添加新的策略

2026-02-04 22:53:52 +08:00
parent 98e309802a
commit 39b5103858
4 changed files with 451 additions and 109 deletions
@@ -53,7 +53,7 @@ class ChanLun():
 		self.time_M_symbols	 = ['2M', '3M', '6M', '1y']
 		self.time_symbols = ['1m', '2m', '3m', '5m', '10m', '15m', '20m', '30m', '45m','1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d', '1w', '2w', '1M', '3M', '6M', '1y']
 		self.tf_df_dict = {}
-		self.ema_symbols = ['1m', '2m', '3m', '5m', '10m', '15m', '20m', '30m', '45m', '1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d']
+		self.ema_symbols = ['5m', '10m', '15m', '20m', '30m', '45m', '1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d']
 		self.tf_df = TF_DF()
 	def init_data(self, dataframe, intervals, timeframes):
 		for index in range(0, len(intervals)):
@@ -61,6 +61,7 @@ class ChanLun():
 			interval = intervals[index]
 			self.tf_df_dict[timeframe] = TF_DF(dataframe, interval, timeframe)
 	def init_dataframes(self, dataframe_m=None, dataframe_15m=None, dataframe_h=None, dataframe_d=None, dataframe_w=None, dataframe_M=None):
+		self.tf_df_dict = {}
 		if dataframe_m is not None:
 			self.tf_df_dict['1m'] = TF_DF(dataframe_m, 1, '1m')
 			self.init_data(dataframe_m, self.time_m_intervals, self.time_m_symbols)
@@ -12,7 +12,7 @@
    "trading_mode": "futures",
    "margin_mode": "isolated",
    "can_short" : true,
-    "timeframe" : "15m",
+    "timeframe" : "1h",
    "process_only_new_candles" : false,
    "unfilledtimeout": {
        "entry": 1,
@@ -89,11 +89,15 @@ class ChanLun_EMA52(IStrategy):
 	# startup_candle_count = 1600
 	big_tf = '1h'
 	small_tf = '15m'
-	last_time = datetime.now()
+	last_time = None
 	chan = ChanLun()
 	last_order = None
 	last_trade = None
 	pair = 'BTC/USDT:USDT'
+	long_tf = '1h'
+	short_tf = '15m'
+	long_time = 60
+	short_time = 15
 	def informative_pairs(self):
 		return [(self.pair, "1h"),
 				(self.pair, "1d"),
@@ -102,16 +106,31 @@ class ChanLun_EMA52(IStrategy):
 				(self.pair, "1w"),
 				]
 	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-		dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
-		if self.last_time + timedelta(minutes=1) < datetime.now():
+		long_df = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1h')
+		long_df['entry_long'] = self.long_entry_condition(long_df)
+		dataframe['rsi'] = ta.RSI(long_df, timeperiod=14)
+		if self.last_time is None or self.last_time + timedelta(minutes=1) < datetime.now():
 			self.last_time = datetime.now()
 			logger.info("init_dataframes----------------------------")
 			last_price = dataframe.iloc[-1]['close']
+			macdstr = str(long_df.iloc[-1]['macd']) + " " + str(long_df.iloc[-1]['macdsignal']) + " " + str(long_df.iloc[-1]['macdhist']))
 			date = dataframe.iloc[-1]['date']
 			tf_ema52_list = self.chan.check_price_ema52(last_price)
 			self.init_dataframes(dataframe)
-			logger.info("Date: " + date.strftime('%Y-%m-%d %H:%M:%S') + " Price: " + str(last_price) + " EMA52_list: " + str(tf_ema52_list))
+			logger.info("Date: " + date.strftime('%Y-%m-%d %H:%M:%S') + " Price: " + str(last_price) + " EMA52_list: " + str(tf_ema52_list) + " MACD: " + macdstr)
 		return dataframe
+	def long_entry_condition(self, long_df):
+		long_df['ema52'] = ta.EMA(long_df, timeperiod=52)
+		long_df['dir52'] = long_df['close'] - long_df['ema52']
+		long_df['ema156'] = ta.EMA(long_df, timeperiod=156)
+		long_df['dir156'] = long_df['close'] - long_df['ema156']
+		long_df_macd = ta.MACD(long_df, fast=12, slow=26, signal=9)
+		long_df['macdsignal'] = long_df_macd['macdsignal']
+		long_df['macd'] = long_df_macd['macd']
+		long_df['macdhist'] = long_df_macd['macdhist']
+		long_entry_condition = (long_df['dir52'] > 0) & (long_df['dir156'] > 0) & (long_df['macdhist'] > 0)
+		return long_entry_condition
+		
 	def init_dataframes(self, dataframe_1m):
 		dataframe_15m = self.dp.get_pair_dataframe(pair=self.pair, timeframe='15m')
 		dataframe_1h = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1h')
@@ -1,115 +1,437 @@
 # --- Do not remove these libs ---
-from statistics import median
 from freqtrade.strategy import IStrategy
-from technical.util import resample_to_interval, resampled_merge
 from pandas import DataFrame
 import talib.abstract as ta
-from technical import qtpylib
+import numpy as np
+
+# 1小时短线趋势追踪策略 V7 - EMA + MACD 综合趋势判断
+# 使用 EMA8, EMA24, EMA72, EMA168 + MACD 识别趋势阶段和强度
+# freqtrade backtesting -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange=20250101-
+# freqtrade download-data -c ./user_data/Chan/config/EMA_Pattern.json -t 1h --pairs BTC/USDT:USDT --timerange=20250101-

-### Now you can use logger.info('asfd') to log
-# freqtrade plot-dataframe --strategy EMA_Pattern --datadir user_data/data/binance -c ./user_data/Chan/EMA_Pattern.json --timerange=20250309-
-# freqtrade backtesting -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange=20251030-
-# freqtrade download-data -c ./user_data/Chan/config/EMA_Pattern.json -t 1m 3m 5m 15m 30m 1h  --pairs BTC/USDT:USDT --timerange=20250405-
-# freqtrade download-data -c ./user_data/Chan/config/EMA_Pattern.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
-# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/EMA_Pattern.json -e 200 --timerange=20250201-20250901
-# freqtrade edge -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
-# freqtrade plot-dataframe -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901

 class EMA_Pattern(IStrategy):
-	time1h = 1440
-	can_short: bool = True
-	timeframe: str = "1m"
-	process_only_new_candles: bool = False
+    """
+    1小时K线短线趋势追踪策略 V7 - EMA + MACD
    
-	# ROI 与止损可根据需要在配置中覆盖
+    核心逻辑:
+    1. EMA多头排列 + MACD金叉确认 = 趋势启动（最佳入场）
+    2. EMA发散 + MACD动能增加 = 趋势发展（可追涨）
+    3. MACD动能减弱/顶背离 = 趋势成熟（减仓）
+    4. EMA死叉 + MACD死叉 = 趋势结束（出场）
+    """
+    
+    can_short: bool = True
+    timeframe: str = "1h"
+    process_only_new_candles: bool = True
+    
+    # EMA参数
+    ema_fast: int = 8
+    ema_short: int = 24
+    ema_mid: int = 72
+    ema_long: int = 168
+    
+    # 趋势强度阈值（放宽以增加交易机会）
+    min_trend_strength: int = 65
+    
+    # ADX阈值
+    min_adx: int = 25  # 有趋势就交易
+    
+    # ROI - V6最佳参数（无杠杆）
    minimal_roi = {
-		"60": 0.005,
-		"30": 0.01,
-		"0": 0.02,
+        "120": 0.02,   # 5天后 2%止盈
+        "72": 0.03,    # 3天后 3%止盈
+        "24": 0.05,    # 1天后 5%止盈
+        "0": 0.08,     # 立即 8%止盈
    }
-	stoploss: float = -0.30
-	# 需要的历史K线数量（包含EMA等指标预热）
-	startup_candle_count: int = 200
+    
+    # 固定止损
+    stoploss: float = -0.05  # 5%止损
+    
+    # 追踪止损
+    trailing_stop: bool = True
+    trailing_stop_positive: float = 0.025  # 盈利2.5%后启动
+    trailing_stop_positive_offset: float = 0.04  # 盈利4%后才触发
+    trailing_only_offset_is_reached: bool = True
+    
+    # 禁用自定义止损（让追踪止损工作）
+    use_custom_stoploss: bool = False
+    
+    # 只做多（做空效果差）
+    can_short: bool = False
+    
+    startup_candle_count: int = 250
+
+    def custom_stoploss(self, pair: str, trade, current_time, current_rate,
+                        current_profit, after_fill, **kwargs) -> float:
+        """
+        动态止损 V3 - 保护利润但不过早出场：
+        1. 盈利超过6%，止损移到盈利3%（锁定一半利润）
+        2. 盈利超过3%，止损移到盈利1%
+        3. 盈利超过1.5%，止损移到保本
+        4. 长时间亏损才考虑缩紧止损
+        """
+        # 持仓时间（小时）
+        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
+        
+        # 盈利时动态止损 - 阶梯式保护利润
+        if current_profit > 0.06:
+            # 盈利超过6%，锁定3%利润
+            return -0.03
+        elif current_profit > 0.03:
+            # 盈利超过3%，锁定1%利润
+            return -0.02
+        elif current_profit > 0.015:
+            # 盈利超过1.5%，移到保本
+            return -0.005
+        
+        # 长时间持仓亏损（超过96小时=4天），才缩紧止损
+        if trade_duration > 96 and current_profit < -0.03:
+            return -0.04  # 缩紧到4%
+        
+        # 默认使用配置的止损
+        return self.stoploss

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if dataframe is None or dataframe.empty:
            return dataframe
-		dataframe = self.add_indicators(dataframe)
-		return dataframe
-	def add_indicators(self, dataframe: DataFrame) -> DataFrame:
-		macd = ta.MACD(dataframe, timeperiod=12, fastperiod=12, slowperiod=26, signalperiod=9)
+        
+        # ==================== EMA指标 ====================
+        dataframe['ema8'] = ta.EMA(dataframe, timeperiod=self.ema_fast)
+        dataframe['ema24'] = ta.EMA(dataframe, timeperiod=self.ema_short)
+        dataframe['ema72'] = ta.EMA(dataframe, timeperiod=self.ema_mid)
+        dataframe['ema168'] = ta.EMA(dataframe, timeperiod=self.ema_long)
+        
+        # ==================== MACD指标 ====================
+        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
        dataframe['macd'] = macd['macd']
-		dataframe['macdsignal'] = macd['macdsignal']
-		dataframe['macdhist'] = macd['macdhist']
-		dataframe['ema6'] = ta.EMA(dataframe, timeperiod=6)
-		dataframe['ema12'] = ta.EMA(dataframe, timeperiod=12)
-		dataframe['ema24'] = ta.EMA(dataframe, timeperiod=24)
-		dataframe['ema52'] = ta.EMA(dataframe, timeperiod=52)
+        dataframe['macd_signal'] = macd['macdsignal']
+        dataframe['macd_hist'] = macd['macdhist']
+        
+        # MACD辅助指标
+        dataframe['macd_hist_change'] = dataframe['macd_hist'] - dataframe['macd_hist'].shift(1)
+        dataframe['macd_hist_ma'] = dataframe['macd_hist'].rolling(5).mean()
+        
+        # ADX趋势强度
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
-		dataframe['strong_trend'] = dataframe['adx'] > 25
-		dataframe['UP_Pattern'] = (dataframe['ema6'] > dataframe['ema12']) & (dataframe['ema12'] > dataframe['ema24']) & (dataframe['ema24'] > dataframe['ema52'])
-		dataframe['DOWN_Pattern'] = (dataframe['ema6'] < dataframe['ema12']) & (dataframe['ema12'] < dataframe['ema24']) & (dataframe['ema24'] < dataframe['ema52'])
-		dataframe['UP_Confirm'] = (dataframe['ema6'] > dataframe['ema6'].shift(1)) & (dataframe['ema12'] > dataframe['ema12'].shift(1)) & (dataframe['ema24'] > dataframe['ema24'].shift(1)) & (dataframe['ema52'] > dataframe['ema52'].shift(1))
-		dataframe['DOWN_Confirm'] = (dataframe['ema6'] < dataframe['ema6'].shift(1)) & (dataframe['ema12'] < dataframe['ema12'].shift(1)) & (dataframe['ema24'] < dataframe['ema24'].shift(1)) & (dataframe['ema52'] < dataframe['ema52'].shift(1))
-		dataframe['EMA52_Cross_EMA24_UP'] = (dataframe['ema52'] < dataframe['ema24']) & (dataframe['ema52'].shift(1) > dataframe['ema24'].shift(1))
-		dataframe['EMA52_Cross_EMA24_DOWN'] = (dataframe['ema52'] > dataframe['ema24']) & (dataframe['ema52'].shift(1) < dataframe['ema24'].shift(1))
-		dataframe['Price_Above_EMA52'] = (dataframe['close'] > dataframe['ema52'])
-		dataframe['Price_Below_EMA52'] = (dataframe['close'] < dataframe['ema52'])
-		dataframe['MACD_Above_Zero'] = (dataframe['macd'] > 0) & (dataframe['macdsignal'] > 0)
-		dataframe['MACD_Below_Zero'] = (dataframe['macd'] < 0) & (dataframe['macdsignal'] < 0)
-		dataframe['BUY_END'] = dataframe['close'] < dataframe['ema52']
-		dataframe['SELL_END'] = dataframe['close'] > dataframe['ema52']
+        
+        # ATR波动率
+        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
+        dataframe['atr_pct'] = dataframe['atr'] / dataframe['close'] * 100
+        
+        # 成交量
+        dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean()
+        dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_ma']
+        
+        # ==================== 均线排列 ====================
+        dataframe['bull_align'] = (
+            (dataframe['ema8'] > dataframe['ema24']) &
+            (dataframe['ema24'] > dataframe['ema72']) &
+            (dataframe['ema72'] > dataframe['ema168'])
+        )
+        
+        dataframe['bear_align'] = (
+            (dataframe['ema8'] < dataframe['ema24']) &
+            (dataframe['ema24'] < dataframe['ema72']) &
+            (dataframe['ema72'] < dataframe['ema168'])
+        )
+        
+        # 简化排列（短中期）
+        dataframe['bull_align_short'] = (
+            (dataframe['ema8'] > dataframe['ema24']) &
+            (dataframe['ema24'] > dataframe['ema72'])
+        )
+        
+        dataframe['bear_align_short'] = (
+            (dataframe['ema8'] < dataframe['ema24']) &
+            (dataframe['ema24'] < dataframe['ema72'])
+        )
+        
+        # ==================== 均线斜率 ====================
+        lookback = 5
+        dataframe['slope_ema8'] = (dataframe['ema8'] - dataframe['ema8'].shift(lookback)) / dataframe['ema8'].shift(lookback) * 100
+        dataframe['slope_ema24'] = (dataframe['ema24'] - dataframe['ema24'].shift(lookback)) / dataframe['ema24'].shift(lookback) * 100
+        dataframe['slope_ema72'] = (dataframe['ema72'] - dataframe['ema72'].shift(lookback)) / dataframe['ema72'].shift(lookback) * 100
+        dataframe['slope_ema168'] = (dataframe['ema168'] - dataframe['ema168'].shift(lookback)) / dataframe['ema168'].shift(lookback) * 100
+        
+        dataframe['slope_bull_confirm'] = (
+            (dataframe['slope_ema8'] > 0) &
+            (dataframe['slope_ema24'] > 0) &
+            (dataframe['slope_ema72'] > 0)
+        )
+        
+        dataframe['slope_bear_confirm'] = (
+            (dataframe['slope_ema8'] < 0) &
+            (dataframe['slope_ema24'] < 0) &
+            (dataframe['slope_ema72'] < 0)
+        )
+        
+        # ==================== 均线间距 ====================
+        dataframe['spread_total'] = (dataframe['ema8'] - dataframe['ema168']) / dataframe['ema168'] * 100
+        dataframe['spread_short'] = (dataframe['ema8'] - dataframe['ema24']) / dataframe['ema24'] * 100
+        dataframe['spread_change'] = dataframe['spread_total'] - dataframe['spread_total'].shift(lookback)
+        dataframe['spread_ma'] = dataframe['spread_total'].rolling(20).mean()
+        
+        spread_abs = dataframe['spread_total'].abs()
+        dataframe['spread_pct_30'] = spread_abs.rolling(50, min_periods=20).quantile(0.3)
+        dataframe['spread_pct_70'] = spread_abs.rolling(50, min_periods=20).quantile(0.7)
+        
+        # ==================== MACD交叉信号 ====================
+        # MACD金叉（MACD线上穿信号线）
+        dataframe['macd_golden_cross'] = (
+            (dataframe['macd'] > dataframe['macd_signal']) &
+            (dataframe['macd'].shift(1) <= dataframe['macd_signal'].shift(1))
+        )
+        
+        # MACD死叉
+        dataframe['macd_death_cross'] = (
+            (dataframe['macd'] < dataframe['macd_signal']) &
+            (dataframe['macd'].shift(1) >= dataframe['macd_signal'].shift(1))
+        )
+        
+        # MACD零轴上方金叉（更强信号）
+        dataframe['macd_strong_golden'] = dataframe['macd_golden_cross'] & (dataframe['macd'] > 0)
+        
+        # ==================== MACD动能判断 ====================
+        # 柱状图动能增加（多头）
+        dataframe['macd_momentum_up'] = (
+            (dataframe['macd_hist'] > 0) &
+            (dataframe['macd_hist'] > dataframe['macd_hist'].shift(1)) &
+            (dataframe['macd_hist'].shift(1) > dataframe['macd_hist'].shift(2))
+        )
+        
+        # 柱状图动能增加（空头）
+        dataframe['macd_momentum_down'] = (
+            (dataframe['macd_hist'] < 0) &
+            (dataframe['macd_hist'] < dataframe['macd_hist'].shift(1)) &
+            (dataframe['macd_hist'].shift(1) < dataframe['macd_hist'].shift(2))
+        )
+        
+        # 柱状图动能减弱（顶部信号）
+        dataframe['macd_momentum_weakening'] = (
+            (dataframe['macd_hist'] > 0) &
+            (dataframe['macd_hist'] < dataframe['macd_hist'].shift(1)) &
+            (dataframe['macd_hist'].shift(1) < dataframe['macd_hist'].shift(2))
+        )
+        
+        # ==================== 趋势阶段（EMA + MACD综合判断）====================
+        # 趋势启动期：EMA开始排列 + MACD金叉 + 柱状图正向增长
+        dataframe['trend_start_bull'] = (
+            dataframe['bull_align_short'] &  # 至少短中期排列
+            (dataframe['macd'] > dataframe['macd_signal']) &  # MACD金叉状态
+            (dataframe['macd_hist'] > 0) &  # 柱状图为正
+            (dataframe['macd_hist'] > dataframe['macd_hist'].shift(1)) &  # 动能增加
+            (spread_abs < dataframe['spread_pct_70'])  # 发散度不是最大
+        )
+        
+        # 趋势发展期：完整排列 + MACD在零轴上方 + 持续放量
+        dataframe['trend_develop_bull'] = (
+            dataframe['bull_align'] &  # 完整4线排列
+            (dataframe['macd'] > 0) &  # MACD在零轴上方
+            (dataframe['macd'] > dataframe['macd_signal']) &  # 金叉状态
+            dataframe['slope_bull_confirm'] &  # 斜率确认
+            (dataframe['spread_change'] > 0)  # 发散度扩大
+        )
+        
+        # 趋势成熟期：发散度大 + MACD动能减弱
+        dataframe['trend_mature_bull'] = (
+            dataframe['bull_align'] &
+            (dataframe['macd'] > 0) &
+            dataframe['macd_momentum_weakening'] &  # 动能减弱
+            (spread_abs > dataframe['spread_pct_70'])  # 发散度很大
+        )
+        
+        # 趋势结束：EMA开始死叉 + MACD死叉
+        dataframe['trend_end_bull'] = (
+            (dataframe['ema8'] < dataframe['ema24']) &  # EMA8死叉EMA24
+            (dataframe['macd'] < dataframe['macd_signal']) &  # MACD死叉
+            (dataframe['macd_hist'] < 0)  # 柱状图转负
+        )
+        
+        # 空头阶段（镜像）
+        dataframe['trend_start_bear'] = (
+            dataframe['bear_align_short'] &
+            (dataframe['macd'] < dataframe['macd_signal']) &
+            (dataframe['macd_hist'] < 0) &
+            (dataframe['macd_hist'] < dataframe['macd_hist'].shift(1)) &
+            (spread_abs < dataframe['spread_pct_70'])
+        )
+        
+        dataframe['trend_develop_bear'] = (
+            dataframe['bear_align'] &
+            (dataframe['macd'] < 0) &
+            (dataframe['macd'] < dataframe['macd_signal']) &
+            dataframe['slope_bear_confirm'] &
+            (dataframe['spread_change'] < 0)
+        )
+        
+        dataframe['trend_end_bear'] = (
+            (dataframe['ema8'] > dataframe['ema24']) &
+            (dataframe['macd'] > dataframe['macd_signal']) &
+            (dataframe['macd_hist'] > 0)
+        )
+        
+        # ==================== 趋势强度计算 (0-100) ====================
+        # 1. 排列得分 (0-25)
+        bull_align_score = (
+            (dataframe['ema8'] > dataframe['ema24']).astype(int) +
+            (dataframe['ema24'] > dataframe['ema72']).astype(int) +
+            (dataframe['ema72'] > dataframe['ema168']).astype(int) +
+            (dataframe['close'] > dataframe['ema8']).astype(int)
+        ) * 6.25
+        
+        bear_align_score = (
+            (dataframe['ema8'] < dataframe['ema24']).astype(int) +
+            (dataframe['ema24'] < dataframe['ema72']).astype(int) +
+            (dataframe['ema72'] < dataframe['ema168']).astype(int) +
+            (dataframe['close'] < dataframe['ema8']).astype(int)
+        ) * 6.25
+        
+        dataframe['align_score'] = np.maximum(bull_align_score, bear_align_score)
+        
+        # 2. 斜率得分 (0-25)
+        dataframe['slope_score'] = np.clip(np.abs(dataframe['slope_ema24']) * 10, 0, 25)
+        
+        # 3. MACD动能得分 (0-25) - 替换原来的间距得分
+        macd_hist_norm = dataframe['macd_hist'].abs() / dataframe['close'] * 1000
+        dataframe['macd_score'] = np.clip(macd_hist_norm * 5, 0, 25)
+        
+        # 4. ADX得分 (0-25)
+        dataframe['adx_score'] = np.clip(dataframe['adx'] - 15, 0, 25)
+        
+        # 综合强度
+        dataframe['trend_strength'] = (
+            dataframe['align_score'] +
+            dataframe['slope_score'] +
+            dataframe['macd_score'].fillna(12.5) +
+            dataframe['adx_score']
+        )
+        
+        # ==================== 入场信号 ====================
+        # EMA金叉
+        dataframe['ema8_cross_ema24_up'] = (
+            (dataframe['ema8'] > dataframe['ema24']) &
+            (dataframe['ema8'].shift(1) <= dataframe['ema24'].shift(1))
+        )
+        
+        dataframe['ema8_cross_ema24_down'] = (
+            (dataframe['ema8'] < dataframe['ema24']) &
+            (dataframe['ema8'].shift(1) >= dataframe['ema24'].shift(1))
+        )
+        
+        # 回踩EMA24反弹
+        dataframe['pullback_buy'] = (
+            (dataframe['low'].shift(1) <= dataframe['ema24'].shift(1) * 1.005) &
+            (dataframe['close'] > dataframe['ema8']) &
+            (dataframe['close'] > dataframe['open']) &
+            (dataframe['macd'] > dataframe['macd_signal'])  # MACD确认
+        )
+        
+        # 突破前高
+        dataframe['high_break'] = dataframe['close'] > dataframe['high'].rolling(24).max().shift(1)
+        dataframe['low_break'] = dataframe['close'] < dataframe['low'].rolling(24).min().shift(1)
+        
+        # ==================== 大周期趋势过滤 ====================
+        dataframe['big_trend_bull'] = (
+            (dataframe['ema168'] > dataframe['ema168'].shift(24)) &
+            (dataframe['close'] > dataframe['ema168'])
+        )
+        
+        dataframe['big_trend_bear'] = (
+            (dataframe['ema168'] < dataframe['ema168'].shift(24)) &
+            (dataframe['close'] < dataframe['ema168'])
+        )
+        
        return dataframe
+
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if dataframe is None or dataframe.empty:
            return dataframe
+        
+        # ==================== 做多入场1：趋势启动（EMA排列 + MACD确认）====================
+        # 核心条件：EMA开始多头排列 + MACD金叉 + 动能增加
        dataframe.loc[
            (
-				(dataframe['UP_Pattern']) &
-				(dataframe['UP_Confirm']) &
-				(dataframe['Price_Above_EMA52']) &
-				(dataframe['MACD_Above_Zero']) &
-				(dataframe['EMA52_Cross_EMA24_UP']) &
-				(dataframe['strong_trend'])
+                dataframe['bull_align_short'] &  # 至少短中期排列（EMA8>24>72）
+                (dataframe['macd'] > dataframe['macd_signal']) &  # MACD金叉
+                (dataframe['macd_hist'] > 0) &  # 柱状图为正
+                (dataframe['macd_hist'] > dataframe['macd_hist'].shift(1)) &  # 动能增加
+                (dataframe['close'] > dataframe['ema24']) &  # 价格在EMA24上方
+                (dataframe['adx'] > 20) &  # 有趋势
+                (dataframe['slope_ema8'] > 0) &  # EMA8向上
+                (dataframe['volume_ratio'] > 0.7)
            ),
            ["enter_long", "enter_tag"],
-		] = (1, "ema_up_trend")
+        ] = (1, "trend_start")
        
+        # ==================== 做多入场2：强势突破（突破前高 + MACD确认）====================
        dataframe.loc[
            (
-				(dataframe['DOWN_Pattern']) &
-				(dataframe['DOWN_Confirm']) &
-				(dataframe['Price_Below_EMA52']) &
-				(dataframe['MACD_Below_Zero']) &
-				(dataframe['EMA52_Cross_EMA24_DOWN']) &
-				(dataframe['strong_trend'])
+                dataframe['bull_align'] &  # 完整排列
+                dataframe['high_break'] &  # 突破前高
+                (dataframe['macd'] > dataframe['macd_signal']) &  # MACD金叉状态
+                (dataframe['adx'] > self.min_adx) &  # 强趋势
+                (dataframe['trend_strength'] >= self.min_trend_strength - 10) &
+                (dataframe['volume_ratio'] > 0.9)
            ),
-			["enter_short", "enter_tag"],
-		] = (1, "ema_down_trend")
+            ["enter_long", "enter_tag"],
+        ] = (1, "bull_breakout")
+        
+        # ==================== 做多入场3：回踩反弹（趋势中回调）====================
+        dataframe.loc[
+            (
+                dataframe['bull_align'] &  # 完整排列
+                (dataframe['low'].shift(1) <= dataframe['ema24'].shift(1) * 1.01) &  # 回踩EMA24附近
+                (dataframe['close'] > dataframe['open']) &  # 阳线
+                (dataframe['close'] > dataframe['ema8']) &  # 收盘价回到EMA8上方
+                (dataframe['macd'] > 0) &  # MACD在零轴上方
+                (dataframe['macd'] > dataframe['macd_signal']) &  # 金叉状态
+                (dataframe['trend_strength'] >= self.min_trend_strength - 15)
+            ),
+            ["enter_long", "enter_tag"],
+        ] = (1, "bull_pullback")
        
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if dataframe is None or dataframe.empty:
            return dataframe
-		dataframe.loc[
-			(
-				(dataframe['BUY_END']) |
-				(dataframe['DOWN_Pattern']) |
-				(dataframe['EMA52_Cross_EMA24_DOWN'])
-			),
-			["exit_long", "exit_tag"],
-		] = (1, "ema_long_exit")
        
+        # ==================== 多头出场1：趋势结束（EMA死叉 + MACD死叉）====================
        dataframe.loc[
-			(
-				(dataframe['SELL_END']) |
-				(dataframe['UP_Pattern']) |
-				(dataframe['EMA52_Cross_EMA24_UP'])
-			),
+            dataframe['trend_end_bull'] &  # EMA8死叉 + MACD死叉 + 柱状图转负
+            (dataframe['close'] < dataframe['ema24']),  # 价格确认跌破EMA24
+            ["exit_long", "exit_tag"],
+        ] = (1, "trend_end")
+        
+        # ==================== 多头出场2：趋势反转（完全转为空头）====================
+        dataframe.loc[
+            dataframe['bear_align'] &  # 完全空头排列
+            (dataframe['macd'] < dataframe['macd_signal']) &  # MACD死叉
+            (dataframe['macd_hist'] < 0) &  # 柱状图为负
+            (dataframe['close'] < dataframe['ema72']) &  # 价格跌破EMA72
+            (dataframe['close'].shift(1) < dataframe['ema72'].shift(1)),  # 连续2根确认
+            ["exit_long", "exit_tag"],
+        ] = (1, "trend_reverse")
+        
+        # ==================== 空头出场1：趋势结束 ====================
+        dataframe.loc[
+            dataframe['trend_end_bear'] &
+            (dataframe['close'] > dataframe['ema24']),
            ["exit_short", "exit_tag"],
-		] = (1, "ema_short_exit")
+        ] = (1, "trend_end")
+        
+        # ==================== 空头出场2：趋势反转 ====================
+        dataframe.loc[
+            dataframe['bull_align'] &
+            (dataframe['macd'] > dataframe['macd_signal']) &
+            (dataframe['macd_hist'] > 0) &
+            (dataframe['close'] > dataframe['ema72']) &
+            (dataframe['close'].shift(1) > dataframe['ema72'].shift(1)),
+            ["exit_short", "exit_tag"],
+        ] = (1, "trend_reverse")
        
        return dataframe
+
    def get_ticker_indicator(self):
        return int(self.timeframe[:-1])