fixes

core/orchestrator.py

@@ -303,25 +303,48 @@ class TradingOrchestrator:
         """Initialize the enhanced orchestrator with full ML capabilities"""
         self.config = get_config()
         self.data_provider = data_provider or DataProvider()
-        self.universal_adapter = UniversalDataAdapter(self.data_provider)
+        # Temporarily disable UniversalDataAdapter to avoid crash
+        self.universal_adapter = None  # UniversalDataAdapter(self.data_provider)
         self.model_manager = None  # Will be initialized later if needed
         self.model_registry = model_registry  # Model registry for dynamic model management
         self.enhanced_rl_training = enhanced_rl_training
-        
+
         # Set primary trading symbol
         self.symbol = self.config.get('primary_symbol', 'ETH/USDT')
         self.ref_symbols = self.config.get('reference_symbols', ['BTC/USDT'])
-        
+
         # Initialize signal accumulator
         self.signal_accumulator = {}
-        
+
         # Initialize confidence threshold
         self.confidence_threshold = self.config.get('confidence_threshold', 0.6)
-        
+
+        # CRITICAL: Initialize prediction tracking attributes FIRST to avoid attribute errors
+        # Model prediction tracking for dashboard visualization
+        self.recent_dqn_predictions: Dict[str, deque] = (
+            {}
+        )  # {symbol: List[Dict]} - Recent DQN predictions
+        self.recent_cnn_predictions: Dict[str, deque] = (
+            {}
+        )  # {symbol: List[Dict]} - Recent CNN predictions
+        self.recent_transformer_predictions: Dict[str, deque] = (
+            {}
+        )  # {symbol: List[Dict]} - Recent Transformer predictions
+        self.prediction_accuracy_history: Dict[str, deque] = (
+            {}
+        )  # {symbol: List[Dict]} - Prediction accuracy tracking
+
+        # Initialize prediction tracking for the primary trading symbol only
+        self.recent_dqn_predictions[self.symbol] = deque(maxlen=100)
+        self.recent_cnn_predictions[self.symbol] = deque(maxlen=50)
+        self.recent_transformer_predictions[self.symbol] = deque(maxlen=50)
+        self.prediction_accuracy_history[self.symbol] = deque(maxlen=200)
+        self.signal_accumulator[self.symbol] = []
+
         # Determine the device to use from config.yaml
         self.device = self._get_device_from_config()
         logger.info(f"Using device: {self.device}")
-    
+
     def _get_device_from_config(self) -> torch.device:
         """Get device from config.yaml or auto-detect"""
         try:
@@ -406,27 +429,6 @@ class TradingOrchestrator:
             {}
         )  # {symbol: {side, size, entry_price, entry_time, pnl}}
         self.trading_executor = None  # Will be set by dashboard or external system
-        # Model prediction tracking for dashboard visualization
-        self.recent_dqn_predictions: Dict[str, deque] = (
-            {}
-        )  # {symbol: List[Dict]} - Recent DQN predictions
-        self.recent_cnn_predictions: Dict[str, deque] = (
-            {}
-        )  # {symbol: List[Dict]} - Recent CNN predictions
-        self.recent_transformer_predictions: Dict[str, deque] = (
-            {}
-        )  # {symbol: List[Dict]} - Recent Transformer predictions
-        self.prediction_accuracy_history: Dict[str, deque] = (
-            {}
-        )  # {symbol: List[Dict]} - Prediction accuracy tracking
-
-        # Initialize prediction tracking for the primary trading symbol only
-        self.recent_dqn_predictions[self.symbol] = deque(maxlen=100)
-        self.recent_cnn_predictions[self.symbol] = deque(maxlen=50)
-        self.recent_transformer_predictions[self.symbol] = deque(maxlen=50)
-        self.prediction_accuracy_history[self.symbol] = deque(maxlen=200)
-        self.signal_accumulator[self.symbol] = []
-        
         # Decision callbacks
         self.decision_callbacks: List[Any] = []