Andy477andGitHub
1aba566216
Fixing buy_ma_count's Int Parameter value since they cause an error.
In more detail, the old version had buy_ma_count starting off at 2, then had a buy-ma plus 1.
Then the smallest period(e.g dataframe["buy-ma-3"]), is 3.
But in the old code, there is dataframe[f"buy-ma-{i-1}"], if i is equal 3, then i - 1 is equal to 2.
So forth causing an exception, as there is no dataframe["buy-ma-2"] as the smallest period is dataframe["buy-ma-3"].
The same thing happens if buy_ma_count's low value is equal to 1.
The same thing happens on the sell-side.
The only way to fix it is to have buy/sell_ma_count's low value set to 0.
89 lines
3.0 KiB
Python
89 lines
3.0 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(0, 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(0, 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(, 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 = {"0": 0.30873, "569": 0.16689, "3211": 0.06473, "7617": 0}
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# Stoploss:
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stoploss = -0.1
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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(
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dataframe, timeperiod=int((i + 1) * self.buy_ma_gap.value)
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)
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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[reduce(lambda x, y: x & y, conditions), "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(
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dataframe, timeperiod=int((i + 1) * self.sell_ma_gap.value)
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)
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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[reduce(lambda x, y: x & y, conditions), "sell"] = 1
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return dataframe
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