# source: https://raw.githubusercontent.com/marcosrioj/cryptosignal/0668b2fc26ee17c51a9acf92aa6e17fe38cedc3f/user_data/strategies/freqtrade-strategies/Supertrend.py """ Github_marcosrioj_cryptosignal__Supertrend__20220514_010047 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_marcosrioj_cryptosignal__Supertrend__20220514_010047 strategy is just one of many possible strategies using `Github_marcosrioj_cryptosignal__Supertrend__20220514_010047` 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.hyper import IntParameter from pandas import DataFrame import talib.abstract as ta import numpy as np class Github_marcosrioj_cryptosignal__Supertrend__20220514_010047(IStrategy): # Buy params, Sell params, ROI, Stoploss and Trailing Stop are values generated by 'freqtrade hyperopt --strategy Github_marcosrioj_cryptosignal__Supertrend__20220514_010047 --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) 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_buy_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 ), 'buy'] = 1 return dataframe def populate_sell_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 ), 'sell'] = 1 return dataframe """ Github_marcosrioj_cryptosignal__Supertrend__20220514_010047 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 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] # Set the Github_marcosrioj_cryptosignal__Supertrend__20220514_010047 value 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 # Mark the trend direction up/down df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.NaN) # 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] })