HKUDS/Vibe-Trading · error · ValueError

confidence must be in (0.0, 1.0), got {confidence}

Error message

confidence must be in (0.0, 1.0), got {confidence}

What it means

vasicek_credit_var evaluates norm.ppf(confidence) to get the quantile of the systematic factor, so confidence must be strictly inside (0.0, 1.0): 0 or 1 map to infinite inverse-normal values and the VaR is undefined.

Source

Thrown at agent/src/quantlib/credit.py:940

    Raises:
        ValueError: If parameters violate domain constraints.
    """
    ead = _require_finite(ead, "ead")
    pd = _require_finite(pd, "pd")
    lgd = _require_finite(lgd, "lgd")
    asset_correlation = _require_finite(asset_correlation, "asset_correlation")
    confidence = _require_finite(confidence, "confidence")
    if ead <= 0.0:
        raise ValueError(f"ead must be strictly positive, got {ead}")
    if not (0.0 < pd < 1.0):
        raise ValueError(f"pd must be in (0.0, 1.0), got {pd}")
    if not (0.0 <= lgd <= 1.0):
        raise ValueError(f"lgd must be in [0.0, 1.0], got {lgd}")
    if not (0.0 <= asset_correlation < 1.0):
        raise ValueError(f"asset_correlation must be in [0.0, 1.0), got {asset_correlation}")
    if not (0.0 < confidence < 1.0):
        raise ValueError(f"confidence must be in (0.0, 1.0), got {confidence}")

    rho = asset_correlation
    inv_pd = float(norm.ppf(pd))
    inv_conf = float(norm.ppf(confidence))

    numerator = inv_pd + np.sqrt(rho) * inv_conf
    denominator = np.sqrt(1.0 - rho)
    wcdr = float(norm.cdf(numerator / denominator))

    el = expected_loss(ead, pd, lgd)
    wcl = float(ead * lgd * wcdr)
    ul = float(max(0.0, wcl - el))
    capital_ratio = float(ul / ead) if ead > 0 else 0.0

    return {
        "expected_loss": el,
        "wcdr": wcdr,
        "worst_case_loss": wcl,

View on GitHub (pinned to 80ffdda44c)

Solutions

1. Use decimal confidence: 0.999 for 99.9%
2. Clamp: confidence = min(max(c, 1e-12), 1 - 1e-12)
3. Validate user-facing inputs that specify confidence as a percent and divide by 100

Example fix

# before
var = vasicek_credit_var(1e6, 0.02, 0.6, 0.2, confidence=99.9)

# after
var = vasicek_credit_var(1e6, 0.02, 0.6, 0.2, confidence=0.999)

confidence = confidence / 100.0 if confidence > 1.0 else confidence
confidence = min(max(confidence, 1e-12), 1.0 - 1e-12)
var = vasicek_credit_var(ead, pd, lgd, rho, confidence)

def is_valid_confidence(c: float) -> bool:
    return isinstance(c, (int, float)) and 0.0 < float(c) < 1.0

try:
    var = vasicek_credit_var(ead, pd, lgd, rho, conf)
except ValueError as e:
    if 'confidence' in str(e):
        var = vasicek_credit_var(ead, pd, lgd, rho, 0.999)
    else:
        raise

When it happens

Trigger: Calling vasicek_credit_var with confidence = 0.0, 1.0, 99.9 (percent), or values like 1.001 from rounding.

Common situations: Passing 99.9 instead of 0.999 (percent vs decimal) is by far the most common slip; floating point rounding that yields exactly 1.0.

Related errors

• ead must be strictly positive, got {ead}
• pd must be in (0.0, 1.0), got {pd}
• asset_correlation must be in [0.0, 1.0), got {asset_correlat
• survival_prob must be in (0.0, 1.0], got {survival_prob}
• tenor_years must be strictly positive, got {tenor_years}

AI-assisted analysis of HKUDS/Vibe-Trading@80ffdda44c (2026-08-28). Data as JSON: /api/errors/bb7e73a448596f45. Report an issue: GitHub.