{"record":{"id":"421a1806d62117a5","repo":"HKUDS/Vibe-Trading","slug":"portfolio-weights-contains-non-finite-values","errorCode":null,"errorMessage":"portfolio_weights contains non-finite values","messagePattern":"portfolio_weights contains non-finite values","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"agent/src/quantlib/factormodel.py","lineNumber":636,"sourceCode":"        exposures: Asset factor exposures (rows = assets, columns = factors).\n        factor_cov: Covariance matrix of factor returns (K x K).\n        specific_variances: Asset-specific (idiosyncratic) return variances.\n            Defaults to zero if omitted.\n\n    Returns:\n        :class:`FactorRiskDecomposition` containing total/factor/specific\n        variances, volatilities, marginal contributions to risk (MCR), and\n        percentage contributions to risk (PCR) per factor and per asset.\n\n    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","sourceCodeStart":618,"sourceCodeEnd":654,"githubUrl":"https://github.com/HKUDS/Vibe-Trading/blob/80ffdda44c5c4db0dd84d70e051cca591cea67df/agent/src/quantlib/factormodel.py#L618-L654","documentation":"factor_risk_decomposition validates that all weights are finite; NaN or ±inf weights make variance wᵀXF Xᵀw undefined, so np.isfinite fails and the error is raised.","triggerScenarios":"A weight value is NaN (missing join) or inf (division by a near-zero NAV when normalizing).","commonSituations":"Weights computed as positions/NAV where NAV was 0; NaN introduced by a reindex/merge on tickers; dirty CSV holdings.","solutions":["Print w_series[~np.isfinite(w_series)] to find the offending asset","Fill or drop bad weights: .fillna(0) or .replace([np.inf,-np.inf],0).dropna()","Fix the NAV normalization that produced inf"],"exampleFix":"# before\nrisk = factor_risk_decomposition(w, X, F)\n# after\nw = w.replace([np.inf, -np.inf], np.nan).dropna()\nrisk = factor_risk_decomposition(w, X, F)","handlingStrategy":"validation","validationCode":"assert np.isfinite(pd.Series(portfolio_weights, dtype=float).values).all()","typeGuard":null,"tryCatchPattern":null,"preventionTips":["Sanitize weights: dropna + replace inf before every risk call","Validate NAV > 0 before normalizing positions"],"tags":["quantlib","factormodel","nan","inf","validation"],"backgroundTag":"non-finite-values","analyzedSha":"80ffdda44c5c4db0dd84d70e051cca591cea67df","analyzedAt":"2026-08-28T12:46:38.989Z","schemaVersion":2},"datasetVersion":"2026-08-28T16:17:29.566Z"}