skfolio.moments.DetoneCovariance#

class skfolio.moments.DetoneCovariance(covariance_estimator=None, n_markets=1, nearest=True, higham=False, higham_max_iteration=100)[source]#

Covariance Detoning estimator.

Financial covariance matrices usually incorporate a market component corresponding to the first eigenvectors [1]. For some applications like clustering, removing the market component (loud tone) allow a greater portion of the covariance to be explained by components that affect specific subsets of the securities.

Parameters:

covariance_estimatorBaseCovariance, optional: Covariance estimator to estimate the covariance matrix prior detoning. The default (None) is to use EmpiricalCovariance.
n_marketsint, default=1: Number of eigenvectors related to the market. The default value is 1.
nearestbool, default=True: If this is set to True, the covariance is replaced by the nearest covariance matrix that is positive definite and with a Cholesky decomposition than can be computed. The variance is left unchanged. A covariance matrix that is not positive definite often occurs in high dimensional problems. It can be due to multicollinearity, floating-point inaccuracies, or when the number of observations is smaller than the number of assets. For more details, see cov_nearest. The default is True.
highambool, default=False: If this is set to True, the Higham (2002) algorithm is used to find the nearest PD covariance, otherwise the eigenvalues are clipped to a threshold above zeros (1e-13). The default is False and uses the clipping method as the Higham algorithm can be slow for large datasets.
higham_max_iterationint, default=100: Maximum number of iterations of the Higham (2002) algorithm. The default value is 100.

Attributes:

covariance_ndarray of shape (n_assets, n_assets): Estimated covariance.
covariance_estimator_BaseCovariance: Fitted covariance_estimator.
n_features_in_int: Number of assets seen during fit.
feature_names_in_ndarray of shape (n_features_in_,): Names of assets seen during fit. Defined only when X has assets names that are all strings.

Methods

`fit`(X[, y])	Fit the Covariance Detoning estimator.
`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.

References

[1]

“Machine Learning for Asset Managers”. Elements in Quantitative Finance. Lòpez de Prado (2020).

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

Fit the Covariance Detoning estimator.

Parameters:

Xarray-like of shape (n_observations, n_assets): Price returns of the assets.
yIgnored: Not used, present for API consistency by convention.
**fit_paramsdict: Parameters to pass to the underlying estimators. Only available if enable_metadata_routing=True, which can be set by using sklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.

Returns:

selfDetoneCovariance: Fitted estimator.

get_metadata_routing()[source]#

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.