HKUDS/Vibe-Trading · error · ValueError

exposures contains non-finite values

Error message

exposures contains non-finite values

What it means

The exposures matrix must contain only finite numbers; a NaN/inf entry would propagate into x_p = Xᵀw and corrupt every risk number, so the function validates up front.

Source

Thrown at agent/src/quantlib/factormodel.py:641

    Returns:
        :class:`FactorRiskDecomposition` containing total/factor/specific
        variances, volatilities, marginal contributions to risk (MCR), and
        percentage contributions to risk (PCR) per factor and per asset.

    Raises:
        ValueError: If weights or matrices are empty, contain non-finite values,
            or share no common assets or factors.
    """
    w_series = pd.Series(portfolio_weights, dtype=float)
    if w_series.empty:
        raise ValueError("portfolio_weights cannot be empty")
    if not np.isfinite(w_series.values).all():
        raise ValueError("portfolio_weights contains non-finite values")

    if not isinstance(exposures, pd.DataFrame) or exposures.empty:
        raise ValueError("exposures must be a non-empty DataFrame")
    if not np.isfinite(exposures.values).all():
        raise ValueError("exposures contains non-finite values")

    if not isinstance(factor_cov, pd.DataFrame) or factor_cov.empty:
        raise ValueError("factor_cov must be a non-empty DataFrame")
    if not np.isfinite(factor_cov.values).all():
        raise ValueError("factor_cov contains non-finite values")

    # Align assets
    assets = w_series.index.intersection(exposures.index)
    if assets.empty:
        raise ValueError(
            f"No matching assets between weights ({sorted(w_series.index)}) and exposures ({sorted(exposures.index)})"
        )

    unmatched_weight = float(w_series.drop(index=assets, errors="ignore").abs().sum())
    w = w_series.loc[assets]
    X = exposures.loc[assets]

    # Align factors

View on GitHub (pinned to 80ffdda44c)

Solutions

1. Locate bad cells: exposures[~np.isfinite(exposures.values)] / exposures.mask(~np.isfinite(exposures))
2. Fill NaNs with 0 exposure or drop the affected assets/factors
3. Fix the standardizer to guard zero std before dividing

Example fix

# before
risk = factor_risk_decomposition(w, X, F)
# after
X = X.fillna(0.0).replace([np.inf, -np.inf], 0.0)
risk = factor_risk_decomposition(w, X, F)

assert np.isfinite(exposures.values).all()

When it happens

Trigger: A NaN in the asset-factor matrix from a partial data join, or inf from a bad z-score division by zero std.

Common situations: Cross-sectional standardization with zero-variance factors; sparse vendor data with missing cells not filled.

Related errors

• portfolio_weights contains non-finite values
• factor_cov contains non-finite values
• specific_variances contains non-finite values
• holdings is empty
• exposures has no factor columns

AI-assisted analysis of HKUDS/Vibe-Trading@80ffdda44c (2026-08-28). Data as JSON: /api/errors/2413733be011aa6b. Report an issue: GitHub.