Guard factor regressions against unidentified models

factor_attribution.py

@@ -293,14 +293,23 @@ def run_factor_regression(
 
     x = regression_frame[factor_cols].astype(float).to_numpy()
     x = np.column_stack([np.ones(len(regression_frame)), x])
+    n_obs = len(regression_frame)
+    param_count = x.shape[1]
+    if n_obs <= param_count:
+        raise ValueError(
+            f"Insufficient observations for regression: need more than {param_count} rows, got {n_obs}"
+        )
+
+    coefficients, _, rank, _ = np.linalg.lstsq(x, y.to_numpy(), rcond=None)
+    if rank < param_count:
+        raise ValueError(
+            "Regression design matrix is rank-deficient; coefficients are not uniquely identified"
+        )
 
-    coefficients, _, _, _ = np.linalg.lstsq(x, y.to_numpy(), rcond=None)
     fitted = x @ coefficients
     residuals = y.to_numpy() - fitted
 
-    n_obs = len(regression_frame)
-    param_count = x.shape[1]
-    dof = max(n_obs - param_count, 1)
+    dof = n_obs - param_count
     residual_variance = float((residuals @ residuals) / dof)
     covariance = residual_variance * np.linalg.pinv(x.T @ x)
     standard_errors = np.sqrt(np.diag(covariance))