src/quant_engine/factor_library.py

@@ -312,7 +312,7 @@ def ols_regress(
     ss_tot = float(((y_arr - y_arr.mean()) ** 2).sum())
     r_sq = 1.0 - ss_res / ss_tot if ss_tot > 0 else np.nan
     sigma2 = ss_res / max(n - k, 1)
-    # 协方差矩阵 = sigma2 * (X'X)^-1
+    # 广义协方差矩阵 = sigma2 * (X'X)^+，伪逆兼容共线因子。
     xtx_inv = np.linalg.pinv(x_arr.T @ x_arr) if sigma2 > 0 else np.full((k, k), np.nan)
     se = np.sqrt(np.diag(xtx_inv) * sigma2)
     t_vals = coef / se if sigma2 > 0 else np.full_like(coef, np.nan)

src/quant_engine/risk.py

@@ -5,14 +5,13 @@
 
 from __future__ import annotations
 
-import numpy as np
-from numpy.typing import NDArray
 from typing import Any
 
+import numpy as np
+from numpy.typing import NDArray
 
-def _validate_inputs(
-    weights: NDArray[Any], cov: NDArray[Any]
-) -> tuple[NDArray[Any], NDArray[Any]]:
+
+def _validate_inputs(weights: NDArray[Any], cov: NDArray[Any]) -> tuple[NDArray[Any], NDArray[Any]]:
     """Normalize a portfolio vector and its covariance matrix."""
     w = np.asarray(weights, dtype=float).ravel()
     covariance = np.asarray(cov, dtype=float)
@@ -20,9 +19,7 @@ def _validate_inputs(
     if k == 0:
         raise ValueError("weights must contain at least one asset")
     if covariance.shape != (k, k):
-        raise ValueError(
-            f"cov shape {covariance.shape} does not match weights length {k}"
-        )
+        raise ValueError(f"cov shape {covariance.shape} does not match weights length {k}")
     return w, covariance