""" 动量趋势跟随策略 基于价格动量和相对强弱的趋势跟随策略 """ from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter from pandas import DataFrame import talib.abstract as ta from technical import qtpylib import numpy as np import logging logger = logging.getLogger(__name__) class MomentumTrendStrategy(IStrategy): """ 动量趋势跟随策略 策略逻辑: 1. 使用RSI、ROC、MACD等动量指标识别趋势 2. 当动量向上且强劲时买入 3. 当动量衰减或反转时卖出 4. 结合趋势确认和成交量分析 5. 使用动态止损跟踪趋势 """ INTERFACE_VERSION = 3 # 基础配置 minimal_roi = { "0": 0.18, # 18%收益立即止盈 "45": 0.10, # 45分钟后10%收益 "90": 0.06, # 1.5小时后6%收益 "180": 0.03, # 3小时后3%收益 "360": 0.01 # 6小时后1%收益 } stoploss = -0.07 # 7%止损 timeframe = '1h' # 1小时时间框架 # 策略控制 can_short = False startup_candle_count = 100 process_only_new_candles = True use_exit_signal = True use_custom_stoploss = True # 可优化参数 # RSI参数 rsi_period = IntParameter(10, 20, default=14, space="buy") rsi_buy_threshold = IntParameter(50, 65, default=55, space="buy") rsi_sell_threshold = IntParameter(65, 85, default=75, space="sell") # ROC (变化率) 参数 roc_period = IntParameter(8, 16, default=12, space="buy") roc_threshold = DecimalParameter(1.0, 5.0, default=2.5, space="buy") # MACD参数 macd_fast = IntParameter(8, 16, default=12, space="buy") macd_slow = IntParameter(21, 35, default=26, space="buy") macd_signal = IntParameter(7, 12, default=9, space="buy") # 动量振荡器参数 mom_period = IntParameter(8, 16, default=10, space="buy") # CCI参数 cci_period = IntParameter(15, 25, default=20, space="buy") cci_buy_threshold = IntParameter(80, 120, default=100, space="buy") cci_sell_threshold = IntParameter(-120, -80, default=-100, space="sell") # 威廉指标参数 williams_period = IntParameter(10, 20, default=14, space="buy") williams_buy_threshold = IntParameter(-80, -60, default=-70, space="buy") williams_sell_threshold = IntParameter(-40, -20, default=-30, space="sell") # 趋势确认参数 ema_short = IntParameter(15, 25, default=20, space="buy") ema_long = IntParameter(40, 60, default=50, space="buy") # ADX参数 adx_period = IntParameter(10, 20, default=14, space="buy") adx_threshold = IntParameter(20, 35, default=25, space="buy") # 成交量参数 volume_factor = DecimalParameter(1.2, 2.5, default=1.8, space="buy") # 止损参数 atr_period = IntParameter(10, 20, default=14, space="sell") atr_multiplier = DecimalParameter(2.0, 3.5, default=2.5, space="sell") def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 计算技术指标 """ # RSI相对强弱指数 dataframe['rsi'] = ta.RSI(dataframe, timeperiod=self.rsi_period.value) # ROC变化率 dataframe['roc'] = ta.ROC(dataframe, timeperiod=self.roc_period.value) # MACD指标 macd = ta.MACD(dataframe, fastperiod=self.macd_fast.value, slowperiod=self.macd_slow.value, signalperiod=self.macd_signal.value) dataframe['macd'] = macd['macd'] dataframe['macd_signal'] = macd['macdsignal'] dataframe['macd_hist'] = macd['macdhist'] # 动量指标 dataframe['momentum'] = ta.MOM(dataframe, timeperiod=self.mom_period.value) # CCI商品通道指数 dataframe['cci'] = ta.CCI(dataframe, timeperiod=self.cci_period.value) # 威廉指标 dataframe['williams_r'] = ta.WILLR(dataframe, timeperiod=self.williams_period.value) # 随机指标 slowk, slowd = ta.STOCH(dataframe['high'], dataframe['low'], dataframe['close']) dataframe['stoch_k'] = slowk dataframe['stoch_d'] = slowd # 趋势EMA dataframe['ema_short'] = ta.EMA(dataframe, timeperiod=self.ema_short.value) dataframe['ema_long'] = ta.EMA(dataframe, timeperiod=self.ema_long.value) # 趋势方向 dataframe['trend_up'] = dataframe['ema_short'] > dataframe['ema_long'] dataframe['price_above_ema_short'] = dataframe['close'] > dataframe['ema_short'] dataframe['price_above_ema_long'] = dataframe['close'] > dataframe['ema_long'] # ADX趋势强度 dataframe['adx'] = ta.ADX(dataframe, timeperiod=self.adx_period.value) dataframe['di_plus'] = ta.PLUS_DI(dataframe, timeperiod=self.adx_period.value) dataframe['di_minus'] = ta.MINUS_DI(dataframe, timeperiod=self.adx_period.value) # ATR波动率 dataframe['atr'] = ta.ATR(dataframe, timeperiod=self.atr_period.value) dataframe['atr_percent'] = dataframe['atr'] / dataframe['close'] # 成交量指标 dataframe['volume_sma'] = dataframe['volume'].rolling(window=20).mean() dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_sma'] # 成交量价格趋势指标 (VPT) dataframe['vpt'] = (dataframe['volume'] * ((dataframe['close'] - dataframe['close'].shift(1)) / dataframe['close'].shift(1))).cumsum() dataframe['vpt_sma'] = dataframe['vpt'].rolling(window=20).mean() dataframe['vpt_signal'] = dataframe['vpt'] > dataframe['vpt_sma'] # 相对活力指数 (RVI) # RVI计算 numerator = (dataframe['close'] - dataframe['open'] + 2 * (dataframe['close'].shift(1) - dataframe['open'].shift(1)) + 2 * (dataframe['close'].shift(2) - dataframe['open'].shift(2)) + (dataframe['close'].shift(3) - dataframe['open'].shift(3))) / 6 denominator = (dataframe['high'] - dataframe['low'] + 2 * (dataframe['high'].shift(1) - dataframe['low'].shift(1)) + 2 * (dataframe['high'].shift(2) - dataframe['low'].shift(2)) + (dataframe['high'].shift(3) - dataframe['low'].shift(3))) / 6 dataframe['rvi'] = numerator.rolling(window=10).sum() / denominator.rolling(window=10).sum() dataframe['rvi_signal'] = dataframe['rvi'].rolling(window=4).mean() # 价格变化率 dataframe['price_change'] = (dataframe['close'] - dataframe['close'].shift(1)) / dataframe['close'].shift(1) dataframe['price_change_5'] = (dataframe['close'] - dataframe['close'].shift(5)) / dataframe['close'].shift(5) # 动量综合评分 # 标准化各指标到0-100范围 rsi_norm = dataframe['rsi'] roc_norm = np.clip((dataframe['roc'] + 10) * 5, 0, 100) # ROC转换到0-100 macd_norm = np.clip((dataframe['macd_hist'] + 1) * 50, 0, 100) # MACD柱状图转换 cci_norm = np.clip((dataframe['cci'] + 200) / 4, 0, 100) # CCI转换 williams_norm = dataframe['williams_r'] + 100 # 威廉指标转换 stoch_norm = dataframe['stoch_k'] # 综合动量评分 dataframe['momentum_score'] = ( rsi_norm * 0.2 + roc_norm * 0.2 + macd_norm * 0.15 + cci_norm * 0.15 + williams_norm * 0.15 + stoch_norm * 0.15 ) # 动量方向 dataframe['momentum_up'] = ( (dataframe['rsi'] > 50) & (dataframe['roc'] > 0) & (dataframe['macd'] > dataframe['macd_signal']) & (dataframe['momentum'] > 0) ) dataframe['momentum_down'] = ( (dataframe['rsi'] < 50) & (dataframe['roc'] < 0) & (dataframe['macd'] < dataframe['macd_signal']) & (dataframe['momentum'] < 0) ) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 定义买入条件 - 动量策略 """ conditions = [ # 主要动量信号 dataframe['momentum_up'], # RSI动量确认 dataframe['rsi'] > self.rsi_buy_threshold.value, dataframe['rsi'] < 80, # 避免极度超买 # ROC变化率确认 dataframe['roc'] > self.roc_threshold.value, # MACD确认 dataframe['macd'] > dataframe['macd_signal'], dataframe['macd_hist'] > 0, dataframe['macd_hist'] > dataframe['macd_hist'].shift(1), # MACD柱状图增强 # CCI确认 dataframe['cci'] > self.cci_buy_threshold.value, # 威廉指标确认 dataframe['williams_r'] > self.williams_buy_threshold.value, # 随机指标确认 dataframe['stoch_k'] > 50, dataframe['stoch_k'] > dataframe['stoch_d'], # K线上穿D线 # 趋势确认 dataframe['trend_up'], dataframe['price_above_ema_short'], # ADX趋势强度 dataframe['adx'] > self.adx_threshold.value, dataframe['di_plus'] > dataframe['di_minus'], # 成交量确认 dataframe['volume_ratio'] > self.volume_factor.value, dataframe['vpt_signal'], # RVI确认 dataframe['rvi'] > dataframe['rvi_signal'], # 价格动量确认 dataframe['price_change'] > 0.005, # 至少0.5%的上涨 dataframe['price_change_5'] > 0.01, # 5周期内至少1%上涨 # 综合动量评分 dataframe['momentum_score'] > 65, ] # 组合条件 dataframe.loc[ ( conditions[0] & # 动量向上 conditions[1] & # RSI下限 conditions[2] & # RSI上限 conditions[3] & # ROC conditions[4] & # MACD金叉 conditions[5] & # MACD柱状图 conditions[6] & # MACD增强 conditions[7] & # CCI conditions[8] & # 威廉指标 conditions[9] & # 随机指标 conditions[10] & # 随机指标交叉 conditions[11] & # 趋势向上 conditions[12] & # 价格位置 conditions[13] & # ADX强度 conditions[14] & # DI确认 conditions[15] & # 成交量比率 conditions[16] & # VPT信号 conditions[17] & # RVI信号 conditions[18] & # 短期价格动量 conditions[19] & # 中期价格动量 conditions[20] # 综合动量评分 ), 'enter_long' ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 定义卖出条件 """ conditions = [ # 主要动量信号转弱 dataframe['momentum_down'], # RSI转弱或超买 dataframe['rsi'] > self.rsi_sell_threshold.value, dataframe['rsi'] < 45, # 动量消失 # ROC转负 dataframe['roc'] < 0, # MACD死叉 dataframe['macd'] < dataframe['macd_signal'], dataframe['macd_hist'] < 0, # CCI转弱 dataframe['cci'] < self.cci_sell_threshold.value, # 威廉指标超买 dataframe['williams_r'] > self.williams_sell_threshold.value, # 随机指标转弱 dataframe['stoch_k'] < 50, dataframe['stoch_k'] < dataframe['stoch_d'], # 趋势转向 ~dataframe['trend_up'], dataframe['close'] < dataframe['ema_short'], # ADX弱化 dataframe['adx'] < 20, dataframe['di_minus'] > dataframe['di_plus'], # RVI转向 dataframe['rvi'] < dataframe['rvi_signal'], # 综合动量评分下降 dataframe['momentum_score'] < 40, ] dataframe.loc[ ( conditions[0] | # 动量转弱 conditions[1] | # RSI超买 conditions[2] | # RSI过低 conditions[3] | # ROC转负 conditions[4] | # MACD死叉 conditions[5] | # MACD柱状图 conditions[6] | # CCI转弱 conditions[7] | # 威廉指标 conditions[8] | # 随机指标 conditions[9] | # 随机指标交叉 conditions[10] | # 趋势转向 conditions[11] | # 价格跌破EMA conditions[12] | # ADX弱化 conditions[13] | # DI转向 conditions[14] | # RVI转向 conditions[15] # 动量评分下降 ), 'exit_long' ] = 1 return dataframe def custom_stoploss(self, pair: str, trade, current_time, current_rate: float, current_profit: float, **kwargs) -> float: """ 动态止损策略 - 动量策略特有 """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # ATR动态止损 atr_distance = last_candle['atr'] * self.atr_multiplier.value atr_stop_distance = atr_distance / current_rate # 基于动量的动态止损 momentum_score = last_candle['momentum_score'] if current_profit > 0.08: # 盈利超过8% # 高盈利时，根据动量强度调整 if momentum_score > 70: return max(-atr_stop_distance * 0.5, -0.015) # 动量强劲，紧跟 else: return max(-atr_stop_distance * 0.7, -0.025) # 动量衰减，稍松 elif current_profit > 0.04: # 盈利超过4% if momentum_score > 60: return max(-atr_stop_distance * 0.7, -0.02) else: return max(-atr_stop_distance * 0.9, -0.03) elif current_profit > 0.02: # 盈利超过2% return max(-atr_stop_distance * 0.8, -0.035) else: # 动量策略需要更多空间 return max(-atr_stop_distance * 1.1, self.stoploss) def custom_exit(self, pair: str, trade, current_time, current_rate: float, current_profit: float, **kwargs) -> str: """ 自定义退出逻辑 - 动量策略特有 """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # 动量衰减退出 if (last_candle['momentum_score'] < 30 and current_profit > 0.02): return "momentum_exhaustion" # 动量反转退出 if (last_candle['momentum_down'] and last_candle['rsi'] < 40 and current_profit > 0.01): return "momentum_reversal" # RSI背离退出 if (current_profit > 0.05 and last_candle['rsi'] < 50 and current_rate > trade.open_rate * 1.03): # 价格新高但RSI下降 return "rsi_divergence" # 成交量萎缩退出 if (last_candle['volume_ratio'] < 0.7 and current_profit > 0.03): return "volume_drying_up" # 时间止损 trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600 if trade_duration > 36 and current_profit < 0.01: # 36小时无显著盈利 return "time_exit" return None def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time, entry_tag: str, side: str, **kwargs) -> bool: """ 交易确认 - 动量策略特有验证 """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) < 1: return False last_candle = dataframe.iloc[-1].squeeze() # 确保动量足够强劲 if last_candle['momentum_score'] < 60: return False # 确保RSI在合理范围 if last_candle['rsi'] < 50 or last_candle['rsi'] > 85: return False # 确保MACD信号明确 if last_candle['macd_hist'] <= 0: return False # 确保趋势明确 if not last_candle['trend_up']: return False # 确保ADX足够强 if last_candle['adx'] < 20: return False # 确保成交量支持 if last_candle['volume_ratio'] < 1.2: return False return True