Source code for skfolio.moments.expected_returns._empirical_mu

"""Empirical Expected Returns (Mu) Estimator."""

# Copyright (c) 2023-2026
# Author: Hugo Delatte <hugo.delatte@skfoliolabs.com>
# SPDX-License-Identifier: BSD-3-Clause
# Implementation derived from:
# scikit-learn, Copyright (c) 2007-2010 David Cournapeau, Fabian Pedregosa, Olivier
# Grisel Licensed under BSD 3 clause.

from __future__ import annotations

import numpy as np
import sklearn.utils.validation as skv

from skfolio.moments.expected_returns._base import BaseMu
from skfolio.typing import ArrayLike
from skfolio.utils.tools import apply_window_size




class EmpiricalMu(BaseMu):
    """Empirical Expected Returns (Mu) estimator.

    Estimates the expected returns with the historical mean.

    Parameters
    ----------
    window_size : int, optional
        Window size. The model is fitted on the last `window_size` observations.
        The default (`None`) is to use all the data.

    Attributes
    ----------
    mu_ : ndarray of shape (n_assets,)
        Estimated expected returns of the assets.

    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.
    """

    def __init__(self, window_size: int | None = None):
        self.window_size = window_size



    def fit(self, X: ArrayLike, y=None) -> EmpiricalMu:
        """Fit the Mu Empirical estimator model.

        Parameters
        ----------
        X : array-like of shape (n_observations, n_assets)
            Price returns of the assets.

        y : Ignored
            Not used, present for API consistency by convention.

        Returns
        -------
        self : EmpiricalMu
            Fitted estimator.
        """
        X = skv.validate_data(self, X)
        X = apply_window_size(X, window_size=self.window_size)
        self.mu_ = np.mean(X, axis=0)
        return self