remove model name mappings

core/orchestrator.py

@@ -18,7 +18,7 @@ import threading
 import numpy as np
 import pandas as pd
 from datetime import datetime, timedelta
-from typing import Dict, List, Optional, Any, Tuple, Union
+from typing import Dict, List, Optional, Any, Tuple, Union, Deque
 from dataclasses import dataclass, field
 from collections import deque
 import json
@@ -325,6 +325,13 @@ class TradingOrchestrator:
             "DECISION": ["decision_fusion", "decision"],
         }
 
+        # Recent inference buffer for vector supervision (configurable length)
+        self.recent_inference_maxlen: int = self.config.orchestrator.get(
+            "recent_inference_buffer", 10
+        )
+        # Model name -> deque of recent inference records
+        self.recent_inferences: Dict[str, Deque[Dict]] = {}
+
         # Configuration - AGGRESSIVE for more training data
         self.confidence_threshold = self.config.orchestrator.get(
             "confidence_threshold", 0.15
@@ -1166,53 +1173,64 @@ class TradingOrchestrator:
         price_direction_pred: torch.Tensor,
         rewards: torch.Tensor,
         actions: torch.Tensor,
-    ) -> torch.Tensor:
+        target_vector: Optional[Dict[str, float]] = None,
+    ) -> Optional[torch.Tensor]:
         """
-        Calculate price direction loss for CNN model
+        Calculate price direction loss for CNN model.
+
+        If target_vector is provided, perform supervised regression towards the
+        explicit direction/confidence. Otherwise, derive weak targets from
+        rewards and actions.
 
         Args:
-            price_direction_pred: Tensor of shape [batch, 2] containing [direction, confidence]
+            price_direction_pred: [batch, 2] = [direction, confidence]
-            rewards: Tensor of shape [batch] containing rewards
+            rewards: [batch]
-            actions: Tensor of shape [batch] containing actions
+            actions: [batch]
+            target_vector: Optional dict {'direction': float, 'confidence': float}
 
         Returns:
-            Price direction loss tensor
+            Loss tensor or None.
         """
         try:
             if price_direction_pred.size(1) != 2:
                 return None
 
             batch_size = price_direction_pred.size(0)
+            direction_pred = price_direction_pred[:, 0]
+            confidence_pred = price_direction_pred[:, 1]
 
-            # Extract direction and confidence predictions
+            # Supervised targets from explicit vector if available
-            direction_pred = price_direction_pred[:, 0]  # -1 to 1
+            if target_vector and isinstance(target_vector, dict):
-            confidence_pred = price_direction_pred[:, 1]  # 0 to 1
+                try:
-
+                    t_dir = float(target_vector.get("direction", 0.0))
-            # Create targets based on rewards and actions
+                    t_conf = float(target_vector.get("confidence", 0.0))
-            with torch.no_grad():
+                    direction_targets = torch.full(
-                # Direction targets: 1 if reward > 0 and action is BUY, -1 if reward > 0 and action is SELL, 0 otherwise
+                        (batch_size,), t_dir, device=price_direction_pred.device, dtype=direction_pred.dtype
-                direction_targets = torch.zeros(
-                    batch_size, device=price_direction_pred.device
                     )
-                for i in range(batch_size):
+                    confidence_targets = torch.full(
-                    if rewards[i] > 0.01:  # Positive reward threshold
+                        (batch_size,), t_conf, device=price_direction_pred.device, dtype=confidence_pred.dtype
-                        if actions[i] == 0:  # BUY action
+                    )
-                            direction_targets[i] = 1.0  # UP
+                    dir_loss = nn.MSELoss()(direction_pred, direction_targets)
-                        elif actions[i] == 1:  # SELL action
+                    conf_loss = nn.MSELoss()(confidence_pred, confidence_targets)
-                            direction_targets[i] = -1.0  # DOWN
+                    return dir_loss + 0.3 * conf_loss
-                    # else: targets remain 0 (sideways)
+                except Exception:
+                    # Fall back to weak supervision below
+                    pass
 
-                # Confidence targets: based on reward magnitude (higher reward = higher confidence)
+            # Weak supervision from rewards/actions
+            with torch.no_grad():
+                direction_targets = torch.zeros(batch_size, device=price_direction_pred.device)
+                for i in range(batch_size):
+                    if rewards[i] > 0.01:
+                        if actions[i] == 0:  # BUY
+                            direction_targets[i] = 1.0
+                        elif actions[i] == 1:  # SELL
+                            direction_targets[i] = -1.0
                 confidence_targets = torch.abs(rewards).clamp(0, 1)
 
-            # Calculate losses for each component
+            dir_loss = nn.MSELoss()(direction_pred, direction_targets)
-            direction_loss = nn.MSELoss()(direction_pred, direction_targets)
+            conf_loss = nn.MSELoss()(confidence_pred, confidence_targets)
-            confidence_loss = nn.MSELoss()(confidence_pred, confidence_targets)
+            return dir_loss + 0.3 * conf_loss
-
-            # Combined loss (direction is more important than confidence)
-            total_loss = direction_loss + 0.3 * confidence_loss
-
-            return total_loss
 
         except Exception as e:
             logger.debug(f"Error calculating CNN price direction loss: {e}")
@@ -1365,13 +1383,13 @@ class TradingOrchestrator:
                 if hasattr(callback, "clear_session"):
                     callback.clear_session()
 
-            logger.info("✅ Orchestrator session data cleared")
+            logger.info("Orchestrator session data cleared")
             logger.info("🧠 Model states preserved for continued training")
             logger.info("📊 Prediction history cleared")
             logger.info("💼 Position tracking reset")
 
         except Exception as e:
-            logger.error(f"❌ Error clearing orchestrator session data: {e}")
+            logger.error(f"Error clearing orchestrator session data: {e}")
 
     def sync_model_states_with_dashboard(self):
         """Sync model states with current dashboard values"""
@@ -2931,6 +2949,14 @@ class TradingOrchestrator:
             # Store only the last inference per model (for immediate training)
             self.last_inference[model_name] = inference_record
 
+            # Push into in-memory recent buffer immediately
+            try:
+                if model_name not in self.recent_inferences:
+                    self.recent_inferences[model_name] = deque(maxlen=self.recent_inference_maxlen)
+                self.recent_inferences[model_name].append(inference_record)
+            except Exception as e:
+                logger.debug(f"Unable to append to recent buffer for {model_name}: {e}")
+
             # Also save to database using database manager for future training and analysis
             asyncio.create_task(
                 self._save_to_database_manager_async(model_name, inference_record)
@@ -3022,6 +3048,8 @@ class TradingOrchestrator:
         # Run database operation in thread pool to avoid blocking
         await asyncio.get_event_loop().run_in_executor(None, save_to_db)
 
+        # Note: in-memory recent buffer is appended in _store_inference_data_async
+
     def get_last_inference_status(self) -> Dict[str, Any]:
         """Get status of last inferences for all models"""
         status = {}
@@ -3299,6 +3327,15 @@ class TradingOrchestrator:
                 f"Triggering immediate training for {model_name} with current price: {current_price}"
             )
 
+            # Before evaluating the single record, compute a short-horizon direction vector
+            # from recent inferences and attach to the prediction for vector supervision.
+            try:
+                vector = self._compute_recent_direction_vector(model_name, symbol)
+                if vector is not None:
+                    inference_record.setdefault("prediction", {})["price_direction"] = vector
+            except Exception as e:
+                logger.debug(f"Vector computation failed for {model_name}: {e}")
+
             # Evaluate the previous prediction and train the model immediately
             await self._evaluate_and_train_on_record(inference_record, current_price)
 
@@ -3441,7 +3478,7 @@ class TradingOrchestrator:
                 ):  # Price stayed stable
                     was_correct = True
 
-            outcome_status = "✅ CORRECT" if was_correct else "❌ INCORRECT"
+            outcome_status = "CORRECT" if was_correct else "INCORRECT"
 
             # Get model statistics for enhanced logging
             model_stats = self.get_model_statistics(model_name)
@@ -4027,6 +4064,86 @@ class TradingOrchestrator:
             logger.error(f"Error calculating price vector bonus: {e}")
             return 0.0
 
+    def _compute_recent_direction_vector(self, model_name: str, symbol: str) -> Optional[Dict[str, float]]:
+        """
+        Compute a price direction vector from recent stored inferences by comparing
+        current price with prices at the times of those inferences.
+
+        Returns a dict: {'direction': float in [-1,1], 'confidence': float in [0,1]}
+        """
+        try:
+            from statistics import median
+            recent = self.recent_inferences.get(model_name)
+            if not recent or len(recent) < 2:
+                return None
+
+            # Gather tuples (delta_pct, age_seconds) for last N inferences with stored price
+            deltas = []
+            now_price = self._get_current_price(symbol)
+            if now_price is None or now_price <= 0:
+                return None
+
+            for rec in list(recent):
+                infer_price = rec.get("inference_price")
+                ts = rec.get("timestamp")
+                if isinstance(ts, str):
+                    try:
+                        ts = datetime.fromisoformat(ts)
+                    except Exception:
+                        ts = None
+                if infer_price is None or infer_price <= 0 or ts is None:
+                    continue
+
+                pct = (now_price - infer_price) / infer_price * 100.0
+                age_sec = max(1.0, (datetime.now() - ts).total_seconds())
+                deltas.append((pct, age_sec))
+
+            if not deltas:
+                return None
+
+            # Weight recent observations more: weight = 1 / sqrt(age_seconds)
+            weighted_sum = 0.0
+            weight_total = 0.0
+            magnitudes = []
+            for pct, age in deltas:
+                w = 1.0 / (age ** 0.5)
+                weighted_sum += pct * w
+                weight_total += w
+                magnitudes.append(abs(pct))
+
+            if weight_total <= 0:
+                return None
+
+            avg_pct = weighted_sum / weight_total  # signed percentage
+
+            # Map avg_pct to direction in [-1, 1] using tanh on scaled percent (2% -> ~1)
+            scale = 2.0
+            direction = float(np.tanh(avg_pct / scale))
+
+            # Confidence combines recency, agreement, and magnitude
+            # Use normalized median magnitude capped at 2%
+            med_mag = median(magnitudes) if magnitudes else 0.0
+            mag_norm = max(0.0, min(1.0, med_mag / 2.0))
+
+            # Agreement: fraction of deltas with the same sign as avg_pct
+            if avg_pct > 0:
+                agree = sum(1 for pct, _ in deltas if pct > 0) / len(deltas)
+            elif avg_pct < 0:
+                agree = sum(1 for pct, _ in deltas if pct < 0) / len(deltas)
+            else:
+                agree = 0.5
+
+            # Recency: average weight normalized
+            recency = max(0.0, min(1.0, (weight_total / len(deltas)) * (1.0 / (1.0 ** 0.5))))
+
+            confidence = float(max(0.0, min(1.0, 0.5 * agree + 0.4 * mag_norm + 0.1 * recency)))
+
+            return {"direction": direction, "confidence": confidence}
+
+        except Exception as e:
+            logger.debug(f"Error computing recent direction vector for {model_name}: {e}")
+            return None
+
     async def _train_model_on_outcome(
         self,
         record: Dict,
@@ -4556,8 +4673,15 @@ class TradingOrchestrator:
                         price_direction_pred is not None
                         and price_direction_pred.shape[0] > 0
                     ):
+                        # Supervised vector target from recent inferences if available
+                        vector_target = None
+                        try:
+                            vector_target = self._compute_recent_direction_vector(model_name, symbol)
+                        except Exception:
+                            vector_target = None
+
                         price_direction_loss = self._calculate_cnn_price_direction_loss(
-                            price_direction_pred, reward_tensor, action_tensor
+                            price_direction_pred, reward_tensor, action_tensor, vector_target
                         )
                         if price_direction_loss is not None:
                             total_loss = total_loss + 0.2 * price_direction_loss
@@ -7167,7 +7291,7 @@ class TradingOrchestrator:
                                     if result and result.get("success"):
                                         positions_closed += 1
                                         logger.info(
-                                            f"✅ Closed {side} position for {symbol}: {size} units"
+                                            f"Closed {side} position for {symbol}: {size} units"
                                         )
                                     else:
                                         logger.warning(
@@ -7184,7 +7308,7 @@ class TradingOrchestrator:
 
             if positions_closed > 0:
                 logger.info(
-                    f"✅ Closed {positions_closed} open positions during session clear"
+                    f"Closed {positions_closed} open positions during session clear"
                 )
             else:
                 logger.debug("No open positions to close")