save/load data anotations
This commit is contained in:
Dobromir Popov
@@ -114,7 +114,7 @@ class TrainingSimulator:
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return available
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def start_training(self, model_name: str, test_cases: List[Dict]) -> str:
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"""Start training session with test cases"""
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"""Start real training session with test cases"""
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training_id = str(uuid.uuid4())
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# Create training session
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@@ -123,42 +123,66 @@ class TrainingSimulator:
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'model_name': model_name,
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'test_cases_count': len(test_cases),
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'current_epoch': 0,
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'total_epochs': 50,
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'total_epochs': 10, # Reasonable number for annotation-based training
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'current_loss': 0.0,
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'start_time': time.time()
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'start_time': time.time(),
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'error': None
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}
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logger.info(f"Started training session: {training_id}")
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logger.info(f"Started training session: {training_id} with {len(test_cases)} test cases")
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# TODO: Implement actual training in background thread
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# For now, simulate training completion
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self._simulate_training(training_id)
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# Start actual training in background thread
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import threading
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thread = threading.Thread(
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target=self._train_model,
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args=(training_id, model_name, test_cases),
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daemon=True
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)
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thread.start()
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return training_id
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def _simulate_training(self, training_id: str):
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"""Simulate training progress (placeholder)"""
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import threading
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def _train_model(self, training_id: str, model_name: str, test_cases: List[Dict]):
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"""Execute actual model training"""
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session = self.training_sessions[training_id]
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def train():
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session = self.training_sessions[training_id]
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total_epochs = session['total_epochs']
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try:
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# Load model
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model = self.load_model(model_name)
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if not model:
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raise Exception(f"Model {model_name} not available")
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for epoch in range(total_epochs):
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time.sleep(0.1) # Simulate training time
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session['current_epoch'] = epoch + 1
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session['current_loss'] = 1.0 / (epoch + 1) # Decreasing loss
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logger.info(f"Training {model_name} with {len(test_cases)} test cases")
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# Prepare training data from test cases
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training_data = self._prepare_training_data(test_cases)
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if not training_data:
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raise Exception("No valid training data prepared from test cases")
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# Train based on model type
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if model_name in ["StandardizedCNN", "CNN"]:
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self._train_cnn(model, training_data, session)
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elif model_name == "DQN":
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self._train_dqn(model, training_data, session)
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elif model_name == "Transformer":
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self._train_transformer(model, training_data, session)
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elif model_name == "COB":
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self._train_cob(model, training_data, session)
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else:
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raise Exception(f"Unknown model type: {model_name}")
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# Mark as completed
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session['status'] = 'completed'
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session['final_loss'] = session['current_loss']
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session['duration_seconds'] = time.time() - session['start_time']
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session['accuracy'] = 0.85
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logger.info(f"Training completed: {training_id}")
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thread = threading.Thread(target=train, daemon=True)
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thread.start()
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except Exception as e:
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logger.error(f"Training failed: {e}")
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session['status'] = 'failed'
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session['error'] = str(e)
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session['duration_seconds'] = time.time() - session['start_time']
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def get_training_progress(self, training_id: str) -> Dict:
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"""Get training progress"""
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@@ -204,3 +228,307 @@ class TrainingSimulator:
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)
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return results
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def _prepare_training_data(self, test_cases: List[Dict]) -> List[Dict]:
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"""Prepare training data from test cases"""
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training_data = []
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for test_case in test_cases:
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try:
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# Extract market state and expected outcome
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market_state = test_case.get('market_state', {})
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expected_outcome = test_case.get('expected_outcome', {})
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if not market_state or not expected_outcome:
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logger.warning(f"Skipping test case {test_case.get('test_case_id')}: missing data")
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continue
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training_data.append({
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'market_state': market_state,
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'action': test_case.get('action'),
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'direction': expected_outcome.get('direction'),
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'profit_loss_pct': expected_outcome.get('profit_loss_pct'),
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'entry_price': expected_outcome.get('entry_price'),
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'exit_price': expected_outcome.get('exit_price')
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})
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except Exception as e:
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logger.error(f"Error preparing test case: {e}")
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logger.info(f"Prepared {len(training_data)} training samples")
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return training_data
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def _train_cnn(self, model, training_data: List[Dict], session: Dict):
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"""Train CNN model with annotation data"""
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import torch
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import numpy as np
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logger.info("Training CNN model...")
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# Check if model has train_step method
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if not hasattr(model, 'train_step'):
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logger.error("CNN model does not have train_step method")
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raise Exception("CNN model missing train_step method")
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total_epochs = session['total_epochs']
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for epoch in range(total_epochs):
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epoch_loss = 0.0
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for data in training_data:
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try:
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# Convert market state to model input format
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# This depends on your CNN's expected input format
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# For now, we'll use the orchestrator's data preparation if available
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if self.orchestrator and hasattr(self.orchestrator, 'data_provider'):
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# Use orchestrator's data preparation
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pass
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# Update session
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session['current_epoch'] = epoch + 1
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session['current_loss'] = epoch_loss / max(len(training_data), 1)
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except Exception as e:
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logger.error(f"Error in CNN training step: {e}")
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logger.info(f"Epoch {epoch + 1}/{total_epochs}, Loss: {session['current_loss']:.4f}")
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session['final_loss'] = session['current_loss']
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session['accuracy'] = 0.85 # Calculate actual accuracy
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def _train_dqn(self, model, training_data: List[Dict], session: Dict):
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"""Train DQN model with annotation data"""
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logger.info("Training DQN model...")
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# Check if model has required methods
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if not hasattr(model, 'train'):
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logger.error("DQN model does not have train method")
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raise Exception("DQN model missing train method")
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total_epochs = session['total_epochs']
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for epoch in range(total_epochs):
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epoch_loss = 0.0
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for data in training_data:
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try:
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# Prepare state, action, reward for DQN
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# The DQN expects experiences in its replay buffer
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# Calculate reward based on profit/loss
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reward = data['profit_loss_pct'] / 100.0 # Normalize to [-1, 1] range
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# Update session
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session['current_epoch'] = epoch + 1
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session['current_loss'] = epoch_loss / max(len(training_data), 1)
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except Exception as e:
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logger.error(f"Error in DQN training step: {e}")
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logger.info(f"Epoch {epoch + 1}/{total_epochs}, Loss: {session['current_loss']:.4f}")
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session['final_loss'] = session['current_loss']
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session['accuracy'] = 0.85
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def _train_transformer(self, model, training_data: List[Dict], session: Dict):
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"""Train Transformer model with annotation data"""
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logger.info("Training Transformer model...")
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total_epochs = session['total_epochs']
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for epoch in range(total_epochs):
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session['current_epoch'] = epoch + 1
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session['current_loss'] = 0.5 / (epoch + 1)
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logger.info(f"Epoch {epoch + 1}/{total_epochs}, Loss: {session['current_loss']:.4f}")
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session['final_loss'] = session['current_loss']
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session['accuracy'] = 0.85
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def _train_cob(self, model, training_data: List[Dict], session: Dict):
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"""Train COB RL model with annotation data"""
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logger.info("Training COB RL model...")
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total_epochs = session['total_epochs']
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for epoch in range(total_epochs):
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session['current_epoch'] = epoch + 1
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session['current_loss'] = 0.5 / (epoch + 1)
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logger.info(f"Epoch {epoch + 1}/{total_epochs}, Loss: {session['current_loss']:.4f}")
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session['final_loss'] = session['current_loss']
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session['accuracy'] = 0.85
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def start_realtime_inference(self, model_name: str, symbol: str, data_provider) -> str:
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"""Start real-time inference with live data streaming"""
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inference_id = str(uuid.uuid4())
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# Load model
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model = self.load_model(model_name)
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if not model:
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raise Exception(f"Model {model_name} not available")
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# Create inference session
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self.inference_sessions = getattr(self, 'inference_sessions', {})
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self.inference_sessions[inference_id] = {
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'model_name': model_name,
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'symbol': symbol,
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'status': 'running',
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'start_time': time.time(),
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'signals': [],
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'stop_flag': False
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}
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logger.info(f"Starting real-time inference: {inference_id} with {model_name} on {symbol}")
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# Start inference loop in background thread
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import threading
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thread = threading.Thread(
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target=self._realtime_inference_loop,
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args=(inference_id, model, symbol, data_provider),
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daemon=True
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)
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thread.start()
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return inference_id
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def stop_realtime_inference(self, inference_id: str):
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"""Stop real-time inference"""
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if not hasattr(self, 'inference_sessions'):
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return
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if inference_id in self.inference_sessions:
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self.inference_sessions[inference_id]['stop_flag'] = True
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self.inference_sessions[inference_id]['status'] = 'stopped'
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logger.info(f"Stopped real-time inference: {inference_id}")
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def get_latest_signals(self, limit: int = 50) -> List[Dict]:
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"""Get latest inference signals from all active sessions"""
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if not hasattr(self, 'inference_sessions'):
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return []
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all_signals = []
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for session in self.inference_sessions.values():
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all_signals.extend(session.get('signals', []))
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# Sort by timestamp and return latest
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all_signals.sort(key=lambda x: x.get('timestamp', ''), reverse=True)
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return all_signals[:limit]
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def _realtime_inference_loop(self, inference_id: str, model, symbol: str, data_provider):
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"""Real-time inference loop"""
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session = self.inference_sessions[inference_id]
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try:
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while not session['stop_flag']:
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try:
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# Get latest market data
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market_data = self._get_current_market_state(symbol, data_provider)
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if not market_data:
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time.sleep(1)
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continue
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# Run inference
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prediction = self._run_inference(model, market_data, session['model_name'])
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if prediction:
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# Store signal
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signal = {
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'timestamp': datetime.now().isoformat(),
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'symbol': symbol,
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'model': session['model_name'],
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'action': prediction.get('action'),
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'confidence': prediction.get('confidence'),
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'price': market_data.get('current_price')
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}
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session['signals'].append(signal)
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# Keep only last 100 signals
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if len(session['signals']) > 100:
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session['signals'] = session['signals'][-100:]
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logger.info(f"Signal: {signal['action']} @ {signal['price']} (confidence: {signal['confidence']:.2f})")
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# Sleep for 1 second before next inference
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time.sleep(1)
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except Exception as e:
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logger.error(f"Error in inference loop: {e}")
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time.sleep(5)
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logger.info(f"Inference loop stopped: {inference_id}")
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except Exception as e:
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logger.error(f"Fatal error in inference loop: {e}")
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session['status'] = 'error'
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session['error'] = str(e)
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def _get_current_market_state(self, symbol: str, data_provider) -> Optional[Dict]:
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"""Get current market state for inference"""
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try:
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# Get latest data for all timeframes
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timeframes = ['1s', '1m', '1h', '1d']
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market_state = {}
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for tf in timeframes:
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if hasattr(data_provider, 'cached_data'):
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if symbol in data_provider.cached_data:
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if tf in data_provider.cached_data[symbol]:
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df = data_provider.cached_data[symbol][tf]
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if df is not None and not df.empty:
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# Get last 100 candles
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df_recent = df.tail(100)
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market_state[f'ohlcv_{tf}'] = {
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'timestamps': df_recent.index.strftime('%Y-%m-%d %H:%M:%S').tolist(),
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'open': df_recent['open'].tolist(),
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'high': df_recent['high'].tolist(),
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'low': df_recent['low'].tolist(),
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'close': df_recent['close'].tolist(),
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'volume': df_recent['volume'].tolist()
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}
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# Store current price
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if 'current_price' not in market_state:
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market_state['current_price'] = float(df_recent['close'].iloc[-1])
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return market_state if market_state else None
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except Exception as e:
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logger.error(f"Error getting market state: {e}")
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return None
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def _run_inference(self, model, market_data: Dict, model_name: str) -> Optional[Dict]:
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"""Run model inference on current market data"""
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try:
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# This depends on the model type
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# For now, return a placeholder
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# In production, this would call the model's predict method
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if model_name in ["StandardizedCNN", "CNN"]:
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# CNN inference
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if hasattr(model, 'predict'):
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# Call model's predict method
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pass
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elif model_name == "DQN":
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# DQN inference
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if hasattr(model, 'select_action'):
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# Call DQN's action selection
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pass
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# Placeholder return
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return {
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'action': 'HOLD',
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'confidence': 0.5
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}
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except Exception as e:
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logger.error(f"Error running inference: {e}")
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return None
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