添加新策略

2025-08-08 21:03:07 +08:00
parent 8f22306766
commit 05942fed8e
4 changed files with 363 additions and 316 deletions
@@ -25,7 +25,7 @@ logger = logging.getLogger(__name__)
 # freqtrade plot-dataframe --strategy ChanLun_BTC_K --datadir user_data/data/binance -c ./user_data/Chan/config/ChanLun_BTC_K.json --timerange=20250309-

 # freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_K.json --strategy ChanLun_BTC_K --strategy-path ./user_data/Chan/strategies
-# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_K.json --strategy ChanLun_BTC_K --strategy-path ./user_data/Chan/strategies --timerange=20250721-
+# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_K.json --strategy ChanLun_BTC_K --strategy-path ./user_data/Chan/strategies --timerange=20250701-
 # freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_K.json -t 1m --pairs BTC/USDT:USDT --timerange=20250405-
 # freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_BTC_K --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_K.json -e 200 --timerange=20250201-20250401

@@ -38,16 +38,29 @@ class ChanLun_BTC_K(IStrategy):
    
    # 策略参数
    minimal_roi = {
-        "0": 0.05,      # 5% 利润即可退出
-        "30": 0.03,     # 30分钟后3%利润退出
-        "60": 0.02,     # 1小时后2%利润退出
-        "120": 0.01     # 2小时后1%利润退出
+        "0": 0.004,     # 0.4%
+        "15": 0.006,    # 15分钟0.6%
+        "30": 0.008,    # 30分钟0.8%
+        "60": 0.01      # 60分钟1.0%
    }
    
    stoploss = -0.03    # 3%止损
+    use_custom_stoploss = True
+    startup_candle_count = 200
+
+    def get_ticker_indicator(self) -> int:
+        """返回基础时间框架的分钟数（如 '1m' -> 1）。"""
+        tf = str(self.timeframe).strip().lower()
+        if tf.endswith('m'):
+            return int(tf[:-1])
+        if tf.endswith('h'):
+            return int(tf[:-1]) * 60
+        if tf.endswith('d'):
+            return int(tf[:-1]) * 60 * 24
+        return 1
    
    # 时间框架
-    timeframe = '1m'
+    timeframe = '5m'
    
    # 指标参数
    macd_fast = 12
@@ -73,25 +86,117 @@ class ChanLun_BTC_K(IStrategy):
        # EMA均线
        dataframe['ema_24'] = ta.EMA(dataframe, timeperiod=self.ema_short)
        dataframe['ema_52'] = ta.EMA(dataframe, timeperiod=self.ema_long)
+
+        # 多时间周期（3x、5x、15x）聚合与指标
+        base_min = self.get_ticker_indicator()
+        intervals = {
+            'x3': base_min * 3,
+            'x5': base_min * 5,
+            'x15': base_min * 15,
+            'x60': base_min * 60,
+        }
+
+        def build_htf(df_resampled: DataFrame, suffix: str) -> DataFrame:
+            macd_htf = ta.MACD(df_resampled, fastperiod=self.macd_fast, slowperiod=self.macd_slow, signalperiod=self.macd_signal)
+            df_resampled[f'macd_{suffix}'] = macd_htf['macd']
+            df_resampled[f'macdsignal_{suffix}'] = macd_htf['macdsignal']
+            df_resampled[f'macdhist_{suffix}'] = macd_htf['macdhist']
+            df_resampled[f'ema_24_{suffix}'] = ta.EMA(df_resampled, timeperiod=self.ema_short)
+            df_resampled[f'ema_52_{suffix}'] = ta.EMA(df_resampled, timeperiod=self.ema_long)
+            # ATR及其百分比（用于波动过滤/动态止损）
+            df_resampled[f'atr_{suffix}'] = ta.ATR(df_resampled, timeperiod=14)
+            df_resampled[f'atr_pct_{suffix}'] = df_resampled[f'atr_{suffix}'] / df_resampled['close']
+            # 近零轴/方向
+            zero_dist = np.sqrt(np.square(df_resampled[f'macd_{suffix}']) + np.square(df_resampled[f'macdsignal_{suffix}']))
+            zero_dist_ema = zero_dist.ewm(span=50, adjust=False).mean()
+            zero_eps = zero_dist_ema * 0.2
+            df_resampled[f'above_zero_{suffix}'] = (df_resampled[f'macd_{suffix}'] > 0) & (df_resampled[f'macdsignal_{suffix}'] > 0)
+            df_resampled[f'below_zero_{suffix}'] = (df_resampled[f'macd_{suffix}'] < 0) & (df_resampled[f'macdsignal_{suffix}'] < 0)
+            df_resampled[f'near_zero_{suffix}'] = (np.abs(df_resampled[f'macd_{suffix}']) < zero_eps) & (np.abs(df_resampled[f'macdsignal_{suffix}']) < zero_eps)
+            df_resampled[f'hist_increasing_{suffix}'] = df_resampled[f'macdhist_{suffix}'] > df_resampled[f'macdhist_{suffix}'].shift(1)
+            df_resampled[f'hist_decreasing_{suffix}'] = df_resampled[f'macdhist_{suffix}'] < df_resampled[f'macdhist_{suffix}'].shift(1)
+            # 高位：远离零轴
+            df_resampled[f'high_position_{suffix}'] = zero_dist > (zero_dist_ema * 1.5)
+            # 金叉/死叉
+            df_resampled[f'macd_cross_up_{suffix}'] = (df_resampled[f'macd_{suffix}'] > df_resampled[f'macdsignal_{suffix}']) & (df_resampled[f'macd_{suffix}'].shift(1) <= df_resampled[f'macdsignal_{suffix}'].shift(1))
+            df_resampled[f'macd_cross_down_{suffix}'] = (df_resampled[f'macd_{suffix}'] < df_resampled[f'macdsignal_{suffix}']) & (df_resampled[f'macd_{suffix}'].shift(1) >= df_resampled[f'macdsignal_{suffix}'].shift(1))
+            return df_resampled[[
+                'date',
+                'close',
+                f'macd_{suffix}', f'macdsignal_{suffix}', f'macdhist_{suffix}',
+                f'ema_24_{suffix}', f'ema_52_{suffix}',
+                f'atr_{suffix}', f'atr_pct_{suffix}',
+                f'above_zero_{suffix}', f'below_zero_{suffix}', f'near_zero_{suffix}', f'hist_increasing_{suffix}', f'hist_decreasing_{suffix}',
+                f'high_position_{suffix}', f'macd_cross_up_{suffix}', f'macd_cross_down_{suffix}'
+            ]]
+
+        for suf, minutes in intervals.items():
+            df_res = resample_to_interval(dataframe, minutes)
+            df_htf = build_htf(df_res, suf)
+            dataframe = resampled_merge(dataframe, df_htf)
        
-        # 零轴判断
+        # 动态阈值与距离定义
+        # 距离零轴的合成距离，用于高位/近零判定
+        dataframe['macd_abs'] = np.abs(dataframe['macd'])
+        dataframe['macdsignal_abs'] = np.abs(dataframe['macdsignal'])
+        dataframe['zero_dist'] = np.sqrt(np.square(dataframe['macd']) + np.square(dataframe['macdsignal']))
+        dataframe['zero_dist_ema'] = dataframe['zero_dist'].ewm(span=50, adjust=False).mean()
+        # 近零动态阈值（零轴“无限接近”的量化）
+        dataframe['zero_eps'] = (dataframe['zero_dist_ema'] * 0.2).clip(lower=1e-8)
+
+        # 零轴判断（方向与近零）
        dataframe['above_zero'] = (dataframe['macd'] > 0) & (dataframe['macdsignal'] > 0)
        dataframe['below_zero'] = (dataframe['macd'] < 0) & (dataframe['macdsignal'] < 0)
-        dataframe['cross_zero'] = (
-            (dataframe['macd'].shift(1) < 0) & (dataframe['macd'] > 0) |
-            (dataframe['macdsignal'].shift(1) < 0) & (dataframe['macdsignal'] > 0)
+        dataframe['near_zero_fast'] = dataframe['macd_abs'] < dataframe['zero_eps']
+        dataframe['near_zero_slow'] = dataframe['macdsignal_abs'] < dataframe['zero_eps']
+        dataframe['near_zero'] = dataframe['near_zero_fast'] & dataframe['near_zero_slow']
+        # MACD与信号线穿越零轴（当根事件，用于阶段/线段识别）
+        dataframe['cross_zero_up'] = (
+            ((dataframe['macd'].shift(1) <= 0) & (dataframe['macd'] > 0)) |
+            ((dataframe['macdsignal'].shift(1) <= 0) & (dataframe['macdsignal'] > 0))
        )
+        dataframe['cross_zero_down'] = (
+            ((dataframe['macd'].shift(1) >= 0) & (dataframe['macd'] < 0)) |
+            ((dataframe['macdsignal'].shift(1) >= 0) & (dataframe['macdsignal'] < 0))
+        )
+        dataframe['cross_zero'] = dataframe['cross_zero_up'] | dataframe['cross_zero_down']
+
+        # 价格触碰/接近EMA52（文档：K线触碰EMA52附近）
+        dataframe['price_above_ema52'] = dataframe['close'] > dataframe['ema_52']
+        dataframe['price_below_ema52'] = dataframe['close'] < dataframe['ema_52']
+        dataframe['price_near_ema52'] = (np.abs(dataframe['close'] - dataframe['ema_52']) / dataframe['ema_52']) < 0.003
+        dataframe['touch_zero_by_price'] = dataframe['price_near_ema52']
+        # MACD白线（DIF）无限接近零轴（文档：白线靠近零轴）
+        dataframe['touch_zero_by_macd'] = dataframe['near_zero_fast']
+
+        # 有效击穿/突破EMA52与零轴（延后确认，信号在确认K线产生，避免前瞻）
+        # 上破EMA52，并在2根K线后仍然在其上
+        cond_break_up = (dataframe['close'].shift(2) > dataframe['ema_52'].shift(2)) & (
+            (dataframe['close'].shift(3) <= dataframe['ema_52'].shift(3))
+        )
+        dataframe['effective_break_ema52_up'] = cond_break_up.fillna(False)
+        # 下破EMA52，并在2根K线后仍然在其下
+        cond_break_down = (dataframe['close'].shift(2) < dataframe['ema_52'].shift(2)) & (
+            (dataframe['close'].shift(3) >= dataframe['ema_52'].shift(3))
+        )
+        dataframe['effective_break_ema52_down'] = cond_break_down.fillna(False)
+
+        # 黄线（慢线：DEA）有效击穿零轴（2根K线后确认）
+        cond_dea_up = (dataframe['macdsignal'].shift(2) > 0) & (dataframe['macdsignal'].shift(3) <= 0)
+        cond_dea_down = (dataframe['macdsignal'].shift(2) < 0) & (dataframe['macdsignal'].shift(3) >= 0)
+        dataframe['effective_dea_cross_up'] = cond_dea_up.fillna(False)
+        dataframe['effective_dea_cross_down'] = cond_dea_down.fillna(False)
        
        # 高位空形态检测
        # 高位空：MACD黄白线处于高位，K线缓慢上涨或横盘，能量柱衰减，形成夹角
-        dataframe['high_position'] = (
-            # MACD黄白线远离零轴（高位）
-            ((dataframe['macd'] > 50) & (dataframe['macdsignal'] > 50)) |
-            ((dataframe['macd'] < -50) & (dataframe['macdsignal'] < -50))
-        )
+        # 高位：距离零轴远离，采用动态阈值（> 1.5x 距离均值）
+        dataframe['high_position'] = dataframe['zero_dist'] > (dataframe['zero_dist_ema'] * 1.5)
        # 能量柱衰减检测
        dataframe['histogram_decreasing'] = dataframe['macdhist'] < dataframe['macdhist'].shift(1)
        dataframe['histogram_increasing'] = dataframe['macdhist'] > dataframe['macdhist'].shift(1)
+        # 线条“横盘”（变化不大）：3根之前差值很小
+        dataframe['macd_flat_3'] = (np.abs(dataframe['macd'] - dataframe['macd'].shift(3)) < dataframe['zero_eps'])
+        dataframe['macdsignal_flat_3'] = (np.abs(dataframe['macdsignal'] - dataframe['macdsignal'].shift(3)) < dataframe['zero_eps'])
        
        # 高位空形态：高位 + 能量柱衰减 + 黄白线横盘
        dataframe['high_position_empty'] = (
@@ -100,26 +205,148 @@ class ChanLun_BTC_K(IStrategy):
            # K线缓慢上涨或横盘（价格变化不大）
            (abs(dataframe['close'] - dataframe['close'].shift(3)) / dataframe['close'].shift(3) < 0.02) &
            # MACD黄白线横盘（变化不大）
-            (abs(dataframe['macd'] - dataframe['macd'].shift(3)) < 0.05) &
-            (abs(dataframe['macdsignal'] - dataframe['macdsignal'].shift(3)) < 0.05)
+            dataframe['macd_flat_3'] &
+            dataframe['macdsignal_flat_3']
        )
        
-        
-        # 归零轴检测
-        dataframe['near_zero'] = (
-            (abs(dataframe['macd']) < 0.1) & (abs(dataframe['macdsignal']) < 0.1)
+        # 归零轴四种走势（近似量化）
+        # 1) 触碰EMA52（由上至下或下至上）
+        dataframe['zero_touch_ema52'] = dataframe['price_near_ema52']
+        # 2) 白线无限接近零轴
+        dataframe['zero_near_fastline'] = dataframe['near_zero_fast']
+        # 3) 零轴粘合：刚穿零轴后，|黄白线|均小，hist不释放反向能量柱，斜率小（横向）
+        small_lines = (dataframe['macd_abs'] < dataframe['zero_eps'] * 1.2) & (dataframe['macdsignal_abs'] < dataframe['zero_eps'] * 1.2)
+        same_side_hist = (
+            ((dataframe['macdhist'] >= 0) & dataframe['cross_zero_up']) |
+            ((dataframe['macdhist'] <= 0) & dataframe['cross_zero_down'])
        )
+        dataframe['zero_axis_adhesion'] = small_lines & same_side_hist
+        # 4) K线先触碰EMA52，而黄白线未归零
+        dataframe['zero_touch_price_first'] = dataframe['price_near_ema52'] & (~dataframe['near_zero'])
+
+        # 零轴纠缠：黄白线反复在近零区上下缠绕（5根内多次变号或绝对值很小）
+        near_zero_many = dataframe['near_zero'].rolling(5).sum() >= 3
+        sign_flip_fast = (np.sign(dataframe['macd']) != np.sign(dataframe['macd'].shift(1)))
+        sign_flip_slow = (np.sign(dataframe['macdsignal']) != np.sign(dataframe['macdsignal'].shift(1)))
+        dataframe['zero_axis_entanglement'] = near_zero_many | (sign_flip_fast & sign_flip_slow & dataframe['near_zero'])
+
+        # 零轴倒挂：靠近零轴、能量柱衰减形成夹角、黄白线交叉并释放反向能量柱
+        macd_cross = ((dataframe['macd'] > dataframe['macdsignal']) & (dataframe['macd'].shift(1) <= dataframe['macdsignal'].shift(1))) | (
+            (dataframe['macd'] < dataframe['macdsignal']) & (dataframe['macd'].shift(1) >= dataframe['macdsignal'].shift(1))
+        )
+        hist_flip = np.sign(dataframe['macdhist']) != np.sign(dataframe['macdhist'].shift(1))
+        dataframe['zero_axis_inverted'] = dataframe['near_zero'] & dataframe['histogram_decreasing'] & macd_cross & hist_flip
        
-        # 价格与EMA52关系
-        dataframe['price_above_ema52'] = dataframe['close'] > dataframe['ema_52']
-        dataframe['price_below_ema52'] = dataframe['close'] < dataframe['ema_52']
-        dataframe['price_near_ema52'] = abs(dataframe['close'] - dataframe['ema_52']) / dataframe['ema_52'] < 0.01
+        # 隐形形态：无能量配合
+        # 高位隐形：远离零轴、价格继续拉升/下跌，但hist未释放同向能量
+        dataframe['hidden_high_bull'] = dataframe['above_zero'] & dataframe['high_position'] & (dataframe['close'] > dataframe['close'].shift(1)) & (dataframe['macdhist'] <= 0)
+        dataframe['hidden_high_bear'] = dataframe['below_zero'] & dataframe['high_position'] & (dataframe['close'] < dataframe['close'].shift(1)) & (dataframe['macdhist'] >= 0)
+        # 归零轴隐形：近零时应释放的支撑/压力能量未出现 -> 可能反向穿零
+        dataframe['hidden_zero_bull_fail'] = dataframe['near_zero'] & dataframe['price_near_ema52'] & (dataframe['macdhist'] <= 0)
+        dataframe['hidden_zero_bear_fail'] = dataframe['near_zero'] & dataframe['price_near_ema52'] & (dataframe['macdhist'] >= 0)
        
-        # 背离检测
+        # 斜率/拐点/金叉死叉（上下文门控）
+        dataframe['ema24_slope_up'] = dataframe['ema_24'] > dataframe['ema_24'].shift(1)
+        dataframe['ema24_slope_down'] = dataframe['ema_24'] < dataframe['ema_24'].shift(1)
+        dataframe['hist_turn_up'] = (dataframe['macdhist'] > dataframe['macdhist'].shift(1)) & (dataframe['macdhist'].shift(1) <= dataframe['macdhist'].shift(2))
+        dataframe['hist_turn_down'] = (dataframe['macdhist'] < dataframe['macdhist'].shift(1)) & (dataframe['macdhist'].shift(1) >= dataframe['macdhist'].shift(2))
+        dataframe['macd_cross_up'] = (dataframe['macd'] > dataframe['macdsignal']) & (dataframe['macd'].shift(1) <= dataframe['macdsignal'].shift(1))
+        dataframe['macd_cross_down'] = (dataframe['macd'] < dataframe['macdsignal']) & (dataframe['macd'].shift(1) >= dataframe['macdsignal'].shift(1))
+
+        # 线段与单位调整周期（近似） - 需在背离检测之前生成
+        # 线段：以黄线穿零轴划分段（上穿为上涨线段，下穿为下跌线段）
+        seg_change = (((dataframe['macdsignal'] <= 0) & (dataframe['macdsignal'].shift(1) > 0)) | ((dataframe['macdsignal'] >= 0) & (dataframe['macdsignal'].shift(1) < 0)))
+        dataframe['segment_id'] = seg_change.cumsum().fillna(0).astype(int)
+        # 单位调整周期：由近零出发-远离-回到近零（用近零作为粗略起止标记）
+        dataframe['near_zero_flag'] = dataframe['near_zero'].astype(int)
+        dataframe['unit_cycle_id'] = (dataframe['near_zero_flag'].diff().fillna(0) > 0).cumsum().astype(int)
+
+        # 背离检测（线段内）
        dataframe = self.detect_divergence(dataframe)
-        
+
        # 跳空检测
        dataframe = self.detect_gaps(dataframe)
+
+        # V字反转：近零+收敛+突破横盘区
+        price_break = dataframe['close'] > dataframe['close'].rolling(10).max().shift(1)
+        macd_converge = dataframe['histogram_decreasing'].rolling(4).sum() >= 3
+        dataframe['v_reversal'] = dataframe['near_zero'] & dataframe['price_above_ema52'] & macd_converge & price_break
+
+        # 抢底原理（第三阶段：底背离/动能不足触发）
+        momentum_lack = (dataframe['below_zero'] & dataframe['histogram_decreasing'] & (dataframe['close'] <= dataframe['close'].shift(1)))
+        dataframe['bottom_snap_buy'] = dataframe['near_zero'] & (dataframe['bottom_divergence'] | momentum_lack)
+
+        # 归零轴强支撑（零轴粘合 + EMA52支撑）
+        dataframe['zero_adhesion_support'] = dataframe['zero_axis_adhesion'] & dataframe['price_near_ema52'] & dataframe['price_above_ema52']
+
+        # 高周期门控（5x 与 60x），注意列名经过 resampled_merge 改名：resample_{minutes}_<col>
+        min3 = intervals['x3']
+        min5 = intervals['x5']
+        min15 = intervals['x15']
+        min60 = intervals['x60']
+        ema24_5 = f'resample_{min5}_ema_24_x5'
+        ema52_5 = f'resample_{min5}_ema_52_x5'
+        above0_5 = f'resample_{min5}_above_zero_x5'
+        near0_5 = f'resample_{min5}_near_zero_x5'
+        macds_5 = f'resample_{min5}_macdsignal_x5'
+        d5 = f'resample_{min5}_date'
+        close5 = f'resample_{min5}_close'
+        ema24_60 = f'resample_{min60}_ema_24_x60'
+        ema52_60 = f'resample_{min60}_ema_52_x60'
+        macds_60 = f'resample_{min60}_macdsignal_x60'
+        above0_60 = f'resample_{min60}_above_zero_x60'
+        near0_60 = f'resample_{min60}_near_zero_x60'
+        date_60 = f'resample_{min60}_date'
+
+        dataframe['htf_buy_gate'] = (
+            (dataframe.get(ema24_60, np.nan) > dataframe.get(ema52_60, np.nan)) &
+            ((dataframe.get(above0_60, False)) | (dataframe.get(near0_60, False)) | (dataframe.get(macds_60, np.nan) >= 0))
+        ).fillna(False)
+        dataframe['htf_sell_gate'] = (
+            (dataframe.get(ema24_60, np.nan) < dataframe.get(ema52_60, np.nan)) | (dataframe.get(macds_60, np.nan) < 0)
+        ).fillna(False)
+
+        # 1m 对 60m 均线关系
+        dataframe['price_above_ema52_x60'] = (dataframe['close'] > dataframe.get(ema52_60, np.nan)).fillna(False)
+        dataframe['price_near_ema52_x60'] = (np.abs(dataframe['close'] - dataframe.get(ema52_60, np.nan)) / dataframe.get(ema52_60, np.nan) < 0.003).fillna(False)
+        # 提供无前缀别名，供买卖条件使用（60m）
+        dataframe['near_zero_x60'] = dataframe.get(near0_60, False)
+        dataframe['macdsignal_x60'] = dataframe.get(macds_60, np.nan)
+        dataframe['ema24_x60'] = dataframe.get(ema24_60, np.nan)
+        dataframe['ema52_x60'] = dataframe.get(ema52_60, np.nan)
+        dataframe['hist_increasing_x60'] = dataframe.get(f'resample_{min60}_hist_increasing_x60', False)
+        dataframe['hist_decreasing_x60'] = dataframe.get(f'resample_{min60}_hist_decreasing_x60', False)
+        dataframe['macd_cross_up_60m'] = dataframe.get(f'resample_{min60}_macd_cross_up_x60', False)
+        dataframe['macd_cross_down_60m'] = dataframe.get(f'resample_{min60}_macd_cross_down_x60', False)
+        # 60m边界触发：仅在新60m开始的一根1m上允许交易（直接比较，避免时区转换问题）
+        d60 = dataframe.get(date_60)
+        dataframe['is_new_60m'] = d60.ne(d60.shift(1)).fillna(False)
+        # 5m边界（用于5m信号仅在新5m产生）
+        d5s = dataframe.get(d5)
+        dataframe['is_new_5m'] = d5s.ne(d5s.shift(1)).fillna(False)
+        # 60m开始后前5分钟内也允许交易
+        if 'date' in dataframe.columns:
+            dt_delta60 = (dataframe['date'] - d60)
+            dataframe['within_first_60m5'] = dt_delta60.dt.total_seconds().div(60).between(0, 10).fillna(False)
+        else:
+            dataframe['within_first_60m5'] = dataframe['is_new_60m']
+        # 60m波动过滤
+        dataframe['atr_pct_x60'] = dataframe.get(f'resample_{min60}_atr_pct_x60', np.nan)
+        dataframe['htf_vol_ok'] = (dataframe['atr_pct_x60'] > 0.0005).fillna(False)
+        # 价格接近1h EMA24（小回踩判定）
+        dataframe['price_near_ema24_x60'] = (np.abs(dataframe['close'] - dataframe.get('ema24_x60', np.nan)) / dataframe.get('ema24_x60', np.nan) < 0.0015).fillna(False)
+        # 5m别名与突破判定
+        dataframe['near_zero_x5'] = dataframe.get(near0_5, False)
+        dataframe['macd_cross_up_5m'] = dataframe.get(f'resample_{min5}_macd_cross_up_x5', False)
+        dataframe['macd_cross_down_5m'] = dataframe.get(f'resample_{min5}_macd_cross_down_x5', False)
+        dataframe['ema24_x5'] = dataframe.get(ema24_5, np.nan)
+        dataframe['ema52_x5'] = dataframe.get(ema52_5, np.nan)
+        dataframe['close_x5'] = dataframe.get(close5, np.nan)
+        # 5m突破：新5m且收盘突破近20根5m最高收盘
+        dataframe['breakout_5m'] = (
+            dataframe['is_new_5m'] &
+            (dataframe['close_x5'] > dataframe['close_x5'].rolling(20).max().shift(1))
+        ).fillna(False)
        
        return dataframe
    
@@ -127,48 +354,61 @@ class ChanLun_BTC_K(IStrategy):
        """
        检测背离形态
        """
-        # 顶背离检测
-        dataframe['top_divergence'] = False
-        dataframe['bottom_divergence'] = False
-        
-        for i in range(self.divergence_lookback, len(dataframe)):
-            # 顶背离：价格创新高，MACD未创新高
-            if (dataframe['close'].iloc[i] > dataframe['close'].iloc[i-self.divergence_lookback:i].max() and
-                dataframe['macd'].iloc[i] < dataframe['macd'].iloc[i-self.divergence_lookback:i].max() and
-                dataframe['above_zero'].iloc[i]):
-                dataframe.loc[dataframe.index[i], 'top_divergence'] = True
-            
-            # 底背离：价格创新低，MACD未创新低
-            if (dataframe['close'].iloc[i] < dataframe['close'].iloc[i-self.divergence_lookback:i].min() and
-                dataframe['macd'].iloc[i] > dataframe['macd'].iloc[i-self.divergence_lookback:i].min() and
-                dataframe['below_zero'].iloc[i]):
-                dataframe.loc[dataframe.index[i], 'bottom_divergence'] = True
-        
-        return dataframe
+        # 顶/底背离（限制在同一线段内比较，避免跨段）
+        df = dataframe
+        df['top_divergence'] = False
+        df['bottom_divergence'] = False
+
+        # 线段内的滚动极值（不跨段）
+        seg_group = df.groupby('segment_id', group_keys=False)
+        seg_close_max_prev = seg_group['close'].apply(lambda s: s.cummax().shift(1))
+        seg_macd_max_prev = seg_group['macd'].apply(lambda s: s.cummax().shift(1))
+        seg_close_min_prev = seg_group['close'].apply(lambda s: s.cummin().shift(1))
+        seg_macd_min_prev = seg_group['macd'].apply(lambda s: s.cummin().shift(1))
+
+        cond_top = (df['close'] > seg_close_max_prev) & (df['macd'] < seg_macd_max_prev) & df['above_zero']
+        cond_bottom = (df['close'] < seg_close_min_prev) & (df['macd'] > seg_macd_min_prev) & df['below_zero']
+        df.loc[cond_top.fillna(False), 'top_divergence'] = True
+        df.loc[cond_bottom.fillna(False), 'bottom_divergence'] = True
+
+        return df
    
    def detect_gaps(self, dataframe: DataFrame) -> DataFrame:
        """
        检测跳空形态
        """
-        # 连续跳空检测
-        dataframe['continuous_gap'] = False
-        dataframe['separate_gap'] = False
-        
-        for i in range(5, len(dataframe)):
-            # 连续跳空：能量柱连续增长
-            if (dataframe['histogram_increasing'].iloc[i-2:i+1].all() and
-                dataframe['macdhist'].iloc[i] > 0 and
-                dataframe['macdhist'].iloc[i] > dataframe['macdhist'].iloc[i-1]):
-                dataframe.loc[dataframe.index[i], 'continuous_gap'] = True
-            
-            # 分立跳空：能量柱被反向能量柱分隔
-            if (i > 10 and
-                dataframe['macdhist'].iloc[i] > 0 and
-                dataframe['macdhist'].iloc[i-5:i].min() < 0 and
-                dataframe['macdhist'].iloc[i] > dataframe['macdhist'].iloc[i-5:i].max()):
-                dataframe.loc[dataframe.index[i], 'separate_gap'] = True
-        
-        return dataframe
+        # 连续跳空/分立跳空（单位周期内的近似判定）
+        df = dataframe
+        df['continuous_gap'] = False
+        df['separate_gap'] = False
+
+        # 能量柱包含在黄白线之内：|hist| <= max(|macd|, |signal|)
+        hist_within_lines = df['macdhist'].abs() <= np.maximum(df['macd_abs'], df['macdsignal_abs'])
+
+        for i in range(5, len(df)):
+            # 连续跳空：同向能量柱在单位周期中由衰减转为增长，且能量柱包含在线内
+            if (
+                df['histogram_increasing'].iloc[i-2:i+1].all() and
+                hist_within_lines.iloc[i-2:i+1].all() and
+                ((df['macdhist'].iloc[i] > 0) | (df['macdhist'].iloc[i] < 0)) and
+                # 近似单位周期：最近出现过near_zero且当前未再次near_zero
+                (df['near_zero'].iloc[i-10:i].any()) and (not df['near_zero'].iloc[i])
+            ):
+                df.loc[df.index[i], 'continuous_gap'] = True
+
+            # 分立跳空：两个同向能量堆被反向能量柱分隔，且远离零轴
+            if i > 10:
+                recent_hist = df['macdhist'].iloc[i-10:i+1]
+                same_dir = (recent_hist.max() > 0 and recent_hist.min() < 0)
+                far_from_zero = df['high_position'].iloc[i]
+                if same_dir and far_from_zero:
+                    # 当前是同向新峰，且此前5根内出现过反向柱
+                    if (df['macdhist'].iloc[i] > 0 and (recent_hist.iloc[-6:-1] < 0).any() and df['macdhist'].iloc[i] > recent_hist.iloc[:-1].max()) or (
+                        df['macdhist'].iloc[i] < 0 and (recent_hist.iloc[-6:-1] > 0).any() and df['macdhist'].iloc[i] < recent_hist.iloc[:-1].min()
+                    ):
+                        df.loc[df.index[i], 'separate_gap'] = True
+
+        return df
    
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
@@ -176,48 +416,54 @@ class ChanLun_BTC_K(IStrategy):
        """
        conditions = []
        
-        # 条件1: 底背离确认买点
+        # 条件1: 60m门控 + 底背离确认买点（同段内）
        conditions.append(
            dataframe['bottom_divergence'] &
            dataframe['below_zero'] &
-            dataframe['price_near_ema52']
+            (dataframe['price_near_ema52_x60'] | dataframe['near_zero_x60'] | dataframe['near_zero']) &
+            (dataframe['ema24_slope_up']) & dataframe['htf_vol_ok'] &
+            (dataframe['htf_buy_gate']) & (dataframe['is_new_60m'] | dataframe['within_first_60m5'])
        )
        
        # 条件2: 单位调整周期内的连续跳空背离
        conditions.append(
            dataframe['continuous_gap'] &
            dataframe['below_zero'] &
-            dataframe['near_zero']
+            dataframe['near_zero'] &
+            (dataframe['ema24_slope_up']) & dataframe['htf_vol_ok'] &
+            (dataframe['price_near_ema52_x60'] | dataframe['near_zero_x60'] | dataframe['price_near_ema24_x60']) &
+            (dataframe['htf_buy_gate'])
        )
        
-        # 条件3: 底部形态V字反转
+        # 条件3: 底部形态V字反转 或 5m突破
+        conditions.append(
+            (
+                dataframe['v_reversal'] & dataframe['macd_cross_up'] |
+                (dataframe['breakout_5m'] & dataframe['macd_cross_up_5m'])
+            ) &
+            (dataframe['price_above_ema52_x60'] | (dataframe['macdsignal_x60'] >= 0)) & dataframe['htf_vol_ok'] &
+            (dataframe['htf_buy_gate'])
+        )
+        
+        # 条件4: 抢底原理（第三阶段：底背离/动能不足叠加）
+        conditions.append(
+            dataframe['bottom_snap_buy'] & (dataframe['macd_cross_up'] | dataframe['hist_turn_up']) &
+            (dataframe['price_near_ema52_x60'] | dataframe['near_zero_x60'] | dataframe['price_near_ema24_x60']) & (dataframe['htf_buy_gate']) & dataframe['htf_vol_ok']
+        )
+        
+        # 条件5: 归零轴反弹（近零+EMA52附近+能量回升）
        conditions.append(
-            dataframe['price_above_ema52'] &
            dataframe['near_zero'] &
+            (dataframe['price_near_ema52_x60'] | dataframe['price_near_ema24_x60']) &
            dataframe['histogram_increasing'] &
-            (dataframe['close'] > dataframe['close'].shift(5))
+            (dataframe['close'] > dataframe['close'].shift(1)) &
+            (dataframe['ema24_slope_up']) & (dataframe['htf_buy_gate']) & dataframe['htf_vol_ok']
        )
        
-        # 条件4: 抢底原理（第三阶段背离/动能不足）
+        # 条件6: 零轴粘合强支撑买入（文档强调强支撑、弱反弹）
        conditions.append(
-            dataframe['below_zero'] &
-            dataframe['near_zero'] &
-            dataframe['histogram_decreasing'] &
-            (dataframe['macd'] > dataframe['macd'].shift(3))  # MACD开始收敛
-        )
-        
-        # 条件5: 归零轴反弹
-        conditions.append(
-            dataframe['near_zero'] &
-            dataframe['price_near_ema52'] &
-            dataframe['histogram_increasing'] &
-            (dataframe['close'] > dataframe['close'].shift(1))
-        )
-        
-        # 条件6: 零轴之下高位空形态（归零轴需求）
-        conditions.append(
-            dataframe['high_position_empty'] &
-            dataframe['below_zero']
+            dataframe['zero_adhesion_support'] & dataframe['macd_cross_up'] &
+            (dataframe['price_above_ema52_x60'] | dataframe['near_zero_x60'] | dataframe['price_near_ema24_x60']) & (dataframe['htf_buy_gate']) & dataframe['htf_vol_ok']
        )
        
        if conditions:
@@ -233,7 +479,7 @@ class ChanLun_BTC_K(IStrategy):
        """
        conditions = []
        
-        # 条件1: 顶背离确认卖点
+        # 条件1: 顶背离确认卖点（同段内）
        conditions.append(
            dataframe['top_divergence'] &
            dataframe['above_zero']
@@ -242,28 +488,26 @@ class ChanLun_BTC_K(IStrategy):
        # 条件2: 高位空形态
        conditions.append(
            dataframe['high_position_empty'] &
-            dataframe['above_zero']
+            dataframe['above_zero'] &
+            (dataframe['macd_cross_down'] | dataframe['hist_turn_down'])
        )
        
-        # 条件3: 穿零轴下跌
+        # 条件3: 有效穿零轴下跌（慢线有效击穿 + EMA52下）
        conditions.append(
-            dataframe['cross_zero'] &
+            (dataframe['effective_dea_cross_down'] | dataframe['cross_zero_down'] | dataframe['htf_sell_gate']) &
            dataframe['price_below_ema52'] &
-            (dataframe['macd'] < 0)
+            (dataframe['ema24_slope_down'])
        )
        
        # 条件4: 能量柱隐形形态（无能量配合的上涨）
        conditions.append(
-            dataframe['above_zero'] &
-            (dataframe['macdhist'] < 0) &
-            (dataframe['close'] > dataframe['close'].shift(1))
+            dataframe['hidden_high_bull'] & (dataframe['macd_cross_down'] | dataframe['hist_turn_down'])
        )
        
-        # 条件5: 线段背离（价格创新高但MACD未创新高）
+        # 条件5: 零轴倒挂（弱支撑弱反弹，易继续下行）
        conditions.append(
-            dataframe['above_zero'] &
-            (dataframe['close'] > dataframe['close'].shift(10).max()) &
-            (dataframe['macd'] < dataframe['macd'].shift(10).max())
+            dataframe['zero_axis_inverted'] &
+            dataframe['above_zero']
        )
        
        if conditions: