Normalize one-point regression residual volatility

factor_attribution.py

@@ -309,6 +309,9 @@ def run_factor_regression(
     fitted = x @ coefficients
     residuals = y.to_numpy() - fitted
     residual_series = pd.Series(residuals, index=regression_frame.index)
+    if len(residual_series) == 1:
+        residual_vol_ann = 0.0
+    else:
         residual_vol_ann = float(residual_series.std(ddof=1) * np.sqrt(TRADING_DAYS_PER_YEAR))
 
     dof = n_obs - param_count

tests/test_factor_attribution.py

@@ -501,6 +501,24 @@ class RegressionTests(unittest.TestCase):
         self.assertTrue(np.isnan(result["adj_r_squared"]))
         self.assertAlmostEqual(result["residual_vol_ann"], 0.0, places=12)
 
+    def test_run_factor_regression_single_observation_intercept_only_has_zero_residual_vol(self):
+        dates = pd.date_range("2024-01-01", periods=1, freq="B")
+        factors = pd.DataFrame(index=dates)
+        strategy = pd.Series([0.0015], index=dates)
+
+        result = run_factor_regression(strategy, factors, factor_cols=[])
+
+        self.assertAlmostEqual(result["alpha_daily"], 0.0015, places=12)
+        self.assertEqual(result["betas"], {})
+        self.assertEqual(result["t_stats"], {})
+        self.assertEqual(result["p_values"], {})
+        self.assertEqual(result["r_squared"], 0.0)
+        self.assertTrue(np.isnan(result["alpha_t_stat"]))
+        self.assertTrue(np.isnan(result["alpha_p_value"]))
+        self.assertTrue(np.isnan(result["adj_r_squared"]))
+        self.assertEqual(result["n_obs"], 1)
+        self.assertAlmostEqual(result["residual_vol_ann"], 0.0, places=12)
+
     def test_run_factor_regression_rejects_rank_deficient_designs(self):
         dates = pd.date_range("2024-01-01", periods=6, freq="B")
         market = np.array([0.01, -0.02, 0.015, 0.005, -0.01, 0.02])