pyepo.data.portfolio
====================

.. py:module:: pyepo.data.portfolio

.. autoapi-nested-parse::

   Synthetic data for portfolio



Functions
---------

.. autoapisummary::

   pyepo.data.portfolio.genData


Module Contents
---------------

.. py:function:: genData(num_data: int, num_features: int, num_assets: int, deg: int = 1, noise_level: float = 1, seed: int = 135) -> tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

   Generate synthetic feature-cost pairs for portfolio optimization.

   Returns the expected returns :math:`\mathbf{r}` (the per-instance cost
   vectors) and a single shared covariance matrix :math:`\mathbf{\Sigma}`
   used in the risk constraint of the predefined portfolio model. The mean
   returns follow a factor-model structure
   :math:`\mathbf{r}_i = \bar{\mathbf{r}}_i + \mathbf{L}\mathbf{f}
   + 0.01 \tau \boldsymbol{\epsilon}`, where the factor loadings
   :math:`\mathbf{L}` and residual noise are both scaled by ``noise_level``
   (:math:`\tau`). Unlike the other generators in ``pyepo.data``, portfolio
   noise is controlled by ``noise_level`` rather than ``noise_width``.

   :param num_data: number of data points
   :param num_features: dimension of features
   :param num_assets: number of assets
   :param deg: polynomial degree of the feature-to-return mapping
   :param noise_level: scales factor loadings L and residual noise (tau)
   :param seed: random seed (default 135 for reproducibility)

   :returns: covariance matrix (np.ndarray), data features (np.ndarray), mean returns (np.ndarray)
   :rtype: tuple