OpenBB-finance/OpenBB · error · ValueError

Window '{window}' is greater than the input data length '{le

Error message

Window '{window}' is greater than the input data length '{len(input_data)}'

What it means

validate_window in openbb_quantitative/helpers.py raises when the requested rolling/statistics window is larger than the number of observations in the input Series/DataFrame, since such a computation would produce all-NaN results.

Source

Thrown at openbb_platform/extensions/quantitative/openbb_quantitative/helpers.py:78


def validate_window(input_data: Union["Series", "DataFrame"], window: int) -> None:
    """Validate the window input.

    Parameters
    ----------
    input_data : Union[Series, DataFrame]
        The input data to be validated.
    window : int
        The window to be validated.

    Raises
    ------
    ValueError
        If the window is greater than the input data length.
    """
    if window > len(input_data):
        raise ValueError(
            f"Window '{window}' is greater than the input data length '{len(input_data)}'"
        )

View on GitHub (pinned to 3e071fcc2c)

Solutions

Reduce the window parameter to at most len(data).
Fetch more history (increase limit / extend start_date) so the window fits.
Compute the window dynamically, e.g. window = min(window, len(data)) when full history is unavailable.
Skip symbols whose history is shorter than the window before calling.

Example fix

# before
obb.quantitative.rolling_window(data, window=200)  # data has 50 rows

# after
window = min(200, len(data))
obb.quantitative.rolling_window(data, window=window)

Defensive patterns

Strategy: validation

Validate before calling

from openbb_quantitative.helpers import validate_window
validate_window(input_data=data, window=window)  # raises before compute with a clear message

Type guard

def window_fits(data, window: int) -> bool:
    return window <= len(data)

Try / catch

try:
    result = compute(data, window=window)
except ValueError as e:
    if "greater than the input data length" in str(e):
        window = len(data)
        result = compute(data, window=window)
    else:
        raise

Prevention

Fetch history at least as long as the window
Derive window from len(data) when history is fixed
Skip short series in batch jobs

When it happens

Trigger: Calling quantitative endpoints (e.g. rolling statistics, summary metrics with a window parameter) with window > len(input_data): passing window=100 with only 50 rows of price history.

Common situations: Short historical fetches (limit=30) combined with large windows; intraday data with fewer bars than expected; hardcoded window values reused across tickers with different listing ages.

Related errors

AI-assisted analysis of OpenBB-finance/OpenBB@3e071fcc2c (2026-08-14). Data as JSON: /api/errors/6a45ed933032aab2. Report an issue: GitHub.