# source: https://raw.githubusercontent.com/dumpsayamrat/my-freqtrade/333c302f84d0fd5fad20fee71ba92beea4b25efc/user_data/strategies/FSupertrendStrategy.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, IntParameter from pandas import DataFrame import talib.abstract as ta import numpy as np import pandas as pd class Github_dumpsayamrat_my_freqtrade__FSupertrendStrategy__20250511_050546(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 INTERFACE_VERSION: int = 3 # Buy hyperspace params: # Buy hyperspace params: buy_params = { "buy_m1": 6, "buy_m2": 6, "buy_m3": 2, "buy_p1": 19, "buy_p2": 20, "buy_p3": 17, } # Sell hyperspace params: sell_params = { "sell_m1": 3, "sell_m2": 7, "sell_m3": 6, "sell_p1": 15, "sell_p2": 15, "sell_p3": 21, } # ROI table: minimal_roi = { "0": 0.309, "144": 0.077, "448": 0.037, "1766": 0 } # Stoploss: stoploss = -0.28 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.127 trailing_stop_positive_offset = 0.216 trailing_only_offset_is_reached = False timeframe = "1h" startup_candle_count = 18 buy_m1 = IntParameter(1, 7, default=1) buy_m2 = IntParameter(1, 7, default=3) buy_m3 = IntParameter(1, 7, default=4) buy_p1 = IntParameter(7, 21, default=14) buy_p2 = IntParameter(7, 21, default=10) buy_p3 = IntParameter(7, 21, default=10) sell_m1 = IntParameter(1, 7, default=1) sell_m2 = IntParameter(1, 7, default=3) sell_m3 = IntParameter(1, 7, default=4) sell_p1 = IntParameter(7, 21, default=14) sell_p2 = IntParameter(7, 21, default=10) sell_p3 = IntParameter(7, 21, default=10) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: frames = [] # BUY supertrends for multiplier, period, idx in [ (m, p, 1) for m in self.buy_m1.range for p in self.buy_p1.range ] + [ (m, p, 2) for m in self.buy_m2.range for p in self.buy_p2.range ] + [ (m, p, 3) for m in self.buy_m3.range for p in self.buy_p3.range ]: colname = f"supertrend_{idx}_buy_{multiplier}_{period}" stx = self.supertrend(dataframe, multiplier, period)["STX"] frames.append(pd.DataFrame({colname: stx}, index=dataframe.index)) # SELL supertrends for multiplier, period, idx in [ (m, p, 1) for m in self.sell_m1.range for p in self.sell_p1.range ] + [ (m, p, 2) for m in self.sell_m2.range for p in self.sell_p2.range ] + [ (m, p, 3) for m in self.sell_m3.range for p in self.sell_p3.range ]: colname = f"supertrend_{idx}_sell_{multiplier}_{period}" stx = self.supertrend(dataframe, multiplier, period)["STX"] frames.append(pd.DataFrame({colname: stx}, index=dataframe.index)) # one big join indicators = pd.concat(frames, axis=1) dataframe = dataframe.join(indicators) 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 ), "enter_long", ] = 1 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 ), "enter_short", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( dataframe[ f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}" ] == "down" ), "exit_long", ] = 1 dataframe.loc[ ( dataframe[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"] == "up" ), "exit_short", ] = 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]})