{"record":{"id":"65f75ac7a35aaca6","repo":"HKUDS/Vibe-Trading","slug":"factor-cov-contains-non-finite-values","errorCode":null,"errorMessage":"factor_cov contains non-finite values","messagePattern":"factor_cov contains non-finite values","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"agent/src/quantlib/factormodel.py","lineNumber":646,"sourceCode":"    Raises:\n        ValueError: If weights or matrices are empty, contain non-finite values,\n            or share no common assets or factors.\n    \"\"\"\n    w_series = pd.Series(portfolio_weights, dtype=float)\n    if w_series.empty:\n        raise ValueError(\"portfolio_weights cannot be empty\")\n    if not np.isfinite(w_series.values).all():\n        raise ValueError(\"portfolio_weights contains non-finite values\")\n\n    if not isinstance(exposures, pd.DataFrame) or exposures.empty:\n        raise ValueError(\"exposures must be a non-empty DataFrame\")\n    if not np.isfinite(exposures.values).all():\n        raise ValueError(\"exposures contains non-finite values\")\n\n    if not isinstance(factor_cov, pd.DataFrame) or factor_cov.empty:\n        raise ValueError(\"factor_cov must be a non-empty DataFrame\")\n    if not np.isfinite(factor_cov.values).all():\n        raise ValueError(\"factor_cov contains non-finite values\")\n\n    # Align assets\n    assets = w_series.index.intersection(exposures.index)\n    if assets.empty:\n        raise ValueError(\n            f\"No matching assets between weights ({sorted(w_series.index)}) and exposures ({sorted(exposures.index)})\"\n        )\n\n    unmatched_weight = float(w_series.drop(index=assets, errors=\"ignore\").abs().sum())\n    w = w_series.loc[assets]\n    X = exposures.loc[assets]\n\n    # Align factors\n    factors = X.columns.intersection(factor_cov.index).intersection(factor_cov.columns)\n    if factors.empty:\n        raise ValueError(\n            f\"No matching factors between exposures ({sorted(X.columns)}) and factor_cov ({sorted(factor_cov.index)})\"\n        )","sourceCodeStart":628,"sourceCodeEnd":664,"githubUrl":"https://github.com/HKUDS/Vibe-Trading/blob/80ffdda44c5c4db0dd84d70e051cca591cea67df/agent/src/quantlib/factormodel.py#L628-L664","documentation":"The factor covariance matrix must be fully finite; NaN or ±inf entries would make eigendecomposition and the variance quadratic form meaningless.","triggerScenarios":"Covariance estimated from returns with NaNs without dropna, or from too few observations producing inf.","commonSituations":"pd.DataFrame.cov() on a frame containing NaN pairs; shrinkage estimator divided by zero; stale factor where all returns were missing.","solutions":["Recompute the covariance after returns.dropna() or with min_periods","Locate bad entries: F[~np.isfinite(F.values)]","Drop factors with insufficient history before estimating F"],"exampleFix":"# before\nF = returns.cov()\n# after\nF = returns.dropna(how='any').cov(min_periods=20)","handlingStrategy":"validation","validationCode":"assert np.isfinite(factor_cov.values).all()","typeGuard":null,"tryCatchPattern":null,"preventionTips":["Estimate covariance only on complete returns (dropna/min_periods)","Drop factors with insufficient history before estimation"],"tags":["quantlib","factormodel","nan","inf","covariance"],"backgroundTag":"non-finite-values","analyzedSha":"80ffdda44c5c4db0dd84d70e051cca591cea67df","analyzedAt":"2026-08-28T12:46:38.989Z","schemaVersion":2},"datasetVersion":"2026-08-28T16:17:29.566Z"}