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Restore summary schema for proxy attribution

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This commit is contained in:
Gahow Wang
2026-04-07 17:25:10 +08:00
parent 69a03f52d9
commit 82a3e63c2b
2 changed files with 58 additions and 40 deletions
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64
factor_attribution.py
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@@ -37,13 +37,13 @@ SUMMARY_BETA_COLUMN_BY_FACTOR = {
"MKT_RF": "beta_mkt",
"MKT": "beta_mkt",
"SMB": "beta_smb",
"SMB_PROXY": "beta_smb_proxy",
"SMB_PROXY": "beta_smb",
"HML": "beta_hml",
"HML_PROXY": "beta_hml_proxy",
"HML_PROXY": "beta_hml",
"RMW": "beta_rmw",
"RMW_PROXY": "beta_rmw_proxy",
"RMW_PROXY": "beta_rmw",
"CMA": "beta_cma",
"CMA_PROXY": "beta_cma_proxy",
"CMA_PROXY": "beta_cma",
"MOM": "beta_mom",
"LOWVOL": "beta_lowvol",
"RECOVERY": "beta_recovery",
@@ -69,10 +69,6 @@ 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",
@@ -526,10 +522,6 @@ 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,
@@ -588,11 +580,34 @@ def _describe_fit(r_squared: float) -> str:
def _top_loading_descriptions(row: pd.Series, limit: int = 2) -> str:
beta_columns = [column for column in row.index if column.startswith("beta_")]
if bool(row.get("proxy_only", False)):
factor_labels = {
"beta_mkt": "MKT",
"beta_smb": "SMB_PROXY",
"beta_hml": "HML_PROXY",
"beta_rmw": "RMW_PROXY",
"beta_cma": "CMA_PROXY",
"beta_mom": "MOM",
"beta_lowvol": "LOWVOL",
"beta_recovery": "RECOVERY",
}
else:
factor_labels = {
"beta_mkt": "MKT",
"beta_smb": "SMB",
"beta_hml": "HML",
"beta_rmw": "RMW",
"beta_cma": "CMA",
"beta_mom": "MOM",
"beta_lowvol": "LOWVOL",
"beta_recovery": "RECOVERY",
}
present = []
for column in beta_columns:
value = row.get(column)
if pd.notna(value):
present.append((column.removeprefix("beta_").upper(), float(value)))
label = factor_labels.get(column)
if label is not None and pd.notna(value):
present.append((label, float(value)))
if not present:
return "no material factor loadings were estimated"
@@ -610,6 +625,8 @@ def print_attribution_summary(summary_df: pd.DataFrame) -> None:
"strategy",
"market",
"model",
"factor_source",
"proxy_only",
"alpha_ann",
"r_squared",
"residual_vol_ann",
@@ -618,16 +635,25 @@ 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.reindex(columns=display_columns).copy()
numeric_columns = [column for column in display_columns if column not in {"strategy", "market", "model"}]
if bool(table["proxy_only"].fillna(False).all()):
table = table.rename(
columns={
"beta_smb": "beta_smb_proxy",
"beta_hml": "beta_hml_proxy",
"beta_rmw": "beta_rmw_proxy",
"beta_cma": "beta_cma_proxy",
}
)
numeric_columns = [
column
for column in table.columns
if column not in {"strategy", "market", "model", "factor_source", "proxy_only"}
]
table.loc[:, numeric_columns] = table.loc[:, numeric_columns].round(4)
print("\nFactor attribution")