HKUDS/Vibe-Trading · error · ValueError

payment_frequency must be positive, got {payment_frequency}

Error message

payment_frequency must be positive, got {payment_frequency}

What it means

cds_price builds a premium schedule with payments every 1/payment_frequency years, so payment_frequency must be a positive integer-like number (e.g. 4 for quarterly). Zero or negative frequencies make the step size invalid and would break or never terminate the payment loop.

Source

Thrown at agent/src/quantlib/credit.py:810

        ValueError: If spread_bps < 0, recovery_rate not in [0, 1), tenor_years <= 0, or notional <= 0.
    """
    spread_bps = _require_finite(spread_bps, "spread_bps")
    recovery_rate = _require_finite(recovery_rate, "recovery_rate")
    tenor_years = _require_finite(tenor_years, "tenor_years")
    risk_free_rate = _require_finite(risk_free_rate, "risk_free_rate")
    coupon_bps = _require_finite(coupon_bps, "coupon_bps")
    notional = _require_finite(notional, "notional")
    payment_frequency = _require_finite(payment_frequency, "payment_frequency")
    if spread_bps < 0.0:
        raise ValueError(f"spread_bps must be non-negative, got {spread_bps}")
    if not (0.0 <= recovery_rate < 1.0):
        raise ValueError(f"recovery_rate must be in [0.0, 1.0), got {recovery_rate}")
    if tenor_years <= 0.0:
        raise ValueError(f"tenor_years must be strictly positive, got {tenor_years}")
    if notional <= 0.0:
        raise ValueError(f"notional must be strictly positive, got {notional}")
    if payment_frequency <= 0:
        raise ValueError(f"payment_frequency must be positive, got {payment_frequency}")

    s_dec = spread_bps / 10_000.0
    c_dec = coupon_bps / 10_000.0
    lgd = 1.0 - recovery_rate

    # Implied hazard rate lambda ≈ s / LGD
    lambda_hazard = float(s_dec / lgd) if lgd > 0 else 0.0

    n_periods = max(1, int(round(tenor_years * payment_frequency)))
    t_grid = np.linspace(tenor_years / n_periods, tenor_years, n_periods)
    t_prev = np.r_[0.0, t_grid[:-1]]
    dts = t_grid - t_prev
    t_mid = 0.5 * (t_prev + t_grid)

    # Survival probabilities Q(t) = exp(-lambda * t)
    q_grid = np.exp(-lambda_hazard * t_grid)
    q_prev = np.r_[1.0, q_grid[:-1]]

View on GitHub (pinned to 80ffdda44c)

Solutions

1. Pass payments per year: 1=annual, 2=semi, 4=quarterly, 12=monthly
2. If you have a period length dt, use payment_frequency = 1/dt rounded to int
3. Validate the value is a positive int in your wrapper before calling

Example fix

# before
pv = cds_price(250, 5.0, payment_frequency=0)

# after
pv = cds_price(250, 5.0, payment_frequency=4)

payment_frequency = int(round(1.0 / period_years)) if period_years else 4
assert payment_frequency > 0
pv = cds_price(250.0, tenor_years=5.0, payment_frequency=payment_frequency)

def is_valid_payment_frequency(f: float) -> bool:
    return isinstance(f, (int, float)) and float(f) > 0 and float(f).is_integer()

try:
    pv = cds_price(250.0, 5.0, payment_frequency=freq)
except ValueError as e:
    if 'payment_frequency' in str(e):
        pv = cds_price(250.0, 5.0, payment_frequency=4)  # default quarterly
    else:
        raise

When it happens

Trigger: Calling cds_price with payment_frequency = 0, a negative number, or accidentally passing the period length in years (0.25) instead of the count per year (4).

Common situations: Confusing frequency (payments per year) with period length (years between payments); passing 0.25 for quarterly and hitting the <= 0 check only for zero, but typically hitting it with 0 from a default int.

Related errors

• recovery_rate must be in [0.0, 1.0), got {recovery_rate}
• notional must be strictly positive, got {notional}
• survival_prob must be in (0.0, 1.0], got {survival_prob}
• tenor_years must be strictly positive, got {tenor_years}
• spread_bps must be non-negative, got {spread_bps}

AI-assisted analysis of HKUDS/Vibe-Trading@80ffdda44c (2026-08-28). Data as JSON: /api/errors/7e50b7ed353860f3. Report an issue: GitHub.