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118 lines
3.9 KiB
Markdown
118 lines
3.9 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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- [Change your strategy](#change-your-strategy)
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- [Add more Indicator](#add-more-indicator)
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## Change your strategy
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The bot is using buy and sell strategies to buy and sell your trades.
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Both are customizable.
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### Buy strategy
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The default buy strategy is located in the file
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[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L73-L92).
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Edit the function `populate_buy_trend()` to update your buy strategy.
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Sample:
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```python
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def populate_buy_trend(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['rsi'] < 35) &
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(dataframe['fastd'] < 35) &
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(dataframe['adx'] > 30) &
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(dataframe['plus_di'] > 0.5)
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) |
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(
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(dataframe['adx'] > 65) &
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(dataframe['plus_di'] > 0.5)
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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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The default buy strategy is located in the file
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[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L95-L115)
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Edit the function `populate_sell_trend()` to update your buy strategy.
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Sample:
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```python
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def populate_sell_trend(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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(
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(crossed_above(dataframe['rsi'], 70)) |
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(crossed_above(dataframe['fastd'], 70))
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) &
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(dataframe['adx'] > 10) &
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(dataframe['minus_di'] > 0)
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) |
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(
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(dataframe['adx'] > 70) &
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(dataframe['minus_di'] > 0.5)
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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 see
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the indicators in the file
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[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L95-L115).
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Of course you can add more indicators by extending the list contained in
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the function `populate_indicators()`.
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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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## 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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