Create model.py

model.py

@@ -0,0 +1,35 @@
+import torch
+import torch.nn as nn
+
+
+class TinyTransformerForecaster(nn.Module):
+    """
+    Small Transformer encoder for time-series (CPU-friendly).
+    Input: (B, T, F=4)
+    Output: (B, 1) predicted log-return
+    """
+    def __init__(self, feature_dim=4, d_model=64, nhead=4, num_layers=2, dropout=0.1):
+        super().__init__()
+        self.in_proj = nn.Linear(feature_dim, d_model)
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=d_model,
+            nhead=nhead,
+            dim_feedforward=4 * d_model,
+            dropout=dropout,
+            batch_first=True,
+            activation="gelu",
+            norm_first=True,
+        )
+        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        self.head = nn.Sequential(
+            nn.LayerNorm(d_model),
+            nn.Linear(d_model, d_model // 2),
+            nn.GELU(),
+            nn.Linear(d_model // 2, 1),
+        )
+
+    def forward(self, x):
+        h = self.in_proj(x)
+        h = self.encoder(h)
+        last = h[:, -1, :]
+        return self.head(last)