Decouple strategy from analyse.py
This commit is contained in:
Gerald Lonlas
0
freqtrade/strategy/__init__.py
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0
freqtrade/strategy/__init__.py
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262
freqtrade/strategy/default_strategy.py
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262
freqtrade/strategy/default_strategy.py
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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from hyperopt import hp
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from functools import reduce
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from typing import Dict, List
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class_name = 'DefaultStrategy'
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class DefaultStrategy(IStrategy):
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"""
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Default Strategy provided by freqtrade bot.
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You can override it with your own strategy
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"""
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# Minimal ROI designed for the strategy
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minimal_roi = {
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"40": 0.0,
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"30": 0.01,
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"20": 0.02,
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"0": 0.04
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}
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# Optimal stoploss designed for the strategy
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stoploss = -0.10
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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"""
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# Momentum Indicator
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# ------------------------------------
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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# Awesome oscillator
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dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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# MACD
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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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# MFI
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dataframe['mfi'] = ta.MFI(dataframe)
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# Minus Directional Indicator / Movement
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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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# Plus Directional Indicator / Movement
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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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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# Stoch fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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# Overlap Studies
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# ------------------------------------
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# Previous Bollinger bands
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# Because ta.BBANDS implementation is broken with small numbers, it actually
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# returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
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# and use middle band instead.
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dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
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"""
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# Bollinger bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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"""
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# EMA - Exponential Moving Average
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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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# SAR Parabol
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dataframe['sar'] = ta.SAR(dataframe)
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# SMA - Simple Moving Average
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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# TEMA - Triple Exponential Moving Average
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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# Cycle Indicator
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# ------------------------------------
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# Hilbert Transform Indicator - SineWave
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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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return dataframe
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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['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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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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(
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(qtpylib.crossed_above(dataframe['rsi'], 70)) |
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(qtpylib.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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def hyperopt_space(self) -> List[Dict]:
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"""
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Define your Hyperopt space for the strategy
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"""
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space = {
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'mfi': hp.choice('mfi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
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]),
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'fastd': hp.choice('fastd', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
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]),
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'adx': hp.choice('adx', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
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]),
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'rsi': hp.choice('rsi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
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]),
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'uptrend_long_ema': hp.choice('uptrend_long_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_short_ema': hp.choice('uptrend_short_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'over_sar': hp.choice('over_sar', [
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{'enabled': False},
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{'enabled': True}
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]),
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'green_candle': hp.choice('green_candle', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_sma': hp.choice('uptrend_sma', [
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{'enabled': False},
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{'enabled': True}
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]),
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'trigger': hp.choice('trigger', [
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{'type': 'lower_bb'},
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{'type': 'faststoch10'},
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{'type': 'ao_cross_zero'},
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{'type': 'ema5_cross_ema10'},
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{'type': 'macd_cross_signal'},
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{'type': 'sar_reversal'},
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{'type': 'stochf_cross'},
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{'type': 'ht_sine'},
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]),
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'stoploss': hp.uniform('stoploss', -0.5, -0.02),
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}
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return space
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def buy_strategy_generator(self, params) -> None:
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"""
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Define the buy strategy parameters to be used by hyperopt
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"""
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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conditions = []
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# GUARDS AND TRENDS
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if params['uptrend_long_ema']['enabled']:
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conditions.append(dataframe['ema50'] > dataframe['ema100'])
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if params['uptrend_short_ema']['enabled']:
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conditions.append(dataframe['ema5'] > dataframe['ema10'])
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if params['mfi']['enabled']:
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conditions.append(dataframe['mfi'] < params['mfi']['value'])
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if params['fastd']['enabled']:
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conditions.append(dataframe['fastd'] < params['fastd']['value'])
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if params['adx']['enabled']:
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conditions.append(dataframe['adx'] > params['adx']['value'])
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if params['rsi']['enabled']:
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conditions.append(dataframe['rsi'] < params['rsi']['value'])
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if params['over_sar']['enabled']:
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conditions.append(dataframe['close'] > dataframe['sar'])
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if params['green_candle']['enabled']:
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conditions.append(dataframe['close'] > dataframe['open'])
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if params['uptrend_sma']['enabled']:
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prevsma = dataframe['sma'].shift(1)
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conditions.append(dataframe['sma'] > prevsma)
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# TRIGGERS
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triggers = {
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'lower_bb': dataframe['tema'] <= dataframe['blower'],
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'faststoch10': (qtpylib.crossed_above(dataframe['fastd'], 10.0)),
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'ao_cross_zero': (qtpylib.crossed_above(dataframe['ao'], 0.0)),
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'ema5_cross_ema10': (
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qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10'])
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),
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'macd_cross_signal': (
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qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])
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),
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'sar_reversal': (qtpylib.crossed_above(dataframe['close'], dataframe['sar'])),
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'stochf_cross': (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])),
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'ht_sine': (qtpylib.crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
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}
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conditions.append(triggers.get(params['trigger']['type']))
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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return populate_buy_trend
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56
freqtrade/strategy/interface.py
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56
freqtrade/strategy/interface.py
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@@ -0,0 +1,56 @@
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from abc import ABC, abstractmethod
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from pandas import DataFrame
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from typing import Dict
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class IStrategy(ABC):
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@property
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def name(self) -> str:
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"""
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Name of the strategy.
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:return: str representation of the class name
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"""
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return self.__class__.__name__
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"""
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Attributes you can use:
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minimal_roi -> Dict: Minimal ROI designed for the strategy
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stoploss -> float: ptimal stoploss designed for the strategy
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"""
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@abstractmethod
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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"""
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Populate indicators that will be used in the Buy and Sell strategy
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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@abstractmethod
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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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:return:
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"""
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@abstractmethod
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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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@abstractmethod
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def hyperopt_space(self) -> Dict:
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"""
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Define your Hyperopt space for the strategy
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"""
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@abstractmethod
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def buy_strategy_generator(self, params) -> None:
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"""
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Define the buy strategy parameters to be used by hyperopt
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"""
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165
freqtrade/strategy/strategy.py
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165
freqtrade/strategy/strategy.py
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@@ -0,0 +1,165 @@
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import os
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import sys
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import logging
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import importlib
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from pandas import DataFrame
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from typing import Dict
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from freqtrade.strategy.interface import IStrategy
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sys.path.insert(0, r'../../user_data/strategies')
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class Strategy(object):
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__instance = None
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DEFAULT_STRATEGY = 'default_strategy'
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def __new__(cls):
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if Strategy.__instance is None:
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Strategy.__instance = object.__new__(cls)
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return Strategy.__instance
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def init(self, config):
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self.logger = logging.getLogger(__name__)
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# Verify the strategy is in the configuration, otherwise fallback to the default strategy
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if 'strategy' in config:
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strategy = config['strategy']
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else:
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strategy = self.DEFAULT_STRATEGY
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# Load the strategy
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self._load_strategy(strategy)
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# Set attributes
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# Check if we need to override configuration
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if 'minimal_roi' in config:
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self.custom_strategy.minimal_roi = config['minimal_roi']
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self.logger.info("Override strategy \'minimal_roi\' with value in config file.")
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if 'stoploss' in config:
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self.custom_strategy.stoploss = config['stoploss']
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self.logger.info("Override strategy \'stoploss\' with value in config file.")
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self.minimal_roi = self.custom_strategy.minimal_roi
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self.stoploss = self.custom_strategy.stoploss
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def _load_strategy(self, strategy_name: str) -> None:
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"""
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Search and load the custom strategy. If no strategy found, fallback on the default strategy
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Set the object into self.custom_strategy
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:param strategy_name: name of the module to import
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:return: None
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"""
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try:
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# Start by sanitizing the file name (remove any extensions)
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strategy_name = self._sanitize_module_name(filename=strategy_name)
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# Search where can be the strategy file
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path = self._search_strategy(filename=strategy_name)
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# Load the strategy
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self.custom_strategy = self._load_class(path + strategy_name)
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# Fallback to the default strategy
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except (ImportError, TypeError):
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self.custom_strategy = self._load_class('.' + self.DEFAULT_STRATEGY)
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def _load_class(self, filename: str) -> IStrategy:
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"""
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Import a strategy as a module
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:param filename: path to the strategy (path from freqtrade/strategy/)
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:return: return the strategy class
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"""
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module = importlib.import_module(filename, __package__)
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custom_strategy = getattr(module, module.class_name)
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self.logger.info("Load strategy class: {} ({}.py)".format(module.class_name, filename))
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return custom_strategy()
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@staticmethod
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def _sanitize_module_name(filename: str) -> str:
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"""
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Remove any extension from filename
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:param filename: filename to sanatize
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:return: return the filename without extensions
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"""
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filename = os.path.basename(filename)
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filename = os.path.splitext(filename)[0]
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return filename
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@staticmethod
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def _search_strategy(filename: str) -> str:
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"""
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Search for the Strategy file in different folder
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1. search into the user_data/strategies folder
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2. search into the freqtrade/strategy folder
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3. if nothing found, return None
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:param strategy_name: module name to search
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:return: module path where is the strategy
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"""
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pwd = os.path.dirname(os.path.realpath(__file__)) + '/'
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user_data = os.path.join(pwd, '..', '..', 'user_data', 'strategies', filename + '.py')
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strategy_folder = os.path.join(pwd, filename + '.py')
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path = None
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if os.path.isfile(user_data):
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path = 'user_data.strategies.'
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elif os.path.isfile(strategy_folder):
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path = '.'
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return path
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def minimal_roi(self) -> Dict:
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"""
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Minimal ROI designed for the strategy
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:return: Dict: Value for the Minimal ROI
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"""
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return
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def stoploss(self) -> float:
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"""
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Optimal stoploss designed for the strategy
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:return: float | return None to disable it
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"""
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return self.custom_strategy.stoploss
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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"""
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Populate indicators that will be used in the Buy and Sell strategy
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
|
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:return: a Dataframe with all mandatory indicators for the strategies
|
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"""
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return self.custom_strategy.populate_indicators(dataframe)
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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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:return:
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"""
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return self.custom_strategy.populate_buy_trend(dataframe)
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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
|
||||
:return: DataFrame with buy column
|
||||
"""
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return self.custom_strategy.populate_sell_trend(dataframe)
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def hyperopt_space(self) -> Dict:
|
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"""
|
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Define your Hyperopt space for the strategy
|
||||
"""
|
||||
return self.custom_strategy.hyperopt_space()
|
||||
|
||||
def buy_strategy_generator(self, params) -> None:
|
||||
"""
|
||||
Define the buy strategy parameters to be used by hyperopt
|
||||
"""
|
||||
return self.custom_strategy.buy_strategy_generator(params)
|
||||
Reference in New Issue
Block a user