skfolio.optimization.StackingOptimization#

class skfolio.optimization.StackingOptimization(estimators, final_estimator=None, cv=None, quantile=0.5, quantile_measure=Sharpe Ratio, n_jobs=None, verbose=0, portfolio_params=None)[source]#

Stack of optimizations with a final optimization.

Stacking Optimization is an ensemble method that consists in stacking the output of individual portfolio optimizations with a final portfolio optimization.

The weights are the dot-product of individual optimizations weights with the final optimization weights.

Stacking allows to use the strength of each individual portfolio optimization by using their output as input of a final portfolio optimization.

To avoid data leakage, out-of-sample estimates are used to fit the outer optimization.

Note that estimators_ are fitted on the full X while final_estimator_ is trained using cross-validated predictions of the base estimators using cross_val_predict.

Parameters:

estimatorslist[tuple[str, BaseOptimization]]

Optimization estimators which will be stacked together. Each element of the list is defined as a tuple of string (i.e. name) and an optimization estimator.

final_estimatorBaseOptimization, optional

A final optimization estimator which will be used to combine the base estimators. The default (None) is to use MeanRisk.

cvBaseCrossValidator | BaseCombinatorialCV | int | “prefit” | “ignore”, optional

Determines the cross-validation splitting strategy used in cross_val_predict to train the final_estimator. The default (None) is to use the 5-fold cross validation KFold(). Possible inputs for cv are:

“ignore”: no cross-validation is used (note that it will likely lead to data leakage with a high risk of overfitting)

integer, to specify the number of folds in a KFold

An object to be used as a cross-validation generator

An iterable yielding train, test splits

“prefit” to assume the estimators are prefit, and skip cross validation

A CombinatorialPurgedCV

If a CombinatorialCV cross-validator is used, each cluster out-of-sample outputs becomes a collection of multiple paths instead of one single path. The selected out-of-sample path among this collection of paths is chosen according to the quantile and quantile_measure parameters.

If “prefit” is passed, it is assumed that all estimators have been fitted already. The final_estimator_ is trained on the estimators predictions on the full training set and are not cross validated predictions. Please note that if the models have been trained on the same data to train the stacking model, there is a very high risk of overfitting.

n_jobsint, optional

The number of jobs to run in parallel for fit of all estimators. The value -1 means using all processors. The default (None) means 1 unless in a joblib.parallel_backend context.

quantilefloat, default=0.5

Quantile for a given measure (quantile_measure) of the out-of-sample inner-estimator paths when the cv parameter is a CombinatorialPurgedCV cross-validator. The default value is 0.5 corresponding to the path with the median measure. (see cv)

quantile_measurePerfMeasure or RatioMeasure or RiskMeasure or ExtraRiskMeasure, default=RatioMeasure.SHARPE_RATIO

Measure used for the quantile path selection (see quantile and cv). The default is RatioMeasure.SHARPE_RATIO.

verboseint, default=0

The verbosity level. The default value is 0.

portfolio_paramsdict, optional

Portfolio parameters passed to the portfolio evaluated by the predict and score methods. If not provided, the name is copied from the optimization model and systematically passed to the portfolio.

Attributes:

weights_ndarray of shape (n_assets,): Weights of the assets.
estimators_list[BaseOptimization]: The elements of the estimators parameter, having been fitted on the training data. When cv="prefit", estimators_ is set to estimators and is not fitted again.
named_estimators_dict[str, BaseOptimization]: Attribute to access any fitted sub-estimators by name.
final_estimator_BaseOptimization: The fitted final_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 Stacking Optimization estimator.
`fit_predict`(X)	Perform `fit` on `X` and returns the predicted `Portfolio` or `Population` of `Portfolio` on `X` based on the fitted `weights`.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get the parameters of an estimator from the ensemble.
`predict`(X)	Predict the `Portfolio` or `Population` of `Portfolio` on `X` based on the fitted weights.
`score`(X[, y])	Prediction score.
`set_params`(**params)	Set the parameters of an estimator from the ensemble.

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

Fit the Stacking Optimization estimator.

Parameters:

Xarray-like of shape (n_observations, n_assets): Price returns of the assets.
yarray-like of shape (n_observations, n_targets), optional: Price returns of factors or a target benchmark. The default is None.
**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:

selfStackingOptimization: Fitted estimator.

fit_predict(X)#

Perform fit on X and returns the predicted Portfolio or Population of Portfolio on X based on the fitted weights. For factor models, use fit(X, y) then predict(X) separately.

Parameters:

Xarray-like of shape (n_observations, n_assets): Price returns of the assets.

Returns:

predictionPortfolio | Population: Portfolio or Population of Portfolio estimated on X based on the fitted weights.

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)[source]#

Get the parameters of an estimator from the ensemble.

Returns the parameters given in the constructor as well as the estimators contained within the estimators parameter.

Parameters:

deepbool, default=True: Setting it to True gets the various estimators and the parameters of the estimators as well.

Returns:

paramsdict: Parameter and estimator names mapped to their values or parameter names mapped to their values.

property named_estimators#

Dictionary to access any fitted sub-estimators by name.

Returns:

Bunch

predict(X)#

Predict the Portfolio or Population of Portfolio on X based on the fitted weights.

Optimization estimators can return a 1D or a 2D array of weights. For a 1D array, the prediction returns a Portfolio. For a 2D array, the prediction returns a Population of Portfolio.

If name is not provided in the portfolio arguments, we use the first 500 characters of the estimator name.

Parameters:

Xarray-like of shape (n_observations, n_assets): Price returns of the assets.

Returns:

predictionPortfolio | Population: Portfolio or Population of Portfolio estimated on X based on the fitted weights.

score(X, y=None)#

Prediction score. If the prediction is a single Portfolio, the score is the Sharpe Ratio. If the prediction is a Population of Portfolio, the score is the mean of all the portfolios Sharpe Ratios in the population.

Parameters:

Xarray-like of shape (n_observations, n_assets): Price returns of the assets.
yIgnored: Not used, present here for API consistency by convention.

Returns:

scorefloat: The Sharpe Ratio of the portfolio if the prediction is a single Portfolio or the mean of all the portfolios Sharpe Ratios if the prediction is a Population of Portfolio.

set_params(**params)[source]#

Set the parameters of an estimator from the ensemble.

Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in estimators.

Parameters:

**paramskeyword arguments: Specific parameters using e.g. set_params(parameter_name=new_value). In addition, to setting the parameters of the estimator, the individual estimator of the estimators can also be set, or can be removed by setting them to ‘drop’.

Returns:

selfobject: Estimator instance.