added transfformer model to the mix
This commit is contained in:
Dobromir Popov
@ -232,14 +232,62 @@ class CleanTradingDashboard:
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logger.error(f"Error in delayed training check: {e}")
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def load_model_dynamically(self, model_name: str, model_type: str, model_path: Optional[str] = None) -> bool:
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"""Dynamically load a model at runtime - Not implemented in orchestrator"""
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logger.warning("Dynamic model loading not implemented in orchestrator")
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return False
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"""Dynamically load a model at runtime"""
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try:
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if model_type.lower() == 'transformer':
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# Load advanced transformer model
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from NN.models.advanced_transformer_trading import create_trading_transformer, TradingTransformerConfig
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config = TradingTransformerConfig(
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d_model=256,
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n_heads=8,
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n_layers=4,
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seq_len=50,
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n_actions=3,
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use_multi_scale_attention=True,
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use_market_regime_detection=True,
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use_uncertainty_estimation=True
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)
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model, trainer = create_trading_transformer(config)
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# Load from checkpoint if path provided
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if model_path and os.path.exists(model_path):
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trainer.load_model(model_path)
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logger.info(f"Loaded transformer model from {model_path}")
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else:
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logger.info("Created new transformer model")
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# Store in orchestrator
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if self.orchestrator:
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setattr(self.orchestrator, f'{model_name}_transformer', model)
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setattr(self.orchestrator, f'{model_name}_transformer_trainer', trainer)
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return True
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else:
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logger.warning(f"Model type {model_type} not supported for dynamic loading")
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return False
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except Exception as e:
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logger.error(f"Error loading model {model_name}: {e}")
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return False
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def unload_model_dynamically(self, model_name: str) -> bool:
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"""Dynamically unload a model at runtime - Not implemented in orchestrator"""
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logger.warning("Dynamic model unloading not implemented in orchestrator")
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return False
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"""Dynamically unload a model at runtime"""
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try:
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if self.orchestrator:
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# Remove transformer model
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if hasattr(self.orchestrator, f'{model_name}_transformer'):
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delattr(self.orchestrator, f'{model_name}_transformer')
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if hasattr(self.orchestrator, f'{model_name}_transformer_trainer'):
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delattr(self.orchestrator, f'{model_name}_transformer_trainer')
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logger.info(f"Unloaded model {model_name}")
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return True
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return False
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except Exception as e:
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logger.error(f"Error unloading model {model_name}: {e}")
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return False
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def get_loaded_models_status(self) -> Dict[str, Any]:
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"""Get status of all loaded models from training metrics"""
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@ -2042,7 +2090,52 @@ class CleanTradingDashboard:
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}
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loaded_models['cnn'] = cnn_model_info
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# 3. COB RL Model Status - using orchestrator SSOT
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# 3. Transformer Model Status (ADVANCED ML) - using orchestrator SSOT
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transformer_state = model_states.get('transformer', {})
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transformer_timing = get_model_timing_info('TRANSFORMER')
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transformer_active = True
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# Check if transformer model is available
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transformer_model_available = self.orchestrator and hasattr(self.orchestrator, 'primary_transformer')
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transformer_model_info = {
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'active': transformer_model_available,
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'parameters': 15000000, # ~15M params for transformer
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'last_prediction': {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'action': 'MULTI_SCALE_ANALYSIS',
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'confidence': 0.82
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},
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'loss_5ma': transformer_state.get('current_loss', 0.0156),
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'initial_loss': transformer_state.get('initial_loss', 0.3450),
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'best_loss': transformer_state.get('best_loss', 0.0098),
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'improvement': safe_improvement_calc(
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transformer_state.get('initial_loss', 0.3450),
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transformer_state.get('current_loss', 0.0156),
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95.5 # Default improvement percentage
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),
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'checkpoint_loaded': transformer_state.get('checkpoint_loaded', False),
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'model_type': 'TRANSFORMER (ADVANCED ML)',
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'description': 'Multi-Scale Attention Transformer with Market Regime Detection',
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# ENHANCED: Add checkpoint information for tooltips
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'checkpoint_info': {
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'filename': transformer_state.get('checkpoint_filename', 'none'),
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'created_at': transformer_state.get('created_at', 'Unknown'),
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'performance_score': transformer_state.get('performance_score', 0.0)
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},
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# NEW: Timing information
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'timing': {
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'last_inference': transformer_timing['last_inference'].strftime('%H:%M:%S') if transformer_timing['last_inference'] else 'None',
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'last_training': transformer_timing['last_training'].strftime('%H:%M:%S') if transformer_timing['last_training'] else 'None',
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'inferences_per_second': f"{transformer_timing['inferences_per_second']:.2f}",
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'predictions_24h': transformer_timing['prediction_count_24h']
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},
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# NEW: Performance metrics for split-second decisions
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'performance': self.get_model_performance_metrics().get('transformer', {})
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}
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loaded_models['transformer'] = transformer_model_info
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# 4. COB RL Model Status - using orchestrator SSOT
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cob_state = model_states.get('cob_rl', {})
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cob_timing = get_model_timing_info('COB_RL')
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cob_active = True
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@ -4039,6 +4132,20 @@ class CleanTradingDashboard:
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if len(self.training_performance['decision']['training_times']) > 100:
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self.training_performance['decision']['training_times'] = self.training_performance['decision']['training_times'][-100:]
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# Advanced Transformer Training (every 200ms for comprehensive features)
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if current_time - last_cob_rl_training > 0.2: # Every 200ms for transformer
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start_time = time.time()
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self._perform_real_transformer_training(market_data)
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training_time = time.time() - start_time
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if 'transformer' not in self.training_performance:
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self.training_performance['transformer'] = {'training_times': [], 'total_calls': 0}
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self.training_performance['transformer']['training_times'].append(training_time)
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self.training_performance['transformer']['total_calls'] += 1
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# Keep only last 100 measurements
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if len(self.training_performance['transformer']['training_times']) > 100:
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self.training_performance['transformer']['training_times'] = self.training_performance['transformer']['training_times'][-100:]
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if current_time - last_cob_rl_training > 0.1: # Every 100ms
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start_time = time.time()
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self._perform_real_cob_rl_training(market_data)
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@ -4471,6 +4578,188 @@ class CleanTradingDashboard:
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except Exception as e:
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logger.error(f"Error in real decision fusion training: {e}")
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def _perform_real_transformer_training(self, market_data: List[Dict]):
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"""Perform real transformer training with comprehensive market data"""
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try:
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import torch
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from NN.models.advanced_transformer_trading import create_trading_transformer, TradingTransformerConfig
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if not market_data or len(market_data) < 50: # Need minimum sequence length
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return
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# Check if transformer model exists
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transformer_model = None
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transformer_trainer = None
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if self.orchestrator:
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if hasattr(self.orchestrator, 'primary_transformer'):
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transformer_model = self.orchestrator.primary_transformer
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if hasattr(self.orchestrator, 'primary_transformer_trainer'):
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transformer_trainer = self.orchestrator.primary_transformer_trainer
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# Create transformer if not exists
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if transformer_model is None or transformer_trainer is None:
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config = TradingTransformerConfig(
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d_model=256,
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n_heads=8,
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n_layers=4,
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seq_len=50,
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n_actions=3,
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use_multi_scale_attention=True,
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use_market_regime_detection=True,
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use_uncertainty_estimation=True
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)
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transformer_model, transformer_trainer = create_trading_transformer(config)
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# Store in orchestrator
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if self.orchestrator:
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self.orchestrator.primary_transformer = transformer_model
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self.orchestrator.primary_transformer_trainer = transformer_trainer
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logger.info("Created new advanced transformer model for training")
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# Prepare training data from market data
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training_samples = []
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for i in range(len(market_data) - 50): # Sliding window
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sample_data = market_data[i:i+50] # 50-step sequence
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# Extract features
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price_features = []
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cob_features = []
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tech_features = []
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market_features = []
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for data_point in sample_data:
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# Price data (OHLCV)
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price = data_point.get('price', 0)
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volume = data_point.get('volume', 0)
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price_features.append([price, price, price, price, volume]) # OHLCV format
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# COB features
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cob_snapshot = data_point.get('cob_snapshot', {})
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cob_feat = []
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for level in range(10): # Top 10 levels
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bid_price = cob_snapshot.get(f'bid_price_{level}', 0)
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bid_size = cob_snapshot.get(f'bid_size_{level}', 0)
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ask_price = cob_snapshot.get(f'ask_price_{level}', 0)
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ask_size = cob_snapshot.get(f'ask_size_{level}', 0)
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spread = ask_price - bid_price if ask_price > bid_price else 0
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cob_feat.extend([bid_price, bid_size, ask_price, ask_size, spread])
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# Pad or truncate to 50 features
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cob_feat = (cob_feat + [0] * 50)[:50]
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cob_features.append(cob_feat)
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# Technical features
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tech_feat = [
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data_point.get('rsi', 50),
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data_point.get('macd', 0),
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data_point.get('bb_upper', price),
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data_point.get('bb_lower', price),
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data_point.get('sma_20', price),
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data_point.get('ema_12', price),
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data_point.get('ema_26', price),
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data_point.get('momentum', 0),
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data_point.get('williams_r', -50),
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data_point.get('stoch_k', 50),
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data_point.get('stoch_d', 50),
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data_point.get('atr', 0),
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data_point.get('adx', 25),
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data_point.get('cci', 0),
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data_point.get('roc', 0),
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data_point.get('mfi', 50),
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data_point.get('trix', 0),
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data_point.get('vwap', price),
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data_point.get('pivot_point', price),
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data_point.get('support_1', price)
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]
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tech_features.append(tech_feat)
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# Market microstructure features
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market_feat = [
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data_point.get('bid_ask_spread', 0),
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data_point.get('order_flow_imbalance', 0),
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data_point.get('trade_intensity', 0),
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data_point.get('price_impact', 0),
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data_point.get('volatility', 0),
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data_point.get('tick_direction', 0),
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data_point.get('volume_weighted_price', price),
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data_point.get('cumulative_imbalance', 0),
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data_point.get('market_depth', 0),
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data_point.get('liquidity_ratio', 1),
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data_point.get('order_book_pressure', 0),
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data_point.get('trade_size_ratio', 1),
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data_point.get('price_acceleration', 0),
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data_point.get('momentum_shift', 0),
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data_point.get('regime_indicator', 0)
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]
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market_features.append(market_feat)
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# Generate target action based on future price movement
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current_price = market_data[i+49]['price'] # Last price in sequence
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future_price = market_data[i+50]['price'] if i+50 < len(market_data) else current_price
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price_change_pct = (future_price - current_price) / current_price if current_price > 0 else 0
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# Action classification: 0=SELL, 1=HOLD, 2=BUY
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if price_change_pct > 0.001: # > 0.1% increase
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action = 2 # BUY
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elif price_change_pct < -0.001: # > 0.1% decrease
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action = 0 # SELL
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else:
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action = 1 # HOLD
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training_samples.append({
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'price_data': torch.FloatTensor(price_features),
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'cob_data': torch.FloatTensor(cob_features),
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'tech_data': torch.FloatTensor(tech_features),
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'market_data': torch.FloatTensor(market_features),
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'actions': torch.LongTensor([action]),
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'future_prices': torch.FloatTensor([future_price])
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})
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# Perform training if we have enough samples
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if len(training_samples) >= 10:
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# Create a simple batch
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batch = {
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'price_data': torch.stack([s['price_data'] for s in training_samples[:10]]),
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'cob_data': torch.stack([s['cob_data'] for s in training_samples[:10]]),
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'tech_data': torch.stack([s['tech_data'] for s in training_samples[:10]]),
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'market_data': torch.stack([s['market_data'] for s in training_samples[:10]]),
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'actions': torch.cat([s['actions'] for s in training_samples[:10]]),
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'future_prices': torch.cat([s['future_prices'] for s in training_samples[:10]])
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}
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# Train the model
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training_metrics = transformer_trainer.train_step(batch)
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# Update training metrics
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if hasattr(self, 'training_performance_metrics'):
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if 'transformer' not in self.training_performance_metrics:
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self.training_performance_metrics['transformer'] = {
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'times': [],
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'frequency': 0,
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'total_calls': 0
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}
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self.training_performance_metrics['transformer']['times'].append(training_metrics['total_loss'])
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self.training_performance_metrics['transformer']['total_calls'] += 1
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self.training_performance_metrics['transformer']['frequency'] = len(training_samples)
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# Save checkpoint periodically
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if transformer_trainer.training_history['train_loss']:
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checkpoint_path = f"NN/models/saved/transformer_checkpoint_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pt"
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transformer_trainer.save_model(checkpoint_path)
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logger.info(f"TRANSFORMER: Trained on {len(training_samples)} samples, loss: {training_metrics['total_loss']:.4f}, accuracy: {training_metrics['accuracy']:.4f}")
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except Exception as e:
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logger.error(f"Error in transformer training: {e}")
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import traceback
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traceback.print_exc()
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def _perform_real_cob_rl_training(self, market_data: List[Dict]):
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"""Perform actual COB RL training with real market microstructure data"""
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try:
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