# source: https://raw.githubusercontent.com/nyp4o/Freqtrade-strategies/4df7da1a1fecda17f475a3a70b5f5e939f4b13d7/Selfie.py # --- Hyperopt buy_params as generated/placed by Freqtrade optimizer --- buy_params = { "adx_entry_long_thresh": 15, "adx_exit_short_thresh": 17, "adx_period": 7, "bbands_std": 2.336, "bbands_window": 10, "cci_entry_long_thresh": -119, "cci_exit_short_thresh": -146, "cci_period": 17, "macd_fast": 10, "macd_signal": 6, "macd_slow": 14, "mfi_entry_long_thresh": 39, "mfi_exit_short_thresh": 30, "mfi_period": 13, "rsi_entry_long_thresh": 36, "rsi_exit_short_thresh": 15, "rsi_period": 11, "sar_af": 0.01, "sar_max": 0.27, "stoch_d": 3, "stoch_k": 14, "use_adx_mode": 1, "use_bbands_mode": 14, "use_cci_mode": 0, "use_htsine_mode": 4, "use_macd_mode": 1, "use_mfi_mode": 7, "use_rsi_mode": 5, "use_sar_mode": 9, "use_stoch_mode": 14, "adx_entry_short_thresh": 22, "adx_exit_long_thresh": 16, "cci_entry_short_thresh": 125, "cci_exit_long_thresh": 60, "mfi_entry_short_thresh": 80, "mfi_exit_long_thresh": 70, "rsi_entry_short_thresh": 62, "rsi_exit_long_thresh": 71, "leverage_opt": 1 } # --- Optimization global switches --- Indicator_optimize = True Optimize_use = True from freqtrade.strategy import IStrategy from pandas import DataFrame import talib.abstract as ta import numpy as np from freqtrade.strategy import IntParameter, DecimalParameter from datetime import datetime class Github_nyp4o_Freqtrade_strategies__Selfie__20250529_080740(IStrategy): INTERFACE_VERSION = 3 minimal_roi = { "0": 0.02, "10": 0.01, "60": 0 } stoploss = -0.1 timeframe = '5m' # Битова маска: 1=Long, 2=Short, 4=Entry, 8=Exit use_rsi_mode = IntParameter(0, 15, default=buy_params['use_rsi_mode'], space="buy", optimize=Optimize_use) use_macd_mode = IntParameter(0, 15, default=buy_params['use_macd_mode'], space="buy", optimize=Optimize_use) use_stoch_mode = IntParameter(0, 15, default=buy_params['use_stoch_mode'], space="buy", optimize=Optimize_use) use_bbands_mode = IntParameter(0, 15, default=buy_params['use_bbands_mode'], space="buy", optimize=Optimize_use) use_adx_mode = IntParameter(0, 15, default=buy_params['use_adx_mode'], space="buy", optimize=Optimize_use) use_mfi_mode = IntParameter(0, 15, default=buy_params['use_mfi_mode'], space="buy", optimize=Optimize_use) use_cci_mode = IntParameter(0, 15, default=buy_params['use_cci_mode'], space="buy", optimize=Optimize_use) use_sar_mode = IntParameter(0, 15, default=buy_params['use_sar_mode'], space="buy", optimize=Optimize_use) use_htsine_mode = IntParameter(0, 15, default=buy_params['use_htsine_mode'], space="buy", optimize=Optimize_use) # RSI (по-стегнати граници за скалпинг) rsi_period = IntParameter(7, 16, default=buy_params['rsi_period'], space="buy", optimize=Indicator_optimize) rsi_entry_long_thresh = IntParameter(30, 45, default=buy_params['rsi_entry_long_thresh'], space="buy", optimize=Indicator_optimize) rsi_exit_long_thresh = IntParameter(55, 75, default=buy_params['rsi_exit_long_thresh'], space="sell", optimize=Indicator_optimize) rsi_entry_short_thresh = IntParameter(60, 80, default=buy_params['rsi_entry_short_thresh'], space="sell", optimize=Indicator_optimize) rsi_exit_short_thresh = IntParameter(15, 40, default=buy_params['rsi_exit_short_thresh'], space="buy", optimize=Indicator_optimize) # MACD (по-реактивен за скалпинг) macd_fast = IntParameter(6, 14, default=buy_params['macd_fast'], space="buy", optimize=Indicator_optimize) macd_slow = IntParameter(12, 24, default=buy_params['macd_slow'], space="buy", optimize=Indicator_optimize) macd_signal = IntParameter(5, 10, default=buy_params['macd_signal'], space="buy", optimize=Indicator_optimize) # Stochastic stoch_k = IntParameter(5, 14, default=buy_params['stoch_k'], space="buy", optimize=Indicator_optimize) stoch_d = IntParameter(3, 7, default=buy_params['stoch_d'], space="buy", optimize=Indicator_optimize) # Bollinger Bands bbands_window = IntParameter(10, 22, default=buy_params['bbands_window'], space="buy", optimize=Indicator_optimize) bbands_std = DecimalParameter(1.5, 2.5, default=buy_params['bbands_std'], space="buy", optimize=Indicator_optimize) # ADX adx_period = IntParameter(7, 14, default=buy_params['adx_period'], space="buy", optimize=Indicator_optimize) adx_entry_long_thresh = IntParameter(10, 25, default=buy_params['adx_entry_long_thresh'], space="buy", optimize=Indicator_optimize) adx_exit_long_thresh = IntParameter(10, 25, default=buy_params['adx_exit_long_thresh'], space="sell", optimize=Indicator_optimize) adx_entry_short_thresh = IntParameter(10, 25, default=buy_params['adx_entry_short_thresh'], space="sell", optimize=Indicator_optimize) adx_exit_short_thresh = IntParameter(10, 25, default=buy_params['adx_exit_short_thresh'], space="buy", optimize=Indicator_optimize) # MFI mfi_period = IntParameter(7, 16, default=buy_params['mfi_period'], space="buy", optimize=Indicator_optimize) mfi_entry_long_thresh = IntParameter(20, 40, default=buy_params['mfi_entry_long_thresh'], space="buy", optimize=Indicator_optimize) mfi_exit_long_thresh = IntParameter(60, 80, default=buy_params['mfi_exit_long_thresh'], space="sell", optimize=Indicator_optimize) mfi_entry_short_thresh = IntParameter(60, 80, default=buy_params['mfi_entry_short_thresh'], space="sell", optimize=Indicator_optimize) mfi_exit_short_thresh = IntParameter(20, 40, default=buy_params['mfi_exit_short_thresh'], space="buy", optimize=Indicator_optimize) # CCI cci_period = IntParameter(7, 20, default=buy_params['cci_period'], space="buy", optimize=Indicator_optimize) cci_entry_long_thresh = IntParameter(-150, -50, default=buy_params['cci_entry_long_thresh'], space="buy", optimize=Indicator_optimize) cci_exit_long_thresh = IntParameter(50, 150, default=buy_params['cci_exit_long_thresh'], space="sell", optimize=Indicator_optimize) cci_entry_short_thresh = IntParameter(50, 150, default=buy_params['cci_entry_short_thresh'], space="sell", optimize=Indicator_optimize) cci_exit_short_thresh = IntParameter(-150, -50, default=buy_params['cci_exit_short_thresh'], space="buy", optimize=Indicator_optimize) # SAR sar_af = DecimalParameter(0.01, 0.05, default=buy_params['sar_af'], decimals=2, space="buy", optimize=Indicator_optimize) sar_max = DecimalParameter(0.1, 0.3, default=buy_params['sar_max'], decimals=2, space="buy", optimize=Indicator_optimize) leverage_opt = IntParameter(1, 5, default=buy_params["leverage_opt"], space="buy", optimize=Indicator_optimize) @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 2 # само 10 минути пауза }, { "method": "MaxDrawdown", "lookback_period_candles": 24, # 2 часа назад "trade_limit": 10, "stop_duration_candles": 2, # 10 минути стоп след drawdown "max_allowed_drawdown": 0.15 # по-строго за скалпинг }, { "method": "StoplossGuard", "lookback_period_candles": 12, # 1 час назад "trade_limit": 2, "stop_duration_candles": 2, "only_per_pair": True # по-добре само за всяка двойка }, { "method": "LowProfitPairs", "lookback_period_candles": 3, # 15 мин назад "trade_limit": 2, "stop_duration_candles": 20, # 100 минути стоп "required_profit": 0.01 # 1% е по-реалистично за скалпинг }, { "method": "LowProfitPairs", "lookback_period_candles": 24, # 2 часа "trade_limit": 4, "stop_duration_candles": 2, # 10 минути "required_profit": 0.005 # 0.5% за по-мек филтър } ] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # RSI if self.use_rsi_mode.value > 0: # noinspection PyUnresolvedReferences dataframe["rsi"] = ta.RSI(dataframe, timeperiod=self.rsi_period.value) # MACD if self.use_macd_mode.value > 0: # noinspection PyUnresolvedReferences macd = ta.MACD( dataframe, fastperiod=self.macd_fast.value, slowperiod=self.macd_slow.value, signalperiod=self.macd_signal.value ) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] # Stochastic if self.use_stoch_mode.value > 0: # noinspection PyUnresolvedReferences stoch = ta.STOCHF( dataframe, fastk_period=self.stoch_k.value, fastd_period=self.stoch_d.value, fastd_matype=0 ) dataframe["fastk"] = stoch["fastk"] dataframe["fastd"] = stoch["fastd"] # Bollinger Bands if self.use_bbands_mode.value > 0: bbands = self.calculate_bollinger_bands( dataframe, window=self.bbands_window.value, stds=self.bbands_std.value ) dataframe["bb_upperband"] = bbands["upper"] dataframe["bb_lowerband"] = bbands["lower"] dataframe["bb_middleband"] = bbands["mid"] # ADX if self.use_adx_mode.value > 0: # noinspection PyUnresolvedReferences dataframe["adx"] = ta.ADX(dataframe, timeperiod=self.adx_period.value) # MFI if self.use_mfi_mode.value > 0: # noinspection PyUnresolvedReferences dataframe["mfi"] = ta.MFI(dataframe, timeperiod=self.mfi_period.value) # CCI if self.use_cci_mode.value > 0: # noinspection PyUnresolvedReferences dataframe["cci"] = ta.CCI(dataframe, timeperiod=self.cci_period.value) # SAR if self.use_sar_mode.value > 0: # noinspection PyUnresolvedReferences dataframe["sar"] = ta.SAR( dataframe, acceleration=self.sar_af.value, maximum=self.sar_max.value ) # HTSINE if self.use_htsine_mode.value > 0: # noinspection PyUnresolvedReferences htsine = ta.HT_SINE(dataframe) dataframe["htsine"] = htsine["sine"] dataframe["htleadsine"] = htsine["leadsine"] return dataframe @staticmethod def calculate_bollinger_bands(dataframe: DataFrame, window: int, stds: float): tp = (dataframe["high"] + dataframe["low"] + dataframe["close"]) / 3 mid = tp.rolling(window).mean() std = tp.rolling(window).std() upper = mid + stds * std lower = mid - stds * std return {"mid": mid, "upper": upper, "lower": lower} # noinspection PyUnusedLocal def build_entry_conditions_long(self, dataframe: DataFrame, metadata: dict) -> list: conditions = [] if (self.use_rsi_mode.value & 1) and (self.use_rsi_mode.value & 4): conditions.append(dataframe["rsi"] < self.rsi_entry_long_thresh.value) if (self.use_macd_mode.value & 1) and (self.use_macd_mode.value & 4): conditions.append(dataframe["macd"] > dataframe["macdsignal"]) if (self.use_stoch_mode.value & 1) and (self.use_stoch_mode.value & 4): conditions.append((dataframe["fastk"] < 30) & (dataframe["fastd"] < 30)) if (self.use_bbands_mode.value & 1) and (self.use_bbands_mode.value & 4): conditions.append(dataframe["close"] < dataframe["bb_lowerband"]) if (self.use_adx_mode.value & 1) and (self.use_adx_mode.value & 4): conditions.append(dataframe["adx"] > self.adx_entry_long_thresh.value) if (self.use_mfi_mode.value & 1) and (self.use_mfi_mode.value & 4): conditions.append(dataframe["mfi"] < self.mfi_entry_long_thresh.value) if (self.use_cci_mode.value & 1) and (self.use_cci_mode.value & 4): conditions.append(dataframe["cci"] < self.cci_entry_long_thresh.value) if (self.use_sar_mode.value & 1) and (self.use_sar_mode.value & 4): conditions.append(dataframe["sar"] > dataframe["close"]) if (self.use_htsine_mode.value & 1) and (self.use_htsine_mode.value & 4): conditions.append(dataframe["htsine"] < dataframe["htleadsine"]) return conditions # noinspection PyUnusedLocal def build_exit_conditions_long(self, dataframe: DataFrame, metadata: dict) -> list: conditions = [] if (self.use_rsi_mode.value & 1) and (self.use_rsi_mode.value & 8): conditions.append(dataframe["rsi"] > self.rsi_exit_long_thresh.value) if (self.use_macd_mode.value & 1) and (self.use_macd_mode.value & 8): conditions.append(dataframe["macd"] < dataframe["macdsignal"]) if (self.use_stoch_mode.value & 1) and (self.use_stoch_mode.value & 8): conditions.append((dataframe["fastk"] > 70) & (dataframe["fastd"] > 70)) if (self.use_bbands_mode.value & 1) and (self.use_bbands_mode.value & 8): conditions.append(dataframe["close"] > dataframe["bb_upperband"]) if (self.use_adx_mode.value & 1) and (self.use_adx_mode.value & 8): conditions.append(dataframe["adx"] < self.adx_exit_long_thresh.value) if (self.use_mfi_mode.value & 1) and (self.use_mfi_mode.value & 8): conditions.append(dataframe["mfi"] > self.mfi_exit_long_thresh.value) if (self.use_cci_mode.value & 1) and (self.use_cci_mode.value & 8): conditions.append(dataframe["cci"] > self.cci_exit_long_thresh.value) if (self.use_sar_mode.value & 1) and (self.use_sar_mode.value & 8): conditions.append(dataframe["sar"] < dataframe["close"]) if (self.use_htsine_mode.value & 1) and (self.use_htsine_mode.value & 8): conditions.append(dataframe["htsine"] > dataframe["htleadsine"]) return conditions # noinspection PyUnusedLocal def build_entry_conditions_short(self, dataframe: DataFrame, metadata: dict) -> list: conditions = [] if (self.use_rsi_mode.value & 2) and (self.use_rsi_mode.value & 4): conditions.append(dataframe["rsi"] > self.rsi_entry_short_thresh.value) if (self.use_macd_mode.value & 2) and (self.use_macd_mode.value & 4): conditions.append(dataframe["macd"] < dataframe["macdsignal"]) if (self.use_stoch_mode.value & 2) and (self.use_stoch_mode.value & 4): conditions.append((dataframe["fastk"] > 70) & (dataframe["fastd"] > 70)) if (self.use_bbands_mode.value & 2) and (self.use_bbands_mode.value & 4): conditions.append(dataframe["close"] > dataframe["bb_upperband"]) if (self.use_adx_mode.value & 2) and (self.use_adx_mode.value & 4): conditions.append(dataframe["adx"] > self.adx_entry_short_thresh.value) if (self.use_mfi_mode.value & 2) and (self.use_mfi_mode.value & 4): conditions.append(dataframe["mfi"] > self.mfi_entry_short_thresh.value) if (self.use_cci_mode.value & 2) and (self.use_cci_mode.value & 4): conditions.append(dataframe["cci"] > self.cci_entry_short_thresh.value) if (self.use_sar_mode.value & 2) and (self.use_sar_mode.value & 4): conditions.append(dataframe["sar"] > dataframe["close"]) if (self.use_htsine_mode.value & 2) and (self.use_htsine_mode.value & 4): conditions.append(dataframe["htsine"] > dataframe["htleadsine"]) return conditions # noinspection PyUnusedLocal def build_exit_conditions_short(self, dataframe: DataFrame, metadata: dict) -> list: conditions = [] if (self.use_rsi_mode.value & 2) and (self.use_rsi_mode.value & 8): conditions.append(dataframe["rsi"] < self.rsi_exit_short_thresh.value) if (self.use_macd_mode.value & 2) and (self.use_macd_mode.value & 8): conditions.append(dataframe["macd"] > dataframe["macdsignal"]) if (self.use_stoch_mode.value & 2) and (self.use_stoch_mode.value & 8): conditions.append((dataframe["fastk"] < 30) & (dataframe["fastd"] < 30)) if (self.use_bbands_mode.value & 2) and (self.use_bbands_mode.value & 8): conditions.append(dataframe["close"] < dataframe["bb_lowerband"]) if (self.use_adx_mode.value & 2) and (self.use_adx_mode.value & 8): conditions.append(dataframe["adx"] < self.adx_exit_short_thresh.value) if (self.use_mfi_mode.value & 2) and (self.use_mfi_mode.value & 8): conditions.append(dataframe["mfi"] < self.mfi_exit_short_thresh.value) if (self.use_cci_mode.value & 2) and (self.use_cci_mode.value & 8): conditions.append(dataframe["cci"] < self.cci_exit_short_thresh.value) if (self.use_sar_mode.value & 2) and (self.use_sar_mode.value & 8): conditions.append(dataframe["sar"] < dataframe["close"]) if (self.use_htsine_mode.value & 2) and (self.use_htsine_mode.value & 8): conditions.append(dataframe["htsine"] < dataframe["htleadsine"]) return conditions def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: long_conditions = self.build_entry_conditions_long(dataframe, metadata) short_conditions = self.build_entry_conditions_short(dataframe, metadata) if long_conditions: dataframe.loc[np.all(long_conditions, axis=0), 'enter_long'] = 1 if short_conditions: dataframe.loc[np.all(short_conditions, axis=0), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: long_exit_conditions = self.build_exit_conditions_long(dataframe, metadata) short_exit_conditions = self.build_exit_conditions_short(dataframe, metadata) if long_exit_conditions: dataframe.loc[np.all(long_exit_conditions, axis=0), 'exit_long'] = 1 if short_exit_conditions: dataframe.loc[np.all(short_exit_conditions, axis=0), 'exit_short'] = 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs) -> float: return float(self.leverage_opt.value) def custom_stoploss(self, pair: str, trade, current_time, current_rate, current_profit, after_fill, **kwargs): if not trade: return self.stoploss # Трейлващ стоп към break-even if current_profit > 0.01: # при 1% печалба return max(-0.002, self.stoploss) # стоп на -0.2% if current_profit > 0.005: # при 0.5% печалба return max(-0.003, self.stoploss) # стоп на -0.3% return self.stoploss