95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
# MultiMa Strategy
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# (First Hyperopt it.A hyperopt file is available)
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#
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# --- Do not remove these libs ---
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from freqtrade.strategy.hyper import IntParameter
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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# --------------------------------
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# Add your lib to import here
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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 functools import reduce
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class MultiMa(IStrategy):
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buy_ma_count = IntParameter(2, 10, default=10, space='buy')
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buy_ma_gap = IntParameter(2, 10, default=2, space='buy')
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buy_ma_shift = IntParameter(0, 10, default=0, space='buy')
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# buy_ma_rolling = IntParameter(0, 10, default=0, space='buy')
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sell_ma_count = IntParameter(2, 10, default=10, space='sell')
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sell_ma_gap = IntParameter(2, 10, default=2, space='sell')
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sell_ma_shift = IntParameter(0, 10, default=0, space='sell')
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# sell_ma_rolling = IntParameter(0, 10, default=0, space='sell')
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# ROI table:
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minimal_roi = {
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"0": 0.30873,
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"569": 0.16689,
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"3211": 0.06473,
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"7617": 0
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}
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# Stoploss:
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stoploss = -0.128
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# Buy hypers
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timeframe = '4h'
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# We will dinamicly generate the indicators
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# cuz this method just run one time in hyperopts
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# if you have static timeframes you can move first loop of buy and sell trends populators inside this method
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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for i in self.buy_ma_count.range:
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dataframe[f'buy-ma-{i+1}'] = ta.SMA(dataframe,
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timeperiod=int((i+1) * self.buy_ma_gap.value))
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conditions = []
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for i in self.buy_ma_count.range:
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if i > 1:
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shift = self.buy_ma_shift.value
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for shift in self.buy_ma_shift.range:
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conditions.append(
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dataframe[f'buy-ma-{i}'].shift(shift) >
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dataframe[f'buy-ma-{i-1}'].shift(shift)
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)
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if conditions:
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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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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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for i in self.sell_ma_count.range:
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dataframe[f'sell-ma-{i+1}'] = ta.SMA(dataframe,
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timeperiod=int((i+1) * self.sell_ma_gap.value))
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conditions = []
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for i in self.sell_ma_count.range:
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if i > 1:
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shift = self.sell_ma_shift.value
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for shift in self.sell_ma_shift.range:
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conditions.append(
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dataframe[f'sell-ma-{i}'].shift(shift) <
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dataframe[f'sell-ma-{i-1}'].shift(shift)
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)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'sell']=1
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return dataframe
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