146 lines
5.2 KiB
Markdown
146 lines
5.2 KiB
Markdown
# Bot Optimization
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This page explains where to customize your strategies, and add new
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indicators.
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## Table of Contents
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- [Install a custom strategy file](#install-a-custom-strategy-file)
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- [Customize your strategy](#change-your-strategy)
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- [Add more Indicator](#add-more-indicator)
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- [Where is the default strategy](#where-is-the-default-strategy)
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Since the version `0.16.0` the bot allows using custom strategy file.
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## Install a custom strategy file
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This is very simple. Copy paste your strategy file into the folder
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`user_data/strategies`.
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Let assume you have a strategy file `awesome-strategy.py`:
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1. Move your file into `user_data/strategies` (you should have `user_data/strategies/awesome-strategy.py`
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2. Start the bot with the param `--strategy awesome-strategy` (the parameter is the name of the file without '.py')
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```bash
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python3 ./freqtrade/main.py --strategy awesome_strategy
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```
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## Change your strategy
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The bot includes a default strategy file. However, we recommend you to
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use your own file to not have to lose your parameters every time the default
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strategy file will be updated on Github. Put your custom strategy file
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into the folder `user_data/strategies`.
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A strategy file contains all the information needed to build a good strategy:
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- Buy strategy rules
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- Sell strategy rules
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- Minimal ROI recommended
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- Stoploss recommended
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- Hyperopt parameter
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The bot also include a sample strategy you can update: `user_data/strategies/test_strategy.py`.
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You can test it with the parameter: `--strategy test_strategy`
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```bash
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python3 ./freqtrade/main.py --strategy awesome_strategy
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```
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**For the following section we will use the [user_data/strategies/test_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py)
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file as reference.**
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### Buy strategy
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Edit the method `populate_buy_trend()` into your strategy file to
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update your buy strategy.
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Sample from `user_data/strategies/test_strategy.py`:
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```python
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def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(dataframe['adx'] > 30) &
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(dataframe['tema'] <= dataframe['blower']) &
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(dataframe['tema'] > dataframe['tema'].shift(1))
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),
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'buy'] = 1
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return dataframe
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```
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### Sell strategy
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Edit the method `populate_sell_trend()` into your strategy file to
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update your sell strategy.
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Sample from `user_data/strategies/test_strategy.py`:
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```python
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def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(dataframe['adx'] > 70) &
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(dataframe['tema'] > dataframe['blower']) &
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(dataframe['tema'] < dataframe['tema'].shift(1))
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),
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'sell'] = 1
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return dataframe
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```
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## Add more Indicator
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As you have seen, buy and sell strategies need indicators. You can add
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more indicators by extending the list contained in
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the method `populate_indicators()` from your strategy file.
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Sample:
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```python
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def populate_indicators(dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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"""
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dataframe['sar'] = ta.SAR(dataframe)
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dataframe['adx'] = ta.ADX(dataframe)
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stoch = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch['fastd']
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dataframe['fastk'] = stoch['fastk']
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dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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dataframe['mfi'] = ta.MFI(dataframe)
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dataframe['rsi'] = ta.RSI(dataframe)
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dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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dataframe['ao'] = awesome_oscillator(dataframe)
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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hilbert = ta.HT_SINE(dataframe)
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dataframe['htsine'] = hilbert['sine']
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dataframe['htleadsine'] = hilbert['leadsine']
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dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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return dataframe
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```
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**Want more indicators example?**
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Look into the [user_data/strategies/test_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py).
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Then uncomment indicators you need.
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### Where is the default strategy?
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The default buy strategy is located in the file
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[freqtrade/default_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/strategy/default_strategy.py).
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## Next step
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Now you have a perfect strategy you probably want to backtesting it.
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Your next step is to learn [How to use the Backtesting](https://github.com/gcarq/freqtrade/blob/develop/docs/backtesting.md).
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