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# skfolio.metrics.diagonal_calibration_ratio

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### skfolio.metrics.diagonal_calibration_ratio(estimator, X_test, y=None)

Diagonal calibration ratio based on marginal variances.

Let $r_t$ be the one-period realized return vector at time $t$,
and let $R^{(h)} = \sum_{t=1}^{h} r_t$ be the aggregated return over
an evaluation window of $h$ observations. This metric uses only the
diagonal of the covariance matrix, ignoring correlations, and compares
each component $R_i^{(h)}$ against the horizon-scaled variance
$h\,\sigma_i^2$:

$$
s = \frac{1}{n}\sum_{i=1}^{n}
    \frac{(R_i^{(h)})^2}{h\,\sigma_i^2}
$$

where $n$ is the number of assets and $\sigma_i^2$ is the
forecast variance for asset $i$.

If the marginal variance forecasts are correct and the aggregated returns are
centered, then $\mathbb{E}[s] = 1$ for any horizon $h$.
Because correlations are ignored, this metric diagnoses the calibration
of marginal scales rather than the full covariance structure.

When `X_test` contains NaNs (e.g. holidays, pre-listing, or post-delisting
periods), each asset uses its own effective horizon $h_i$, equal to
the number of finite observations for that asset, so the ratio retains the
same target under missing data. In skfolio, NaN diagonal entries in the
forecast covariance mark inactive assets, which are excluded from the
evaluation.

* **Parameters:**
  **estimator** *BaseEstimator*
  : Fitted estimator, must expose `covariance_` or `return_distribution_.covariance`.

  **X_test** *array-like of shape (n_observations, n_assets)*
  : Realized returns for the test window.

  **y** *Ignored*
  : Present for scikit-learn API compatibility.
* **Returns:**
  float
  : Calibration ratio. Values near `1.0` indicate well-calibrated
    marginal variance forecasts.

#### SEE ALSO
[`diagonal_calibration_loss`](https://skfolio.org/generated/skfolio.metrics.diagonal_calibration_loss.html.md#skfolio.metrics.diagonal_calibration_loss)
: Absolute deviation from the calibration target of `1.0`.

[`mahalanobis_calibration_ratio`](https://skfolio.org/generated/skfolio.metrics.mahalanobis_calibration_ratio.html.md#skfolio.metrics.mahalanobis_calibration_ratio)
: Calibration ratio using the full covariance structure.