MultiMa V2(Errors fixed+Shift removed+offline indicator population)
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girdakan
@@ -1,8 +1,7 @@
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# MultiMa Strategy
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# MultiMa Strategy V2
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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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@@ -17,72 +16,84 @@ from functools import reduce
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class MultiMa(IStrategy):
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# 111/2000: 18 trades. 12/4/2 Wins/Draws/Losses. Avg profit 9.72%. Median profit 3.01%. Total profit 733.01234143 USDT ( 73.30%). Avg duration 2 days, 18:40:00 min. Objective: 1.67048
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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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# Buy hyperspace params:
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buy_params = {
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"buy_ma_count": 4,
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"buy_ma_gap": 15,
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}
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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(0, 10, default=0, space="sell")
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# sell_ma_rolling = IntParameter(0, 10, default=0, space='sell')
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# Sell hyperspace params:
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sell_params = {
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"sell_ma_count": 12,
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"sell_ma_gap": 68,
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}
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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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minimal_roi = {
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"0": 0.523,
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"1553": 0.123,
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"2332": 0.076,
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"3169": 0
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}
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# Stoploss:
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stoploss = -0.1
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stoploss = -0.345
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# Buy hypers
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# Trailing stop:
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trailing_stop = False # value loaded from strategy
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trailing_stop_positive = None # value loaded from strategy
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trailing_stop_positive_offset = 0.0 # value loaded from strategy
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trailing_only_offset_is_reached = False # value loaded from strategy
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# Opimal Timeframe
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timeframe = "4h"
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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count_max = 20
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gap_max = 100
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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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buy_ma_count = IntParameter(1, count_max, default=7, space="buy")
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buy_ma_gap = IntParameter(1, gap_max, default=7, space="buy")
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sell_ma_count = IntParameter(1, count_max, default=7, space="sell")
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sell_ma_gap = IntParameter(1, gap_max, default=94, space="sell")
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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for count in range(self.count_max):
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for gap in range(self.gap_max):
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if count*gap > 1 and count*gap not in dataframe.keys():
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dataframe[count*gap] = ta.TEMA(
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dataframe, timeperiod=int(count*gap)
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)
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print(" ", metadata['pair'], end="\t\r")
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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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# I used range(self.buy_ma_count.value) instade of self.buy_ma_count.range
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# Cuz it returns range(7,8) but we need range(8) for all modes hyperopt, backtest and etc
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for ma_count in range(self.buy_ma_count.value):
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key = ma_count*self.buy_ma_gap.value
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past_key = (ma_count-1)*self.buy_ma_gap.value
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if past_key > 1 and key in dataframe.keys() and past_key in dataframe.keys():
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conditions.append(dataframe[key] < dataframe[past_key])
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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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for ma_count in range(self.sell_ma_count.value):
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key = ma_count*self.sell_ma_gap.value
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past_key = (ma_count-1)*self.sell_ma_gap.value
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if past_key > 1 and key in dataframe.keys() and past_key in dataframe.keys():
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conditions.append(dataframe[key] > dataframe[past_key])
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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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dataframe.loc[reduce(lambda x, y: x | y, conditions), "sell"] = 1
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return dataframe
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