Returns estimators API¶
CAPMReturns¶
skportfolio.riskreturn._returns_estimators.CAPMReturns(returns_data=False, frequency=252, risk_free_rate=0.0, benchmark=None)Base class for return estimator.
It provides the basic infrastructure for all estimators of expected returns.
It either can take price data or returns data, by specifying the returns_data=False or True respectively.
The returns calculation is done with the standard .pct_change() of Pandas, unless otherwise specified by
specification of the returns_function callable.
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.
CompoundedHistoricalLinearReturns¶
skportfolio.riskreturn._returns_estimators.CompoundedHistoricalLinearReturns(returns_data=False, frequency=252, returns_function=None)Base class for return estimator.
It provides the basic infrastructure for all estimators of expected returns.
It either can take price data or returns data, by specifying the returns_data=False or True respectively.
The returns calculation is done with the standard .pct_change() of Pandas, unless otherwise specified by
specification of the returns_function callable.
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.
CompoundedHistoricalLogReturns¶
skportfolio.riskreturn._returns_estimators.CompoundedHistoricalLogReturns(returns_data=False, frequency=252, returns_function=None)Base class for return estimator.
It provides the basic infrastructure for all estimators of expected returns.
It either can take price data or returns data, by specifying the returns_data=False or True respectively.
The returns calculation is done with the standard .pct_change() of Pandas, unless otherwise specified by
specification of the returns_function callable.
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.
EMAHistoricalReturns¶
skportfolio.riskreturn._returns_estimators.EMAHistoricalReturns(returns_data=False, frequency=252, span=180)Base class for return estimator.
It provides the basic infrastructure for all estimators of expected returns.
It either can take price data or returns data, by specifying the returns_data=False or True respectively.
The returns calculation is done with the standard .pct_change() of Pandas, unless otherwise specified by
specification of the returns_function callable.
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.
MeanHistoricalLinearReturns¶
skportfolio.riskreturn._returns_estimators.MeanHistoricalLinearReturns(returns_data=False, frequency=252, returns_function=None)Base class for return estimator.
It provides the basic infrastructure for all estimators of expected returns.
It either can take price data or returns data, by specifying the returns_data=False or True respectively.
The returns calculation is done with the standard .pct_change() of Pandas, unless otherwise specified by
specification of the returns_function callable.
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.
MeanHistoricalLogReturns¶
skportfolio.riskreturn._returns_estimators.MeanHistoricalLogReturns(returns_data=False, frequency=252, returns_function=None)Base class for return estimator.
It provides the basic infrastructure for all estimators of expected returns.
It either can take price data or returns data, by specifying the returns_data=False or True respectively.
The returns calculation is done with the standard .pct_change() of Pandas, unless otherwise specified by
specification of the returns_function callable.
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.
MedianHistoricalLinearReturns¶
skportfolio.riskreturn._returns_estimators.MedianHistoricalLinearReturns(returns_data=False, frequency=252, returns_function=None)Base class for return estimator.
It provides the basic infrastructure for all estimators of expected returns.
It either can take price data or returns data, by specifying the returns_data=False or True respectively.
The returns calculation is done with the standard .pct_change() of Pandas, unless otherwise specified by
specification of the returns_function callable.
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.
MedianHistoricalLogReturns¶
skportfolio.riskreturn._returns_estimators.MedianHistoricalLogReturns(returns_data=False, frequency=252, returns_function=None)Base class for return estimator.
It provides the basic infrastructure for all estimators of expected returns.
It either can take price data or returns data, by specifying the returns_data=False or True respectively.
The returns calculation is done with the standard .pct_change() of Pandas, unless otherwise specified by
specification of the returns_function callable.
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.
RollingMedianReturns¶
skportfolio.riskreturn._returns_estimators.RollingMedianReturns(returns_data=False, frequency=252, window=20)Base class for return estimator.
It provides the basic infrastructure for all estimators of expected returns.
It either can take price data or returns data, by specifying the returns_data=False or True respectively.
The returns calculation is done with the standard .pct_change() of Pandas, unless otherwise specified by
specification of the returns_function callable.
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.