HKUDS/Vibe-Trading · error · ValueError

returns contains no finite observation

Error message

returns contains no finite observation

What it means

Raised by _clean_returns when the returns series passed to a risk statistic (historical_var, parametric_var, historical_cvar, fit_gpd_tail) contains zero finite values — i.e. it is empty, or every element is NaN/inf. The library requires at least one finite observation to compute any tail statistic, so it fails fast rather than returning NaN.

Solutions

1. Check the input series length and np.isfinite(values).sum() before calling the risk function
2. Drop NaN/inf rows: returns = pd.Series(returns).replace([np.inf,-np.inf], np.nan).dropna()
3. Log the series head/dtype to find the upstream pipeline stage that emptied it
4. If an empty input is legitimate in your flow, guard with a length check and skip or return NaN explicitly

Example fix

// before
var = historical_var(returns, confidence=0.95)  # returns is all NaN
// after
returns = pd.Series(returns).replace([np.inf, -np.inf], np.nan).dropna()
var = historical_var(returns, confidence=0.95) if len(returns) else float("nan")

import numpy as np
def finite_returns_ok(r):
    v = np.asarray(r, dtype=float).ravel()
    return np.isfinite(v).sum() > 0
# call only when finite_returns_ok(returns)

def has_finite_returns(returns) -> bool:
    import numpy as np
    v = np.asarray(returns, dtype=float).ravel()
    return bool(np.isfinite(v).any())

try:
    var = historical_var(returns, 0.95)
except ValueError as e:
    if "no finite observation" in str(e):
        logger.warning("empty return series; skipping VaR")
        var = float("nan")
    else:
        raise

When it happens

Trigger: Calling historical_var([], 0.95), parametric_var([np.nan]*10), or fit_gpd_tail with a series of all-NaN (e.g. a price series diff'd after leading NaNs, or an empty dataframe column).

Common situations: Loading a CSV with wrong column name (all NaN), a pandas pipeline that produced an empty slice (e.g. df[df.date > max_date]), or forward-filled price data turned into returns where the NaN row was not dropped.

Related errors

• confidence must be in (0, 1), got
• equity contains no finite observation
• horizon must be >= 1, got
• s0 must be > 0, got
• threshold_pct must be in (0, 100), got

AI-assisted analysis of HKUDS/Vibe-Trading@80ffdda44c (2026-08-28). Data as JSON: /api/errors/b575081784670034. Report an issue: GitHub.

Appendix: source

Thrown at agent/src/quantlib/risk.py:95

    """Coerce a return series to a finite 1-D float array.

    Args:
        returns: Return observations as a pandas Series, numpy array or any
            sequence of floats. NaN and infinite values are dropped.

    Returns:
        A 1-D float64 array of the finite observations, in input order.

    Raises:
        ValueError: If the input is not 1-D or holds no finite observation.
    """
    values = np.asarray(returns, dtype=float)
    if values.ndim > 1:
        raise ValueError(f"returns must be 1-D, got shape {values.shape}")
    values = values.ravel()
    finite = values[np.isfinite(values)]
    if finite.size == 0:
        raise ValueError("returns contains no finite observation")
    return finite


def _validate_confidence(confidence: float) -> None:
    """Check that a confidence level is a strict probability.

    Args:
        confidence: Confidence level, e.g. 0.95.

    Raises:
        ValueError: If ``confidence`` is not strictly between 0 and 1.
    """
    if not 0.0 < confidence < 1.0:
        raise ValueError(f"confidence must be in (0, 1), got {confidence}")


def _validate_horizon(horizon: int) -> None:
    """Check that a holding period is a positive whole number of periods.

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