feat: 新增 slope_r2_idm 和 slope_r2_ensemble 动量因子

- slope_r2_idm: slope_r2 × IDM(信息离散动量)，惩罚靠少数大涨日撑起来的假动量
- slope_r2_ensemble: 多窗口(63/126/252天) slope_r2 等权融合，捕捉不同周期趋势信号
- 新增 info_dispersal_momentum() 计算正收益天数占比
- 新增 slope_r2_idm_score() 和 slope_r2_ensemble_score() 因子函数
- ensemble 因子需要更长预加载窗口(504天)和计算窗口(252天)
- crash filter 仍使用原始 n_days 窗口

rotation/config_loader.py

@@ -35,6 +35,8 @@ class FactorType(str, Enum):
     WEIGHTED_MOMENTUM = "weighted_momentum"
     VOL_ADJUSTED_MOMENTUM = "vol_adjusted_momentum"
     STANDARDIZED_SLOPE = "standardized_slope"
+    SLOPE_R2_IDM = "slope_r2_idm"  # slope_r2 * IDM (信息离散动量融合)
+    SLOPE_R2_ENSEMBLE = "slope_r2_ensemble"  # 多窗口融合 (63/126/252天)
 
 
 class PremiumMode(str, Enum):

rotation/simple_rotation.py

@@ -123,6 +123,59 @@ def slope_r2_score(prices: np.ndarray) -> float:
     return 10000 * slope * r2
 
 
+def info_dispersal_momentum(prices: np.ndarray) -> float:
+    """Information Dispersal Momentum (IDM): 正收益天数占比
+    
+    衡量上涨的持续性和广度，过滤靠少数极端日撑起来的假动量。
+    范围 [0, 1]，值越高表示上涨天数越多。
+    """
+    if len(prices) < 2:
+        return 0.0
+    returns = np.diff(prices)
+    positive_days = np.sum(returns > 0)
+    return positive_days / len(returns)
+
+
+def slope_r2_idm_score(prices: np.ndarray) -> float:
+    """Slope * R² * IDM: 趋势强度 × 拟合质量 × 上涨持续性
+    
+    将 slope_r2 与信息离散动量 (IDM) 通过乘法融合：
+    - slope_r2 捕捉趋势方向和稳定性
+    - IDM 作为折扣系数，惩罚靠少数大涨日撑起来的动量
+    """
+    sr2 = slope_r2_score(prices)
+    idm = info_dispersal_momentum(prices)
+    return sr2 * idm
+
+
+def slope_r2_ensemble_score(prices: np.ndarray, windows: list = None) -> float:
+    """多窗口 slope_r2 等权融合
+    
+    使用多个时间窗口（默认 63/126/252 天）计算 slope_r2，取等权平均。
+    金融语义：捕捉不同周期的趋势信号，降低单窗口风险。
+    
+    对 slope_r2 偏好的影响：
+    - 削弱对"高波动+间歇性趋势"资产的偏好
+    - 增强对"低波动+持续性趋势"资产的识别
+    - 让因子更偏向"持续性趋势"而非"爆发性趋势"
+    """
+    if windows is None:
+        windows = [63, 126, 252]  # 3月、6月、12月
+    
+    scores = []
+    for w in windows:
+        if len(prices) >= w:
+            # 取最后 w 天的价格计算 slope_r2
+            window_prices = prices[-w:]
+            score = slope_r2_score(window_prices)
+            scores.append(score)
+    
+    if not scores:
+        return 0.0
+    
+    return sum(scores) / len(scores)
+
+
 def standardized_slope_score(prices: np.ndarray) -> float:
     """Standardized slope (t-statistic): slope / SE(slope)
 
@@ -459,7 +512,9 @@ class SimpleRotationStrategy:
         """Preload all historical data"""
         start_date = self.config.backtest.start_date
         end_date = self.config.backtest.end_date or datetime.now().strftime('%Y-%m-%d')
-        preload_start = (pd.Timestamp(start_date) - timedelta(days=self.n_days * 2)).strftime('%Y-%m-%d')
+        # ensemble 因子需要更长的历史（252天窗口，预加载 2 倍）
+        preload_days = 504 if self.config.factor.type == FactorType.SLOPE_R2_ENSEMBLE else self.n_days * 2
+        preload_start = (pd.Timestamp(start_date) - timedelta(days=preload_days)).strftime('%Y-%m-%d')
 
         print("\n[1/4] Preloading signal sources (index raw)...")
         for code in self.signal_codes:
@@ -498,13 +553,17 @@ class SimpleRotationStrategy:
         df = self.index_data[signal_code]
         mask = df.index <= date
         recent = df.loc[mask]
-        if len(recent) < self.n_days:
+        
+        # ensemble 因子需要更长的窗口（252天）
+        required_days = 252 if self.config.factor.type == FactorType.SLOPE_R2_ENSEMBLE else self.n_days
+        
+        if len(recent) < required_days:
             return None
-        prices = recent['close'].values[-self.n_days:]
+        prices = recent['close'].values[-required_days:]
 
         base_momentum = self._compute_base_momentum(prices)
 
-        if is_crash(prices):
+        if is_crash(prices[-self.n_days:]):  # crash filter 仍然用 n_days 窗口
             return 0.0
         return base_momentum
 
@@ -517,17 +576,21 @@ class SimpleRotationStrategy:
         df = self.index_data[signal_code]
         mask = df.index <= date
         recent = df.loc[mask]
-        if len(recent) < self.n_days:
+        
+        # ensemble 因子需要更长的窗口（252天）
+        required_days = 252 if self.config.factor.type == FactorType.SLOPE_R2_ENSEMBLE else self.n_days
+        
+        if len(recent) < required_days:
             return None
-        prices = recent['close'].values[-self.n_days:]
+        prices = recent['close'].values[-required_days:]
 
         if self.config.factor.type == FactorType.VOL_ADJUSTED_MOMENTUM:
-            base_momentum = weighted_momentum_score(prices)
+            base_momentum = weighted_momentum_score(prices[-self.n_days:])
         else:
             # Use the same score function as ranking
             base_momentum = self._compute_base_momentum(prices)
 
-        if is_crash(prices):
+        if is_crash(prices[-self.n_days:]):
             return 0.0
         return base_momentum
 
@@ -538,6 +601,10 @@ class SimpleRotationStrategy:
             return vol_adjusted_momentum_score(prices)
         elif ft == FactorType.SLOPE_R2:
             return slope_r2_score(prices)
+        elif ft == FactorType.SLOPE_R2_IDM:
+            return slope_r2_idm_score(prices)
+        elif ft == FactorType.SLOPE_R2_ENSEMBLE:
+            return slope_r2_ensemble_score(prices)
         elif ft == FactorType.STANDARDIZED_SLOPE:
             return standardized_slope_score(prices)
         elif ft == FactorType.MOMENTUM: