diff --git a/core/retrospective_trainer.py b/core/retrospective_trainer.py
new file mode 100644
index 0000000..f7a0017
--- /dev/null
+++ b/core/retrospective_trainer.py
@@ -0,0 +1,453 @@
+"""
+Retrospective Training System
+
+This module implements a retrospective training system that:
+1. Triggers training when trades close with known P&L outcomes
+2. Uses captured model inputs from trade entry to train models
+3. Optimizes for profit by learning from profitable vs unprofitable patterns
+4. Supports simultaneous inference and training without weight reloading
+5. Implements reinforcement learning with immediate reward feedback
+"""
+
+import logging
+import threading
+import time
+import queue
+from datetime import datetime
+from typing import Dict, List, Optional, Any, Callable
+from dataclasses import dataclass
+import numpy as np
+from collections import deque
+
+logger = logging.getLogger(__name__)
+
+@dataclass
+class TrainingCase:
+    """Represents a completed trade case for retrospective training"""
+    case_id: str
+    symbol: str
+    action: str  # 'BUY' or 'SELL'
+    entry_price: float
+    exit_price: float
+    entry_time: datetime
+    exit_time: datetime
+    pnl: float
+    fees: float
+    confidence: float
+    model_inputs: Dict[str, Any]
+    market_state: Dict[str, Any]
+    outcome_label: int  # 1 for profit, 0 for loss, 2 for breakeven
+    reward_signal: float  # Scaled reward for RL training
+    leverage: float = 1.0
+
+class RetrospectiveTrainer:
+    """Retrospective training system for real-time model optimization"""
+    
+    def __init__(self, orchestrator=None, config: Optional[Dict[str, Any]] = None):
+        """Initialize the retrospective trainer"""
+        self.orchestrator = orchestrator
+        self.config = config or {}
+        
+        # Training configuration
+        self.batch_size = self.config.get('batch_size', 32)
+        self.min_cases_for_training = self.config.get('min_cases_for_training', 5)
+        self.profit_threshold = self.config.get('profit_threshold', 0.0)
+        self.training_frequency = self.config.get('training_frequency_seconds', 120)  # 2 minutes
+        self.max_training_cases = self.config.get('max_training_cases', 1000)
+        
+        # Training state
+        self.training_queue = queue.Queue()
+        self.completed_cases = deque(maxlen=self.max_training_cases)
+        self.training_stats = {
+            'total_cases': 0,
+            'profitable_cases': 0,
+            'loss_cases': 0,
+            'breakeven_cases': 0,
+            'avg_profit': 0.0,
+            'last_training_time': datetime.now(),
+            'training_sessions': 0,
+            'model_updates': 0
+        }
+        
+        # Threading
+        self.training_thread = None
+        self.is_training_active = False
+        self.training_lock = threading.Lock()
+        
+        logger.info("RetrospectiveTrainer initialized")
+        logger.info(f"Configuration: batch_size={self.batch_size}, "
+                   f"min_cases={self.min_cases_for_training}, "
+                   f"training_freq={self.training_frequency}s")
+    
+    def add_completed_trade(self, trade_record: Dict[str, Any], model_inputs: Dict[str, Any]) -> bool:
+        """Add a completed trade for retrospective training"""
+        try:
+            # Create training case from trade record
+            case = self._create_training_case(trade_record, model_inputs)
+            if case is None:
+                return False
+            
+            # Add to completed cases
+            self.completed_cases.append(case)
+            self.training_queue.put(case)
+            
+            # Update statistics
+            self.training_stats['total_cases'] += 1
+            if case.outcome_label == 1:  # Profit
+                self.training_stats['profitable_cases'] += 1
+            elif case.outcome_label == 0:  # Loss
+                self.training_stats['loss_cases'] += 1
+            else:  # Breakeven
+                self.training_stats['breakeven_cases'] += 1
+            
+            # Calculate running average profit
+            total_pnl = sum(c.pnl for c in self.completed_cases)
+            self.training_stats['avg_profit'] = total_pnl / len(self.completed_cases)
+            
+            logger.info(f"RETROSPECTIVE: Added training case {case.case_id} "
+                       f"(P&L: ${case.pnl:.3f}, Label: {case.outcome_label})")
+            
+            # Trigger training if we have enough cases
+            self._maybe_trigger_training()
+            
+            return True
+            
+        except Exception as e:
+            logger.error(f"Error adding completed trade for retrospective training: {e}")
+            return False
+    
+    def _create_training_case(self, trade_record: Dict[str, Any], model_inputs: Dict[str, Any]) -> Optional[TrainingCase]:
+        """Create a training case from trade record and model inputs"""
+        try:
+            # Extract trade information
+            symbol = trade_record.get('symbol', 'UNKNOWN')
+            side = trade_record.get('side', 'UNKNOWN')
+            pnl = trade_record.get('pnl', 0.0)
+            fees = trade_record.get('fees', 0.0)
+            confidence = trade_record.get('confidence', 0.0)
+            
+            # Calculate net P&L after fees
+            net_pnl = pnl - fees
+            
+            # Determine outcome label and reward signal
+            if net_pnl > self.profit_threshold:
+                outcome_label = 1  # Profitable
+                # Scale reward by profit magnitude and confidence
+                reward_signal = min(10.0, net_pnl * confidence * 10)  # Amplify for training
+            elif net_pnl < -self.profit_threshold:
+                outcome_label = 0  # Loss
+                # Negative reward scaled by loss magnitude
+                reward_signal = max(-10.0, net_pnl * confidence * 10)  # Negative reward
+            else:
+                outcome_label = 2  # Breakeven
+                reward_signal = 0.0
+            
+            # Create case ID
+            timestamp_str = datetime.now().strftime('%Y%m%d_%H%M%S')
+            case_id = f"retro_{timestamp_str}_{symbol.replace('/', '')}_{side}_pnl_{abs(net_pnl):.3f}".replace('.', 'p')
+            
+            # Create training case
+            case = TrainingCase(
+                case_id=case_id,
+                symbol=symbol,
+                action=side,
+                entry_price=trade_record.get('entry_price', 0.0),
+                exit_price=trade_record.get('exit_price', 0.0),
+                entry_time=trade_record.get('entry_time', datetime.now()),
+                exit_time=trade_record.get('exit_time', datetime.now()),
+                pnl=net_pnl,
+                fees=fees,
+                confidence=confidence,
+                model_inputs=model_inputs,
+                market_state=model_inputs.get('market_state', {}),
+                outcome_label=outcome_label,
+                reward_signal=reward_signal,
+                leverage=trade_record.get('leverage', 1.0)
+            )
+            
+            return case
+            
+        except Exception as e:
+            logger.error(f"Error creating training case: {e}")
+            return None
+    
+    def _maybe_trigger_training(self):
+        """Check if we should trigger a training session"""
+        try:
+            # Check if we have enough cases
+            if len(self.completed_cases) < self.min_cases_for_training:
+                return
+            
+            # Check if enough time has passed since last training
+            time_since_last = (datetime.now() - self.training_stats['last_training_time']).total_seconds()
+            if time_since_last < self.training_frequency:
+                return
+            
+            # Check if training thread is not already running
+            if self.is_training_active:
+                logger.debug("Training already in progress, skipping trigger")
+                return
+            
+            # Start training in background thread
+            self._start_training_session()
+            
+        except Exception as e:
+            logger.error(f"Error checking training trigger: {e}")
+    
+    def _start_training_session(self):
+        """Start a training session in background thread"""
+        try:
+            if self.training_thread and self.training_thread.is_alive():
+                logger.debug("Training thread already running")
+                return
+            
+            self.training_thread = threading.Thread(
+                target=self._run_training_session,
+                daemon=True,
+                name="RetrospectiveTrainer"
+            )
+            self.training_thread.start()
+            logger.info("RETROSPECTIVE: Started training session")
+            
+        except Exception as e:
+            logger.error(f"Error starting training session: {e}")
+    
+    def _run_training_session(self):
+        """Run a complete training session"""
+        try:
+            with self.training_lock:
+                self.is_training_active = True
+                start_time = time.time()
+                
+                logger.info(f"RETROSPECTIVE: Training with {len(self.completed_cases)} cases")
+                
+                # Train models if orchestrator available
+                training_results = {}
+                if self.orchestrator:
+                    training_results = self._train_models()
+                
+                # Update statistics
+                self.training_stats['last_training_time'] = datetime.now()
+                self.training_stats['training_sessions'] += 1
+                self.training_stats['model_updates'] += len(training_results)
+                
+                elapsed_time = time.time() - start_time
+                logger.info(f"RETROSPECTIVE: Training completed in {elapsed_time:.2f}s - {training_results}")
+                
+        except Exception as e:
+            logger.error(f"Error in retrospective training session: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+        finally:
+            self.is_training_active = False
+    
+    def _train_models(self) -> Dict[str, Any]:
+        """Train available models using retrospective data"""
+        results = {}
+        
+        try:
+            # Prepare training data
+            profitable_cases = [c for c in self.completed_cases if c.outcome_label == 1]
+            loss_cases = [c for c in self.completed_cases if c.outcome_label == 0]
+            
+            if len(profitable_cases) == 0 and len(loss_cases) == 0:
+                return {'error': 'No labeled cases for training'}
+            
+            logger.info(f"RETROSPECTIVE: Training data - Profitable: {len(profitable_cases)}, Loss: {len(loss_cases)}")
+            
+            # Train DQN agent if available
+            if self.orchestrator and hasattr(self.orchestrator, 'rl_agent') and self.orchestrator.rl_agent:
+                try:
+                    dqn_result = self._train_dqn_retrospective()
+                    results['dqn'] = dqn_result
+                    logger.info(f"RETROSPECTIVE: DQN training result: {dqn_result}")
+                except Exception as e:
+                    logger.warning(f"DQN retrospective training failed: {e}")
+                    results['dqn'] = {'error': str(e)}
+            
+            # Train other models
+            if self.orchestrator and hasattr(self.orchestrator, 'extrema_trainer') and self.orchestrator.extrema_trainer:
+                try:
+                    # Update extrema trainer with retrospective feedback
+                    extrema_feedback = self._create_extrema_feedback()
+                    if extrema_feedback:
+                        results['extrema'] = {'feedback_cases': len(extrema_feedback)}
+                        logger.info(f"RETROSPECTIVE: Extrema feedback provided for {len(extrema_feedback)} cases")
+                except Exception as e:
+                    logger.warning(f"Extrema retrospective training failed: {e}")
+            
+            return results
+            
+        except Exception as e:
+            logger.error(f"Error training models retrospectively: {e}")
+            return {'error': str(e)}
+    
+    def _train_dqn_retrospective(self) -> Dict[str, Any]:
+        """Train DQN agent using retrospective experience replay"""
+        try:
+            if not self.orchestrator or not hasattr(self.orchestrator, 'rl_agent') or not self.orchestrator.rl_agent:
+                return {'error': 'DQN agent not available'}
+            
+            dqn_agent = self.orchestrator.rl_agent
+            experiences_added = 0
+            
+            # Add retrospective experiences to DQN replay buffer
+            for case in self.completed_cases:
+                try:
+                    # Extract state from model inputs
+                    state = self._extract_state_vector(case.model_inputs)
+                    if state is None:
+                        continue
+                    
+                    # Action mapping: BUY=0, SELL=1
+                    action = 0 if case.action == 'BUY' else 1
+                    
+                    # Use reward signal as immediate reward
+                    reward = case.reward_signal
+                    
+                    # For retrospective training, next_state is None (terminal)
+                    next_state = np.zeros_like(state)  # Terminal state
+                    done = True
+                    
+                    # Add experience to DQN replay buffer
+                    if hasattr(dqn_agent, 'add_experience'):
+                        dqn_agent.add_experience(state, action, reward, next_state, done)
+                        experiences_added += 1
+                    
+                except Exception as e:
+                    logger.debug(f"Error adding DQN experience: {e}")
+                    continue
+            
+            # Train DQN if we have enough experiences
+            if experiences_added > 0 and hasattr(dqn_agent, 'train'):
+                try:
+                    # Perform multiple training steps on retrospective data
+                    training_steps = min(10, experiences_added // 4)  # Conservative training
+                    for _ in range(training_steps):
+                        loss = dqn_agent.train()
+                        if loss is None:
+                            break
+                    
+                    return {
+                        'experiences_added': experiences_added,
+                        'training_steps': training_steps,
+                        'method': 'retrospective_experience_replay'
+                    }
+                except Exception as e:
+                    logger.warning(f"DQN training step failed: {e}")
+                    return {'experiences_added': experiences_added, 'training_error': str(e)}
+            
+            return {'experiences_added': experiences_added, 'training_steps': 0}
+            
+        except Exception as e:
+            logger.error(f"Error in DQN retrospective training: {e}")
+            return {'error': str(e)}
+    
+    def _extract_state_vector(self, model_inputs: Dict[str, Any]) -> Optional[np.ndarray]:
+        """Extract state vector for DQN training from model inputs"""
+        try:
+            # Try to get pre-built RL state
+            if 'dqn_state' in model_inputs:
+                state = model_inputs['dqn_state']
+                if isinstance(state, dict) and 'state_vector' in state:
+                    return np.array(state['state_vector'])
+            
+            # Build state from market features
+            market_state = model_inputs.get('market_state', {})
+            features = []
+            
+            # Price features
+            for key in ['current_price', 'price_sma_5', 'price_sma_20', 'price_std_20', 'price_rsi']:
+                features.append(market_state.get(key, 0.0))
+            
+            # Volume features
+            for key in ['volume_current', 'volume_sma_20', 'volume_ratio']:
+                features.append(market_state.get(key, 0.0))
+            
+            # Technical indicators
+            indicators = model_inputs.get('technical_indicators', {})
+            for key in ['sma_10', 'sma_20', 'bb_upper', 'bb_lower', 'bb_position', 'macd', 'volatility']:
+                features.append(indicators.get(key, 0.0))
+            
+            if len(features) < 5:  # Minimum required features
+                return None
+            
+            return np.array(features, dtype=np.float32)
+            
+        except Exception as e:
+            logger.debug(f"Error extracting state vector: {e}")
+            return None
+    
+    def _create_extrema_feedback(self) -> List[Dict[str, Any]]:
+        """Create feedback data for extrema trainer"""
+        feedback = []
+        
+        try:
+            for case in self.completed_cases:
+                if case.outcome_label in [0, 1]:  # Only profit/loss cases
+                    feedback_item = {
+                        'symbol': case.symbol,
+                        'action': case.action,
+                        'entry_price': case.entry_price,
+                        'exit_price': case.exit_price,
+                        'was_profitable': case.outcome_label == 1,
+                        'reward_signal': case.reward_signal,
+                        'market_state': case.market_state
+                    }
+                    feedback.append(feedback_item)
+            
+            return feedback
+            
+        except Exception as e:
+            logger.error(f"Error creating extrema feedback: {e}")
+            return []
+    
+    def get_training_stats(self) -> Dict[str, Any]:
+        """Get current training statistics"""
+        stats = self.training_stats.copy()
+        stats['total_cases_in_memory'] = len(self.completed_cases)
+        stats['training_queue_size'] = self.training_queue.qsize()
+        stats['is_training_active'] = self.is_training_active
+        
+        # Calculate profit metrics
+        if len(self.completed_cases) > 0:
+            profitable_count = sum(1 for c in self.completed_cases if c.pnl > 0)
+            stats['profit_rate'] = profitable_count / len(self.completed_cases)
+            stats['total_pnl'] = sum(c.pnl for c in self.completed_cases)
+            stats['avg_reward'] = sum(c.reward_signal for c in self.completed_cases) / len(self.completed_cases)
+        
+        return stats
+    
+    def force_training_session(self) -> bool:
+        """Force a training session regardless of timing constraints"""
+        try:
+            if self.is_training_active:
+                logger.warning("Training already in progress")
+                return False
+            
+            if len(self.completed_cases) < 1:
+                logger.warning("No completed cases available for training")
+                return False
+            
+            logger.info("RETROSPECTIVE: Forcing training session")
+            self._start_training_session()
+            return True
+            
+        except Exception as e:
+            logger.error(f"Error forcing training session: {e}")
+            return False
+    
+    def stop(self):
+        """Stop the retrospective trainer"""
+        try:
+            self.is_training_active = False
+            if self.training_thread and self.training_thread.is_alive():
+                self.training_thread.join(timeout=10)
+            logger.info("RetrospectiveTrainer stopped")
+        except Exception as e:
+            logger.error(f"Error stopping RetrospectiveTrainer: {e}")
+
+
+def create_retrospective_trainer(orchestrator=None, config: Optional[Dict[str, Any]] = None) -> RetrospectiveTrainer:
+    """Factory function to create a RetrospectiveTrainer instance"""
+    return RetrospectiveTrainer(orchestrator=orchestrator, config=config)
diff --git a/web/clean_dashboard.py b/web/clean_dashboard.py
index a10271a..aeeea1b 100644
--- a/web/clean_dashboard.py
+++ b/web/clean_dashboard.py
@@ -1368,7 +1368,7 @@ class CleanTradingDashboard:
                         result = self.trading_executor.execute_trade(symbol, action, size)
                         if result:
                             signal['executed'] = True
-                            logger.info(f"✅ EXECUTED {action} signal: {symbol} @ ${signal.get('price', 0):.2f} "
+                            logger.info(f"EXECUTED {action} signal: {symbol} @ ${signal.get('price', 0):.2f} "
                                       f"(conf: {signal['confidence']:.2f}, size: {size}) - {execution_reason}")
                             
                             # Create trade record for tracking
@@ -1436,7 +1436,7 @@ class CleanTradingDashboard:
                         else:
                             signal['blocked'] = True
                             signal['block_reason'] = "Trading executor failed"
-                            logger.warning(f"❌ BLOCKED {action} signal: executor failed")
+                            logger.warning(f"BLOCKED {action} signal: executor failed")
                     else:
                         signal['blocked'] = True
                         signal['block_reason'] = "No trading executor or invalid action"
@@ -1444,7 +1444,7 @@ class CleanTradingDashboard:
                 except Exception as e:
                     signal['blocked'] = True
                     signal['block_reason'] = str(e)
-                    logger.error(f"❌ EXECUTION ERROR for {signal.get('action', 'UNKNOWN')}: {e}")
+                    logger.error(f"EXECUTION ERROR for {signal.get('action', 'UNKNOWN')}: {e}")
             else:
                 # Determine which threshold was not met
                 if action == 'BUY':