# source: https://raw.githubusercontent.com/andythierry/freqtrade-strategies/e4701f998bc8077a3fc9d55cb0132cdb46c2416c/Supertrend.py """ Github_andythierry_freqtrade_strategies__Supertrend__20250901_063243 strategy: * Description: Generate a 3 supertrend indicators for 'buy' strategies & 3 supertrend indicators for 'sell' strategies Buys if the 3 'buy' indicators are 'up' Sells if the 3 'sell' indicators are 'down' * Author: @juankysoriano (Juan Carlos Soriano) * github: https://github.com/juankysoriano/ *** NOTE: This Github_andythierry_freqtrade_strategies__Supertrend__20250901_063243 strategy is just one of many possible strategies using `Github_andythierry_freqtrade_strategies__Supertrend__20250901_063243` as indicator. It should on any case used at your own risk. It comes with at least a couple of caveats: 1. The implementation for the `supertrend` indicator is based on the following discussion: https://github.com/freqtrade/freqtrade-strategies/issues/30 . Concretelly https://github.com/freqtrade/freqtrade-strategies/issues/30#issuecomment-853042401 2. The implementation for `supertrend` on this strategy is not validated; meaning this that is not proven to match the results by the paper where it was originally introduced or any other trusted academic resources """ import logging from numpy.lib import math from freqtrade.strategy import IStrategy, IntParameter from pandas import DataFrame import talib.abstract as ta import numpy as np class Github_andythierry_freqtrade_strategies__Supertrend__20250901_063243(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": 0.087, "372": 0.058, "861": 0.029, "2221": 0 } stoploss = -0.265 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 = 199 buy_m1 = IntParameter(1, 7, default=4) buy_m2 = IntParameter(1, 7, default=4) buy_m3 = IntParameter(1, 7, default=4) buy_p1 = IntParameter(7, 21, default=14) buy_p2 = IntParameter(7, 21, default=14) buy_p3 = IntParameter(7, 21, default=14) sell_m1 = IntParameter(1, 7, default=4) sell_m2 = IntParameter(1, 7, default=4) sell_m3 = IntParameter(1, 7, default=4) sell_p1 = IntParameter(7, 21, default=14) sell_p2 = IntParameter(7, 21, default=14) sell_p3 = IntParameter(7, 21, default=14) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: for multiplier in self.buy_m1.range: for period in self.buy_p1.range: dataframe[f'supertrend_1_buy_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX'] for multiplier in self.buy_m2.range: for period in self.buy_p2.range: dataframe[f'supertrend_2_buy_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX'] for multiplier in self.buy_m3.range: for period in self.buy_p3.range: dataframe[f'supertrend_3_buy_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX'] for multiplier in self.sell_m1.range: for period in self.sell_p1.range: dataframe[f'supertrend_1_sell_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX'] for multiplier in self.sell_m2.range: for period in self.sell_p2.range: dataframe[f'supertrend_2_sell_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX'] for multiplier in self.sell_m3.range: for period in self.sell_p3.range: dataframe[f'supertrend_3_sell_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe[f'supertrend_1_buy_{self.buy_m1.value}_{self.buy_p1.value}'] == 'up') & (dataframe[f'supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}'] == 'up') & (dataframe[f'supertrend_3_buy_{self.buy_m3.value}_{self.buy_p3.value}'] == 'up') & (dataframe['volume'] > 0) ), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe[f'supertrend_1_sell_{self.sell_m1.value}_{self.sell_p1.value}'] == 'down') & (dataframe[f'supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}'] == 'down') & (dataframe[f'supertrend_3_sell_{self.sell_m3.value}_{self.sell_p3.value}'] == 'down') & (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 = 'ST_' + str(period) + '_' + str(multiplier) stx = 'STX_' + str(period) + '_' + str(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)): df[st].iat[i] = df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] <= df['final_ub'].iat[i] else df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] > df['final_ub'].iat[i] else df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] >= df['final_lb'].iat[i] else df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] < df['final_lb'].iat[i] else 0.00 df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), 'unknown') 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] })