# source: https://raw.githubusercontent.com/0xkl/freqtrade-strategies-load/219a2adbebbf5c4d592ee17f3198e4ea68507988/BandtasticFiboHyper_opt79_885.py import talib.abstract as ta import numpy as np import pandas as pd from functools import reduce from pandas import DataFrame import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy import ( IStrategy, CategoricalParameter, DecimalParameter, IntParameter, RealParameter ) # strategy_Github_0xkl_freqtrade_strategies_load__BandtasticFiboHyper_opt79_885__20251129_081439 超参数硬编码结果 # 885/1000: 805 trades. 457/87/261 Wins/Draws/Losses. # Avg profit 0.45%. # Median profit 0.71%. # Total profit 827.41331745 USDT ( 82.74%). # Avg duration 2:20:00 min.5 # Objective: 0.95259 # max_open_trades = 5 class Github_0xkl_freqtrade_strategies_load__BandtasticFiboHyper_opt79_885__20251129_081439(IStrategy): INTERFACE_VERSION = 3 can_short = True timeframe = '5m' # ROI table (from hyperspace) minimal_roi = { "0": 0.222, "11": 0.097, "52": 0.036, "119": 0 } stoploss = -0.301 startup_candle_count = 999 trailing_stop = True trailing_stop_positive = 0.345 trailing_stop_positive_offset = 0.444 trailing_only_offset_is_reached = False # Max open trades max_open_trades = 5 # ========= 杠杆参数 ========= max_leverage = DecimalParameter(1.0, 5.0, default=3.67, space='protection', optimize=False) max_short_leverage = DecimalParameter(1.0, 3.0, default=2.859, space='protection', optimize=False) atr_threshold_low = DecimalParameter(0.005, 0.03, default=0.025, space='protection', optimize=False) atr_threshold_high = DecimalParameter(0.02, 0.08, default=0.067, space='protection', optimize=False) # ========= long 参数 ========= buy_fastema = IntParameter(1, 236, default=152, space='buy', optimize=False) buy_slowema = IntParameter(1, 250, default=40, space='buy', optimize=False) buy_rsi = IntParameter(15, 70, default=67, space='buy', optimize=False) buy_mfi = IntParameter(15, 70, default=42, space='buy', optimize=False) buy_rsi_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False) buy_mfi_enabled = CategoricalParameter([True, False], default=False, space='buy', optimize=False) buy_ema_enabled = CategoricalParameter([True, False], default=False, space='buy', optimize=False) buy_trigger = CategoricalParameter(['bb_lower1', 'bb_lower2', 'bb_lower3', 'bb_lower4', 'fibonacci'], default='bb_lower4', space='buy', optimize=False) buy_fib_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False) buy_fib_level = CategoricalParameter(['fib_236', 'fib_382', 'fib_5', 'fib_618', 'fib_786'], default='fib_618', space='buy', optimize=False) # ====== Short 参数 ========== short_fastema = IntParameter(1, 250, default=35, space='sell', optimize=False) short_slowema = IntParameter(1, 250, default=143, space='sell', optimize=False) short_rsi = IntParameter(30, 100, default=92, space='sell', optimize=False) short_mfi = IntParameter(30, 100, default=68, space='sell', optimize=False) short_rsi_enabled = CategoricalParameter([True, False], default=False, space='sell', optimize=False) short_mfi_enabled = CategoricalParameter([True, False], default=True, space='sell', optimize=False) short_ema_enabled = CategoricalParameter([True, False], default=False, space='sell', optimize=False) short_trigger = CategoricalParameter(['bb_upper1', 'bb_upper2', 'bb_upper3', 'bb_upper4'], default='bb_upper4', space='sell', optimize=False) # ========= Sell 参数 ========= sell_fastema = IntParameter(1, 365, default=212, space='sell', optimize=False) sell_slowema = IntParameter(1, 365, default=213, space='sell', optimize=False) sell_rsi = IntParameter(30, 100, default=63, space='sell', optimize=False) sell_mfi = IntParameter(30, 100, default=53, space='sell', optimize=False) sell_rsi_enabled = CategoricalParameter([True, False], default=False, space='sell', optimize=False) sell_mfi_enabled = CategoricalParameter([True, False], default=False, space='sell', optimize=False) sell_ema_enabled = CategoricalParameter([True, False], default=False, space='sell', optimize=False) sell_trigger = CategoricalParameter(['sell-bb_upper1', 'sell-bb_upper2', 'sell-bb_upper3', 'sell-bb_upper4'], default='sell-bb_upper3', space='sell', optimize=False) cover_fastema = IntParameter(1, 250, default=45, space='buy', optimize=False) cover_slowema = IntParameter(1, 250, default=235, space='buy', optimize=False) cover_rsi = IntParameter(10, 70, default=64, space='buy', optimize=False) cover_mfi = IntParameter(10, 70, default=63, space='buy', optimize=False) cover_rsi_enabled = CategoricalParameter([True, False], default=False, space='buy', optimize=False) cover_mfi_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False) cover_ema_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False) cover_trigger = CategoricalParameter(['bb_lower1', 'bb_lower2', 'bb_lower3', 'bb_lower4', 'fibonacci'], default='bb_lower1', space='buy', optimize=False) cover_fib_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False) cover_fib_level = CategoricalParameter(['fib_236', 'fib_382', 'fib_5', 'fib_618', 'fib_786'], default='fib_382', space='buy', optimize=False) def leverage(self, pair: str, current_time: 'datetime', current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: """ 使用 ATR/价格 标准化波动率动态调整杠杆。 """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if dataframe is None or len(dataframe) < 20: return 2.0 # fallback 默认值 close = dataframe['close'].iloc[-1] atr = ta.ATR(dataframe, timeperiod=14).iloc[-1] normalized_atr = atr / close if close > 0 else 0 if normalized_atr < self.atr_threshold_low.value: lev = 4.0 elif normalized_atr < self.atr_threshold_high.value: lev = 2.5 else: lev = 1.5 if side == 'short': lev = min(lev, self.max_short_leverage.value) return min(lev, float(self.max_leverage.value)) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi'] = ta.RSI(dataframe) dataframe['mfi'] = ta.MFI(dataframe) dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['normalized_atr'] = dataframe['atr'] / dataframe['close'] for std in range(1, 5): bb = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=std) dataframe[f'bb_lowerband{std}'] = bb['lower'] dataframe[f'bb_middleband{std}'] = bb['mid'] dataframe[f'bb_upperband{std}'] = bb['upper'] ema_periods = set([ int(self.buy_fastema.value), int(self.buy_slowema.value), int(self.sell_fastema.value), int(self.sell_slowema.value), int(self.short_fastema.value), int(self.short_slowema.value), int(self.cover_fastema.value), int(self.cover_slowema.value) ]) for period in ema_periods: if period > 0 and len(dataframe) >= period: dataframe[f'EMA_{period}'] = ta.EMA(dataframe, timeperiod=period) # Fibonacci Levels lookback = 50 if len(dataframe) >= lookback: recent_max = dataframe['high'].rolling(lookback).max() recent_min = dataframe['low'].rolling(lookback).min() diff = recent_max - recent_min dataframe['fib_236'] = recent_max - diff * 0.236 dataframe['fib_382'] = recent_max - diff * 0.382 dataframe['fib_5'] = recent_max - diff * 0.5 dataframe['fib_618'] = recent_max - diff * 0.618 dataframe['fib_786'] = recent_max - diff * 0.786 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # -------- 做多逻辑 ---------- long_conditions = [] if self.buy_rsi_enabled.value: long_conditions.append(dataframe['rsi'] < self.buy_rsi.value) if self.buy_mfi_enabled.value: long_conditions.append(dataframe['mfi'] < self.buy_mfi.value) if self.buy_ema_enabled.value: fast_col = f'EMA_{self.buy_fastema.value}' slow_col = f'EMA_{self.buy_slowema.value}' if fast_col in dataframe and slow_col in dataframe: long_conditions.append(dataframe[fast_col] > dataframe[slow_col]) if self.buy_trigger.value.startswith('bb_lower'): bb_col = f'bb_lowerband{self.buy_trigger.value[-1]}' long_conditions.append(dataframe['close'] < dataframe[bb_col]) if self.buy_trigger.value == 'fibonacci' and self.buy_fib_enabled.value: fib_col = self.buy_fib_level.value if fib_col in dataframe.columns: long_conditions.append(dataframe['close'] < dataframe[fib_col]) long_conditions.append(dataframe['volume'] > 0) if long_conditions: dataframe.loc[reduce(lambda x, y: x & y, long_conditions), 'enter_long'] = 1 # -------- 做空逻辑 ---------- short_conditions = [] if self.short_rsi_enabled.value: short_conditions.append(dataframe['rsi'] > self.short_rsi.value) if self.short_mfi_enabled.value: short_conditions.append(dataframe['mfi'] > self.short_mfi.value) if self.short_ema_enabled.value: fast_col = f'EMA_{self.short_fastema.value}' slow_col = f'EMA_{self.short_slowema.value}' if fast_col in dataframe and slow_col in dataframe: short_conditions.append(dataframe[fast_col] < dataframe[slow_col]) if self.short_trigger.value.startswith('bb_upper'): bb_col = f'bb_upperband{self.short_trigger.value[-1]}' short_conditions.append(dataframe['close'] > dataframe[bb_col]) short_conditions.append(dataframe['volume'] > 0) if short_conditions: dataframe.loc[reduce(lambda x, y: x & y, short_conditions), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # -------- 做多平仓 ---------- long_exit = [] if self.sell_rsi_enabled.value: long_exit.append(dataframe['rsi'] > self.sell_rsi.value) if self.sell_mfi_enabled.value: long_exit.append(dataframe['mfi'] > self.sell_mfi.value) if self.sell_ema_enabled.value: fast_col = f'EMA_{self.sell_fastema.value}' slow_col = f'EMA_{self.sell_slowema.value}' if fast_col in dataframe and slow_col in dataframe: long_exit.append(dataframe[fast_col] < dataframe[slow_col]) if self.sell_trigger.value.startswith('sell-bb_upper'): bb_col = f'bb_upperband{self.sell_trigger.value[-1]}' long_exit.append(dataframe['close'] > dataframe[bb_col]) long_exit.append(dataframe['volume'] > 0) if long_exit: dataframe.loc[reduce(lambda x, y: x & y, long_exit), 'exit_long'] = 1 # -------- 做空平仓逻辑 ---------- short_exit = [] if self.cover_rsi_enabled.value: short_exit.append(dataframe['rsi'] < self.cover_rsi.value) if self.cover_mfi_enabled.value: short_exit.append(dataframe['mfi'] < self.cover_mfi.value) if self.cover_ema_enabled.value: fast_col = f'EMA_{self.cover_fastema.value}' slow_col = f'EMA_{self.cover_slowema.value}' if fast_col in dataframe and slow_col in dataframe: short_exit.append(dataframe[fast_col] > dataframe[slow_col]) if self.cover_trigger.value.startswith('bb_lower'): bb_col = f'bb_lowerband{self.cover_trigger.value[-1]}' short_exit.append(dataframe['close'] < dataframe[bb_col]) if self.cover_trigger.value == 'fibonacci' and self.cover_fib_enabled.value: fib_col = self.cover_fib_level.value if fib_col in dataframe.columns: short_exit.append(dataframe['close'] < dataframe[fib_col]) short_exit.append(dataframe['volume'] > 0) if short_exit: dataframe.loc[reduce(lambda x, y: x & y, short_exit), 'exit_short'] = 1 return dataframe