skfolio.alpha.BaseAlpha#

class skfolio.alpha.BaseAlpha[source]#

Base class for all Alpha estimators in skfolio.

Methods

`fit`(X[, y])	Fit the alpha estimator and store the latest alpha forecast in `alpha_`.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`set_params`(**params)	Set the parameters of this estimator.

abstractmethod fit(X, y=None, **fit_params)[source]#

Fit the alpha estimator and store the latest alpha forecast in alpha_.

Parameters:

XAssetPanel: Input panel data.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters passed to the sub-estimators.

Returns:

selfBaseAlpha: Fitted estimator.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

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:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.