Reject colliding attribution semantics headers

factor_attribution.py

@@ -475,7 +475,7 @@ def _resolve_beta_semantics(row: pd.Series) -> dict[str, str]:
                 parsed_mapping = {str(key): str(value) for key, value in parsed.items()}
                 if set(parsed_mapping) == set(SEMANTIC_BETA_COLUMNS) and all(
                     value.strip() for value in parsed_mapping.values()
-                ):
+                ) and _semantics_have_unique_headers(parsed_mapping):
                     return parsed_mapping
     return canonical
 
@@ -488,6 +488,11 @@ def _beta_header_name(factor_name: str) -> str:
     return f"beta_{suffix}"
 
 
+def _semantics_have_unique_headers(semantics: dict[str, str]) -> bool:
+    headers = [_beta_header_name(semantics[column]) for column in SEMANTIC_BETA_COLUMNS]
+    return len(headers) == len(set(headers))
+
+
 def _section_beta_header_map(semantics: dict[str, str]) -> dict[str, str]:
     header_map: dict[str, str] = {}
     for beta_column, factor_name in semantics.items():

tests/test_factor_attribution.py

@@ -1017,6 +1017,112 @@ class AttributionIntegrationTests(unittest.TestCase):
         self.assertNotIn("MKT_RF 1.05", output)
         self.assertNotIn("SMB_PROXY -0.30", output)
 
+    def test_print_attribution_summary_ignores_duplicate_beta_semantics_labels(self):
+        summary = pd.DataFrame(
+            [
+                {
+                    "strategy": "US Strategy",
+                    "market": "us",
+                    "model": "ff5",
+                    "factor_source": "external+local",
+                    "proxy_only": False,
+                    "beta_semantics": json.dumps(
+                        {
+                            "beta_mkt": "DUPLICATE",
+                            "beta_smb": "DUPLICATE",
+                            "beta_hml": "VALUE",
+                            "beta_rmw": "QUALITY",
+                            "beta_cma": "INVESTMENT",
+                            "beta_mom": "MOMENTUM",
+                            "beta_lowvol": "MINVOL",
+                            "beta_recovery": "BOUNCE",
+                        }
+                    ),
+                    "start_date": "2025-01-02",
+                    "end_date": "2026-03-24",
+                    "n_obs": 319,
+                    "alpha_daily": 0.0004,
+                    "alpha_ann": 0.1008,
+                    "alpha_t_stat": 2.1,
+                    "alpha_p_value": 0.04,
+                    "r_squared": 0.82,
+                    "adj_r_squared": 0.81,
+                    "residual_vol_ann": 0.12,
+                    "beta_mkt": 1.05,
+                    "beta_smb": -0.20,
+                    "beta_hml": 0.30,
+                    "beta_rmw": 0.05,
+                    "beta_cma": np.nan,
+                    "beta_mom": np.nan,
+                    "beta_lowvol": np.nan,
+                    "beta_recovery": np.nan,
+                }
+            ]
+        )
+
+        buffer = io.StringIO()
+        with contextlib.redirect_stdout(buffer):
+            print_attribution_summary(summary)
+
+        output = buffer.getvalue()
+        self.assertIn("beta_smb", output)
+        self.assertIn("MKT_RF 1.05", output)
+        self.assertNotIn("beta_duplicate", output)
+        self.assertNotIn("DUPLICATE 1.05", output)
+
+    def test_print_attribution_summary_ignores_colliding_normalized_beta_semantics_labels(self):
+        summary = pd.DataFrame(
+            [
+                {
+                    "strategy": "CN Strategy",
+                    "market": "cn",
+                    "model": "proxy",
+                    "factor_source": "proxy_only",
+                    "proxy_only": True,
+                    "beta_semantics": json.dumps(
+                        {
+                            "beta_mkt": "A-B",
+                            "beta_smb": "A B",
+                            "beta_hml": "VALUE_PROXY_CUSTOM",
+                            "beta_rmw": "QUALITY_PROXY_CUSTOM",
+                            "beta_cma": "INVEST_PROXY_CUSTOM",
+                            "beta_mom": "TREND",
+                            "beta_lowvol": "DEFENSIVE",
+                            "beta_recovery": "RECOVERY_PROXY",
+                        }
+                    ),
+                    "start_date": "2025-01-02",
+                    "end_date": "2026-03-24",
+                    "n_obs": 319,
+                    "alpha_daily": 0.0002,
+                    "alpha_ann": 0.0504,
+                    "alpha_t_stat": 1.5,
+                    "alpha_p_value": 0.12,
+                    "r_squared": 0.72,
+                    "adj_r_squared": 0.70,
+                    "residual_vol_ann": 0.14,
+                    "beta_mkt": 0.85,
+                    "beta_smb": -0.30,
+                    "beta_hml": 0.25,
+                    "beta_rmw": 0.10,
+                    "beta_cma": -0.05,
+                    "beta_mom": 0.20,
+                    "beta_lowvol": np.nan,
+                    "beta_recovery": np.nan,
+                }
+            ]
+        )
+
+        buffer = io.StringIO()
+        with contextlib.redirect_stdout(buffer):
+            print_attribution_summary(summary)
+
+        output = buffer.getvalue()
+        self.assertIn("beta_smb_proxy", output)
+        self.assertIn("SMB_PROXY -0.30", output)
+        self.assertNotIn("beta_a_b", output)
+        self.assertNotIn("A B -0.30", output)
+
     def test_print_attribution_summary_splits_standard_and_proxy_sections_for_mixed_frames(self):
         summary = pd.DataFrame(
             [