HKUDS/Vibe-Trading · error · ValueError

scenario_iv_values may contain at most {_MAX_IV_SCENARIOS} e

Error message

scenario_iv_values may contain at most {_MAX_IV_SCENARIOS} entries

What it means

Thrown by _scenario_ivs when the supplied scenario_iv_values list exceeds _MAX_IV_SCENARIOS entries. The cap keeps the scenario matrix output bounded; it fires after the non-empty array check and before numeric conversion.

Source

Thrown at agent/src/tools/options_payoff_tool.py:331

        raise ValueError("spot_max must be greater than spot_min")
    return spot_min, spot_max


def _scenario_ivs(raw: Any, entry_iv: float) -> np.ndarray:
    """Resolve bounded explicit IV scenarios or the skill's five defaults."""
    if raw is None:
        values = [
            entry_iv * 0.5,
            entry_iv * 0.75,
            entry_iv,
            entry_iv * 1.25,
            entry_iv * 1.5,
        ]
    else:
        if not isinstance(raw, list) or not raw:
            raise ValueError("scenario_iv_values must be a non-empty array")
        if len(raw) > _MAX_IV_SCENARIOS:
            raise ValueError(f"scenario_iv_values may contain at most {_MAX_IV_SCENARIOS} entries")
        try:
            values = [float(value) for value in raw]
        except (TypeError, ValueError, OverflowError) as exc:
            raise ValueError("scenario_iv_values must contain numbers") from exc
    array = np.asarray(values, dtype=float)
    if not np.isfinite(array).all() or (array <= 0).any():
        raise ValueError("scenario_iv_values must contain positive finite values")
    return array


def _rounded(value: float) -> float:
    """Round a finite scalar for stable, compact JSON."""
    return round(float(value), 6)


def _rounded_array(values: np.ndarray) -> list[float]:
    """Round a numeric array for stable, compact JSON."""
    return [round(float(value), 6) for value in np.asarray(values).tolist()]

View on GitHub (pinned to 80ffdda44c)

Solutions

1. Downsample to at most _MAX_IV_SCENARIOS representative IVs
2. Pick key scenarios: [0.5, 0.75, 1.0, 1.25, 1.5] * entry_iv
3. Check the constant at the top of options_payoff_tool.py for the exact cap

Example fix

// before
execute({..., "scenario_iv_values": np.linspace(0.1, 0.9, 50).tolist()})
// after
execute({..., "scenario_iv_values": [0.15, 0.20, 0.25, 0.30, 0.35]})

from agent.src.tools.options_payoff_tool import _MAX_IV_SCENARIOS
ivs = kwargs.get("scenario_iv_values")
if ivs and len(ivs) > _MAX_IV_SCENARIOS:
    step = len(ivs) / _MAX_IV_SCENARIOS
    kwargs["scenario_iv_values"] = [ivs[int(i*step)] for i in range(_MAX_IV_SCENARIOS)]

def iv_count_ok(ivs) -> bool:
    return ivs is None or 0 < len(ivs) <= _MAX_IV_SCENARIOS

try:
    execute(kwargs)
except ValueError as e:
    if "at most" in str(e) and "scenario_iv" in str(e):
        downsample_and_retry(kwargs)

When it happens

Trigger: Passing a finely grained IV grid (e.g. np.linspace(0.1, 0.9, 50).tolist()) as scenario values.

Common situations: Reusing plotting grids as scenario inputs; sensitivity-sweep scripts; LLMs over-generating scenario lists.

Related errors

• legs may contain at most {_MAX_LEGS} entries
• spot_points must be between {_MIN_SPOT_POINTS} and {_MAX_SPO
• T must be > 0 to imply a volatility, got {T}
• valuations may contain at most {_MAX_VALUATIONS} entries
• legs must be a non-empty array

AI-assisted analysis of HKUDS/Vibe-Trading@80ffdda44c (2026-08-28). Data as JSON: /api/errors/cb73bb6a534fb06d. Report an issue: GitHub.