display predictions
This commit is contained in:
Dobromir Popov
@ -71,6 +71,34 @@ class EnhancedRealtimeTrainingSystem:
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'validation': 0.0
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}
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# Model prediction tracking - NEW for dashboard visualization
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self.recent_dqn_predictions = {
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'ETH/USDT': deque(maxlen=100),
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'BTC/USDT': deque(maxlen=100)
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}
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self.recent_cnn_predictions = {
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'ETH/USDT': deque(maxlen=50),
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'BTC/USDT': deque(maxlen=50)
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}
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self.prediction_accuracy_history = {
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'ETH/USDT': deque(maxlen=200),
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'BTC/USDT': deque(maxlen=200)
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}
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# FIXED: Forward-looking prediction system
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self.pending_predictions = {
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'ETH/USDT': deque(maxlen=100), # Predictions waiting for validation
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'BTC/USDT': deque(maxlen=100)
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}
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self.last_prediction_time = {
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'ETH/USDT': 0,
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'BTC/USDT': 0
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}
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self.prediction_intervals = {
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'dqn': 30, # Make DQN prediction every 30 seconds
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'cnn': 60 # Make CNN prediction every 60 seconds
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}
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# Real-time data streams
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self.real_time_data = {
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'ticks': deque(maxlen=1000),
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@ -146,24 +174,27 @@ class EnhancedRealtimeTrainingSystem:
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current_time = time.time()
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self.training_iteration += 1
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# 1. DQN Training (every 5 seconds with enough data)
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# 1. FORWARD-LOOKING PREDICTIONS - Generate real predictions for future validation
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self.generate_forward_looking_predictions()
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# 2. DQN Training (every 5 seconds with enough data)
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if (current_time - self.last_training_times['dqn'] > self.training_config['dqn_training_interval']
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and len(self.experience_buffer) >= self.training_config['min_training_samples']):
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self._perform_enhanced_dqn_training()
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self.last_training_times['dqn'] = current_time
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# 2. CNN Training (every 10 seconds)
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# 3. CNN Training (every 10 seconds)
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if (current_time - self.last_training_times['cnn'] > self.training_config['cnn_training_interval']
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and len(self.real_time_data['ohlcv_1m']) >= 20):
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self._perform_enhanced_cnn_training()
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self.last_training_times['cnn'] = current_time
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# 3. Validation (every minute)
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# 4. Validation (every minute)
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if current_time - self.last_training_times['validation'] > self.training_config['validation_interval']:
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self._perform_validation()
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self.last_training_times['validation'] = current_time
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# 4. Adaptive learning rate adjustment
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# 5. Adaptive learning rate adjustment
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if self.training_iteration % 100 == 0:
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self._adapt_learning_parameters()
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@ -911,6 +942,11 @@ class EnhancedRealtimeTrainingSystem:
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'dqn_loss_count': len(self.performance_history['dqn_losses']),
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'cnn_loss_count': len(self.performance_history['cnn_losses']),
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'validation_count': len(self.performance_history['validation_scores'])
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},
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'prediction_stats': {
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'dqn_predictions': {symbol: len(predictions) for symbol, predictions in self.recent_dqn_predictions.items()},
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'cnn_predictions': {symbol: len(predictions) for symbol, predictions in self.recent_cnn_predictions.items()},
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'accuracy_history': {symbol: len(history) for symbol, history in self.prediction_accuracy_history.items()}
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}
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}
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@ -927,4 +963,492 @@ class EnhancedRealtimeTrainingSystem:
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except Exception as e:
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logger.error(f"Error getting training statistics: {e}")
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return {'error': str(e)}
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return {'error': str(e)}
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def capture_dqn_prediction(self, symbol: str, state: np.ndarray, q_values: List[float], action: int, confidence: float, price: float):
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"""Capture DQN prediction for dashboard visualization"""
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try:
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prediction = {
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'timestamp': datetime.now(),
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'symbol': symbol,
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'state': state.tolist() if hasattr(state, 'tolist') else state,
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'q_values': q_values,
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'action': action, # 0=BUY, 1=SELL, 2=HOLD
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'confidence': confidence,
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'price': price
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}
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if symbol in self.recent_dqn_predictions:
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self.recent_dqn_predictions[symbol].append(prediction)
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logger.debug(f"DQN prediction captured: {symbol} action={action} confidence={confidence:.2f}")
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except Exception as e:
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logger.debug(f"Error capturing DQN prediction: {e}")
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def capture_cnn_prediction(self, symbol: str, current_price: float, predicted_price: float, direction: int, confidence: float, features: Optional[np.ndarray] = None):
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"""Capture CNN prediction for dashboard visualization"""
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try:
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prediction = {
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'timestamp': datetime.now(),
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'symbol': symbol,
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'current_price': current_price,
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'predicted_price': predicted_price,
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'direction': direction, # 0=DOWN, 1=SAME, 2=UP
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'confidence': confidence,
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'features': features.tolist() if features is not None and hasattr(features, 'tolist') else None
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}
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if symbol in self.recent_cnn_predictions:
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self.recent_cnn_predictions[symbol].append(prediction)
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logger.debug(f"CNN prediction captured: {symbol} direction={direction} confidence={confidence:.2f}")
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except Exception as e:
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logger.debug(f"Error capturing CNN prediction: {e}")
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def validate_prediction_accuracy(self, symbol: str, prediction_type: str, predicted_action: int, actual_price_change: float, confidence: float):
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"""Validate prediction accuracy and store results"""
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try:
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# Determine if prediction was correct
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was_correct = False
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if prediction_type == 'DQN':
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# For DQN: BUY (0) should be followed by price increase, SELL (1) by decrease
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if predicted_action == 0 and actual_price_change > 0.001: # BUY + price up
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was_correct = True
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elif predicted_action == 1 and actual_price_change < -0.001: # SELL + price down
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was_correct = True
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elif predicted_action == 2 and abs(actual_price_change) <= 0.001: # HOLD + no change
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was_correct = True
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elif prediction_type == 'CNN':
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# For CNN: direction prediction accuracy
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if predicted_action == 2 and actual_price_change > 0.001: # UP + price up
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was_correct = True
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elif predicted_action == 0 and actual_price_change < -0.001: # DOWN + price down
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was_correct = True
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elif predicted_action == 1 and abs(actual_price_change) <= 0.001: # SAME + no change
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was_correct = True
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# Calculate accuracy score based on confidence and correctness
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accuracy_score = confidence if was_correct else (1.0 - confidence)
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accuracy_data = {
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'timestamp': datetime.now(),
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'symbol': symbol,
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'prediction_type': prediction_type,
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'correct': was_correct,
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'accuracy_score': accuracy_score,
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'confidence': confidence,
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'actual_price_change': actual_price_change,
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'predicted_action': predicted_action
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}
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if symbol in self.prediction_accuracy_history:
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self.prediction_accuracy_history[symbol].append(accuracy_data)
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logger.debug(f"Prediction accuracy validated: {symbol} {prediction_type} correct={was_correct} score={accuracy_score:.2f}")
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except Exception as e:
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logger.debug(f"Error validating prediction accuracy: {e}")
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def get_prediction_summary(self, symbol: str) -> Dict[str, Any]:
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"""Get prediction summary for a symbol"""
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try:
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summary = {
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'symbol': symbol,
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'dqn_predictions': len(self.recent_dqn_predictions.get(symbol, [])),
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'cnn_predictions': len(self.recent_cnn_predictions.get(symbol, [])),
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'accuracy_history': len(self.prediction_accuracy_history.get(symbol, [])),
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'pending_predictions': len(self.pending_predictions.get(symbol, []))
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}
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# Calculate accuracy statistics
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if symbol in self.prediction_accuracy_history and self.prediction_accuracy_history[symbol]:
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accuracy_data = list(self.prediction_accuracy_history[symbol])
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total_predictions = len(accuracy_data)
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correct_predictions = sum(1 for acc in accuracy_data if acc['correct'])
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summary['total_predictions'] = total_predictions
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summary['correct_predictions'] = correct_predictions
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summary['accuracy_rate'] = correct_predictions / total_predictions if total_predictions > 0 else 0.0
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# Calculate accuracy by prediction type
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dqn_accuracy_data = [acc for acc in accuracy_data if acc['prediction_type'] == 'DQN']
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cnn_accuracy_data = [acc for acc in accuracy_data if acc['prediction_type'] == 'CNN']
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if dqn_accuracy_data:
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dqn_correct = sum(1 for acc in dqn_accuracy_data if acc['correct'])
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summary['dqn_accuracy_rate'] = dqn_correct / len(dqn_accuracy_data)
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else:
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summary['dqn_accuracy_rate'] = 0.0
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if cnn_accuracy_data:
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cnn_correct = sum(1 for acc in cnn_accuracy_data if acc['correct'])
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summary['cnn_accuracy_rate'] = cnn_correct / len(cnn_accuracy_data)
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else:
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summary['cnn_accuracy_rate'] = 0.0
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return summary
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except Exception as e:
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logger.error(f"Error getting prediction summary: {e}")
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return {'error': str(e)}
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def generate_forward_looking_predictions(self):
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"""Generate forward-looking predictions based on current market data"""
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try:
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current_time = time.time()
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for symbol in ['ETH/USDT', 'BTC/USDT']:
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# Check if it's time to make new predictions
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time_since_last = current_time - self.last_prediction_time.get(symbol, 0)
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# Generate DQN prediction every 30 seconds
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if time_since_last >= self.prediction_intervals['dqn']:
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self._generate_forward_dqn_prediction(symbol, current_time)
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# Generate CNN prediction every 60 seconds
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if time_since_last >= self.prediction_intervals['cnn']:
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self._generate_forward_cnn_prediction(symbol, current_time)
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# Validate pending predictions
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self._validate_pending_predictions(symbol, current_time)
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except Exception as e:
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logger.error(f"Error generating forward-looking predictions: {e}")
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def _generate_forward_dqn_prediction(self, symbol: str, current_time: float):
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"""Generate a DQN prediction for future price movement"""
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try:
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# Get current market state (only historical data)
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current_state = self._build_comprehensive_state()
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current_price = self._get_current_price_from_data(symbol)
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if current_price is None:
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return
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# Use DQN model to predict action (if available)
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if (self.orchestrator and hasattr(self.orchestrator, 'rl_agent')
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and self.orchestrator.rl_agent):
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# Get Q-values from model
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q_values = self.orchestrator.rl_agent.act(current_state, return_q_values=True)
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if isinstance(q_values, tuple):
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action, q_vals = q_values
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q_values = q_vals.tolist() if hasattr(q_vals, 'tolist') else [0, 0, 0]
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else:
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action = q_values
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q_values = [0.33, 0.33, 0.34] # Default uniform distribution
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confidence = max(q_values) / sum(q_values) if sum(q_values) > 0 else 0.33
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else:
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# Fallback to technical analysis-based prediction
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action, q_values, confidence = self._technical_analysis_prediction(symbol)
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# Create forward-looking prediction
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prediction_time = datetime.now()
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target_time = prediction_time + timedelta(minutes=5) # Predict 5 minutes ahead
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prediction = {
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'id': f"dqn_{symbol}_{int(current_time)}",
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'type': 'DQN',
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'symbol': symbol,
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'prediction_time': prediction_time,
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'target_time': target_time,
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'current_price': current_price,
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'predicted_action': action,
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'q_values': q_values,
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'confidence': confidence,
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'state': current_state.tolist() if hasattr(current_state, 'tolist') else current_state,
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'validated': False
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}
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# Add to pending predictions for future validation
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if symbol in self.pending_predictions:
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self.pending_predictions[symbol].append(prediction)
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# Add to recent predictions for display (only if confident enough)
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if confidence > 0.4:
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display_prediction = {
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'timestamp': prediction_time,
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'price': current_price,
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'action': action,
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'confidence': confidence,
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'q_values': q_values
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}
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if symbol in self.recent_dqn_predictions:
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self.recent_dqn_predictions[symbol].append(display_prediction)
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self.last_prediction_time[symbol] = current_time
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logger.info(f"Forward DQN prediction: {symbol} action={['BUY','SELL','HOLD'][action]} confidence={confidence:.2f} target={target_time.strftime('%H:%M:%S')}")
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except Exception as e:
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logger.error(f"Error generating forward DQN prediction: {e}")
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def _generate_forward_cnn_prediction(self, symbol: str, current_time: float):
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"""Generate a CNN prediction for future price direction"""
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try:
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# Get current price and historical sequence (only past data)
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current_price = self._get_current_price_from_data(symbol)
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price_sequence = self._get_historical_price_sequence(symbol, periods=15)
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if current_price is None or len(price_sequence) < 15:
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return
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# Use CNN model to predict direction (if available)
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if (self.orchestrator and hasattr(self.orchestrator, 'cnn_model')
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and self.orchestrator.cnn_model):
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# Prepare features for CNN
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features = self._prepare_cnn_features(price_sequence)
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try:
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# Get prediction from CNN model
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prediction_output = self.orchestrator.cnn_model.predict(features)
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if hasattr(prediction_output, 'tolist'):
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pred_probs = prediction_output.tolist()
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else:
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pred_probs = [0.33, 0.33, 0.34] # Default
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direction = int(np.argmax(pred_probs)) # 0=DOWN, 1=SAME, 2=UP
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confidence = max(pred_probs)
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except Exception as e:
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logger.debug(f"CNN model prediction failed: {e}")
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direction, confidence = self._technical_direction_prediction(symbol)
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else:
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# Fallback to technical analysis
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direction, confidence = self._technical_direction_prediction(symbol)
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# Calculate predicted price based on direction
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price_change_percent = self._estimate_price_change(direction, confidence)
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predicted_price = current_price * (1 + price_change_percent)
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# Create forward-looking prediction
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prediction_time = datetime.now()
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target_time = prediction_time + timedelta(minutes=10) # Predict 10 minutes ahead
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prediction = {
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'id': f"cnn_{symbol}_{int(current_time)}",
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'type': 'CNN',
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'symbol': symbol,
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'prediction_time': prediction_time,
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'target_time': target_time,
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'current_price': current_price,
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'predicted_price': predicted_price,
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'direction': direction,
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'confidence': confidence,
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'features': features.tolist() if hasattr(features, 'tolist') else None,
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'validated': False
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}
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# Add to pending predictions for future validation
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if symbol in self.pending_predictions:
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self.pending_predictions[symbol].append(prediction)
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# Add to recent predictions for display (only if confident enough)
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if confidence > 0.5:
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display_prediction = {
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'timestamp': prediction_time,
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'current_price': current_price,
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'predicted_price': predicted_price,
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'direction': direction,
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'confidence': confidence
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}
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if symbol in self.recent_cnn_predictions:
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self.recent_cnn_predictions[symbol].append(display_prediction)
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logger.info(f"Forward CNN prediction: {symbol} direction={['DOWN','SAME','UP'][direction]} confidence={confidence:.2f} target={target_time.strftime('%H:%M:%S')}")
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except Exception as e:
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logger.error(f"Error generating forward CNN prediction: {e}")
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def _validate_pending_predictions(self, symbol: str, current_time: float):
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"""Validate pending predictions when their target time arrives"""
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try:
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if symbol not in self.pending_predictions:
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return
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current_datetime = datetime.now()
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validated_predictions = []
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# Check each pending prediction
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for prediction in list(self.pending_predictions[symbol]):
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target_time = prediction['target_time']
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# If target time has passed, validate the prediction
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if current_datetime >= target_time:
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actual_price = self._get_current_price_from_data(symbol)
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if actual_price is not None:
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# Calculate actual price change
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predicted_price = prediction.get('predicted_price', prediction['current_price'])
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actual_change = (actual_price - prediction['current_price']) / prediction['current_price']
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predicted_change = (predicted_price - prediction['current_price']) / prediction['current_price']
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# Validate based on prediction type
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if prediction['type'] == 'DQN':
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was_correct = self._validate_dqn_prediction(prediction, actual_change)
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else: # CNN
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was_correct = self._validate_cnn_prediction(prediction, actual_change)
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# Store accuracy result
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accuracy_data = {
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'timestamp': current_datetime,
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'symbol': symbol,
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'prediction_type': prediction['type'],
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'correct': was_correct,
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'accuracy_score': prediction['confidence'] if was_correct else (1.0 - prediction['confidence']),
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'confidence': prediction['confidence'],
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'actual_price_change': actual_change,
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'predicted_action': prediction.get('predicted_action', prediction.get('direction', 0)),
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'actual_price': actual_price
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}
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if symbol in self.prediction_accuracy_history:
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self.prediction_accuracy_history[symbol].append(accuracy_data)
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validated_predictions.append(prediction['id'])
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|
||||
logger.info(f"Validated {prediction['type']} prediction: {symbol} correct={was_correct} confidence={prediction['confidence']:.2f}")
|
||||
|
||||
# Remove validated predictions from pending list
|
||||
if validated_predictions:
|
||||
self.pending_predictions[symbol] = deque([
|
||||
p for p in self.pending_predictions[symbol]
|
||||
if p['id'] not in validated_predictions
|
||||
], maxlen=100)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error validating pending predictions: {e}")
|
||||
|
||||
def _validate_dqn_prediction(self, prediction: Dict, actual_change: float) -> bool:
|
||||
"""Validate DQN action prediction"""
|
||||
predicted_action = prediction['predicted_action']
|
||||
threshold = 0.005 # 0.5% threshold for significant movement
|
||||
|
||||
if predicted_action == 0: # BUY prediction
|
||||
return actual_change > threshold
|
||||
elif predicted_action == 1: # SELL prediction
|
||||
return actual_change < -threshold
|
||||
else: # HOLD prediction
|
||||
return abs(actual_change) <= threshold
|
||||
|
||||
def _validate_cnn_prediction(self, prediction: Dict, actual_change: float) -> bool:
|
||||
"""Validate CNN direction prediction"""
|
||||
predicted_direction = prediction['direction']
|
||||
threshold = 0.002 # 0.2% threshold for direction
|
||||
|
||||
if predicted_direction == 2: # UP prediction
|
||||
return actual_change > threshold
|
||||
elif predicted_direction == 0: # DOWN prediction
|
||||
return actual_change < -threshold
|
||||
else: # SAME prediction
|
||||
return abs(actual_change) <= threshold
|
||||
|
||||
def _get_current_price_from_data(self, symbol: str) -> Optional[float]:
|
||||
"""Get current price from real-time data streams"""
|
||||
try:
|
||||
if len(self.real_time_data['ohlcv_1m']) > 0:
|
||||
return self.real_time_data['ohlcv_1m'][-1]['close']
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.debug(f"Error getting current price: {e}")
|
||||
return None
|
||||
|
||||
def _get_historical_price_sequence(self, symbol: str, periods: int = 15) -> List[float]:
|
||||
"""Get historical price sequence for CNN features"""
|
||||
try:
|
||||
if len(self.real_time_data['ohlcv_1m']) >= periods:
|
||||
return [bar['close'] for bar in list(self.real_time_data['ohlcv_1m'])[-periods:]]
|
||||
return []
|
||||
except Exception as e:
|
||||
logger.debug(f"Error getting price sequence: {e}")
|
||||
return []
|
||||
|
||||
def _technical_analysis_prediction(self, symbol: str) -> Tuple[int, List[float], float]:
|
||||
"""Fallback technical analysis prediction for DQN"""
|
||||
try:
|
||||
# Simple momentum-based prediction
|
||||
if len(self.real_time_data['ohlcv_1m']) >= 5:
|
||||
recent_prices = [bar['close'] for bar in list(self.real_time_data['ohlcv_1m'])[-5:]]
|
||||
momentum = (recent_prices[-1] - recent_prices[0]) / recent_prices[0]
|
||||
|
||||
if momentum > 0.01: # 1% upward momentum
|
||||
return 0, [0.6, 0.2, 0.2], 0.6 # BUY
|
||||
elif momentum < -0.01: # 1% downward momentum
|
||||
return 1, [0.2, 0.6, 0.2], 0.6 # SELL
|
||||
else:
|
||||
return 2, [0.2, 0.2, 0.6], 0.6 # HOLD
|
||||
|
||||
return 2, [0.33, 0.33, 0.34], 0.33 # Default HOLD
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error in technical analysis prediction: {e}")
|
||||
return 2, [0.33, 0.33, 0.34], 0.33
|
||||
|
||||
def _technical_direction_prediction(self, symbol: str) -> Tuple[int, float]:
|
||||
"""Fallback technical analysis for CNN direction"""
|
||||
try:
|
||||
if len(self.real_time_data['ohlcv_1m']) >= 3:
|
||||
recent_prices = [bar['close'] for bar in list(self.real_time_data['ohlcv_1m'])[-3:]]
|
||||
short_momentum = (recent_prices[-1] - recent_prices[-2]) / recent_prices[-2]
|
||||
|
||||
if short_momentum > 0.005: # 0.5% short-term up
|
||||
return 2, 0.65 # UP
|
||||
elif short_momentum < -0.005: # 0.5% short-term down
|
||||
return 0, 0.65 # DOWN
|
||||
else:
|
||||
return 1, 0.55 # SAME
|
||||
|
||||
return 1, 0.5 # Default SAME
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error in technical direction prediction: {e}")
|
||||
return 1, 0.5
|
||||
|
||||
def _prepare_cnn_features(self, price_sequence: List[float]) -> np.ndarray:
|
||||
"""Prepare features for CNN model"""
|
||||
try:
|
||||
# Normalize prices relative to first price
|
||||
if len(price_sequence) >= 15:
|
||||
base_price = price_sequence[0]
|
||||
normalized = [(p - base_price) / base_price for p in price_sequence]
|
||||
|
||||
# Create feature matrix (15 x 20, flattened)
|
||||
features = np.zeros((15, 20))
|
||||
for i, norm_price in enumerate(normalized):
|
||||
features[i, 0] = norm_price # Normalized price
|
||||
if i > 0:
|
||||
features[i, 1] = normalized[i] - normalized[i-1] # Price change
|
||||
|
||||
return features.flatten()
|
||||
|
||||
return np.zeros(300) # Default feature vector
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error preparing CNN features: {e}")
|
||||
return np.zeros(300)
|
||||
|
||||
def _estimate_price_change(self, direction: int, confidence: float) -> float:
|
||||
"""Estimate price change percentage based on direction and confidence"""
|
||||
try:
|
||||
# Base change scaled by confidence
|
||||
base_change = 0.01 * confidence # Up to 1% change
|
||||
|
||||
if direction == 2: # UP
|
||||
return base_change
|
||||
elif direction == 0: # DOWN
|
||||
return -base_change
|
||||
else: # SAME
|
||||
return 0.0
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error estimating price change: {e}")
|
||||
return 0.0
|
||||
309
test_model_predictions_visualization.py
Normal file
309
test_model_predictions_visualization.py
Normal file
@ -0,0 +1,309 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test Model Predictions Visualization
|
||||
|
||||
This script demonstrates the enhanced model prediction visualization system
|
||||
that shows DQN actions, CNN price predictions, and accuracy feedback on the price chart.
|
||||
|
||||
Features tested:
|
||||
- DQN action predictions (BUY/SELL/HOLD) as directional arrows with confidence-based sizing
|
||||
- CNN price direction predictions as trend lines with target markers
|
||||
- Prediction accuracy feedback with color-coded results
|
||||
- Real-time prediction tracking and storage
|
||||
- Mock prediction generation for demonstration
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import time
|
||||
import numpy as np
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
from core.config import get_config
|
||||
from core.data_provider import DataProvider
|
||||
from core.orchestrator import TradingOrchestrator
|
||||
from core.trading_executor import TradingExecutor
|
||||
from web.clean_dashboard import create_clean_dashboard
|
||||
from enhanced_realtime_training import EnhancedRealtimeTrainingSystem
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class ModelPredictionTester:
|
||||
"""Test model prediction visualization capabilities"""
|
||||
|
||||
def __init__(self):
|
||||
self.config = get_config()
|
||||
self.data_provider = DataProvider()
|
||||
self.trading_executor = TradingExecutor()
|
||||
self.orchestrator = TradingOrchestrator(
|
||||
data_provider=self.data_provider,
|
||||
enhanced_rl_training=True,
|
||||
model_registry={}
|
||||
)
|
||||
|
||||
# Initialize enhanced training system
|
||||
self.training_system = EnhancedRealtimeTrainingSystem(
|
||||
orchestrator=self.orchestrator,
|
||||
data_provider=self.data_provider,
|
||||
dashboard=None # Will be set after dashboard creation
|
||||
)
|
||||
|
||||
# Create dashboard with enhanced prediction visualization
|
||||
self.dashboard = create_clean_dashboard(
|
||||
data_provider=self.data_provider,
|
||||
orchestrator=self.orchestrator,
|
||||
trading_executor=self.trading_executor
|
||||
)
|
||||
|
||||
# Connect training system to dashboard
|
||||
self.training_system.dashboard = self.dashboard
|
||||
self.dashboard.training_system = self.training_system
|
||||
|
||||
# Test data
|
||||
self.test_symbols = ['ETH/USDT', 'BTC/USDT']
|
||||
self.prediction_count = 0
|
||||
|
||||
logger.info("Model Prediction Tester initialized")
|
||||
|
||||
def generate_mock_dqn_predictions(self, symbol: str, count: int = 10):
|
||||
"""Generate mock DQN predictions for testing"""
|
||||
try:
|
||||
current_price = self.data_provider.get_current_price(symbol) or 2400.0
|
||||
|
||||
for i in range(count):
|
||||
# Generate realistic state vector
|
||||
state = np.random.random(100) # 100-dimensional state
|
||||
|
||||
# Generate Q-values with some logic
|
||||
q_values = [np.random.random(), np.random.random(), np.random.random()]
|
||||
action = np.argmax(q_values) # Best action
|
||||
confidence = max(q_values) / sum(q_values) # Confidence based on Q-value distribution
|
||||
|
||||
# Add some price variation
|
||||
pred_price = current_price + np.random.normal(0, 20)
|
||||
|
||||
# Capture prediction
|
||||
self.training_system.capture_dqn_prediction(
|
||||
symbol=symbol,
|
||||
state=state,
|
||||
q_values=q_values,
|
||||
action=action,
|
||||
confidence=confidence,
|
||||
price=pred_price
|
||||
)
|
||||
|
||||
self.prediction_count += 1
|
||||
|
||||
logger.info(f"Generated DQN prediction {i+1}/{count}: {symbol} action={['BUY', 'SELL', 'HOLD'][action]} confidence={confidence:.2f}")
|
||||
|
||||
# Small delay between predictions
|
||||
time.sleep(0.1)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating DQN predictions: {e}")
|
||||
|
||||
def generate_mock_cnn_predictions(self, symbol: str, count: int = 8):
|
||||
"""Generate mock CNN predictions for testing"""
|
||||
try:
|
||||
current_price = self.data_provider.get_current_price(symbol) or 2400.0
|
||||
|
||||
for i in range(count):
|
||||
# Generate direction with some logic
|
||||
direction = np.random.choice([0, 1, 2], p=[0.3, 0.2, 0.5]) # Slightly bullish
|
||||
confidence = 0.4 + np.random.random() * 0.5 # 0.4-0.9 confidence
|
||||
|
||||
# Calculate predicted price based on direction
|
||||
if direction == 2: # UP
|
||||
price_change = np.random.uniform(5, 50)
|
||||
elif direction == 0: # DOWN
|
||||
price_change = -np.random.uniform(5, 50)
|
||||
else: # SAME
|
||||
price_change = np.random.uniform(-5, 5)
|
||||
|
||||
predicted_price = current_price + price_change
|
||||
|
||||
# Generate features
|
||||
features = np.random.random((15, 20)).flatten() # Flattened CNN features
|
||||
|
||||
# Capture prediction
|
||||
self.training_system.capture_cnn_prediction(
|
||||
symbol=symbol,
|
||||
current_price=current_price,
|
||||
predicted_price=predicted_price,
|
||||
direction=direction,
|
||||
confidence=confidence,
|
||||
features=features
|
||||
)
|
||||
|
||||
self.prediction_count += 1
|
||||
|
||||
logger.info(f"Generated CNN prediction {i+1}/{count}: {symbol} direction={['DOWN', 'SAME', 'UP'][direction]} confidence={confidence:.2f}")
|
||||
|
||||
# Small delay between predictions
|
||||
time.sleep(0.2)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating CNN predictions: {e}")
|
||||
|
||||
def generate_mock_accuracy_data(self, symbol: str, count: int = 15):
|
||||
"""Generate mock prediction accuracy data for testing"""
|
||||
try:
|
||||
current_price = self.data_provider.get_current_price(symbol) or 2400.0
|
||||
|
||||
for i in range(count):
|
||||
# Randomly choose prediction type
|
||||
prediction_type = np.random.choice(['DQN', 'CNN'])
|
||||
predicted_action = np.random.choice([0, 1, 2])
|
||||
confidence = 0.3 + np.random.random() * 0.6
|
||||
|
||||
# Generate realistic price change
|
||||
actual_price_change = np.random.normal(0, 0.01) # ±1% typical change
|
||||
|
||||
# Validate accuracy
|
||||
self.training_system.validate_prediction_accuracy(
|
||||
symbol=symbol,
|
||||
prediction_type=prediction_type,
|
||||
predicted_action=predicted_action,
|
||||
actual_price_change=actual_price_change,
|
||||
confidence=confidence
|
||||
)
|
||||
|
||||
logger.info(f"Generated accuracy data {i+1}/{count}: {symbol} {prediction_type} action={predicted_action}")
|
||||
|
||||
# Small delay
|
||||
time.sleep(0.1)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating accuracy data: {e}")
|
||||
|
||||
def run_prediction_generation_test(self):
|
||||
"""Run comprehensive prediction generation test"""
|
||||
try:
|
||||
logger.info("Starting Model Prediction Visualization Test")
|
||||
logger.info("=" * 60)
|
||||
|
||||
# Test for each symbol
|
||||
for symbol in self.test_symbols:
|
||||
logger.info(f"\nGenerating predictions for {symbol}...")
|
||||
|
||||
# Generate DQN predictions
|
||||
logger.info(f"Generating DQN predictions for {symbol}...")
|
||||
self.generate_mock_dqn_predictions(symbol, count=12)
|
||||
|
||||
# Generate CNN predictions
|
||||
logger.info(f"Generating CNN predictions for {symbol}...")
|
||||
self.generate_mock_cnn_predictions(symbol, count=8)
|
||||
|
||||
# Generate accuracy data
|
||||
logger.info(f"Generating accuracy data for {symbol}...")
|
||||
self.generate_mock_accuracy_data(symbol, count=20)
|
||||
|
||||
# Get prediction summary
|
||||
summary = self.training_system.get_prediction_summary(symbol)
|
||||
logger.info(f"Prediction summary for {symbol}: {summary}")
|
||||
|
||||
# Log total statistics
|
||||
training_stats = self.training_system.get_training_statistics()
|
||||
logger.info("\nTraining System Statistics:")
|
||||
logger.info(f"Total predictions generated: {self.prediction_count}")
|
||||
logger.info(f"Prediction stats: {training_stats.get('prediction_stats', {})}")
|
||||
|
||||
logger.info("\n" + "=" * 60)
|
||||
logger.info("Prediction generation test completed successfully!")
|
||||
logger.info("Dashboard should now show enhanced model predictions on the price chart:")
|
||||
logger.info("- Green/Red arrows for DQN BUY/SELL predictions")
|
||||
logger.info("- Gray circles for DQN HOLD predictions")
|
||||
logger.info("- Colored trend lines for CNN price direction predictions")
|
||||
logger.info("- Diamond markers for CNN prediction targets")
|
||||
logger.info("- Green/Red X marks for correct/incorrect prediction feedback")
|
||||
logger.info("- Hover tooltips showing confidence, Q-values, and accuracy scores")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in prediction generation test: {e}")
|
||||
|
||||
def start_dashboard_with_predictions(self, host='127.0.0.1', port=8051):
|
||||
"""Start dashboard with enhanced prediction visualization"""
|
||||
try:
|
||||
logger.info(f"Starting dashboard with model predictions at http://{host}:{port}")
|
||||
|
||||
# Run prediction generation in background
|
||||
import threading
|
||||
pred_thread = threading.Thread(target=self.run_prediction_generation_test, daemon=True)
|
||||
pred_thread.start()
|
||||
|
||||
# Start training system
|
||||
self.training_system.start_training()
|
||||
|
||||
# Start dashboard
|
||||
self.dashboard.run_server(host=host, port=port, debug=False)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error starting dashboard with predictions: {e}")
|
||||
|
||||
def test_prediction_accuracy_validation(self):
|
||||
"""Test prediction accuracy validation logic"""
|
||||
try:
|
||||
logger.info("Testing prediction accuracy validation...")
|
||||
|
||||
# Test DQN accuracy validation
|
||||
test_cases = [
|
||||
('DQN', 0, 0.01, 0.8, True), # BUY + price up = correct
|
||||
('DQN', 1, -0.01, 0.7, True), # SELL + price down = correct
|
||||
('DQN', 2, 0.0005, 0.6, True), # HOLD + no change = correct
|
||||
('DQN', 0, -0.01, 0.8, False), # BUY + price down = incorrect
|
||||
('CNN', 2, 0.01, 0.9, True), # UP + price up = correct
|
||||
('CNN', 0, -0.01, 0.8, True), # DOWN + price down = correct
|
||||
('CNN', 1, 0.0005, 0.7, True), # SAME + no change = correct
|
||||
('CNN', 2, -0.01, 0.9, False), # UP + price down = incorrect
|
||||
]
|
||||
|
||||
for prediction_type, action, price_change, confidence, expected_correct in test_cases:
|
||||
self.training_system.validate_prediction_accuracy(
|
||||
symbol='ETH/USDT',
|
||||
prediction_type=prediction_type,
|
||||
predicted_action=action,
|
||||
actual_price_change=price_change,
|
||||
confidence=confidence
|
||||
)
|
||||
|
||||
# Check if validation worked correctly
|
||||
if self.training_system.prediction_accuracy_history['ETH/USDT']:
|
||||
latest = list(self.training_system.prediction_accuracy_history['ETH/USDT'])[-1]
|
||||
actual_correct = latest['correct']
|
||||
|
||||
status = "✓" if actual_correct == expected_correct else "✗"
|
||||
logger.info(f"{status} {prediction_type} action={action} change={price_change:.4f} -> correct={actual_correct}")
|
||||
|
||||
logger.info("Prediction accuracy validation test completed")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error testing prediction accuracy validation: {e}")
|
||||
|
||||
def main():
|
||||
"""Main test function"""
|
||||
try:
|
||||
# Create tester
|
||||
tester = ModelPredictionTester()
|
||||
|
||||
# Run accuracy validation test first
|
||||
tester.test_prediction_accuracy_validation()
|
||||
|
||||
# Start dashboard with enhanced predictions
|
||||
logger.info("\nStarting dashboard with enhanced model prediction visualization...")
|
||||
logger.info("Visit http://127.0.0.1:8051 to see the enhanced price chart with model predictions")
|
||||
|
||||
tester.start_dashboard_with_predictions()
|
||||
|
||||
except KeyboardInterrupt:
|
||||
logger.info("Test interrupted by user")
|
||||
except Exception as e:
|
||||
logger.error(f"Error in main test: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -106,6 +106,10 @@ class CleanTradingDashboard:
|
||||
else:
|
||||
self.orchestrator = orchestrator
|
||||
|
||||
# Initialize enhanced training system for predictions
|
||||
self.training_system = None
|
||||
self._initialize_enhanced_training_system()
|
||||
|
||||
# Initialize layout and component managers
|
||||
self.layout_manager = DashboardLayoutManager(
|
||||
starting_balance=self._get_initial_balance(),
|
||||
@ -711,9 +715,9 @@ class CleanTradingDashboard:
|
||||
x=0.5, y=0.5, showarrow=False)
|
||||
|
||||
def _add_model_predictions_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
|
||||
"""Add model predictions to the chart - ONLY EXECUTED TRADES on main chart"""
|
||||
"""Add enhanced model predictions to the chart with real-time feedback"""
|
||||
try:
|
||||
# Only show EXECUTED TRADES on the main 1m chart
|
||||
# 1. Add executed trades (existing functionality)
|
||||
executed_signals = [signal for signal in self.recent_decisions if self._get_signal_attribute(signal, 'executed', False)]
|
||||
|
||||
if executed_signals:
|
||||
@ -721,8 +725,7 @@ class CleanTradingDashboard:
|
||||
buy_trades = []
|
||||
sell_trades = []
|
||||
|
||||
for signal in executed_signals[-50:]: # Last 50 executed trades (increased from 20)
|
||||
# Try to get full timestamp first, fall back to string timestamp
|
||||
for signal in executed_signals[-50:]: # Last 50 executed trades
|
||||
signal_time = self._get_signal_attribute(signal, 'full_timestamp')
|
||||
if not signal_time:
|
||||
signal_time = self._get_signal_attribute(signal, 'timestamp')
|
||||
@ -732,10 +735,9 @@ class CleanTradingDashboard:
|
||||
signal_confidence = self._get_signal_attribute(signal, 'confidence', 0)
|
||||
|
||||
if signal_time and signal_price and signal_confidence > 0:
|
||||
# FIXED: Better timestamp conversion to prevent race conditions
|
||||
# Enhanced timestamp handling
|
||||
if isinstance(signal_time, str):
|
||||
try:
|
||||
# Handle time-only format with current date
|
||||
if ':' in signal_time and len(signal_time.split(':')) == 3:
|
||||
now = datetime.now()
|
||||
time_parts = signal_time.split(':')
|
||||
@ -745,7 +747,6 @@ class CleanTradingDashboard:
|
||||
second=int(time_parts[2]),
|
||||
microsecond=0
|
||||
)
|
||||
# Handle day boundary issues - if signal seems from future, subtract a day
|
||||
if signal_time > now + timedelta(minutes=5):
|
||||
signal_time -= timedelta(days=1)
|
||||
else:
|
||||
@ -754,7 +755,6 @@ class CleanTradingDashboard:
|
||||
logger.debug(f"Error parsing timestamp {signal_time}: {e}")
|
||||
continue
|
||||
elif not isinstance(signal_time, datetime):
|
||||
# Convert other timestamp formats to datetime
|
||||
try:
|
||||
signal_time = pd.to_datetime(signal_time)
|
||||
except Exception as e:
|
||||
@ -766,7 +766,7 @@ class CleanTradingDashboard:
|
||||
elif signal_action == 'SELL':
|
||||
sell_trades.append({'x': signal_time, 'y': signal_price, 'confidence': signal_confidence})
|
||||
|
||||
# Add EXECUTED BUY trades (large green circles)
|
||||
# Add executed trades with enhanced visualization
|
||||
if buy_trades:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
@ -790,7 +790,6 @@ class CleanTradingDashboard:
|
||||
row=row, col=1
|
||||
)
|
||||
|
||||
# Add EXECUTED SELL trades (large red circles)
|
||||
if sell_trades:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
@ -813,9 +812,363 @@ class CleanTradingDashboard:
|
||||
),
|
||||
row=row, col=1
|
||||
)
|
||||
|
||||
# 2. NEW: Add real-time model predictions overlay
|
||||
self._add_dqn_predictions_to_chart(fig, symbol, df_main, row)
|
||||
self._add_cnn_predictions_to_chart(fig, symbol, df_main, row)
|
||||
self._add_prediction_accuracy_feedback(fig, symbol, df_main, row)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error adding executed trades to main chart: {e}")
|
||||
logger.warning(f"Error adding model predictions to chart: {e}")
|
||||
|
||||
def _add_dqn_predictions_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
|
||||
"""Add DQN action predictions as directional arrows"""
|
||||
try:
|
||||
# Get recent DQN predictions from orchestrator
|
||||
dqn_predictions = self._get_recent_dqn_predictions(symbol)
|
||||
|
||||
if not dqn_predictions:
|
||||
return
|
||||
|
||||
# Separate predictions by action
|
||||
buy_predictions = []
|
||||
sell_predictions = []
|
||||
hold_predictions = []
|
||||
|
||||
for pred in dqn_predictions[-30:]: # Last 30 DQN predictions
|
||||
action = pred.get('action', 2) # 0=BUY, 1=SELL, 2=HOLD
|
||||
confidence = pred.get('confidence', 0)
|
||||
timestamp = pred.get('timestamp', datetime.now())
|
||||
price = pred.get('price', 0)
|
||||
|
||||
if confidence > 0.3: # Only show predictions with reasonable confidence
|
||||
pred_data = {
|
||||
'x': timestamp,
|
||||
'y': price,
|
||||
'confidence': confidence,
|
||||
'q_values': pred.get('q_values', [0, 0, 0])
|
||||
}
|
||||
|
||||
if action == 0: # BUY
|
||||
buy_predictions.append(pred_data)
|
||||
elif action == 1: # SELL
|
||||
sell_predictions.append(pred_data)
|
||||
else: # HOLD
|
||||
hold_predictions.append(pred_data)
|
||||
|
||||
# Add DQN BUY predictions (green arrows pointing up)
|
||||
if buy_predictions:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=[p['x'] for p in buy_predictions],
|
||||
y=[p['y'] for p in buy_predictions],
|
||||
mode='markers',
|
||||
marker=dict(
|
||||
symbol='triangle-up',
|
||||
size=[8 + p['confidence'] * 12 for p in buy_predictions], # Size based on confidence
|
||||
color=[f'rgba(0, 200, 0, {0.3 + p["confidence"] * 0.7})' for p in buy_predictions], # Opacity based on confidence
|
||||
line=dict(width=1, color='darkgreen')
|
||||
),
|
||||
name='DQN BUY Prediction',
|
||||
showlegend=True,
|
||||
hovertemplate="<b>DQN BUY PREDICTION</b><br>" +
|
||||
"Price: $%{y:.2f}<br>" +
|
||||
"Time: %{x}<br>" +
|
||||
"Confidence: %{customdata[0]:.1%}<br>" +
|
||||
"Q-Values: [%{customdata[1]:.3f}, %{customdata[2]:.3f}, %{customdata[3]:.3f}]<extra></extra>",
|
||||
customdata=[[p['confidence']] + p['q_values'] for p in buy_predictions]
|
||||
),
|
||||
row=row, col=1
|
||||
)
|
||||
|
||||
# Add DQN SELL predictions (red arrows pointing down)
|
||||
if sell_predictions:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=[p['x'] for p in sell_predictions],
|
||||
y=[p['y'] for p in sell_predictions],
|
||||
mode='markers',
|
||||
marker=dict(
|
||||
symbol='triangle-down',
|
||||
size=[8 + p['confidence'] * 12 for p in sell_predictions],
|
||||
color=[f'rgba(200, 0, 0, {0.3 + p["confidence"] * 0.7})' for p in sell_predictions],
|
||||
line=dict(width=1, color='darkred')
|
||||
),
|
||||
name='DQN SELL Prediction',
|
||||
showlegend=True,
|
||||
hovertemplate="<b>DQN SELL PREDICTION</b><br>" +
|
||||
"Price: $%{y:.2f}<br>" +
|
||||
"Time: %{x}<br>" +
|
||||
"Confidence: %{customdata[0]:.1%}<br>" +
|
||||
"Q-Values: [%{customdata[1]:.3f}, %{customdata[2]:.3f}, %{customdata[3]:.3f}]<extra></extra>",
|
||||
customdata=[[p['confidence']] + p['q_values'] for p in sell_predictions]
|
||||
),
|
||||
row=row, col=1
|
||||
)
|
||||
|
||||
# Add DQN HOLD predictions (small gray circles)
|
||||
if hold_predictions:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=[p['x'] for p in hold_predictions],
|
||||
y=[p['y'] for p in hold_predictions],
|
||||
mode='markers',
|
||||
marker=dict(
|
||||
symbol='circle',
|
||||
size=[4 + p['confidence'] * 6 for p in hold_predictions],
|
||||
color=[f'rgba(128, 128, 128, {0.2 + p["confidence"] * 0.5})' for p in hold_predictions],
|
||||
line=dict(width=1, color='gray')
|
||||
),
|
||||
name='DQN HOLD Prediction',
|
||||
showlegend=True,
|
||||
hovertemplate="<b>DQN HOLD PREDICTION</b><br>" +
|
||||
"Price: $%{y:.2f}<br>" +
|
||||
"Time: %{x}<br>" +
|
||||
"Confidence: %{customdata[0]:.1%}<br>" +
|
||||
"Q-Values: [%{customdata[1]:.3f}, %{customdata[2]:.3f}, %{customdata[3]:.3f}]<extra></extra>",
|
||||
customdata=[[p['confidence']] + p['q_values'] for p in hold_predictions]
|
||||
),
|
||||
row=row, col=1
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error adding DQN predictions to chart: {e}")
|
||||
|
||||
def _add_cnn_predictions_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
|
||||
"""Add CNN price direction predictions as trend lines"""
|
||||
try:
|
||||
# Get recent CNN predictions from orchestrator
|
||||
cnn_predictions = self._get_recent_cnn_predictions(symbol)
|
||||
|
||||
if not cnn_predictions:
|
||||
return
|
||||
|
||||
# Create trend prediction lines
|
||||
prediction_lines = []
|
||||
|
||||
for i, pred in enumerate(cnn_predictions[-20:]): # Last 20 CNN predictions
|
||||
direction = pred.get('direction', 1) # 0=DOWN, 1=SAME, 2=UP
|
||||
confidence = pred.get('confidence', 0)
|
||||
timestamp = pred.get('timestamp', datetime.now())
|
||||
current_price = pred.get('current_price', 0)
|
||||
predicted_price = pred.get('predicted_price', current_price)
|
||||
|
||||
if confidence > 0.4 and current_price > 0: # Only show confident predictions
|
||||
# Calculate prediction end point (5 minutes ahead)
|
||||
end_time = timestamp + timedelta(minutes=5)
|
||||
|
||||
# Determine color based on direction
|
||||
if direction == 2: # UP
|
||||
color = f'rgba(0, 255, 0, {0.3 + confidence * 0.4})'
|
||||
line_color = 'green'
|
||||
prediction_name = 'CNN UP'
|
||||
elif direction == 0: # DOWN
|
||||
color = f'rgba(255, 0, 0, {0.3 + confidence * 0.4})'
|
||||
line_color = 'red'
|
||||
prediction_name = 'CNN DOWN'
|
||||
else: # SAME
|
||||
color = f'rgba(128, 128, 128, {0.2 + confidence * 0.3})'
|
||||
line_color = 'gray'
|
||||
prediction_name = 'CNN FLAT'
|
||||
|
||||
# Add prediction line
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=[timestamp, end_time],
|
||||
y=[current_price, predicted_price],
|
||||
mode='lines',
|
||||
line=dict(
|
||||
color=line_color,
|
||||
width=2 + confidence * 3, # Line width based on confidence
|
||||
dash='dot' if direction == 1 else 'solid'
|
||||
),
|
||||
name=f'{prediction_name} Prediction',
|
||||
showlegend=i == 0, # Only show legend for first instance
|
||||
hovertemplate=f"<b>{prediction_name} PREDICTION</b><br>" +
|
||||
"From: $%{y[0]:.2f}<br>" +
|
||||
"To: $%{y[1]:.2f}<br>" +
|
||||
"Time: %{x[0]} → %{x[1]}<br>" +
|
||||
f"Confidence: {confidence:.1%}<br>" +
|
||||
f"Direction: {['DOWN', 'SAME', 'UP'][direction]}<extra></extra>"
|
||||
),
|
||||
row=row, col=1
|
||||
)
|
||||
|
||||
# Add prediction end point marker
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=[end_time],
|
||||
y=[predicted_price],
|
||||
mode='markers',
|
||||
marker=dict(
|
||||
symbol='diamond',
|
||||
size=6 + confidence * 8,
|
||||
color=color,
|
||||
line=dict(width=1, color=line_color)
|
||||
),
|
||||
name=f'{prediction_name} Target',
|
||||
showlegend=False,
|
||||
hovertemplate=f"<b>{prediction_name} TARGET</b><br>" +
|
||||
"Target Price: $%{y:.2f}<br>" +
|
||||
"Target Time: %{x}<br>" +
|
||||
f"Confidence: {confidence:.1%}<extra></extra>"
|
||||
),
|
||||
row=row, col=1
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error adding CNN predictions to chart: {e}")
|
||||
|
||||
def _add_prediction_accuracy_feedback(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
|
||||
"""Add prediction accuracy feedback with color-coded results"""
|
||||
try:
|
||||
# Get prediction accuracy history
|
||||
accuracy_data = self._get_prediction_accuracy_history(symbol)
|
||||
|
||||
if not accuracy_data:
|
||||
return
|
||||
|
||||
# Add accuracy feedback markers
|
||||
correct_predictions = []
|
||||
incorrect_predictions = []
|
||||
|
||||
for acc in accuracy_data[-50:]: # Last 50 accuracy points
|
||||
timestamp = acc.get('timestamp', datetime.now())
|
||||
price = acc.get('actual_price', 0)
|
||||
was_correct = acc.get('correct', False)
|
||||
prediction_type = acc.get('prediction_type', 'unknown')
|
||||
accuracy_score = acc.get('accuracy_score', 0)
|
||||
|
||||
if price > 0:
|
||||
acc_data = {
|
||||
'x': timestamp,
|
||||
'y': price,
|
||||
'type': prediction_type,
|
||||
'score': accuracy_score
|
||||
}
|
||||
|
||||
if was_correct:
|
||||
correct_predictions.append(acc_data)
|
||||
else:
|
||||
incorrect_predictions.append(acc_data)
|
||||
|
||||
# Add correct prediction markers (green checkmarks)
|
||||
if correct_predictions:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=[p['x'] for p in correct_predictions],
|
||||
y=[p['y'] for p in correct_predictions],
|
||||
mode='markers',
|
||||
marker=dict(
|
||||
symbol='x',
|
||||
size=8,
|
||||
color='rgba(0, 255, 0, 0.8)',
|
||||
line=dict(width=2, color='darkgreen')
|
||||
),
|
||||
name='Correct Predictions',
|
||||
showlegend=True,
|
||||
hovertemplate="<b>CORRECT PREDICTION</b><br>" +
|
||||
"Price: $%{y:.2f}<br>" +
|
||||
"Time: %{x}<br>" +
|
||||
"Type: %{customdata[0]}<br>" +
|
||||
"Accuracy: %{customdata[1]:.1%}<extra></extra>",
|
||||
customdata=[[p['type'], p['score']] for p in correct_predictions]
|
||||
),
|
||||
row=row, col=1
|
||||
)
|
||||
|
||||
# Add incorrect prediction markers (red X marks)
|
||||
if incorrect_predictions:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=[p['x'] for p in incorrect_predictions],
|
||||
y=[p['y'] for p in incorrect_predictions],
|
||||
mode='markers',
|
||||
marker=dict(
|
||||
symbol='x',
|
||||
size=8,
|
||||
color='rgba(255, 0, 0, 0.8)',
|
||||
line=dict(width=2, color='darkred')
|
||||
),
|
||||
name='Incorrect Predictions',
|
||||
showlegend=True,
|
||||
hovertemplate="<b>INCORRECT PREDICTION</b><br>" +
|
||||
"Price: $%{y:.2f}<br>" +
|
||||
"Time: %{x}<br>" +
|
||||
"Type: %{customdata[0]}<br>" +
|
||||
"Accuracy: %{customdata[1]:.1%}<extra></extra>",
|
||||
customdata=[[p['type'], p['score']] for p in incorrect_predictions]
|
||||
),
|
||||
row=row, col=1
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error adding prediction accuracy feedback to chart: {e}")
|
||||
|
||||
def _get_recent_dqn_predictions(self, symbol: str) -> List[Dict]:
|
||||
"""Get recent DQN predictions from enhanced training system (forward-looking only)"""
|
||||
try:
|
||||
predictions = []
|
||||
|
||||
# Get REAL forward-looking predictions from enhanced training system
|
||||
if hasattr(self, 'training_system') and self.training_system:
|
||||
if hasattr(self.training_system, 'recent_dqn_predictions'):
|
||||
predictions.extend(self.training_system.recent_dqn_predictions.get(symbol, []))
|
||||
|
||||
# Get from orchestrator as fallback
|
||||
if hasattr(self.orchestrator, 'recent_dqn_predictions'):
|
||||
predictions.extend(self.orchestrator.recent_dqn_predictions.get(symbol, []))
|
||||
|
||||
# REMOVED: Mock prediction generation - now using REAL predictions only
|
||||
# No more artificial past predictions or random data
|
||||
|
||||
return sorted(predictions, key=lambda x: x.get('timestamp', datetime.now()))
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error getting DQN predictions: {e}")
|
||||
return []
|
||||
|
||||
def _get_recent_cnn_predictions(self, symbol: str) -> List[Dict]:
|
||||
"""Get recent CNN predictions from enhanced training system (forward-looking only)"""
|
||||
try:
|
||||
predictions = []
|
||||
|
||||
# Get REAL forward-looking predictions from enhanced training system
|
||||
if hasattr(self, 'training_system') and self.training_system:
|
||||
if hasattr(self.training_system, 'recent_cnn_predictions'):
|
||||
predictions.extend(self.training_system.recent_cnn_predictions.get(symbol, []))
|
||||
|
||||
# Get from orchestrator as fallback
|
||||
if hasattr(self.orchestrator, 'recent_cnn_predictions'):
|
||||
predictions.extend(self.orchestrator.recent_cnn_predictions.get(symbol, []))
|
||||
|
||||
# REMOVED: Mock prediction generation - now using REAL predictions only
|
||||
# No more artificial past predictions or random data
|
||||
|
||||
return sorted(predictions, key=lambda x: x.get('timestamp', datetime.now()))
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error getting CNN predictions: {e}")
|
||||
return []
|
||||
|
||||
def _get_prediction_accuracy_history(self, symbol: str) -> List[Dict]:
|
||||
"""Get REAL prediction accuracy history from validated forward-looking predictions"""
|
||||
try:
|
||||
accuracy_data = []
|
||||
|
||||
# Get REAL accuracy data from training system validation
|
||||
if hasattr(self, 'training_system') and self.training_system:
|
||||
if hasattr(self.training_system, 'prediction_accuracy_history'):
|
||||
accuracy_data.extend(self.training_system.prediction_accuracy_history.get(symbol, []))
|
||||
|
||||
# REMOVED: Mock accuracy data generation - now using REAL validation results only
|
||||
# Accuracy is now based on actual prediction outcomes, not random data
|
||||
|
||||
return sorted(accuracy_data, key=lambda x: x.get('timestamp', datetime.now()))
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error getting prediction accuracy history: {e}")
|
||||
return []
|
||||
|
||||
def _add_signals_to_mini_chart(self, fig: go.Figure, symbol: str, ws_data_1s: pd.DataFrame, row: int = 2):
|
||||
"""Add ALL signals (executed and non-executed) to the 1s mini chart"""
|
||||
@ -2566,6 +2919,33 @@ class CleanTradingDashboard:
|
||||
except Exception as e:
|
||||
logger.warning(f"Error clearing old signals: {e}")
|
||||
|
||||
def _initialize_enhanced_training_system(self):
|
||||
"""Initialize enhanced training system for model predictions"""
|
||||
try:
|
||||
# Try to import and initialize enhanced training system
|
||||
from enhanced_realtime_training import EnhancedRealtimeTrainingSystem
|
||||
|
||||
self.training_system = EnhancedRealtimeTrainingSystem(
|
||||
orchestrator=self.orchestrator,
|
||||
data_provider=self.data_provider,
|
||||
dashboard=self
|
||||
)
|
||||
|
||||
# Initialize prediction storage
|
||||
if not hasattr(self.orchestrator, 'recent_dqn_predictions'):
|
||||
self.orchestrator.recent_dqn_predictions = {}
|
||||
if not hasattr(self.orchestrator, 'recent_cnn_predictions'):
|
||||
self.orchestrator.recent_cnn_predictions = {}
|
||||
|
||||
logger.info("Enhanced training system initialized for model predictions")
|
||||
|
||||
except ImportError:
|
||||
logger.warning("Enhanced training system not available - using mock predictions")
|
||||
self.training_system = None
|
||||
except Exception as e:
|
||||
logger.error(f"Error initializing enhanced training system: {e}")
|
||||
self.training_system = None
|
||||
|
||||
def _initialize_cob_integration(self):
|
||||
"""Initialize COB integration with high-frequency data handling"""
|
||||
try:
|
||||
|
||||
Reference in New Issue
Block a user