<a id="skfolio-descriptor-ewmarketbeta"></a>

# skfolio.descriptor.EWMarketBeta

<a id="skfolio.descriptor.EWMarketBeta"></a>

### *class* skfolio.descriptor.EWMarketBeta(half_life=60.0, aggregation_period=1, min_periods=None, shrinkage_group=None, min_group_size=5, shrinkage_bounds=(0.0, 1.0), eps=1e-12)

Exponentially weighted market beta descriptor.

Measures each asset’s sensitivity to the market portfolio using exponentially
weighted covariance and variance estimates [[1]](#r317192b78e0b-1):

$$
\beta_i = \frac{\text{Cov}(r_i, r_m)}{\text{Var}(r_m)}
$$

where $r_i$ is the return of asset $i$, $r_m$ is the cap-weighted
market return computed on the estimation universe and the EWMA uses decay
$\lambda = \exp(-\ln(2) / \text{half\_life})$.

* **Parameters:**
  **half_life** *float, default=60.0*
  : EWMA half-life in observations after aggregation. For example, with
    `aggregation_period=5` and `half_life=60`, the EWMA decays over 60 aggregated
    periods, equivalent to 300 raw observations.

  **aggregation_period** *int, default=1*
  : Number of consecutive observations to aggregate before updating EWMA statistics.
    Aggregation can reduce desynchronization effects. Returns are aggregated with
    the mean of finite values. If an asset has no finite returns in an aggregation
    window, its state is unchanged.

  **min_periods** *int, optional*
  : Minimum number of market observations and valid asset returns required before
    computing market betas. Until both counts reach this value, the asset’s output
    is NaN. This warm-up period avoids exposing early EWMA values before the beta
    estimate has sufficiently converged from its zero initialization. If `None`,
    defaults to $\lceil\text{half\_life}\rceil$, with a minimum of 1.

  **shrinkage_group** *str, optional*
  : Name of a categorical field containing group labels (e.g., `"industry"`) for
    Bayesian shrinkage. When provided, raw betas are shrunk toward the cap-weighted
    group mean using an empirical Bayes approach:
    $$
    \beta_i^{\text{shrunk}} = w_i \cdot \beta_i^{\text{raw}}
    + (1 - w_i) \cdot \mu_g
    $$
    <br/>
    where $w_i = \tau_g^2 / (\tau_g^2 + \sigma_i^2)$, $\mu_g$ is the
    cap-weighted group mean, $\tau_g^2$ is the prior variance (cross-sectional
    variance minus noise) and $\sigma_i^2$ is the estimation error variance.
    <br/>
    Missing category codes are excluded from shrinkage. If `None` (default), no
    shrinkage is applied.

  **min_group_size** *int, default=5*
  : Minimum number of assets in a group to compute group-specific statistics.
    Groups with fewer assets fall back to global (cross-sectional) statistics.
    Only used when `shrinkage_group` is provided.

  **shrinkage_bounds** *tuple of float, default=(0.0, 1.0)*
  : Lower and upper bounds `(w_min, w_max)` for the raw-beta weight
    $w_i$. Lower values apply more shrinkage toward the group mean, while
    higher values keep more of the raw beta. The coefficient is clipped to this
    range after estimation.
    <br/>
    For example, `(0.1, 0.9)` keeps at least 10% weight on the raw beta and at
    least 10% weight on the group mean. Only used when `shrinkage_group` is
    provided.

  **eps** *float, default=1e-12*
  : Small constant for numerical stability in
    $1 / \text{Var}(\text{market})$.
* **Attributes:**
  **n_assets_** *int*
  : Number of assets seen during fitting.

  **asset_names_** *ndarray of shape (n_assets,)*
  : Asset names seen during fitting.

  **market_beta_** *ndarray of shape (n_assets,)*
  : Last fitted market beta value for each asset.

### Methods

| [`fit_transform`](#skfolio.descriptor.EWMarketBeta.fit_transform)(X[, y])         | Compute exponentially weighted market betas.            |
|--------------------------------------------------------------------------------|---------------------------------------------------------|
| [`get_metadata_routing`](#skfolio.descriptor.EWMarketBeta.get_metadata_routing)()        | Get metadata routing of this object.                    |
| [`get_params`](#skfolio.descriptor.EWMarketBeta.get_params)([deep])            | Get parameters for this estimator.                      |
| [`partial_fit_transform`](#skfolio.descriptor.EWMarketBeta.partial_fit_transform)(X[, y]) | Update EWMA state on X and return betas for this batch. |
| [`set_params`](#skfolio.descriptor.EWMarketBeta.set_params)(\*\*params)        | Set the parameters of this estimator.                   |

### Notes

NaNs are allowed as missing observations. Non-missing `returns` values must be
finite. The market variance is updated at every observation. Asset covariances are
updated only for assets with valid returns and each asset’s valid-observation count
controls when its output starts. This avoids emitting initialized values for
late-listed or sparsely observed assets. The `active_mask` property of
[`AssetPanel`](https://skfolio.org/generated/skfolio.containers.AssetPanel.html.md#skfolio.containers.AssetPanel) distinguishes holidays from delistings.

Market returns are computed from the estimation universe (`estimation_mask` of
[`AssetPanel`](https://skfolio.org/generated/skfolio.containers.AssetPanel.html.md#skfolio.containers.AssetPanel)). If no estimable asset has both finite
returns and finite `market_cap` at an observation, the market return is undefined
and a `ValueError` is raised.

### References

* <a id='r317192b78e0b-1'>**[1]**</a> “Capital asset prices: A theory of market equilibrium under conditions of risk”. The Journal of Finance. Sharpe, W. F. (1964).

<a id="skfolio.descriptor.EWMarketBeta.fit_transform"></a>

#### fit_transform(X, y=None, \*\*fit_params)

Compute exponentially weighted market betas.

* **Parameters:**
  **X** *AssetPanel*
  : Input panel containing `returns` and `market_cap`, and when
    `shrinkage_group` is set, that group characteristic.

  **y** *None*
  : Ignored. Present for compatibility with scikit-learn’s API.

  **\*\*fit_params** *dict*
  : Additional fit parameters. Ignored.
* **Returns:**
  **betas** *ndarray of shape (n_observations, n_assets)*
  : Market beta for each observation and asset.

<a id="skfolio.descriptor.EWMarketBeta.get_metadata_routing"></a>

#### get_metadata_routing()

Get metadata routing of this object.

Please check [User Guide](https://skfolio.org/user_guide/metadata_routing.html.md#metadata-routing) on how the routing
mechanism works.

* **Returns:**
  **routing** *MetadataRequest*
  : A `MetadataRequest` encapsulating
    routing information.

<a id="skfolio.descriptor.EWMarketBeta.get_params"></a>

#### get_params(deep=True)

Get parameters for this estimator.

* **Parameters:**
  **deep** *bool, default=True*
  : If True, will return the parameters for this estimator and
    contained subobjects that are estimators.
* **Returns:**
  **params** *dict*
  : Parameter names mapped to their values.

<a id="skfolio.descriptor.EWMarketBeta.partial_fit_transform"></a>

#### partial_fit_transform(X, y=None, \*\*fit_params)

Update EWMA state on X and return betas for this batch.

This method supports online updates by continuing from the current fitted state.
Use `fit_transform` to start from a clean state.

* **Parameters:**
  **X** *AssetPanel*
  : Input panel containing at least `"returns"` and `"market_cap"`.
    If shrinkage is enabled, it must also contain the field specified
    by `shrinkage_group`.

  **y** *None*
  : Ignored. Present for compatibility with scikit-learn’s API.

  **\*\*fit_params** *dict*
  : Additional fit parameters. Ignored.
* **Returns:**
  **betas** *ndarray of shape (n_observations, n_assets)*
  : Market beta for each observation and asset.

<a id="skfolio.descriptor.EWMarketBeta.set_params"></a>

#### set_params(\*\*params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects
(such as `Pipeline`). The latter have
parameters of the form `<component>__<parameter>` so that it’s
possible to update each component of a nested object.

* **Parameters:**
  **\*\*params** *dict*
  : Estimator parameters.
* **Returns:**
  **self** *estimator instance*
  : Estimator instance.