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Fix proxy attribution benchmark and labeling

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This commit is contained in:
Gahow Wang
2026-04-07 17:18:29 +08:00
parent 9c4a219c68
commit 69a03f52d9
2 changed files with 117 additions and 10 deletions
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36
factor_attribution.py
View File

@@ -32,17 +32,18 @@ PROXY_FACTOR_COLUMNS = [
"CMA_PROXY",
] + EXTENSION_FACTOR_COLUMNS
TRADING_DAYS_PER_YEAR = 252
MISSING_BENCHMARK_SENTINEL = "__missing_benchmark__"
SUMMARY_BETA_COLUMN_BY_FACTOR = {
"MKT_RF": "beta_mkt",
"MKT": "beta_mkt",
"SMB": "beta_smb",
"SMB_PROXY": "beta_smb",
"SMB_PROXY": "beta_smb_proxy",
"HML": "beta_hml",
"HML_PROXY": "beta_hml",
"HML_PROXY": "beta_hml_proxy",
"RMW": "beta_rmw",
"RMW_PROXY": "beta_rmw",
"RMW_PROXY": "beta_rmw_proxy",
"CMA": "beta_cma",
"CMA_PROXY": "beta_cma",
"CMA_PROXY": "beta_cma_proxy",
"MOM": "beta_mom",
"LOWVOL": "beta_lowvol",
"RECOVERY": "beta_recovery",
@@ -68,6 +69,10 @@ SUMMARY_COLUMNS = [
"beta_hml",
"beta_rmw",
"beta_cma",
"beta_smb_proxy",
"beta_hml_proxy",
"beta_rmw_proxy",
"beta_cma_proxy",
"beta_mom",
"beta_lowvol",
"beta_recovery",
@@ -429,6 +434,12 @@ def _select_model_names(
return list(available_models)
def _resolve_benchmark_symbol(benchmark: str | None) -> str:
if benchmark is None:
return MISSING_BENCHMARK_SENTINEL
return benchmark
def attribute_strategies(
results_df: pd.DataFrame,
benchmark_label: str,
@@ -438,10 +449,7 @@ def attribute_strategies(
benchmark: str | None = None,
external_factors: pd.DataFrame | None = None,
) -> tuple[pd.DataFrame, pd.DataFrame]:
benchmark_symbol = benchmark
if benchmark_symbol is None:
matching_columns = [column for column in price_data.columns if column in benchmark_label]
benchmark_symbol = matching_columns[0] if matching_columns else price_data.columns[-1]
benchmark_symbol = _resolve_benchmark_symbol(benchmark)
extension_factors = build_extension_factors(price_data, benchmark=benchmark_symbol, market=market)
@@ -518,6 +526,10 @@ def attribute_strategies(
"beta_hml": np.nan,
"beta_rmw": np.nan,
"beta_cma": np.nan,
"beta_smb_proxy": np.nan,
"beta_hml_proxy": np.nan,
"beta_rmw_proxy": np.nan,
"beta_cma_proxy": np.nan,
"beta_mom": np.nan,
"beta_lowvol": np.nan,
"beta_recovery": np.nan,
@@ -575,7 +587,7 @@ def _describe_fit(r_squared: float) -> str:
def _top_loading_descriptions(row: pd.Series, limit: int = 2) -> str:
beta_columns = [column for column in SUMMARY_COLUMNS if column.startswith("beta_")]
beta_columns = [column for column in row.index if column.startswith("beta_")]
present = []
for column in beta_columns:
value = row.get(column)
@@ -606,11 +618,15 @@ def print_attribution_summary(summary_df: pd.DataFrame) -> None:
"beta_hml",
"beta_rmw",
"beta_cma",
"beta_smb_proxy",
"beta_hml_proxy",
"beta_rmw_proxy",
"beta_cma_proxy",
"beta_mom",
"beta_lowvol",
"beta_recovery",
]
table = summary_df.loc[:, display_columns].copy()
table = summary_df.reindex(columns=display_columns).copy()
numeric_columns = [column for column in display_columns if column not in {"strategy", "market", "model"}]
table.loc[:, numeric_columns] = table.loc[:, numeric_columns].round(4)

91
tests/test_factor_attribution.py
View File

@@ -655,6 +655,10 @@ class AttributionIntegrationTests(unittest.TestCase):
"beta_hml",
"beta_rmw",
"beta_cma",
"beta_smb_proxy",
"beta_hml_proxy",
"beta_rmw_proxy",
"beta_cma_proxy",
"beta_mom",
"beta_lowvol",
"beta_recovery",
@@ -667,6 +671,7 @@ class AttributionIntegrationTests(unittest.TestCase):
self.assertAlmostEqual(summary.loc[0, "beta_mkt"], 1.10, places=3)
self.assertAlmostEqual(summary.loc[0, "beta_smb"], -0.25, places=3)
self.assertAlmostEqual(summary.loc[0, "beta_hml"], 0.35, places=3)
self.assertTrue(np.isnan(summary.loc[0, "beta_smb_proxy"]))
self.assertTrue(np.isnan(summary.loc[0, "beta_mom"]))
self.assertListEqual(
@@ -704,11 +709,53 @@ class AttributionIntegrationTests(unittest.TestCase):
self.assertEqual(summary.loc[0, "model"], "proxy")
self.assertEqual(summary.loc[0, "factor_source"], "proxy_only")
self.assertTrue(bool(summary.loc[0, "proxy_only"]))
self.assertIn("beta_smb_proxy", summary.columns)
self.assertIn("beta_hml_proxy", summary.columns)
self.assertIn("beta_rmw_proxy", summary.columns)
self.assertIn("beta_cma_proxy", summary.columns)
self.assertTrue(np.isnan(summary.loc[0, "beta_smb"]))
self.assertTrue(np.isnan(summary.loc[0, "beta_hml"]))
self.assertTrue(np.isnan(summary.loc[0, "beta_rmw"]))
self.assertTrue(np.isnan(summary.loc[0, "beta_cma"]))
self.assertEqual(
set(loadings["factor"]),
{"MKT", "SMB_PROXY", "HML_PROXY", "RMW_PROXY", "CMA_PROXY", "MOM", "LOWVOL", "RECOVERY"},
)
def test_attribute_strategies_without_benchmark_uses_equal_weight_proxy_market(self):
dates = pd.date_range("2025-01-01", periods=320, freq="B")
prices = self._make_price_frame(dates, benchmark="000300.SS").drop(columns=["000300.SS"])
equal_weight_returns = prices.pct_change().mean(axis=1).fillna(0.0)
results = pd.DataFrame(
{
"Strategy": 100_000.0 * (1.0 + 0.0002 + 0.8 * equal_weight_returns).cumprod(),
"External Benchmark": 100_000.0 * (1.0 + 0.0001 + 0.6 * equal_weight_returns).cumprod(),
},
index=dates,
)
summary_missing, loadings_missing = attribute_strategies(
results_df=results,
benchmark_label="External Benchmark",
benchmark=None,
price_data=prices,
market="cn",
model_selection="ff5",
external_factors=None,
)
summary_explicit, loadings_explicit = attribute_strategies(
results_df=results,
benchmark_label="External Benchmark",
benchmark="MISSING_BENCHMARK",
price_data=prices,
market="cn",
model_selection="ff5",
external_factors=None,
)
pd.testing.assert_frame_equal(summary_missing, summary_explicit, check_dtype=False)
pd.testing.assert_frame_equal(loadings_missing, loadings_explicit, check_dtype=False)
def test_print_attribution_summary_prints_compact_table_and_interpretation(self):
summary = pd.DataFrame(
[
@@ -751,6 +798,50 @@ class AttributionIntegrationTests(unittest.TestCase):
self.assertIn("alpha_ann", output)
self.assertIn("Interpretation", output)
def test_print_attribution_summary_keeps_proxy_factor_labels_in_output(self):
summary = pd.DataFrame(
[
{
"strategy": "Strategy",
"market": "cn",
"model": "proxy",
"factor_source": "proxy_only",
"proxy_only": True,
"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": np.nan,
"beta_hml": np.nan,
"beta_rmw": np.nan,
"beta_cma": np.nan,
"beta_smb_proxy": -0.30,
"beta_hml_proxy": 0.25,
"beta_rmw_proxy": 0.10,
"beta_cma_proxy": -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("beta_hml_proxy", output)
self.assertIn("SMB_PROXY", output)
def _make_price_frame(self, dates: pd.DatetimeIndex, benchmark: str) -> pd.DataFrame:
steps = np.arange(len(dates), dtype=float)
data = {}