T model was only using 1t data so far. fixing WIP

NN/models/advanced_transformer_trading.py

@@ -349,8 +349,17 @@ class AdvancedTradingTransformer(nn.Module):
         super().__init__()
         self.config = config
         
-        # Input projections
-        self.price_projection = nn.Linear(5, config.d_model)  # OHLCV
+        # Multi-timeframe input projections
+        # Each timeframe gets its own projection to learn timeframe-specific patterns
+        self.timeframes = ['1s', '1m', '1h', '1d']
+        self.price_projections = nn.ModuleDict({
+            tf: nn.Linear(5, config.d_model) for tf in self.timeframes  # OHLCV per timeframe
+        })
+        
+        # Reference symbol projection (BTC 1m)
+        self.btc_projection = nn.Linear(5, config.d_model)
+        
+        # Other input projections
         self.cob_projection = nn.Linear(config.cob_features, config.d_model)
         self.tech_projection = nn.Linear(config.tech_features, config.d_model)
         self.market_projection = nn.Linear(config.market_features, config.d_model)
@@ -367,6 +376,9 @@ class AdvancedTradingTransformer(nn.Module):
             nn.Linear(config.d_model // 2, config.d_model)  # 512 -> 1024
         )
         
+        # Timeframe importance weights (learnable)
+        self.timeframe_weights = nn.Parameter(torch.ones(len(self.timeframes) + 1))  # +1 for BTC
+        
         # Positional encoding
         if config.use_relative_position:
             self.pos_encoding = RelativePositionalEncoding(config.d_model)
@@ -435,7 +447,7 @@ class AdvancedTradingTransformer(nn.Module):
         
         # NEW: Next candle OHLCV prediction heads for each timeframe (1s, 1m, 1h, 1d)
         # Each timeframe predicts: [open, high, low, close, volume] = 5 values
-        self.timeframes = ['1s', '1m', '1h', '1d']
+        # Note: self.timeframes already defined above in input projections
         self.next_candle_heads = nn.ModuleDict({
             tf: nn.Sequential(
                 nn.Linear(config.d_model, config.d_model // 2),
@@ -448,6 +460,17 @@ class AdvancedTradingTransformer(nn.Module):
             ) for tf in self.timeframes
         })
         
+        # BTC next candle prediction head
+        self.btc_next_candle_head = nn.Sequential(
+            nn.Linear(config.d_model, config.d_model // 2),
+            nn.GELU(),
+            nn.Dropout(config.dropout),
+            nn.Linear(config.d_model // 2, config.d_model // 4),
+            nn.GELU(),
+            nn.Dropout(config.dropout),
+            nn.Linear(config.d_model // 4, 5)  # OHLCV for BTC
+        )
+        
         # NEW: Next pivot point prediction heads for L1-L5 levels
         # Each level predicts: [price, type_prob_high, type_prob_low, confidence]
         # type_prob_high + type_prob_low = 1 (softmax), but we output separately for clarity