added more predictions

NN/models/simple_cnn.py

@@ -125,6 +125,14 @@ class SimpleCNN(nn.Module):
         
         # Extrema detection head
         self.extrema_head = nn.Linear(256, 3)  # 0=bottom, 1=top, 2=neither
+        
+        # Price prediction heads for different timeframes
+        self.price_pred_immediate = nn.Linear(256, 3)  # Up, Down, Sideways for immediate term (1s, 1m)
+        self.price_pred_midterm = nn.Linear(256, 3)    # Up, Down, Sideways for mid-term (1h)
+        self.price_pred_longterm = nn.Linear(256, 3)   # Up, Down, Sideways for long-term (1d)
+        
+        # Regression heads for exact price prediction
+        self.price_pred_value = nn.Linear(256, 4)  # Predicts % change for each timeframe (1s, 1m, 1h, 1d)
     
     def _check_rebuild_network(self, features):
         """Check if network needs to be rebuilt for different feature dimensions"""
@@ -140,7 +148,7 @@ class SimpleCNN(nn.Module):
     def forward(self, x):
         """
         Forward pass through the network
-        Returns both action values and extrema predictions
+        Returns action values, extrema predictions, and price movement predictions for multiple timeframes
         """
         # Handle different input shapes
         if len(x.shape) == 2:  # [batch_size, features]
@@ -173,7 +181,50 @@ class SimpleCNN(nn.Module):
         # Extrema predictions
         extrema_pred = self.extrema_head(fc_out)
         
-        return action_values, extrema_pred
+        # Price movement predictions for different timeframes
+        price_immediate = self.price_pred_immediate(fc_out)  # 1s, 1m
+        price_midterm = self.price_pred_midterm(fc_out)      # 1h
+        price_longterm = self.price_pred_longterm(fc_out)    # 1d
+        
+        # Regression values for exact price predictions (percentage changes)
+        price_values = self.price_pred_value(fc_out)
+        
+        # Return all predictions in a structured dictionary
+        price_predictions = {
+            'immediate': price_immediate,
+            'midterm': price_midterm,
+            'longterm': price_longterm,
+            'values': price_values
+        }
+        
+        return action_values, extrema_pred, price_predictions
+        
+    def save(self, path):
+        """Save model weights and architecture"""
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        torch.save({
+            'state_dict': self.state_dict(),
+            'input_shape': self.input_shape,
+            'n_actions': self.n_actions,
+            'feature_dim': self.feature_dim
+        }, f"{path}.pt")
+        logger.info(f"Model saved to {path}.pt")
+        
+    def load(self, path):
+        """Load model weights and architecture"""
+        try:
+            checkpoint = torch.load(f"{path}.pt", map_location=self.device)
+            self.input_shape = checkpoint['input_shape']
+            self.n_actions = checkpoint['n_actions']
+            self.feature_dim = checkpoint['feature_dim']
+            self._build_network()
+            self.load_state_dict(checkpoint['state_dict'])
+            self.to(self.device)
+            logger.info(f"Model loaded from {path}.pt")
+            return True
+        except Exception as e:
+            logger.error(f"Error loading model: {str(e)}")
+            return False
 
 class CNNModelPyTorch(nn.Module):
     """