pyepo.data.portfolio

Synthetic data for portfolio

Functions

genData(→ tuple[numpy.ndarray, numpy.ndarray, ...)

Generate synthetic feature-cost pairs for portfolio optimization.

Module Contents

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

Parameters:

  • num_data – number of data points

  • num_features – dimension of features

  • num_assets – number of assets

  • deg – polynomial degree of the feature-to-return mapping

  • noise_level – scales factor loadings L and residual noise (tau)

  • seed – random seed (default 135 for reproducibility)

Returns:

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

Return type:

tuple