# source: https://raw.githubusercontent.com/sriram221189/freqtrade/e97856a41bf264e17c647b809b6202694771be10/supertrend_bb.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 import IStrategy from freqtrade.strategy import IntParameter, BooleanParameter from pandas import DataFrame, concat import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib class github_sriram221189_freqtrade__supertrend_bb__20230325_123557(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": 3, "buy_p1": 15, "buy_bb_window": 14, "buy_bb_std": 2 } # Sell hyperspace params: sell_params = { # "sell_m1": 3, # "sell_p1": 15 } # ROI table: minimal_roi = { "0": 0.332, "101": 0.047, "215": 0.032, "474": 0 } # Stoploss: stoploss = -0.297 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.069 trailing_stop_positive_offset = 0.086 trailing_only_offset_is_reached = True timeframe = '15m' startup_candle_count = 18 buy_m1 = IntParameter(1, 7, default=3) buy_p1 = IntParameter(7, 21, default=15) buy_bb_window = IntParameter(5, 16, default=14) buy_bb_std = IntParameter(1,3, default=2) # sell_m1 = IntParameter(1, 7, default=3) # sell_p1 = IntParameter(7, 21, default=15) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: calc_dict = {} for multiplier in self.buy_m1.range: for period in self.buy_p1.range: calc_dict['supertrend_1_buy_'+str(multiplier)+"_"+str(period)] = self.supertrend(dataframe, multiplier, period)[ 'STX'] for window in self.buy_bb_window.range: for std in self.buy_bb_std.range: bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=window, stds=std) calc_dict['bb_lower_'+str(window)+"_"+str(std)] = bollinger['lower'] calc_dict['bb_middle_' + str(window) + "_" + str(std)] = bollinger['mid'] calc_dict['bb_upper_' + str(window) + "_" + str(std)] = bollinger['upper'] # for multiplier in self.sell_m1.range: # for period in self.sell_p1.range: # calc_dict['supertrend_1_sell_' + str(multiplier) + "_" + str(period)] = self.supertrend(dataframe, multiplier, period)[ # 'STX'] supertrend_bb_data = DataFrame.from_dict(calc_dict) dataframe = concat([dataframe, supertrend_bb_data],axis=1) #print(metadata) #print(dataframe.columns) 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['close'] < dataframe[f'bb_lower_{self.buy_bb_window.value}_{self.buy_bb_std.value}']) & # (dataframe[f'supertrend_1_buy_{self.buy_m1.value}_{self.buy_p1.value}'].iloc[-2] == 'down') & # (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 """ 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) # 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 Supertrend 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] }) def supertrend_vec(self, dataframe: DataFrame, period, multiplier): """ data: pandas dataframe with Close price data period: period for moving average calculation multiplier: multiplier for ATR calculation """ # Calculate ATR st = 'ST_' + str(period) + '_' + str(multiplier) stx = 'STX_' + str(period) + '_' + str(multiplier) dataframe['ATR'] = np.mean(dataframe[['high', 'low']].rolling(period).max() - dataframe[['high', 'low']].rolling(period).min(), axis=1) # Calculate SuperTrend dataframe['SuperTrend'] = dataframe['close'].rolling(period).mean() dataframe['UpperBand'] = dataframe['SuperTrend'] + multiplier * dataframe['ATR'] dataframe['LowerBand'] = dataframe['SuperTrend'] - multiplier * dataframe['ATR'] dataframe[st] = np.where( (dataframe['close'] <= dataframe['UpperBand'].shift(1)) & (dataframe['close'] > dataframe['UpperBand']), dataframe['LowerBand'], np.where((dataframe['close'] >= dataframe['LowerBand'].shift(1)) & (dataframe['close'] < dataframe['LowerBand']), dataframe['UpperBand'], dataframe['SuperTrend'].shift(1))) dataframe[stx] = np.where((dataframe[st] > 0.00), np.where((dataframe['close'] < dataframe[st]), 'down', 'up'), np.NaN) dataframe.fillna(0, inplace=True) return DataFrame(index=dataframe.index, data={ 'ST': dataframe[st], 'STX': dataframe[stx] })