# skfolio.utils.stats.squared_standardized_euclidean_dist ### skfolio.utils.stats.squared_standardized_euclidean_dist(returns, covariance) Squared standardized Euclidean distance. $$ d^2 = \sum_i (r_i\,/\,\sigma_i)^2 $$ This is the squared Mahalanobis distance using only the diagonal of the covariance matrix (ignoring correlations). * **Parameters:** **returns** *ndarray of shape (n_assets,)* : Asset return vector. **covariance** *ndarray of shape (n_assets, n_assets)* : Covariance matrix. * **Returns:** float : Sum of squared standardized returns (non-negative). Under correct calibration: $\mathbb{E}[d^2] = n_{\text{assets}}$.