Risk covariance estimators API¶
SampleCovariance¶
skportfolio.riskreturn._risk_estimators.SampleCovariance(returns_data=False, frequency=252)The standard sample covariance estimator, based on historical data.
fit_transform(X,y,**fit_params)(X_new : ndarray array of shape (n_samples, n_features_new)) — Fit to data, then transform it.get_params(deep)(params : dict) — Get parameters for this estimator.set_params(**params)(self : estimator instance) — Set the parameters of this estimator.
get_params(deep=True)Get parameters for this estimator.
deep(bool, default=True) — If True, will return the parameters for this estimator and contained subobjects that are estimators.
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 :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
**params(dict) — Estimator parameters.
Estimator instance.
fit_transform(X, y=None, **fit_params)Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
X(array-like of shape (n_samples, n_features)) — Input samples.y(array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) — Target values (None for unsupervised transformations).**fit_params(dict) — Additional fit parameters.
Transformed array.
CovarianceExp¶
skportfolio.riskreturn._risk_estimators.CovarianceExp(returns_data=False, frequency=252, span=60)Estimator of covariance based on exponential weighted average of returns
fit_transform(X,y,**fit_params)(X_new : ndarray array of shape (n_samples, n_features_new)) — Fit to data, then transform it.get_params(deep)(params : dict) — Get parameters for this estimator.set_params(**params)(self : estimator instance) — Set the parameters of this estimator.
get_params(deep=True)Get parameters for this estimator.
deep(bool, default=True) — If True, will return the parameters for this estimator and contained subobjects that are estimators.
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 :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
**params(dict) — Estimator parameters.
Estimator instance.
fit_transform(X, y=None, **fit_params)Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
X(array-like of shape (n_samples, n_features)) — Input samples.y(array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) — Target values (None for unsupervised transformations).**fit_params(dict) — Additional fit parameters.
Transformed array.
CovarianceGlasso¶
skportfolio.riskreturn._risk_estimators.CovarianceGlasso(returns_data=False, frequency=252)Estimator of covariance based on GLASSO algorithm
fit_transform(X,y,**fit_params)(X_new : ndarray array of shape (n_samples, n_features_new)) — Fit to data, then transform it.get_params(deep)(params : dict) — Get parameters for this estimator.set_params(**params)(self : estimator instance) — Set the parameters of this estimator.
get_params(deep=True)Get parameters for this estimator.
deep(bool, default=True) — If True, will return the parameters for this estimator and contained subobjects that are estimators.
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 :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
**params(dict) — Estimator parameters.
Estimator instance.
fit_transform(X, y=None, **fit_params)Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
X(array-like of shape (n_samples, n_features)) — Input samples.y(array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) — Target values (None for unsupervised transformations).**fit_params(dict) — Additional fit parameters.
Transformed array.
CovarianceHierarchicalFilterAverage¶
skportfolio.riskreturn._risk_estimators.CovarianceHierarchicalFilterAverage(returns_data=False, frequency=252)Estimator of covariance based on hierarchical filtering approach.
fit_transform(X,y,**fit_params)(X_new : ndarray array of shape (n_samples, n_features_new)) — Fit to data, then transform it.get_params(deep)(params : dict) — Get parameters for this estimator.set_params(**params)(self : estimator instance) — Set the parameters of this estimator.
get_params(deep=True)Get parameters for this estimator.
deep(bool, default=True) — If True, will return the parameters for this estimator and contained subobjects that are estimators.
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 :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
**params(dict) — Estimator parameters.
Estimator instance.
fit_transform(X, y=None, **fit_params)Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
X(array-like of shape (n_samples, n_features)) — Input samples.y(array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) — Target values (None for unsupervised transformations).**fit_params(dict) — Additional fit parameters.
Transformed array.
CovarianceHierarchicalFilterComplete¶
skportfolio.riskreturn._risk_estimators.CovarianceHierarchicalFilterComplete(returns_data=False, frequency=252)Estimator of covariance based on hierarchical filtering approach.
fit_transform(X,y,**fit_params)(X_new : ndarray array of shape (n_samples, n_features_new)) — Fit to data, then transform it.get_params(deep)(params : dict) — Get parameters for this estimator.set_params(**params)(self : estimator instance) — Set the parameters of this estimator.
get_params(deep=True)Get parameters for this estimator.
deep(bool, default=True) — If True, will return the parameters for this estimator and contained subobjects that are estimators.
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 :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
**params(dict) — Estimator parameters.
Estimator instance.
fit_transform(X, y=None, **fit_params)Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
X(array-like of shape (n_samples, n_features)) — Input samples.y(array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) — Target values (None for unsupervised transformations).**fit_params(dict) — Additional fit parameters.
Transformed array.
CovarianceHierarchicalFilterSingle¶
skportfolio.riskreturn._risk_estimators.CovarianceHierarchicalFilterSingle(returns_data=False, frequency=252)Estimator of covariance based on hierarchical filtering approach.
fit_transform(X,y,**fit_params)(X_new : ndarray array of shape (n_samples, n_features_new)) — Fit to data, then transform it.get_params(deep)(params : dict) — Get parameters for this estimator.set_params(**params)(self : estimator instance) — Set the parameters of this estimator.
get_params(deep=True)Get parameters for this estimator.
deep(bool, default=True) — If True, will return the parameters for this estimator and contained subobjects that are estimators.
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 :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
**params(dict) — Estimator parameters.
Estimator instance.
fit_transform(X, y=None, **fit_params)Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
X(array-like of shape (n_samples, n_features)) — Input samples.y(array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) — Target values (None for unsupervised transformations).**fit_params(dict) — Additional fit parameters.
Transformed array.
CovarianceLedoitWolf¶
skportfolio.riskreturn._risk_estimators.CovarianceLedoitWolf(returns_data=False, frequency=252, shrinkage_target='constant_variance')Estimator of covariance based on the Ledoit-Wolf shrinkage
fit_transform(X,y,**fit_params)(X_new : ndarray array of shape (n_samples, n_features_new)) — Fit to data, then transform it.get_params(deep)(params : dict) — Get parameters for this estimator.set_params(**params)(self : estimator instance) — Set the parameters of this estimator.
get_params(deep=True)Get parameters for this estimator.
deep(bool, default=True) — If True, will return the parameters for this estimator and contained subobjects that are estimators.
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 :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
**params(dict) — Estimator parameters.
Estimator instance.
fit_transform(X, y=None, **fit_params)Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
X(array-like of shape (n_samples, n_features)) — Input samples.y(array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) — Target values (None for unsupervised transformations).**fit_params(dict) — Additional fit parameters.
Transformed array.
CovarianceRMT¶
skportfolio.riskreturn._risk_estimators.CovarianceRMT(returns_data=False, frequency=252)Estimator of covariance based on Random Matrix Theory
fit_transform(X,y,**fit_params)(X_new : ndarray array of shape (n_samples, n_features_new)) — Fit to data, then transform it.get_params(deep)(params : dict) — Get parameters for this estimator.set_params(**params)(self : estimator instance) — Set the parameters of this estimator.
get_params(deep=True)Get parameters for this estimator.
deep(bool, default=True) — If True, will return the parameters for this estimator and contained subobjects that are estimators.
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 :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
**params(dict) — Estimator parameters.
Estimator instance.
fit_transform(X, y=None, **fit_params)Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
X(array-like of shape (n_samples, n_features)) — Input samples.y(array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) — Target values (None for unsupervised transformations).**fit_params(dict) — Additional fit parameters.
Transformed array.
SemiCovariance¶
skportfolio.riskreturn._risk_estimators.SemiCovariance(returns_data=False, frequency=252)The semicovariance, a.k.a. the covariance matrix estimated from only the positive returns.
fit_transform(X,y,**fit_params)(X_new : ndarray array of shape (n_samples, n_features_new)) — Fit to data, then transform it.get_params(deep)(params : dict) — Get parameters for this estimator.set_params(**params)(self : estimator instance) — Set the parameters of this estimator.
get_params(deep=True)Get parameters for this estimator.
deep(bool, default=True) — If True, will return the parameters for this estimator and contained subobjects that are estimators.
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 :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
**params(dict) — Estimator parameters.
Estimator instance.
fit_transform(X, y=None, **fit_params)Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
X(array-like of shape (n_samples, n_features)) — Input samples.y(array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) — Target values (None for unsupervised transformations).**fit_params(dict) — Additional fit parameters.
Transformed array.