the bot in dry-run mode to see how your bot will
behave and how is the performance of your strategy. In Dry-run mode the
bot does not engage your money. It only runs a live simulation without
creating trades.
### To switch your bot in Dry-run mode:
1. Edit your `config.json` file
2. Switch dry-run to true and specify db_url for a persistent db
```json
"dry_run": true,
"db_url": "sqlite///tradesv3.dryrun.sqlite",
```
3. Remove your Exchange API key (change them by fake api credentials)
```json
"exchange": {
"name": "bittrex",
"key": "key",
"secret": "secret",
...
}
}
```
Once you will be happy with your bot performance, you can switch it to
production mode.
## 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
you run it in production mode.
### To switch your bot in production mode:
1. Edit your `config.json` file
2. Switch dry-run to false and don't forget to adapt your database URL if set
```json
"dry_run": false,
```
3. Insert your Exchange API key (change them by fake api keys)
```json
"exchange": {
"name": "bittrex",
@ -160,10 +193,37 @@ you run it in production mode.
"secret": "08a9dc6db3d7b53e1acebd9275677f4b0a04f1a5",
...
}
```
If you have not your Bittrex API key yet,
[see our tutorial](https://github.com/freqtrade/freqtrade/blob/develop/docs/pre-requisite.md).
If you have not your Bittrex API key yet, [see our tutorial](https://github.com/freqtrade/freqtrade/blob/develop/docs/pre-requisite.md).
### Embedding Strategies
FreqTrade provides you with with an easy way to embed the strategy into your configuration file.
This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
in your chosen config file.
##### Encoding a string as BASE64
This is a quick example, how to generate the BASE64 string in python
```python
from base64 import urlsafe_b64encode
with open(file, 'r') as f:
content = f.read()
content = urlsafe_b64encode(content.encode('utf-8'))
```
The variable 'content', will contain the strategy file in a BASE64 encoded form. Which can now be set in your configurations file as following
```json
"strategy": "NameOfStrategy:BASE64String"
```
Please ensure that 'NameOfStrategy' is identical to the strategy name!
## Next step
Now you have configured your config.json, the next step is to
[start your bot](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md).
Now you have configured your config.json, the next step is to [start your bot](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-usage.md).

300
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@ -1,155 +1,114 @@
# Hyperopt
This page explains how to tune your strategy by finding the optimal
parameters with 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
search will burn all your CPU cores, make your laptop sound like a fighter jet
and still take a long time.
## Table of Contents
- [Prepare your Hyperopt](#prepare-hyperopt)
- [1. Configure your Guards and Triggers](#1-configure-your-guards-and-triggers)
- [2. Update the hyperopt config file](#2-update-the-hyperopt-config-file)
- [Advanced Hyperopt notions](#advanced-notions)
- [Understand the Guards and Triggers](#understand-the-guards-and-triggers)
- [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)
## Prepare Hyperopt
Before we start digging in Hyperopt, we recommend you to take a look at
your strategy file located into [user_data/strategies/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py)
## 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)
### 1. Configure your Guards and Triggers
There are two places you need to change in your strategy file to add a
new buy strategy for testing:
- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L278-L294).
- Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297) known as `SPACE`.
### 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.
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".
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.
"buy when EMA5 crosses over EMA10" or "buy when close price touches lower
bollinger band".
HyperOpt will, for each eval round, pick just ONE trigger, and possibly
multiple guards. So that the constructed strategy will be something like
Hyperoptimization will, for each eval round, pick one trigger and possibly
multiple guards. The constructed strategy will be something like
"*buy exactly when close price touches lower bollinger band, BUT only if
ADX > 10*".
If you have updated the buy strategy, means change the content of
If you have updated the buy strategy, ie. changed the contents of
`populate_buy_trend()` method you have to update the `guards` and
`triggers` hyperopts must used.
`triggers` hyperopts must use.
As for an example if your `populate_buy_trend()` method is:
```python
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
dataframe.loc[
(dataframe['rsi'] < 35) &
(dataframe['adx'] > 65),
'buy'] = 1
## Solving a Mystery
return dataframe
```
Let's say you are curious: should you use MACD crossings or lower Bollinger
Bands to trigger your buys. And you also wonder should you use RSI or ADX to
help with those buy decisions. If you decide to use RSI or ADX, which values
should I use for them? So let's use hyperparameter optimization to solve this
mystery.
Your hyperopt file must contain `guards` to find the right value for
`(dataframe['adx'] > 65)` & and `(dataframe['plus_di'] > 0.5)`. That
means you will need to enable/disable triggers.
In our case the `SPACE` and `populate_buy_trend` in your strategy file
will look like:
```python
space = {
'rsi': hp.choice('rsi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
]),
'adx': hp.choice('adx', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
]),
'trigger': hp.choice('trigger', [
{'type': 'lower_bb'},
{'type': 'faststoch10'},
{'type': 'ao_cross_zero'},
{'type': 'ema5_cross_ema10'},
{'type': 'macd_cross_signal'},
{'type': 'sar_reversal'},
{'type': 'stochf_cross'},
{'type': 'ht_sine'},
]),
}
...
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
if params['adx']['enabled']:
conditions.append(dataframe['adx'] > params['adx']['value'])
if params['rsi']['enabled']:
conditions.append(dataframe['rsi'] < params['rsi']['value'])
# TRIGGERS
triggers = {
'lower_bb': dataframe['tema'] <= dataframe['blower'],
'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
}
...
```
### 2. Update the hyperopt config file
Hyperopt is using a dedicated config file. Currently hyperopt
cannot use your config file. It is also made on purpose to allow you
testing your strategy with different configurations.
The Hyperopt configuration is located in
[user_data/hyperopt_conf.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopt_conf.py).
## Advanced notions
### Understand the Guards and Triggers
When you need to add the new guards and triggers to be hyperopt
parameters, you do this by adding them into the [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297).
If it's a trigger, you add one line to the 'trigger' choice group and that's it.
If it's a guard, you will add a line like this:
```
'rsi': hp.choice('rsi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
]),
```
This says, "*one of the guards is RSI, it can have two values, enabled or
disabled. If it is enabled, try different values for it between 20 and 40*".
So, the part of the strategy builder using the above setting looks like
this:
We will start by defining a search space:
```
if params['rsi']['enabled']:
conditions.append(dataframe['rsi'] < params['rsi']['value'])
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(20, 40, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal'], name='trigger')
]
```
It checks if Hyperopt wants the RSI guard to be enabled for this
round `params['rsi']['enabled']` and if it is, then it will add a
condition that says RSI must be smaller than the value hyperopt picked
for this evaluation, which is given in the `params['rsi']['value']`.
Above definition says: I have five parameters I want you to randomly combine
to find the best combination. Two of them are integer values (`adx-value`
and `rsi-value`) and I want you test in the range of values 20 to 40.
Then we have three category variables. First two are either `True` or `False`.
We use these to either enable or disable the ADX and RSI guards. The last
one we call `trigger` and use it to decide which buy trigger we want to use.
That's it. Now you can add new parts of strategies to Hyperopt and it
will try all the combinations with all different values in the search
for best working algo.
So let's write the buy strategy using these values:
```
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
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'])
### Add a new Indicators
If you want to test an indicator that isn't used by the bot currently,
you need to add it to the `populate_indicators()` method in `hyperopt.py`.
# 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']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
```
Hyperopting will now call this `populate_buy_trend` as many times you ask it (`epochs`)
with different value combinations. It will then use the given historical data and make
buys based on the buy signals generated with the above function and based on the results
it will end with telling you which paramter combination produced the best profits.
The search for best parameters starts with a few random combinations and then uses a
regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination
that minimizes the value of the objective function `calculate_loss` in `hyperopt.py`.
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
When you want to test an indicator that isn't used by the bot currently, remember to
add it to the `populate_indicators()` method in `hyperopt.py`.
## Execute Hyperopt
Once you have updated your hyperopt configuration you can run it.
@ -164,12 +123,12 @@ python3 ./freqtrade/main.py -c config.json hyperopt -e 5000
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
### 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
### Running Hyperopt with Smaller Testset
Use the `--timeperiod` argument to change how much of the testset
you want to use. The last N ticks/timeframes will be used.
Example:
@ -178,7 +137,7 @@ Example:
python3 ./freqtrade/main.py hyperopt --timeperiod -200
```
### Running hyperopt with smaller search space
### 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.
@ -193,87 +152,44 @@ 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
Once Hyperopt is completed you can use the result to adding new buy
signal. Given following result from hyperopt:
```
Best parameters:
{
"adx": {
"enabled": true,
"value": 15.0
},
"fastd": {
"enabled": true,
"value": 40.0
},
"green_candle": {
"enabled": true
},
"mfi": {
"enabled": false
},
"over_sar": {
"enabled": false
},
"rsi": {
"enabled": true,
"value": 37.0
},
"trigger": {
"type": "lower_bb"
},
"uptrend_long_ema": {
"enabled": true
},
"uptrend_short_ema": {
"enabled": false
},
"uptrend_sma": {
"enabled": false
}
}
## Understand the Hyperopts Result
Once Hyperopt is completed you can use the result to create a new strategy.
Given the following result from hyperopt:
Best Result:
2197 trades. Avg profit 1.84%. Total profit 0.79367541 BTC. Avg duration 241.0 mins.
```
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'}
```
You should understand this result like:
- You should **consider** the guard "adx" (`"adx"` is `"enabled": true`)
and the best value is `15.0` (`"value": 15.0,`)
- You should **consider** the guard "fastd" (`"fastd"` is `"enabled":
true`) and the best value is `40.0` (`"value": 40.0,`)
- You should **consider** to enable the guard "green_candle"
(`"green_candle"` is `"enabled": true`) but this guards as no
customizable value.
- You should **ignore** the guard "mfi" (`"mfi"` is `"enabled": false`)
- and so on...
- 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.
So for example you had `adx:` with the `value: 15.0` so we would look
at `adx`-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['adx'] > 15.0)
(dataframe['rsi'] < 29.0)
```
Translating your whole hyperopt result to as the new buy-signal
would be the following:
Translating your whole hyperopt result as the new buy-signal
would then look like:
```
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 15.0) & # adx-value
(dataframe['fastd'] < 40.0) & # fastd-value
(dataframe['close'] > dataframe['open']) & # green_candle
(dataframe['rsi'] < 37.0) & # rsi-value
(dataframe['ema50'] > dataframe['ema100']) # uptrend_long_ema
(dataframe['rsi'] < 29.0) & # rsi-value
dataframe['close'] < dataframe['bb_lowerband'] # trigger
),
'buy'] = 1
return dataframe
```
## Next step
## 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).

4
docs/index.md
View File

@ -1,4 +1,5 @@
# 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
@ -6,6 +7,7 @@ Pull-request. Do not hesitate to reach us on
if you do not find the answer to your questions.
## Table of Contents
- [Pre-requisite](https://github.com/freqtrade/freqtrade/blob/develop/docs/pre-requisite.md)
- [Setup your Bittrex account](https://github.com/freqtrade/freqtrade/blob/develop/docs/pre-requisite.md#setup-your-bittrex-account)
- [Setup your Telegram bot](https://github.com/freqtrade/freqtrade/blob/develop/docs/pre-requisite.md#setup-your-telegram-bot)
@ -25,8 +27,10 @@ Pull-request. Do not hesitate to reach us on
- [Test your strategy with Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.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)
- [Contribute to the project](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
- [How to contribute](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
- [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))

15
docs/installation.md
View File

@ -56,23 +56,29 @@ Reset parameter will hard reset your branch (only if you are on `master` or `dev
Config parameter is a `config.json` configurator. This script will ask you questions to setup your bot and create your `config.json`.
## Manual installation - Linux/MacOS
The following steps are made for Linux/MacOS environment
**1. Clone the repo**
### 1. Clone the repo
```bash
git clone git@github.com:freqtrade/freqtrade.git
git checkout develop
cd freqtrade
```
**2. Create the config file**
### 2. Create the config file
Switch `"dry_run": true,`
```bash
cp config.json.example config.json
vi config.json
```
**3. Build your docker image and run it**
### 3. Build your docker image and run it
```bash
docker build -t freqtrade .
docker run --rm -v /etc/localtime:/etc/localtime:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
@ -261,6 +267,7 @@ Official webpage: https://mrjbq7.github.io/ta-lib/install.html
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 "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h
./configure --prefix=/usr
make
make install

8
docs/plotting.md
View File

@ -24,7 +24,7 @@ script/plot_dataframe.py [-h] [-p pair] [--live]
Example
```
python scripts/plot_dataframe.py -p BTC_ETH
python scripts/plot_dataframe.py -p BTC/ETH
```
The `-p` pair argument, can be used to specify what
@ -34,18 +34,18 @@ pair you would like to plot.
To plot the current live price use the `--live` flag:
```
python scripts/plot_dataframe.py -p BTC_ETH --live
python scripts/plot_dataframe.py -p BTC/ETH --live
```
To plot a timerange (to zoom in):
```
python scripts/plot_dataframe.py -p BTC_ETH --timerange=100-200
python scripts/plot_dataframe.py -p BTC/ETH --timerange=100-200
```
Timerange doesn't work with live data.
To plot trades stored in a database use `--db-url` argument:
```
python scripts/plot_dataframe.py --db-url tradesv3.dry_run.sqlite -p BTC_ETH
python scripts/plot_dataframe.py --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH
```
To plot a test strategy the strategy should have first be backtested.

151
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@ -0,0 +1,151 @@
# Sandbox API testing
Where an exchange provides a sandbox for risk-free integration, or end-to-end, testing CCXT provides access to these.
This document is a *light overview of configuring Freqtrade and GDAX sandbox.
This can be useful to developers and trader alike as Freqtrade is quite customisable.
When testing your API connectivity, make sure to use the following URLs.
***Website**
https://public.sandbox.gdax.com
***REST API**
https://api-public.sandbox.gdax.com
---
# Configure a Sandbox account on Gdax
Aim of this document section
- An sanbox account
- create 2FA (needed to create an API)
- Add test 50BTC to account
- Create :
- - API-KEY
- - API-Secret
- - API Password
## Acccount
This link will redirect to the sandbox main page to login / create account dialogues:
https://public.sandbox.pro.coinbase.com/orders/
After registration and Email confimation you wil be redirected into your sanbox account. It is easy to verify you're in sandbox by checking the URL bar.
> https://public.sandbox.pro.coinbase.com/
## Enable 2Fa (a prerequisite to creating sandbox API Keys)
From within sand box site select your profile, top right.
>Or as a direct link: https://public.sandbox.pro.coinbase.com/profile
From the menu panel to the left of the screen select
> Security: "*View or Update*"
In the new site select "enable authenticator" as typical google Authenticator.
- open Google Authenticator on your phone
- scan barcode
- enter your generated 2fa
## Enable API Access
From within sandbox select profile>api>create api-keys
>or as a direct link: https://public.sandbox.pro.coinbase.com/profile/api
Click on "create one" and ensure **view** and **trade** are "checked" and sumbit your 2Fa
- **Copy and paste the Passphase** into a notepade this will be needed later
- **Copy and paste the API Secret** popup into a notepad this will needed later
- **Copy and paste the API Key** into a notepad this will needed later
## Add 50 BTC test funds
To add funds, use the web interface deposit and withdraw buttons.
To begin select 'Wallets' from the top menu.
> Or as a direct link: https://public.sandbox.pro.coinbase.com/wallets
- Deposits (bottom left of screen)
- - Deposit Funds Bitcoin
- - - Coinbase BTC Wallet
- - - - Max (50 BTC)
- - - - - Deposit
*This process may be repeated for other currencies, ETH as example*
---
# Configure Freqtrade to use Gax Sandbox
The aim of this document section
- Enable sandbox URLs in Freqtrade
- Configure API
- - secret
- - key
- - passphrase
## Sandbox URLs
Freqtrade makes use of CCXT which in turn provides a list of URLs to Freqtrade.
These include `['test']` and `['api']`.
- `[Test]` if available will point to an Exchanges sandbox.
- `[Api]` normally used, and resolves to live API target on the exchange
To make use of sandbox / test add "sandbox": true, to your config.json
```
"exchange": {
"name": "gdax",
"sandbox": true,
"key": "5wowfxemogxeowo;heiohgmd",
"secret": "/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==",
"password": "1bkjfkhfhfu6sr",
"pair_whitelist": [
"BTC/USD"
```
Also insert your
- api-key (noted earlier)
- api-secret (noted earlier)
- password (the passphrase - noted earlier)
---
## You should now be ready to test your sandbox!
Ensure Freqtrade logs show the sandbox URL, and trades made are shown in sandbox.
** Typically the BTC/USD has the most activity in sandbox to test against.
## GDAX - Old Candles problem
It is my experience that GDAX sandbox candles may be 20+- minutes out of date. This can cause trades to fail as one of Freqtrades safety checks
To disable this check, edit:
>strategy/interface.py
Look for the following section:
```
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + 5))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
(arrow.utcnow() - signal_date).seconds // 60
)
return False, False
```
You could Hash out the entire check as follows:
```
# # Check if dataframe is out of date
# signal_date = arrow.get(latest['date'])
# interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
# if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + 5))):
# logger.warning(
# 'Outdated history for pair %s. Last tick is %s minutes old',
# pair,
# (arrow.utcnow() - signal_date).seconds // 60
# )
# return False, False
```
Or inrease the timeout to offer a level of protection/alignment of this test to freqtrade in live.
As example, to allow an additional 30 minutes. "(interval_minutes * 2 + 5 + 30)"
```
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + 5 + 30))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
(arrow.utcnow() - signal_date).seconds // 60
)
return False, False
```

2
docs/sql_cheatsheet.md
View File

@ -59,7 +59,7 @@ SELECT * FROM trades;
```sql
UPDATE trades
SET is_open=0, close_date=<close_date>, close_rate=<close_rate>, close_profit=close_rate/open_rate
SET is_open=0, close_date=<close_date>, close_rate=<close_rate>, close_profit=close_rate/open_rate-1
WHERE id=<trade_ID_to_update>;
```

51
docs/stoploss.md Normal file
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@ -0,0 +1,51 @@
# Stop Loss support
At this stage the bot contains the following stoploss support modes:
1. static stop loss, defined in either the strategy or configuration
2. trailing stop loss, defined in the configuration
3. trailing stop loss, custom positive loss, defined in configuration
## Static Stop Loss
This is very simple, basically you define a stop loss of x in your strategy file or alternative in the configuration, which
will overwrite the strategy definition. This will basically try to sell your asset, the second the loss exceeds the defined loss.
## Trail Stop Loss
The initial value for this stop loss, is defined in your strategy or configuration. Just as you would define your Stop Loss normally.
To enable this Feauture all you have to do is to define the configuration element:
``` json
"trailing_stop" : True
```
This will now activate an algorithm, which automatically moves your stop loss up every time the price of your asset increases.
For example, simplified math,
* you buy an asset at a price of 100$
* your stop loss is defined at 2%
* which means your stop loss, gets triggered once your asset dropped below 98$
* assuming your asset now increases to 102$
* your stop loss, will now be 2% of 102$ or 99.96$
* now your asset drops in value to 101$, your stop loss, will still be 99.96$
basically what this means is that your stop loss will be adjusted to be always be 2% of the highest observed price
### Custom positive loss
Due to demand, it is possible to have a default stop loss, when you are in the red with your buy, but once your profit surpasses a certain percentage,
the system will utilize a new stop loss, which can be a different value. For example your default stop loss is 5%, but once you have 1.1% profit,
it will be changed to be only a 1% stop loss, which trails the green candles until it goes below them.
Both values can be configured in the main configuration file and requires `"trailing_stop": true` to be set to true.
``` json
"trailing_stop_positive": 0.01,
"trailing_stop_positive_offset": 0.011,
```
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.

74
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@ -0,0 +1,74 @@
# Webhook usage
This page explains how to configure your bot to talk to webhooks.
## Configuration
Enable webhooks by adding a webhook-section to your configuration file, and setting `webhook.enabled` to `true`.
Sample configuration (tested using IFTTT).
```json
"webhook": {
"enabled": true,
"url": "https://maker.ifttt.com/trigger/<YOUREVENT>/with/key/<YOURKEY>/",
"webhookbuy": {
"value1": "Buying {pair}",
"value2": "limit {limit:8f}",
"value3": "{stake_amount:8f} {stake_currency}"
},
"webhooksell": {
"value1": "Selling {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency}"
},
"webhookstatus": {
"value1": "Status: {status}",
"value2": "",
"value3": ""
}
},
```
The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert our event and key to the url.
Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
### Webhookbuy
The fields in `webhook.webhookbuy` are filled when the bot executes a buy. Parameters are filled using string.format.
Possible parameters are:
* exchange
* pair
* market_url
* limit
* stake_amount
* stake_amount_fiat
* stake_currency
* fiat_currency
### Webhooksell
The fields in `webhook.webhooksell` are filled when the bot sells a trade. Parameters are filled using string.format.
Possible parameters are:
* exchange
* pair
* gain
* market_url
* limit
* amount
* open_rate
* current_rate
* profit_amount
* profit_percent
* profit_fiat
* stake_currency
* fiat_currency
### Webhookstatus
The fields in `webhook.webhookstatus` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
The only possible value here is `{status}`.

2
freqtrade/__init__.py
View File

@ -1,5 +1,5 @@
""" FreqTrade bot """
__version__ = '0.17.0'
__version__ = '0.17.2'
class DependencyException(BaseException):

2
freqtrade/__main__.py
View File

@ -7,8 +7,8 @@ To launch Freqtrade as a module
"""
import sys
from freqtrade import main
from freqtrade import main
if __name__ == '__main__':
main.set_loggers()

237
freqtrade/analyze.py
View File

@ -1,237 +0,0 @@
"""
Functions to analyze ticker data with indicators and produce buy and sell signals
"""
import logging
from datetime import datetime, timedelta
from enum import Enum
from typing import Dict, List, Tuple
import arrow
from pandas import DataFrame, to_datetime
from freqtrade import constants
from freqtrade.exchange import Exchange
from freqtrade.persistence import Trade
from freqtrade.strategy.resolver import StrategyResolver, IStrategy
logger = logging.getLogger(__name__)
class SignalType(Enum):
"""
Enum to distinguish between buy and sell signals
"""
BUY = "buy"
SELL = "sell"
class Analyze(object):
"""
Analyze class contains everything the bot need to determine if the situation is good for
buying or selling.
"""
def __init__(self, config: dict) -> None:
"""
Init Analyze
:param config: Bot configuration (use the one from Configuration())
"""
self.config = config
self.strategy: IStrategy = StrategyResolver(self.config).strategy
@staticmethod
def parse_ticker_dataframe(ticker: list) -> DataFrame:
"""
Analyses the trend for the given ticker history
:param ticker: See exchange.get_ticker_history
:return: DataFrame
"""
cols = ['date', 'open', 'high', 'low', 'close', 'volume']
frame = DataFrame(ticker, columns=cols)
frame['date'] = to_datetime(frame['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
# group by index and aggregate results to eliminate duplicate ticks
frame = frame.groupby(by='date', as_index=False, sort=True).agg({
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'volume': 'max',
})
frame.drop(frame.tail(1).index, inplace=True) # eliminate partial candle
return frame
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
return self.strategy.populate_indicators(dataframe=dataframe)
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
return self.strategy.populate_buy_trend(dataframe=dataframe)
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
return self.strategy.populate_sell_trend(dataframe=dataframe)
def get_ticker_interval(self) -> str:
"""
Return ticker interval to use
:return: Ticker interval value to use
"""
return self.strategy.ticker_interval
def get_stoploss(self) -> float:
"""
Return stoploss to use
:return: Strategy stoploss value to use
"""
return self.strategy.stoploss
def analyze_ticker(self, ticker_history: List[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 = self.parse_ticker_dataframe(ticker_history)
dataframe = self.populate_indicators(dataframe)
dataframe = self.populate_buy_trend(dataframe)
dataframe = self.populate_sell_trend(dataframe)
return dataframe
def get_signal(self, exchange: Exchange, pair: str, interval: str) -> 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)
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
ticker_hist = exchange.get_ticker_history(pair, interval)
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return False, False
try:
dataframe = self.analyze_ticker(ticker_hist)
except ValueError as error:
logger.warning(
'Unable to analyze ticker for pair %s: %s',
pair,
str(error)
)
return False, False
except Exception as error:
logger.exception(
'Unexpected error when analyzing ticker for pair %s: %s',
pair,
str(error)
)
return False, False
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return False, False
latest = dataframe.iloc[-1]
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
if signal_date < (arrow.utcnow() - timedelta(minutes=(interval_minutes + 5))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
(arrow.utcnow() - signal_date).seconds // 60
)
return False, False
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
logger.debug(
'trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'],
pair,
str(buy),
str(sell)
)
return buy, sell
def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool, sell: bool) -> bool:
"""
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)
if self.stop_loss_reached(current_profit=current_profit):
return True
experimental = self.config.get('experimental', {})
if buy and experimental.get('ignore_roi_if_buy_signal', False):
logger.debug('Buy signal still active - not selling.')
return False
# Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
if self.min_roi_reached(trade=trade, current_profit=current_profit, current_time=date):
logger.debug('Required profit reached. Selling..')
return True
if experimental.get('sell_profit_only', False):
logger.debug('Checking if trade is profitable..')
if trade.calc_profit(rate=rate) <= 0:
return False
if sell and not buy and experimental.get('use_sell_signal', False):
logger.debug('Sell signal received. Selling..')
return True
return False
def stop_loss_reached(self, current_profit: float) -> bool:
"""Based on current profit of the trade and configured stoploss, decides to sell or not"""
if self.strategy.stoploss is not None and current_profit < self.strategy.stoploss:
logger.debug('Stop loss hit.')
return True
return False
def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
"""
Based an earlier trade and current price and ROI configuration, decides whether bot should
sell
:return True if bot should sell at current rate
"""
# Check if time matches and current rate is above threshold
time_diff = (current_time.timestamp() - trade.open_date.timestamp()) / 60
for duration, threshold in self.strategy.minimal_roi.items():
if time_diff <= duration:
return False
if current_profit > threshold:
return True
return False
def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given ticker data
"""
return {pair: self.populate_indicators(self.parse_ticker_dataframe(pair_data))
for pair, pair_data in tickerdata.items()}

47
freqtrade/arguments.py
View File

@ -2,12 +2,12 @@
This module contains the argument manager class
"""
import os
import argparse
import logging
import os
import re
from typing import List, NamedTuple, Optional
import arrow
from typing import List, Optional, NamedTuple
from freqtrade import __version__, constants
@ -63,11 +63,10 @@ class Arguments(object):
"""
self.parser.add_argument(
'-v', '--verbose',
help='be verbose',
action='store_const',
help='verbose mode (-vv for more, -vvv to get all messages)',
action='count',
dest='loglevel',
const=logging.DEBUG,
default=logging.INFO,
default=0,
)
self.parser.add_argument(
'--version',
@ -143,6 +142,16 @@ class Arguments(object):
action='store_true',
dest='refresh_pairs',
)
parser.add_argument(
'--strategy-list',
help='Provide a commaseparated list of strategies to backtest '
'Please note that ticker-interval needs to be set either in config '
'or via command line. When using this together with --export trades, '
'the strategy-name is injected into the filename '
'(so backtest-data.json becomes backtest-data-DefaultStrategy.json',
nargs='+',
dest='strategy_list',
)
parser.add_argument(
'--export',
help='export backtest results, argument are: trades\
@ -177,11 +186,22 @@ class Arguments(object):
type=str,
)
parser.add_argument(
'--realistic-simulation',
help='uses max_open_trades from config to simulate real world limitations',
'--eps', '--enable-position-stacking',
help='Allow buying the same pair multiple times (position stacking)',
action='store_true',
dest='realistic_simulation',
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(
'--timerange',
help='specify what timerange of data to use.',
@ -334,3 +354,10 @@ class Arguments(object):
nargs='+',
dest='timeframes',
)
self.parser.add_argument(
'--erase',
help='Clean all existing data for the selected exchange/pairs/timeframes',
dest='erase',
action='store_true'
)

70
freqtrade/configuration.py
View File

@ -1,21 +1,33 @@
"""
This module contains the configuration class
"""
import os
import json
import logging
import os
from argparse import Namespace
from typing import Optional, Dict, Any
from typing import Any, Dict, Optional
import ccxt
from jsonschema import Draft4Validator, validate
from jsonschema.exceptions import ValidationError, best_match
import ccxt
from freqtrade import OperationalException, constants
logger = logging.getLogger(__name__)
def set_loggers(log_level: int = 0) -> None:
"""
Set the logger level for Third party libs
:return: None
"""
logging.getLogger('requests').setLevel(logging.INFO if log_level <= 1 else logging.DEBUG)
logging.getLogger("urllib3").setLevel(logging.INFO if log_level <= 1 else logging.DEBUG)
logging.getLogger('ccxt.base.exchange').setLevel(
logging.INFO if log_level <= 2 else logging.DEBUG)
logging.getLogger('telegram').setLevel(logging.INFO)
class Configuration(object):
"""
Class to read and init the bot configuration
@ -62,8 +74,8 @@ class Configuration(object):
conf = json.load(file)
except FileNotFoundError:
raise OperationalException(
'Config file "{}" not found!'
' Please create a config file or check whether it exists.'.format(path))
f'Config file "{path}" not found!'
' Please create a config file or check whether it exists.')
if 'internals' not in conf:
conf['internals'] = {}
@ -79,12 +91,15 @@ class Configuration(object):
# Log level
if 'loglevel' in self.args and self.args.loglevel:
config.update({'loglevel': self.args.loglevel})
logging.basicConfig(
level=config['loglevel'],
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
logger.info('Log level set to %s', logging.getLevelName(config['loglevel']))
config.update({'verbosity': self.args.loglevel})
else:
config.update({'verbosity': 0})
logging.basicConfig(
level=logging.INFO if config['verbosity'] < 1 else logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
set_loggers(config['verbosity'])
logger.info('Verbosity set to %s', config['verbosity'])
# Add dynamic_whitelist if found
if 'dynamic_whitelist' in self.args and self.args.dynamic_whitelist:
@ -109,7 +124,7 @@ class Configuration(object):
config['db_url'] = constants.DEFAULT_DB_PROD_URL
logger.info('Dry run is disabled')
logger.info('Using DB: "{}"'.format(config['db_url']))
logger.info(f'Using DB: "{config["db_url"]}"')
# Check if the exchange set by the user is supported
self.check_exchange(config)
@ -142,11 +157,18 @@ class Configuration(object):
config.update({'live': True})
logger.info('Parameter -l/--live detected ...')
# If --realistic-simulation is used we add it to the configuration
if 'realistic_simulation' in self.args and self.args.realistic_simulation:
config.update({'realistic_simulation': True})
logger.info('Parameter --realistic-simulation detected ...')
logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
# If --enable-position-stacking is used we add it to the configuration
if 'position_stacking' in self.args and self.args.position_stacking:
config.update({'position_stacking': True})
logger.info('Parameter --enable-position-stacking detected ...')
# If --disable-max-market-positions is used we add it to the configuration
if 'use_max_market_positions' in self.args and not self.args.use_max_market_positions:
config.update({'use_max_market_positions': False})
logger.info('Parameter --disable-max-market-positions detected ...')
logger.info('max_open_trades set to unlimited ...')
else:
logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
# If --timerange is used we add it to the configuration
if 'timerange' in self.args and self.args.timerange:
@ -165,6 +187,14 @@ class Configuration(object):
config.update({'refresh_pairs': True})
logger.info('Parameter -r/--refresh-pairs-cached detected ...')
if 'strategy_list' in self.args and self.args.strategy_list:
config.update({'strategy_list': self.args.strategy_list})
logger.info('Using strategy list of %s Strategies', len(self.args.strategy_list))
if 'ticker_interval' in self.args and self.args.ticker_interval:
config.update({'ticker_interval': self.args.ticker_interval})
logger.info('Overriding ticker interval with Command line argument')
# If --export is used we add it to the configuration
if 'export' in self.args and self.args.export:
config.update({'export': self.args.export})
@ -182,7 +212,7 @@ class Configuration(object):
Extract information for sys.argv and load Hyperopt configuration
:return: configuration as dictionary
"""
# If --realistic-simulation is used we add it to the configuration
# If --epochs is used we add it to the configuration
if 'epochs' in self.args and self.args.epochs:
config.update({'epochs': self.args.epochs})
logger.info('Parameter --epochs detected ...')

28
freqtrade/constants.py
View File

@ -36,7 +36,7 @@ SUPPORTED_FIAT = [
"EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY",
"KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN",
"RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD",
"BTC", "ETH", "XRP", "LTC", "BCH", "USDT"
"BTC", "XBT", "ETH", "XRP", "LTC", "BCH", "USDT"
]
# Required json-schema for user specified config
@ -45,7 +45,7 @@ CONF_SCHEMA = {
'properties': {
'max_open_trades': {'type': 'integer', 'minimum': 0},
'ticker_interval': {'type': 'string', 'enum': list(TICKER_INTERVAL_MINUTES.keys())},
'stake_currency': {'type': 'string', 'enum': ['BTC', 'ETH', 'USDT', 'EUR', 'USD']},
'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
'stake_amount': {
"type": ["number", "string"],
"minimum": 0.0005,
@ -61,7 +61,16 @@ CONF_SCHEMA = {
'minProperties': 1
},
'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True},
'unfilledtimeout': {'type': 'integer', 'minimum': 0},
'trailing_stop': {'type': 'boolean'},
'trailing_stop_positive': {'type': 'number', 'minimum': 0, 'maximum': 1},
'trailing_stop_positive_offset': {'type': 'number', 'minimum': 0, 'maximum': 1},
'unfilledtimeout': {
'type': 'object',
'properties': {
'buy': {'type': 'number', 'minimum': 3},
'sell': {'type': 'number', 'minimum': 10}
}
},
'bid_strategy': {
'type': 'object',
'properties': {
@ -92,6 +101,15 @@ CONF_SCHEMA = {
},
'required': ['enabled', 'token', 'chat_id']
},
'webhook': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'webhookbuy': {'type': 'object'},
'webhooksell': {'type': 'object'},
'webhookstatus': {'type': 'object'},
},
},
'db_url': {'type': 'string'},
'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
'internals': {
@ -107,8 +125,11 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'name': {'type': 'string'},
'sandbox': {'type': 'boolean'},
'key': {'type': 'string'},
'secret': {'type': 'string'},
'password': {'type': 'string'},
'uid': {'type': 'string'},
'pair_whitelist': {
'type': 'array',
'items': {
@ -136,7 +157,6 @@ CONF_SCHEMA = {
'max_open_trades',
'stake_currency',
'stake_amount',
'fiat_display_currency',
'dry_run',
'bid_strategy',
'telegram'

60
freqtrade/exchange/__init__.py
View File

@ -4,6 +4,7 @@ import logging
from random import randint
from typing import List, Dict, Any, Optional
from datetime import datetime
from math import floor, ceil
import ccxt
import arrow
@ -70,6 +71,10 @@ class Exchange(object):
# Check if all pairs are available
self.validate_pairs(config['exchange']['pair_whitelist'])
if config.get('ticker_interval'):
# Check if timeframe is available
self.validate_timeframes(config['ticker_interval'])
def _init_ccxt(self, exchange_config: dict) -> ccxt.Exchange:
"""
Initialize ccxt with given config and return valid
@ -86,11 +91,13 @@ class Exchange(object):
'secret': exchange_config.get('secret'),
'password': exchange_config.get('password'),
'uid': exchange_config.get('uid', ''),
'enableRateLimit': True,
'enableRateLimit': exchange_config.get('ccxt_rate_limit', True),
})
except (KeyError, AttributeError):
raise OperationalException(f'Exchange {name} is not supported')
self.set_sandbox(api, exchange_config, name)
return api
@property
@ -103,6 +110,16 @@ class Exchange(object):
"""exchange ccxt id"""
return self._api.id
def set_sandbox(self, api, exchange_config: dict, name: str):
if exchange_config.get('sandbox'):
if api.urls.get('test'):
api.urls['api'] = api.urls['test']
logger.info("Enabled Sandbox API on %s", name)
else:
logger.warning(self._api.name, "No Sandbox URL in CCXT, exiting. "
"Please check your config.json")
raise OperationalException(f'Exchange {name} does not provide a sandbox api')
def validate_pairs(self, pairs: List[str]) -> None:
"""
Checks if all given pairs are tradable on the current exchange.
@ -128,6 +145,15 @@ class Exchange(object):
raise OperationalException(
f'Pair {pair} is not available at {self.name}')
def validate_timeframes(self, timeframe: List[str]) -> None:
"""
Checks if ticker interval from config is a supported timeframe on the exchange
"""
timeframes = self._api.timeframes
if timeframe not in timeframes:
raise OperationalException(
f'Invalid ticker {timeframe}, this Exchange supports {timeframes}')
def exchange_has(self, endpoint: str) -> bool:
"""
Checks if exchange implements a specific API endpoint.
@ -137,6 +163,28 @@ class Exchange(object):
"""
return endpoint in self._api.has and self._api.has[endpoint]
def symbol_amount_prec(self, pair, amount: float):
'''
Returns the amount to buy or sell to a precision the Exchange accepts
Rounded down
'''
if self._api.markets[pair]['precision']['amount']:
symbol_prec = self._api.markets[pair]['precision']['amount']
big_amount = amount * pow(10, symbol_prec)
amount = floor(big_amount) / pow(10, symbol_prec)
return amount
def symbol_price_prec(self, pair, price: float):
'''
Returns the price buying or selling with to the precision the Exchange accepts
Rounds up
'''
if self._api.markets[pair]['precision']['price']:
symbol_prec = self._api.markets[pair]['precision']['price']
big_price = price * pow(10, symbol_prec)
price = ceil(big_price) / pow(10, symbol_prec)
return price
def buy(self, pair: str, rate: float, amount: float) -> Dict:
if self._conf['dry_run']:
order_id = f'dry_run_buy_{randint(0, 10**6)}'
@ -154,6 +202,10 @@ class Exchange(object):
return {'id': order_id}
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)
return self._api.create_limit_buy_order(pair, amount, rate)
except ccxt.InsufficientFunds as e:
raise DependencyException(
@ -187,6 +239,10 @@ class Exchange(object):
return {'id': order_id}
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)
return self._api.create_limit_sell_order(pair, amount, rate)
except ccxt.InsufficientFunds as e:
raise DependencyException(
@ -274,7 +330,7 @@ class Exchange(object):
return self._cached_ticker[pair]
@retrier
def get_ticker_history(self, pair: str, tick_interval: str,
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]

33
freqtrade/exchange/exchange_helpers.py Normal file
View File

@ -0,0 +1,33 @@
"""
Functions to analyze ticker data with indicators and produce buy and sell signals
"""
import logging
from pandas import DataFrame, to_datetime
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
:return: DataFrame
"""
cols = ['date', 'open', 'high', 'low', 'close', 'volume']
frame = DataFrame(ticker, columns=cols)
frame['date'] = to_datetime(frame['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
# group by index and aggregate results to eliminate duplicate ticks
frame = frame.groupby(by='date', as_index=False, sort=True).agg({
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'volume': 'max',
})
frame.drop(frame.tail(1).index, inplace=True) # eliminate partial candle
return frame

7
freqtrade/fiat_convert.py
View File

@ -8,9 +8,10 @@ import time
from typing import Dict, List
from coinmarketcap import Market
from requests.exceptions import RequestException
from freqtrade.constants import SUPPORTED_FIAT
logger = logging.getLogger(__name__)
@ -88,10 +89,10 @@ class CryptoToFiatConverter(object):
coinlistings = self._coinmarketcap.listings()
self._cryptomap = dict(map(lambda coin: (coin["symbol"], str(coin["id"])),
coinlistings["data"]))
except (ValueError, RequestException) as exception:
except (BaseException) as exception:
logger.error(
"Could not load FIAT Cryptocurrency map for the following problem: %s",
exception
type(exception).__name__
)
def convert_amount(self, crypto_amount: float, crypto_symbol: str, fiat_symbol: str) -> float:

237
freqtrade/freqtradebot.py
View File

@ -7,22 +7,20 @@ import logging
import time
import traceback
from datetime import datetime
from typing import Dict, List, Optional, Any, Callable
from typing import Any, Callable, Dict, List, Optional
import arrow
import requests
from cachetools import TTLCache, cached
from freqtrade import (
DependencyException, OperationalException, TemporaryError, persistence, __version__,
)
from freqtrade import constants
from freqtrade.analyze import Analyze
from freqtrade import (DependencyException, OperationalException,
TemporaryError, __version__, constants, persistence)
from freqtrade.exchange import Exchange
from freqtrade.fiat_convert import CryptoToFiatConverter
from freqtrade.persistence import Trade
from freqtrade.rpc.rpc_manager import RPCManager
from freqtrade.rpc import RPCManager, RPCMessageType
from freqtrade.state import State
from freqtrade.strategy.interface import SellType
from freqtrade.strategy.resolver import IStrategy, StrategyResolver
logger = logging.getLogger(__name__)
@ -50,12 +48,10 @@ class FreqtradeBot(object):
# Init objects
self.config = config
self.analyze = Analyze(self.config)
self.fiat_converter = CryptoToFiatConverter()
self.strategy: IStrategy = StrategyResolver(self.config).strategy
self.rpc: RPCManager = RPCManager(self)
self.persistence = None
self.exchange = Exchange(self.config)
self._init_modules()
def _init_modules(self) -> None:
@ -93,8 +89,13 @@ class FreqtradeBot(object):
# Log state transition
state = self.state
if state != old_state:
self.rpc.send_msg(f'*Status:* `{state.name.lower()}`')
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'{state.name.lower()}'
})
logger.info('Changing state to: %s', state.name)
if state == State.RUNNING:
self._startup_messages()
if state == State.STOPPED:
time.sleep(1)
@ -111,6 +112,38 @@ class FreqtradeBot(object):
nb_assets=nb_assets)
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
@ -160,7 +193,7 @@ class FreqtradeBot(object):
if 'unfilledtimeout' in self.config:
# Check and handle any timed out open orders
self.check_handle_timedout(self.config['unfilledtimeout'])
self.check_handle_timedout()
Trade.session.flush()
except TemporaryError as error:
@ -169,9 +202,10 @@ class FreqtradeBot(object):
except OperationalException:
tb = traceback.format_exc()
hint = 'Issue `/start` if you think it is safe to restart.'
self.rpc.send_msg(
f'*Status:* OperationalException:\n```\n{tb}```{hint}'
)
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'OperationalException:\n```\n{tb}```{hint}'
})
logger.exception('OperationalException. Stopping trader ...')
self.state = State.STOPPED
return state_changed
@ -245,6 +279,11 @@ class FreqtradeBot(object):
return ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
def _get_trade_stake_amount(self) -> 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'])
@ -277,18 +316,21 @@ class FreqtradeBot(object):
return None
min_stake_amounts = []
if 'cost' in market['limits'] and 'min' in market['limits']['cost']:
min_stake_amounts.append(market['limits']['cost']['min'])
limits = market['limits']
if ('cost' in limits and 'min' in limits['cost']
and limits['cost']['min'] is not None):
min_stake_amounts.append(limits['cost']['min'])
if 'amount' in market['limits'] and 'min' in market['limits']['amount']:
min_stake_amounts.append(market['limits']['amount']['min'] * price)
if ('amount' in limits and 'min' in limits['amount']
and limits['amount']['min'] is not None):
min_stake_amounts.append(limits['amount']['min'] * price)
if not min_stake_amounts:
return None
amount_reserve_percent = 1 - 0.05 # reserve 5% + stoploss
if self.analyze.get_stoploss() is not None:
amount_reserve_percent += self.analyze.get_stoploss()
if self.strategy.stoploss is not None:
amount_reserve_percent += self.strategy.stoploss
# it should not be more than 50%
amount_reserve_percent = max(amount_reserve_percent, 0.5)
return min(min_stake_amounts)/amount_reserve_percent
@ -299,14 +341,11 @@ class FreqtradeBot(object):
if one pair triggers the buy_signal a new trade record gets created
:return: True if a trade object has been created and persisted, False otherwise
"""
interval = self.analyze.get_ticker_interval()
interval = self.strategy.ticker_interval
stake_amount = self._get_trade_stake_amount()
if not stake_amount:
return False
stake_currency = self.config['stake_currency']
fiat_currency = self.config['fiat_display_currency']
exc_name = self.exchange.name
logger.info(
'Checking buy signals to create a new trade with stake_amount: %f ...',
@ -325,14 +364,23 @@ class FreqtradeBot(object):
# Pick pair based on buy signals
for _pair in whitelist:
(buy, sell) = self.analyze.get_signal(self.exchange, _pair, interval)
thistory = self.exchange.get_candle_history(_pair, interval)
(buy, sell) = self.strategy.get_signal(_pair, interval, thistory)
if buy and not sell:
pair = _pair
break
else:
return False
return self.execute_buy(_pair, stake_amount)
return False
def execute_buy(self, pair: str, stake_amount: float) -> bool:
"""
Executes a limit buy for the given pair
:param pair: pair for which we want to create a LIMIT_BUY
:return: None
"""
pair_s = pair.replace('_', '/')
pair_url = self.exchange.get_pair_detail_url(pair)
stake_currency = self.config['stake_currency']
fiat_currency = self.config.get('fiat_display_currency', None)
# Calculate amount
buy_limit = self.get_target_bid(self.exchange.get_ticker(pair))
@ -349,18 +397,16 @@ class FreqtradeBot(object):
order_id = self.exchange.buy(pair, buy_limit, amount)['id']
stake_amount_fiat = self.fiat_converter.convert_amount(
stake_amount,
stake_currency,
fiat_currency
)
# Create trade entity and return
self.rpc.send_msg(
f"""*{exc_name}:* Buying [{pair_s}]({pair_url}) \
with limit `{buy_limit:.8f} ({stake_amount:.6f} \
{stake_currency}, {stake_amount_fiat:.3f} {fiat_currency})`"""
)
self.rpc.send_msg({
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': pair_s,
'market_url': pair_url,
'limit': buy_limit,
'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(
@ -373,7 +419,9 @@ with limit `{buy_limit:.8f} ({stake_amount:.6f} \
open_rate_requested=buy_limit,
open_date=datetime.utcnow(),
exchange=self.exchange.id,
open_order_id=order_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()
@ -483,22 +531,27 @@ with limit `{buy_limit:.8f} ({stake_amount:.6f} \
(buy, sell) = (False, False)
experimental = self.config.get('experimental', {})
if experimental.get('use_sell_signal') or experimental.get('ignore_roi_if_buy_signal'):
(buy, sell) = self.analyze.get_signal(self.exchange,
trade.pair, self.analyze.get_ticker_interval())
ticker = self.exchange.get_candle_history(trade.pair, self.strategy.ticker_interval)
(buy, sell) = self.strategy.get_signal(trade.pair, self.strategy.ticker_interval,
ticker)
if self.analyze.should_sell(trade, current_rate, datetime.utcnow(), buy, sell):
self.execute_sell(trade, current_rate)
should_sell = self.strategy.should_sell(trade, current_rate, datetime.utcnow(), buy, sell)
if should_sell.sell_flag:
self.execute_sell(trade, current_rate, should_sell.sell_type)
return True
logger.info('Found no sell signals for whitelisted currencies. Trying again..')
return False
def check_handle_timedout(self, timeoutvalue: int) -> None:
def check_handle_timedout(self) -> None:
"""
Check if any orders are timed out and cancel if neccessary
:param timeoutvalue: Number of minutes until order is considered timed out
:return: None
"""
timeoutthreashold = arrow.utcnow().shift(minutes=-timeoutvalue).datetime
buy_timeout = self.config['unfilledtimeout']['buy']
sell_timeout = self.config['unfilledtimeout']['sell']
buy_timeoutthreashold = arrow.utcnow().shift(minutes=-buy_timeout).datetime
sell_timeoutthreashold = arrow.utcnow().shift(minutes=-sell_timeout).datetime
for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
try:
@ -521,10 +574,12 @@ with limit `{buy_limit:.8f} ({stake_amount:.6f} \
if int(order['remaining']) == 0:
continue
if order['side'] == 'buy' and ordertime < timeoutthreashold:
self.handle_timedout_limit_buy(trade, order)
elif order['side'] == 'sell' and ordertime < timeoutthreashold:
self.handle_timedout_limit_sell(trade, order)
# 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)
elif order['side'] == 'sell' and ordertime < sell_timeoutthreashold:
self.handle_timedout_limit_sell(trade, order)
# FIX: 20180110, why is cancel.order unconditionally here, whereas
# it is conditionally called in the
@ -540,7 +595,10 @@ with limit `{buy_limit:.8f} ({stake_amount:.6f} \
Trade.session.delete(trade)
Trade.session.flush()
logger.info('Buy order timeout for %s.', trade)
self.rpc.send_msg(f'*Timeout:* Unfilled buy order for {pair_s} cancelled')
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'Unfilled buy order for {pair_s} cancelled due to timeout'
})
return True
# if trade is partially complete, edit the stake details for the trade
@ -549,7 +607,10 @@ with limit `{buy_limit:.8f} ({stake_amount:.6f} \
trade.stake_amount = trade.amount * trade.open_rate
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
self.rpc.send_msg(f'*Timeout:* Remaining buy order for {pair_s} cancelled')
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'Remaining buy order for {pair_s} cancelled due to timeout'
})
return False
# FIX: 20180110, should cancel_order() be cond. or unconditionally called?
@ -567,65 +628,59 @@ with limit `{buy_limit:.8f} ({stake_amount:.6f} \
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
self.rpc.send_msg(f'*Timeout:* Unfilled sell order for {pair_s} cancelled')
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'Unfilled sell order for {pair_s} cancelled due to timeout'
})
logger.info('Sell order timeout for %s.', trade)
return True
# TODO: figure out how to handle partially complete sell orders
return False
def execute_sell(self, trade: Trade, limit: float) -> None:
def execute_sell(self, trade: Trade, limit: float, sell_reason: SellType) -> None:
"""
Executes a limit sell for the given trade and limit
:param trade: Trade instance
:param limit: limit rate for the sell order
:param sellreason: Reason the sell was triggered
:return: None
"""
exc = trade.exchange
pair = trade.pair
# Execute sell and update trade record
order_id = self.exchange.sell(str(trade.pair), limit, trade.amount)['id']
trade.open_order_id = order_id
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.value
fmt_exp_profit = round(trade.calc_profit_percent(rate=limit) * 100, 2)
profit_trade = trade.calc_profit(rate=limit)
current_rate = self.exchange.get_ticker(trade.pair)['bid']
profit = trade.calc_profit_percent(limit)
profit_percent = trade.calc_profit_percent(limit)
pair_url = self.exchange.get_pair_detail_url(trade.pair)
gain = "profit" if fmt_exp_profit > 0 else "loss"
gain = "profit" if profit_percent > 0 else "loss"
message = f"*{exc}:* Selling\n" \
f"*Current Pair:* [{pair}]({pair_url})\n" \
f"*Limit:* `{limit}`\n" \
f"*Amount:* `{round(trade.amount, 8)}`\n" \
f"*Open Rate:* `{trade.open_rate:.8f}`\n" \
f"*Current Rate:* `{current_rate:.8f}`\n" \
f"*Profit:* `{round(profit * 100, 2):.2f}%`" \
""
msg = {
'type': RPCMessageType.SELL_NOTIFICATION,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'gain': gain,
'market_url': pair_url,
'limit': limit,
'amount': trade.amount,
'open_rate': trade.open_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_percent': profit_percent,
}
# For regular case, when the configuration exists
if 'stake_currency' in self.config and 'fiat_display_currency' in self.config:
stake = self.config['stake_currency']
fiat = self.config['fiat_display_currency']
fiat_converter = CryptoToFiatConverter()
profit_fiat = fiat_converter.convert_amount(
profit_trade,
stake,
fiat
)
message += f'` ({gain}: {fmt_exp_profit:.2f}%, {profit_trade:.8f} {stake}`' \
f'` / {profit_fiat:.3f} {fiat})`'\
''
# Because telegram._forcesell does not have the configuration
# Ignore the FIAT value and does not show the stake_currency as well
else:
message += '` ({gain}: {profit_percent:.2f}%, {profit_coin:.8f})`'.format(
gain="profit" if fmt_exp_profit > 0 else "loss",
profit_percent=fmt_exp_profit,
profit_coin=profit_trade
)
stake_currency = self.config['stake_currency']
fiat_currency = self.config['fiat_display_currency']
msg.update({
'stake_currency': stake_currency,
'fiat_currency': fiat_currency,
})
# Send the message
self.rpc.send_msg(message)
self.rpc.send_msg(msg)
Trade.session.flush()

2
freqtrade/indicator_helpers.py
View File

@ -1,4 +1,4 @@
from math import exp, pi, sqrt, cos
from math import cos, exp, pi, sqrt
import numpy as np
import talib as ta

27
freqtrade/main.py
View File

@ -10,9 +10,10 @@ from typing import List
from freqtrade import OperationalException
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.configuration import Configuration, set_loggers
from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.state import State
from freqtrade.rpc import RPCMessageType
logger = logging.getLogger('freqtrade')
@ -59,7 +60,10 @@ def main(sysargv: List[str]) -> None:
logger.exception('Fatal exception!')
finally:
if freqtrade:
freqtrade.rpc.send_msg('*Status:* `Process died ...`')
freqtrade.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': 'process died'
})
freqtrade.cleanup()
sys.exit(return_code)
@ -73,24 +77,13 @@ def reconfigure(freqtrade: FreqtradeBot, args: Namespace) -> FreqtradeBot:
# Create new instance
freqtrade = FreqtradeBot(Configuration(args).get_config())
freqtrade.rpc.send_msg(
'*Status:* `Config reloaded ...`'.format(
freqtrade.state.name.lower()
)
)
freqtrade.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': 'config reloaded'
})
return freqtrade
def set_loggers() -> None:
"""
Set the logger level for Third party libs
:return: None
"""
logging.getLogger('requests.packages.urllib3').setLevel(logging.INFO)
logging.getLogger('ccxt.base.exchange').setLevel(logging.INFO)
logging.getLogger('telegram').setLevel(logging.INFO)
if __name__ == '__main__':
set_loggers()
main(sys.argv[1:])

2
freqtrade/misc.py
View File

@ -2,10 +2,10 @@
Various tool function for Freqtrade and scripts
"""
import gzip
import json
import logging
import re
import gzip
from datetime import datetime
from typing import Dict

9
freqtrade/optimize/__init__.py
View File

@ -54,11 +54,8 @@ def load_tickerdata_file(
:return dict OR empty if unsuccesful
"""
path = make_testdata_path(datadir)
pair_file_string = pair.replace('/', '_')
file = os.path.join(path, '{pair}-{ticker_interval}.json'.format(
pair=pair_file_string,
ticker_interval=ticker_interval,
))
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.
@ -222,7 +219,7 @@ def download_backtesting_testdata(datadir: str,
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')
new_data = exchange.get_ticker_history(pair=pair, tick_interval=tick_interval,
new_data = exchange.get_candle_history(pair=pair, tick_interval=tick_interval,
since_ms=since_ms)
data.extend(new_data)

246
freqtrade/optimize/backtesting.py
View File

@ -6,21 +6,24 @@ This module contains the backtesting logic
import logging
import operator
from argparse import Namespace
from datetime import datetime
from typing import Dict, Tuple, Any, List, Optional, NamedTuple
from copy import deepcopy
from datetime import datetime, timedelta
from pathlib import Path
from typing import Any, Dict, List, NamedTuple, Optional, Tuple
import arrow
from pandas import DataFrame
from tabulate import tabulate
import freqtrade.optimize as optimize
from freqtrade import constants, DependencyException
from freqtrade.exchange import Exchange
from freqtrade.analyze import Analyze
from freqtrade import DependencyException, constants
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.exchange import Exchange
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
logger = logging.getLogger(__name__)
@ -38,6 +41,9 @@ class BacktestResult(NamedTuple):
close_index: int
trade_duration: float
open_at_end: bool
open_rate: float
close_rate: float
sell_reason: SellType
class Backtesting(object):
@ -48,13 +54,9 @@ class Backtesting(object):
backtesting = Backtesting(config)
backtesting.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
self.analyze = Analyze(self.config)
self.ticker_interval = self.analyze.strategy.ticker_interval
self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe
self.populate_buy_trend = self.analyze.populate_buy_trend
self.populate_sell_trend = self.analyze.populate_sell_trend
# Reset keys for backtesting
self.config['exchange']['key'] = ''
@ -62,9 +64,36 @@ class Backtesting(object):
self.config['exchange']['password'] = ''
self.config['exchange']['uid'] = ''
self.config['dry_run'] = True
self.strategylist: List[IStrategy] = []
if self.config.get('strategy_list', None):
# Force one interval
self.ticker_interval = str(self.config.get('ticker_interval'))
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
self.strategylist.append(StrategyResolver(stratconf).strategy)
else:
# only one strategy
strat = StrategyResolver(self.config).strategy
self.strategylist.append(StrategyResolver(self.config).strategy)
# Load one strategy
self._set_strategy(self.strategylist[0])
self.exchange = Exchange(self.config)
self.fee = self.exchange.get_fee()
def _set_strategy(self, strategy):
"""
Load strategy into backtesting
"""
self.strategy = strategy
self.ticker_interval = self.config.get('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]:
"""
@ -73,7 +102,7 @@ class Backtesting(object):
:return: tuple containing min_date, max_date
"""
timeframe = [
(arrow.get(min(frame.date)), arrow.get(max(frame.date)))
(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
for frame in data.values()
]
return min(timeframe, key=operator.itemgetter(0))[0], \
@ -86,9 +115,9 @@ class Backtesting(object):
"""
stake_currency = str(self.config.get('stake_currency'))
floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['pair', 'buy count', 'avg profit %',
headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
for pair in data:
result = results[results.pair == pair]
@ -96,8 +125,10 @@ class Backtesting(object):
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_percent.sum() * 100.0,
result.profit_abs.sum(),
result.trade_duration.mean(),
str(timedelta(
minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
len(result[result.profit_abs > 0]),
len(result[result.profit_abs < 0])
])
@ -107,22 +138,63 @@ class Backtesting(object):
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.trade_duration.mean(),
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
def _store_backtest_result(self, recordfilename: Optional[str], results: DataFrame) -> None:
def _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str:
"""
Generate small table outlining Backtest results
"""
tabular_data = []
headers = ['Sell Reason', 'Count']
for reason, count in results['sell_reason'].value_counts().iteritems():
tabular_data.append([reason.value, count])
return tabulate(tabular_data, headers=headers, tablefmt="pipe")
records = [(trade_entry.pair, trade_entry.profit_percent,
trade_entry.open_time.timestamp(),
trade_entry.close_time.timestamp(),
trade_entry.open_index - 1, trade_entry.trade_duration)
for index, trade_entry in results.iterrows()]
def _generate_text_table_strategy(self, all_results: dict) -> str:
"""
Generate summary table per strategy
"""
stake_currency = str(self.config.get('stake_currency'))
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
for strategy, results in all_results.items():
tabular_data.append([
strategy,
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
def _store_backtest_result(self, recordfilename: str, results: DataFrame,
strategyname: Optional[str] = None) -> None:
records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
for index, t in results.iterrows()]
if records:
if strategyname:
# Inject strategyname to filename
recname = Path(recordfilename)
recordfilename = str(Path.joinpath(
recname.parent, f'{recname.stem}-{strategyname}').with_suffix(recname.suffix))
logger.info('Dumping backtest results to %s', recordfilename)
file_dump_json(recordfilename, records)
@ -133,7 +205,7 @@ class Backtesting(object):
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(
open_rate=buy_row.close,
open_rate=buy_row.open,
open_date=buy_row.date,
stake_amount=stake_amount,
amount=stake_amount / buy_row.open,
@ -148,31 +220,40 @@ class Backtesting(object):
trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
buy_signal = sell_row.buy
if self.analyze.should_sell(trade, sell_row.close, sell_row.date, buy_signal,
sell_row.sell):
sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
sell_row.sell)
if sell.sell_flag:
return BacktestResult(pair=pair,
profit_percent=trade.calc_profit_percent(rate=sell_row.close),
profit_abs=trade.calc_profit(rate=sell_row.close),
profit_percent=trade.calc_profit_percent(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=(sell_row.date - buy_row.date).seconds // 60,
trade_duration=int((
sell_row.date - buy_row.date).total_seconds() // 60),
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=False
open_at_end=False,
open_rate=buy_row.open,
close_rate=sell_row.open,
sell_reason=sell.sell_type
)
if partial_ticker:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ticker[-1]
btr = BacktestResult(pair=pair,
profit_percent=trade.calc_profit_percent(rate=sell_row.close),
profit_abs=trade.calc_profit(rate=sell_row.close),
profit_percent=trade.calc_profit_percent(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=(sell_row.date - buy_row.date).seconds // 60,
trade_duration=int((
sell_row.date - buy_row.date).total_seconds() // 60),
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=True
open_at_end=True,
open_rate=buy_row.open,
close_rate=sell_row.open,
sell_reason=SellType.FORCE_SELL
)
logger.debug('Force_selling still open trade %s with %s perc - %s', btr.pair,
btr.profit_percent, btr.profit_abs)
@ -191,20 +272,20 @@ class Backtesting(object):
stake_amount: btc amount to use for each trade
processed: a processed dictionary with format {pair, data}
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
realistic: do we try to simulate realistic trades? (default: True)
position_stacking: do we allow position stacking? (default: False)
:return: DataFrame
"""
headers = ['date', 'buy', 'open', 'close', 'sell']
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
realistic = args.get('realistic', False)
position_stacking = args.get('position_stacking', False)
trades = []
trade_count_lock: Dict = {}
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
ticker_data = self.populate_sell_trend(
self.populate_buy_trend(pair_data))[headers].copy()
ticker_data = self.advise_sell(
self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
@ -221,7 +302,7 @@ class Backtesting(object):
if row.buy == 0 or row.sell == 1:
continue # skip rows where no buy signal or that would immediately sell off
if realistic:
if not position_stacking:
if lock_pair_until is not None and row.date <= lock_pair_until:
continue
if max_open_trades > 0:
@ -257,7 +338,7 @@ class Backtesting(object):
if self.config.get('live'):
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = self.exchange.get_ticker_history(pair, self.ticker_interval)
data[pair] = self.exchange.get_candle_history(pair, self.ticker_interval)
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
@ -275,58 +356,61 @@ class Backtesting(object):
if not data:
logger.critical("No data found. Terminating.")
return
# Ignore max_open_trades in backtesting, except realistic flag was passed
if self.config.get('realistic_simulation', False):
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
max_open_trades = self.config['max_open_trades']
else:
logger.info('Ignoring max_open_trades (realistic_simulation not set) ...')
logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
all_results = {}
preprocessed = self.tickerdata_to_dataframe(data)
for strat in self.strategylist:
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
self._set_strategy(strat)
# 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
)
# need to reprocess data every time to populate signals
preprocessed = self.tickerdata_to_dataframe(data)
# Execute backtest and print results
results = self.backtest(
{
'stake_amount': self.config.get('stake_amount'),
'processed': preprocessed,
'max_open_trades': max_open_trades,
'realistic': self.config.get('realistic_simulation', False),
}
)
if self.config.get('export', False):
self._store_backtest_result(self.config.get('exportfilename'), results)
logger.info(
'\n======================================== '
'BACKTESTING REPORT'
' =========================================\n'
'%s',
self._generate_text_table(
data,
results
# 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
)
)
logger.info(
'\n====================================== '
'LEFT OPEN TRADES REPORT'
' ======================================\n'
'%s',
self._generate_text_table(
data,
results.loc[results.open_at_end]
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest(
{
'stake_amount': self.config.get('stake_amount'),
'processed': preprocessed,
'max_open_trades': max_open_trades,
'position_stacking': self.config.get('position_stacking', False),
}
)
)
for strategy, results in all_results.items():
if self.config.get('export', False):
self._store_backtest_result(self.config['exportfilename'], results,
strategy if len(self.strategylist) > 1 else None)
print(f"Result for strategy {strategy}")
print(' BACKTESTING REPORT '.center(119, '='))
print(self._generate_text_table(data, results))
print(' SELL REASON STATS '.center(119, '='))
print(self._generate_text_table_sell_reason(data, results))
print(' LEFT OPEN TRADES REPORT '.center(119, '='))
print(self._generate_text_table(data, results.loc[results.open_at_end]))
print()
if len(all_results) > 1:
# Print Strategy summary table
print(' Strategy Summary '.center(119, '='))
print(self._generate_text_table_strategy(all_results))
print('\nFor more details, please look at the detail tables above')
def setup_configuration(args: Namespace) -> Dict[str, Any]:

507
freqtrade/optimize/hyperopt.py
View File

@ -4,22 +4,21 @@
This module contains the hyperopt logic
"""
import json
import logging
import multiprocessing
import os
import pickle
import signal
import sys
from argparse import Namespace
from functools import reduce
from math import exp
from operator import itemgetter
from typing import Dict, Any, Callable, Optional
from typing import Any, Callable, Dict, List
import numpy
import talib.abstract as ta
from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
from pandas import DataFrame
from sklearn.externals.joblib import Parallel, delayed, dump, load
from skopt import Optimizer
from skopt.space import Categorical, Dimension, Integer, Real
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.arguments import Arguments
@ -29,6 +28,9 @@ from freqtrade.optimize.backtesting import Backtesting
logger = logging.getLogger(__name__)
MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization
TICKERDATA_PICKLE = os.path.join('user_data', 'hyperopt_tickerdata.pkl')
class Hyperopt(Backtesting):
"""
@ -44,7 +46,6 @@ class Hyperopt(Backtesting):
# to the number of days
self.target_trades = 600
self.total_tries = config.get('epochs', 0)
self.current_tries = 0
self.current_best_loss = 100
# max average trade duration in minutes
@ -56,130 +57,38 @@ class Hyperopt(Backtesting):
# check that the reported Σ% values do not exceed this!
self.expected_max_profit = 3.0
# Configuration and data used by hyperopt
self.processed: Optional[Dict[str, Any]] = None
# Previous evaluations
self.trials_file = os.path.join('user_data', 'hyperopt_results.pickle')
self.trials: List = []
# Hyperopt Trials
self.trials_file = os.path.join('user_data', 'hyperopt_trials.pickle')
self.trials = Trials()
def get_args(self, params):
dimensions = self.hyperopt_space()
# Ensure the number of dimensions match
# the number of parameters in the list x.
if len(params) != len(dimensions):
raise ValueError('Mismatch in number of search-space dimensions. '
f'len(dimensions)=={len(dimensions)} and len(x)=={len(params)}')
# Create a dict where the keys are the names of the dimensions
# and the values are taken from the list of parameters x.
arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
return arg_dict
@staticmethod
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
dataframe['cci'] = ta.CCI(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['roc'] = ta.ROC(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Stoch RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
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['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# EMA - Exponential Moving Average
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# SAR Parabolic
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
"""
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# Inverted Hammer: values [0, 100]
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# Dragonfly Doji: values [0, 100]
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# Piercing Line: values [0, 100]
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# Morningstar: values [0, 100]
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# Three White Soldiers: values [0, 100]
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
"""
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
"""
# Hanging Man: values [0, 100]
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# Shooting Star: values [0, 100]
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# Gravestone Doji: values [0, 100]
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# Dark Cloud Cover: values [0, 100]
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# Evening Doji Star: values [0, 100]
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# Evening Star: values [0, 100]
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
"""
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
"""
# Three Line Strike: values [0, -100, 100]
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# Spinning Top: values [0, -100, 100]
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# Engulfing: values [0, -100, 100]
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# Harami: values [0, -100, 100]
dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# Three Outside Up/Down: values [0, -100, 100]
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# Three Inside Up/Down: values [0, -100, 100]
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
"""
# Chart type
# ------------------------------------
# Heikinashi stategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
return dataframe
@ -187,15 +96,16 @@ class Hyperopt(Backtesting):
"""
Save hyperopt trials to file
"""
logger.info('Saving Trials to \'%s\'', self.trials_file)
pickle.dump(self.trials, open(self.trials_file, 'wb'))
if self.trials:
logger.info('Saving %d evaluations to \'%s\'', len(self.trials), self.trials_file)
dump(self.trials, self.trials_file)
def read_trials(self) -> Trials:
def read_trials(self) -> List:
"""
Read hyperopt trials file
"""
logger.info('Reading Trials from \'%s\'', self.trials_file)
trials = pickle.load(open(self.trials_file, 'rb'))
trials = load(self.trials_file)
os.remove(self.trials_file)
return trials
@ -203,22 +113,27 @@ class Hyperopt(Backtesting):
"""
Display Best hyperopt result
"""
vals = json.dumps(self.trials.best_trial['misc']['vals'], indent=4)
results = self.trials.best_trial['result']['result']
logger.info('Best result:\n%s\nwith values:\n%s', results, vals)
results = sorted(self.trials, key=itemgetter('loss'))
best_result = results[0]
logger.info(
'Best result:\n%s\nwith values:\n%s',
best_result['result'],
best_result['params']
)
if 'roi_t1' in best_result['params']:
logger.info('ROI table:\n%s', self.generate_roi_table(best_result['params']))
def log_results(self, results) -> None:
"""
Log results if it is better than any previous evaluation
"""
if results['loss'] < self.current_best_loss:
current = results['current_tries']
total = results['total_tries']
res = results['result']
loss = results['loss']
self.current_best_loss = results['loss']
log_msg = '\n{:5d}/{}: {}. Loss {:.5f}'.format(
results['current_tries'],
results['total_tries'],
results['result'],
results['loss']
)
log_msg = f'\n{current:5d}/{total}: {res}. Loss {loss:.5f}'
print(log_msg)
else:
print('.', end='')
@ -231,7 +146,8 @@ class Hyperopt(Backtesting):
trade_loss = 1 - 0.25 * exp(-(trade_count - self.target_trades) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / self.expected_max_profit)
duration_loss = 0.4 * min(trade_duration / self.max_accepted_trade_duration, 1)
return trade_loss + profit_loss + duration_loss
result = trade_loss + profit_loss + duration_loss
return result
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
@ -247,87 +163,44 @@ class Hyperopt(Backtesting):
return roi_table
@staticmethod
def roi_space() -> Dict[str, Any]:
def roi_space() -> List[Dimension]:
"""
Values to search for each ROI steps
"""
return {
'roi_t1': hp.quniform('roi_t1', 10, 120, 20),
'roi_t2': hp.quniform('roi_t2', 10, 60, 15),
'roi_t3': hp.quniform('roi_t3', 10, 40, 10),
'roi_p1': hp.quniform('roi_p1', 0.01, 0.04, 0.01),
'roi_p2': hp.quniform('roi_p2', 0.01, 0.07, 0.01),
'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01),
}
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() -> Dict[str, Any]:
def stoploss_space() -> List[Dimension]:
"""
Stoploss Value to search
Stoploss search space
"""
return {
'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
}
return [
Real(-0.5, -0.02, name='stoploss'),
]
@staticmethod
def indicator_space() -> Dict[str, Any]:
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return {
'macd_below_zero': hp.choice('macd_below_zero', [
{'enabled': False},
{'enabled': True}
]),
'mfi': hp.choice('mfi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('mfi-value', 10, 25, 5)}
]),
'fastd': hp.choice('fastd', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('fastd-value', 15, 45, 5)}
]),
'adx': hp.choice('adx', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('adx-value', 20, 50, 5)}
]),
'rsi': hp.choice('rsi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 5)}
]),
'uptrend_long_ema': hp.choice('uptrend_long_ema', [
{'enabled': False},
{'enabled': True}
]),
'uptrend_short_ema': hp.choice('uptrend_short_ema', [
{'enabled': False},
{'enabled': True}
]),
'over_sar': hp.choice('over_sar', [
{'enabled': False},
{'enabled': True}
]),
'green_candle': hp.choice('green_candle', [
{'enabled': False},
{'enabled': True}
]),
'uptrend_sma': hp.choice('uptrend_sma', [
{'enabled': False},
{'enabled': True}
]),
'trigger': hp.choice('trigger', [
{'type': 'lower_bb'},
{'type': 'lower_bb_tema'},
{'type': 'faststoch10'},
{'type': 'ao_cross_zero'},
{'type': 'ema3_cross_ema10'},
{'type': 'macd_cross_signal'},
{'type': 'sar_reversal'},
{'type': 'ht_sine'},
{'type': 'heiken_reversal_bull'},
{'type': 'di_cross'},
]),
}
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:
"""
@ -337,17 +210,17 @@ class Hyperopt(Backtesting):
return True
return False
def hyperopt_space(self) -> Dict[str, Any]:
def hyperopt_space(self) -> List[Dimension]:
"""
Return the space to use during Hyperopt
"""
spaces: Dict = {}
spaces: List[Dimension] = []
if self.has_space('buy'):
spaces = {**spaces, **Hyperopt.indicator_space()}
spaces += Hyperopt.indicator_space()
if self.has_space('roi'):
spaces = {**spaces, **Hyperopt.roi_space()}
spaces += Hyperopt.roi_space()
if self.has_space('stoploss'):
spaces = {**spaces, **Hyperopt.stoploss_space()}
spaces += Hyperopt.stoploss_space()
return spaces
@staticmethod
@ -355,69 +228,32 @@ class Hyperopt(Backtesting):
"""
Define the buy strategy parameters to be used by hyperopt
"""
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use
"""
conditions = []
# GUARDS AND TRENDS
if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
conditions.append(dataframe['ema50'] > dataframe['ema100'])
if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
conditions.append(dataframe['macd'] < 0)
if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
conditions.append(dataframe['ema5'] > dataframe['ema10'])
if 'mfi' in params and params['mfi']['enabled']:
conditions.append(dataframe['mfi'] < params['mfi']['value'])
if 'fastd' in params and params['fastd']['enabled']:
conditions.append(dataframe['fastd'] < params['fastd']['value'])
if 'adx' in params and params['adx']['enabled']:
conditions.append(dataframe['adx'] > params['adx']['value'])
if 'rsi' in params and params['rsi']['enabled']:
conditions.append(dataframe['rsi'] < params['rsi']['value'])
if 'over_sar' in params and params['over_sar']['enabled']:
conditions.append(dataframe['close'] > dataframe['sar'])
if 'green_candle' in params and params['green_candle']['enabled']:
conditions.append(dataframe['close'] > dataframe['open'])
if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
prevsma = dataframe['sma'].shift(1)
conditions.append(dataframe['sma'] > prevsma)
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
triggers = {
'lower_bb': (
dataframe['close'] < dataframe['bb_lowerband']
),
'lower_bb_tema': (
dataframe['tema'] < dataframe['bb_lowerband']
),
'faststoch10': (qtpylib.crossed_above(
dataframe['fastd'], 10.0
)),
'ao_cross_zero': (qtpylib.crossed_above(
dataframe['ao'], 0.0
)),
'ema3_cross_ema10': (qtpylib.crossed_above(
dataframe['ema3'], dataframe['ema10']
)),
'macd_cross_signal': (qtpylib.crossed_above(
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']
)),
'sar_reversal': (qtpylib.crossed_above(
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
)),
'ht_sine': (qtpylib.crossed_above(
dataframe['htleadsine'], dataframe['htsine']
)),
'heiken_reversal_bull': (
(qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
(dataframe['ha_low'] == dataframe['ha_open'])
),
'di_cross': (qtpylib.crossed_above(
dataframe['plus_di'], dataframe['minus_di']
)),
}
conditions.append(triggers.get(params['trigger']['type']))
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@ -427,21 +263,24 @@ class Hyperopt(Backtesting):
return populate_buy_trend
def generate_optimizer(self, params: Dict) -> Dict:
def generate_optimizer(self, _params) -> Dict:
params = self.get_args(_params)
if self.has_space('roi'):
self.analyze.strategy.minimal_roi = self.generate_roi_table(params)
self.strategy.minimal_roi = self.generate_roi_table(params)
if self.has_space('buy'):
self.populate_buy_trend = self.buy_strategy_generator(params)
self.advise_buy = self.buy_strategy_generator(params)
if self.has_space('stoploss'):
self.analyze.strategy.stoploss = params['stoploss']
self.strategy.stoploss = params['stoploss']
processed = load(TICKERDATA_PICKLE)
results = self.backtest(
{
'stake_amount': self.config['stake_amount'],
'processed': self.processed,
'realistic': self.config.get('realistic_simulation', False),
'processed': processed,
'position_stacking': self.config.get('position_stacking', True),
}
)
result_explanation = self.format_results(results)
@ -450,30 +289,18 @@ class Hyperopt(Backtesting):
trade_count = len(results.index)
trade_duration = results.trade_duration.mean()
if trade_count == 0 or trade_duration > self.max_accepted_trade_duration:
print('.', end='')
sys.stdout.flush()
if trade_count == 0:
return {
'status': STATUS_FAIL,
'loss': float('inf')
'loss': MAX_LOSS,
'params': params,
'result': result_explanation,
}
loss = self.calculate_loss(total_profit, trade_count, trade_duration)
self.current_tries += 1
self.log_results(
{
'loss': loss,
'current_tries': self.current_tries,
'total_tries': self.total_tries,
'result': result_explanation,
}
)
return {
'loss': loss,
'status': STATUS_OK,
'params': params,
'result': result_explanation,
}
@ -481,15 +308,37 @@ class Hyperopt(Backtesting):
"""
Return the format result in a string
"""
return ('{:6d} trades. Avg profit {: 5.2f}%. '
'Total profit {: 11.8f} {} ({:.4f}Σ%). Avg duration {:5.1f} mins.').format(
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_abs.sum(),
self.config['stake_currency'],
results.profit_percent.sum(),
results.trade_duration.mean(),
)
trades = len(results.index)
avg_profit = results.profit_percent.mean() * 100.0
total_profit = results.profit_abs.sum()
stake_cur = self.config['stake_currency']
profit = results.profit_percent.sum()
duration = results.trade_duration.mean()
return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
f'Total profit {total_profit: 11.8f} {stake_cur} '
f'({profit:.4f}Σ%). Avg duration {duration:5.1f} mins.')
def get_optimizer(self, cpu_count) -> Optimizer:
return Optimizer(
self.hyperopt_space(),
base_estimator="ET",
acq_optimizer="auto",
n_initial_points=30,
acq_optimizer_kwargs={'n_jobs': cpu_count}
)
def run_optimizer_parallel(self, parallel, asked) -> List:
return parallel(delayed(self.generate_optimizer)(v) for v in asked)
def load_previous_results(self):
""" read trials file if we have one """
if os.path.exists(self.trials_file) and os.path.getsize(self.trials_file) > 0:
self.trials = self.read_trials()
logger.info(
'Loaded %d previous evaluations from disk.',
len(self.trials)
)
def start(self) -> None:
timerange = Arguments.parse_timerange(None if self.config.get(
@ -502,68 +351,36 @@ class Hyperopt(Backtesting):
)
if self.has_space('buy'):
self.analyze.populate_indicators = Hyperopt.populate_indicators # type: ignore
self.processed = self.tickerdata_to_dataframe(data)
self.strategy.advise_indicators = Hyperopt.populate_indicators # type: ignore
dump(self.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
self.exchange = None # type: ignore
self.load_previous_results()
logger.info('Preparing Trials..')
signal.signal(signal.SIGINT, self.signal_handler)
# read trials file if we have one
if os.path.exists(self.trials_file) and os.path.getsize(self.trials_file) > 0:
self.trials = self.read_trials()
self.current_tries = len(self.trials.results)
self.total_tries += self.current_tries
logger.info(
'Continuing with trials. Current: %d, Total: %d',
self.current_tries,
self.total_tries
)
cpus = multiprocessing.cpu_count()
logger.info(f'Found {cpus} CPU cores. Let\'s make them scream!')
opt = self.get_optimizer(cpus)
EVALS = max(self.total_tries // cpus, 1)
try:
best_parameters = fmin(
fn=self.generate_optimizer,
space=self.hyperopt_space(),
algo=tpe.suggest,
max_evals=self.total_tries,
trials=self.trials
)
with Parallel(n_jobs=cpus) as parallel:
for i in range(EVALS):
asked = opt.ask(n_points=cpus)
f_val = self.run_optimizer_parallel(parallel, asked)
opt.tell(asked, [i['loss'] for i in f_val])
results = sorted(self.trials.results, key=itemgetter('loss'))
best_result = results[0]['result']
except ValueError:
best_parameters = {}
best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
'try with more epochs (param: -e).'
# Improve best parameter logging display
if best_parameters:
best_parameters = space_eval(
self.hyperopt_space(),
best_parameters
)
logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
if 'roi_t1' in best_parameters:
logger.info('ROI table:\n%s', self.generate_roi_table(best_parameters))
logger.info('Best Result:\n%s', best_result)
# Store trials result to file to resume next time
self.save_trials()
def signal_handler(self, sig, frame) -> None:
"""
Hyperopt SIGINT handler
"""
logger.info(
'Hyperopt received %s',
signal.Signals(sig).name
)
self.trials += f_val
for j in range(cpus):
self.log_results({
'loss': f_val[j]['loss'],
'current_tries': i * cpus + j,
'total_tries': self.total_tries,
'result': f_val[j]['result'],
})
except KeyboardInterrupt:
print('User interrupted..')
self.save_trials()
self.log_trials_result()
sys.exit(0)
def start(args: Namespace) -> None:

139
freqtrade/persistence.py
View File

@ -5,12 +5,11 @@ This module contains the class to persist trades into SQLite
import logging
from datetime import datetime
from decimal import Decimal, getcontext
from typing import Dict, Optional, Any
from typing import Any, Dict, Optional
import arrow
from sqlalchemy import (Boolean, Column, DateTime, Float, Integer, String,
create_engine)
from sqlalchemy import inspect
create_engine, inspect)
from sqlalchemy.exc import NoSuchModuleError
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm.scoping import scoped_session
@ -22,6 +21,7 @@ from freqtrade import OperationalException
logger = logging.getLogger(__name__)
_DECL_BASE: Any = declarative_base()
_SQL_DOCS_URL = 'http://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls'
def init(config: Dict) -> None:
@ -46,10 +46,8 @@ def init(config: Dict) -> None:
try:
engine = create_engine(db_url, **kwargs)
except NoSuchModuleError:
error = 'Given value for db_url: \'{}\' is no valid database URL! (See {}).'.format(
db_url, 'http://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls'
)
raise OperationalException(error)
raise OperationalException(f'Given value for db_url: \'{db_url}\' '
f'is no valid database URL! (See {_SQL_DOCS_URL})')
session = scoped_session(sessionmaker(bind=engine, autoflush=True, autocommit=True))
Trade.session = session()
@ -66,6 +64,10 @@ def has_column(columns, searchname: str) -> bool:
return len(list(filter(lambda x: x["name"] == searchname, columns))) == 1
def get_column_def(columns, column: str, default: str) -> str:
return default if not has_column(columns, column) else column
def check_migrate(engine) -> None:
"""
Checks if migration is necessary and migrates if necessary
@ -73,18 +75,38 @@ def check_migrate(engine) -> None:
inspector = inspect(engine)
cols = inspector.get_columns('trades')
tabs = inspector.get_table_names()
table_back_name = 'trades_bak'
for i, table_back_name in enumerate(tabs):
table_back_name = f'trades_bak{i}'
logger.info(f'trying {table_back_name}')
# Check for latest column
if not has_column(cols, 'ticker_interval'):
fee_open = get_column_def(cols, 'fee_open', 'fee')
fee_close = get_column_def(cols, 'fee_close', 'fee')
open_rate_requested = get_column_def(cols, 'open_rate_requested', 'null')
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')
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')
ticker_interval = get_column_def(cols, 'ticker_interval', 'null')
if not has_column(cols, 'fee_open'):
# Schema migration necessary
engine.execute("alter table trades rename to trades_bak")
engine.execute(f"alter table trades rename to {table_back_name}")
# let SQLAlchemy create the schema as required
_DECL_BASE.metadata.create_all(engine)
# Copy data back - following the correct schema
engine.execute("""insert into trades
engine.execute(f"""insert into trades
(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)
stake_amount, amount, open_date, close_date, open_order_id,
stop_loss, initial_stop_loss, max_rate, sell_reason, strategy,
ticker_interval
)
select id, lower(exchange),
case
when instr(pair, '_') != 0 then
@ -93,22 +115,20 @@ def check_migrate(engine) -> None:
else pair
end
pair,
is_open, fee fee_open, fee fee_close,
open_rate, null open_rate_requested, close_rate,
null close_rate_requested, close_profit,
stake_amount, amount, open_date, close_date, open_order_id
from trades_bak
is_open, {fee_open} fee_open, {fee_close} fee_close,
open_rate, {open_rate_requested} open_rate_requested, close_rate,
{close_rate_requested} close_rate_requested, close_profit,
stake_amount, amount, open_date, close_date, open_order_id,
{stop_loss} stop_loss, {initial_stop_loss} initial_stop_loss,
{max_rate} max_rate, {sell_reason} sell_reason, {strategy} strategy,
{ticker_interval} ticker_interval
from {table_back_name}
""")
# Reread columns - the above recreated the table!
inspector = inspect(engine)
cols = inspector.get_columns('trades')
if not has_column(cols, 'open_rate_requested'):
engine.execute("alter table trades add open_rate_requested float")
if not has_column(cols, 'close_rate_requested'):
engine.execute("alter table trades add close_rate_requested float")
def cleanup() -> None:
"""
@ -137,8 +157,8 @@ class Trade(_DECL_BASE):
id = Column(Integer, primary_key=True)
exchange = Column(String, nullable=False)
pair = Column(String, nullable=False)
is_open = Column(Boolean, nullable=False, default=True)
pair = Column(String, nullable=False, index=True)
is_open = Column(Boolean, nullable=False, default=True, index=True)
fee_open = Column(Float, nullable=False, default=0.0)
fee_close = Column(Float, nullable=False, default=0.0)
open_rate = Column(Float)
@ -151,15 +171,60 @@ class Trade(_DECL_BASE):
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
close_date = Column(DateTime)
open_order_id = Column(String)
# absolute value of the stop loss
stop_loss = Column(Float, nullable=True, default=0.0)
# absolute value of the initial stop loss
initial_stop_loss = Column(Float, nullable=True, default=0.0)
# absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0)
sell_reason = Column(String, nullable=True)
strategy = Column(String, nullable=True)
ticker_interval = Column(Integer, nullable=True)
def __repr__(self):
return 'Trade(id={}, pair={}, amount={:.8f}, open_rate={:.8f}, open_since={})'.format(
self.id,
self.pair,
self.amount,
self.open_rate,
arrow.get(self.open_date).humanize() if self.is_open else 'closed'
)
open_since = arrow.get(self.open_date).humanize() if self.is_open else 'closed'
return (f'Trade(id={self.id}, pair={self.pair}, amount={self.amount:.8f}, '
f'open_rate={self.open_rate:.8f}, open_since={open_since})')
def adjust_stop_loss(self, current_price: float, stoploss: float, initial: bool = False):
"""this adjusts the stop loss to it's most recently observed setting"""
if initial and not (self.stop_loss is None or self.stop_loss == 0):
# Don't modify if called with initial and nothing to do
return
new_loss = float(current_price * (1 - abs(stoploss)))
# keeping track of the highest observed rate for this trade
if self.max_rate is None:
self.max_rate = current_price
else:
if current_price > self.max_rate:
self.max_rate = current_price
# no stop loss assigned yet
if not self.stop_loss:
logger.debug("assigning new stop loss")
self.stop_loss = new_loss
self.initial_stop_loss = new_loss
# evaluate if the stop loss needs to be updated
else:
if new_loss > self.stop_loss: # stop losses only walk up, never down!
self.stop_loss = new_loss
logger.debug("adjusted stop loss")
else:
logger.debug("keeping current stop loss")
logger.debug(
f"{self.pair} - current price {current_price:.8f}, "
f"bought at {self.open_rate:.8f} and calculated "
f"stop loss is at: {self.initial_stop_loss:.8f} initial "
f"stop at {self.stop_loss:.8f}. "
f"trailing stop loss saved us: "
f"{float(self.stop_loss) - float(self.initial_stop_loss):.8f} "
f"and max observed rate was {self.max_rate:.8f}")
def update(self, order: Dict) -> None:
"""
@ -167,6 +232,7 @@ class Trade(_DECL_BASE):
:param order: order retrieved by exchange.get_order()
:return: None
"""
order_type = order['type']
# Ignore open and cancelled orders
if order['status'] == 'open' or order['price'] is None:
return
@ -174,16 +240,16 @@ class Trade(_DECL_BASE):
logger.info('Updating trade (id=%d) ...', self.id)
getcontext().prec = 8 # Bittrex do not go above 8 decimal
if order['type'] == 'limit' and order['side'] == 'buy':
if order_type == '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)
self.open_order_id = None
elif order['type'] == 'limit' and order['side'] == 'sell':
elif order_type == 'limit' and order['side'] == 'sell':
self.close(order['price'])
else:
raise ValueError('Unknown order type: {}'.format(order['type']))
raise ValueError(f'Unknown order type: {order_type}')
cleanup()
def close(self, rate: float) -> None:
@ -254,7 +320,8 @@ class Trade(_DECL_BASE):
rate=(rate or self.close_rate),
fee=(fee or self.fee_close)
)
return float("{0:.8f}".format(close_trade_price - open_trade_price))
profit = close_trade_price - open_trade_price
return float(f"{profit:.8f}")
def calc_profit_percent(
self,
@ -274,5 +341,5 @@ class Trade(_DECL_BASE):
rate=(rate or self.close_rate),
fee=(fee or self.fee_close)
)
return float("{0:.8f}".format((close_trade_price / open_trade_price) - 1))
profit_percent = (close_trade_price / open_trade_price) - 1
return float(f"{profit_percent:.8f}")

2
freqtrade/rpc/__init__.py
View File

@ -0,0 +1,2 @@
from .rpc import RPC, RPCMessageType, RPCException # noqa
from .rpc_manager import RPCManager # noqa

197
freqtrade/rpc/rpc.py
View File

@ -3,22 +3,36 @@ This module contains class to define a RPC communications
"""
import logging
from abc import abstractmethod
from datetime import datetime, timedelta, date
from datetime import timedelta, datetime, date
from decimal import Decimal
from typing import Dict, Tuple, Any, List
from enum import Enum
from typing import Dict, Any, List, Optional
import arrow
import sqlalchemy as sql
from numpy import mean, nan_to_num
from pandas import DataFrame
from freqtrade.fiat_convert import CryptoToFiatConverter
from freqtrade.misc import shorten_date
from freqtrade.persistence import Trade
from freqtrade.state import State
from freqtrade.strategy.interface import SellType
logger = logging.getLogger(__name__)
class RPCMessageType(Enum):
STATUS_NOTIFICATION = 'status'
WARNING_NOTIFICATION = 'warning'
CUSTOM_NOTIFICATION = 'custom'
BUY_NOTIFICATION = 'buy'
SELL_NOTIFICATION = 'sell'
def __repr__(self):
return self.value
class RPCException(Exception):
"""
Should be raised with a rpc-formatted message in an _rpc_* method
@ -26,13 +40,21 @@ class RPCException(Exception):
raise RPCException('*Status:* `no active trade`')
"""
pass
def __init__(self, message: str) -> None:
super().__init__(self)
self.message = message
def __str__(self):
return self.message
class RPC(object):
"""
RPC class can be used to have extra feature, like bot data, and access to DB data
"""
# Bind _fiat_converter if needed in each RPC handler
_fiat_converter: Optional[CryptoToFiatConverter] = None
def __init__(self, freqtrade) -> None:
"""
Initializes all enabled rpc modules
@ -41,20 +63,20 @@ class RPC(object):
"""
self._freqtrade = freqtrade
@property
def name(self) -> str:
""" Returns the lowercase name of the implementation """
return self.__class__.__name__.lower()
@abstractmethod
def cleanup(self) -> None:
""" Cleanup pending module resources """
@property
@abstractmethod
def name(self) -> str:
""" Returns the lowercase name of this module """
@abstractmethod
def send_msg(self, msg: str) -> None:
def send_msg(self, msg: Dict[str, str]) -> None:
""" Sends a message to all registered rpc modules """
def _rpc_trade_status(self) -> List[str]:
def _rpc_trade_status(self) -> List[Dict[str, Any]]:
"""
Below follows the RPC backend it is prefixed with rpc_ to raise awareness that it is
a remotely exposed function
@ -62,11 +84,11 @@ class RPC(object):
# Fetch open trade
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
if self._freqtrade.state != State.RUNNING:
raise RPCException('*Status:* `trader is not running`')
raise RPCException('trader is not running')
elif not trades:
raise RPCException('*Status:* `no active trade`')
raise RPCException('no active trade')
else:
result = []
results = []
for trade in trades:
order = None
if trade.open_order_id:
@ -74,53 +96,42 @@ class RPC(object):
# calculate profit and send message to user
current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
current_profit = trade.calc_profit_percent(current_rate)
fmt_close_profit = '{:.2f}%'.format(
round(trade.close_profit * 100, 2)
) if trade.close_profit else None
message = "*Trade ID:* `{trade_id}`\n" \
"*Current Pair:* [{pair}]({market_url})\n" \
"*Open Since:* `{date}`\n" \
"*Amount:* `{amount}`\n" \
"*Open Rate:* `{open_rate:.8f}`\n" \
"*Close Rate:* `{close_rate}`\n" \
"*Current Rate:* `{current_rate:.8f}`\n" \
"*Close Profit:* `{close_profit}`\n" \
"*Current Profit:* `{current_profit:.2f}%`\n" \
"*Open Order:* `{open_order}`"\
.format(
trade_id=trade.id,
pair=trade.pair,
market_url=self._freqtrade.exchange.get_pair_detail_url(trade.pair),
date=arrow.get(trade.open_date).humanize(),
open_rate=trade.open_rate,
close_rate=trade.close_rate,
current_rate=current_rate,
amount=round(trade.amount, 8),
close_profit=fmt_close_profit,
current_profit=round(current_profit * 100, 2),
open_order='({} {} rem={:.8f})'.format(
order['type'], order['side'], order['remaining']
) if order else None,
)
result.append(message)
return result
fmt_close_profit = (f'{round(trade.close_profit * 100, 2):.2f}%'
if trade.close_profit else None)
results.append(dict(
trade_id=trade.id,
pair=trade.pair,
market_url=self._freqtrade.exchange.get_pair_detail_url(trade.pair),
date=arrow.get(trade.open_date),
open_rate=trade.open_rate,
close_rate=trade.close_rate,
current_rate=current_rate,
amount=round(trade.amount, 8),
close_profit=fmt_close_profit,
current_profit=round(current_profit * 100, 2),
open_order='({} {} rem={:.8f})'.format(
order['type'], order['side'], order['remaining']
) if order else None,
))
return results
def _rpc_status_table(self) -> DataFrame:
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
if self._freqtrade.state != State.RUNNING:
raise RPCException('*Status:* `trader is not running`')
raise RPCException('trader is not running')
elif not trades:
raise RPCException('*Status:* `no active order`')
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']
trade_perc = (100 * trade.calc_profit_percent(current_rate))
trades_list.append([
trade.id,
trade.pair,
shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
'{:.2f}%'.format(100 * trade.calc_profit_percent(current_rate))
f'{trade_perc:.2f}%'
])
columns = ['ID', 'Pair', 'Since', 'Profit']
@ -135,9 +146,8 @@ class RPC(object):
profit_days: Dict[date, Dict] = {}
if not (isinstance(timescale, int) and timescale > 0):
raise RPCException('*Daily [n]:* `must be an integer greater than 0`')
raise RPCException('timescale must be an integer greater than 0')
fiat = self._freqtrade.fiat_converter
for day in range(0, timescale):
profitday = today - timedelta(days=day)
trades = Trade.query \
@ -148,7 +158,7 @@ class RPC(object):
.all()
curdayprofit = sum(trade.calc_profit() for trade in trades)
profit_days[profitday] = {
'amount': format(curdayprofit, '.8f'),
'amount': f'{curdayprofit:.8f}',
'trades': len(trades)
}
@ -160,11 +170,11 @@ class RPC(object):
symbol=stake_currency
),
'{value:.3f} {symbol}'.format(
value=fiat.convert_amount(
value=self._fiat_converter.convert_amount(
value['amount'],
stake_currency,
fiat_display_currency
),
) if self._fiat_converter else 0,
symbol=fiat_display_currency
),
'{value} trade{s}'.format(
@ -215,34 +225,33 @@ class RPC(object):
.order_by(sql.text('profit_sum DESC')).first()
if not best_pair:
raise RPCException('*Status:* `no closed trade`')
raise RPCException('no closed trade')
bp_pair, bp_rate = best_pair
# FIX: we want to keep fiatconverter in a state/environment,
# doing this will utilize its caching functionallity, instead we reinitialize it here
fiat = self._freqtrade.fiat_converter
# Prepare data to display
profit_closed_coin = round(sum(profit_closed_coin), 8)
profit_closed_coin_sum = round(sum(profit_closed_coin), 8)
profit_closed_percent = round(nan_to_num(mean(profit_closed_percent)) * 100, 2)
profit_closed_fiat = fiat.convert_amount(
profit_closed_coin,
profit_closed_fiat = self._fiat_converter.convert_amount(
profit_closed_coin_sum,
stake_currency,
fiat_display_currency
)
profit_all_coin = round(sum(profit_all_coin), 8)
) if self._fiat_converter else 0
profit_all_coin_sum = round(sum(profit_all_coin), 8)
profit_all_percent = round(nan_to_num(mean(profit_all_percent)) * 100, 2)
profit_all_fiat = fiat.convert_amount(
profit_all_coin,
profit_all_fiat = self._fiat_converter.convert_amount(
profit_all_coin_sum,
stake_currency,
fiat_display_currency
)
) if self._fiat_converter else 0
num = float(len(durations) or 1)
return {
'profit_closed_coin': profit_closed_coin,
'profit_closed_coin': profit_closed_coin_sum,
'profit_closed_percent': profit_closed_percent,
'profit_closed_fiat': profit_closed_fiat,
'profit_all_coin': profit_all_coin,
'profit_all_coin': profit_all_coin_sum,
'profit_all_percent': profit_all_percent,
'profit_all_fiat': profit_all_fiat,
'trade_count': len(trades),
@ -253,7 +262,7 @@ class RPC(object):
'best_rate': round(bp_rate * 100, 2),
}
def _rpc_balance(self, fiat_display_currency: str) -> Tuple[List[Dict], float, str, float]:
def _rpc_balance(self, fiat_display_currency: str) -> Dict:
""" Returns current account balance per crypto """
output = []
total = 0.0
@ -270,45 +279,47 @@ class RPC(object):
rate = self._freqtrade.exchange.get_ticker(coin + '/BTC', False)['bid']
est_btc: float = rate * balance['total']
total = total + est_btc
output.append(
{
'currency': coin,
'available': balance['free'],
'balance': balance['total'],
'pending': balance['used'],
'est_btc': est_btc
}
)
output.append({
'currency': coin,
'available': balance['free'],
'balance': balance['total'],
'pending': balance['used'],
'est_btc': est_btc,
})
if total == 0.0:
raise RPCException('`All balances are zero.`')
raise RPCException('all balances are zero')
fiat = self._freqtrade.fiat_converter
symbol = fiat_display_currency
value = fiat.convert_amount(total, 'BTC', symbol)
return output, total, symbol, value
value = self._fiat_converter.convert_amount(total, 'BTC',
symbol) if self._fiat_converter else 0
return {
'currencies': output,
'total': total,
'symbol': symbol,
'value': value,
}
def _rpc_start(self) -> str:
def _rpc_start(self) -> Dict[str, str]:
""" Handler for start """
if self._freqtrade.state == State.RUNNING:
return '*Status:* `already running`'
return {'status': 'already running'}
self._freqtrade.state = State.RUNNING
return '`Starting trader ...`'
return {'status': 'starting trader ...'}
def _rpc_stop(self) -> str:
def _rpc_stop(self) -> Dict[str, str]:
""" Handler for stop """
if self._freqtrade.state == State.RUNNING:
self._freqtrade.state = State.STOPPED
return '`Stopping trader ...`'
return {'status': 'stopping trader ...'}
return '*Status:* `already stopped`'
return {'status': 'already stopped'}
def _rpc_reload_conf(self) -> str:
def _rpc_reload_conf(self) -> Dict[str, str]:
""" Handler for reload_conf. """
self._freqtrade.state = State.RELOAD_CONF
return '*Status:* `Reloading config ...`'
return {'status': 'reloading config ...'}
# FIX: no test for this!!!!
def _rpc_forcesell(self, trade_id) -> None:
"""
Handler for forcesell <id>.
@ -338,11 +349,11 @@ class RPC(object):
# Get current rate and execute sell
current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
self._freqtrade.execute_sell(trade, current_rate)
self._freqtrade.execute_sell(trade, current_rate, SellType.FORCE_SELL)
# ---- EOF def _exec_forcesell ----
if self._freqtrade.state != State.RUNNING:
raise RPCException('`trader is not running`')
raise RPCException('trader is not running')
if trade_id == 'all':
# Execute sell for all open orders
@ -359,7 +370,7 @@ class RPC(object):
).first()
if not trade:
logger.warning('forcesell: Invalid argument received')
raise RPCException('Invalid argument.')
raise RPCException('invalid argument')
_exec_forcesell(trade)
Trade.session.flush()
@ -370,7 +381,7 @@ class RPC(object):
Shows a performance statistic from finished trades
"""
if self._freqtrade.state != State.RUNNING:
raise RPCException('`trader is not running`')
raise RPCException('trader is not running')
pair_rates = Trade.session.query(Trade.pair,
sql.func.sum(Trade.close_profit).label('profit_sum'),
@ -387,6 +398,6 @@ class RPC(object):
def _rpc_count(self) -> List[Trade]:
""" Returns the number of trades running """
if self._freqtrade.state != State.RUNNING:
raise RPCException('`trader is not running`')
raise RPCException('trader is not running')
return Trade.query.filter(Trade.is_open.is_(True)).all()

21
freqtrade/rpc/rpc_manager.py
View File

@ -2,9 +2,9 @@
This module contains class to manage RPC communications (Telegram, Slack, ...)
"""
import logging
from typing import List
from typing import List, Dict, Any
from freqtrade.rpc.rpc import RPC
from freqtrade.rpc import RPC
logger = logging.getLogger(__name__)
@ -23,6 +23,12 @@ class RPCManager(object):
from freqtrade.rpc.telegram import Telegram
self.registered_modules.append(Telegram(freqtrade))
# Enable Webhook
if freqtrade.config.get('webhook', {}).get('enabled', False):
logger.info('Enabling rpc.webhook ...')
from freqtrade.rpc.webhook import Webhook
self.registered_modules.append(Webhook(freqtrade))
def cleanup(self) -> None:
""" Stops all enabled rpc modules """
logger.info('Cleaning up rpc modules ...')
@ -32,11 +38,14 @@ class RPCManager(object):
mod.cleanup()
del mod
def send_msg(self, msg: str) -> None:
def send_msg(self, msg: Dict[str, Any]) -> None:
"""
Send given markdown message to all registered rpc modules
:param msg: message
:return: None
Send given message to all registered rpc modules.
A message consists of one or more key value pairs of strings.
e.g.:
{
'status': 'stopping bot'
}
"""
logger.info('Sending rpc message: %s', msg)
for mod in self.registered_modules:

172
freqtrade/rpc/telegram.py
View File

@ -4,7 +4,7 @@
This module manage Telegram communication
"""
import logging
from typing import Any, Callable
from typing import Any, Callable, Dict
from tabulate import tabulate
from telegram import Bot, ParseMode, ReplyKeyboardMarkup, Update
@ -12,7 +12,8 @@ from telegram.error import NetworkError, TelegramError
from telegram.ext import CommandHandler, Updater
from freqtrade.__init__ import __version__
from freqtrade.rpc.rpc import RPC, RPCException
from freqtrade.fiat_convert import CryptoToFiatConverter
from freqtrade.rpc import RPC, RPCException, RPCMessageType
logger = logging.getLogger(__name__)
@ -55,10 +56,6 @@ def authorized_only(command_handler: Callable[[Any, Bot, Update], None]) -> Call
class Telegram(RPC):
""" This class handles all telegram communication """
@property
def name(self) -> str:
return "telegram"
def __init__(self, freqtrade) -> None:
"""
Init the Telegram call, and init the super class RPC
@ -70,6 +67,8 @@ class Telegram(RPC):
self._updater: Updater = None
self._config = freqtrade.config
self._init()
if self._config.get('fiat_display_currency', None):
self._fiat_converter = CryptoToFiatConverter()
def _init(self) -> None:
"""
@ -114,9 +113,57 @@ class Telegram(RPC):
"""
self._updater.stop()
def send_msg(self, msg: str) -> None:
def send_msg(self, msg: Dict[str, Any]) -> None:
""" Send a message to telegram channel """
self._send_msg(msg)
if msg['type'] == RPCMessageType.BUY_NOTIFICATION:
if self._fiat_converter:
msg['stake_amount_fiat'] = self._fiat_converter.convert_amount(
msg['stake_amount'], msg['stake_currency'], msg['fiat_currency'])
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)
if msg.get('fiat_currency', None):
message += ",{stake_amount_fiat:.3f} {fiat_currency}".format(**msg)
message += ")`"
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
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)
# Check if all sell properties are available.
# This might not be the case if the message origin is triggered by /forcesell
if (all(prop in msg for prop in ['gain', 'fiat_currency', 'stake_currency'])
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)
elif msg['type'] == RPCMessageType.STATUS_NOTIFICATION:
message = '*Status:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.WARNING_NOTIFICATION:
message = '*Warning:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.CUSTOM_NOTIFICATION:
message = '{status}'.format(**msg)
else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
self._send_msg(message)
@authorized_only
def _status(self, bot: Bot, update: Update) -> None:
@ -136,8 +183,26 @@ class Telegram(RPC):
return
try:
for trade_msg in self._rpc_trade_status():
self._send_msg(trade_msg, bot=bot)
results = self._rpc_trade_status()
# pre format data
for result in results:
result['date'] = result['date'].humanize()
messages = [
"*Trade ID:* `{trade_id}`\n"
"*Current Pair:* [{pair}]({market_url})\n"
"*Open Since:* `{date}`\n"
"*Amount:* `{amount}`\n"
"*Open Rate:* `{open_rate:.8f}`\n"
"*Close Rate:* `{close_rate}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
"*Close Profit:* `{close_profit}`\n"
"*Current Profit:* `{current_profit:.2f}%`\n"
"*Open Order:* `{open_order}`".format(**result)
for result in results
]
for msg in messages:
self._send_msg(msg, bot=bot)
except RPCException as e:
self._send_msg(str(e), bot=bot)
@ -153,7 +218,7 @@ class Telegram(RPC):
try:
df_statuses = self._rpc_status_table()
message = tabulate(df_statuses, headers='keys', tablefmt='simple')
self._send_msg("<pre>{}</pre>".format(message), parse_mode=ParseMode.HTML)
self._send_msg(f"<pre>{message}</pre>", parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e), bot=bot)
@ -166,6 +231,8 @@ class Telegram(RPC):
:param update: message update
:return: None
"""
stake_cur = self._config['stake_currency']
fiat_disp_cur = self._config.get('fiat_display_currency', '')
try:
timescale = int(update.message.text.replace('/daily', '').strip())
except (TypeError, ValueError):
@ -173,18 +240,17 @@ class Telegram(RPC):
try:
stats = self._rpc_daily_profit(
timescale,
self._config['stake_currency'],
self._config['fiat_display_currency']
stake_cur,
fiat_disp_cur
)
stats = tabulate(stats,
headers=[
'Day',
'Profit {}'.format(self._config['stake_currency']),
'Profit {}'.format(self._config['fiat_display_currency'])
f'Profit {stake_cur}',
f'Profit {fiat_disp_cur}'
],
tablefmt='simple')
message = '<b>Daily Profit over the last {} days</b>:\n<pre>{}</pre>'\
.format(timescale, stats)
message = f'<b>Daily Profit over the last {timescale} days</b>:\n<pre>{stats}</pre>'
self._send_msg(message, bot=bot, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e), bot=bot)
@ -198,39 +264,38 @@ class Telegram(RPC):
:param update: message update
:return: None
"""
stake_cur = self._config['stake_currency']
fiat_disp_cur = self._config.get('fiat_display_currency', '')
try:
stats = self._rpc_trade_statistics(
self._config['stake_currency'],
self._config['fiat_display_currency'])
stake_cur,
fiat_disp_cur)
profit_closed_coin = stats['profit_closed_coin']
profit_closed_percent = stats['profit_closed_percent']
profit_closed_fiat = stats['profit_closed_fiat']
profit_all_coin = stats['profit_all_coin']
profit_all_percent = stats['profit_all_percent']
profit_all_fiat = stats['profit_all_fiat']
trade_count = stats['trade_count']
first_trade_date = stats['first_trade_date']
latest_trade_date = stats['latest_trade_date']
avg_duration = stats['avg_duration']
best_pair = stats['best_pair']
best_rate = stats['best_rate']
# Message to display
markdown_msg = "*ROI:* Close trades\n" \
"∙ `{profit_closed_coin:.8f} {coin} ({profit_closed_percent:.2f}%)`\n" \
"∙ `{profit_closed_fiat:.3f} {fiat}`\n" \
"*ROI:* All trades\n" \
"∙ `{profit_all_coin:.8f} {coin} ({profit_all_percent:.2f}%)`\n" \
"∙ `{profit_all_fiat:.3f} {fiat}`\n" \
"*Total Trade Count:* `{trade_count}`\n" \
"*First Trade opened:* `{first_trade_date}`\n" \
"*Latest Trade opened:* `{latest_trade_date}`\n" \
"*Avg. Duration:* `{avg_duration}`\n" \
"*Best Performing:* `{best_pair}: {best_rate:.2f}%`"\
.format(
coin=self._config['stake_currency'],
fiat=self._config['fiat_display_currency'],
profit_closed_coin=stats['profit_closed_coin'],
profit_closed_percent=stats['profit_closed_percent'],
profit_closed_fiat=stats['profit_closed_fiat'],
profit_all_coin=stats['profit_all_coin'],
profit_all_percent=stats['profit_all_percent'],
profit_all_fiat=stats['profit_all_fiat'],
trade_count=stats['trade_count'],
first_trade_date=stats['first_trade_date'],
latest_trade_date=stats['latest_trade_date'],
avg_duration=stats['avg_duration'],
best_pair=stats['best_pair'],
best_rate=stats['best_rate']
)
f"∙ `{profit_closed_coin:.8f} {stake_cur} "\
f"({profit_closed_percent:.2f}%)`\n" \
f"∙ `{profit_closed_fiat:.3f} {fiat_disp_cur}`\n" \
f"*ROI:* All trades\n" \
f"∙ `{profit_all_coin:.8f} {stake_cur} ({profit_all_percent:.2f}%)`\n" \
f"∙ `{profit_all_fiat:.3f} {fiat_disp_cur}`\n" \
f"*Total Trade Count:* `{trade_count}`\n" \
f"*First Trade opened:* `{first_trade_date}`\n" \
f"*Latest Trade opened:* `{latest_trade_date}`\n" \
f"*Avg. Duration:* `{avg_duration}`\n" \
f"*Best Performing:* `{best_pair}: {best_rate:.2f}%`"
self._send_msg(markdown_msg, bot=bot)
except RPCException as e:
self._send_msg(str(e), bot=bot)
@ -239,10 +304,9 @@ class Telegram(RPC):
def _balance(self, bot: Bot, update: Update) -> None:
""" Handler for /balance """
try:
currencys, total, symbol, value = \
self._rpc_balance(self._config['fiat_display_currency'])
result = self._rpc_balance(self._config.get('fiat_display_currency', ''))
output = ''
for currency in currencys:
for currency in result['currencies']:
output += "*{currency}:*\n" \
"\t`Available: {available: .8f}`\n" \
"\t`Balance: {balance: .8f}`\n" \
@ -250,8 +314,8 @@ class Telegram(RPC):
"\t`Est. BTC: {est_btc: .8f}`\n".format(**currency)
output += "\n*Estimated Value*:\n" \
"\t`BTC: {0: .8f}`\n" \
"\t`{1}: {2: .2f}`\n".format(total, symbol, value)
"\t`BTC: {total: .8f}`\n" \
"\t`{symbol}: {value: .2f}`\n".format(**result)
self._send_msg(output, bot=bot)
except RPCException as e:
self._send_msg(str(e), bot=bot)
@ -266,7 +330,7 @@ class Telegram(RPC):
:return: None
"""
msg = self._rpc_start()
self._send_msg(msg, bot=bot)
self._send_msg('Status: `{status}`'.format(**msg), bot=bot)
@authorized_only
def _stop(self, bot: Bot, update: Update) -> None:
@ -278,7 +342,7 @@ class Telegram(RPC):
:return: None
"""
msg = self._rpc_stop()
self._send_msg(msg, bot=bot)
self._send_msg('Status: `{status}`'.format(**msg), bot=bot)
@authorized_only
def _reload_conf(self, bot: Bot, update: Update) -> None:
@ -290,7 +354,7 @@ class Telegram(RPC):
:return: None
"""
msg = self._rpc_reload_conf()
self._send_msg(msg, bot=bot)
self._send_msg('Status: `{status}`'.format(**msg), bot=bot)
@authorized_only
def _forcesell(self, bot: Bot, update: Update) -> None:

66
freqtrade/rpc/webhook.py Normal file
View File

@ -0,0 +1,66 @@
"""
This module manages webhook communication
"""
import logging
from typing import Any, Dict
from requests import post, RequestException
from freqtrade.rpc import RPC, RPCMessageType
logger = logging.getLogger(__name__)
logger.debug('Included module rpc.webhook ...')
class Webhook(RPC):
""" This class handles all webhook communication """
def __init__(self, freqtrade) -> None:
"""
Init the Webhook class, and init the super class RPC
:param freqtrade: Instance of a freqtrade bot
:return: None
"""
super().__init__(freqtrade)
self._config = freqtrade.config
self._url = self._config['webhook']['url']
def cleanup(self) -> None:
"""
Cleanup pending module resources.
This will do nothing for webhooks, they will simply not be called anymore
"""
pass
def send_msg(self, msg: Dict[str, Any]) -> None:
""" Send a message to telegram channel """
try:
if msg['type'] == RPCMessageType.BUY_NOTIFICATION:
valuedict = self._config['webhook'].get('webhookbuy', None)
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
valuedict = self._config['webhook'].get('webhooksell', None)
elif msg['type'] == RPCMessageType.STATUS_NOTIFICATION:
valuedict = self._config['webhook'].get('webhookstatus', None)
else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
if not valuedict:
logger.info("Message type %s not configured for webhooks", msg['type'])
return
payload = {key: value.format(**msg) for (key, value) in valuedict.items()}
self._send_msg(payload)
except KeyError as exc:
logger.exception("Problem calling Webhook. Please check your webhook configuration. "
"Exception: %s", exc)
def _send_msg(self, payload: dict) -> None:
"""do the actual call to the webhook"""
try:
post(self._url, data=payload)
except RequestException as exc:
logger.warning("Could not call webhook url. Exception: %s", exc)

4
freqtrade/strategy/__init__.py
View File

@ -7,7 +7,7 @@ from freqtrade.strategy.interface import IStrategy
logger = logging.getLogger(__name__)
def import_strategy(strategy: IStrategy) -> IStrategy:
def import_strategy(strategy: IStrategy, config: dict) -> IStrategy:
"""
Imports given Strategy instance to global scope
of freqtrade.strategy and returns an instance of it
@ -29,4 +29,4 @@ def import_strategy(strategy: IStrategy) -> IStrategy:
# Modify global scope to declare class
globals()[name] = clazz
return clazz()
return clazz(config)

11
freqtrade/strategy/default_strategy.py
View File

@ -28,13 +28,16 @@ class DefaultStrategy(IStrategy):
# Optimal ticker interval for the strategy
ticker_interval = '5m'
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# Momentum Indicator
@ -196,10 +199,11 @@ class DefaultStrategy(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
@ -217,10 +221,11 @@ class DefaultStrategy(IStrategy):
return dataframe
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
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
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[

284
freqtrade/strategy/interface.py
View File

@ -2,11 +2,50 @@
IStrategy interface
This module defines the interface to apply for strategies
"""
from typing import Dict
import logging
from abc import ABC, abstractmethod
from datetime import datetime
from enum import Enum
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__)
class SignalType(Enum):
"""
Enum to distinguish between buy and sell signals
"""
BUY = "buy"
SELL = "sell"
class SellType(Enum):
"""
Enum to distinguish between sell reasons
"""
ROI = "roi"
STOP_LOSS = "stop_loss"
TRAILING_STOP_LOSS = "trailing_stop_loss"
SELL_SIGNAL = "sell_signal"
FORCE_SELL = "force_sell"
NONE = ""
class SellCheckTuple(NamedTuple):
"""
NamedTuple for Sell type + reason
"""
sell_flag: bool
sell_type: SellType
class IStrategy(ABC):
"""
@ -19,30 +58,267 @@ class IStrategy(ABC):
ticker_interval -> str: value of the ticker interval to use for the strategy
"""
_populate_fun_len: int = 0
_buy_fun_len: int = 0
_sell_fun_len: int = 0
# associated minimal roi
minimal_roi: Dict
# associated stoploss
stoploss: float
# associated ticker interval
ticker_interval: str
def __init__(self, config: dict) -> None:
self.config = config
@abstractmethod
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
def populate_indicators(self, 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()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
@abstractmethod
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
@abstractmethod
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
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
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with sell column
"""
def get_strategy_name(self) -> str:
"""
Returns strategy class name
"""
return self.__class__.__name__
def analyze_ticker(self, ticker_history: List[Dict], 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)
dataframe = self.advise_indicators(dataframe, metadata)
dataframe = self.advise_buy(dataframe, metadata)
dataframe = self.advise_sell(dataframe, metadata)
return dataframe
def get_signal(self, pair: str, interval: str, ticker_hist: List[Dict]) -> 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)
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return False, False
try:
dataframe = self.analyze_ticker(ticker_hist, {'pair': pair})
except ValueError as error:
logger.warning(
'Unable to analyze ticker for pair %s: %s',
pair,
str(error)
)
return False, False
except Exception as error:
logger.exception(
'Unexpected error when analyzing ticker for pair %s: %s',
pair,
str(error)
)
return False, False
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return False, False
latest = dataframe.iloc[-1]
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + 5))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
(arrow.utcnow() - signal_date).seconds // 60
)
return False, False
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
logger.debug(
'trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'],
pair,
str(buy),
str(sell)
)
return buy, sell
def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
sell: bool) -> 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)
if stoplossflag.sell_flag:
return stoplossflag
experimental = self.config.get('experimental', {})
if buy and experimental.get('ignore_roi_if_buy_signal', False):
logger.debug('Buy signal still active - not selling.')
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
# Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
if self.min_roi_reached(trade=trade, current_profit=current_profit, current_time=date):
logger.debug('Required profit reached. Selling..')
return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)
if experimental.get('sell_profit_only', False):
logger.debug('Checking if trade is profitable..')
if trade.calc_profit(rate=rate) <= 0:
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
if sell and not buy and experimental.get('use_sell_signal', False):
logger.debug('Sell signal received. Selling..')
return SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)
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:
"""
Based on current profit of the trade and configured (trailing) stoploss,
decides to sell or not
:param current_profit: current profit in percent
"""
trailing_stop = self.config.get('trailing_stop', False)
trade.adjust_stop_loss(trade.open_rate, self.stoploss, initial=True)
# evaluate if the stoploss was hit
if self.stoploss is not None and trade.stop_loss >= current_rate:
selltype = SellType.STOP_LOSS
if trailing_stop:
selltype = SellType.TRAILING_STOP_LOSS
logger.debug(
f"HIT STOP: current price at {current_rate:.6f}, "
f"stop loss is {trade.stop_loss:.6f}, "
f"initial stop loss was at {trade.initial_stop_loss:.6f}, "
f"trade opened at {trade.open_rate:.6f}")
logger.debug(f"trailing stop saved {trade.stop_loss - trade.initial_stop_loss:.6f}")
logger.debug('Stop loss hit.')
return SellCheckTuple(sell_flag=True, sell_type=selltype)
# update the stop loss afterwards, after all by definition it's supposed to be hanging
if trailing_stop:
# check if we have a special stop loss for positive condition
# and if profit is positive
stop_loss_value = self.stoploss
sl_offset = self.config.get('trailing_stop_positive_offset', 0.0)
if 'trailing_stop_positive' in self.config and current_profit > sl_offset:
# Ignore mypy error check in configuration that this is a float
stop_loss_value = self.config.get('trailing_stop_positive') # type: ignore
logger.debug(f"using positive stop loss mode: {stop_loss_value} "
f"with offset {sl_offset:.4g} "
f"since we have profit {current_profit:.4f}%")
trade.adjust_stop_loss(current_rate, stop_loss_value)
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
"""
Based an earlier trade and current price and ROI configuration, decides whether bot should
sell
:return True if bot should sell at current rate
"""
# Check if time matches and current rate is above threshold
time_diff = (current_time.timestamp() - trade.open_date.timestamp()) / 60
for duration, threshold in self.minimal_roi.items():
if time_diff <= duration:
return False
if current_profit > threshold:
return True
return False
def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given ticker data
"""
return {pair: self.advise_indicators(parse_ticker_dataframe(pair_data), {'pair': pair})
for pair, pair_data in tickerdata.items()}
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
This method should not be overridden.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
if self._populate_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
return self.populate_indicators(dataframe) # type: ignore
else:
return self.populate_indicators(dataframe, metadata)
def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
This method should not be overridden.
:param dataframe: DataFrame
:param pair: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
if self._buy_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
return self.populate_buy_trend(dataframe) # type: ignore
else:
return self.populate_buy_trend(dataframe, metadata)
def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
This method should not be overridden.
:param dataframe: DataFrame
:param pair: Additional information, like the currently traded pair
:return: DataFrame with sell column
"""
if self._sell_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
return self.populate_sell_trend(dataframe) # type: ignore
else:
return self.populate_sell_trend(dataframe, metadata)

47
freqtrade/strategy/resolver.py
View File

@ -7,14 +7,16 @@ import importlib.util
import inspect
import logging
import os
import tempfile
from base64 import urlsafe_b64decode
from collections import OrderedDict
from typing import Optional, Dict, Type
from pathlib import Path
from typing import Dict, Optional, Type
from freqtrade import constants
from freqtrade.strategy import import_strategy
from freqtrade.strategy.interface import IStrategy
logger = logging.getLogger(__name__)
@ -35,6 +37,7 @@ class StrategyResolver(object):
# Verify the strategy is in the configuration, otherwise fallback to the default strategy
strategy_name = config.get('strategy') or constants.DEFAULT_STRATEGY
self.strategy: IStrategy = self._load_strategy(strategy_name,
config=config,
extra_dir=config.get('strategy_path'))
# Set attributes
@ -42,12 +45,16 @@ class StrategyResolver(object):
if 'minimal_roi' in config:
self.strategy.minimal_roi = config['minimal_roi']
logger.info("Override strategy \'minimal_roi\' with value in config file.")
else:
config['minimal_roi'] = self.strategy.minimal_roi
if 'stoploss' in config:
self.strategy.stoploss = config['stoploss']
logger.info(
"Override strategy \'stoploss\' with value in config file: %s.", config['stoploss']
)
else:
config['stoploss'] = self.strategy.stoploss
if 'ticker_interval' in config:
self.strategy.ticker_interval = config['ticker_interval']
@ -55,6 +62,8 @@ class StrategyResolver(object):
"Override strategy \'ticker_interval\' with value in config file: %s.",
config['ticker_interval']
)
else:
config['ticker_interval'] = self.strategy.ticker_interval
# Sort and apply type conversions
self.strategy.minimal_roi = OrderedDict(sorted(
@ -63,10 +72,11 @@ class StrategyResolver(object):
self.strategy.stoploss = float(self.strategy.stoploss)
def _load_strategy(
self, strategy_name: str, extra_dir: Optional[str] = None) -> IStrategy:
self, strategy_name: str, config: dict, extra_dir: Optional[str] = None) -> IStrategy:
"""
Search and loads the specified strategy.
:param strategy_name: name of the module to import
:param config: configuration for the strategy
:param extra_dir: additional directory to search for the given strategy
:return: Strategy instance or None
"""
@ -80,12 +90,35 @@ class StrategyResolver(object):
# Add extra strategy directory on top of search paths
abs_paths.insert(0, extra_dir)
if ":" in strategy_name:
logger.info("loading base64 endocded strategy")
strat = strategy_name.split(":")
if len(strat) == 2:
temp = Path(tempfile.mkdtemp("freq", "strategy"))
name = strat[0] + ".py"
temp.joinpath(name).write_text(urlsafe_b64decode(strat[1]).decode('utf-8'))
temp.joinpath("__init__.py").touch()
strategy_name = os.path.splitext(name)[0]
# register temp path with the bot
abs_paths.insert(0, str(temp.resolve()))
for path in abs_paths:
try:
strategy = self._search_strategy(path, strategy_name)
strategy = self._search_strategy(path, strategy_name=strategy_name, config=config)
if strategy:
logger.info('Using resolved strategy %s from \'%s\'', strategy_name, path)
return import_strategy(strategy)
strategy._populate_fun_len = len(
inspect.getfullargspec(strategy.populate_indicators).args)
strategy._buy_fun_len = len(
inspect.getfullargspec(strategy.populate_buy_trend).args)
strategy._sell_fun_len = len(
inspect.getfullargspec(strategy.populate_sell_trend).args)
return import_strategy(strategy, config=config)
except FileNotFoundError:
logger.warning('Path "%s" does not exist', path)
@ -115,7 +148,7 @@ class StrategyResolver(object):
return next(valid_strategies_gen, None)
@staticmethod
def _search_strategy(directory: str, strategy_name: str) -> Optional[IStrategy]:
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
@ -131,5 +164,5 @@ class StrategyResolver(object):
os.path.abspath(os.path.join(directory, entry)), strategy_name
)
if strategy:
return strategy()
return strategy(config)
return None

19
freqtrade/tests/conftest.py
View File

@ -2,17 +2,15 @@
import json
import logging
from datetime import datetime
from typing import Dict, Optional
from functools import reduce
from typing import Dict, Optional
from unittest.mock import MagicMock
import arrow
import pytest
from jsonschema import validate
from telegram import Chat, Message, Update
from freqtrade.analyze import Analyze
from freqtrade import constants
from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
from freqtrade.exchange import Exchange
from freqtrade.freqtradebot import FreqtradeBot
@ -20,7 +18,7 @@ logging.getLogger('').setLevel(logging.INFO)
def log_has(line, logs):
# caplog mocker returns log as a tuple: ('freqtrade.analyze', logging.WARNING, 'foobar')
# caplog mocker returns log as a tuple: ('freqtrade.something', logging.WARNING, 'foobar')
# and we want to match line against foobar in the tuple
return reduce(lambda a, b: a or b,
filter(lambda x: x[2] == line, logs),
@ -29,6 +27,7 @@ def log_has(line, logs):
def patch_exchange(mocker, api_mock=None) -> None:
mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
if api_mock:
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
else:
@ -51,13 +50,11 @@ def get_patched_freqtradebot(mocker, config) -> FreqtradeBot:
"""
# mocker.patch('freqtrade.fiat_convert.Market', {'price_usd': 12345.0})
patch_coinmarketcap(mocker, {'price_usd': 12345.0})
mocker.patch('freqtrade.freqtradebot.Analyze', MagicMock())
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
mocker.patch('freqtrade.freqtradebot.persistence.init', MagicMock())
patch_exchange(mocker, None)
mocker.patch('freqtrade.freqtradebot.RPCManager._init', MagicMock())
mocker.patch('freqtrade.freqtradebot.RPCManager.send_msg', MagicMock())
mocker.patch('freqtrade.freqtradebot.Analyze.get_signal', MagicMock())
return FreqtradeBot(config)
@ -100,7 +97,10 @@ def default_conf():
"0": 0.04
},
"stoploss": -0.10,
"unfilledtimeout": 600,
"unfilledtimeout": {
"buy": 10,
"sell": 30
},
"bid_strategy": {
"ask_last_balance": 0.0
},
@ -125,7 +125,6 @@ def default_conf():
"db_url": "sqlite://",
"loglevel": logging.DEBUG,
}
validate(configuration, constants.CONF_SCHEMA)
return configuration
@ -613,7 +612,7 @@ def tickers():
@pytest.fixture
def result():
with open('freqtrade/tests/testdata/UNITTEST_BTC-1m.json') as data_file:
return Analyze.parse_ticker_dataframe(json.load(data_file))
return parse_ticker_dataframe(json.load(data_file))
# FIX:
# Create an fixture/function

344
freqtrade/tests/exchange/test_exchange.py
View File

@ -1,17 +1,30 @@
# pragma pylint: disable=missing-docstring, C0103, bad-continuation, global-statement
# pragma pylint: disable=protected-access
import logging
from copy import deepcopy
from random import randint
from datetime import datetime
from random import randint
from unittest.mock import MagicMock, PropertyMock
import ccxt
import pytest
from freqtrade import OperationalException, DependencyException, TemporaryError
from freqtrade.exchange import Exchange, API_RETRY_COUNT
from freqtrade.tests.conftest import log_has, get_patched_exchange
from freqtrade import DependencyException, OperationalException, TemporaryError
from freqtrade.exchange import API_RETRY_COUNT, Exchange
from freqtrade.tests.conftest import get_patched_exchange, log_has
def ccxt_exceptionhandlers(mocker, default_conf, api_mock, fun, mock_ccxt_fun, **kwargs):
with pytest.raises(TemporaryError):
api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
getattr(exchange, fun)(**kwargs)
assert api_mock.__dict__[mock_ccxt_fun].call_count == API_RETRY_COUNT + 1
with pytest.raises(OperationalException):
api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
getattr(exchange, fun)(**kwargs)
assert api_mock.__dict__[mock_ccxt_fun].call_count == 1
def test_init(default_conf, mocker, caplog):
@ -20,7 +33,7 @@ def test_init(default_conf, mocker, caplog):
assert log_has('Instance is running with dry_run enabled', caplog.record_tuples)
def test_init_exception(default_conf):
def test_init_exception(default_conf, mocker):
default_conf['exchange']['name'] = 'wrong_exchange_name'
with pytest.raises(
@ -28,6 +41,100 @@ def test_init_exception(default_conf):
match='Exchange {} is not supported'.format(default_conf['exchange']['name'])):
Exchange(default_conf)
default_conf['exchange']['name'] = 'binance'
with pytest.raises(
OperationalException,
match='Exchange {} is not supported'.format(default_conf['exchange']['name'])):
mocker.patch("ccxt.binance", MagicMock(side_effect=AttributeError))
Exchange(default_conf)
def test_symbol_amount_prec(default_conf, mocker):
'''
Test rounds down to 4 Decimal places
'''
api_mock = MagicMock()
api_mock.load_markets = MagicMock(return_value={
'ETH/BTC': '', 'LTC/BTC': '', 'XRP/BTC': '', 'NEO/BTC': ''
})
mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value='binance'))
markets = PropertyMock(return_value={'ETH/BTC': {'precision': {'amount': 4}}})
type(api_mock).markets = markets
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
exchange = Exchange(default_conf)
amount = 2.34559
pair = 'ETH/BTC'
amount = exchange.symbol_amount_prec(pair, amount)
assert amount == 2.3455
def test_symbol_price_prec(default_conf, mocker):
'''
Test rounds up to 4 decimal places
'''
api_mock = MagicMock()
api_mock.load_markets = MagicMock(return_value={
'ETH/BTC': '', 'LTC/BTC': '', 'XRP/BTC': '', 'NEO/BTC': ''
})
mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value='binance'))
markets = PropertyMock(return_value={'ETH/BTC': {'precision': {'price': 4}}})
type(api_mock).markets = markets
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
exchange = Exchange(default_conf)
price = 2.34559
pair = 'ETH/BTC'
price = exchange.symbol_price_prec(pair, price)
assert price == 2.3456
def test_set_sandbox(default_conf, mocker):
"""
Test working scenario
"""
api_mock = MagicMock()
api_mock.load_markets = MagicMock(return_value={
'ETH/BTC': '', 'LTC/BTC': '', 'XRP/BTC': '', 'NEO/BTC': ''
})
url_mock = PropertyMock(return_value={'test': "api-public.sandbox.gdax.com",
'api': 'https://api.gdax.com'})
type(api_mock).urls = url_mock
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
exchange = Exchange(default_conf)
liveurl = exchange._api.urls['api']
default_conf['exchange']['sandbox'] = True
exchange.set_sandbox(exchange._api, default_conf['exchange'], 'Logname')
assert exchange._api.urls['api'] != liveurl
def test_set_sandbox_exception(default_conf, mocker):
"""
Test Fail scenario
"""
api_mock = MagicMock()
api_mock.load_markets = MagicMock(return_value={
'ETH/BTC': '', 'LTC/BTC': '', 'XRP/BTC': '', 'NEO/BTC': ''
})
url_mock = PropertyMock(return_value={'api': 'https://api.gdax.com'})
type(api_mock).urls = url_mock
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
with pytest.raises(OperationalException, match=r'does not provide a sandbox api'):
exchange = Exchange(default_conf)
default_conf['exchange']['sandbox'] = True
exchange.set_sandbox(exchange._api, default_conf['exchange'], 'Logname')
def test_validate_pairs(default_conf, mocker):
api_mock = MagicMock()
@ -38,6 +145,7 @@ def test_validate_pairs(default_conf, mocker):
type(api_mock).id = id_mock
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
Exchange(default_conf)
@ -45,6 +153,7 @@ def test_validate_pairs_not_available(default_conf, mocker):
api_mock = MagicMock()
api_mock.load_markets = MagicMock(return_value={})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
with pytest.raises(OperationalException, match=r'not available'):
Exchange(default_conf)
@ -55,12 +164,11 @@ def test_validate_pairs_not_compatible(default_conf, mocker):
api_mock.load_markets = MagicMock(return_value={
'ETH/BTC': '', 'TKN/BTC': '', 'TRST/BTC': '', 'SWT/BTC': '', 'BCC/BTC': ''
})
conf = deepcopy(default_conf)
conf['stake_currency'] = 'ETH'
default_conf['stake_currency'] = 'ETH'
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
with pytest.raises(OperationalException, match=r'not compatible'):
Exchange(conf)
Exchange(default_conf)
def test_validate_pairs_exception(default_conf, mocker, caplog):
@ -70,6 +178,7 @@ def test_validate_pairs_exception(default_conf, mocker, caplog):
api_mock.load_markets = MagicMock(return_value={})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', api_mock)
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
with pytest.raises(OperationalException, match=r'Pair ETH/BTC is not available at Binance'):
Exchange(default_conf)
@ -84,17 +193,80 @@ def test_validate_pairs_exception(default_conf, mocker, caplog):
def test_validate_pairs_stake_exception(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
conf = deepcopy(default_conf)
conf['stake_currency'] = 'ETH'
default_conf['stake_currency'] = 'ETH'
api_mock = MagicMock()
api_mock.name = MagicMock(return_value='binance')
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', api_mock)
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
with pytest.raises(
OperationalException,
match=r'Pair ETH/BTC not compatible with stake_currency: ETH'
):
Exchange(conf)
Exchange(default_conf)
def test_validate_timeframes(default_conf, mocker):
default_conf["ticker_interval"] = "5m"
api_mock = MagicMock()
id_mock = PropertyMock(return_value='test_exchange')
type(api_mock).id = id_mock
timeframes = PropertyMock(return_value={'1m': '1m',
'5m': '5m',
'15m': '15m',
'1h': '1h'})
type(api_mock).timeframes = timeframes
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
Exchange(default_conf)
def test_validate_timeframes_failed(default_conf, mocker):
default_conf["ticker_interval"] = "3m"
api_mock = MagicMock()
id_mock = PropertyMock(return_value='test_exchange')
type(api_mock).id = id_mock
timeframes = PropertyMock(return_value={'1m': '1m',
'5m': '5m',
'15m': '15m',
'1h': '1h'})
type(api_mock).timeframes = timeframes
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
with pytest.raises(OperationalException, match=r'Invalid ticker 3m, this Exchange supports.*'):
Exchange(default_conf)
def test_validate_timeframes_not_in_config(default_conf, mocker):
del default_conf["ticker_interval"]
api_mock = MagicMock()
id_mock = PropertyMock(return_value='test_exchange')
type(api_mock).id = id_mock
timeframes = PropertyMock(return_value={'1m': '1m',
'5m': '5m',
'15m': '15m',
'1h': '1h'})
type(api_mock).timeframes = timeframes
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
Exchange(default_conf)
def test_exchange_has(default_conf, mocker):
exchange = get_patched_exchange(mocker, default_conf)
assert not exchange.exchange_has('ASDFASDF')
api_mock = MagicMock()
type(api_mock).has = PropertyMock(return_value={'deadbeef': True})
exchange = get_patched_exchange(mocker, default_conf, api_mock)
assert exchange.exchange_has("deadbeef")
type(api_mock).has = PropertyMock(return_value={'deadbeef': False})
exchange = get_patched_exchange(mocker, default_conf, api_mock)
assert not exchange.exchange_has("deadbeef")
def test_buy_dry_run(default_conf, mocker):
@ -216,6 +388,11 @@ def test_get_balance_prod(default_conf, mocker):
exchange.get_balance(currency='BTC')
with pytest.raises(TemporaryError, match=r'.*balance due to malformed exchange response:.*'):
exchange = get_patched_exchange(mocker, default_conf, api_mock)
mocker.patch('freqtrade.exchange.Exchange.get_balances', MagicMock(return_value={}))
exchange.get_balance(currency='BTC')
def test_get_balances_dry_run(default_conf, mocker):
default_conf['dry_run'] = True
@ -243,17 +420,8 @@ def test_get_balances_prod(default_conf, mocker):
assert exchange.get_balances()['1ST']['total'] == 10.0
assert exchange.get_balances()['1ST']['used'] == 0.0
with pytest.raises(TemporaryError):
api_mock.fetch_balance = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_balances()
assert api_mock.fetch_balance.call_count == API_RETRY_COUNT + 1
with pytest.raises(OperationalException):
api_mock.fetch_balance = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_balances()
assert api_mock.fetch_balance.call_count == 1
ccxt_exceptionhandlers(mocker, default_conf, api_mock,
"get_balances", "fetch_balance")
def test_get_tickers(default_conf, mocker):
@ -282,15 +450,8 @@ def test_get_tickers(default_conf, mocker):
assert tickers['BCH/BTC']['bid'] == 0.6
assert tickers['BCH/BTC']['ask'] == 0.5
with pytest.raises(TemporaryError): # test retrier
api_mock.fetch_tickers = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_tickers()
with pytest.raises(OperationalException):
api_mock.fetch_tickers = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_tickers()
ccxt_exceptionhandlers(mocker, default_conf, api_mock,
"get_tickers", "fetch_tickers")
with pytest.raises(OperationalException):
api_mock.fetch_tickers = MagicMock(side_effect=ccxt.NotSupported)
@ -345,15 +506,9 @@ def test_get_ticker(default_conf, mocker):
exchange.get_ticker(pair='ETH/BTC', refresh=False)
assert api_mock.fetch_ticker.call_count == 0
with pytest.raises(TemporaryError): # test retrier
api_mock.fetch_ticker = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_ticker(pair='ETH/BTC', refresh=True)
with pytest.raises(OperationalException):
api_mock.fetch_ticker = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_ticker(pair='ETH/BTC', refresh=True)
ccxt_exceptionhandlers(mocker, default_conf, api_mock,
"get_ticker", "fetch_ticker",
pair='ETH/BTC', refresh=True)
api_mock.fetch_ticker = MagicMock(return_value={})
exchange = get_patched_exchange(mocker, default_conf, api_mock)
@ -369,7 +524,7 @@ def make_fetch_ohlcv_mock(data):
return fetch_ohlcv_mock
def test_get_ticker_history(default_conf, mocker):
def test_get_candle_history(default_conf, mocker):
api_mock = MagicMock()
tick = [
[
@ -386,7 +541,7 @@ def test_get_ticker_history(default_conf, mocker):
exchange = get_patched_exchange(mocker, default_conf, api_mock)
# retrieve original ticker
ticks = exchange.get_ticker_history('ETH/BTC', default_conf['ticker_interval'])
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
@ -408,7 +563,7 @@ def test_get_ticker_history(default_conf, mocker):
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_ticker_history('ETH/BTC', default_conf['ticker_interval'])
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
@ -416,20 +571,17 @@ def test_get_ticker_history(default_conf, mocker):
assert ticks[0][4] == 9
assert ticks[0][5] == 10
with pytest.raises(TemporaryError): # test retrier
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.NetworkError)
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)
# new symbol to get around cache
exchange.get_ticker_history('ABCD/BTC', default_conf['ticker_interval'])
with pytest.raises(OperationalException):
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
# new symbol to get around cache
exchange.get_ticker_history('EFGH/BTC', default_conf['ticker_interval'])
exchange.get_candle_history(pair='ABCD/BTC', tick_interval=default_conf['ticker_interval'])
def test_get_ticker_history_sort(default_conf, mocker):
def test_get_candle_history_sort(default_conf, mocker):
api_mock = MagicMock()
# GDAX use-case (real data from GDAX)
@ -452,7 +604,7 @@ def test_get_ticker_history_sort(default_conf, mocker):
exchange = get_patched_exchange(mocker, default_conf, api_mock)
# Test the ticker history sort
ticks = exchange.get_ticker_history('ETH/BTC', default_conf['ticker_interval'])
ticks = exchange.get_candle_history('ETH/BTC', default_conf['ticker_interval'])
assert ticks[0][0] == 1527830400000
assert ticks[0][1] == 0.07649
assert ticks[0][2] == 0.07651
@ -485,7 +637,7 @@ def test_get_ticker_history_sort(default_conf, mocker):
api_mock.fetch_ohlcv = MagicMock(side_effect=make_fetch_ohlcv_mock(tick))
exchange = get_patched_exchange(mocker, default_conf, api_mock)
# Test the ticker history sort
ticks = exchange.get_ticker_history('ETH/BTC', default_conf['ticker_interval'])
ticks = exchange.get_candle_history('ETH/BTC', default_conf['ticker_interval'])
assert ticks[0][0] == 1527827700000
assert ticks[0][1] == 0.07659999
assert ticks[0][2] == 0.0766
@ -515,24 +667,15 @@ def test_cancel_order(default_conf, mocker):
exchange = get_patched_exchange(mocker, default_conf, api_mock)
assert exchange.cancel_order(order_id='_', pair='TKN/BTC') == 123
with pytest.raises(TemporaryError):
api_mock.cancel_order = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.cancel_order(order_id='_', pair='TKN/BTC')
assert api_mock.cancel_order.call_count == API_RETRY_COUNT + 1
with pytest.raises(DependencyException):
api_mock.cancel_order = MagicMock(side_effect=ccxt.InvalidOrder)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.cancel_order(order_id='_', pair='TKN/BTC')
assert api_mock.cancel_order.call_count == API_RETRY_COUNT + 1
with pytest.raises(OperationalException):
api_mock.cancel_order = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.cancel_order(order_id='_', pair='TKN/BTC')
assert api_mock.cancel_order.call_count == 1
ccxt_exceptionhandlers(mocker, default_conf, api_mock,
"cancel_order", "cancel_order",
order_id='_', pair='TKN/BTC')
def test_get_order(default_conf, mocker):
@ -550,23 +693,15 @@ def test_get_order(default_conf, mocker):
exchange = get_patched_exchange(mocker, default_conf, api_mock)
assert exchange.get_order('X', 'TKN/BTC') == 456
with pytest.raises(TemporaryError):
api_mock.fetch_order = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_order(order_id='_', pair='TKN/BTC')
assert api_mock.fetch_order.call_count == API_RETRY_COUNT + 1
with pytest.raises(DependencyException):
api_mock.fetch_order = MagicMock(side_effect=ccxt.InvalidOrder)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_order(order_id='_', pair='TKN/BTC')
assert api_mock.fetch_order.call_count == API_RETRY_COUNT + 1
with pytest.raises(OperationalException):
api_mock.fetch_order = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_order(order_id='_', pair='TKN/BTC')
assert api_mock.fetch_order.call_count == 1
ccxt_exceptionhandlers(mocker, default_conf, api_mock,
'get_order', 'fetch_order',
order_id='_', pair='TKN/BTC')
def test_name(default_conf, mocker):
@ -651,19 +786,12 @@ def test_get_trades_for_order(default_conf, mocker):
assert len(orders) == 1
assert orders[0]['price'] == 165
# test Exceptions
with pytest.raises(OperationalException):
api_mock = MagicMock()
api_mock.fetch_my_trades = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_trades_for_order(order_id, 'LTC/BTC', since)
ccxt_exceptionhandlers(mocker, default_conf, api_mock,
'get_trades_for_order', 'fetch_my_trades',
order_id=order_id, pair='LTC/BTC', since=since)
with pytest.raises(TemporaryError):
api_mock = MagicMock()
api_mock.fetch_my_trades = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_trades_for_order(order_id, 'LTC/BTC', since)
assert api_mock.fetch_my_trades.call_count == API_RETRY_COUNT + 1
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=False))
assert exchange.get_trades_for_order(order_id, 'LTC/BTC', since) == []
def test_get_markets(default_conf, mocker, markets):
@ -677,19 +805,8 @@ def test_get_markets(default_conf, mocker, markets):
assert ret[0]["id"] == "ethbtc"
assert ret[0]["symbol"] == "ETH/BTC"
# test Exceptions
with pytest.raises(OperationalException):
api_mock = MagicMock()
api_mock.fetch_markets = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_markets()
with pytest.raises(TemporaryError):
api_mock = MagicMock()
api_mock.fetch_markets = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_markets()
assert api_mock.fetch_markets.call_count == API_RETRY_COUNT + 1
ccxt_exceptionhandlers(mocker, default_conf, api_mock,
'get_markets', 'fetch_markets')
def test_get_fee(default_conf, mocker):
@ -704,19 +821,8 @@ def test_get_fee(default_conf, mocker):
assert exchange.get_fee() == 0.025
# test Exceptions
with pytest.raises(OperationalException):
api_mock = MagicMock()
api_mock.calculate_fee = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_fee()
with pytest.raises(TemporaryError):
api_mock = MagicMock()
api_mock.calculate_fee = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.get_fee()
assert api_mock.calculate_fee.call_count == API_RETRY_COUNT + 1
ccxt_exceptionhandlers(mocker, default_conf, api_mock,
'get_fee', 'calculate_fee')
def test_get_amount_lots(default_conf, mocker):

21
freqtrade/tests/exchange/test_exchange_helpers.py Normal file
View File

@ -0,0 +1,21 @@
# pragma pylint: disable=missing-docstring, C0103
from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
def test_dataframe_correct_length(result):
dataframe = parse_ticker_dataframe(result)
assert len(result.index) - 1 == len(dataframe.index) # last partial candle removed
def test_dataframe_correct_columns(result):
assert result.columns.tolist() == \
['date', 'open', 'high', 'low', 'close', 'volume']
def test_parse_ticker_dataframe(ticker_history):
columns = ['date', 'open', 'high', 'low', 'close', 'volume']
# Test file with BV data
dataframe = parse_ticker_dataframe(ticker_history)
assert dataframe.columns.tolist() == columns

386
freqtrade/tests/optimize/test_backtesting.py
View File

@ -3,20 +3,21 @@
import json
import math
import random
import pytest
from copy import deepcopy
from typing import List
from unittest.mock import MagicMock
import numpy as np
import pandas as pd
import pytest
from arrow import Arrow
from freqtrade import optimize, constants, DependencyException
from freqtrade.analyze import Analyze
from freqtrade import DependencyException, constants, optimize
from freqtrade.arguments import Arguments, TimeRange
from freqtrade.optimize.backtesting import Backtesting, start, setup_configuration
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
def get_args(args) -> List[str]:
@ -95,7 +96,7 @@ def simple_backtest(config, contour, num_results, mocker) -> None:
'stake_amount': config['stake_amount'],
'processed': processed,
'max_open_trades': 1,
'realistic': True
'position_stacking': False
}
)
# results :: <class 'pandas.core.frame.DataFrame'>
@ -109,7 +110,7 @@ def mocked_load_data(datadir, pairs=[], ticker_interval='0m', refresh_pairs=Fals
return pairdata
# use for mock freqtrade.exchange.get_ticker_history'
# use for mock freqtrade.exchange.get_candle_history'
def _load_pair_as_ticks(pair, tickfreq):
ticks = optimize.load_data(None, ticker_interval=tickfreq, pairs=[pair])
ticks = trim_dictlist(ticks, -201)
@ -126,7 +127,7 @@ def _make_backtest_conf(mocker, conf=None, pair='UNITTEST/BTC', record=None):
'stake_amount': conf['stake_amount'],
'processed': backtesting.tickerdata_to_dataframe(data),
'max_open_trades': 10,
'realistic': True,
'position_stacking': False,
'record': record
}
@ -144,7 +145,7 @@ def _trend(signals, buy_value, sell_value):
return signals
def _trend_alternate(dataframe=None):
def _trend_alternate(dataframe=None, metadata=None):
signals = dataframe
low = signals['low']
n = len(low)
@ -162,9 +163,6 @@ def _trend_alternate(dataframe=None):
# Unit tests
def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
"""
Test setup_configuration() function
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
@ -192,8 +190,8 @@ def test_setup_configuration_without_arguments(mocker, default_conf, caplog) ->
assert 'live' not in config
assert not log_has('Parameter -l/--live detected ...', caplog.record_tuples)
assert 'realistic_simulation' not in config
assert not log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples)
assert 'position_stacking' not in config
assert not log_has('Parameter --enable-position-stacking detected ...', caplog.record_tuples)
assert 'refresh_pairs' not in config
assert not log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
@ -203,9 +201,6 @@ def test_setup_configuration_without_arguments(mocker, default_conf, caplog) ->
def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> None:
"""
Test setup_configuration() function
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
@ -217,7 +212,8 @@ def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> Non
'backtesting',
'--ticker-interval', '1m',
'--live',
'--realistic-simulation',
'--enable-position-stacking',
'--disable-max-market-positions',
'--refresh-pairs-cached',
'--timerange', ':100',
'--export', '/bar/foo',
@ -245,9 +241,12 @@ def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> Non
assert 'live' in config
assert log_has('Parameter -l/--live detected ...', caplog.record_tuples)
assert 'realistic_simulation' in config
assert log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples)
assert log_has('Using max_open_trades: 1 ...', caplog.record_tuples)
assert 'position_stacking' in config
assert log_has('Parameter --enable-position-stacking detected ...', caplog.record_tuples)
assert 'use_max_market_positions' in config
assert log_has('Parameter --disable-max-market-positions detected ...', caplog.record_tuples)
assert log_has('max_open_trades set to unlimited ...', caplog.record_tuples)
assert 'refresh_pairs' in config
assert log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
@ -270,15 +269,10 @@ def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> Non
def test_setup_configuration_unlimited_stake_amount(mocker, default_conf, caplog) -> None:
"""
Test setup_configuration() function
"""
conf = deepcopy(default_conf)
conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
default_conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(conf)
read_data=json.dumps(default_conf)
))
args = [
@ -292,9 +286,6 @@ def test_setup_configuration_unlimited_stake_amount(mocker, default_conf, caplog
def test_start(mocker, fee, default_conf, caplog) -> None:
"""
Test start() function
"""
start_mock = MagicMock()
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
@ -317,26 +308,19 @@ def test_start(mocker, fee, default_conf, caplog) -> None:
def test_backtesting_init(mocker, default_conf) -> None:
"""
Test Backtesting._init() method
"""
patch_exchange(mocker)
get_fee = mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.5))
backtesting = Backtesting(default_conf)
assert backtesting.config == default_conf
assert isinstance(backtesting.analyze, Analyze)
assert backtesting.ticker_interval == '5m'
assert callable(backtesting.tickerdata_to_dataframe)
assert callable(backtesting.populate_buy_trend)
assert callable(backtesting.populate_sell_trend)
assert callable(backtesting.advise_buy)
assert callable(backtesting.advise_sell)
get_fee.assert_called()
assert backtesting.fee == 0.5
def test_tickerdata_to_dataframe(default_conf, mocker) -> None:
"""
Test Backtesting.tickerdata_to_dataframe() method
"""
patch_exchange(mocker)
timerange = TimeRange(None, 'line', 0, -100)
tick = optimize.load_tickerdata_file(None, 'UNITTEST/BTC', '1m', timerange=timerange)
@ -346,16 +330,13 @@ def test_tickerdata_to_dataframe(default_conf, mocker) -> None:
data = backtesting.tickerdata_to_dataframe(tickerlist)
assert len(data['UNITTEST/BTC']) == 99
# Load Analyze to compare the result between Backtesting function and Analyze are the same
analyze = Analyze(default_conf)
data2 = analyze.tickerdata_to_dataframe(tickerlist)
# Load strategy to compare the result between Backtesting function and strategy are the same
strategy = DefaultStrategy(default_conf)
data2 = strategy.tickerdata_to_dataframe(tickerlist)
assert data['UNITTEST/BTC'].equals(data2['UNITTEST/BTC'])
def test_get_timeframe(default_conf, mocker) -> None:
"""
Test Backtesting.get_timeframe() method
"""
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
@ -372,9 +353,6 @@ def test_get_timeframe(default_conf, mocker) -> None:
def test_generate_text_table(default_conf, mocker):
"""
Test Backtesting.generate_text_table() method
"""
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
@ -390,29 +368,94 @@ def test_generate_text_table(default_conf, mocker):
)
result_str = (
'| pair | buy count | avg profit % | '
'total profit BTC | avg duration | profit | loss |\n'
'|:--------|------------:|---------------:|'
'-------------------:|---------------:|---------:|-------:|\n'
'| ETH/BTC | 2 | 15.00 | '
'0.60000000 | 20.0 | 2 | 0 |\n'
'| TOTAL | 2 | 15.00 | '
'0.60000000 | 20.0 | 2 | 0 |'
'| pair | buy count | avg profit % | cum profit % | '
'total profit BTC | avg duration | profit | loss |\n'
'|:--------|------------:|---------------:|---------------:|'
'-------------------:|:---------------|---------:|-------:|\n'
'| ETH/BTC | 2 | 15.00 | 30.00 | '
'0.60000000 | 0:20:00 | 2 | 0 |\n'
'| TOTAL | 2 | 15.00 | 30.00 | '
'0.60000000 | 0:20:00 | 2 | 0 |'
)
assert backtesting._generate_text_table(data={'ETH/BTC': {}}, results=results) == result_str
def test_backtesting_start(default_conf, mocker, caplog) -> None:
"""
Test Backtesting.start() method
"""
def test_generate_text_table_sell_reason(default_conf, mocker):
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
results = pd.DataFrame(
{
'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
'profit_percent': [0.1, 0.2, 0.3],
'profit_abs': [0.2, 0.4, 0.5],
'trade_duration': [10, 30, 10],
'profit': [2, 0, 0],
'loss': [0, 0, 1],
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
}
)
result_str = (
'| Sell Reason | Count |\n'
'|:--------------|--------:|\n'
'| roi | 2 |\n'
'| stop_loss | 1 |'
)
assert backtesting._generate_text_table_sell_reason(
data={'ETH/BTC': {}}, results=results) == result_str
def test_generate_text_table_strategyn(default_conf, mocker):
"""
Test Backtesting.generate_text_table_sell_reason() method
"""
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
results = {}
results['ETH/BTC'] = pd.DataFrame(
{
'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
'profit_percent': [0.1, 0.2, 0.3],
'profit_abs': [0.2, 0.4, 0.5],
'trade_duration': [10, 30, 10],
'profit': [2, 0, 0],
'loss': [0, 0, 1],
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
}
)
results['LTC/BTC'] = pd.DataFrame(
{
'pair': ['LTC/BTC', 'LTC/BTC', 'LTC/BTC'],
'profit_percent': [0.4, 0.2, 0.3],
'profit_abs': [0.4, 0.4, 0.5],
'trade_duration': [15, 30, 15],
'profit': [4, 1, 0],
'loss': [0, 0, 1],
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
}
)
result_str = (
'| Strategy | buy count | avg profit % | cum profit % '
'| total profit BTC | avg duration | profit | loss |\n'
'|:-----------|------------:|---------------:|---------------:'
'|-------------------:|:---------------|---------:|-------:|\n'
'| ETH/BTC | 3 | 20.00 | 60.00 '
'| 1.10000000 | 0:17:00 | 3 | 0 |\n'
'| LTC/BTC | 3 | 30.00 | 90.00 '
'| 1.30000000 | 0:20:00 | 3 | 0 |'
)
print(backtesting._generate_text_table_strategy(all_results=results))
assert backtesting._generate_text_table_strategy(all_results=results) == result_str
def test_backtesting_start(default_conf, mocker, caplog) -> None:
def get_timeframe(input1, input2):
return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)
mocker.patch('freqtrade.freqtradebot.Analyze', MagicMock())
mocker.patch('freqtrade.optimize.load_data', mocked_load_data)
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history')
mocker.patch('freqtrade.exchange.Exchange.get_candle_history')
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.optimize.backtesting.Backtesting',
@ -421,15 +464,14 @@ def test_backtesting_start(default_conf, mocker, caplog) -> None:
get_timeframe=get_timeframe,
)
conf = deepcopy(default_conf)
conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
conf['ticker_interval'] = 1
conf['live'] = False
conf['datadir'] = None
conf['export'] = None
conf['timerange'] = '-100'
default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
default_conf['ticker_interval'] = 1
default_conf['live'] = False
default_conf['datadir'] = None
default_conf['export'] = None
default_conf['timerange'] = '-100'
backtesting = Backtesting(conf)
backtesting = Backtesting(default_conf)
backtesting.start()
# check the logs, that will contain the backtest result
exists = [
@ -444,16 +486,11 @@ def test_backtesting_start(default_conf, mocker, caplog) -> None:
def test_backtesting_start_no_data(default_conf, mocker, caplog) -> None:
"""
Test Backtesting.start() method if no data is found
"""
def get_timeframe(input1, input2):
return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)
mocker.patch('freqtrade.freqtradebot.Analyze', MagicMock())
mocker.patch('freqtrade.optimize.load_data', MagicMock(return_value={}))
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history')
mocker.patch('freqtrade.exchange.Exchange.get_candle_history')
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.optimize.backtesting.Backtesting',
@ -462,15 +499,14 @@ def test_backtesting_start_no_data(default_conf, mocker, caplog) -> None:
get_timeframe=get_timeframe,
)
conf = deepcopy(default_conf)
conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
conf['ticker_interval'] = "1m"
conf['live'] = False
conf['datadir'] = None
conf['export'] = None
conf['timerange'] = '20180101-20180102'
default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
default_conf['ticker_interval'] = "1m"
default_conf['live'] = False
default_conf['datadir'] = None
default_conf['export'] = None
default_conf['timerange'] = '20180101-20180102'
backtesting = Backtesting(conf)
backtesting = Backtesting(default_conf)
backtesting.start()
# check the logs, that will contain the backtest result
@ -478,31 +514,53 @@ def test_backtesting_start_no_data(default_conf, mocker, caplog) -> None:
def test_backtest(default_conf, fee, mocker) -> None:
"""
Test Backtesting.backtest() method
"""
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
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)
results = backtesting.backtest(
{
'stake_amount': default_conf['stake_amount'],
'processed': backtesting.tickerdata_to_dataframe(data),
'processed': data_processed,
'max_open_trades': 10,
'realistic': True
'position_stacking': False
}
)
assert not results.empty
assert len(results) == 2
expected = pd.DataFrame(
{'pair': [pair, pair],
'profit_percent': [0.00029975, 0.00056708],
'profit_abs': [1.49e-06, 7.6e-07],
'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_index': [125, 193],
'trade_duration': [240, 50],
'open_at_end': [False, False],
'open_rate': [0.104445, 0.10302485],
'close_rate': [0.105, 0.10359999],
'sell_reason': [SellType.ROI, SellType.ROI]
})
pd.testing.assert_frame_equal(results, expected)
data_pair = data_processed[pair]
for _, t in results.iterrows():
ln = data_pair.loc[data_pair["date"] == t["open_time"]]
# 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
ln = data_pair.loc[data_pair["date"] == t["close_time"]]
assert round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6)
def test_backtest_1min_ticker_interval(default_conf, fee, mocker) -> None:
"""
Test Backtesting.backtest() method with 1 min ticker
"""
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
@ -515,7 +573,7 @@ def test_backtest_1min_ticker_interval(default_conf, fee, mocker) -> None:
'stake_amount': default_conf['stake_amount'],
'processed': backtesting.tickerdata_to_dataframe(data),
'max_open_trades': 1,
'realistic': True
'position_stacking': False
}
)
assert not results.empty
@ -523,9 +581,6 @@ def test_backtest_1min_ticker_interval(default_conf, fee, mocker) -> None:
def test_processed(default_conf, mocker) -> None:
"""
Test Backtesting.backtest() method with offline data
"""
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
@ -551,42 +606,42 @@ 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).populate_buy_trend
fun = Backtesting(default_conf).advise_buy
for _ in ticks:
backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
backtesting = Backtesting(default_conf)
backtesting.populate_buy_trend = fun # Override
backtesting.populate_sell_trend = fun # Override
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):
def fun(dataframe=None, pair=None):
buy_value = 1
sell_value = 1
return _trend(dataframe, buy_value, sell_value)
backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
backtesting = Backtesting(default_conf)
backtesting.populate_buy_trend = fun # Override
backtesting.populate_sell_trend = fun # Override
backtesting.advise_buy = fun # Override
backtesting.advise_sell = fun # Override
results = backtesting.backtest(backtest_conf)
assert results.empty
def test_backtest_only_sell(mocker, default_conf):
# Override the default buy trend function in our default_strategy
def fun(dataframe=None):
def fun(dataframe=None, pair=None):
buy_value = 0
sell_value = 1
return _trend(dataframe, buy_value, sell_value)
backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
backtesting = Backtesting(default_conf)
backtesting.populate_buy_trend = fun # Override
backtesting.populate_sell_trend = fun # Override
backtesting.advise_buy = fun # Override
backtesting.advise_sell = fun # Override
results = backtesting.backtest(backtest_conf)
assert results.empty
@ -595,8 +650,8 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker):
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
backtest_conf = _make_backtest_conf(mocker, conf=default_conf, pair='UNITTEST/BTC')
backtesting = Backtesting(default_conf)
backtesting.populate_buy_trend = _trend_alternate # Override
backtesting.populate_sell_trend = _trend_alternate # Override
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
@ -627,9 +682,15 @@ def test_backtest_record(default_conf, fee, mocker):
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
"open_index": [1, 119, 153, 185],
"close_index": [118, 151, 184, 199],
"trade_duration": [123, 34, 31, 14]})
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
})
backtesting._store_backtest_result("backtest-result.json", results)
assert len(results) == 4
# Assert file_dump_json was only called once
@ -637,15 +698,32 @@ def test_backtest_record(default_conf, fee, mocker):
records = records[0]
# Ensure records are of correct type
assert len(records) == 4
# reset test to test with strategy name
names = []
records = []
backtesting._store_backtest_result("backtest-result.json", results, "DefStrat")
assert len(results) == 4
# Assert file_dump_json was only called once
assert names == ['backtest-result-DefStrat.json']
records = records[0]
# Ensure records are of correct type
assert len(records) == 4
# ('UNITTEST/BTC', 0.00331158, '1510684320', '1510691700', 0, 117)
# Below follows just a typecheck of the schema/type of trade-records
oix = None
for (pair, profit, date_buy, date_sell, buy_index, dur) in records:
for (pair, profit, date_buy, date_sell, buy_index, dur,
openr, closer, open_at_end, sell_reason) in records:
assert pair == 'UNITTEST/BTC'
isinstance(profit, float)
assert isinstance(profit, float)
# FIX: buy/sell should be converted to ints
isinstance(date_buy, str)
isinstance(date_sell, str)
assert isinstance(date_buy, float)
assert isinstance(date_sell, float)
assert isinstance(openr, float)
assert isinstance(closer, float)
assert isinstance(open_at_end, bool)
assert isinstance(sell_reason, str)
isinstance(buy_index, pd._libs.tslib.Timestamp)
if oix:
assert buy_index > oix
@ -654,26 +732,16 @@ def test_backtest_record(default_conf, fee, mocker):
def test_backtest_start_live(default_conf, mocker, caplog):
conf = deepcopy(default_conf)
conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history',
default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
mocker.patch('freqtrade.exchange.Exchange.get_candle_history',
new=lambda s, n, i: _load_pair_as_ticks(n, i))
patch_exchange(mocker)
mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table', MagicMock())
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(conf)
read_data=json.dumps(default_conf)
))
args = MagicMock()
args.ticker_interval = 1
args.level = 10
args.live = True
args.datadir = None
args.export = None
args.strategy = 'DefaultStrategy'
args.timerange = '-100' # needed due to MagicMock malleability
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
@ -682,7 +750,8 @@ def test_backtest_start_live(default_conf, mocker, caplog):
'--ticker-interval', '1m',
'--live',
'--timerange', '-100',
'--realistic-simulation'
'--enable-position-stacking',
'--disable-max-market-positions'
]
args = get_args(args)
start(args)
@ -691,14 +760,71 @@ def test_backtest_start_live(default_conf, mocker, caplog):
'Parameter -i/--ticker-interval detected ...',
'Using ticker_interval: 1m ...',
'Parameter -l/--live detected ...',
'Using max_open_trades: 1 ...',
'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
'Parameter --timerange detected: -100 ...',
'Using data folder: freqtrade/tests/testdata ...',
'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...',
'Downloading data for all pairs in whitelist ...',
'Measuring data from 2017-11-14T19:31:00+00:00 up to 2017-11-14T22:58:00+00:00 (0 days)..',
'Parameter --realistic-simulation detected ...'
'Parameter --enable-position-stacking detected ...'
]
for line in exists:
assert log_has(line, caplog.record_tuples)
def test_backtest_start_multi_strat(default_conf, mocker, caplog):
default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
mocker.patch('freqtrade.exchange.Exchange.get_candle_history',
new=lambda s, n, i: _load_pair_as_ticks(n, i))
patch_exchange(mocker)
backtestmock = MagicMock()
mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
gen_table_mock = MagicMock()
mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table', gen_table_mock)
gen_strattable_mock = MagicMock()
mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table_strategy',
gen_strattable_mock)
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = [
'--config', 'config.json',
'--datadir', 'freqtrade/tests/testdata',
'backtesting',
'--ticker-interval', '1m',
'--live',
'--timerange', '-100',
'--enable-position-stacking',
'--disable-max-market-positions',
'--strategy-list',
'DefaultStrategy',
'TestStrategy',
]
args = get_args(args)
start(args)
# 2 backtests, 4 tables
assert backtestmock.call_count == 2
assert gen_table_mock.call_count == 4
assert gen_strattable_mock.call_count == 1
# check the logs, that will contain the backtest result
exists = [
'Parameter -i/--ticker-interval detected ...',
'Using ticker_interval: 1m ...',
'Parameter -l/--live detected ...',
'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
'Parameter --timerange detected: -100 ...',
'Using data folder: freqtrade/tests/testdata ...',
'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...',
'Downloading data for all pairs in whitelist ...',
'Measuring data from 2017-11-14T19:31:00+00:00 up to 2017-11-14T22:58:00+00:00 (0 days)..',
'Parameter --enable-position-stacking detected ...',
'Running backtesting for Strategy DefaultStrategy',
'Running backtesting for Strategy TestStrategy',
]
for line in exists:

410
freqtrade/tests/optimize/test_hyperopt.py
View File

@ -1,7 +1,5 @@
# pragma pylint: disable=missing-docstring,W0212,C0103
import os
import signal
from copy import deepcopy
from unittest.mock import MagicMock
import pandas as pd
@ -13,52 +11,32 @@ from freqtrade.strategy.resolver import StrategyResolver
from freqtrade.tests.conftest import log_has, patch_exchange
from freqtrade.tests.optimize.test_backtesting import get_args
# Avoid to reinit the same object again and again
_HYPEROPT_INITIALIZED = False
_HYPEROPT = None
@pytest.fixture(scope='function')
def init_hyperopt(default_conf, mocker):
global _HYPEROPT_INITIALIZED, _HYPEROPT
if not _HYPEROPT_INITIALIZED:
patch_exchange(mocker)
_HYPEROPT = Hyperopt(default_conf)
_HYPEROPT_INITIALIZED = True
def hyperopt(default_conf, mocker):
patch_exchange(mocker)
return Hyperopt(default_conf)
# Functions for recurrent object patching
def create_trials(mocker) -> None:
def create_trials(mocker, hyperopt) -> None:
"""
When creating trials, mock the hyperopt Trials so that *by default*
- we don't create any pickle'd files in the filesystem
- we might have a pickle'd file so make sure that we return
false when looking for it
"""
_HYPEROPT.trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
hyperopt.trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
mocker.patch('freqtrade.optimize.hyperopt.os.path.exists', return_value=False)
mocker.patch('freqtrade.optimize.hyperopt.os.path.getsize', return_value=1)
mocker.patch('freqtrade.optimize.hyperopt.os.remove', return_value=True)
mocker.patch('freqtrade.optimize.hyperopt.pickle.dump', return_value=None)
mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None)
return mocker.Mock(
results=[
{
'loss': 1,
'result': 'foo',
'status': 'ok'
}
],
best_trial={'misc': {'vals': {'adx': 999}}}
)
return [{'loss': 1, 'result': 'foo', 'params': {}}]
# Unit tests
def test_start(mocker, default_conf, caplog) -> None:
"""
Test start() function
"""
start_mock = MagicMock()
mocker.patch(
'freqtrade.configuration.Configuration._load_config_file',
@ -87,11 +65,7 @@ def test_start(mocker, default_conf, caplog) -> None:
assert start_mock.call_count == 1
def test_loss_calculation_prefer_correct_trade_count(init_hyperopt) -> None:
"""
Test Hyperopt.calculate_loss()
"""
hyperopt = _HYPEROPT
def test_loss_calculation_prefer_correct_trade_count(hyperopt) -> None:
StrategyResolver({'strategy': 'DefaultStrategy'})
correct = hyperopt.calculate_loss(1, hyperopt.target_trades, 20)
@ -101,20 +75,13 @@ def test_loss_calculation_prefer_correct_trade_count(init_hyperopt) -> None:
assert under > correct
def test_loss_calculation_prefer_shorter_trades(init_hyperopt) -> None:
"""
Test Hyperopt.calculate_loss()
"""
hyperopt = _HYPEROPT
def test_loss_calculation_prefer_shorter_trades(hyperopt) -> None:
shorter = hyperopt.calculate_loss(1, 100, 20)
longer = hyperopt.calculate_loss(1, 100, 30)
assert shorter < longer
def test_loss_calculation_has_limited_profit(init_hyperopt) -> None:
hyperopt = _HYPEROPT
def test_loss_calculation_has_limited_profit(hyperopt) -> None:
correct = hyperopt.calculate_loss(hyperopt.expected_max_profit, hyperopt.target_trades, 20)
over = hyperopt.calculate_loss(hyperopt.expected_max_profit * 2, hyperopt.target_trades, 20)
under = hyperopt.calculate_loss(hyperopt.expected_max_profit / 2, hyperopt.target_trades, 20)
@ -122,8 +89,7 @@ def test_loss_calculation_has_limited_profit(init_hyperopt) -> None:
assert under > correct
def test_log_results_if_loss_improves(init_hyperopt, capsys) -> None:
hyperopt = _HYPEROPT
def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
hyperopt.current_best_loss = 2
hyperopt.log_results(
{
@ -134,11 +100,10 @@ def test_log_results_if_loss_improves(init_hyperopt, capsys) -> None:
}
)
out, err = capsys.readouterr()
assert ' 1/2: foo. Loss 1.00000'in out
assert ' 1/2: foo. Loss 1.00000' in out
def test_no_log_if_loss_does_not_improve(init_hyperopt, caplog) -> None:
hyperopt = _HYPEROPT
def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
hyperopt.current_best_loss = 2
hyperopt.log_results(
{
@ -148,166 +113,23 @@ def test_no_log_if_loss_does_not_improve(init_hyperopt, caplog) -> None:
assert caplog.record_tuples == []
def test_fmin_best_results(mocker, init_hyperopt, default_conf, caplog) -> None:
fmin_result = {
"macd_below_zero": 0,
"adx": 1,
"adx-value": 15.0,
"fastd": 1,
"fastd-value": 40.0,
"green_candle": 1,
"mfi": 0,
"over_sar": 0,
"rsi": 1,
"rsi-value": 37.0,
"trigger": 2,
"uptrend_long_ema": 1,
"uptrend_short_ema": 0,
"uptrend_sma": 0,
"stoploss": -0.1,
"roi_t1": 1,
"roi_t2": 2,
"roi_t3": 3,
"roi_p1": 1,
"roi_p2": 2,
"roi_p3": 3,
}
conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'epochs': 1})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value=fmin_result)
patch_exchange(mocker)
StrategyResolver({'strategy': 'DefaultStrategy'})
hyperopt = Hyperopt(conf)
hyperopt.trials = create_trials(mocker)
hyperopt.tickerdata_to_dataframe = MagicMock()
hyperopt.start()
exists = [
'Best parameters:',
'"adx": {\n "enabled": true,\n "value": 15.0\n },',
'"fastd": {\n "enabled": true,\n "value": 40.0\n },',
'"green_candle": {\n "enabled": true\n },',
'"macd_below_zero": {\n "enabled": false\n },',
'"mfi": {\n "enabled": false\n },',
'"over_sar": {\n "enabled": false\n },',
'"roi_p1": 1.0,',
'"roi_p2": 2.0,',
'"roi_p3": 3.0,',
'"roi_t1": 1.0,',
'"roi_t2": 2.0,',
'"roi_t3": 3.0,',
'"rsi": {\n "enabled": true,\n "value": 37.0\n },',
'"stoploss": -0.1,',
'"trigger": {\n "type": "faststoch10"\n },',
'"uptrend_long_ema": {\n "enabled": true\n },',
'"uptrend_short_ema": {\n "enabled": false\n },',
'"uptrend_sma": {\n "enabled": false\n }',
'ROI table:\n{0: 6.0, 3.0: 3.0, 5.0: 1.0, 6.0: 0}',
'Best Result:\nfoo'
]
for line in exists:
assert line in caplog.text
def test_fmin_throw_value_error(mocker, init_hyperopt, default_conf, caplog) -> None:
mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.fmin', side_effect=ValueError())
conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'epochs': 1})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
patch_exchange(mocker)
StrategyResolver({'strategy': 'DefaultStrategy'})
hyperopt = Hyperopt(conf)
hyperopt.trials = create_trials(mocker)
hyperopt.tickerdata_to_dataframe = MagicMock()
hyperopt.start()
exists = [
'Best Result:',
'Sorry, Hyperopt was not able to find good parameters. Please try with more epochs '
'(param: -e).',
]
for line in exists:
assert line in caplog.text
def test_resuming_previous_hyperopt_results_succeeds(mocker, init_hyperopt, default_conf) -> None:
trials = create_trials(mocker)
conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'epochs': 1})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
mocker.patch('freqtrade.optimize.hyperopt.os.path.exists', return_value=True)
mocker.patch('freqtrade.optimize.hyperopt.len', return_value=len(trials.results))
mock_read = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.read_trials',
return_value=trials
)
mock_save = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.save_trials',
return_value=None
)
mocker.patch('freqtrade.optimize.hyperopt.sorted', return_value=trials.results)
mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
patch_exchange(mocker)
StrategyResolver({'strategy': 'DefaultStrategy'})
hyperopt = Hyperopt(conf)
def test_save_trials_saves_trials(mocker, hyperopt, caplog) -> None:
trials = create_trials(mocker, hyperopt)
mock_dump = mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None)
hyperopt.trials = trials
hyperopt.tickerdata_to_dataframe = MagicMock()
hyperopt.start()
mock_read.assert_called_once()
mock_save.assert_called_once()
current_tries = hyperopt.current_tries
total_tries = hyperopt.total_tries
assert current_tries == len(trials.results)
assert total_tries == (current_tries + len(trials.results))
def test_save_trials_saves_trials(mocker, init_hyperopt, caplog) -> None:
create_trials(mocker)
mock_dump = mocker.patch('freqtrade.optimize.hyperopt.pickle.dump', return_value=None)
hyperopt = _HYPEROPT
mocker.patch('freqtrade.optimize.hyperopt.open', return_value=hyperopt.trials_file)
hyperopt.save_trials()
trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
assert log_has(
'Saving Trials to \'{}\''.format(trials_file),
'Saving 1 evaluations to \'{}\''.format(trials_file),
caplog.record_tuples
)
mock_dump.assert_called_once()
def test_read_trials_returns_trials_file(mocker, init_hyperopt, caplog) -> None:
trials = create_trials(mocker)
mock_load = mocker.patch('freqtrade.optimize.hyperopt.pickle.load', return_value=trials)
mock_open = mocker.patch('freqtrade.optimize.hyperopt.open', return_value=mock_load)
hyperopt = _HYPEROPT
def test_read_trials_returns_trials_file(mocker, hyperopt, caplog) -> None:
trials = create_trials(mocker, hyperopt)
mock_load = mocker.patch('freqtrade.optimize.hyperopt.load', return_value=trials)
hyperopt_trial = hyperopt.read_trials()
trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
assert log_has(
@ -315,11 +137,10 @@ def test_read_trials_returns_trials_file(mocker, init_hyperopt, caplog) -> None:
caplog.record_tuples
)
assert hyperopt_trial == trials
mock_open.assert_called_once()
mock_load.assert_called_once()
def test_roi_table_generation(init_hyperopt) -> None:
def test_roi_table_generation(hyperopt) -> None:
params = {
'roi_t1': 5,
'roi_t2': 10,
@ -329,36 +150,35 @@ def test_roi_table_generation(init_hyperopt) -> None:
'roi_p3': 3,
}
hyperopt = _HYPEROPT
assert hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
def test_start_calls_fmin(mocker, init_hyperopt, default_conf) -> None:
trials = create_trials(mocker)
mocker.patch('freqtrade.optimize.hyperopt.sorted', return_value=trials.results)
def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.multiprocessing.cpu_count', MagicMock(return_value=1))
parallel = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
MagicMock(return_value=[{'loss': 1, 'result': 'foo result', 'params': {}}])
)
patch_exchange(mocker)
mock_fmin = mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'epochs': 1})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
default_conf.update({'config': 'config.json.example'})
default_conf.update({'epochs': 1})
default_conf.update({'timerange': None})
default_conf.update({'spaces': 'all'})
hyperopt = Hyperopt(conf)
hyperopt.trials = trials
hyperopt = Hyperopt(default_conf)
hyperopt.tickerdata_to_dataframe = MagicMock()
hyperopt.start()
mock_fmin.assert_called_once()
parallel.assert_called_once()
assert 'Best result:\nfoo result\nwith values:\n{}' in caplog.text
assert dumper.called
def test_format_results(init_hyperopt):
"""
Test Hyperopt.format_results()
"""
def test_format_results(hyperopt):
# Test with BTC as stake_currency
trades = [
('ETH/BTC', 2, 2, 123),
@ -368,7 +188,7 @@ def test_format_results(init_hyperopt):
labels = ['currency', 'profit_percent', 'profit_abs', 'trade_duration']
df = pd.DataFrame.from_records(trades, columns=labels)
result = _HYPEROPT.format_results(df)
result = hyperopt.format_results(df)
assert result.find(' 66.67%')
assert result.find('Total profit 1.00000000 BTC')
assert result.find('2.0000Σ %')
@ -380,117 +200,61 @@ def test_format_results(init_hyperopt):
('XPR/EUR', -1, -2, -246)
]
df = pd.DataFrame.from_records(trades, columns=labels)
result = _HYPEROPT.format_results(df)
result = hyperopt.format_results(df)
assert result.find('Total profit 1.00000000 EUR')
def test_signal_handler(mocker, init_hyperopt):
"""
Test Hyperopt.signal_handler()
"""
m = MagicMock()
mocker.patch('sys.exit', m)
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.save_trials', m)
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.log_trials_result', m)
def test_has_space(hyperopt):
hyperopt.config.update({'spaces': ['buy', 'roi']})
assert hyperopt.has_space('roi')
assert hyperopt.has_space('buy')
assert not hyperopt.has_space('stoploss')
hyperopt = _HYPEROPT
hyperopt.signal_handler(signal.SIGTERM, None)
assert m.call_count == 3
hyperopt.config.update({'spaces': ['all']})
assert hyperopt.has_space('buy')
def test_has_space(init_hyperopt):
"""
Test Hyperopt.has_space() method
"""
_HYPEROPT.config.update({'spaces': ['buy', 'roi']})
assert _HYPEROPT.has_space('roi')
assert _HYPEROPT.has_space('buy')
assert not _HYPEROPT.has_space('stoploss')
_HYPEROPT.config.update({'spaces': ['all']})
assert _HYPEROPT.has_space('buy')
def test_populate_indicators(init_hyperopt) -> None:
"""
Test Hyperopt.populate_indicators()
"""
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'])
dataframes = hyperopt.tickerdata_to_dataframe(tickerlist)
dataframe = 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
assert 'ao' in dataframe
assert 'cci' in dataframe
assert 'mfi' in dataframe
assert 'rsi' in dataframe
def test_buy_strategy_generator(init_hyperopt) -> None:
"""
Test Hyperopt.buy_strategy_generator()
"""
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'])
dataframes = hyperopt.tickerdata_to_dataframe(tickerlist)
dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
populate_buy_trend = _HYPEROPT.buy_strategy_generator(
populate_buy_trend = hyperopt.buy_strategy_generator(
{
'uptrend_long_ema': {
'enabled': True
},
'macd_below_zero': {
'enabled': True
},
'uptrend_short_ema': {
'enabled': True
},
'mfi': {
'enabled': True,
'value': 20
},
'fastd': {
'enabled': True,
'value': 20
},
'adx': {
'enabled': True,
'value': 20
},
'rsi': {
'enabled': True,
'value': 20
},
'over_sar': {
'enabled': True,
},
'green_candle': {
'enabled': True,
},
'uptrend_sma': {
'enabled': True,
},
'trigger': {
'type': 'lower_bb'
}
'adx-value': 20,
'fastd-value': 20,
'mfi-value': 20,
'rsi-value': 20,
'adx-enabled': True,
'fastd-enabled': True,
'mfi-enabled': True,
'rsi-enabled': True,
'trigger': 'bb_lower'
}
)
result = populate_buy_trend(dataframe)
result = populate_buy_trend(dataframe, {'pair': 'UNITTEST/BTC'})
# Check if some indicators are generated. We will not test all of them
assert 'buy' in result
assert 1 in result['buy']
def test_generate_optimizer(mocker, init_hyperopt, default_conf) -> None:
"""
Test Hyperopt.generate_optimizer() function
"""
conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
def test_generate_optimizer(mocker, default_conf) -> None:
default_conf.update({'config': 'config.json.example'})
default_conf.update({'timerange': None})
default_conf.update({'spaces': 'all'})
trades = [
('POWR/BTC', 0.023117, 0.000233, 100)
@ -503,35 +267,33 @@ def test_generate_optimizer(mocker, init_hyperopt, default_conf) -> None:
MagicMock(return_value=backtest_result)
)
patch_exchange(mocker)
mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock())
optimizer_param = {
'adx': {'enabled': False},
'fastd': {'enabled': True, 'value': 35.0},
'green_candle': {'enabled': True},
'macd_below_zero': {'enabled': True},
'mfi': {'enabled': False},
'over_sar': {'enabled': False},
'roi_p1': 0.01,
'roi_p2': 0.01,
'roi_p3': 0.1,
'adx-value': 0,
'fastd-value': 35,
'mfi-value': 0,
'rsi-value': 0,
'adx-enabled': False,
'fastd-enabled': True,
'mfi-enabled': False,
'rsi-enabled': False,
'trigger': 'macd_cross_signal',
'roi_t1': 60.0,
'roi_t2': 30.0,
'roi_t3': 20.0,
'rsi': {'enabled': False},
'roi_p1': 0.01,
'roi_p2': 0.01,
'roi_p3': 0.1,
'stoploss': -0.4,
'trigger': {'type': 'macd_cross_signal'},
'uptrend_long_ema': {'enabled': False},
'uptrend_short_ema': {'enabled': True},
'uptrend_sma': {'enabled': True}
}
response_expected = {
'loss': 1.9840569076926293,
'result': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
'(0.0231Σ%). Avg duration 100.0 mins.',
'status': 'ok'
'params': optimizer_param
}
hyperopt = Hyperopt(conf)
generate_optimizer_value = hyperopt.generate_optimizer(optimizer_param)
hyperopt = Hyperopt(default_conf)
generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values()))
assert generate_optimizer_value == response_expected

45
freqtrade/tests/optimize/test_optimize.py
View File

@ -3,16 +3,19 @@
import json
import os
import uuid
import arrow
from shutil import copyfile
import arrow
from freqtrade import optimize
from freqtrade.misc import file_dump_json
from freqtrade.optimize.__init__ import make_testdata_path, download_pairs, \
download_backtesting_testdata, load_tickerdata_file, trim_tickerlist, \
load_cached_data_for_updating
from freqtrade.arguments import TimeRange
from freqtrade.tests.conftest import log_has, get_patched_exchange
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
@ -50,10 +53,7 @@ def _clean_test_file(file: str) -> None:
def test_load_data_30min_ticker(ticker_history, mocker, caplog, default_conf) -> None:
"""
Test load_data() with 30 min ticker
"""
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=ticker_history)
mocker.patch('freqtrade.exchange.Exchange.get_candle_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')
@ -63,10 +63,7 @@ def test_load_data_30min_ticker(ticker_history, mocker, caplog, default_conf) ->
def test_load_data_5min_ticker(ticker_history, mocker, caplog, default_conf) -> None:
"""
Test load_data() with 5 min ticker
"""
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=ticker_history)
mocker.patch('freqtrade.exchange.Exchange.get_candle_history', return_value=ticker_history)
file = os.path.join(os.path.dirname(__file__), '..', 'testdata', 'UNITTEST_BTC-5m.json')
_backup_file(file, copy_file=True)
@ -77,11 +74,7 @@ def test_load_data_5min_ticker(ticker_history, mocker, caplog, default_conf) ->
def test_load_data_1min_ticker(ticker_history, mocker, caplog) -> None:
"""
Test load_data() with 1 min ticker
"""
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=ticker_history)
mocker.patch('freqtrade.exchange.Exchange.get_candle_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'])
@ -94,7 +87,7 @@ def test_load_data_with_new_pair_1min(ticker_history, mocker, caplog, default_co
"""
Test load_data() with 1 min ticker
"""
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=ticker_history)
mocker.patch('freqtrade.exchange.Exchange.get_candle_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')
@ -125,7 +118,7 @@ def test_testdata_path() -> None:
def test_download_pairs(ticker_history, mocker, default_conf) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=ticker_history)
mocker.patch('freqtrade.exchange.Exchange.get_candle_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')
@ -268,7 +261,7 @@ def test_load_cached_data_for_updating(mocker) -> None:
def test_download_pairs_exception(ticker_history, mocker, caplog, default_conf) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=ticker_history)
mocker.patch('freqtrade.exchange.Exchange.get_candle_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)
@ -286,7 +279,7 @@ def test_download_pairs_exception(ticker_history, mocker, caplog, default_conf)
def test_download_backtesting_testdata(ticker_history, mocker, default_conf) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=ticker_history)
mocker.patch('freqtrade.exchange.Exchange.get_candle_history', return_value=ticker_history)
exchange = get_patched_exchange(mocker, default_conf)
# Download a 1 min ticker file
@ -311,7 +304,7 @@ def test_download_backtesting_testdata2(mocker, default_conf) -> None:
[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_ticker_history', return_value=tick)
mocker.patch('freqtrade.exchange.Exchange.get_candle_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')
@ -418,10 +411,6 @@ def test_trim_tickerlist() -> None:
def test_file_dump_json() -> None:
"""
Test file_dump_json()
:return: None
"""
file = os.path.join(os.path.dirname(__file__), '..', 'testdata',
'test_{id}.json'.format(id=str(uuid.uuid4())))
data = {'bar': 'foo'}

151
freqtrade/tests/rpc/test_rpc.py
View File

@ -1,19 +1,18 @@
# pragma pylint: disable=missing-docstring, C0103
# pragma pylint: disable=invalid-sequence-index, invalid-name, too-many-arguments
"""
Unit test file for rpc/rpc.py
"""
from datetime import datetime
from unittest.mock import MagicMock
from unittest.mock import MagicMock, ANY
import pytest
from freqtrade.fiat_convert import CryptoToFiatConverter
from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.persistence import Trade
from freqtrade.rpc.rpc import RPC, RPCException
from freqtrade.rpc import RPC, RPCException
from freqtrade.state import State
from freqtrade.tests.test_freqtradebot import patch_get_signal, patch_coinmarketcap
from freqtrade.tests.test_freqtradebot import patch_get_signal
from freqtrade.tests.conftest import patch_coinmarketcap
# Functions for recurrent object patching
@ -26,10 +25,6 @@ def prec_satoshi(a, b) -> float:
# Unit tests
def test_rpc_trade_status(default_conf, ticker, fee, markets, mocker) -> None:
"""
Test rpc_trade_status() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
@ -41,6 +36,7 @@ def test_rpc_trade_status(default_conf, ticker, fee, markets, mocker) -> None:
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
freqtradebot.state = State.STOPPED
@ -52,31 +48,24 @@ def test_rpc_trade_status(default_conf, ticker, fee, markets, mocker) -> None:
rpc._rpc_trade_status()
freqtradebot.create_trade()
trades = rpc._rpc_trade_status()
trade = trades[0]
results = rpc._rpc_trade_status()
result_message = [
'*Trade ID:* `1`\n'
'*Current Pair:* '
'[ETH/BTC](https://bittrex.com/Market/Index?MarketName=BTC-ETH)\n'
'*Open Since:* `just now`\n'
'*Amount:* `90.99181074`\n'
'*Open Rate:* `0.00001099`\n'
'*Close Rate:* `None`\n'
'*Current Rate:* `0.00001098`\n'
'*Close Profit:* `None`\n'
'*Current Profit:* `-0.59%`\n'
'*Open Order:* `(limit buy rem=0.00000000)`'
]
assert trades == result_message
assert trade.find('[ETH/BTC]') >= 0
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': 1.098e-05,
'amount': 90.99181074,
'close_profit': None,
'current_profit': -0.59,
'open_order': '(limit buy rem=0.00000000)'
} == results[0]
def test_rpc_status_table(default_conf, ticker, fee, markets, mocker) -> None:
"""
Test rpc_status_table() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
@ -88,14 +77,15 @@ def test_rpc_status_table(default_conf, ticker, fee, markets, mocker) -> None:
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
freqtradebot.state = State.STOPPED
with pytest.raises(RPCException, match=r'.*\*Status:\* `trader is not running``*'):
with pytest.raises(RPCException, match=r'.*trader is not running*'):
rpc._rpc_status_table()
freqtradebot.state = State.RUNNING
with pytest.raises(RPCException, match=r'.*\*Status:\* `no active order`*'):
with pytest.raises(RPCException, match=r'.*no active order*'):
rpc._rpc_status_table()
freqtradebot.create_trade()
@ -107,10 +97,6 @@ def test_rpc_status_table(default_conf, ticker, fee, markets, mocker) -> None:
def test_rpc_daily_profit(default_conf, update, ticker, fee,
limit_buy_order, limit_sell_order, markets, mocker) -> None:
"""
Test rpc_daily_profit() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
@ -122,11 +108,12 @@ def test_rpc_daily_profit(default_conf, update, ticker, fee,
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
stake_currency = default_conf['stake_currency']
fiat_display_currency = default_conf['fiat_display_currency']
rpc = RPC(freqtradebot)
rpc._fiat_converter = CryptoToFiatConverter()
# Create some test data
freqtradebot.create_trade()
trade = Trade.query.first()
@ -159,15 +146,12 @@ 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:
"""
Test rpc_trade_statistics() method
"""
patch_get_signal(mocker, (True, False))
mocker.patch.multiple(
'freqtrade.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
)
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
@ -178,10 +162,12 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
stake_currency = default_conf['stake_currency']
fiat_display_currency = default_conf['fiat_display_currency']
rpc = RPC(freqtradebot)
rpc._fiat_converter = CryptoToFiatConverter()
with pytest.raises(RPCException, match=r'.*no closed trade*'):
rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
@ -236,10 +222,6 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
# trade.open_rate (it is set to None)
def test_rpc_trade_statistics_closed(mocker, default_conf, ticker, fee, markets,
ticker_sell_up, limit_buy_order, limit_sell_order):
"""
Test rpc_trade_statistics() method
"""
patch_get_signal(mocker, (True, False))
mocker.patch.multiple(
'freqtrade.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
@ -255,6 +237,7 @@ def test_rpc_trade_statistics_closed(mocker, default_conf, ticker, fee, markets,
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
stake_currency = default_conf['stake_currency']
fiat_display_currency = default_conf['fiat_display_currency']
@ -295,9 +278,6 @@ def test_rpc_trade_statistics_closed(mocker, default_conf, ticker, fee, markets,
def test_rpc_balance_handle(default_conf, mocker):
"""
Test rpc_balance() method
"""
mock_balance = {
'BTC': {
'free': 10.0,
@ -311,12 +291,12 @@ def test_rpc_balance_handle(default_conf, mocker):
}
}
patch_get_signal(mocker, (True, False))
mocker.patch.multiple(
'freqtrade.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
)
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
@ -325,25 +305,24 @@ def test_rpc_balance_handle(default_conf, mocker):
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
rpc._fiat_converter = CryptoToFiatConverter()
output, total, symbol, value = rpc._rpc_balance(default_conf['fiat_display_currency'])
assert prec_satoshi(total, 12)
assert prec_satoshi(value, 180000)
assert 'USD' in symbol
assert len(output) == 1
assert 'BTC' in output[0]['currency']
assert prec_satoshi(output[0]['available'], 10)
assert prec_satoshi(output[0]['balance'], 12)
assert prec_satoshi(output[0]['pending'], 2)
assert prec_satoshi(output[0]['est_btc'], 12)
result = rpc._rpc_balance(default_conf['fiat_display_currency'])
assert prec_satoshi(result['total'], 12)
assert prec_satoshi(result['value'], 180000)
assert 'USD' == result['symbol']
assert result['currencies'] == [{
'currency': 'BTC',
'available': 10.0,
'balance': 12.0,
'pending': 2.0,
'est_btc': 12.0,
}]
def test_rpc_start(mocker, default_conf) -> None:
"""
Test rpc_start() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
@ -353,23 +332,20 @@ def test_rpc_start(mocker, default_conf) -> None:
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
freqtradebot.state = State.STOPPED
result = rpc._rpc_start()
assert '`Starting trader ...`' in result
assert {'status': 'starting trader ...'} == result
assert freqtradebot.state == State.RUNNING
result = rpc._rpc_start()
assert '*Status:* `already running`' in result
assert {'status': 'already running'} == result
assert freqtradebot.state == State.RUNNING
def test_rpc_stop(mocker, default_conf) -> None:
"""
Test rpc_stop() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
@ -379,23 +355,21 @@ def test_rpc_stop(mocker, default_conf) -> None:
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
freqtradebot.state = State.RUNNING
result = rpc._rpc_stop()
assert '`Stopping trader ...`' in result
assert {'status': 'stopping trader ...'} == result
assert freqtradebot.state == State.STOPPED
result = rpc._rpc_stop()
assert '*Status:* `already stopped`' in result
assert {'status': 'already stopped'} == result
assert freqtradebot.state == State.STOPPED
def test_rpc_forcesell(default_conf, ticker, fee, mocker, markets) -> None:
"""
Test rpc_forcesell() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
@ -417,14 +391,15 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker, markets) -> None:
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
freqtradebot.state = State.STOPPED
with pytest.raises(RPCException, match=r'.*`trader is not running`*'):
with pytest.raises(RPCException, match=r'.*trader is not running*'):
rpc._rpc_forcesell(None)
freqtradebot.state = State.RUNNING
with pytest.raises(RPCException, match=r'.*Invalid argument.*'):
with pytest.raises(RPCException, match=r'.*invalid argument*'):
rpc._rpc_forcesell(None)
rpc._rpc_forcesell('all')
@ -435,10 +410,10 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker, markets) -> None:
rpc._rpc_forcesell('1')
freqtradebot.state = State.STOPPED
with pytest.raises(RPCException, match=r'.*`trader is not running`*'):
with pytest.raises(RPCException, match=r'.*trader is not running*'):
rpc._rpc_forcesell(None)
with pytest.raises(RPCException, match=r'.*`trader is not running`*'):
with pytest.raises(RPCException, match=r'.*trader is not running*'):
rpc._rpc_forcesell('all')
freqtradebot.state = State.RUNNING
@ -496,10 +471,6 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker, markets) -> None:
def test_performance_handle(default_conf, ticker, limit_buy_order, fee,
limit_sell_order, markets, mocker) -> None:
"""
Test rpc_performance() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
@ -512,6 +483,7 @@ def test_performance_handle(default_conf, ticker, limit_buy_order, fee,
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
# Create some test data
@ -535,10 +507,6 @@ def test_performance_handle(default_conf, ticker, limit_buy_order, fee,
def test_rpc_count(mocker, default_conf, ticker, fee, markets) -> None:
"""
Test rpc_count() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
@ -551,6 +519,7 @@ def test_rpc_count(mocker, default_conf, ticker, fee, markets) -> None:
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
rpc = RPC(freqtradebot)
trades = rpc._rpc_count()

90
freqtrade/tests/rpc/test_rpc_manager.py
View File

@ -1,50 +1,31 @@
"""
Unit test file for rpc/rpc_manager.py
"""
# pragma pylint: disable=missing-docstring, C0103
import logging
from copy import deepcopy
from unittest.mock import MagicMock
from freqtrade.rpc.rpc_manager import RPCManager
from freqtrade.rpc import RPCMessageType, RPCManager
from freqtrade.tests.conftest import log_has, get_patched_freqtradebot
def test_rpc_manager_object() -> None:
""" Test the Arguments object has the mandatory methods """
assert hasattr(RPCManager, 'send_msg')
assert hasattr(RPCManager, 'cleanup')
def test__init__(mocker, default_conf) -> None:
""" Test __init__() method """
conf = deepcopy(default_conf)
conf['telegram']['enabled'] = False
default_conf['telegram']['enabled'] = False
rpc_manager = RPCManager(get_patched_freqtradebot(mocker, conf))
rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
assert rpc_manager.registered_modules == []
def test_init_telegram_disabled(mocker, default_conf, caplog) -> None:
""" Test _init() method with Telegram disabled """
caplog.set_level(logging.DEBUG)
conf = deepcopy(default_conf)
conf['telegram']['enabled'] = False
rpc_manager = RPCManager(get_patched_freqtradebot(mocker, conf))
default_conf['telegram']['enabled'] = False
rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
assert not log_has('Enabling rpc.telegram ...', caplog.record_tuples)
assert rpc_manager.registered_modules == []
def test_init_telegram_enabled(mocker, default_conf, caplog) -> None:
"""
Test _init() method with Telegram enabled
"""
caplog.set_level(logging.DEBUG)
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
assert log_has('Enabling rpc.telegram ...', caplog.record_tuples)
@ -54,16 +35,11 @@ def test_init_telegram_enabled(mocker, default_conf, caplog) -> None:
def test_cleanup_telegram_disabled(mocker, default_conf, caplog) -> None:
"""
Test cleanup() method with Telegram disabled
"""
caplog.set_level(logging.DEBUG)
telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.cleanup', MagicMock())
default_conf['telegram']['enabled'] = False
conf = deepcopy(default_conf)
conf['telegram']['enabled'] = False
freqtradebot = get_patched_freqtradebot(mocker, conf)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
rpc_manager = RPCManager(freqtradebot)
rpc_manager.cleanup()
@ -72,9 +48,6 @@ def test_cleanup_telegram_disabled(mocker, default_conf, caplog) -> None:
def test_cleanup_telegram_enabled(mocker, default_conf, caplog) -> None:
"""
Test cleanup() method with Telegram enabled
"""
caplog.set_level(logging.DEBUG)
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.cleanup', MagicMock())
@ -92,32 +65,51 @@ def test_cleanup_telegram_enabled(mocker, default_conf, caplog) -> None:
def test_send_msg_telegram_disabled(mocker, default_conf, caplog) -> None:
"""
Test send_msg() method with Telegram disabled
"""
telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock())
default_conf['telegram']['enabled'] = False
conf = deepcopy(default_conf)
conf['telegram']['enabled'] = False
freqtradebot = get_patched_freqtradebot(mocker, conf)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
rpc_manager = RPCManager(freqtradebot)
rpc_manager.send_msg('test')
rpc_manager.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': 'test'
})
assert log_has('Sending rpc message: test', caplog.record_tuples)
assert log_has("Sending rpc message: {'type': status, 'status': 'test'}", caplog.record_tuples)
assert telegram_mock.call_count == 0
def test_send_msg_telegram_enabled(mocker, default_conf, caplog) -> None:
"""
Test send_msg() method with Telegram disabled
"""
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.send_msg('test')
rpc_manager.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': 'test'
})
assert log_has('Sending rpc message: test', caplog.record_tuples)
assert log_has("Sending rpc message: {'type': status, 'status': 'test'}", caplog.record_tuples)
assert telegram_mock.call_count == 1
def test_init_webhook_disabled(mocker, default_conf, caplog) -> None:
caplog.set_level(logging.DEBUG)
default_conf['telegram']['enabled'] = False
default_conf['webhook'] = {'enabled': False}
rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
assert not log_has('Enabling rpc.webhook ...', caplog.record_tuples)
assert rpc_manager.registered_modules == []
def test_init_webhook_enabled(mocker, default_conf, caplog) -> None:
caplog.set_level(logging.DEBUG)
default_conf['telegram']['enabled'] = False
default_conf['webhook'] = {'enabled': True, 'url': "https://DEADBEEF.com"}
rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
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]

507
freqtrade/tests/rpc/test_rpc_telegram.py
View File

@ -1,27 +1,27 @@
# pragma pylint: disable=missing-docstring, C0103
# pragma pylint: disable=protected-access, unused-argument, invalid-name
# pragma pylint: disable=too-many-lines, too-many-arguments
"""
Unit test file for rpc/telegram.py
"""
import re
from copy import deepcopy
from datetime import datetime
from random import randint
from unittest.mock import MagicMock
from unittest.mock import MagicMock, ANY
from telegram import Update, Message, Chat
import arrow
import pytest
from telegram import Chat, Message, Update
from telegram.error import NetworkError
from freqtrade import __version__
from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.persistence import Trade
from freqtrade.rpc.telegram import Telegram
from freqtrade.rpc.telegram import authorized_only
from freqtrade.rpc import RPCMessageType
from freqtrade.rpc.telegram import Telegram, authorized_only
from freqtrade.state import State
from freqtrade.tests.conftest import get_patched_freqtradebot, patch_exchange, log_has
from freqtrade.tests.test_freqtradebot import patch_get_signal, patch_coinmarketcap
from freqtrade.tests.conftest import (get_patched_freqtradebot, log_has,
patch_exchange)
from freqtrade.tests.test_freqtradebot import patch_get_signal
from freqtrade.tests.conftest import patch_coinmarketcap
class DummyCls(Telegram):
@ -51,9 +51,6 @@ class DummyCls(Telegram):
def test__init__(default_conf, mocker) -> None:
"""
Test __init__() method
"""
mocker.patch('freqtrade.rpc.telegram.Updater', MagicMock())
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
@ -63,7 +60,6 @@ def test__init__(default_conf, mocker) -> None:
def test_init(default_conf, mocker, caplog) -> None:
""" Test _init() method """
start_polling = MagicMock()
mocker.patch('freqtrade.rpc.telegram.Updater', MagicMock(return_value=start_polling))
@ -82,9 +78,6 @@ def test_init(default_conf, mocker, caplog) -> None:
def test_cleanup(default_conf, mocker) -> None:
"""
Test cleanup() method
"""
updater_mock = MagicMock()
updater_mock.stop = MagicMock()
mocker.patch('freqtrade.rpc.telegram.Updater', updater_mock)
@ -95,10 +88,6 @@ def test_cleanup(default_conf, mocker) -> None:
def test_authorized_only(default_conf, mocker, caplog) -> None:
"""
Test authorized_only() method when we are authorized
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
patch_exchange(mocker, None)
@ -106,9 +95,10 @@ def test_authorized_only(default_conf, mocker, caplog) -> None:
update = Update(randint(1, 100))
update.message = Message(randint(1, 100), 0, datetime.utcnow(), chat)
conf = deepcopy(default_conf)
conf['telegram']['enabled'] = False
dummy = DummyCls(FreqtradeBot(conf))
default_conf['telegram']['enabled'] = False
bot = FreqtradeBot(default_conf)
patch_get_signal(bot, (True, False))
dummy = DummyCls(bot)
dummy.dummy_handler(bot=MagicMock(), update=update)
assert dummy.state['called'] is True
assert log_has(
@ -126,19 +116,16 @@ def test_authorized_only(default_conf, mocker, caplog) -> None:
def test_authorized_only_unauthorized(default_conf, mocker, caplog) -> None:
"""
Test authorized_only() method when we are unauthorized
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
patch_exchange(mocker, None)
chat = Chat(0xdeadbeef, 0)
update = Update(randint(1, 100))
update.message = Message(randint(1, 100), 0, datetime.utcnow(), chat)
conf = deepcopy(default_conf)
conf['telegram']['enabled'] = False
dummy = DummyCls(FreqtradeBot(conf))
default_conf['telegram']['enabled'] = False
bot = FreqtradeBot(default_conf)
patch_get_signal(bot, (True, False))
dummy = DummyCls(bot)
dummy.dummy_handler(bot=MagicMock(), update=update)
assert dummy.state['called'] is False
assert not log_has(
@ -156,19 +143,18 @@ def test_authorized_only_unauthorized(default_conf, mocker, caplog) -> None:
def test_authorized_only_exception(default_conf, mocker, caplog) -> None:
"""
Test authorized_only() method when an exception is thrown
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
patch_exchange(mocker)
update = Update(randint(1, 100))
update.message = Message(randint(1, 100), 0, datetime.utcnow(), Chat(0, 0))
conf = deepcopy(default_conf)
conf['telegram']['enabled'] = False
dummy = DummyCls(FreqtradeBot(conf))
default_conf['telegram']['enabled'] = False
bot = FreqtradeBot(default_conf)
patch_get_signal(bot, (True, False))
dummy = DummyCls(bot)
dummy.dummy_exception(bot=MagicMock(), update=update)
assert dummy.state['called'] is False
assert not log_has(
@ -186,16 +172,12 @@ def test_authorized_only_exception(default_conf, mocker, caplog) -> None:
def test_status(default_conf, update, mocker, fee, ticker, markets) -> None:
"""
Test _status() method
"""
update.message.chat.id = 123
conf = deepcopy(default_conf)
conf['telegram']['enabled'] = False
conf['telegram']['chat_id'] = 123
default_conf['telegram']['enabled'] = False
default_conf['telegram']['chat_id'] = 123
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
validate_pairs=MagicMock(),
@ -209,13 +191,26 @@ def test_status(default_conf, update, mocker, fee, ticker, markets) -> None:
mocker.patch.multiple(
'freqtrade.rpc.telegram.Telegram',
_init=MagicMock(),
_rpc_trade_status=MagicMock(return_value=[1, 2, 3]),
_rpc_trade_status=MagicMock(return_value=[{
'trade_id': 1,
'pair': 'ETH/BTC',
'market_url': 'https://bittrex.com/Market/Index?MarketName=BTC-ETH',
'date': arrow.utcnow(),
'open_rate': 1.099e-05,
'close_rate': None,
'current_rate': 1.098e-05,
'amount': 90.99181074,
'close_profit': None,
'current_profit': -0.59,
'open_order': '(limit buy rem=0.00000000)'
}]),
_status_table=status_table,
_send_msg=msg_mock
)
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
freqtradebot = FreqtradeBot(conf)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
# Create some test data
@ -223,7 +218,7 @@ def test_status(default_conf, update, mocker, fee, ticker, markets) -> None:
freqtradebot.create_trade()
telegram._status(bot=MagicMock(), update=update)
assert msg_mock.call_count == 3
assert msg_mock.call_count == 1
update.message.text = MagicMock()
update.message.text.replace = MagicMock(return_value='table 2 3')
@ -232,10 +227,6 @@ def test_status(default_conf, update, mocker, fee, ticker, markets) -> None:
def test_status_handle(default_conf, update, ticker, fee, markets, mocker) -> None:
"""
Test _status() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
@ -255,6 +246,8 @@ def test_status_handle(default_conf, update, ticker, fee, markets, mocker) -> No
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
freqtradebot.state = State.STOPPED
@ -279,10 +272,6 @@ def test_status_handle(default_conf, update, ticker, fee, markets, mocker) -> No
def test_status_table_handle(default_conf, update, ticker, fee, markets, mocker) -> None:
"""
Test _status_table() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
@ -300,9 +289,10 @@ def test_status_table_handle(default_conf, update, ticker, fee, markets, mocker)
)
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
conf = deepcopy(default_conf)
conf['stake_amount'] = 15.0
freqtradebot = FreqtradeBot(conf)
default_conf['stake_amount'] = 15.0
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
freqtradebot.state = State.STOPPED
@ -333,13 +323,9 @@ def test_status_table_handle(default_conf, update, ticker, fee, markets, mocker)
def test_daily_handle(default_conf, update, ticker, limit_buy_order, fee,
limit_sell_order, markets, mocker) -> None:
"""
Test _daily() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch(
'freqtrade.fiat_convert.CryptoToFiatConverter._find_price',
'freqtrade.rpc.rpc.CryptoToFiatConverter._find_price',
return_value=15000.0
)
mocker.patch.multiple(
@ -358,6 +344,7 @@ def test_daily_handle(default_conf, update, ticker, limit_buy_order, fee,
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
# Create some test data
@ -407,10 +394,6 @@ def test_daily_handle(default_conf, update, ticker, limit_buy_order, fee,
def test_daily_wrong_input(default_conf, update, ticker, mocker) -> None:
"""
Test _daily() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
@ -426,6 +409,7 @@ def test_daily_wrong_input(default_conf, update, ticker, mocker) -> None:
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
# Try invalid data
@ -446,12 +430,8 @@ def test_daily_wrong_input(default_conf, update, ticker, mocker) -> None:
def test_profit_handle(default_conf, update, ticker, ticker_sell_up, fee,
limit_buy_order, limit_sell_order, markets, mocker) -> None:
"""
Test _profit() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
validate_pairs=MagicMock(),
@ -468,6 +448,7 @@ def test_profit_handle(default_conf, update, ticker, ticker_sell_up, fee,
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
telegram._profit(bot=MagicMock(), update=update)
@ -507,10 +488,6 @@ def test_profit_handle(default_conf, update, ticker, ticker_sell_up, fee,
def test_telegram_balance_handle(default_conf, update, mocker) -> None:
"""
Test _balance() method
"""
mock_balance = {
'BTC': {
'total': 12.0,
@ -535,9 +512,6 @@ def test_telegram_balance_handle(default_conf, update, mocker) -> None:
}
def mock_ticker(symbol, refresh):
"""
Mock Bittrex.get_ticker() response
"""
if symbol == 'BTC/USDT':
return {
'bid': 10000.00,
@ -551,7 +525,6 @@ def test_telegram_balance_handle(default_conf, update, mocker) -> None:
'last': 0.1,
}
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch('freqtrade.exchange.Exchange.get_balances', return_value=mock_balance)
mocker.patch('freqtrade.exchange.Exchange.get_ticker', side_effect=mock_ticker)
@ -564,6 +537,8 @@ def test_telegram_balance_handle(default_conf, update, mocker) -> None:
)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
telegram._balance(bot=MagicMock(), update=update)
@ -577,11 +552,7 @@ def test_telegram_balance_handle(default_conf, update, mocker) -> None:
assert 'BTC: 14.00000000' in result
def test_zero_balance_handle(default_conf, update, mocker) -> None:
"""
Test _balance() method when the Exchange platform returns nothing
"""
patch_get_signal(mocker, (True, False))
def test_balance_handle_empty_response(default_conf, update, mocker) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_balances', return_value={})
msg_mock = MagicMock()
@ -592,18 +563,17 @@ def test_zero_balance_handle(default_conf, update, mocker) -> None:
)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
telegram._balance(bot=MagicMock(), update=update)
result = msg_mock.call_args_list[0][0][0]
assert msg_mock.call_count == 1
assert '`All balances are zero.`' in result
assert 'all balances are zero' in result
def test_start_handle(default_conf, update, mocker) -> None:
"""
Test _start() method
"""
msg_mock = MagicMock()
mocker.patch.multiple(
'freqtrade.rpc.telegram.Telegram',
@ -622,9 +592,6 @@ def test_start_handle(default_conf, update, mocker) -> None:
def test_start_handle_already_running(default_conf, update, mocker) -> None:
"""
Test _start() method
"""
msg_mock = MagicMock()
mocker.patch.multiple(
'freqtrade.rpc.telegram.Telegram',
@ -644,9 +611,6 @@ def test_start_handle_already_running(default_conf, update, mocker) -> None:
def test_stop_handle(default_conf, update, mocker) -> None:
"""
Test _stop() method
"""
patch_coinmarketcap(mocker)
msg_mock = MagicMock()
mocker.patch.multiple(
@ -663,13 +627,10 @@ def test_stop_handle(default_conf, update, mocker) -> None:
telegram._stop(bot=MagicMock(), update=update)
assert freqtradebot.state == State.STOPPED
assert msg_mock.call_count == 1
assert 'Stopping trader' in msg_mock.call_args_list[0][0][0]
assert 'stopping trader' in msg_mock.call_args_list[0][0][0]
def test_stop_handle_already_stopped(default_conf, update, mocker) -> None:
"""
Test _stop() method
"""
patch_coinmarketcap(mocker)
msg_mock = MagicMock()
mocker.patch.multiple(
@ -690,7 +651,6 @@ def test_stop_handle_already_stopped(default_conf, update, mocker) -> None:
def test_reload_conf_handle(default_conf, update, mocker) -> None:
""" Test _reload_conf() method """
patch_coinmarketcap(mocker)
msg_mock = MagicMock()
mocker.patch.multiple(
@ -707,17 +667,13 @@ def test_reload_conf_handle(default_conf, update, mocker) -> None:
telegram._reload_conf(bot=MagicMock(), update=update)
assert freqtradebot.state == State.RELOAD_CONF
assert msg_mock.call_count == 1
assert 'Reloading config' in msg_mock.call_args_list[0][0][0]
assert 'reloading config' in msg_mock.call_args_list[0][0][0]
def test_forcesell_handle(default_conf, update, ticker, fee,
ticker_sell_up, markets, mocker) -> None:
"""
Test _forcesell() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=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(
@ -729,6 +685,7 @@ def test_forcesell_handle(default_conf, update, ticker, fee,
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
# Create some test data
@ -744,20 +701,26 @@ def test_forcesell_handle(default_conf, update, ticker, fee,
telegram._forcesell(bot=MagicMock(), update=update)
assert rpc_mock.call_count == 2
assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
assert '[ETH/BTC]' in rpc_mock.call_args_list[-1][0][0]
assert 'Amount' in rpc_mock.call_args_list[-1][0][0]
assert '0.00001172' in rpc_mock.call_args_list[-1][0][0]
assert 'profit: 6.11%, 0.00006126' in rpc_mock.call_args_list[-1][0][0]
assert '0.919 USD' in rpc_mock.call_args_list[-1][0][0]
last_msg = rpc_mock.call_args_list[-1][0][0]
assert {
'type': RPCMessageType.SELL_NOTIFICATION,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'gain': 'profit',
'market_url': 'https://bittrex.com/Market/Index?MarketName=BTC-ETH',
'limit': 1.172e-05,
'amount': 90.99181073703367,
'open_rate': 1.099e-05,
'current_rate': 1.172e-05,
'profit_amount': 6.126e-05,
'profit_percent': 0.06110514,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
} == last_msg
def test_forcesell_down_handle(default_conf, update, ticker, fee,
ticker_sell_down, markets, mocker) -> None:
"""
Test _forcesell() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
rpc_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock())
@ -771,6 +734,7 @@ def test_forcesell_down_handle(default_conf, update, ticker, fee,
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
# Create some test data
@ -790,19 +754,26 @@ def test_forcesell_down_handle(default_conf, update, ticker, fee,
telegram._forcesell(bot=MagicMock(), update=update)
assert rpc_mock.call_count == 2
assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
assert '[ETH/BTC]' in rpc_mock.call_args_list[-1][0][0]
assert 'Amount' in rpc_mock.call_args_list[-1][0][0]
assert '0.00001044' in rpc_mock.call_args_list[-1][0][0]
assert 'loss: -5.48%, -0.00005492' in rpc_mock.call_args_list[-1][0][0]
assert '-0.824 USD' in rpc_mock.call_args_list[-1][0][0]
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': 1.044e-05,
'amount': 90.99181073703367,
'open_rate': 1.099e-05,
'current_rate': 1.044e-05,
'profit_amount': -5.492e-05,
'profit_percent': -0.05478343,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
} == last_msg
def test_forcesell_all_handle(default_conf, update, ticker, fee, markets, mocker) -> None:
"""
Test _forcesell() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
rpc_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock())
@ -817,6 +788,7 @@ def test_forcesell_all_handle(default_conf, update, ticker, fee, markets, mocker
)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
# Create some test data
@ -828,17 +800,25 @@ def test_forcesell_all_handle(default_conf, update, ticker, fee, markets, mocker
telegram._forcesell(bot=MagicMock(), update=update)
assert rpc_mock.call_count == 4
for args in rpc_mock.call_args_list:
assert '0.00001098' in args[0][0]
assert 'loss: -0.59%, -0.00000591 BTC' in args[0][0]
assert '-0.089 USD' in args[0][0]
msg = rpc_mock.call_args_list[0][0][0]
assert {
'type': RPCMessageType.SELL_NOTIFICATION,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'gain': 'loss',
'market_url': ANY,
'limit': 1.098e-05,
'amount': 90.99181073703367,
'open_rate': 1.099e-05,
'current_rate': 1.098e-05,
'profit_amount': -5.91e-06,
'profit_percent': -0.00589292,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
} == msg
def test_forcesell_handle_invalid(default_conf, update, mocker) -> None:
"""
Test _forcesell() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
msg_mock = MagicMock()
@ -850,6 +830,7 @@ def test_forcesell_handle_invalid(default_conf, update, mocker) -> None:
mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
# Trader is not running
@ -865,7 +846,7 @@ def test_forcesell_handle_invalid(default_conf, update, mocker) -> None:
update.message.text = '/forcesell'
telegram._forcesell(bot=MagicMock(), update=update)
assert msg_mock.call_count == 1
assert 'Invalid argument' in msg_mock.call_args_list[0][0][0]
assert 'invalid argument' in msg_mock.call_args_list[0][0][0]
# Invalid argument
msg_mock.reset_mock()
@ -873,15 +854,11 @@ def test_forcesell_handle_invalid(default_conf, update, mocker) -> None:
update.message.text = '/forcesell 123456'
telegram._forcesell(bot=MagicMock(), update=update)
assert msg_mock.call_count == 1
assert 'Invalid argument.' in msg_mock.call_args_list[0][0][0]
assert 'invalid argument' in msg_mock.call_args_list[0][0][0]
def test_performance_handle(default_conf, update, ticker, fee,
limit_buy_order, limit_sell_order, markets, mocker) -> None:
"""
Test _performance() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
msg_mock = MagicMock()
mocker.patch.multiple(
@ -898,6 +875,7 @@ def test_performance_handle(default_conf, update, ticker, fee,
)
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
# Create some test data
@ -920,10 +898,6 @@ def test_performance_handle(default_conf, update, ticker, fee,
def test_performance_handle_invalid(default_conf, update, mocker) -> None:
"""
Test _performance() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
msg_mock = MagicMock()
mocker.patch.multiple(
@ -933,6 +907,7 @@ def test_performance_handle_invalid(default_conf, update, mocker) -> None:
)
mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
# Trader is not running
@ -943,10 +918,6 @@ def test_performance_handle_invalid(default_conf, update, mocker) -> None:
def test_count_handle(default_conf, update, ticker, fee, markets, mocker) -> None:
"""
Test _count() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
msg_mock = MagicMock()
mocker.patch.multiple(
@ -963,6 +934,7 @@ def test_count_handle(default_conf, update, ticker, fee, markets, mocker) -> Non
)
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
freqtradebot = FreqtradeBot(default_conf)
patch_get_signal(freqtradebot, (True, False))
telegram = Telegram(freqtradebot)
freqtradebot.state = State.STOPPED
@ -987,9 +959,6 @@ def test_count_handle(default_conf, update, ticker, fee, markets, mocker) -> Non
def test_help_handle(default_conf, update, mocker) -> None:
"""
Test _help() method
"""
patch_coinmarketcap(mocker)
msg_mock = MagicMock()
mocker.patch.multiple(
@ -1007,9 +976,6 @@ def test_help_handle(default_conf, update, mocker) -> None:
def test_version_handle(default_conf, update, mocker) -> None:
"""
Test _version() method
"""
patch_coinmarketcap(mocker)
msg_mock = MagicMock()
mocker.patch.multiple(
@ -1025,15 +991,224 @@ def test_version_handle(default_conf, update, mocker) -> None:
assert '*Version:* `{}`'.format(__version__) in msg_mock.call_args_list[0][0][0]
def test_send_msg(default_conf, mocker) -> None:
"""
Test send_msg() method
"""
def test_send_msg_buy_notification(default_conf, mocker) -> None:
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.send_msg({
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'market_url': 'https://bittrex.com/Market/Index?MarketName=BTC-ETH',
'limit': 1.099e-05,
'stake_amount': 0.001,
'stake_amount_fiat': 0.0,
'stake_currency': 'BTC',
'fiat_currency': 'USD'
})
assert msg_mock.call_args[0][0] \
== '*Bittrex:* Buying [ETH/BTC](https://bittrex.com/Market/Index?MarketName=BTC-ETH)\n' \
'with limit `0.00001099\n' \
'(0.001000 BTC,0.000 USD)`'
def test_send_msg_sell_notification(default_conf, mocker) -> None:
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)
old_convamount = telegram._fiat_converter.convert_amount
telegram._fiat_converter.convert_amount = lambda a, b, c: -24.812
telegram.send_msg({
'type': RPCMessageType.SELL_NOTIFICATION,
'exchange': 'Binance',
'pair': 'KEY/ETH',
'gain': 'loss',
'market_url': 'https://www.binance.com/tradeDetail.html?symbol=KEY_ETH',
'limit': 3.201e-05,
'amount': 1333.3333333333335,
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_percent': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD'
})
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)`'
msg_mock.reset_mock()
telegram.send_msg({
'type': RPCMessageType.SELL_NOTIFICATION,
'exchange': 'Binance',
'pair': 'KEY/ETH',
'gain': 'loss',
'market_url': 'https://www.binance.com/tradeDetail.html?symbol=KEY_ETH',
'limit': 3.201e-05,
'amount': 1333.3333333333335,
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_percent': -0.57405275,
'stake_currency': 'ETH',
})
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%`'
# Reset singleton function to avoid random breaks
telegram._fiat_converter.convert_amount = old_convamount
def test_send_msg_status_notification(default_conf, mocker) -> None:
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.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': 'running'
})
assert msg_mock.call_args[0][0] == '*Status:* `running`'
def test_warning_notification(default_conf, mocker) -> None:
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.send_msg({
'type': RPCMessageType.WARNING_NOTIFICATION,
'status': 'message'
})
assert msg_mock.call_args[0][0] == '*Warning:* `message`'
def test_custom_notification(default_conf, mocker) -> None:
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.send_msg({
'type': RPCMessageType.CUSTOM_NOTIFICATION,
'status': '*Custom:* `Hello World`'
})
assert msg_mock.call_args[0][0] == '*Custom:* `Hello World`'
def test_send_msg_unknown_type(default_conf, mocker) -> None:
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)
with pytest.raises(NotImplementedError, match=r'Unknown message type: None'):
telegram.send_msg({
'type': None,
})
def test_send_msg_buy_notification_no_fiat(default_conf, mocker) -> None:
del default_conf['fiat_display_currency']
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.send_msg({
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'market_url': 'https://bittrex.com/Market/Index?MarketName=BTC-ETH',
'limit': 1.099e-05,
'stake_amount': 0.001,
'stake_amount_fiat': 0.0,
'stake_currency': 'BTC',
'fiat_currency': None
})
assert msg_mock.call_args[0][0] \
== '*Bittrex:* Buying [ETH/BTC](https://bittrex.com/Market/Index?MarketName=BTC-ETH)\n' \
'with limit `0.00001099\n' \
'(0.001000 BTC)`'
def test_send_msg_sell_notification_no_fiat(default_conf, mocker) -> None:
del default_conf['fiat_display_currency']
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.send_msg({
'type': RPCMessageType.SELL_NOTIFICATION,
'exchange': 'Binance',
'pair': 'KEY/ETH',
'gain': 'loss',
'market_url': 'https://www.binance.com/tradeDetail.html?symbol=KEY_ETH',
'limit': 3.201e-05,
'amount': 1333.3333333333335,
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_percent': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD'
})
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%`'
def test__send_msg(default_conf, mocker) -> None:
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
conf = deepcopy(default_conf)
bot = MagicMock()
freqtradebot = get_patched_freqtradebot(mocker, conf)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
telegram = Telegram(freqtradebot)
telegram._config['telegram']['enabled'] = True
@ -1041,16 +1216,12 @@ def test_send_msg(default_conf, mocker) -> None:
assert len(bot.method_calls) == 1
def test_send_msg_network_error(default_conf, mocker, caplog) -> None:
"""
Test send_msg() method
"""
def test__send_msg_network_error(default_conf, mocker, caplog) -> None:
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
conf = deepcopy(default_conf)
bot = MagicMock()
bot.send_message = MagicMock(side_effect=NetworkError('Oh snap'))
freqtradebot = get_patched_freqtradebot(mocker, conf)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
telegram = Telegram(freqtradebot)
telegram._config['telegram']['enabled'] = True

166
freqtrade/tests/rpc/test_rpc_webhook.py Normal file
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@ -0,0 +1,166 @@
# pragma pylint: disable=missing-docstring, C0103, protected-access
from unittest.mock import MagicMock
import pytest
from requests import RequestException
from freqtrade.rpc import RPCMessageType
from freqtrade.rpc.webhook import Webhook
from freqtrade.tests.conftest import get_patched_freqtradebot, log_has
def get_webhook_dict() -> dict:
return {
"enabled": True,
"url": "https://maker.ifttt.com/trigger/freqtrade_test/with/key/c764udvJ5jfSlswVRukZZ2/",
"webhookbuy": {
"value1": "Buying {pair}",
"value2": "limit {limit:8f}",
"value3": "{stake_amount:8f} {stake_currency}"
},
"webhooksell": {
"value1": "Selling {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency}"
},
"webhookstatus": {
"value1": "Status: {status}",
"value2": "",
"value3": ""
}
}
def test__init__(mocker, default_conf):
default_conf['webhook'] = {'enabled': True, 'url': "https://DEADBEEF.com"}
webhook = Webhook(get_patched_freqtradebot(mocker, default_conf))
assert webhook._config == default_conf
def test_send_msg(default_conf, mocker):
default_conf["webhook"] = get_webhook_dict()
msg_mock = MagicMock()
mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
webhook = Webhook(get_patched_freqtradebot(mocker, default_conf))
msg = {
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'market_url': "http://mockedurl/ETH_BTC",
'limit': 0.005,
'stake_amount': 0.8,
'stake_amount_fiat': 500,
'stake_currency': 'BTC',
'fiat_currency': 'EUR'
}
msg_mock = MagicMock()
mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
webhook.send_msg(msg=msg)
assert msg_mock.call_count == 1
assert (msg_mock.call_args[0][0]["value1"] ==
default_conf["webhook"]["webhookbuy"]["value1"].format(**msg))
assert (msg_mock.call_args[0][0]["value2"] ==
default_conf["webhook"]["webhookbuy"]["value2"].format(**msg))
assert (msg_mock.call_args[0][0]["value3"] ==
default_conf["webhook"]["webhookbuy"]["value3"].format(**msg))
# Test sell
msg_mock = MagicMock()
mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
msg = {
'type': RPCMessageType.SELL_NOTIFICATION,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'gain': "profit",
'market_url': "http://mockedurl/ETH_BTC",
'limit': 0.005,
'amount': 0.8,
'open_rate': 0.004,
'current_rate': 0.005,
'profit_amount': 0.001,
'profit_percent': 0.20,
'stake_currency': 'BTC',
}
webhook.send_msg(msg=msg)
assert msg_mock.call_count == 1
assert (msg_mock.call_args[0][0]["value1"] ==
default_conf["webhook"]["webhooksell"]["value1"].format(**msg))
assert (msg_mock.call_args[0][0]["value2"] ==
default_conf["webhook"]["webhooksell"]["value2"].format(**msg))
assert (msg_mock.call_args[0][0]["value3"] ==
default_conf["webhook"]["webhooksell"]["value3"].format(**msg))
# Test notification
msg = {
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': 'Unfilled sell order for BTC cancelled due to timeout'
}
msg_mock = MagicMock()
mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
webhook.send_msg(msg)
assert msg_mock.call_count == 1
assert (msg_mock.call_args[0][0]["value1"] ==
default_conf["webhook"]["webhookstatus"]["value1"].format(**msg))
assert (msg_mock.call_args[0][0]["value2"] ==
default_conf["webhook"]["webhookstatus"]["value2"].format(**msg))
assert (msg_mock.call_args[0][0]["value3"] ==
default_conf["webhook"]["webhookstatus"]["value3"].format(**msg))
def test_exception_send_msg(default_conf, mocker, caplog):
default_conf["webhook"] = get_webhook_dict()
default_conf["webhook"]["webhookbuy"] = None
webhook = Webhook(get_patched_freqtradebot(mocker, default_conf))
webhook.send_msg({'type': RPCMessageType.BUY_NOTIFICATION})
assert log_has(f"Message type {RPCMessageType.BUY_NOTIFICATION} not configured for webhooks",
caplog.record_tuples)
default_conf["webhook"] = get_webhook_dict()
default_conf["webhook"]["webhookbuy"]["value1"] = "{DEADBEEF:8f}"
msg_mock = MagicMock()
mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
webhook = Webhook(get_patched_freqtradebot(mocker, default_conf))
msg = {
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'market_url': "http://mockedurl/ETH_BTC",
'limit': 0.005,
'stake_amount': 0.8,
'stake_amount_fiat': 500,
'stake_currency': 'BTC',
'fiat_currency': 'EUR'
}
webhook.send_msg(msg)
assert log_has("Problem calling Webhook. Please check your webhook configuration. "
"Exception: 'DEADBEEF'", caplog.record_tuples)
msg_mock = MagicMock()
mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
msg = {
'type': 'DEADBEEF',
'status': 'whatever'
}
with pytest.raises(NotImplementedError):
webhook.send_msg(msg)
def test__send_msg(default_conf, mocker, caplog):
default_conf["webhook"] = get_webhook_dict()
webhook = Webhook(get_patched_freqtradebot(mocker, default_conf))
msg = {'value1': 'DEADBEEF',
'value2': 'ALIVEBEEF',
'value3': 'FREQTRADE'}
post = MagicMock()
mocker.patch("freqtrade.rpc.webhook.post", post)
webhook._send_msg(msg)
assert post.call_count == 1
assert post.call_args[1] == {'data': msg}
assert post.call_args[0] == (default_conf['webhook']['url'], )
post = MagicMock(side_effect=RequestException)
mocker.patch("freqtrade.rpc.webhook.post", post)
webhook._send_msg(msg)
assert log_has('Could not call webhook url. Exception: ', caplog.record_tuples)

235
freqtrade/tests/strategy/legacy_strategy.py Normal file
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@ -0,0 +1,235 @@
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
# This class is a sample. Feel free to customize it.
class TestStrategyLegacy(IStrategy):
"""
This is a test strategy using the legacy function headers, which will be
removed in a future update.
Please do not use this as a template, but refer to user_data/strategy/TestStrategy.py
for a uptodate version of this template.
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# Momentum Indicator
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
"""
# Awesome oscillator
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# Minus Directional Indicator / Movement
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# ROC
dataframe['roc'] = ta.ROC(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Stoch RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
"""
# Overlap Studies
# ------------------------------------
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
"""
# EMA - Exponential Moving Average
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
"""
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
"""
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# Inverted Hammer: values [0, 100]
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# Dragonfly Doji: values [0, 100]
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# Piercing Line: values [0, 100]
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# Morningstar: values [0, 100]
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# Three White Soldiers: values [0, 100]
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
"""
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
"""
# Hanging Man: values [0, 100]
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# Shooting Star: values [0, 100]
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# Gravestone Doji: values [0, 100]
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# Dark Cloud Cover: values [0, 100]
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# Evening Doji Star: values [0, 100]
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# Evening Star: values [0, 100]
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
"""
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
"""
# Three Line Strike: values [0, -100, 100]
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# Spinning Top: values [0, -100, 100]
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# Engulfing: values [0, -100, 100]
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# Harami: values [0, -100, 100]
dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# Three Outside Up/Down: values [0, -100, 100]
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# Three Inside Up/Down: values [0, -100, 100]
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
"""
# Chart type
# ------------------------------------
"""
# Heikinashi stategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
"""
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
: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
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
: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

13
freqtrade/tests/strategy/test_default_strategy.py
View File

@ -3,14 +3,14 @@ import json
import pytest
from pandas import DataFrame
from freqtrade.analyze import Analyze
from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
from freqtrade.strategy.default_strategy import DefaultStrategy
@pytest.fixture
def result():
with open('freqtrade/tests/testdata/ETH_BTC-1m.json') as data_file:
return Analyze.parse_ticker_dataframe(json.load(data_file))
return parse_ticker_dataframe(json.load(data_file))
def test_default_strategy_structure():
@ -23,12 +23,13 @@ def test_default_strategy_structure():
def test_default_strategy(result):
strategy = DefaultStrategy()
strategy = DefaultStrategy({})
metadata = {'pair': 'ETH/BTC'}
assert type(strategy.minimal_roi) is dict
assert type(strategy.stoploss) is float
assert type(strategy.ticker_interval) is str
indicators = strategy.populate_indicators(result)
indicators = strategy.populate_indicators(result, metadata)
assert type(indicators) is DataFrame
assert type(strategy.populate_buy_trend(indicators)) is DataFrame
assert type(strategy.populate_sell_trend(indicators)) is DataFrame
assert type(strategy.populate_buy_trend(indicators, metadata)) is DataFrame
assert type(strategy.populate_sell_trend(indicators, metadata)) is DataFrame

107
freqtrade/tests/strategy/test_interface.py Normal file
View File

@ -0,0 +1,107 @@
# pragma pylint: disable=missing-docstring, C0103
import logging
from unittest.mock import MagicMock
import arrow
from pandas import DataFrame
from freqtrade.arguments import TimeRange
from freqtrade.optimize.__init__ import load_tickerdata_file
from freqtrade.tests.conftest import get_patched_exchange, log_has
from freqtrade.strategy.default_strategy import DefaultStrategy
# Avoid to reinit the same object again and again
_STRATEGY = DefaultStrategy(config={})
def test_returns_latest_buy_signal(mocker, default_conf):
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)
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)
def test_returns_latest_sell_signal(mocker, default_conf):
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)
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)
def test_get_signal_empty(default_conf, mocker, caplog):
assert (False, False) == _STRATEGY.get_signal('foo', default_conf['ticker_interval'],
None)
assert log_has('Empty ticker history for pair foo', caplog.record_tuples)
def test_get_signal_exception_valueerror(default_conf, mocker, caplog):
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 log_has('Unable to analyze ticker for pair foo: xyz', caplog.record_tuples)
def test_get_signal_empty_dataframe(default_conf, mocker, caplog):
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 log_has('Empty dataframe for pair xyz', caplog.record_tuples)
def test_get_signal_old_dataframe(default_conf, mocker, caplog):
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
oldtime = arrow.utcnow().shift(minutes=-16)
ticks = DataFrame([{'buy': 1, 'date': oldtime}])
mocker.patch.object(
_STRATEGY, 'analyze_ticker',
return_value=DataFrame(ticks)
)
assert (False, False) == _STRATEGY.get_signal('xyz', default_conf['ticker_interval'], 1)
assert log_has(
'Outdated history for pair xyz. Last tick is 16 minutes old',
caplog.record_tuples
)
def test_get_signal_handles_exceptions(mocker, default_conf):
mocker.patch('freqtrade.exchange.Exchange.get_candle_history', return_value=MagicMock())
exchange = get_patched_exchange(mocker, default_conf)
mocker.patch.object(
_STRATEGY, 'analyze_ticker',
side_effect=Exception('invalid ticker history ')
)
assert _STRATEGY.get_signal(exchange, 'ETH/BTC', '5m') == (False, False)
def test_tickerdata_to_dataframe(default_conf) -> None:
strategy = DefaultStrategy(default_conf)
timerange = TimeRange(None, 'line', 0, -100)
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m', timerange=timerange)
tickerlist = {'UNITTEST/BTC': tick}
data = strategy.tickerdata_to_dataframe(tickerlist)
assert len(data['UNITTEST/BTC']) == 99 # partial candle was removed

128
freqtrade/tests/strategy/test_strategy.py
View File

@ -1,8 +1,11 @@
# pragma pylint: disable=missing-docstring, protected-access, C0103
import logging
import os
from base64 import urlsafe_b64encode
from os import path
import warnings
import pytest
from pandas import DataFrame
from freqtrade.strategy import import_strategy
from freqtrade.strategy.default_strategy import DefaultStrategy
@ -12,14 +15,15 @@ from freqtrade.strategy.resolver import StrategyResolver
def test_import_strategy(caplog):
caplog.set_level(logging.DEBUG)
default_config = {}
strategy = DefaultStrategy()
strategy = DefaultStrategy(default_config)
strategy.some_method = lambda *args, **kwargs: 42
assert strategy.__module__ == 'freqtrade.strategy.default_strategy'
assert strategy.some_method() == 42
imported_strategy = import_strategy(strategy)
imported_strategy = import_strategy(strategy, default_config)
assert dir(strategy) == dir(imported_strategy)
@ -35,25 +39,42 @@ def test_import_strategy(caplog):
def test_search_strategy():
default_location = os.path.join(os.path.dirname(
os.path.realpath(__file__)), '..', '..', 'strategy'
default_config = {}
default_location = path.join(path.dirname(
path.realpath(__file__)), '..', '..', 'strategy'
)
assert isinstance(
StrategyResolver._search_strategy(default_location, 'DefaultStrategy'), IStrategy
StrategyResolver._search_strategy(
default_location,
config=default_config,
strategy_name='DefaultStrategy'
),
IStrategy
)
assert StrategyResolver._search_strategy(default_location, 'NotFoundStrategy') is None
assert StrategyResolver._search_strategy(
default_location,
config=default_config,
strategy_name='NotFoundStrategy'
) is None
def test_load_strategy(result):
resolver = StrategyResolver({'strategy': 'TestStrategy'})
assert hasattr(resolver.strategy, 'populate_indicators')
assert 'adx' in resolver.strategy.populate_indicators(result)
metadata = {'pair': 'ETH/BTC'}
assert 'adx' in resolver.strategy.advise_indicators(result, metadata=metadata)
def test_load_strategy_byte64(result):
with open("freqtrade/tests/strategy/test_strategy.py", "r") as file:
encoded_string = urlsafe_b64encode(file.read().encode("utf-8")).decode("utf-8")
resolver = StrategyResolver({'strategy': 'TestStrategy:{}'.format(encoded_string)})
assert 'adx' in resolver.strategy.advise_indicators(result, 'ETH/BTC')
def test_load_strategy_invalid_directory(result, caplog):
resolver = StrategyResolver()
extra_dir = os.path.join('some', 'path')
resolver._load_strategy('TestStrategy', extra_dir)
extra_dir = path.join('some', 'path')
resolver._load_strategy('TestStrategy', config={}, extra_dir=extra_dir)
assert (
'freqtrade.strategy.resolver',
@ -61,8 +82,7 @@ def test_load_strategy_invalid_directory(result, caplog):
'Path "{}" does not exist'.format(extra_dir),
) in caplog.record_tuples
assert hasattr(resolver.strategy, 'populate_indicators')
assert 'adx' in resolver.strategy.populate_indicators(result)
assert 'adx' in resolver.strategy.advise_indicators(result, {'pair': 'ETH/BTC'})
def test_load_not_found_strategy():
@ -70,27 +90,30 @@ def test_load_not_found_strategy():
with pytest.raises(ImportError,
match=r'Impossible to load Strategy \'NotFoundStrategy\'.'
r' This class does not exist or contains Python code errors'):
strategy._load_strategy('NotFoundStrategy')
strategy._load_strategy(strategy_name='NotFoundStrategy', config={})
def test_strategy(result):
resolver = StrategyResolver({'strategy': 'DefaultStrategy'})
config = {'strategy': 'DefaultStrategy'}
assert hasattr(resolver.strategy, 'minimal_roi')
resolver = StrategyResolver(config)
metadata = {'pair': 'ETH/BTC'}
assert resolver.strategy.minimal_roi[0] == 0.04
assert config["minimal_roi"]['0'] == 0.04
assert hasattr(resolver.strategy, 'stoploss')
assert resolver.strategy.stoploss == -0.10
assert config['stoploss'] == -0.10
assert hasattr(resolver.strategy, 'populate_indicators')
assert 'adx' in resolver.strategy.populate_indicators(result)
assert resolver.strategy.ticker_interval == '5m'
assert config['ticker_interval'] == '5m'
assert hasattr(resolver.strategy, 'populate_buy_trend')
dataframe = resolver.strategy.populate_buy_trend(resolver.strategy.populate_indicators(result))
df_indicators = resolver.strategy.advise_indicators(result, metadata=metadata)
assert 'adx' in df_indicators
dataframe = resolver.strategy.advise_buy(df_indicators, metadata=metadata)
assert 'buy' in dataframe.columns
assert hasattr(resolver.strategy, 'populate_sell_trend')
dataframe = resolver.strategy.populate_sell_trend(resolver.strategy.populate_indicators(result))
dataframe = resolver.strategy.advise_sell(df_indicators, metadata=metadata)
assert 'sell' in dataframe.columns
@ -104,7 +127,6 @@ def test_strategy_override_minimal_roi(caplog):
}
resolver = StrategyResolver(config)
assert hasattr(resolver.strategy, 'minimal_roi')
assert resolver.strategy.minimal_roi[0] == 0.5
assert ('freqtrade.strategy.resolver',
logging.INFO,
@ -120,7 +142,6 @@ def test_strategy_override_stoploss(caplog):
}
resolver = StrategyResolver(config)
assert hasattr(resolver.strategy, 'stoploss')
assert resolver.strategy.stoploss == -0.5
assert ('freqtrade.strategy.resolver',
logging.INFO,
@ -137,9 +158,64 @@ def test_strategy_override_ticker_interval(caplog):
}
resolver = StrategyResolver(config)
assert hasattr(resolver.strategy, 'ticker_interval')
assert resolver.strategy.ticker_interval == 60
assert ('freqtrade.strategy.resolver',
logging.INFO,
'Override strategy \'ticker_interval\' with value in config file: 60.'
) in caplog.record_tuples
def test_deprecate_populate_indicators(result):
default_location = path.join(path.dirname(path.realpath(__file__)))
resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
'strategy_path': default_location})
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
indicators = resolver.strategy.advise_indicators(result, 'ETH/BTC')
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "deprecated - check out the Sample strategy to see the current function headers!" \
in str(w[-1].message)
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
resolver.strategy.advise_buy(indicators, 'ETH/BTC')
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "deprecated - check out the Sample strategy to see the current function headers!" \
in str(w[-1].message)
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
resolver.strategy.advise_sell(indicators, 'ETH_BTC')
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "deprecated - check out the Sample strategy to see the current function headers!" \
in str(w[-1].message)
def test_call_deprecated_function(result, monkeypatch):
default_location = path.join(path.dirname(path.realpath(__file__)))
resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
'strategy_path': default_location})
metadata = {'pair': 'ETH/BTC'}
# Make sure we are using a legacy function
assert resolver.strategy._populate_fun_len == 2
assert resolver.strategy._buy_fun_len == 2
assert resolver.strategy._sell_fun_len == 2
indicator_df = resolver.strategy.advise_indicators(result, metadata=metadata)
assert type(indicator_df) is DataFrame
assert 'adx' in indicator_df.columns
buydf = resolver.strategy.advise_buy(result, metadata=metadata)
assert type(buydf) is DataFrame
assert 'buy' in buydf.columns
selldf = resolver.strategy.advise_sell(result, metadata=metadata)
assert type(selldf) is DataFrame
assert 'sell' in selldf

5
freqtrade/tests/test_acl_pair.py
View File

@ -1,8 +1,9 @@
# pragma pylint: disable=missing-docstring,C0103,protected-access
import freqtrade.tests.conftest as tt # test tools
from unittest.mock import MagicMock
import freqtrade.tests.conftest as tt # test tools
# 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
@ -10,7 +11,6 @@ from unittest.mock import MagicMock
def whitelist_conf():
config = tt.default_conf()
config['stake_currency'] = 'BTC'
config['exchange']['pair_whitelist'] = [
'ETH/BTC',
@ -19,7 +19,6 @@ def whitelist_conf():
'SWT/BTC',
'BCC/BTC'
]
config['exchange']['pair_blacklist'] = [
'BLK/BTC'
]

197
freqtrade/tests/test_analyze.py
View File

@ -1,197 +0,0 @@
# pragma pylint: disable=missing-docstring, C0103
"""
Unit test file for analyse.py
"""
import datetime
import logging
from unittest.mock import MagicMock
import arrow
from pandas import DataFrame
from freqtrade.analyze import Analyze, SignalType
from freqtrade.optimize.__init__ import load_tickerdata_file
from freqtrade.arguments import TimeRange
from freqtrade.tests.conftest import log_has, get_patched_exchange
# Avoid to reinit the same object again and again
_ANALYZE = Analyze({'strategy': 'DefaultStrategy'})
def test_signaltype_object() -> None:
"""
Test the SignalType object has the mandatory Constants
:return: None
"""
assert hasattr(SignalType, 'BUY')
assert hasattr(SignalType, 'SELL')
def test_analyze_object() -> None:
"""
Test the Analyze object has the mandatory methods
:return: None
"""
assert hasattr(Analyze, 'parse_ticker_dataframe')
assert hasattr(Analyze, 'populate_indicators')
assert hasattr(Analyze, 'populate_buy_trend')
assert hasattr(Analyze, 'populate_sell_trend')
assert hasattr(Analyze, 'analyze_ticker')
assert hasattr(Analyze, 'get_signal')
assert hasattr(Analyze, 'should_sell')
assert hasattr(Analyze, 'min_roi_reached')
def test_dataframe_correct_length(result):
dataframe = Analyze.parse_ticker_dataframe(result)
assert len(result.index) - 1 == len(dataframe.index) # last partial candle removed
def test_dataframe_correct_columns(result):
assert result.columns.tolist() == \
['date', 'open', 'high', 'low', 'close', 'volume']
def test_populates_buy_trend(result):
# Load the default strategy for the unit test, because this logic is done in main.py
dataframe = _ANALYZE.populate_buy_trend(_ANALYZE.populate_indicators(result))
assert 'buy' in dataframe.columns
def test_populates_sell_trend(result):
# Load the default strategy for the unit test, because this logic is done in main.py
dataframe = _ANALYZE.populate_sell_trend(_ANALYZE.populate_indicators(result))
assert 'sell' in dataframe.columns
def test_returns_latest_buy_signal(mocker, default_conf):
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=MagicMock())
exchange = get_patched_exchange(mocker, default_conf)
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
return_value=DataFrame([{'buy': 1, 'sell': 0, 'date': arrow.utcnow()}])
)
)
assert _ANALYZE.get_signal(exchange, 'ETH/BTC', '5m') == (True, False)
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
return_value=DataFrame([{'buy': 0, 'sell': 1, 'date': arrow.utcnow()}])
)
)
assert _ANALYZE.get_signal(exchange, 'ETH/BTC', '5m') == (False, True)
def test_returns_latest_sell_signal(mocker, default_conf):
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=MagicMock())
exchange = get_patched_exchange(mocker, default_conf)
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
return_value=DataFrame([{'sell': 1, 'buy': 0, 'date': arrow.utcnow()}])
)
)
assert _ANALYZE.get_signal(exchange, 'ETH/BTC', '5m') == (False, True)
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
return_value=DataFrame([{'sell': 0, 'buy': 1, 'date': arrow.utcnow()}])
)
)
assert _ANALYZE.get_signal(exchange, 'ETH/BTC', '5m') == (True, False)
def test_get_signal_empty(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=None)
exchange = get_patched_exchange(mocker, default_conf)
assert (False, False) == _ANALYZE.get_signal(exchange, 'foo', default_conf['ticker_interval'])
assert log_has('Empty ticker history for pair foo', caplog.record_tuples)
def test_get_signal_exception_valueerror(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=1)
exchange = get_patched_exchange(mocker, default_conf)
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
side_effect=ValueError('xyz')
)
)
assert (False, False) == _ANALYZE.get_signal(exchange, 'foo', default_conf['ticker_interval'])
assert log_has('Unable to analyze ticker for pair foo: xyz', caplog.record_tuples)
def test_get_signal_empty_dataframe(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=1)
exchange = get_patched_exchange(mocker, default_conf)
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
return_value=DataFrame([])
)
)
assert (False, False) == _ANALYZE.get_signal(exchange, 'xyz', default_conf['ticker_interval'])
assert log_has('Empty dataframe for pair xyz', caplog.record_tuples)
def test_get_signal_old_dataframe(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=1)
exchange = get_patched_exchange(mocker, default_conf)
# FIX: The get_signal function has hardcoded 10, which we must inturn hardcode
oldtime = arrow.utcnow() - datetime.timedelta(minutes=11)
ticks = DataFrame([{'buy': 1, 'date': oldtime}])
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
return_value=DataFrame(ticks)
)
)
assert (False, False) == _ANALYZE.get_signal(exchange, 'xyz', default_conf['ticker_interval'])
assert log_has(
'Outdated history for pair xyz. Last tick is 11 minutes old',
caplog.record_tuples
)
def test_get_signal_handles_exceptions(mocker, default_conf):
mocker.patch('freqtrade.exchange.Exchange.get_ticker_history', return_value=MagicMock())
exchange = get_patched_exchange(mocker, default_conf)
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
side_effect=Exception('invalid ticker history ')
)
)
assert _ANALYZE.get_signal(exchange, 'ETH/BTC', '5m') == (False, False)
def test_parse_ticker_dataframe(ticker_history):
columns = ['date', 'open', 'high', 'low', 'close', 'volume']
# Test file with BV data
dataframe = Analyze.parse_ticker_dataframe(ticker_history)
assert dataframe.columns.tolist() == columns
def test_tickerdata_to_dataframe(default_conf) -> None:
"""
Test Analyze.tickerdata_to_dataframe() method
"""
analyze = Analyze(default_conf)
timerange = TimeRange(None, 'line', 0, -100)
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m', timerange=timerange)
tickerlist = {'UNITTEST/BTC': tick}
data = analyze.tickerdata_to_dataframe(tickerlist)
assert len(data['UNITTEST/BTC']) == 99 # partial candle was removed

35
freqtrade/tests/test_arguments.py
View File

@ -1,41 +1,24 @@
# pragma pylint: disable=missing-docstring, C0103
"""
Unit test file for arguments.py
"""
import argparse
import logging
import pytest
from freqtrade.arguments import Arguments, TimeRange
def test_arguments_object() -> None:
"""
Test the Arguments object has the mandatory methods
:return: None
"""
assert hasattr(Arguments, 'get_parsed_arg')
assert hasattr(Arguments, 'parse_args')
assert hasattr(Arguments, 'parse_timerange')
assert hasattr(Arguments, 'scripts_options')
# Parse common command-line-arguments. Used for all tools
def test_parse_args_none() -> None:
arguments = Arguments([], '')
assert isinstance(arguments, Arguments)
assert isinstance(arguments.parser, argparse.ArgumentParser)
assert isinstance(arguments.parser, argparse.ArgumentParser)
def test_parse_args_defaults() -> None:
args = Arguments([], '').get_parsed_arg()
assert args.config == 'config.json'
assert args.dynamic_whitelist is None
assert args.loglevel == logging.INFO
assert args.loglevel == 0
def test_parse_args_config() -> None:
@ -53,10 +36,10 @@ def test_parse_args_db_url() -> None:
def test_parse_args_verbose() -> None:
args = Arguments(['-v'], '').get_parsed_arg()
assert args.loglevel == logging.DEBUG
assert args.loglevel == 1
args = Arguments(['--verbose'], '').get_parsed_arg()
assert args.loglevel == logging.DEBUG
assert args.loglevel == 1
def test_scripts_options() -> None:
@ -149,15 +132,21 @@ def test_parse_args_backtesting_custom() -> None:
'backtesting',
'--live',
'--ticker-interval', '1m',
'--refresh-pairs-cached']
'--refresh-pairs-cached',
'--strategy-list',
'DefaultStrategy',
'TestStrategy'
]
call_args = Arguments(args, '').get_parsed_arg()
assert call_args.config == 'test_conf.json'
assert call_args.live is True
assert call_args.loglevel == logging.INFO
assert call_args.loglevel == 0
assert call_args.subparser == 'backtesting'
assert call_args.func is not None
assert call_args.ticker_interval == '1m'
assert call_args.refresh_pairs is True
assert type(call_args.strategy_list) is list
assert len(call_args.strategy_list) == 2
def test_parse_args_hyperopt_custom() -> None:
@ -170,7 +159,7 @@ def test_parse_args_hyperopt_custom() -> None:
call_args = Arguments(args, '').get_parsed_arg()
assert call_args.config == 'test_conf.json'
assert call_args.epochs == 20
assert call_args.loglevel == logging.INFO
assert call_args.loglevel == 0
assert call_args.subparser == 'hyperopt'
assert call_args.spaces == ['buy']
assert call_args.func is not None

252
freqtrade/tests/test_configuration.py
View File

@ -1,76 +1,46 @@
# pragma pylint: disable=protected-access, invalid-name
# pragma pylint: disable=missing-docstring, protected-access, invalid-name
"""
Unit test file for configuration.py
"""
import json
from copy import deepcopy
from unittest.mock import MagicMock
from argparse import Namespace
import logging
from unittest.mock import MagicMock
import pytest
from jsonschema import ValidationError
from jsonschema import validate, ValidationError
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.constants import DEFAULT_DB_PROD_URL, DEFAULT_DB_DRYRUN_URL
from freqtrade.tests.conftest import log_has
from freqtrade import constants
from freqtrade import OperationalException
def test_configuration_object() -> None:
"""
Test the Constants object has the mandatory Constants
"""
assert hasattr(Configuration, 'load_config')
assert hasattr(Configuration, '_load_config_file')
assert hasattr(Configuration, '_validate_config')
assert hasattr(Configuration, '_load_common_config')
assert hasattr(Configuration, '_load_backtesting_config')
assert hasattr(Configuration, '_load_hyperopt_config')
assert hasattr(Configuration, 'get_config')
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration, set_loggers
from freqtrade.constants import DEFAULT_DB_DRYRUN_URL, DEFAULT_DB_PROD_URL
from freqtrade.tests.conftest import log_has
def test_load_config_invalid_pair(default_conf) -> None:
"""
Test the configuration validator with an invalid PAIR format
"""
conf = deepcopy(default_conf)
conf['exchange']['pair_whitelist'].append('ETH-BTC')
default_conf['exchange']['pair_whitelist'].append('ETH-BTC')
with pytest.raises(ValidationError, match=r'.*does not match.*'):
configuration = Configuration(Namespace())
configuration._validate_config(conf)
configuration._validate_config(default_conf)
def test_load_config_missing_attributes(default_conf) -> None:
"""
Test the configuration validator with a missing attribute
"""
conf = deepcopy(default_conf)
conf.pop('exchange')
default_conf.pop('exchange')
with pytest.raises(ValidationError, match=r'.*\'exchange\' is a required property.*'):
configuration = Configuration(Namespace())
configuration._validate_config(conf)
configuration._validate_config(default_conf)
def test_load_config_incorrect_stake_amount(default_conf) -> None:
"""
Test the configuration validator with a missing attribute
"""
conf = deepcopy(default_conf)
conf['stake_amount'] = 'fake'
default_conf['stake_amount'] = 'fake'
with pytest.raises(ValidationError, match=r'.*\'fake\' does not match \'unlimited\'.*'):
configuration = Configuration(Namespace())
configuration._validate_config(conf)
configuration._validate_config(default_conf)
def test_load_config_file(default_conf, mocker, caplog) -> None:
"""
Test Configuration._load_config_file() method
"""
file_mock = mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
@ -84,13 +54,9 @@ def test_load_config_file(default_conf, mocker, caplog) -> None:
def test_load_config_max_open_trades_zero(default_conf, mocker, caplog) -> None:
"""
Test Configuration._load_config_file() method
"""
conf = deepcopy(default_conf)
conf['max_open_trades'] = 0
default_conf['max_open_trades'] = 0
file_mock = mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(conf)
read_data=json.dumps(default_conf)
))
Configuration(Namespace())._load_config_file('somefile')
@ -99,9 +65,6 @@ def test_load_config_max_open_trades_zero(default_conf, mocker, caplog) -> None:
def test_load_config_file_exception(mocker) -> None:
"""
Test Configuration._load_config_file() method
"""
mocker.patch(
'freqtrade.configuration.open',
MagicMock(side_effect=FileNotFoundError('File not found'))
@ -113,9 +76,6 @@ def test_load_config_file_exception(mocker) -> None:
def test_load_config(default_conf, mocker) -> None:
"""
Test Configuration.load_config() without any cli params
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
@ -130,13 +90,9 @@ def test_load_config(default_conf, mocker) -> None:
def test_load_config_with_params(default_conf, mocker) -> None:
"""
Test Configuration.load_config() with cli params used
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
arglist = [
'--dynamic-whitelist', '10',
'--strategy', 'TestStrategy',
@ -144,7 +100,6 @@ def test_load_config_with_params(default_conf, mocker) -> None:
'--db-url', 'sqlite:///someurl',
]
args = Arguments(arglist, '').get_parsed_arg()
configuration = Configuration(args)
validated_conf = configuration.load_config()
@ -161,10 +116,10 @@ def test_load_config_with_params(default_conf, mocker) -> None:
))
arglist = [
'--dynamic-whitelist', '10',
'--strategy', 'TestStrategy',
'--strategy-path', '/some/path'
]
'--dynamic-whitelist', '10',
'--strategy', 'TestStrategy',
'--strategy-path', '/some/path'
]
args = Arguments(arglist, '').get_parsed_arg()
configuration = Configuration(args)
@ -192,16 +147,12 @@ def test_load_config_with_params(default_conf, mocker) -> None:
def test_load_custom_strategy(default_conf, mocker) -> None:
"""
Test Configuration.load_config() without any cli params
"""
custom_conf = deepcopy(default_conf)
custom_conf.update({
default_conf.update({
'strategy': 'CustomStrategy',
'strategy_path': '/tmp/strategies',
})
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(custom_conf)
read_data=json.dumps(default_conf)
))
args = Arguments([], '').get_parsed_arg()
@ -213,13 +164,9 @@ def test_load_custom_strategy(default_conf, mocker) -> None:
def test_show_info(default_conf, mocker, caplog) -> None:
"""
Test Configuration.show_info()
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
arglist = [
'--dynamic-whitelist', '10',
'--strategy', 'TestStrategy',
@ -236,19 +183,14 @@ def test_show_info(default_conf, mocker, caplog) -> None:
'(not applicable with Backtesting and Hyperopt)',
caplog.record_tuples
)
assert log_has('Using DB: "sqlite:///tmp/testdb"', caplog.record_tuples)
assert log_has('Dry run is enabled', caplog.record_tuples)
def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
"""
Test setup_configuration() function
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
arglist = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
@ -275,8 +217,8 @@ def test_setup_configuration_without_arguments(mocker, default_conf, caplog) ->
assert 'live' not in config
assert not log_has('Parameter -l/--live detected ...', caplog.record_tuples)
assert 'realistic_simulation' not in config
assert not log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples)
assert 'position_stacking' not in config
assert not log_has('Parameter --enable-position-stacking detected ...', caplog.record_tuples)
assert 'refresh_pairs' not in config
assert not log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
@ -286,9 +228,6 @@ def test_setup_configuration_without_arguments(mocker, default_conf, caplog) ->
def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> None:
"""
Test setup_configuration() function
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
@ -300,7 +239,8 @@ def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> Non
'backtesting',
'--ticker-interval', '1m',
'--live',
'--realistic-simulation',
'--enable-position-stacking',
'--disable-max-market-positions',
'--refresh-pairs-cached',
'--timerange', ':100',
'--export', '/bar/foo'
@ -330,9 +270,12 @@ def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> Non
assert 'live' in config
assert log_has('Parameter -l/--live detected ...', caplog.record_tuples)
assert 'realistic_simulation'in config
assert log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples)
assert log_has('Using max_open_trades: 1 ...', caplog.record_tuples)
assert 'position_stacking'in config
assert log_has('Parameter --enable-position-stacking detected ...', caplog.record_tuples)
assert 'use_max_market_positions' in config
assert log_has('Parameter --disable-max-market-positions detected ...', caplog.record_tuples)
assert log_has('max_open_trades set to unlimited ...', caplog.record_tuples)
assert 'refresh_pairs'in config
assert log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
@ -349,7 +292,7 @@ def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> Non
)
def test_hyperopt_with_arguments(mocker, default_conf, caplog) -> None:
def test_setup_configuration_with_stratlist(mocker, default_conf, caplog) -> None:
"""
Test setup_configuration() function
"""
@ -357,12 +300,62 @@ def test_hyperopt_with_arguments(mocker, default_conf, caplog) -> None:
read_data=json.dumps(default_conf)
))
arglist = [
'--config', 'config.json',
'backtesting',
'--ticker-interval', '1m',
'--export', '/bar/foo',
'--strategy-list',
'DefaultStrategy',
'TestStrategy'
]
args = Arguments(arglist, '').get_parsed_arg()
configuration = Configuration(args)
config = configuration.get_config()
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 'strategy_list' in config
assert log_has('Using strategy list of 2 Strategies', caplog.record_tuples)
assert 'position_stacking' not in config
assert 'use_max_market_positions' not in config
assert 'timerange' not in config
assert 'export' in config
assert log_has(
'Parameter --export detected: {} ...'.format(config['export']),
caplog.record_tuples
)
def test_hyperopt_with_arguments(mocker, default_conf, caplog) -> None:
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
arglist = [
'hyperopt',
'--epochs', '10',
'--spaces', 'all',
]
args = Arguments(arglist, '').get_parsed_arg()
configuration = Configuration(args)
@ -379,26 +372,79 @@ def test_hyperopt_with_arguments(mocker, default_conf, caplog) -> None:
def test_check_exchange(default_conf) -> None:
"""
Test the configuration validator with a missing attribute
"""
conf = deepcopy(default_conf)
configuration = Configuration(Namespace())
# Test a valid exchange
conf.get('exchange').update({'name': 'BITTREX'})
assert configuration.check_exchange(conf)
default_conf.get('exchange').update({'name': 'BITTREX'})
assert configuration.check_exchange(default_conf)
# Test a valid exchange
conf.get('exchange').update({'name': 'binance'})
assert configuration.check_exchange(conf)
default_conf.get('exchange').update({'name': 'binance'})
assert configuration.check_exchange(default_conf)
# Test a invalid exchange
conf.get('exchange').update({'name': 'unknown_exchange'})
configuration.config = conf
default_conf.get('exchange').update({'name': 'unknown_exchange'})
configuration.config = default_conf
with pytest.raises(
OperationalException,
match=r'.*Exchange "unknown_exchange" not supported.*'
):
configuration.check_exchange(conf)
configuration.check_exchange(default_conf)
def test_cli_verbose_with_params(default_conf, mocker, caplog) -> None:
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)))
# Prevent setting loggers
mocker.patch('freqtrade.configuration.set_loggers', MagicMock)
arglist = ['-vvv']
args = Arguments(arglist, '').get_parsed_arg()
configuration = Configuration(args)
validated_conf = configuration.load_config()
assert validated_conf.get('verbosity') == 3
assert log_has('Verbosity set to 3', caplog.record_tuples)
def test_set_loggers() -> None:
# Reset Logging to Debug, otherwise this fails randomly as it's set globally
logging.getLogger('requests').setLevel(logging.DEBUG)
logging.getLogger("urllib3").setLevel(logging.DEBUG)
logging.getLogger('ccxt.base.exchange').setLevel(logging.DEBUG)
logging.getLogger('telegram').setLevel(logging.DEBUG)
previous_value1 = logging.getLogger('requests').level
previous_value2 = logging.getLogger('ccxt.base.exchange').level
previous_value3 = logging.getLogger('telegram').level
set_loggers()
value1 = logging.getLogger('requests').level
assert previous_value1 is not value1
assert value1 is logging.INFO
value2 = logging.getLogger('ccxt.base.exchange').level
assert previous_value2 is not value2
assert value2 is logging.INFO
value3 = logging.getLogger('telegram').level
assert previous_value3 is not value3
assert value3 is logging.INFO
set_loggers(log_level=2)
assert logging.getLogger('requests').level is logging.DEBUG
assert logging.getLogger('ccxt.base.exchange').level is logging.INFO
assert logging.getLogger('telegram').level is logging.INFO
set_loggers(log_level=3)
assert logging.getLogger('requests').level is logging.DEBUG
assert logging.getLogger('ccxt.base.exchange').level is logging.DEBUG
assert logging.getLogger('telegram').level is logging.INFO
def test_validate_default_conf(default_conf) -> None:
validate(default_conf, constants.CONF_SCHEMA)

25
freqtrade/tests/test_constants.py
View File

@ -1,25 +0,0 @@
"""
Unit test file for constants.py
"""
from freqtrade import constants
def test_constant_object() -> None:
"""
Test the Constants object has the mandatory Constants
"""
assert hasattr(constants, 'CONF_SCHEMA')
assert hasattr(constants, 'DYNAMIC_WHITELIST')
assert hasattr(constants, 'PROCESS_THROTTLE_SECS')
assert hasattr(constants, 'TICKER_INTERVAL')
assert hasattr(constants, 'HYPEROPT_EPOCH')
assert hasattr(constants, 'RETRY_TIMEOUT')
assert hasattr(constants, 'DEFAULT_STRATEGY')
def test_conf_schema() -> None:
"""
Test the CONF_SCHEMA is from the right type
"""
assert isinstance(constants.CONF_SCHEMA, dict)

14
freqtrade/tests/test_dataframe.py
View File

@ -2,33 +2,31 @@
import pandas
from freqtrade.analyze import Analyze
from freqtrade.optimize import load_data
from freqtrade.strategy.resolver import StrategyResolver
_pairs = ['ETH/BTC']
def load_dataframe_pair(pairs):
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]]
analyze = Analyze({'strategy': 'DefaultStrategy'})
dataframe = analyze.analyze_ticker(dataframe)
dataframe = strategy.analyze_ticker(dataframe, {'pair': pairs[0]})
return dataframe
def test_dataframe_load():
StrategyResolver({'strategy': 'DefaultStrategy'})
dataframe = load_dataframe_pair(_pairs)
strategy = StrategyResolver({'strategy': 'DefaultStrategy'}).strategy
dataframe = load_dataframe_pair(_pairs, strategy)
assert isinstance(dataframe, pandas.core.frame.DataFrame)
def test_dataframe_columns_exists():
StrategyResolver({'strategy': 'DefaultStrategy'})
dataframe = load_dataframe_pair(_pairs)
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

19
freqtrade/tests/test_fiat_convert.py
View File

@ -5,7 +5,6 @@ import time
from unittest.mock import MagicMock
import pytest
from requests.exceptions import RequestException
from freqtrade.fiat_convert import CryptoFiat, CryptoToFiatConverter
@ -184,6 +183,24 @@ def test_fiat_convert_without_network(mocker):
CryptoToFiatConverter._coinmarketcap = cmc_temp
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',
listings=listmock,
)
# with pytest.raises(RequestEsxception):
fiat_convert = CryptoToFiatConverter()
fiat_convert._cryptomap = {}
fiat_convert._load_cryptomap()
length_cryptomap = len(fiat_convert._cryptomap)
assert length_cryptomap == 0
assert log_has('Could not load FIAT Cryptocurrency map for the following problem: TypeError',
caplog.record_tuples)
def test_convert_amount(mocker):
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter.get_price', return_value=12345.0)

841
freqtrade/tests/test_freqtradebot.py
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4
freqtrade/tests/test_indicator_helpers.py
View File

@ -1,6 +1,8 @@
# pragma pylint: disable=missing-docstring
import pandas as pd
from freqtrade.indicator_helpers import went_up, went_down
from freqtrade.indicator_helpers import went_down, went_up
def test_went_up():

54
freqtrade/tests/test_main.py
View File

@ -1,8 +1,5 @@
"""
Unit test file for main.py
"""
# pragma pylint: disable=missing-docstring
import logging
from copy import deepcopy
from unittest.mock import MagicMock
@ -11,7 +8,7 @@ import pytest
from freqtrade import OperationalException
from freqtrade.arguments import Arguments
from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.main import main, set_loggers, reconfigure
from freqtrade.main import main, reconfigure
from freqtrade.state import State
from freqtrade.tests.conftest import log_has, patch_exchange
@ -27,49 +24,24 @@ def test_parse_args_backtesting(mocker) -> None:
call_args = backtesting_mock.call_args[0][0]
assert call_args.config == 'config.json'
assert call_args.live is False
assert call_args.loglevel == 20
assert call_args.loglevel == 0
assert call_args.subparser == 'backtesting'
assert call_args.func is not None
assert call_args.ticker_interval is None
def test_main_start_hyperopt(mocker) -> None:
"""
Test that main() can start hyperopt
"""
hyperopt_mock = mocker.patch('freqtrade.optimize.hyperopt.start', MagicMock())
main(['hyperopt'])
assert hyperopt_mock.call_count == 1
call_args = hyperopt_mock.call_args[0][0]
assert call_args.config == 'config.json'
assert call_args.loglevel == 20
assert call_args.loglevel == 0
assert call_args.subparser == 'hyperopt'
assert call_args.func is not None
def test_set_loggers() -> None:
"""
Test set_loggers() update the logger level for third-party libraries
"""
previous_value1 = logging.getLogger('requests.packages.urllib3').level
previous_value2 = logging.getLogger('telegram').level
set_loggers()
value1 = logging.getLogger('requests.packages.urllib3').level
assert previous_value1 is not value1
assert value1 is logging.INFO
value2 = logging.getLogger('telegram').level
assert previous_value2 is not value2
assert value2 is logging.INFO
def test_main_fatal_exception(mocker, default_conf, caplog) -> None:
"""
Test main() function
In this test we are skipping the while True loop by throwing an exception.
"""
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.freqtradebot.FreqtradeBot',
@ -81,7 +53,6 @@ def test_main_fatal_exception(mocker, default_conf, caplog) -> None:
'freqtrade.configuration.Configuration._load_config_file',
lambda *args, **kwargs: default_conf
)
mocker.patch('freqtrade.freqtradebot.CryptoToFiatConverter', MagicMock())
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
args = ['-c', 'config.json.example']
@ -94,10 +65,6 @@ def test_main_fatal_exception(mocker, default_conf, caplog) -> None:
def test_main_keyboard_interrupt(mocker, default_conf, caplog) -> None:
"""
Test main() function
In this test we are skipping the while True loop by throwing an exception.
"""
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.freqtradebot.FreqtradeBot',
@ -109,7 +76,6 @@ def test_main_keyboard_interrupt(mocker, default_conf, caplog) -> None:
'freqtrade.configuration.Configuration._load_config_file',
lambda *args, **kwargs: default_conf
)
mocker.patch('freqtrade.freqtradebot.CryptoToFiatConverter', MagicMock())
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
args = ['-c', 'config.json.example']
@ -122,10 +88,6 @@ def test_main_keyboard_interrupt(mocker, default_conf, caplog) -> None:
def test_main_operational_exception(mocker, default_conf, caplog) -> None:
"""
Test main() function
In this test we are skipping the while True loop by throwing an exception.
"""
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.freqtradebot.FreqtradeBot',
@ -137,7 +99,6 @@ def test_main_operational_exception(mocker, default_conf, caplog) -> None:
'freqtrade.configuration.Configuration._load_config_file',
lambda *args, **kwargs: default_conf
)
mocker.patch('freqtrade.freqtradebot.CryptoToFiatConverter', MagicMock())
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
args = ['-c', 'config.json.example']
@ -150,10 +111,6 @@ def test_main_operational_exception(mocker, default_conf, caplog) -> None:
def test_main_reload_conf(mocker, default_conf, caplog) -> None:
"""
Test main() function
In this test we are skipping the while True loop by throwing an exception.
"""
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.freqtradebot.FreqtradeBot',
@ -165,7 +122,6 @@ def test_main_reload_conf(mocker, default_conf, caplog) -> None:
'freqtrade.configuration.Configuration._load_config_file',
lambda *args, **kwargs: default_conf
)
mocker.patch('freqtrade.freqtradebot.CryptoToFiatConverter', MagicMock())
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
# Raise exception as side effect to avoid endless loop
@ -181,7 +137,6 @@ def test_main_reload_conf(mocker, default_conf, caplog) -> None:
def test_reconfigure(mocker, default_conf) -> None:
""" Test recreate() function """
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.freqtradebot.FreqtradeBot',
@ -193,7 +148,6 @@ def test_reconfigure(mocker, default_conf) -> None:
'freqtrade.configuration.Configuration._load_config_file',
lambda *args, **kwargs: default_conf
)
mocker.patch('freqtrade.freqtradebot.CryptoToFiatConverter', MagicMock())
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
freqtrade = FreqtradeBot(default_conf)

37
freqtrade/tests/test_misc.py
View File

@ -1,34 +1,23 @@
# pragma pylint: disable=missing-docstring,C0103
"""
Unit test file for misc.py
"""
import datetime
from unittest.mock import MagicMock
from freqtrade.analyze import Analyze
from freqtrade.misc import (shorten_date, datesarray_to_datetimearray,
common_datearray, file_dump_json, format_ms_time)
from freqtrade.exchange.exchange_helpers 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.strategy.default_strategy import DefaultStrategy
def test_shorten_date() -> None:
"""
Test shorten_date() function
:return: None
"""
str_data = '1 day, 2 hours, 3 minutes, 4 seconds ago'
str_shorten_data = '1 d, 2 h, 3 min, 4 sec ago'
assert shorten_date(str_data) == str_shorten_data
def test_datesarray_to_datetimearray(ticker_history):
"""
Test datesarray_to_datetimearray() function
:return: None
"""
dataframes = Analyze.parse_ticker_dataframe(ticker_history)
dataframes = parse_ticker_dataframe(ticker_history)
dates = datesarray_to_datetimearray(dataframes['date'])
assert isinstance(dates[0], datetime.datetime)
@ -43,14 +32,10 @@ def test_datesarray_to_datetimearray(ticker_history):
def test_common_datearray(default_conf) -> None:
"""
Test common_datearray()
:return: None
"""
analyze = Analyze(default_conf)
strategy = DefaultStrategy(default_conf)
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
tickerlist = {'UNITTEST/BTC': tick}
dataframes = analyze.tickerdata_to_dataframe(tickerlist)
dataframes = strategy.tickerdata_to_dataframe(tickerlist)
dates = common_datearray(dataframes)
@ -60,10 +45,6 @@ def test_common_datearray(default_conf) -> None:
def test_file_dump_json(mocker) -> None:
"""
Test file_dump_json()
:return: None
"""
file_open = mocker.patch('freqtrade.misc.open', MagicMock())
json_dump = mocker.patch('json.dump', MagicMock())
file_dump_json('somefile', [1, 2, 3])
@ -77,10 +58,6 @@ def test_file_dump_json(mocker) -> None:
def test_format_ms_time() -> None:
"""
test format_ms_time()
:return: None
"""
# Date 2018-04-10 18:02:01
date_in_epoch_ms = 1523383321000
date = format_ms_time(date_in_epoch_ms)

167
freqtrade/tests/test_persistence.py
View File

@ -1,12 +1,12 @@
# pragma pylint: disable=missing-docstring, C0103
from copy import deepcopy
from unittest.mock import MagicMock
import pytest
from sqlalchemy import create_engine
from freqtrade import constants, OperationalException
from freqtrade.persistence import Trade, init, clean_dry_run_db
from freqtrade import OperationalException, constants
from freqtrade.persistence import Trade, clean_dry_run_db, init
from freqtrade.tests.conftest import log_has
@pytest.fixture(scope='function')
@ -22,46 +22,40 @@ def test_init_create_session(default_conf):
def test_init_custom_db_url(default_conf, mocker):
conf = deepcopy(default_conf)
# Update path to a value other than default, but still in-memory
conf.update({'db_url': 'sqlite:///tmp/freqtrade2_test.sqlite'})
default_conf.update({'db_url': 'sqlite:///tmp/freqtrade2_test.sqlite'})
create_engine_mock = mocker.patch('freqtrade.persistence.create_engine', MagicMock())
init(conf)
init(default_conf)
assert create_engine_mock.call_count == 1
assert create_engine_mock.mock_calls[0][1][0] == 'sqlite:///tmp/freqtrade2_test.sqlite'
def test_init_invalid_db_url(default_conf):
conf = deepcopy(default_conf)
# Update path to a value other than default, but still in-memory
conf.update({'db_url': 'unknown:///some.url'})
default_conf.update({'db_url': 'unknown:///some.url'})
with pytest.raises(OperationalException, match=r'.*no valid database URL*'):
init(conf)
init(default_conf)
def test_init_prod_db(default_conf, mocker):
conf = deepcopy(default_conf)
conf.update({'dry_run': False})
conf.update({'db_url': constants.DEFAULT_DB_PROD_URL})
default_conf.update({'dry_run': False})
default_conf.update({'db_url': constants.DEFAULT_DB_PROD_URL})
create_engine_mock = mocker.patch('freqtrade.persistence.create_engine', MagicMock())
init(conf)
init(default_conf)
assert create_engine_mock.call_count == 1
assert create_engine_mock.mock_calls[0][1][0] == 'sqlite:///tradesv3.sqlite'
def test_init_dryrun_db(default_conf, mocker):
conf = deepcopy(default_conf)
conf.update({'dry_run': True})
conf.update({'db_url': constants.DEFAULT_DB_DRYRUN_URL})
default_conf.update({'dry_run': True})
default_conf.update({'db_url': constants.DEFAULT_DB_DRYRUN_URL})
create_engine_mock = mocker.patch('freqtrade.persistence.create_engine', MagicMock())
init(conf)
init(default_conf)
assert create_engine_mock.call_count == 1
assert create_engine_mock.mock_calls[0][1][0] == 'sqlite://'
@ -400,13 +394,17 @@ def test_migrate_old(mocker, default_conf, fee):
assert trade.stake_amount == default_conf.get("stake_amount")
assert trade.pair == "ETC/BTC"
assert trade.exchange == "bittrex"
assert trade.max_rate == 0.0
assert trade.stop_loss == 0.0
assert trade.initial_stop_loss == 0.0
def test_migrate_new(mocker, default_conf, fee):
def test_migrate_new(mocker, default_conf, fee, caplog):
"""
Test Database migration (starting with new pairformat)
"""
amount = 103.223
# Always create all columns apart from the last!
create_table_old = """CREATE TABLE IF NOT EXISTS "trades" (
id INTEGER NOT NULL,
exchange VARCHAR NOT NULL,
@ -421,14 +419,21 @@ def test_migrate_new(mocker, default_conf, fee):
open_date DATETIME NOT NULL,
close_date DATETIME,
open_order_id VARCHAR,
stop_loss FLOAT,
initial_stop_loss FLOAT,
max_rate FLOAT,
sell_reason VARCHAR,
strategy VARCHAR,
PRIMARY KEY (id),
CHECK (is_open IN (0, 1))
);"""
insert_table_old = """INSERT INTO trades (exchange, pair, is_open, fee,
open_rate, stake_amount, amount, open_date)
open_rate, stake_amount, amount, open_date,
stop_loss, initial_stop_loss, max_rate)
VALUES ('binance', 'ETC/BTC', 1, {fee},
0.00258580, {stake}, {amount},
'2019-11-28 12:44:24.000000')
'2019-11-28 12:44:24.000000',
0.0, 0.0, 0.0)
""".format(fee=fee.return_value,
stake=default_conf.get("stake_amount"),
amount=amount
@ -439,6 +444,11 @@ def test_migrate_new(mocker, default_conf, fee):
# Create table using the old format
engine.execute(create_table_old)
engine.execute(insert_table_old)
# fake previous backup
engine.execute("create table trades_bak as select * from trades")
engine.execute("create table trades_bak1 as select * from trades")
# Run init to test migration
init(default_conf)
@ -453,3 +463,116 @@ def test_migrate_new(mocker, default_conf, fee):
assert trade.stake_amount == default_conf.get("stake_amount")
assert trade.pair == "ETC/BTC"
assert trade.exchange == "binance"
assert trade.max_rate == 0.0
assert trade.stop_loss == 0.0
assert trade.initial_stop_loss == 0.0
assert trade.sell_reason is None
assert trade.strategy is None
assert trade.ticker_interval is None
assert log_has("trying trades_bak1", caplog.record_tuples)
assert log_has("trying trades_bak2", caplog.record_tuples)
def test_migrate_mid_state(mocker, default_conf, fee, caplog):
"""
Test Database migration (starting with new pairformat)
"""
amount = 103.223
create_table_old = """CREATE TABLE IF NOT EXISTS "trades" (
id INTEGER NOT NULL,
exchange VARCHAR NOT NULL,
pair VARCHAR NOT NULL,
is_open BOOLEAN NOT NULL,
fee_open FLOAT NOT NULL,
fee_close FLOAT NOT NULL,
open_rate FLOAT,
close_rate FLOAT,
close_profit FLOAT,
stake_amount FLOAT NOT NULL,
amount FLOAT,
open_date DATETIME NOT NULL,
close_date DATETIME,
open_order_id VARCHAR,
PRIMARY KEY (id),
CHECK (is_open IN (0, 1))
);"""
insert_table_old = """INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close,
open_rate, stake_amount, amount, open_date)
VALUES ('binance', 'ETC/BTC', 1, {fee}, {fee},
0.00258580, {stake}, {amount},
'2019-11-28 12:44:24.000000')
""".format(fee=fee.return_value,
stake=default_conf.get("stake_amount"),
amount=amount
)
engine = create_engine('sqlite://')
mocker.patch('freqtrade.persistence.create_engine', lambda *args, **kwargs: engine)
# Create table using the old format
engine.execute(create_table_old)
engine.execute(insert_table_old)
# Run init to test migration
init(default_conf)
assert len(Trade.query.filter(Trade.id == 1).all()) == 1
trade = Trade.query.filter(Trade.id == 1).first()
assert trade.fee_open == fee.return_value
assert trade.fee_close == fee.return_value
assert trade.open_rate_requested is None
assert trade.close_rate_requested is None
assert trade.is_open == 1
assert trade.amount == amount
assert trade.stake_amount == default_conf.get("stake_amount")
assert trade.pair == "ETC/BTC"
assert trade.exchange == "binance"
assert trade.max_rate == 0.0
assert trade.stop_loss == 0.0
assert trade.initial_stop_loss == 0.0
assert log_has("trying trades_bak0", caplog.record_tuples)
def test_adjust_stop_loss(limit_buy_order, limit_sell_order, fee):
trade = Trade(
pair='ETH/BTC',
stake_amount=0.001,
fee_open=fee.return_value,
fee_close=fee.return_value,
exchange='bittrex',
open_rate=1,
)
trade.adjust_stop_loss(trade.open_rate, 0.05, True)
assert trade.stop_loss == 0.95
assert trade.max_rate == 1
assert trade.initial_stop_loss == 0.95
# Get percent of profit with a lowre rate
trade.adjust_stop_loss(0.96, 0.05)
assert trade.stop_loss == 0.95
assert trade.max_rate == 1
assert trade.initial_stop_loss == 0.95
# Get percent of profit with a custom rate (Higher than open rate)
trade.adjust_stop_loss(1.3, -0.1)
assert round(trade.stop_loss, 8) == 1.17
assert trade.max_rate == 1.3
assert trade.initial_stop_loss == 0.95
# current rate lower again ... should not change
trade.adjust_stop_loss(1.2, 0.1)
assert round(trade.stop_loss, 8) == 1.17
assert trade.max_rate == 1.3
assert trade.initial_stop_loss == 0.95
# current rate higher... should raise stoploss
trade.adjust_stop_loss(1.4, 0.1)
assert round(trade.stop_loss, 8) == 1.26
assert trade.max_rate == 1.4
assert trade.initial_stop_loss == 0.95
# Initial is true but stop_loss set - so doesn't do anything
trade.adjust_stop_loss(1.7, 0.1, True)
assert round(trade.stop_loss, 8) == 1.26
assert trade.max_rate == 1.4
assert trade.initial_stop_loss == 0.95

14
freqtrade/tests/test_state.py
View File

@ -1,14 +0,0 @@
"""
Unit test file for constants.py
"""
from freqtrade.state import State
def test_state_object() -> None:
"""
Test the State object has the mandatory states
:return: None
"""
assert hasattr(State, 'RUNNING')
assert hasattr(State, 'STOPPED')

16
freqtrade/tests/test_talib.py Normal file
View File

@ -0,0 +1,16 @@
import talib.abstract as ta
import pandas as pd
def test_talib_bollingerbands_near_zero_values():
inputs = pd.DataFrame([
{'close': 0.00000010},
{'close': 0.00000011},
{'close': 0.00000012},
{'close': 0.00000013},
{'close': 0.00000014}
])
bollinger = ta.BBANDS(inputs, matype=0, timeperiod=2)
assert (bollinger['upperband'][3] != bollinger['middleband'][3])

2
install_ta-lib.sh
View File

@ -1,6 +1,6 @@
if [ ! -f "ta-lib/CHANGELOG.TXT" ]; then
tar zxvf ta-lib-0.4.0-src.tar.gz
cd ta-lib && ./configure && make && sudo make install && cd ..
cd ta-lib && sed -i "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 ..

20
requirements.txt
View File

@ -1,25 +1,25 @@
ccxt==1.14.256
SQLAlchemy==1.2.8
ccxt==1.17.126
SQLAlchemy==1.2.10
python-telegram-bot==10.1.0
arrow==0.12.1
cachetools==2.1.0
requests==2.19.1
urllib3==1.22
wrapt==1.10.11
pandas==0.23.1
scikit-learn==0.19.1
pandas==0.23.4
scikit-learn==0.19.2
scipy==1.1.0
jsonschema==2.6.0
numpy==1.14.5
numpy==1.15.0
TA-Lib==0.4.17
pytest==3.6.2
pytest==3.7.1
pytest-mock==1.10.0
pytest-cov==2.5.1
hyperopt==0.1
# do not upgrade networkx before this is fixed https://github.com/hyperopt/hyperopt/issues/325
networkx==1.11 # pyup: ignore
tabulate==0.8.2
coinmarketcap==5.0.3
# Required for hyperopt
scikit-optimize==0.5.2
# Required for plotting data
#plotly==2.7.0
#plotly==3.1.1

7
scripts/convert_backtestdata.py
View File

@ -143,15 +143,14 @@ def convert_main(args: Namespace) -> None:
interval = str_interval
break
# change order on pairs if old ticker interval found
filename_new = path.join(path.dirname(filename),
"{}_{}-{}.json".format(currencies[1],
currencies[0], interval))
f"{currencies[1]}_{currencies[0]}-{interval}.json")
elif ret_string:
interval = ret_string.group(0)
filename_new = path.join(path.dirname(filename),
"{}_{}-{}.json".format(currencies[0],
currencies[1], interval))
f"{currencies[0]}_{currencies[1]}-{interval}.json")
else:
logger.warning("file %s could not be converted, interval not found", filename)

48
scripts/download_backtest_data.py
View File

@ -3,11 +3,14 @@
"""This script generate json data from bittrex"""
import json
import sys
import os
from pathlib import Path
import arrow
from freqtrade import (arguments, misc)
from freqtrade import arguments
from freqtrade.arguments import TimeRange
from freqtrade.exchange import Exchange
from freqtrade.optimize import download_backtesting_testdata
DEFAULT_DL_PATH = 'user_data/data'
@ -17,25 +20,27 @@ args = arguments.parse_args()
timeframes = args.timeframes
dl_path = os.path.join(DEFAULT_DL_PATH, args.exchange)
dl_path = Path(DEFAULT_DL_PATH).joinpath(args.exchange)
if args.export:
dl_path = args.export
dl_path = Path(args.export)
if not os.path.isdir(dl_path):
if not dl_path.is_dir():
sys.exit(f'Directory {dl_path} does not exist.')
pairs_file = args.pairs_file if args.pairs_file else os.path.join(dl_path, 'pairs.json')
if not os.path.isfile(pairs_file):
pairs_file = Path(args.pairs_file) if args.pairs_file else dl_path.joinpath('pairs.json')
if not pairs_file.exists():
sys.exit(f'No pairs file found with path {pairs_file}.')
with open(pairs_file) as file:
with pairs_file.open() as file:
PAIRS = list(set(json.load(file)))
PAIRS.sort()
since_time = None
timerange = TimeRange()
if args.days:
since_time = arrow.utcnow().shift(days=-args.days).timestamp * 1000
time_since = arrow.utcnow().shift(days=-args.days).strftime("%Y%m%d")
timerange = arguments.parse_timerange(f'{time_since}-')
print(f'About to download pairs: {PAIRS} to {dl_path}')
@ -59,21 +64,18 @@ for pair in PAIRS:
print(f"skipping pair {pair}")
continue
for tick_interval in timeframes:
print(f'downloading pair {pair}, interval {tick_interval}')
data = exchange.get_ticker_history(pair, tick_interval, since_ms=since_time)
if not data:
print('\tNo data was downloaded')
break
print('\tData was downloaded for period %s - %s' % (
arrow.get(data[0][0] / 1000).format(),
arrow.get(data[-1][0] / 1000).format()))
# save data
pair_print = pair.replace('/', '_')
filename = f'{pair_print}-{tick_interval}.json'
misc.file_dump_json(os.path.join(dl_path, filename), data)
dl_file = dl_path.joinpath(filename)
if args.erase and dl_file.exists():
print(f'Deleting existing data for pair {pair}, interval {tick_interval}')
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)
if pairs_not_available:

93
scripts/get_market_pairs.py Normal file
View File

@ -0,0 +1,93 @@
import os
import sys
root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(root + '/python')
import ccxt # noqa: E402
def style(s, style):
return style + s + '\033[0m'
def green(s):
return style(s, '\033[92m')
def blue(s):
return style(s, '\033[94m')
def yellow(s):
return style(s, '\033[93m')
def red(s):
return style(s, '\033[91m')
def pink(s):
return style(s, '\033[95m')
def bold(s):
return style(s, '\033[1m')
def underline(s):
return style(s, '\033[4m')
def dump(*args):
print(' '.join([str(arg) for arg in args]))
def print_supported_exchanges():
dump('Supported exchanges:', green(', '.join(ccxt.exchanges)))
try:
id = sys.argv[1] # get exchange id from command line arguments
# check if the exchange is supported by ccxt
exchange_found = id in ccxt.exchanges
if exchange_found:
dump('Instantiating', green(id), 'exchange')
# instantiate the exchange by id
exchange = getattr(ccxt, id)({
# 'proxy':'https://cors-anywhere.herokuapp.com/',
})
# load all markets from the exchange
markets = exchange.load_markets()
# output a list of all market symbols
dump(green(id), 'has', len(exchange.symbols), 'symbols:', exchange.symbols)
tuples = list(ccxt.Exchange.keysort(markets).items())
# debug
for (k, v) in tuples:
print(v)
# output a table of all markets
dump(pink('{:<15} {:<15} {:<15} {:<15}'.format('id', 'symbol', 'base', 'quote')))
for (k, v) in tuples:
dump('{:<15} {:<15} {:<15} {:<15}'.format(v['id'], v['symbol'], v['base'], v['quote']))
else:
dump('Exchange ' + red(id) + ' not found')
print_supported_exchanges()
except Exception as e:
dump('[' + type(e).__name__ + ']', str(e))
dump("Usage: python " + sys.argv[0], green('id'))
print_supported_exchanges()

112
scripts/plot_dataframe.py
View File

@ -24,27 +24,76 @@ Example of usage:
> python3 scripts/plot_dataframe.py --pair BTC/EUR -d user_data/data/ --indicators1 sma,ema3
--indicators2 fastk,fastd
"""
import json
import logging
import os
import sys
from argparse import Namespace
from pathlib import Path
from typing import Dict, List, Any
import pandas as pd
import plotly.graph_objs as go
import pytz
from plotly import tools
from plotly.offline import plot
import freqtrade.optimize as optimize
from freqtrade import persistence
from freqtrade.analyze import Analyze
from freqtrade.arguments import Arguments
from freqtrade.arguments import Arguments, TimeRange
from freqtrade.exchange import Exchange
from freqtrade.optimize.backtesting import setup_configuration
from freqtrade.persistence import Trade
from freqtrade.strategy.resolver import StrategyResolver
logger = logging.getLogger(__name__)
_CONF: Dict[str, Any] = {}
timeZone = pytz.UTC
def load_trades(args: Namespace, pair: str, timerange: TimeRange) -> pd.DataFrame:
trades: pd.DataFrame = pd.DataFrame()
if args.db_url:
persistence.init(_CONF)
columns = ["pair", "profit", "opents", "closets", "open_rate", "close_rate", "duration"]
for x in Trade.query.all():
print("date: {}".format(x.open_date))
trades = pd.DataFrame([(t.pair, t.calc_profit(),
t.open_date.replace(tzinfo=timeZone),
t.close_date.replace(tzinfo=timeZone) if t.close_date else None,
t.open_rate, t.close_rate,
t.close_date.timestamp() - t.open_date.timestamp() if t.close_date else None)
for t in Trade.query.filter(Trade.pair.is_(pair)).all()],
columns=columns)
elif args.exportfilename:
file = Path(args.exportfilename)
# must align with columns in backtest.py
columns = ["pair", "profit", "opents", "closets", "index", "duration",
"open_rate", "close_rate", "open_at_end"]
with file.open() as f:
data = json.load(f)
trades = pd.DataFrame(data, columns=columns)
trades = trades.loc[trades["pair"] == pair]
if timerange:
if timerange.starttype == 'date':
trades = trades.loc[trades["opents"] >= timerange.startts]
if timerange.stoptype == 'date':
trades = trades.loc[trades["opents"] <= timerange.stopts]
trades['opents'] = pd.to_datetime(trades['opents'],
unit='s',
utc=True,
infer_datetime_format=True)
trades['closets'] = pd.to_datetime(trades['closets'],
unit='s',
utc=True,
infer_datetime_format=True)
return trades
def plot_analyzed_dataframe(args: Namespace) -> None:
"""
@ -56,6 +105,7 @@ def plot_analyzed_dataframe(args: Namespace) -> None:
# Load the configuration
_CONF.update(setup_configuration(args))
print(_CONF)
# Set the pair to audit
pair = args.pair
@ -72,7 +122,7 @@ def plot_analyzed_dataframe(args: Namespace) -> None:
# Load the strategy
try:
analyze = Analyze(_CONF)
strategy = StrategyResolver(_CONF).strategy
exchange = Exchange(_CONF)
except AttributeError:
logger.critical(
@ -82,20 +132,21 @@ def plot_analyzed_dataframe(args: Namespace) -> None:
exit()
# Set the ticker to use
tick_interval = analyze.get_ticker_interval()
tick_interval = strategy.ticker_interval
# Load pair tickers
tickers = {}
if args.live:
logger.info('Downloading pair.')
tickers[pair] = exchange.get_ticker_history(pair, tick_interval)
tickers[pair] = exchange.get_candle_history(pair, tick_interval)
else:
tickers = optimize.load_data(
datadir=_CONF.get("datadir"),
pairs=[pair],
ticker_interval=tick_interval,
refresh_pairs=_CONF.get('refresh_pairs', False),
timerange=timerange
timerange=timerange,
exchange=Exchange(_CONF)
)
# No ticker found, or impossible to download
@ -103,30 +154,31 @@ def plot_analyzed_dataframe(args: Namespace) -> None:
exit()
# Get trades already made from the DB
trades: List[Trade] = []
if args.db_url:
persistence.init(_CONF)
trades = Trade.query.filter(Trade.pair.is_(pair)).all()
trades = load_trades(args, pair, timerange)
dataframes = strategy.tickerdata_to_dataframe(tickers)
dataframes = analyze.tickerdata_to_dataframe(tickers)
dataframe = dataframes[pair]
dataframe = analyze.populate_buy_trend(dataframe)
dataframe = analyze.populate_sell_trend(dataframe)
dataframe = strategy.advise_buy(dataframe, {'pair': pair})
dataframe = strategy.advise_sell(dataframe, {'pair': pair})
if len(dataframe.index) > 750:
logger.warning('Ticker contained more than 750 candles, clipping.')
if len(dataframe.index) > args.plot_limit:
logger.warning('Ticker contained more than %s candles as defined '
'with --plot-limit, clipping.', args.plot_limit)
dataframe = dataframe.tail(args.plot_limit)
trades = trades.loc[trades['opents'] >= dataframe.iloc[0]['date']]
fig = generate_graph(
pair=pair,
trades=trades,
data=dataframe.tail(750),
data=dataframe,
args=args
)
plot(fig, filename=os.path.join('user_data', 'freqtrade-plot.html'))
plot(fig, filename=str(Path('user_data').joinpath('freqtrade-plot.html')))
def generate_graph(pair, trades, data, args) -> tools.make_subplots:
def generate_graph(pair, trades: pd.DataFrame, data: pd.DataFrame, args) -> tools.make_subplots:
"""
Generate the graph from the data generated by Backtesting or from DB
:param pair: Pair to Display on the graph
@ -187,8 +239,8 @@ def generate_graph(pair, trades, data, args) -> tools.make_subplots:
)
trade_buys = go.Scattergl(
x=[t.open_date.isoformat() for t in trades],
y=[t.open_rate for t in trades],
x=trades["opents"],
y=trades["open_rate"],
mode='markers',
name='trade_buy',
marker=dict(
@ -199,8 +251,8 @@ def generate_graph(pair, trades, data, args) -> tools.make_subplots:
)
)
trade_sells = go.Scattergl(
x=[t.close_date.isoformat() for t in trades],
y=[t.close_rate for t in trades],
x=trades["closets"],
y=trades["close_rate"],
mode='markers',
name='trade_sell',
marker=dict(
@ -219,7 +271,7 @@ def generate_graph(pair, trades, data, args) -> tools.make_subplots:
x=data.date,
y=data.bb_lowerband,
name='BB lower',
line={'color': "transparent"},
line={'color': 'rgba(255,255,255,0)'},
)
bb_upper = go.Scatter(
x=data.date,
@ -227,7 +279,7 @@ def generate_graph(pair, trades, data, args) -> tools.make_subplots:
name='BB upper',
fill="tonexty",
fillcolor="rgba(0,176,246,0.2)",
line={'color': "transparent"},
line={'color': 'rgba(255,255,255,0)'},
)
fig.append_trace(bb_lower, 1, 1)
fig.append_trace(bb_upper, 1, 1)
@ -299,11 +351,17 @@ def plot_parse_args(args: List[str]) -> Namespace:
default='macd',
dest='indicators2',
)
arguments.parser.add_argument(
'--plot-limit',
help='Specify tick limit for plotting - too high values cause huge files - '
'Default: %(default)s',
dest='plot_limit',
default=750,
type=int,
)
arguments.common_args_parser()
arguments.optimizer_shared_options(arguments.parser)
arguments.backtesting_options(arguments.parser)
return arguments.parse_args()

10
scripts/plot_profit.py
View File

@ -26,9 +26,8 @@ import plotly.graph_objs as go
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.analyze import Analyze
from freqtrade import constants
from freqtrade.strategy.resolver import StrategyResolver
import freqtrade.optimize as optimize
import freqtrade.misc as misc
@ -87,7 +86,8 @@ def plot_profit(args: Namespace) -> None:
# Init strategy
try:
analyze = Analyze({'strategy': config.get('strategy')})
strategy = StrategyResolver({'strategy': config.get('strategy')}).strategy
except AttributeError:
logger.critical(
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
@ -113,7 +113,7 @@ def plot_profit(args: Namespace) -> None:
else:
filter_pairs = config['exchange']['pair_whitelist']
tick_interval = analyze.strategy.ticker_interval
tick_interval = strategy.ticker_interval
pairs = config['exchange']['pair_whitelist']
if filter_pairs:
@ -127,7 +127,7 @@ def plot_profit(args: Namespace) -> None:
refresh_pairs=False,
timerange=timerange
)
dataframes = analyze.tickerdata_to_dataframe(tickers)
dataframes = strategy.tickerdata_to_dataframe(tickers)
# NOTE: the dataframes are of unequal length,
# 'dates' is an merged date array of them all.

3
setup.py
View File

@ -18,7 +18,7 @@ setup(name='freqtrade',
license='GPLv3',
packages=['freqtrade'],
scripts=['bin/freqtrade'],
setup_requires=['pytest-runner'],
setup_requires=['pytest-runner', 'numpy'],
tests_require=['pytest', 'pytest-mock', 'pytest-cov'],
install_requires=[
'ccxt',
@ -36,6 +36,7 @@ setup(name='freqtrade',
'tabulate',
'cachetools',
'coinmarketcap',
'scikit-optimize',
],
include_package_data=True,
zip_safe=False,

17
user_data/strategies/test_strategy.py
View File

@ -12,11 +12,13 @@ import numpy # noqa
# This class is a sample. Feel free to customize it.
class TestStrategy(IStrategy):
__test__ = False # pytest expects to find tests here because of the name
"""
This is a test strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
@ -43,13 +45,16 @@ class TestStrategy(IStrategy):
# Optimal ticker interval for the strategy
ticker_interval = '5m'
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# Momentum Indicator
@ -210,10 +215,11 @@ class TestStrategy(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
@ -226,10 +232,11 @@ class TestStrategy(IStrategy):
return dataframe
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[