feat: 新增 standardized_slope (t-statistic) 因子并实验验证

- simple_rotation.py: 新增 standardized_slope_score 函数 (slope/SE)
- config_loader.py: FactorType 枚举新增 STANDARDIZED_SLOPE
- 对比实验结果: standardized_slope 年化 13.73% vs slope_r2 19.84%
- 结论: t-statistic 过度惩罚高波动资产的有效趋势信号，不适合本场景
- 文档更新: 动量因子对比调研报告新增 3.3 节详细分析

rotation/simple_rotation.py

@@ -123,6 +123,39 @@ def slope_r2_score(prices: np.ndarray) -> float:
     return 10000 * slope * r2
 
 
+def standardized_slope_score(prices: np.ndarray) -> float:
+    """Standardized slope (t-statistic): slope / SE(slope)
+
+    Academic basis:
+    - Uses normalized prices (p/p[0]) for cross-asset comparability,
+      consistent with slope_r2_score.
+    - Divides slope by its standard error, yielding a statistical significance
+      measure rather than raw magnitude.
+    - Equivalent to the t-value for H0: slope=0, penalizing noisy trends.
+
+    Formula:
+        SE(slope) = sqrt(MSE / Sxx)
+        MSE = SS_res / (n - 2)
+        Sxx = sum((xi - x_bar)^2) = n*(n-1)*(n+1)/12  for x = 0..n-1
+    """
+    n = len(prices)
+    if n < 5:
+        return 0.0
+    prices = np.clip(prices, 0.01, None)
+    y = prices / prices[0]  # normalize
+    x = np.arange(n)
+    slope, intercept = np.polyfit(x, y, 1)
+    y_pred = slope * x + intercept
+    ss_res = np.sum((y - y_pred) ** 2)
+    # Standard error of slope
+    mse = ss_res / (n - 2)          # unbiased MSE
+    sxx = n * (n - 1) * (n + 1) / 12  # sum of squared deviations of x
+    se_slope = math.sqrt(mse / sxx) if sxx > 0 else 1e-9
+    if se_slope < 1e-12:
+        se_slope = 1e-12
+    return slope / se_slope
+
+
 def momentum_score(prices: np.ndarray) -> float:
     """Simple price return: (last / first) - 1"""
     if len(prices) < 5:
@@ -409,6 +442,8 @@ class SimpleRotationStrategy:
             return vol_adjusted_momentum_score(prices)
         elif ft == FactorType.SLOPE_R2:
             return slope_r2_score(prices)
+        elif ft == FactorType.STANDARDIZED_SLOPE:
+            return standardized_slope_score(prices)
         elif ft == FactorType.MOMENTUM:
             return momentum_score(prices)
         return weighted_momentum_score(prices)