# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Supertrend_296.py """ Supertrend 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 Supertrend strategy is just one of many possible strategies using `Supertrend` 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 datetime import datetime from numpy.lib import math from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import IntParameter, DecimalParameter, stoploss_from_open from pandas import DataFrame import talib.abstract as ta import numpy as np class Github_remiotore_ccxt_freqtrade__Supertrend_296__20260111_210550(IStrategy): 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, "pHSL": -0.32, "pPF_1": 0.02, "pPF_2": 0.047, "pSL_1": 0.02, "pSL_2": 0.046, } minimal_roi = { "0": 0.087, "372": 0.058, "861": 0.029, "2221": 0 } pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', load=True) pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True) pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True) stoploss = -0.265 trailing_stop = False trailing_stop_positive = 0.05 trailing_stop_positive_offset = 0.144 trailing_only_offset_is_reached = False use_custom_stoploss = True timeframe = '1h' startup_candle_count = 18 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) -> 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_buy_trend(self, dataframe: DataFrame) -> 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') & # The three indicators are 'up' for the current candle (dataframe['volume'] > 0) # There is at least some trading volume ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame) -> 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') & # The three indicators are 'down' for the current candle (dataframe['volume'] > 0) # There is at least some trading volume ), 'sell'] = 1 return dataframe def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value if (current_profit > PF_2): sl_profit = SL_2 + (current_profit - PF_2) elif (current_profit > PF_1): sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL if (sl_profit >= current_profit): return -0.99 return stoploss_from_open(sl_profit, current_profit) """ Supertrend Indicator; adapted for freqtrade from: https://github.com/freqtrade/freqtrade-strategies/issues/30 """ 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'), 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] })