{"record":{"id":"e20ccd7ad048a379","repo":"HKUDS/Vibe-Trading","slug":"factor-cov-matrix-must-be-symmetric","errorCode":null,"errorMessage":"factor_cov matrix must be symmetric","messagePattern":"factor_cov matrix must be symmetric","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"agent/src/quantlib/factormodel.py","lineNumber":670,"sourceCode":"            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        )\n\n    X = X[factors]\n    F = factor_cov.loc[factors, factors]\n    F_mat = F.to_numpy(dtype=float)\n    if not np.allclose(F_mat, F_mat.T, atol=1e-8):\n        raise ValueError(\"factor_cov matrix must be symmetric\")\n    eigvals = np.linalg.eigvalsh(F_mat)\n    if np.min(eigvals) < -1e-8:\n        raise ValueError(\"factor_cov matrix must be positive semi-definite\")\n\n    # Align specific variances\n    if specific_variances is not None:\n        spec_var_s = pd.Series(specific_variances, dtype=float)\n        if not np.isfinite(spec_var_s.values).all():\n            raise ValueError(\"specific_variances contains non-finite values\")\n        d = spec_var_s.reindex(assets, fill_value=0.0).clip(lower=0.0)\n    else:\n        d = pd.Series(0.0, index=assets, dtype=float)\n\n    # Portfolio factor exposure: x_p = X^T w (K x 1)\n    x_p = X.T.dot(w)\n\n    # Factor variance: x_p^T F x_p\n    F_x_p = F.dot(x_p)","sourceCodeStart":652,"sourceCodeEnd":688,"githubUrl":"https://github.com/HKUDS/Vibe-Trading/blob/80ffdda44c5c4db0dd84d70e051cca591cea67df/agent/src/quantlib/factormodel.py#L652-L688","documentation":"factor_risk_decomposition uses eigvalsh and a quadratic form that both assume a symmetric covariance; if F is not (numerically) symmetric within atol=1e-8 the math is invalid, so symmetry is enforced first.","triggerScenarios":"A hand-built covariance with F[i,j] != F[j,i], or asymmetric input after loc-based slicing with duplicated labels.","commonSituations":"Covariance patched cell-by-cell (e.g. overriding one off-diagonal); data read from a long-format table that lost symmetry.","solutions":["Symmetrize: F = (F + F.T) / 2 before calling","Rebuild the matrix from a symmetric source","Check for duplicated index labels causing misaligned .loc slicing"],"exampleFix":"# before\nrisk = factor_risk_decomposition(w, X, F)\n# after\nF = (F + F.T) / 2\nrisk = factor_risk_decomposition(w, X, F)","handlingStrategy":"validation","validationCode":"F = (F + F.T) / 2\nassert np.allclose(F.values, F.values.T, atol=1e-8)","typeGuard":null,"tryCatchPattern":null,"preventionTips":["Symmetrize immediately after any manual matrix edit","Prefer pairwise construction that writes both off-diagonals"],"tags":["quantlib","factormodel","covariance","symmetry","linear-algebra"],"backgroundTag":"non-symmetric-covariance-matrix","analyzedSha":"80ffdda44c5c4db0dd84d70e051cca591cea67df","analyzedAt":"2026-08-28T12:46:38.989Z","schemaVersion":2},"datasetVersion":"2026-08-28T16:17:29.566Z"}