# source: https://raw.githubusercontent.com/IvPalmer/Master-Trader/c3a837bc9907fdcab2f25cc43d250faabaf2e408/ft_userdata/user_data/strategies/ClucHAnix.py # directory_url: https://github.com/IvPalmer/Master-Trader/blob/main/ft_userdata/user_data/strategies/ # User: IvPalmer # Repository: Master-Trader # --------------------import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, DecimalParameter, stoploss_from_open, stoploss_from_absolute, RealParameter from pandas import DataFrame, Series from datetime import datetime def bollinger_bands(stock_price, window_size, num_of_std): rolling_mean = stock_price.rolling(window=window_size).mean() rolling_std = stock_price.rolling(window=window_size).std() lower_band = rolling_mean - (rolling_std * num_of_std) return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band) def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3. return Series(index=bars.index, data=res) class Github_IvPalmer_Master_Trader__ClucHAnix__20260923_203754(IStrategy): INTERFACE_VERSION = 3 buy_params = { 'bbdelta_close': 0.01965, 'bbdelta_tail': 0.95089, 'close_bblower': 0.00799, 'closedelta_close': 0.00556, 'rocr_1h': 0.54904 } sell_params = { "pHSL": -0.08, "pPF_1": 0.016, # DecimalParameter default (was 0.02 — zero cushion) "pPF_2": 0.080, # DecimalParameter default (was 0.047) "pSL_1": 0.011, # DecimalParameter default (was 0.02) "pSL_2": 0.040, # DecimalParameter default (was 0.046) 'sell-fisher': 0.38414, 'sell-bbmiddle-close': 1.07634 } minimal_roi = { "70": 0 } stoploss = -0.10 # safety net for custom_stoploss failures/restarts trailing_stop = False timeframe = '5m' # Changed from 1m for practical backtesting use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False use_custom_stoploss = True process_only_new_candles = True startup_candle_count = 168 @property def protections(self): return [ {"method": "CooldownPeriod", "stop_duration_candles": 5}, {"method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 3, "stop_duration_candles": 12, "only_per_pair": True}, {"method": "LowProfitPairs", "lookback_period_candles": 72, "trade_limit": 2, "stop_duration_candles": 72, "required_profit": -0.02}, {"method": "MaxDrawdown", "lookback_period_candles": 576, "max_allowed_drawdown": 0.20, "stop_duration_candles": 72, "trade_limit": 1}, ] order_types = { 'entry': 'limit', 'exit': 'limit', 'emergency_exit': 'limit', 'force_entry': "limit", 'force_exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } # buy params rocr_1h = RealParameter(0.5, 1.0, default=0.54904, space='buy', optimize=True) bbdelta_close = RealParameter(0.0005, 0.02, default=0.01965, space='buy', optimize=True) closedelta_close = RealParameter(0.0005, 0.02, default=0.00556, space='buy', optimize=True) bbdelta_tail = RealParameter(0.7, 1.0, default=0.95089, space='buy', optimize=True) close_bblower = RealParameter(0.0005, 0.02, default=0.00799, space='buy', optimize=True) # custom stoploss params pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', load=True) # MAE data: winners never dipped below -1.72%, -8% is 4x buffer pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True) pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # --- Time-based exit tightening (5m strategy: 93% of winners close in <50min) --- trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600 if trade_duration > 8: # Force close after 8h — 5m dip thesis is dead return -0.001 elif trade_duration > 4: # Tight stop: accept max -3% loss (if already past -3%, force close) time_sl = stoploss_from_open(-0.03, current_profit) if time_sl != -0.99: return time_sl else: return -0.001 # already past -3%, force close now elif trade_duration > 2: # Tighten via ATR 2x atr_sl = self._atr_stoploss(pair, current_rate, multiplier=2.0) if atr_sl is not None: return atr_sl # --- Original stepped profit-lock stoploss --- HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value if (current_profit > PF_2): sl_profit = SL_2 + (current_profit - PF_2) elif (current_profit > PF_1): sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL if (sl_profit >= current_profit): return HSL # maintain hard stoploss floor (was -0.99 which disabled SL entirely) return stoploss_from_open(sl_profit, current_profit) def _atr_stoploss(self, pair: str, current_rate: float, multiplier: float = 3.0): """ATR-based dynamic stoploss.""" dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) < 1: return None atr = dataframe.iloc[-1]['regime_atr_14'] if np.isnan(atr) or atr <= 0: return None stoploss_price = current_rate - (atr * multiplier) return stoploss_from_absolute(stoploss_price, current_rate=current_rate, is_short=False, leverage=1.0) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] mid, lower = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2) dataframe['lower'] = lower dataframe['mid'] = mid dataframe['bbdelta'] = (mid - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs() dataframe['bb_lowerband'] = dataframe['lower'] dataframe['bb_middleband'] = dataframe['mid'] dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3) dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) rsi = ta.RSI(dataframe) dataframe["rsi"] = rsi rsi = 0.1 * (rsi - 50) dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) inf_tf = '1h' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) inf_heikinashi = qtpylib.heikinashi(informative) informative['ha_close'] = inf_heikinashi['close'] informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) # --- 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'] > 35).astype(int) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( dataframe['rocr_1h'].gt(self.rocr_1h.value) ) & (( (dataframe['lower'].shift().gt(0)) & (dataframe['bbdelta'].gt(dataframe['ha_close'] * self.bbdelta_close.value)) & (dataframe['closedelta'].gt(dataframe['ha_close'] * self.closedelta_close.value)) & (dataframe['tail'].lt(dataframe['bbdelta'] * self.bbdelta_tail.value)) & (dataframe['ha_close'].lt(dataframe['lower'].shift())) & (dataframe['ha_close'].le(dataframe['ha_close'].shift())) ) | ( (dataframe['ha_close'] < dataframe['ema_slow']) & (dataframe['ha_close'] < self.close_bblower.value * dataframe['bb_lowerband']) )) & (dataframe['regime_volatile'] == 0) & # Don't enter during high volatility (dataframe['regime_trending'] == 0), # Mean reversion: only enter in ranging markets (ADX < 35) 'enter_long' ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe['fisher'] > self.sell_params['sell-fisher']) & (dataframe['ha_high'].le(dataframe['ha_high'].shift(1))) & (dataframe['ha_high'].shift(1).le(dataframe['ha_high'].shift(2))) & (dataframe['ha_close'].le(dataframe['ha_close'].shift(1))) & (dataframe['ema_fast'] > dataframe['ha_close']) & ((dataframe['ha_close'] * self.sell_params['sell-bbmiddle-close']) > dataframe['bb_middleband']) & (dataframe['volume'] > 0) | ((dataframe['regime_atr_14'] > 2.5 * dataframe['regime_atr_sma_50']) & (dataframe['volume'] > 0)), # Exit on volatility spike 'exit_long' ] = 1 return dataframe