HKUDS/Vibe-Trading · error · ValueError

factor_cov contains non-finite values

Error message

factor_cov contains non-finite values

What it means

The factor covariance matrix must be fully finite; NaN or ±inf entries would make eigendecomposition and the variance quadratic form meaningless.

Source

Thrown at agent/src/quantlib/factormodel.py:646

    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
    factors = X.columns.intersection(factor_cov.index).intersection(factor_cov.columns)
    if factors.empty:
        raise ValueError(
            f"No matching factors between exposures ({sorted(X.columns)}) and factor_cov ({sorted(factor_cov.index)})"
        )

View on GitHub (pinned to 80ffdda44c)

Solutions

1. Recompute the covariance after returns.dropna() or with min_periods
2. Locate bad entries: F[~np.isfinite(F.values)]
3. Drop factors with insufficient history before estimating F

Example fix

# before
F = returns.cov()
# after
F = returns.dropna(how='any').cov(min_periods=20)

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

When it happens

Trigger: Covariance estimated from returns with NaNs without dropna, or from too few observations producing inf.

Common situations: pd.DataFrame.cov() on a frame containing NaN pairs; shrinkage estimator divided by zero; stale factor where all returns were missing.

Related errors

• portfolio_weights contains non-finite values
• exposures contains non-finite values
• specific_variances contains non-finite values
• factor_cov must be a non-empty DataFrame
• factor_cov matrix must be symmetric

AI-assisted analysis of HKUDS/Vibe-Trading@80ffdda44c (2026-08-28). Data as JSON: /api/errors/65f75ac7a35aaca6. Report an issue: GitHub.