# source: https://raw.githubusercontent.com/jepeloa/mapplyStrategy/0f2da105098c58c0a3b88a8dc6aaa12ef9c9e3ce/mapplySupertrend_lateral.py import logging from freqtrade.strategy import IStrategy, IntParameter from pandas import DataFrame import talib.abstract as ta import numpy as np class Github_jepeloa_mapplyStrategy__mapplySupertrend_lateral__20241211_201923(IStrategy): INTERFACE_VERSION: int = 3 buy_params = { "buy_m1": 4, "buy_m2": 7, "buy_m3": 1, "buy_p1": 8, "buy_p2": 9, "buy_p3": 8, } sell_params = { "sell_m1": 1, "sell_m2": 3, "sell_m3": 6, "sell_p1": 16, "sell_p2": 18, "sell_p3": 18, } minimal_roi = { "0": 0.05, "372": 0.03, "861": 0.01, "2221": 0 } stoploss = -0.12 trailing_stop = True trailing_stop_positive = 0.05 trailing_stop_positive_offset = 0.144 trailing_only_offset_is_reached = False timeframe = '1h' startup_candle_count = 200 buy_m1 = IntParameter(1, 7, default=4, space='buy') buy_m2 = IntParameter(1, 7, default=4, space='buy') buy_m3 = IntParameter(1, 7, default=4, space='buy') buy_p1 = IntParameter(7, 21, default=14, space='buy') buy_p2 = IntParameter(7, 21, default=14, space='buy') buy_p3 = IntParameter(7, 21, default=14, space='buy') sell_m1 = IntParameter(1, 7, default=4, space='sell') sell_m2 = IntParameter(1, 7, default=4, space='sell') sell_m3 = IntParameter(1, 7, default=4, space='sell') sell_p1 = IntParameter(7, 21, default=14, space='sell') sell_p2 = IntParameter(7, 21, default=14, space='sell') sell_p3 = IntParameter(7, 21, default=14, space='sell') def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Calcular supertrends para la configuración actual supertrend_1_buy = self.supertrend(dataframe, self.buy_m1.value, self.buy_p1.value) supertrend_2_buy = self.supertrend(dataframe, self.buy_m2.value, self.buy_p2.value) supertrend_3_buy = self.supertrend(dataframe, self.buy_m3.value, self.buy_p3.value) supertrend_1_sell = self.supertrend(dataframe, self.sell_m1.value, self.sell_p1.value) supertrend_2_sell = self.supertrend(dataframe, self.sell_m2.value, self.sell_p2.value) supertrend_3_sell = self.supertrend(dataframe, self.sell_m3.value, self.sell_p3.value) dataframe['supertrend_1_buy'] = supertrend_1_buy['STX'] dataframe['supertrend_2_buy'] = supertrend_2_buy['STX'] dataframe['supertrend_3_buy'] = supertrend_3_buy['STX'] dataframe['supertrend_1_sell'] = supertrend_1_sell['STX'] dataframe['supertrend_2_sell'] = supertrend_2_sell['STX'] dataframe['supertrend_3_sell'] = supertrend_3_sell['STX'] # SMA de 200 para filtro de tendencia dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) # ADX + DI+ y DI- dataframe['ADX'] = ta.ADX(dataframe, timeperiod=14) dataframe['DI_plus'] = ta.PLUS_DI(dataframe, timeperiod=14) dataframe['DI_minus'] = ta.MINUS_DI(dataframe, timeperiod=14) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Filtro ADX: tendencia fuerte (por ej. ADX > 20) adx_threshold = 20 di_difference_threshold = 5 dataframe.loc[ ( (dataframe['supertrend_1_sell'] == 'down') & (dataframe['supertrend_2_sell'] == 'down') & (dataframe['supertrend_3_sell'] == 'down') & (dataframe['ADX'] < adx_threshold) & # Mercado lateral (abs(dataframe['DI_plus'] - dataframe['DI_minus']) < di_difference_threshold) & # Confirmar lateralidad (abs(dataframe['close'] - dataframe['sma_200']) < dataframe['sma_200'] * 0.02) & # Precio cerca de SMA200 (dataframe['volume'] > 0) # Filtro de volumen ), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Mantenemos la salida igual, o podríamos exigir también condiciones de ADX/DI para salir. # Por simplicidad, dejemos que la salida sea más libre. adx_threshold=20 dataframe.loc[ ( (dataframe['supertrend_1_buy'] == 'up') & (dataframe['supertrend_2_buy'] == 'up') & (dataframe['supertrend_3_buy'] == 'up') & (dataframe['volume'] > 0) ), 'exit_long'] = 1 return dataframe def supertrend(self, dataframe: DataFrame, multiplier, period): df = dataframe.copy() df['TR'] = ta.TRANGE(df) df['ATR'] = ta.SMA(df['TR'], period) st = f'ST_{period}_{multiplier}' stx = f'STX_{period}_{multiplier}' df['basic_ub'] = (df['high'] + df['low']) / 2 + multiplier * df['ATR'] df['basic_lb'] = (df['high'] + df['low']) / 2 - multiplier * df['ATR'] df['final_ub'] = 0.00 df['final_lb'] = 0.00 for i in range(period, len(df)): df['final_ub'].iat[i] = (df['basic_ub'].iat[i] if df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1] or df['close'].iat[i - 1] > df['final_ub'].iat[i - 1] else df['final_ub'].iat[i - 1]) df['final_lb'].iat[i] = (df['basic_lb'].iat[i] if df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1] or df['close'].iat[i - 1] < df['final_lb'].iat[i - 1] else df['final_lb'].iat[i - 1]) df[st] = 0.00 for i in range(period, len(df)): if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] <= df['final_ub'].iat[i]: df[st].iat[i] = df['final_ub'].iat[i] elif df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] > df['final_ub'].iat[i]: df[st].iat[i] = df['final_lb'].iat[i] elif df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] >= df['final_lb'].iat[i]: df[st].iat[i] = df['final_lb'].iat[i] elif df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] < df['final_lb'].iat[i]: df[st].iat[i] = df['final_ub'].iat[i] df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.NaN) df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1) df.fillna(0, inplace=True) return DataFrame(index=df.index, data={ 'ST': df[st], 'STX': df[stx] })