# source: https://raw.githubusercontent.com/IvPalmer/Master-Trader/c7955a15d4bfe82e567bc2bd315a372000a442fd/ft_userdata/user_data/strategies/SupertrendStrategyLaunch.py # directory_url: https://github.com/IvPalmer/Master-Trader/blob/main/ft_userdata/user_data/strategies/ # User: IvPalmer # Repository: Master-Trader # --------------------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_IvPalmer_Master_Trader__SupertrendStrategyLaunch__20260923_012354(IStrategy): INTERFACE_VERSION: int = 3 buy_params = { "buy_m1": 4, "buy_m2": 7, "buy_m3": 1, "buy_p1": 8, "buy_p2": 9, "buy_p3": 8, } sell_params = { "sell_m1": 1, "sell_m2": 3, "sell_m3": 6, "sell_p1": 16, "sell_p2": 18, "sell_p3": 18, } minimal_roi = { "0": 0.087, "372": 0.058, "861": 0.029, "2221": 0 } stoploss = -0.10 # MAE data: worst winner MAE was -4.90%, -10% for 1h buffer 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 = 199 @property def protections(self): return [ {"method": "CooldownPeriod", "stop_duration_candles": 2}, {"method": "StoplossGuard", "lookback_period_candles": 48, "trade_limit": 2, "stop_duration_candles": 24, "only_per_pair": False}, {"method": "LowProfitPairs", "lookback_period_candles": 288, "trade_limit": 4, "stop_duration_candles": 48, "required_profit": -0.05}, {"method": "MaxDrawdown", "lookback_period_candles": 48, "max_allowed_drawdown": 0.20, "stop_duration_candles": 12, "trade_limit": 1}, ] 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: new_cols = [] for multiplier in self.buy_m1.range: for period in self.buy_p1.range: st = self.supertrend(dataframe, multiplier, period)[['STX']].rename( columns={'STX': f'supertrend_1_buy_{multiplier}_{period}'}) new_cols.append(st) for multiplier in self.buy_m2.range: for period in self.buy_p2.range: st = self.supertrend(dataframe, multiplier, period)[['STX']].rename( columns={'STX': f'supertrend_2_buy_{multiplier}_{period}'}) new_cols.append(st) for multiplier in self.buy_m3.range: for period in self.buy_p3.range: st = self.supertrend(dataframe, multiplier, period)[['STX']].rename( columns={'STX': f'supertrend_3_buy_{multiplier}_{period}'}) new_cols.append(st) for multiplier in self.sell_m1.range: for period in self.sell_p1.range: st = self.supertrend(dataframe, multiplier, period)[['STX']].rename( columns={'STX': f'supertrend_1_sell_{multiplier}_{period}'}) new_cols.append(st) for multiplier in self.sell_m2.range: for period in self.sell_p2.range: st = self.supertrend(dataframe, multiplier, period)[['STX']].rename( columns={'STX': f'supertrend_2_sell_{multiplier}_{period}'}) new_cols.append(st) for multiplier in self.sell_m3.range: for period in self.sell_p3.range: st = self.supertrend(dataframe, multiplier, period)[['STX']].rename( columns={'STX': f'supertrend_3_sell_{multiplier}_{period}'}) new_cols.append(st) if new_cols: dataframe = pd.concat([dataframe] + new_cols, axis=1) # --- Regime Detection --- # ATR regime — detect crash/high volatility periods dataframe['regime_atr_14'] = ta.ATR(dataframe, timeperiod=14) dataframe['regime_atr_sma_50'] = dataframe['regime_atr_14'].rolling(50).mean() dataframe['regime_volatile'] = (dataframe['regime_atr_14'] > 2.0 * dataframe['regime_atr_sma_50']).astype(int) # ADX regime — detect trending vs ranging dataframe['regime_adx_14'] = ta.ADX(dataframe, timeperiod=14) dataframe['regime_trending'] = (dataframe['regime_adx_14'] > 25).astype(int) 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') & (dataframe['volume'] > 0) & (dataframe['regime_volatile'] == 0) & # Don't enter during high volatility (dataframe['regime_trending'] == 1) # Trend following: only enter in trending markets (ADX > 25) ), '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') & (dataframe['volume'] > 0) ) | (dataframe['regime_atr_14'] > 2.5 * dataframe['regime_atr_sma_50']), # Exit on volatility spike 'exit_long'] = 1 return dataframe def supertrend(self, dataframe: pd.DataFrame, multiplier, period): df = dataframe.copy() high = df['high'].values low = df['low'].values close = df['close'].values length = len(df) tr = ta.TRANGE(df['high'], df['low'], df['close']) atr = pd.Series(tr).rolling(period).mean().to_numpy() basic_ub = (high + low) / 2 + multiplier * atr basic_lb = (high + low) / 2 - multiplier * atr final_ub = np.zeros(length) final_lb = np.zeros(length) for i in range(period, length): 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] st = np.zeros(length) for i in range(period, length): if st[i-1] == final_ub[i-1]: st[i] = final_ub[i] if close[i] <= final_ub[i] else final_lb[i] elif st[i-1] == final_lb[i-1]: st[i] = final_lb[i] if close[i] >= final_lb[i] else final_ub[i] stx = np.where(st > 0, np.where(close < st, 'down', 'up'), None) result = pd.DataFrame({'ST': st, 'STX': stx}, index=df.index) result.fillna(0, inplace=True) return result