# source: https://raw.githubusercontent.com/XQuant42/ft_pantheon/b7127cbcf1e809f94ae075d73604b6dad7ad8cb3/user_data/strategies/FastSupertrendOpt.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 numpy.lib import math from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import IntParameter from pandas import DataFrame import talib.abstract as ta import numpy as np import time import pandas as pd class Github_XQuant42_ft_pantheon__FastSupertrendOpt__20251018_032502(IStrategy): # Buy params, Sell params, ROI, Stoploss and Trailing Stop are values generated by 'freqtrade hyperopt --strategy Supertrend --hyperopt-loss ShortTradeDurHyperOptLoss --timerange=20210101- --timeframe=1h --spaces all' # It's encourage you find the values that better suites your needs and risk management strategies # Buy hyperspace params: buy_params = { "buy_m1": 4, "buy_m2": 7, "buy_m3": 1, "buy_p1": 8, "buy_p2": 9, "buy_p3": 8, } # Sell hyperspace params: sell_params = { "sell_m1": 1, "sell_m2": 3, "sell_m3": 6, "sell_p1": 16, "sell_p2": 18, "sell_p3": 18, } # ROI table: minimal_roi = { "0": 0.087, "372": 0.058, "861": 0.029, "2221": 0 } # Stoploss: stoploss = -0.265 # Trailing stop: 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 = 18 buy_m1 = IntParameter(1, 7, default=4, space='buy', load=True, optimize=True) buy_m2 = IntParameter(1, 7, default=4, space='buy', load=True, optimize=True) buy_m3 = IntParameter(1, 7, default=4, space='buy', load=True, optimize=True) buy_p1 = IntParameter(7, 21, default=14, space='buy', load=True, optimize=True) buy_p2 = IntParameter(7, 21, default=14, space='buy', load=True, optimize=True) buy_p3 = IntParameter(7, 21, default=14, space='buy', load=True, optimize=True) sell_m1 = IntParameter(1, 7, default=4, space='sell', load=True, optimize=True) sell_m2 = IntParameter(1, 7, default=4, space='sell', load=True, optimize=True) sell_m3 = IntParameter(1, 7, default=4, space='sell', load=True, optimize=True) sell_p1 = IntParameter(7, 21, default=14, space='sell', load=True, optimize=True) sell_p2 = IntParameter(7, 21, default=14, space='sell', load=True, optimize=True) sell_p3 = IntParameter(7, 21, default=14, space='sell', load=True, optimize=True) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['supertrend_1_buy'] = self.supertrend(dataframe, self.buy_m1.value, int(self.buy_p1.value))['STX'] dataframe['supertrend_2_buy'] = self.supertrend(dataframe, self.buy_m2.value, int(self.buy_p2.value))['STX'] dataframe['supertrend_3_buy'] = self.supertrend(dataframe, self.buy_m3.value, int(self.buy_p3.value))['STX'] dataframe['supertrend_1_sell'] = self.supertrend(dataframe, self.sell_m1.value, int(self.sell_p1.value))['STX'] dataframe['supertrend_2_sell'] = self.supertrend(dataframe, self.sell_m2.value, int(self.sell_p2.value))['STX'] dataframe['supertrend_3_sell'] = self.supertrend(dataframe, self.sell_m3.value, int(self.sell_p3.value))['STX'] dataframe.loc[ ( (dataframe[f'supertrend_1_buy'] == 'up') & (dataframe[f'supertrend_2_buy'] == 'up') & (dataframe[f'supertrend_3_buy'] == '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, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe[f'supertrend_1_sell'] == 'down') & (dataframe[f'supertrend_2_sell'] == 'down') & (dataframe[f'supertrend_3_sell'] == 'down') & # The three indicators are 'down' for the current candle (dataframe['volume'] > 0) # There is at least some trading volume ), 'sell'] = 1 return dataframe """ Supertrend Indicator; adapted for freqtrade from: https://github.com/freqtrade/freqtrade-strategies/issues/30 """ def supertrend(self, dataframe: DataFrame, multiplier, period): start_time = time.time() df = dataframe.copy() last_row = dataframe.tail(1).index.item() df['TR'] = ta.TRANGE(df) df['ATR'] = ta.SMA(df['TR'], period) st = 'ST_' + str(period) + '_' + str(multiplier) stx = 'STX_' + str(period) + '_' + str(multiplier) # Compute basic upper and lower bands BASIC_UB = ((df['high'] + df['low']) / 2 + multiplier * df['ATR']).values BASIC_LB = ((df['high'] + df['low']) / 2 - multiplier * df['ATR']).values FINAL_UB = np.zeros(last_row + 1) FINAL_LB = np.zeros(last_row + 1) ST = np.zeros(last_row + 1) CLOSE = df['close'].values # Compute final upper and lower bands for i in range(period, last_row + 1): FINAL_UB[i] = BASIC_UB[i] if BASIC_UB[i] < FINAL_UB[i - 1] or CLOSE[i - 1] > FINAL_UB[i - 1] else FINAL_UB[i - 1] FINAL_LB[i] = BASIC_LB[i] if BASIC_LB[i] > FINAL_LB[i - 1] or CLOSE[i - 1] < FINAL_LB[i - 1] else FINAL_LB[i - 1] # Set the Supertrend value for i in range(period, last_row + 1): ST[i] = FINAL_UB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] <= FINAL_UB[i] else \ FINAL_LB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] > FINAL_UB[i] else \ FINAL_LB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] >= FINAL_LB[i] else \ FINAL_UB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] < FINAL_LB[i] else 0.00 df_ST = pd.DataFrame(ST, columns=[st]) df = pd.concat([df, df_ST],axis=1) # Mark the trend direction up/down df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.NaN) df.fillna(0, inplace=True) end_time = time.time() # print("total time taken this loop: ", end_time - start_time) return DataFrame(index=df.index, data={ 'ST' : df[st], 'STX' : df[stx] })