macrosynergy.learning.forecasting.meta_estimators.probability#

class ProbabilityEstimator(classifier)[source]#

Bases: BaseEstimator, MetaEstimatorMixin, ClassifierMixin

Meta estimator to create trading signals based on the probability of going long.

Parameters:

classifier (ClassifierMixin) – A scikit-learn classifier.

Notes

This class stores feature importances as the feature importances of the base estimator as well as defining a create_signal method that returns the probability of going long in excess of 0.5. This is taken into account when used in the SignalOptimizer class in this package.

fit(X, y)[source]#

Fit the underlying classifier.

Parameters:

  • X (pd.DataFrame or np.ndarray) – Pandas dataframe or numpy array of input features.

  • y (pd.Series or pd.DataFrame or np.ndarray) – Pandas series, dataframe or numpy array of targets associated with each sample in X.

predict(X)[source]#

Predict the class labels for the provided data.

Parameters:

X (pd.DataFrame or numpy array) – Input feature matrix.

Returns:

y_pred – Numpy array of predictions.

Return type:

np.ndarray

create_signal(X)[source]#

Create a trading signal based on the probability of going long.

Parameters:

X (pd.DataFrame or numpy array) – Input feature matrix.

Returns:

y_pred – Numpy array of signals.

Return type:

np.ndarray

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → ProbabilityEstimator#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object