diff --git a/web/clean_dashboard.py b/web/clean_dashboard.py
index 8b63f43..a6d74a2 100644
--- a/web/clean_dashboard.py
+++ b/web/clean_dashboard.py
@@ -175,6 +175,17 @@ class CleanTradingDashboard:
             'ETH/USDT': {},
             'BTC/USDT': {}
         }
+
+        # Confidence calibration tracking
+        self.confidence_calibration: Dict[str, Dict] = {
+            'cob_liquidity_imbalance': {
+                'total_predictions': 0,
+                'correct_predictions': 0,
+                'accuracy_by_confidence': {},  # Track accuracy by confidence ranges
+                'confidence_adjustment': 1.0,  # Multiplier for future confidence levels
+                'last_calibration': None
+            }
+        }
         
         # Initialize timezone
         timezone_name = self.config.get('system', {}).get('timezone', 'Europe/Sofia')
@@ -3821,7 +3832,10 @@ class CleanTradingDashboard:
             # Train ALL models on the signal (if executed)
             if signal['executed']:
                 self._train_all_models_on_signal(signal)
-            
+
+            # Immediate price feedback training (always runs if enabled, regardless of execution)
+            self._immediate_price_feedback_training(signal)
+
             # Log signal processing
             status = "EXECUTED" if signal['executed'] else ("BLOCKED" if signal['blocked'] else "PENDING")
             logger.info(f"[{status}] {signal['action']} signal for {signal['symbol']} "
@@ -3829,7 +3843,249 @@ class CleanTradingDashboard:
             
         except Exception as e:
             logger.error(f"Error processing dashboard signal: {e}")
-    
+
+    # immediate price feedback training 
+    # ToDo: review/revise
+    def _immediate_price_feedback_training(self, signal: Dict):
+        """Immediate training fine-tuning based on current price feedback - rewards profitable predictions"""
+        try:
+            # Check if any model training is enabled - immediate training is part of core training
+            training_enabled = (
+                getattr(self, 'dqn_training_enabled', True) or
+                getattr(self, 'cnn_training_enabled', True) or
+                (hasattr(self.orchestrator, 'cob_rl_agent') and self.orchestrator.cob_rl_agent is not None) or
+                (hasattr(self.orchestrator, 'model_manager') and self.orchestrator.model_manager is not None)
+            )
+
+            if not training_enabled:
+                return
+
+            symbol = signal.get('symbol', 'ETH/USDT')
+            signal_price = signal.get('price', 0)
+            predicted_action = signal.get('action', 'HOLD')
+            signal_confidence = signal.get('confidence', 0.5)
+            signal_timestamp = signal.get('timestamp')
+
+            if signal_price == 0 or predicted_action == 'HOLD':
+                return
+
+            # Get current price for immediate feedback
+            current_price = self._get_current_price(symbol)
+            if current_price == 0:
+                return
+
+            # Calculate immediate price movement since signal generation
+            price_change_pct = (current_price - signal_price) / signal_price
+            price_change_abs = abs(price_change_pct)
+
+            # Determine if prediction was correct
+            predicted_direction = 1 if predicted_action == 'BUY' else -1
+            actual_direction = 1 if price_change_pct > 0 else -1
+            prediction_correct = predicted_direction == actual_direction
+
+            # Calculate reward based on prediction accuracy and price movement
+            base_reward = price_change_abs * 1000  # Scale by price movement
+
+            if prediction_correct:
+                # Reward correct predictions
+                reward = base_reward
+                confidence_bonus = signal_confidence * base_reward * 0.5  # Bonus for high confidence correct predictions
+                reward += confidence_bonus
+            else:
+                # Punish incorrect predictions
+                reward = -base_reward
+                confidence_penalty = (1 - signal_confidence) * base_reward * 0.3  # Less penalty for low confidence wrong predictions
+                reward -= confidence_penalty
+
+            # Scale reward by time elapsed (more recent = higher weight)
+            time_elapsed = (datetime.now() - signal_timestamp).total_seconds() if signal_timestamp else 0
+            time_weight = max(0.1, 1.0 - (time_elapsed / 300))  # Decay over 5 minutes
+            final_reward = reward * time_weight
+
+            # Create immediate training data
+            training_data = {
+                'symbol': symbol,
+                'signal_price': signal_price,
+                'current_price': current_price,
+                'price_change_pct': price_change_pct,
+                'predicted_action': predicted_action,
+                'actual_direction': 'UP' if actual_direction > 0 else 'DOWN',
+                'prediction_correct': prediction_correct,
+                'signal_confidence': signal_confidence,
+                'reward': final_reward,
+                'time_elapsed': time_elapsed,
+                'timestamp': datetime.now()
+            }
+
+            # Train models immediately with price feedback
+            self._train_models_on_immediate_feedback(signal, training_data, final_reward)
+
+            # Update confidence calibration
+            self._update_confidence_calibration(signal, prediction_correct, price_change_abs)
+
+            logger.debug(f"💰 IMMEDIATE TRAINING: {symbol} {predicted_action} signal - "
+                        f"Price: {signal_price:.2f} → {current_price:.2f} ({price_change_pct:+.2%}) - "
+                        f"{'✅' if prediction_correct else '❌'} Correct - Reward: {final_reward:.2f}")
+
+        except Exception as e:
+            logger.debug(f"Error in immediate price feedback training: {e}")
+
+    def _train_models_on_immediate_feedback(self, signal: Dict, training_data: Dict, reward: float):
+        """Train models immediately on price feedback"""
+        try:
+            symbol = signal.get('symbol', 'ETH/USDT')
+            action = 0 if signal.get('action') == 'BUY' else 1
+
+            # Train COB RL model immediately if COB RL training is enabled
+            if (self.orchestrator and hasattr(self.orchestrator, 'cob_rl_agent') and
+                self.orchestrator.cob_rl_agent and hasattr(self.orchestrator, 'model_manager')):
+                try:
+                    # Get COB features for immediate training
+                    cob_features = self._get_cob_features_for_training(symbol, signal.get('price', 0))
+                    if cob_features:
+                        # Store immediate experience
+                        if hasattr(self.orchestrator.cob_rl_agent, 'remember'):
+                            self.orchestrator.cob_rl_agent.remember(
+                                cob_features, action, reward, cob_features, done=False  # Not done for immediate feedback
+                            )
+
+                        # Immediate training if enough samples
+                        if hasattr(self.orchestrator.cob_rl_agent, 'memory') and len(self.orchestrator.cob_rl_agent.memory) > 16:
+                            if hasattr(self.orchestrator.cob_rl_agent, 'replay'):
+                                loss = self.orchestrator.cob_rl_agent.replay(batch_size=8)  # Smaller batch for immediate training
+                                if loss is not None:
+                                    logger.debug(f"COB RL immediate training - loss: {loss:.4f}, reward: {reward:.2f}")
+
+                except Exception as e:
+                    logger.debug(f"Error training COB RL on immediate feedback: {e}")
+
+            # Train DQN model immediately if DQN training is enabled
+            if (self.orchestrator and hasattr(self.orchestrator, 'rl_agent') and
+                self.orchestrator.rl_agent and getattr(self, 'dqn_training_enabled', True)):
+                try:
+                    # Create immediate DQN experience
+                    state = self._get_rl_state_for_training(symbol, signal.get('price', 0))
+                    if state:
+                        if hasattr(self.orchestrator.rl_agent, 'remember'):
+                            self.orchestrator.rl_agent.remember(state, action, reward, state, done=False)
+
+                        # Immediate training
+                        if hasattr(self.orchestrator.rl_agent, 'replay') and hasattr(self.orchestrator.rl_agent, 'memory'):
+                            if len(self.orchestrator.rl_agent.memory) > 16:
+                                loss = self.orchestrator.rl_agent.replay(batch_size=8)
+                                if loss is not None:
+                                    logger.debug(f"DQN immediate training - loss: {loss:.4f}, reward: {reward:.2f}")
+
+                except Exception as e:
+                    logger.debug(f"Error training DQN on immediate feedback: {e}")
+
+            # Train CNN model immediately if CNN training is enabled
+            if (self.orchestrator and hasattr(self.orchestrator, 'cnn_model') and
+                self.orchestrator.cnn_model and getattr(self, 'cnn_training_enabled', True)):
+                try:
+                    # Create immediate CNN training data
+                    cnn_features = self._create_cnn_cob_features(symbol, {
+                        'current_snapshot': {'price': signal.get('price', 0), 'imbalance': 0},
+                        'history': self.cob_data_history.get(symbol, [])[-10:],
+                        'timestamp': datetime.now()
+                    })
+
+                    if cnn_features:
+                        # For CNN, we can update internal training data or use model-specific training
+                        if hasattr(self.orchestrator.cnn_model, 'update_training_data'):
+                            self.orchestrator.cnn_model.update_training_data(cnn_features, action, reward)
+
+                        logger.debug(f"CNN immediate training data updated - action: {action}, reward: {reward:.2f}")
+
+                except Exception as e:
+                    logger.debug(f"Error training CNN on immediate feedback: {e}")
+
+        except Exception as e:
+            logger.debug(f"Error in immediate model training: {e}")
+
+    def _update_confidence_calibration(self, signal: Dict, prediction_correct: bool, price_change_abs: float):
+        """Update confidence calibration based on prediction accuracy"""
+        try:
+            signal_type = signal.get('type', 'unknown')
+            signal_confidence = signal.get('confidence', 0.5)
+
+            if signal_type not in self.confidence_calibration:
+                return
+
+            calibration = self.confidence_calibration[signal_type]
+
+            # Track total predictions and accuracy
+            calibration['total_predictions'] += 1
+            if prediction_correct:
+                calibration['correct_predictions'] += 1
+
+            # Track accuracy by confidence ranges
+            confidence_range = f"{int(signal_confidence * 10) / 10:.1f}"  # 0.0-1.0 in 0.1 increments
+
+            if confidence_range not in calibration['accuracy_by_confidence']:
+                calibration['accuracy_by_confidence'][confidence_range] = {
+                    'total': 0,
+                    'correct': 0,
+                    'avg_price_change': 0.0
+                }
+
+            range_stats = calibration['accuracy_by_confidence'][confidence_range]
+            range_stats['total'] += 1
+            if prediction_correct:
+                range_stats['correct'] += 1
+            range_stats['avg_price_change'] = (
+                (range_stats['avg_price_change'] * (range_stats['total'] - 1)) + price_change_abs
+            ) / range_stats['total']
+
+            # Update confidence adjustment every 50 predictions
+            if calibration['total_predictions'] % 50 == 0:
+                self._recalibrate_confidence_levels(signal_type)
+
+        except Exception as e:
+            logger.debug(f"Error updating confidence calibration: {e}")
+
+    def _recalibrate_confidence_levels(self, signal_type: str):
+        """Recalibrate confidence levels based on historical performance"""
+        try:
+            calibration = self.confidence_calibration[signal_type]
+            accuracy_by_confidence = calibration['accuracy_by_confidence']
+
+            # Calculate expected vs actual accuracy for each confidence range
+            total_adjustment = 0.0
+            valid_ranges = 0
+
+            for conf_range, stats in accuracy_by_confidence.items():
+                if stats['total'] >= 5:  # Need at least 5 predictions for reliable calibration
+                    expected_accuracy = float(conf_range)  # Confidence should match accuracy
+                    actual_accuracy = stats['correct'] / stats['total']
+                    adjustment = actual_accuracy / expected_accuracy if expected_accuracy > 0 else 1.0
+                    total_adjustment += adjustment
+                    valid_ranges += 1
+
+            if valid_ranges > 0:
+                calibration['confidence_adjustment'] = total_adjustment / valid_ranges
+                calibration['last_calibration'] = datetime.now()
+
+                logger.info(f"🔧 CONFIDENCE CALIBRATION: {signal_type} adjustment = {calibration['confidence_adjustment']:.3f} "
+                           f"(based on {valid_ranges} confidence ranges)")
+
+        except Exception as e:
+            logger.debug(f"Error recalibrating confidence levels: {e}")
+
+    def _get_calibrated_confidence(self, signal_type: str, raw_confidence: float) -> float:
+        """Get calibrated confidence level based on historical performance"""
+        try:
+            if signal_type in self.confidence_calibration:
+                adjustment = self.confidence_calibration[signal_type]['confidence_adjustment']
+                calibrated = raw_confidence * adjustment
+                return max(0.0, min(1.0, calibrated))  # Clamp to [0,1]
+            return raw_confidence
+        except Exception as e:
+            logger.debug(f"Error getting calibrated confidence: {e}")
+            return raw_confidence
+
+    # This function is used to train all models on a signal
+    # ToDo: review this function and make sure it is correct
     def _train_all_models_on_signal(self, signal: Dict):
         """Train ALL models on executed trade signal - Comprehensive training system"""
         try:
@@ -5311,7 +5567,10 @@ class CleanTradingDashboard:
             # Generate signal if imbalance exceeds threshold
             if abs_imbalance > threshold:
                 # Calculate more realistic confidence (never exactly 1.0)
-                final_confidence = min(0.95, base_confidence + confidence_boost)
+                raw_confidence = min(0.95, base_confidence + confidence_boost)
+
+                # Apply confidence calibration based on historical performance
+                final_confidence = self._get_calibrated_confidence('cob_liquidity_imbalance', raw_confidence)
                 
                 signal = {
                     'timestamp': datetime.now(),
@@ -5354,6 +5613,7 @@ class CleanTradingDashboard:
                 'history': self.cob_data_history[symbol][-15:],  # Last 15 seconds
                 'bucketed_data': self.cob_bucketed_data[symbol],
                 'cumulative_imbalance': cumulative_imbalance,
+                'cob_imbalance_ma': self.cob_imbalance_ma.get(symbol, {}),  # ✅ ADD MOVING AVERAGES
                 'timestamp': cob_snapshot['timestamp'],
                 'stats': cob_snapshot.get('stats', {}),
                 'bids': cob_snapshot.get('bids', []),
diff --git a/web/component_manager.py b/web/component_manager.py
index 0a1e4ae..54f91af 100644
--- a/web/component_manager.py
+++ b/web/component_manager.py
@@ -374,18 +374,16 @@ class DashboardComponentManager:
             html.Div(imbalance_stats_display),
 
             # COB Imbalance Moving Averages
-            ma_display = []
-            if imbalance_ma_data:
-                ma_display.append(html.H6("Imbalance MAs", className="mt-3 mb-2 small text-muted text-uppercase"))
-                for timeframe, ma_value in imbalance_ma_data.items():
-                    ma_color = "text-success" if ma_value > 0 else "text-danger"
-                    ma_text = f"MA {timeframe}: {ma_value:.3f}"
-                    ma_display.append(html.Div([
+            html.Div([
+                html.H6("Imbalance MAs", className="mt-3 mb-2 small text-muted text-uppercase"),
+                *[
+                    html.Div([
                         html.Strong(f"{timeframe}: ", className="small"),
-                        html.Span(ma_text, className=f"small {ma_color}")
-                    ], className="mb-1"))
-
-            html.Div(ma_display),
+                        html.Span(f"MA {timeframe}: {ma_value:.3f}", className=f"small {'text-success' if ma_value > 0 else 'text-danger'}")
+                    ], className="mb-1")
+                    for timeframe, ma_value in (imbalance_ma_data or {}).items()
+                ]
+            ]) if imbalance_ma_data else html.Div(),
 
             html.Hr(className="my-2"),