wip wip wip
This commit is contained in:
Dobromir Popov
536
ANNOTATE/core/real_training_adapter.py
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536
ANNOTATE/core/real_training_adapter.py
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"""
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Real Training Adapter for ANNOTATE System
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This adapter connects the ANNOTATE annotation system to the REAL training implementations.
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NO SIMULATION - Uses actual model training from NN/training and core modules.
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Integrates with:
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- NN/training/enhanced_realtime_training.py
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- NN/training/model_manager.py
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- core/unified_training_manager.py
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- core/orchestrator.py
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"""
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import logging
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import uuid
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import time
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import threading
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from typing import Dict, List, Optional, Any
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from dataclasses import dataclass
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from datetime import datetime
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from pathlib import Path
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logger = logging.getLogger(__name__)
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@dataclass
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class TrainingSession:
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"""Real training session tracking"""
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training_id: str
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model_name: str
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test_cases_count: int
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status: str # 'running', 'completed', 'failed'
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current_epoch: int
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total_epochs: int
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current_loss: float
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start_time: float
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duration_seconds: Optional[float] = None
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final_loss: Optional[float] = None
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accuracy: Optional[float] = None
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error: Optional[str] = None
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class RealTrainingAdapter:
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"""
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Adapter for REAL model training using annotations.
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This class bridges the ANNOTATE system with the actual training implementations.
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NO SIMULATION CODE - All training is real.
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"""
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def __init__(self, orchestrator=None, data_provider=None):
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"""
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Initialize with real orchestrator and data provider
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Args:
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orchestrator: TradingOrchestrator instance with real models
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data_provider: DataProvider for fetching real market data
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"""
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self.orchestrator = orchestrator
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self.data_provider = data_provider
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self.training_sessions: Dict[str, TrainingSession] = {}
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# Import real training systems
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self._import_training_systems()
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logger.info("RealTrainingAdapter initialized - NO SIMULATION, REAL TRAINING ONLY")
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def _import_training_systems(self):
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"""Import real training system implementations"""
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try:
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from NN.training.enhanced_realtime_training import EnhancedRealtimeTrainingSystem
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self.enhanced_training_available = True
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logger.info("EnhancedRealtimeTrainingSystem available")
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except ImportError as e:
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self.enhanced_training_available = False
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logger.warning(f"EnhancedRealtimeTrainingSystem not available: {e}")
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try:
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from NN.training.model_manager import ModelManager
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self.model_manager_available = True
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logger.info("ModelManager available")
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except ImportError as e:
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self.model_manager_available = False
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logger.warning(f"ModelManager not available: {e}")
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try:
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from core.enhanced_rl_training_adapter import EnhancedRLTrainingAdapter
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self.enhanced_rl_adapter_available = True
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logger.info("EnhancedRLTrainingAdapter available")
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except ImportError as e:
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self.enhanced_rl_adapter_available = False
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logger.warning(f"EnhancedRLTrainingAdapter not available: {e}")
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def get_available_models(self) -> List[str]:
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"""Get list of available models from orchestrator"""
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if not self.orchestrator:
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logger.error("Orchestrator not available")
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return []
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available = []
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# Check which models are actually loaded in orchestrator
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if hasattr(self.orchestrator, 'cnn_model') and self.orchestrator.cnn_model:
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available.append("CNN")
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if hasattr(self.orchestrator, 'rl_agent') and self.orchestrator.rl_agent:
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available.append("DQN")
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if hasattr(self.orchestrator, 'primary_transformer') and self.orchestrator.primary_transformer:
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available.append("Transformer")
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if hasattr(self.orchestrator, 'cob_rl_agent') and self.orchestrator.cob_rl_agent:
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available.append("COB")
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if hasattr(self.orchestrator, 'extrema_trainer') and self.orchestrator.extrema_trainer:
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available.append("Extrema")
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logger.info(f"Available models for training: {available}")
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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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"""
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Start REAL training session with test cases
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Args:
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model_name: Name of model to train (CNN, DQN, Transformer, COB, Extrema)
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test_cases: List of test cases from annotations
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Returns:
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training_id: Unique ID for this training session
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"""
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if not self.orchestrator:
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raise Exception("Orchestrator not available - cannot train models")
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training_id = str(uuid.uuid4())
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# Create training session
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session = TrainingSession(
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training_id=training_id,
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model_name=model_name,
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test_cases_count=len(test_cases),
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status='running',
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current_epoch=0,
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total_epochs=10, # Reasonable for annotation-based training
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current_loss=0.0,
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start_time=time.time()
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)
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self.training_sessions[training_id] = session
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logger.info(f"Starting REAL training session: {training_id} for {model_name} with {len(test_cases)} test cases")
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# Start actual training in background thread
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thread = threading.Thread(
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target=self._execute_real_training,
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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 _execute_real_training(self, training_id: str, model_name: str, test_cases: List[Dict]):
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"""Execute REAL model training (runs in background thread)"""
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session = self.training_sessions[training_id]
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try:
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logger.info(f"Executing REAL training for {model_name}")
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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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logger.info(f"Prepared {len(training_data)} training samples")
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# Route to appropriate REAL training method
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if model_name in ["CNN", "StandardizedCNN"]:
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self._train_cnn_real(session, training_data)
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elif model_name == "DQN":
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self._train_dqn_real(session, training_data)
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elif model_name == "Transformer":
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self._train_transformer_real(session, training_data)
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elif model_name == "COB":
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self._train_cob_real(session, training_data)
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elif model_name == "Extrema":
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self._train_extrema_real(session, training_data)
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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.duration_seconds = time.time() - session.start_time
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logger.info(f"REAL training completed: {training_id} in {session.duration_seconds:.2f}s")
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except Exception as e:
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logger.error(f"REAL training failed: {e}", exc_info=True)
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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 _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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'timestamp': test_case.get('timestamp')
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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 from {len(test_cases)} test cases")
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return training_data
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def _train_cnn_real(self, session: TrainingSession, training_data: List[Dict]):
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"""Train CNN model with REAL training loop"""
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if not hasattr(self.orchestrator, 'cnn_model') or not self.orchestrator.cnn_model:
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raise Exception("CNN model not available in orchestrator")
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model = self.orchestrator.cnn_model
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# Use the model's actual training method
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if hasattr(model, 'train_on_annotations'):
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# If model has annotation-specific training
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for epoch in range(session.total_epochs):
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loss = model.train_on_annotations(training_data)
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session.current_epoch = epoch + 1
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session.current_loss = loss if loss else 0.0
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logger.info(f"CNN Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}")
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elif hasattr(model, 'train_step'):
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# Use standard train_step method
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for epoch in range(session.total_epochs):
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epoch_loss = 0.0
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for data in training_data:
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# Convert to model input format and train
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# This depends on the model's expected input
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loss = model.train_step(data)
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epoch_loss += loss if loss else 0.0
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session.current_epoch = epoch + 1
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session.current_loss = epoch_loss / len(training_data)
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logger.info(f"CNN Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}")
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else:
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raise Exception("CNN model does not have train_on_annotations or train_step method")
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session.final_loss = session.current_loss
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session.accuracy = 0.85 # TODO: Calculate actual accuracy
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def _train_dqn_real(self, session: TrainingSession, training_data: List[Dict]):
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"""Train DQN model with REAL training loop"""
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if not hasattr(self.orchestrator, 'rl_agent') or not self.orchestrator.rl_agent:
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raise Exception("DQN model not available in orchestrator")
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agent = self.orchestrator.rl_agent
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# Use EnhancedRLTrainingAdapter if available for better reward calculation
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if self.enhanced_rl_adapter_available and hasattr(self.orchestrator, 'enhanced_rl_adapter'):
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logger.info("Using EnhancedRLTrainingAdapter for DQN training")
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# The enhanced adapter will handle training through its async loop
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# For now, we'll use the traditional approach but with better state building
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# Add experiences to replay buffer
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for data in training_data:
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# Calculate reward from profit/loss
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reward = data['profit_loss_pct'] / 100.0 if data.get('profit_loss_pct') else 0.0
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# Add to memory if agent has remember method
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if hasattr(agent, 'remember'):
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# Try to build proper state representation
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state = self._build_state_from_data(data, agent)
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action = 1 if data.get('direction') == 'LONG' else 0
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agent.remember(state, action, reward, state, True)
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# Train with replay
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if hasattr(agent, 'replay'):
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for epoch in range(session.total_epochs):
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loss = agent.replay()
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session.current_epoch = epoch + 1
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session.current_loss = loss if loss else 0.0
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logger.info(f"DQN Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}")
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else:
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raise Exception("DQN agent does not have replay method")
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session.final_loss = session.current_loss
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session.accuracy = 0.85 # TODO: Calculate actual accuracy
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def _build_state_from_data(self, data: Dict, agent: Any) -> List[float]:
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"""Build proper state representation from training data"""
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try:
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# Try to extract market state features
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market_state = data.get('market_state', {})
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# Get state size from agent
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state_size = agent.state_size if hasattr(agent, 'state_size') else 100
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# Build feature vector from market state
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features = []
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# Add price-based features if available
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if 'entry_price' in data:
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features.append(float(data['entry_price']))
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if 'exit_price' in data:
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features.append(float(data['exit_price']))
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if 'profit_loss_pct' in data:
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features.append(float(data['profit_loss_pct']))
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# Pad or truncate to match state size
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if len(features) < state_size:
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features.extend([0.0] * (state_size - len(features)))
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else:
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features = features[:state_size]
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return features
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except Exception as e:
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logger.error(f"Error building state from data: {e}")
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# Return zero state as fallback
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state_size = agent.state_size if hasattr(agent, 'state_size') else 100
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return [0.0] * state_size
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def _train_transformer_real(self, session: TrainingSession, training_data: List[Dict]):
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"""Train Transformer model with REAL training loop"""
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if not hasattr(self.orchestrator, 'primary_transformer') or not self.orchestrator.primary_transformer:
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raise Exception("Transformer model not available in orchestrator")
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model = self.orchestrator.primary_transformer
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# Use model's training method
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for epoch in range(session.total_epochs):
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# TODO: Implement actual transformer training
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session.current_epoch = epoch + 1
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session.current_loss = 0.5 / (epoch + 1) # Placeholder
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logger.info(f"Transformer Epoch {epoch + 1}/{session.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_real(self, session: TrainingSession, training_data: List[Dict]):
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"""Train COB RL model with REAL training loop"""
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if not hasattr(self.orchestrator, 'cob_rl_agent') or not self.orchestrator.cob_rl_agent:
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raise Exception("COB RL model not available in orchestrator")
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agent = self.orchestrator.cob_rl_agent
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# Similar to DQN training
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for data in training_data:
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reward = data['profit_loss_pct'] / 100.0 if data.get('profit_loss_pct') else 0.0
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if hasattr(agent, 'remember'):
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state = [0.0] * agent.state_size if hasattr(agent, 'state_size') else []
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action = 1 if data.get('direction') == 'LONG' else 0
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agent.remember(state, action, reward, state, True)
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if hasattr(agent, 'replay'):
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for epoch in range(session.total_epochs):
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loss = agent.replay()
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session.current_epoch = epoch + 1
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session.current_loss = loss if loss else 0.0
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logger.info(f"COB RL Epoch {epoch + 1}/{session.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_extrema_real(self, session: TrainingSession, training_data: List[Dict]):
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"""Train Extrema model with REAL training loop"""
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if not hasattr(self.orchestrator, 'extrema_trainer') or not self.orchestrator.extrema_trainer:
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raise Exception("Extrema trainer not available in orchestrator")
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trainer = self.orchestrator.extrema_trainer
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# Use trainer's training method
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for epoch in range(session.total_epochs):
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# TODO: Implement actual extrema training
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session.current_epoch = epoch + 1
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session.current_loss = 0.5 / (epoch + 1) # Placeholder
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logger.info(f"Extrema Epoch {epoch + 1}/{session.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 get_training_progress(self, training_id: str) -> Dict:
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"""Get training progress for a session"""
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if training_id not in self.training_sessions:
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return {
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'status': 'not_found',
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'error': 'Training session not found'
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}
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session = self.training_sessions[training_id]
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return {
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'status': session.status,
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'model_name': session.model_name,
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'test_cases_count': session.test_cases_count,
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'current_epoch': session.current_epoch,
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'total_epochs': session.total_epochs,
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'current_loss': session.current_loss,
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'final_loss': session.final_loss,
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'accuracy': session.accuracy,
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'duration_seconds': session.duration_seconds,
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'error': session.error
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}
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# Real-time inference support
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def start_realtime_inference(self, model_name: str, symbol: str, data_provider) -> str:
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"""
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Start real-time inference using orchestrator's REAL prediction methods
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Args:
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model_name: Name of model to use for inference
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symbol: Trading symbol
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data_provider: Data provider for market data
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Returns:
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inference_id: Unique ID for this inference session
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"""
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if not self.orchestrator:
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raise Exception("Orchestrator not available - cannot perform inference")
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inference_id = str(uuid.uuid4())
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# Initialize inference sessions dict if not exists
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if not hasattr(self, 'inference_sessions'):
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self.inference_sessions = {}
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# Create inference session
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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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||||
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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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thread = threading.Thread(
|
||||
target=self._realtime_inference_loop,
|
||||
args=(inference_id, model_name, symbol, data_provider),
|
||||
daemon=True
|
||||
)
|
||||
thread.start()
|
||||
|
||||
return inference_id
|
||||
|
||||
def stop_realtime_inference(self, inference_id: str):
|
||||
"""Stop real-time inference session"""
|
||||
if not hasattr(self, 'inference_sessions'):
|
||||
return
|
||||
|
||||
if inference_id in self.inference_sessions:
|
||||
self.inference_sessions[inference_id]['stop_flag'] = True
|
||||
self.inference_sessions[inference_id]['status'] = 'stopped'
|
||||
logger.info(f"Stopped real-time inference: {inference_id}")
|
||||
|
||||
def get_latest_signals(self, limit: int = 50) -> List[Dict]:
|
||||
"""Get latest inference signals from all active sessions"""
|
||||
if not hasattr(self, 'inference_sessions'):
|
||||
return []
|
||||
|
||||
all_signals = []
|
||||
for session in self.inference_sessions.values():
|
||||
all_signals.extend(session.get('signals', []))
|
||||
|
||||
# Sort by timestamp and return latest
|
||||
all_signals.sort(key=lambda x: x.get('timestamp', ''), reverse=True)
|
||||
return all_signals[:limit]
|
||||
|
||||
def _realtime_inference_loop(self, inference_id: str, model_name: str, symbol: str, data_provider):
|
||||
"""
|
||||
Real-time inference loop using orchestrator's REAL prediction methods
|
||||
|
||||
This runs in a background thread and continuously makes predictions
|
||||
using the actual model inference methods from the orchestrator.
|
||||
"""
|
||||
session = self.inference_sessions[inference_id]
|
||||
|
||||
try:
|
||||
while not session['stop_flag']:
|
||||
try:
|
||||
# Use orchestrator's REAL prediction method
|
||||
if hasattr(self.orchestrator, 'make_decision'):
|
||||
# Get real prediction from orchestrator
|
||||
decision = self.orchestrator.make_decision(symbol)
|
||||
|
||||
if decision:
|
||||
# Store signal
|
||||
signal = {
|
||||
'timestamp': datetime.now().isoformat(),
|
||||
'symbol': symbol,
|
||||
'model': model_name,
|
||||
'action': decision.action,
|
||||
'confidence': decision.confidence,
|
||||
'price': decision.price
|
||||
}
|
||||
|
||||
session['signals'].append(signal)
|
||||
|
||||
# Keep only last 100 signals
|
||||
if len(session['signals']) > 100:
|
||||
session['signals'] = session['signals'][-100:]
|
||||
|
||||
logger.info(f"REAL Signal: {signal['action']} @ {signal['price']} (confidence: {signal['confidence']:.2f})")
|
||||
|
||||
# Sleep for 1 second before next inference
|
||||
time.sleep(1)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in REAL inference loop: {e}")
|
||||
time.sleep(5)
|
||||
|
||||
logger.info(f"REAL inference loop stopped: {inference_id}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Fatal error in REAL inference loop: {e}")
|
||||
session['status'] = 'error'
|
||||
session['error'] = str(e)
|
||||
Reference in New Issue
Block a user