# source: https://raw.githubusercontent.com/Kblack0610/freqtrade_trading_attempt/46a1c5ee93464f818fd134d3555c82976007f379/user_data/strategies/Supertrend.py # directory_url: https://github.com/Kblack0610/freqtrade_trading_attempt/blob/master/user_data/strategies/ # User: Kblack0610 # Repository: freqtrade_trading_attempt # --------------------""" Github_Kblack0610_freqtrade_trading_attempt__Supertrend__20261105_012206 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_Kblack0610_freqtrade_trading_attempt__Supertrend__20261105_012206 strategy is just one of many possible strategies using `Github_Kblack0610_freqtrade_trading_attempt__Supertrend__20261105_012206` 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_Kblack0610_freqtrade_trading_attempt__Supertrend__20261105_012206(IStrategy): # Buy params, Sell params, ROI, Stoploss and Trailing Stop are values generated by 'freqtrade hyperopt --strategy Github_Kblack0610_freqtrade_trading_attempt__Supertrend__20261105_012206 --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 INTERFACE_VERSION: int = 3 # 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) 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') & # The three indicators are 'up' for the current candle (dataframe['volume'] > 0) # There is at least some trading volume ), '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') & # The three indicators are 'down' for the current candle (dataframe['volume'] > 0) # There is at least some trading volume ), 'exit_long'] = 1 return dataframe """ Github_Kblack0610_freqtrade_trading_attempt__Supertrend__20261105_012206 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) # Compute basic upper and lower bands df['basic_ub'] = (df['high'] + df['low']) / 2 + multiplier * df['ATR'] df['basic_lb'] = (df['high'] + df['low']) / 2 - multiplier * df['ATR'] # Compute final upper and lower bands. The loops run on numpy arrays: chained # df[col].iat[i] = x writes to a copy under pandas Copy-on-Write and is lost. close = df['close'].to_numpy() basic_ub = df['basic_ub'].to_numpy() basic_lb = df['basic_lb'].to_numpy() final_ub = np.zeros(len(df)) final_lb = np.zeros(len(df)) for i in range(period, len(df)): 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 Github_Kblack0610_freqtrade_trading_attempt__Supertrend__20261105_012206 value st_val = np.zeros(len(df)) for i in range(period, len(df)): st_val[i] = final_ub[i] if st_val[i - 1] == final_ub[i - 1] and close[i] <= final_ub[i] else \ final_lb[i] if st_val[i - 1] == final_ub[i - 1] and close[i] > final_ub[i] else \ final_lb[i] if st_val[i - 1] == final_lb[i - 1] and close[i] >= final_lb[i] else \ final_ub[i] if st_val[i - 1] == final_lb[i - 1] and close[i] < final_lb[i] else 0.00 df['final_ub'] = final_ub df['final_lb'] = final_lb df[st] = st_val # Mark the trend direction up/down df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), '') # Remove basic and final bands from the columns 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] })