skfolio.factor_exposure.GlobalFactor#

class skfolio.factor_exposure.GlobalFactor(*, family='market')[source]#

Constant factor exposure equal to one for every asset.

GlobalFactor represents a cross-sectional regression intercept or broad market factor in a characteristics factor model. It does not depend on any characteristic field. It uses the panel dimensions to return an exposure matrix of ones with shape (n_observations, n_assets).

For each observation \(t\) and asset \(i\), the exposure is:

\[x_{t,i} = 1\]

This factor is typically used when the cross-sectional regression should estimate a common return component in addition to characteristic-based style industry or country factors.

Parameters:

familystr, default=”market”: The factor family this exposure belongs to. Factor families group related factors for basket-neutral constraints, neutralization, attribution and reporting. The default is "market".

Attributes:

n_assets_int: Number of assets seen during fitting.
asset_names_ndarray of shape (n_assets,): Asset names seen during fitting.

Methods

`fit_transform`(X[, y])	Return a constant exposure matrix.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit_transform`(X[, y])	Stateless class delegation to `fit_transform`.
`set_params`(**params)	Set the parameters of this estimator.

Examples

>>> from skfolio.factor_exposure import GlobalFactor
>>> from skfolio.prior import CharacteristicsFactorModel
>>>
>>> model = CharacteristicsFactorModel(
...     factors=[("market", GlobalFactor())]
... )

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

Return a constant exposure matrix.

Parameters:

XAssetPanel: Input panel used to determine the number of observations and assets.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters. They are ignored.

Returns:

exposurendarray of shape (n_observations, n_assets): Constant factor exposure equal to one for every asset in every observation.

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.

partial_fit_transform(X, y=None, **fit_params)#: Stateless class delegation to fit_transform.

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.