HKUDS/Vibe-Trading · error · ValueError

bootstrap_statistic needs n_bootstrap >= 1, got {n_bootstrap

Error message

bootstrap_statistic needs n_bootstrap >= 1, got {n_bootstrap}

What it means

bootstrap_statistic requires n_bootstrap >= 1; zero or negative resamples cannot produce a confidence interval, so the function validates the count before allocating the results array.

Source

Thrown at agent/src/quantlib/timeseries.py:804

        data: One-dimensional sample of observations.
        statistic_func: Callable mapping a resample to a scalar, e.g. ``np.mean``.
        n_bootstrap: Number of bootstrap resamples.
        confidence: Confidence level in (0, 1), e.g. 0.95 for a 95% interval.
        seed: Seed for the random generator; pass an int for reproducible output.

    Returns:
        Dict with keys ``point_estimate``, ``bootstrap_mean``, ``bootstrap_std``,
        ``ci_lower``, ``ci_upper`` (all float) and ``confidence`` (float, echoed).

    Raises:
        ValueError: If ``data`` is empty, ``n_bootstrap`` is below 1, or
            ``confidence`` is not strictly inside (0, 1).
    """
    sample = np.asarray(data, dtype=float).ravel()
    if sample.size == 0:
        raise ValueError("bootstrap_statistic needs a non-empty sample")
    if n_bootstrap < 1:
        raise ValueError(f"bootstrap_statistic needs n_bootstrap >= 1, got {n_bootstrap}")
    if not 0.0 < confidence < 1.0:
        raise ValueError(f"bootstrap_statistic needs confidence in (0, 1), got {confidence}")

    rng = np.random.default_rng(seed)
    n = sample.size
    # Resample one draw at a time. Materialising the whole (n_bootstrap, n)
    # index matrix would be ~160MB at the default 10000 draws over 2000 bars.
    bootstrap_stats = np.empty(n_bootstrap, dtype=float)
    for i in range(n_bootstrap):
        bootstrap_stats[i] = float(statistic_func(sample[rng.integers(0, n, size=n)]))

    alpha = 1 - confidence
    return {
        "point_estimate": float(statistic_func(sample)),
        "bootstrap_mean": float(np.mean(bootstrap_stats)),
        "bootstrap_std": float(np.std(bootstrap_stats)),
        "ci_lower": float(np.percentile(bootstrap_stats, alpha / 2 * 100)),
        "ci_upper": float(np.percentile(bootstrap_stats, (1 - alpha / 2) * 100)),

View on GitHub (pinned to 80ffdda44c)

Solutions

Pass n_bootstrap >= 1 (default 10000 is typical).
Clamp computed values: n_bootstrap = max(1, computed).
Validate config before the run loop.

Example fix

// before
bootstrap_statistic(data, n_bootstrap=n_draws)  # n_draws == 0
// after
bootstrap_statistic(data, n_bootstrap=max(1, n_draws))

Defensive patterns

Strategy: validation

Validate before calling

if n_bootstrap is None or int(n_bootstrap) < 1:
    n_bootstrap = 10_000
bootstrap_statistic(data, n_bootstrap=n_bootstrap)

Prevention

Clamp computed draw counts with max(1, ...).
Validate numeric config knobs (n_bootstrap, confidence) once at startup.

When it happens

Trigger: bootstrap_statistic(data, n_bootstrap=0) or a negative value; commonly a config knob (e.g. n_bootstrap = int(confidence * 0)) that evaluates to 0.

Common situations: Parameter sweeps or YAML configs where the resample count is computed and can floor to 0; CLI flags parsed with a missing default.

Understand the failure class

Background: "Must be a positive integer", "Invalid value", "Unsupported": the invalid-argument-value error family, when a library rejects the value you pass — this error's family across 35 libraries.

Related errors

AI-assisted analysis of HKUDS/Vibe-Trading@80ffdda44c (2026-08-28). Data as JSON: /api/errors/eb72415e22fa1bb7. Report an issue: GitHub.