Masoud AziziandGitHub
0dfcb17017
I just used and splited for loop at first of methods cuz of be easy to move it upside in indicator populators and make timeframes static
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 = -1
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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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