skfolio.measures.skew#

skfolio.measures.skew(returns, sample_weight=None)[source]#

Compute the Skew.

The Skew is a measure of the lopsidedness of the distribution. A symmetric distribution have a Skew of zero. Higher Skew corresponds to longer right tail.

Parameters:

returnsndarray of shape (n_observations,) or (n_observations, n_assets): Array of return values.
sample_weightndarray of shape (n_observations,), optional: Relative observation weights. Must be finite and nonnegative. If None, equal weights are assumed.

Returns:

valuefloat or ndarray of shape (n_assets,): Skew. If returns is a 1D-array, the result is a float. If returns is a 2D-array, the result is a ndarray of shape (n_assets,).

Notes

NaN handling: NaN returns are excluded from each column’s calculation. Remaining sample weights are rescaled to sum to one. The result is NaN if no observations or no positive weight remain.

Nearly constant returns produce NaN when the second central moment is at most (eps * mean)**2. Here eps is machine precision and mean uses the same sample weights.