| """ |
| ⚡ ULTRON Systems // Multi-Task Financial Loss Function |
| Combines: |
| 1. Huber Loss (Smooth L1) on forward return magnitudes (robust against fat-tailed black swan spikes). |
| 2. Binary Cross-Entropy with Logits on directional movement sign. |
| 3. Directional Accuracy evaluation metric. |
| """ |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from typing import Tuple, Dict |
|
|
| class MultiTaskFinancialLoss(nn.Module): |
| def __init__(self, dir_weight: float = 0.5, huber_delta: float = 0.01): |
| super().__init__() |
| self.dir_weight = dir_weight |
| self.huber = nn.HuberLoss(delta=huber_delta) |
| self.bce = nn.BCEWithLogitsLoss() |
|
|
| def forward( |
| self, |
| pred_returns: torch.Tensor, |
| pred_logits: torch.Tensor, |
| target_returns: torch.Tensor |
| ) -> Tuple[torch.Tensor, Dict[str, float]]: |
| """ |
| pred_returns: [Batch, Horiz] |
| pred_logits: [Batch, Horiz] |
| target_returns: [Batch, Horiz] |
| """ |
| |
| reg_loss = self.huber(pred_returns, target_returns) |
| |
| |
| target_dir = (target_returns > 0.0).float() |
| dir_loss = self.bce(pred_logits, target_dir) |
| |
| |
| total_loss = reg_loss + (self.dir_weight * dir_loss) |
| |
| |
| with torch.no_grad(): |
| pred_dir_bool = (pred_logits > 0.0) |
| target_dir_bool = (target_returns > 0.0) |
| dir_acc = (pred_dir_bool == target_dir_bool).float().mean().item() * 100.0 |
| |
| |
| reg_dir_acc = ((pred_returns > 0.0) == target_dir_bool).float().mean().item() * 100.0 |
| |
| metrics = { |
| "total_loss": total_loss.item(), |
| "reg_loss": reg_loss.item(), |
| "dir_loss": dir_loss.item(), |
| "dir_acc_bce": dir_acc, |
| "dir_acc_reg": reg_dir_acc |
| } |
| |
| return total_loss, metrics |
|
|
