Source code for fynance.models.loss.omega

#!/usr/bin/env python3
# coding: utf-8

""" Differentiable Omega-ratio loss. """

from __future__ import annotations

# Third-party packages
import torch

# Local packages
from ._base import BaseLoss

__all__ = ['OmegaLoss']




class OmegaLoss(BaseLoss):
    r""" Negative Omega ratio as a differentiable loss.

    :math:`\Omega = \frac{E[\max(r - L, 0)]}{E[\max(L - r, 0)] + \varepsilon}`,
    the ratio of expected gains to expected losses relative to a threshold
    ``L``. Fully differentiable through :func:`torch.relu`. Minimizing the
    loss maximizes the Omega ratio.

    Parameters
    ----------
    threshold : float, optional
        Return threshold ``L`` separating gains from losses. Default 0.
    **kwargs
        Forwarded to :class:`BaseLoss` (``rf``, ``period``, ``eps``).

    """

    def __init__(self, threshold: float = 0., **kwargs):
        super().__init__(**kwargs)
        self.threshold = threshold



    def forward(
        self, y_pred: torch.Tensor, y_true: torch.Tensor | None = None,
    ) -> torch.Tensor:
        """ Compute the negative Omega ratio (scalar). """
        self._check_tensor(y_pred)
        diff = y_pred - self.threshold
        gains = torch.relu(diff).mean()
        losses = torch.relu(-diff).mean()

        return -(gains / (losses + self.eps))