1628 lines
73 KiB
Python
1628 lines
73 KiB
Python
"""
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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, timedelta, timezone
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from pathlib import Path
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try:
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import pytz
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except ImportError:
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pytz = None
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logger = logging.getLogger(__name__)
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def parse_timestamp_to_utc(timestamp_str: str) -> datetime:
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"""
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Unified timestamp parser that handles all formats and ensures UTC timezone.
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Handles:
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- ISO format with timezone: '2025-10-27T14:00:00+00:00'
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- ISO format with Z: '2025-10-27T14:00:00Z'
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- Space-separated with seconds: '2025-10-27 14:00:00'
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- Space-separated without seconds: '2025-10-27 14:00'
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Args:
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timestamp_str: Timestamp string in various formats
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Returns:
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Timezone-aware datetime object in UTC
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Raises:
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ValueError: If timestamp cannot be parsed
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"""
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if not timestamp_str:
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raise ValueError("Empty timestamp string")
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# Try ISO format first (handles T separator and timezone info)
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if 'T' in timestamp_str or '+' in timestamp_str:
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try:
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# Handle 'Z' suffix (Zulu time = UTC)
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if timestamp_str.endswith('Z'):
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timestamp_str = timestamp_str[:-1] + '+00:00'
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return datetime.fromisoformat(timestamp_str)
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except ValueError:
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pass
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# Try space-separated formats
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# Replace space with T for fromisoformat compatibility
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if ' ' in timestamp_str:
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try:
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# Try parsing with fromisoformat after converting space to T
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dt = datetime.fromisoformat(timestamp_str.replace(' ', 'T'))
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# Make timezone-aware if naive
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=timezone.utc)
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return dt
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except ValueError:
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pass
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# Try explicit format parsing as fallback
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formats = [
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'%Y-%m-%d %H:%M:%S', # With seconds
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'%Y-%m-%d %H:%M', # Without seconds
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'%Y-%m-%dT%H:%M:%S', # ISO without timezone
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'%Y-%m-%dT%H:%M', # ISO without seconds or timezone
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]
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for fmt in formats:
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try:
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dt = datetime.strptime(timestamp_str, fmt)
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# Make timezone-aware
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=timezone.utc)
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return dt
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except ValueError:
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continue
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# If all parsing attempts fail
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raise ValueError(f"Could not parse timestamp: '{timestamp_str}'")
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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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logger.info(f" Training ID: {training_id}")
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logger.info(f" Test cases: {len(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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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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logger.info(" Starting CNN training...")
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self._train_cnn_real(session, training_data)
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elif model_name == "DQN":
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logger.info(" Starting DQN training...")
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self._train_dqn_real(session, training_data)
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elif model_name == "Transformer":
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logger.info(" Starting Transformer training...")
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self._train_transformer_real(session, training_data)
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elif model_name == "COB":
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logger.info(" Starting COB training...")
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self._train_cob_real(session, training_data)
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elif model_name == "Extrema":
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logger.info(" Starting Extrema training...")
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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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logger.info(f" Final loss: {session.final_loss}")
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logger.info(f" Accuracy: {session.accuracy}")
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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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logger.error(f"Training session {training_id} failed after {session.duration_seconds:.2f}s")
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def _get_secondary_symbol(self, primary_symbol: str) -> str:
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"""
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Determine secondary symbol based on primary symbol
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Rules:
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- ETH/USDT -> BTC/USDT
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- SOL/USDT -> BTC/USDT
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- BTC/USDT -> ETH/USDT
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Args:
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primary_symbol: Primary trading symbol
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Returns:
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Secondary symbol for correlation analysis
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"""
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if 'BTC' in primary_symbol:
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return 'ETH/USDT'
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else:
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return 'BTC/USDT'
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def _fetch_market_state_for_test_case(self, test_case: Dict) -> Dict:
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"""
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Fetch market state dynamically for a test case from DuckDB storage
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This fetches HISTORICAL data at the specific timestamp from the annotation,
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not current/latest data.
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Args:
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test_case: Test case dictionary with timestamp, symbol, etc.
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Returns:
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Market state dictionary with OHLCV data for all timeframes
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"""
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try:
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if not self.data_provider:
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logger.warning("DataProvider not available, cannot fetch market state")
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return {}
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|
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symbol = test_case.get('symbol', 'ETH/USDT')
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timestamp_str = test_case.get('timestamp')
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|
|
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if not timestamp_str:
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logger.warning("No timestamp in test case")
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return {}
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|
|
|
# Parse timestamp using unified parser
|
|
try:
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timestamp = parse_timestamp_to_utc(timestamp_str)
|
|
except Exception as e:
|
|
logger.warning(f"Could not parse timestamp '{timestamp_str}': {e}")
|
|
return {}
|
|
|
|
# Get training config
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|
training_config = test_case.get('training_config', {})
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timeframes = training_config.get('timeframes', ['1s', '1m', '1h', '1d'])
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candles_per_timeframe = training_config.get('candles_per_timeframe', 600) # 600 candles per batch
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|
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# Determine secondary symbol based on primary symbol
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# ETH/SOL -> BTC, BTC -> ETH
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secondary_symbol = self._get_secondary_symbol(symbol)
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|
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logger.info(f" Fetching HISTORICAL market state for {symbol} at {timestamp}")
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logger.info(f" Primary symbol: {symbol} - Timeframes: {timeframes}")
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logger.info(f" Secondary symbol: {secondary_symbol} - Timeframe: 1m")
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logger.info(f" Candles per batch: {candles_per_timeframe}")
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|
|
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# Calculate time range based on candles needed
|
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# For 600 candles at 1m = 600 minutes = 10 hours
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from datetime import timedelta
|
|
|
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# Calculate time window for each timeframe to get 600 candles
|
|
time_windows = {
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'1s': timedelta(seconds=candles_per_timeframe), # 600 seconds = 10 minutes
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'1m': timedelta(minutes=candles_per_timeframe), # 600 minutes = 10 hours
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'1h': timedelta(hours=candles_per_timeframe), # 600 hours = 25 days
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'1d': timedelta(days=candles_per_timeframe) # 600 days = ~1.6 years
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}
|
|
|
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# Use the largest window to ensure we have enough data for all timeframes
|
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max_window = max(time_windows.values())
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start_time = timestamp - max_window
|
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end_time = timestamp
|
|
|
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# Fetch data for primary symbol (all timeframes) and secondary symbol (1m only)
|
|
market_state = {
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'symbol': symbol,
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'timestamp': timestamp_str,
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'timeframes': {},
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'secondary_symbol': secondary_symbol,
|
|
'secondary_timeframes': {}
|
|
}
|
|
|
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# Try to get data from DuckDB storage first (historical data)
|
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duckdb_storage = None
|
|
if hasattr(self.data_provider, 'duckdb_storage'):
|
|
duckdb_storage = self.data_provider.duckdb_storage
|
|
|
|
# Fetch primary symbol data (all timeframes)
|
|
logger.info(f" Fetching primary symbol data: {symbol}")
|
|
for timeframe in timeframes:
|
|
df = None
|
|
limit = candles_per_timeframe # Always fetch 600 candles
|
|
|
|
# Try DuckDB storage first (has historical data)
|
|
if duckdb_storage:
|
|
try:
|
|
df = duckdb_storage.get_ohlcv_data(
|
|
symbol=symbol,
|
|
timeframe=timeframe,
|
|
start_time=start_time,
|
|
end_time=end_time,
|
|
limit=limit,
|
|
direction='latest'
|
|
)
|
|
if df is not None and not df.empty:
|
|
logger.debug(f" {timeframe}: {len(df)} candles from DuckDB (historical)")
|
|
except Exception as e:
|
|
logger.debug(f" {timeframe}: DuckDB query failed: {e}")
|
|
|
|
# Fallback to data_provider (might have cached data)
|
|
if df is None or df.empty:
|
|
try:
|
|
# Use get_historical_data_replay for time-specific data
|
|
replay_data = self.data_provider.get_historical_data_replay(
|
|
symbol=symbol,
|
|
start_time=start_time,
|
|
end_time=end_time,
|
|
timeframes=[timeframe]
|
|
)
|
|
df = replay_data.get(timeframe)
|
|
if df is not None and not df.empty:
|
|
logger.debug(f" {timeframe}: {len(df)} candles from replay")
|
|
except Exception as e:
|
|
logger.debug(f" {timeframe}: Replay failed: {e}")
|
|
|
|
# Last resort: get latest data (not ideal but better than nothing)
|
|
if df is None or df.empty:
|
|
logger.warning(f" {timeframe}: No historical data found, using latest data as fallback")
|
|
df = self.data_provider.get_historical_data(
|
|
symbol=symbol,
|
|
timeframe=timeframe,
|
|
limit=limit # Use calculated limit
|
|
)
|
|
|
|
if df is not None and not df.empty:
|
|
# Convert to dict format
|
|
market_state['timeframes'][timeframe] = {
|
|
'timestamps': df.index.strftime('%Y-%m-%d %H:%M:%S').tolist(),
|
|
'open': df['open'].tolist(),
|
|
'high': df['high'].tolist(),
|
|
'low': df['low'].tolist(),
|
|
'close': df['close'].tolist(),
|
|
'volume': df['volume'].tolist()
|
|
}
|
|
logger.info(f" {symbol} {timeframe}: {len(df)} candles")
|
|
else:
|
|
logger.warning(f" {symbol} {timeframe}: No data available")
|
|
|
|
# Fetch secondary symbol data (1m timeframe only, 600 candles)
|
|
logger.info(f" Fetching secondary symbol data: {secondary_symbol} (1m)")
|
|
secondary_df = None
|
|
|
|
# Try DuckDB first
|
|
if duckdb_storage:
|
|
try:
|
|
secondary_df = duckdb_storage.get_ohlcv_data(
|
|
symbol=secondary_symbol,
|
|
timeframe='1m',
|
|
start_time=start_time,
|
|
end_time=end_time,
|
|
limit=candles_per_timeframe,
|
|
direction='latest'
|
|
)
|
|
if secondary_df is not None and not secondary_df.empty:
|
|
logger.debug(f" {secondary_symbol} 1m: {len(secondary_df)} candles from DuckDB")
|
|
except Exception as e:
|
|
logger.debug(f" {secondary_symbol} 1m: DuckDB query failed: {e}")
|
|
|
|
# Fallback to replay
|
|
if secondary_df is None or secondary_df.empty:
|
|
try:
|
|
replay_data = self.data_provider.get_historical_data_replay(
|
|
symbol=secondary_symbol,
|
|
start_time=start_time,
|
|
end_time=end_time,
|
|
timeframes=['1m']
|
|
)
|
|
secondary_df = replay_data.get('1m')
|
|
if secondary_df is not None and not secondary_df.empty:
|
|
logger.debug(f" {secondary_symbol} 1m: {len(secondary_df)} candles from replay")
|
|
except Exception as e:
|
|
logger.debug(f" {secondary_symbol} 1m: Replay failed: {e}")
|
|
|
|
# Last resort: latest data
|
|
if secondary_df is None or secondary_df.empty:
|
|
logger.warning(f" {secondary_symbol} 1m: No historical data, using latest as fallback")
|
|
secondary_df = self.data_provider.get_historical_data(
|
|
symbol=secondary_symbol,
|
|
timeframe='1m',
|
|
limit=candles_per_timeframe
|
|
)
|
|
|
|
# Store secondary symbol data
|
|
if secondary_df is not None and not secondary_df.empty:
|
|
market_state['secondary_timeframes']['1m'] = {
|
|
'timestamps': secondary_df.index.strftime('%Y-%m-%d %H:%M:%S').tolist(),
|
|
'open': secondary_df['open'].tolist(),
|
|
'high': secondary_df['high'].tolist(),
|
|
'low': secondary_df['low'].tolist(),
|
|
'close': secondary_df['close'].tolist(),
|
|
'volume': secondary_df['volume'].tolist()
|
|
}
|
|
logger.info(f" {secondary_symbol} 1m: {len(secondary_df)} candles")
|
|
else:
|
|
logger.warning(f" {secondary_symbol} 1m: No data available")
|
|
|
|
# Verify we have data
|
|
if market_state['timeframes']:
|
|
total_primary = sum(len(tf_data.get('timestamps', [])) for tf_data in market_state['timeframes'].values())
|
|
total_secondary = sum(len(tf_data.get('timestamps', [])) for tf_data in market_state['secondary_timeframes'].values())
|
|
logger.info(f" [OK] Fetched {len(market_state['timeframes'])} primary timeframes ({total_primary} total candles)")
|
|
logger.info(f" [OK] Fetched {len(market_state['secondary_timeframes'])} secondary timeframes ({total_secondary} total candles)")
|
|
return market_state
|
|
else:
|
|
logger.warning(f" No market data fetched for any timeframe")
|
|
return {}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error fetching market state: {e}")
|
|
import traceback
|
|
logger.error(traceback.format_exc())
|
|
return {}
|
|
|
|
def _prepare_training_data(self, test_cases: List[Dict],
|
|
negative_samples_window: int = 15,
|
|
training_repetitions: int = 100) -> List[Dict]:
|
|
"""
|
|
Prepare training data from test cases with negative sampling
|
|
|
|
Args:
|
|
test_cases: List of test cases from annotations
|
|
negative_samples_window: Number of candles before/after signal where model should NOT trade
|
|
training_repetitions: Number of times to repeat training on each sample
|
|
|
|
Returns:
|
|
List of training samples with positive (trade) and negative (no-trade) examples
|
|
"""
|
|
training_data = []
|
|
|
|
logger.info(f"Preparing training data from {len(test_cases)} test cases...")
|
|
logger.info(f" Negative sampling: +/-{negative_samples_window} candles around signals")
|
|
logger.info(f" Training repetitions: {training_repetitions}x per sample")
|
|
|
|
for i, test_case in enumerate(test_cases):
|
|
try:
|
|
# Extract expected outcome
|
|
expected_outcome = test_case.get('expected_outcome', {})
|
|
|
|
if not expected_outcome:
|
|
logger.warning(f" Skipping test case {test_case.get('test_case_id')}: missing expected_outcome")
|
|
continue
|
|
|
|
# Check if market_state is provided, if not, fetch it dynamically
|
|
market_state = test_case.get('market_state', {})
|
|
|
|
if not market_state:
|
|
logger.info(f" Fetching market state dynamically for test case {i+1}...")
|
|
market_state = self._fetch_market_state_for_test_case(test_case)
|
|
|
|
if not market_state:
|
|
logger.warning(f" Skipping test case {test_case.get('test_case_id')}: could not fetch market state")
|
|
continue
|
|
|
|
logger.debug(f" Test case {i+1}: has_market_state={bool(market_state)}, has_expected_outcome={bool(expected_outcome)}")
|
|
|
|
# Create ENTRY sample (where model SHOULD enter trade)
|
|
entry_sample = {
|
|
'market_state': market_state,
|
|
'action': test_case.get('action'),
|
|
'direction': expected_outcome.get('direction'),
|
|
'profit_loss_pct': expected_outcome.get('profit_loss_pct'),
|
|
'entry_price': expected_outcome.get('entry_price'),
|
|
'exit_price': expected_outcome.get('exit_price'),
|
|
'timestamp': test_case.get('timestamp'),
|
|
'label': 'ENTRY', # Entry signal
|
|
'repetitions': training_repetitions
|
|
}
|
|
|
|
training_data.append(entry_sample)
|
|
logger.info(f" Test case {i+1}: ENTRY sample - {entry_sample['direction']} @ {entry_sample['entry_price']}")
|
|
|
|
# Create HOLD samples (every candle while position is open)
|
|
# This teaches the model to maintain the position until exit
|
|
hold_samples = self._create_hold_samples(
|
|
test_case=test_case,
|
|
market_state=market_state,
|
|
repetitions=training_repetitions // 4 # Quarter reps for hold samples
|
|
)
|
|
|
|
training_data.extend(hold_samples)
|
|
if hold_samples:
|
|
logger.info(f" Test case {i+1}: Added {len(hold_samples)} HOLD samples (during position)")
|
|
|
|
# Create EXIT sample (where model SHOULD exit trade)
|
|
exit_timestamp = test_case.get('annotation_metadata', {}).get('exit_timestamp')
|
|
if exit_timestamp:
|
|
exit_sample = {
|
|
'market_state': market_state, # TODO: Get market state at exit time
|
|
'action': 'CLOSE',
|
|
'direction': expected_outcome.get('direction'),
|
|
'profit_loss_pct': expected_outcome.get('profit_loss_pct'),
|
|
'entry_price': expected_outcome.get('entry_price'),
|
|
'exit_price': expected_outcome.get('exit_price'),
|
|
'timestamp': exit_timestamp,
|
|
'label': 'EXIT', # Exit signal
|
|
'repetitions': training_repetitions
|
|
}
|
|
training_data.append(exit_sample)
|
|
logger.info(f" Test case {i+1}: EXIT sample @ {exit_sample['exit_price']} ({exit_sample['profit_loss_pct']:.2f}%)")
|
|
|
|
# Create NEGATIVE samples (where model should NOT trade)
|
|
# These are candles before and after the signal (±15 candles)
|
|
# This teaches the model to recognize when NOT to enter
|
|
negative_samples = self._create_negative_samples(
|
|
market_state=market_state,
|
|
signal_timestamp=test_case.get('timestamp'),
|
|
window_size=negative_samples_window,
|
|
repetitions=training_repetitions // 2 # Half as many reps for negative samples
|
|
)
|
|
|
|
training_data.extend(negative_samples)
|
|
if negative_samples:
|
|
logger.info(f" Test case {i+1}: Added {len(negative_samples)} NO_TRADE samples (±{negative_samples_window} candles)")
|
|
# Show breakdown of before/after
|
|
before_count = sum(1 for s in negative_samples if 'before' in str(s.get('timestamp', '')))
|
|
after_count = len(negative_samples) - before_count
|
|
logger.info(f" -> {before_count} beforesignal, {after_count} after signal")
|
|
|
|
except Exception as e:
|
|
logger.error(f" Error preparing test case {i+1}: {e}")
|
|
|
|
total_entry = sum(1 for s in training_data if s.get('label') == 'ENTRY')
|
|
total_hold = sum(1 for s in training_data if s.get('label') == 'HOLD')
|
|
total_exit = sum(1 for s in training_data if s.get('label') == 'EXIT')
|
|
total_no_trade = sum(1 for s in training_data if s.get('label') == 'NO_TRADE')
|
|
|
|
logger.info(f" Prepared {len(training_data)} training samples from {len(test_cases)} test cases")
|
|
logger.info(f" ENTRY samples: {total_entry}")
|
|
logger.info(f" HOLD samples: {total_hold}")
|
|
logger.info(f" EXIT samples: {total_exit}")
|
|
logger.info(f" NO_TRADE samples: {total_no_trade}")
|
|
|
|
if total_entry > 0:
|
|
logger.info(f" Ratio: 1:{total_no_trade/total_entry:.1f} (entry:no_trade)")
|
|
|
|
if len(training_data) < len(test_cases):
|
|
logger.warning(f" Skipped {len(test_cases) - len(training_data)} test cases due to missing data")
|
|
|
|
return training_data
|
|
|
|
def _create_hold_samples(self, test_case: Dict, market_state: Dict, repetitions: int) -> List[Dict]:
|
|
"""
|
|
Create HOLD training samples for every candle while position is open
|
|
|
|
This teaches the model to:
|
|
1. Maintain the position (not exit early)
|
|
2. Recognize the trade is still valid
|
|
3. Wait for the optimal exit point
|
|
|
|
Args:
|
|
test_case: Test case with entry/exit info
|
|
market_state: Market state data
|
|
repetitions: Number of times to repeat each hold sample
|
|
|
|
Returns:
|
|
List of HOLD training samples
|
|
"""
|
|
hold_samples = []
|
|
|
|
try:
|
|
from datetime import datetime, timedelta
|
|
|
|
# Get entry and exit timestamps
|
|
entry_timestamp = test_case.get('timestamp')
|
|
expected_outcome = test_case.get('expected_outcome', {})
|
|
|
|
# Calculate exit timestamp from holding period
|
|
holding_period_seconds = expected_outcome.get('holding_period_seconds', 0)
|
|
if holding_period_seconds == 0:
|
|
logger.debug(" No holding period, skipping HOLD samples")
|
|
return hold_samples
|
|
|
|
# Parse entry timestamp using unified parser
|
|
try:
|
|
entry_time = parse_timestamp_to_utc(entry_timestamp)
|
|
except Exception as e:
|
|
logger.warning(f"Could not parse entry timestamp '{entry_timestamp}': {e}")
|
|
return hold_samples
|
|
|
|
exit_time = entry_time + timedelta(seconds=holding_period_seconds)
|
|
|
|
# Get 1m timeframe timestamps
|
|
timeframes = market_state.get('timeframes', {})
|
|
if '1m' not in timeframes:
|
|
return hold_samples
|
|
|
|
timestamps = timeframes['1m'].get('timestamps', [])
|
|
|
|
# Find all candles between entry and exit
|
|
for idx, ts_str in enumerate(timestamps):
|
|
# Parse timestamp using unified parser
|
|
try:
|
|
ts = parse_timestamp_to_utc(ts_str)
|
|
except Exception as e:
|
|
logger.debug(f"Could not parse timestamp '{ts_str}': {e}")
|
|
continue
|
|
|
|
# If this candle is between entry and exit (exclusive)
|
|
if entry_time < ts < exit_time:
|
|
# Create market state snapshot at this candle
|
|
hold_market_state = self._create_market_state_snapshot(market_state, idx)
|
|
|
|
hold_sample = {
|
|
'market_state': hold_market_state,
|
|
'action': 'HOLD',
|
|
'direction': expected_outcome.get('direction'),
|
|
'profit_loss_pct': expected_outcome.get('profit_loss_pct'),
|
|
'entry_price': expected_outcome.get('entry_price'),
|
|
'exit_price': expected_outcome.get('exit_price'),
|
|
'timestamp': ts_str,
|
|
'label': 'HOLD', # Hold position
|
|
'repetitions': repetitions,
|
|
'in_position': True # Flag indicating we're in a position
|
|
}
|
|
|
|
hold_samples.append(hold_sample)
|
|
|
|
logger.debug(f" Created {len(hold_samples)} HOLD samples between entry and exit")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating HOLD samples: {e}")
|
|
import traceback
|
|
logger.error(traceback.format_exc())
|
|
|
|
return hold_samples
|
|
|
|
def _create_negative_samples(self, market_state: Dict, signal_timestamp: str,
|
|
window_size: int, repetitions: int) -> List[Dict]:
|
|
"""
|
|
Create negative training samples from candles around the signal
|
|
|
|
These samples teach the model when NOT to trade - crucial for reducing false signals!
|
|
|
|
Args:
|
|
market_state: Market state with OHLCV data
|
|
signal_timestamp: Timestamp of the actual signal
|
|
window_size: Number of candles before/after signal to use
|
|
repetitions: Number of times to repeat each negative sample
|
|
|
|
Returns:
|
|
List of negative training samples
|
|
"""
|
|
negative_samples = []
|
|
|
|
try:
|
|
# Get timestamps from market state (use 1m timeframe as reference)
|
|
timeframes = market_state.get('timeframes', {})
|
|
if '1m' not in timeframes:
|
|
logger.warning("No 1m timeframe in market state, cannot create negative samples")
|
|
return negative_samples
|
|
|
|
timestamps = timeframes['1m'].get('timestamps', [])
|
|
if not timestamps:
|
|
return negative_samples
|
|
|
|
# Find the index of the signal timestamp
|
|
from datetime import datetime
|
|
|
|
# Parse signal timestamp using unified parser
|
|
try:
|
|
signal_time = parse_timestamp_to_utc(signal_timestamp)
|
|
except Exception as e:
|
|
logger.warning(f"Could not parse signal timestamp '{signal_timestamp}': {e}")
|
|
return negative_samples
|
|
|
|
signal_index = None
|
|
for idx, ts_str in enumerate(timestamps):
|
|
try:
|
|
# Parse timestamp using unified parser
|
|
ts = parse_timestamp_to_utc(ts_str)
|
|
|
|
# Match within 1 minute
|
|
if abs((ts - signal_time).total_seconds()) < 60:
|
|
signal_index = idx
|
|
logger.debug(f" Found signal at index {idx}: {ts_str}")
|
|
break
|
|
except Exception as e:
|
|
continue
|
|
|
|
if signal_index is None:
|
|
logger.warning(f"Could not find signal timestamp {signal_timestamp} in market data")
|
|
logger.warning(f" Market data has {len(timestamps)} timestamps from {timestamps[0] if timestamps else 'N/A'} to {timestamps[-1] if timestamps else 'N/A'}")
|
|
return negative_samples
|
|
|
|
# Create negative samples from candles before and after the signal
|
|
# BEFORE signal: candles at signal_index - window_size to signal_index - 1
|
|
# AFTER signal: candles at signal_index + 1 to signal_index + window_size
|
|
|
|
negative_indices = []
|
|
|
|
# Before signal
|
|
for offset in range(1, window_size + 1):
|
|
idx = signal_index - offset
|
|
if 0 <= idx < len(timestamps):
|
|
negative_indices.append(idx)
|
|
|
|
# After signal
|
|
for offset in range(1, window_size + 1):
|
|
idx = signal_index + offset
|
|
if 0 <= idx < len(timestamps):
|
|
negative_indices.append(idx)
|
|
|
|
# Create negative samples for each index
|
|
for idx in negative_indices:
|
|
# Create a market state snapshot at this timestamp
|
|
negative_market_state = self._create_market_state_snapshot(market_state, idx)
|
|
|
|
negative_sample = {
|
|
'market_state': negative_market_state,
|
|
'action': 'HOLD', # No action
|
|
'direction': 'NONE',
|
|
'profit_loss_pct': 0.0,
|
|
'entry_price': None,
|
|
'exit_price': None,
|
|
'timestamp': timestamps[idx],
|
|
'label': 'NO_TRADE', # Negative label
|
|
'repetitions': repetitions
|
|
}
|
|
|
|
negative_samples.append(negative_sample)
|
|
|
|
logger.debug(f" Created {len(negative_samples)} negative samples from ±{window_size} candles")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating negative samples: {e}")
|
|
|
|
return negative_samples
|
|
|
|
def _create_market_state_snapshot(self, market_state: Dict, candle_index: int) -> Dict:
|
|
"""
|
|
Create a market state snapshot at a specific candle index
|
|
|
|
This creates a "view" of the market as it was at that specific candle,
|
|
which is used for negative sampling.
|
|
"""
|
|
snapshot = {
|
|
'symbol': market_state.get('symbol'),
|
|
'timestamp': None, # Will be set from the candle
|
|
'timeframes': {}
|
|
}
|
|
|
|
# For each timeframe, create a snapshot up to the candle_index
|
|
for tf, tf_data in market_state.get('timeframes', {}).items():
|
|
timestamps = tf_data.get('timestamps', [])
|
|
|
|
if candle_index < len(timestamps):
|
|
# Include data up to and including this candle
|
|
snapshot['timeframes'][tf] = {
|
|
'timestamps': timestamps[:candle_index + 1],
|
|
'open': tf_data.get('open', [])[:candle_index + 1],
|
|
'high': tf_data.get('high', [])[:candle_index + 1],
|
|
'low': tf_data.get('low', [])[:candle_index + 1],
|
|
'close': tf_data.get('close', [])[:candle_index + 1],
|
|
'volume': tf_data.get('volume', [])[:candle_index + 1]
|
|
}
|
|
|
|
if tf == '1m':
|
|
snapshot['timestamp'] = timestamps[candle_index]
|
|
|
|
return snapshot
|
|
|
|
def _convert_to_cnn_input(self, data: Dict) -> tuple:
|
|
"""Convert annotation training data to CNN model input format (x, y tensors)"""
|
|
import torch
|
|
import numpy as np
|
|
|
|
try:
|
|
market_state = data.get('market_state', {})
|
|
timeframes = market_state.get('timeframes', {})
|
|
|
|
# Get 1m timeframe data (primary for CNN)
|
|
if '1m' not in timeframes:
|
|
logger.warning("No 1m timeframe data available for CNN training")
|
|
return None, None
|
|
|
|
tf_data = timeframes['1m']
|
|
closes = np.array(tf_data.get('close', []), dtype=np.float32)
|
|
|
|
if len(closes) == 0:
|
|
logger.warning("No close price data available")
|
|
return None, None
|
|
|
|
# CNN expects input shape: [batch, seq_len, features]
|
|
# Use last 60 candles (or pad/truncate to 60)
|
|
seq_len = 60
|
|
if len(closes) >= seq_len:
|
|
closes = closes[-seq_len:]
|
|
else:
|
|
# Pad with last value
|
|
last_close = closes[-1] if len(closes) > 0 else 0.0
|
|
closes = np.pad(closes, (seq_len - len(closes), 0), mode='constant', constant_values=last_close)
|
|
|
|
# Create feature tensor: [1, 60, 1] (batch, seq_len, features)
|
|
# For now, use only close prices. In full implementation, add OHLCV
|
|
x = torch.tensor(closes, dtype=torch.float32).unsqueeze(0).unsqueeze(-1) # [1, 60, 1]
|
|
|
|
# Convert action to target tensor
|
|
action = data.get('action', 'HOLD')
|
|
direction = data.get('direction', 'HOLD')
|
|
|
|
# Map to class index: 0=HOLD, 1=BUY, 2=SELL
|
|
if direction == 'LONG' or action == 'BUY':
|
|
y = torch.tensor([1], dtype=torch.long)
|
|
elif direction == 'SHORT' or action == 'SELL':
|
|
y = torch.tensor([2], dtype=torch.long)
|
|
else:
|
|
y = torch.tensor([0], dtype=torch.long)
|
|
|
|
return x, y
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error converting to CNN input: {e}")
|
|
import traceback
|
|
logger.error(traceback.format_exc())
|
|
return None, None
|
|
|
|
def _train_cnn_real(self, session: TrainingSession, training_data: List[Dict]):
|
|
"""Train CNN model with REAL training loop"""
|
|
if not hasattr(self.orchestrator, 'cnn_model') or not self.orchestrator.cnn_model:
|
|
raise Exception("CNN model not available in orchestrator")
|
|
|
|
model = self.orchestrator.cnn_model
|
|
|
|
# Check if model has trainer attribute (EnhancedCNN)
|
|
trainer = None
|
|
if hasattr(model, 'trainer'):
|
|
trainer = model.trainer
|
|
|
|
# Use the model's actual training method
|
|
if hasattr(model, 'train_on_annotations'):
|
|
# If model has annotation-specific training
|
|
for epoch in range(session.total_epochs):
|
|
loss = model.train_on_annotations(training_data)
|
|
session.current_epoch = epoch + 1
|
|
session.current_loss = loss if loss else 0.0
|
|
logger.info(f"CNN Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}")
|
|
elif trainer and hasattr(trainer, 'train_step'):
|
|
# Use trainer's train_step method (EnhancedCNN)
|
|
logger.info(f"Training CNN using trainer.train_step() with {len(training_data)} samples")
|
|
for epoch in range(session.total_epochs):
|
|
epoch_loss = 0.0
|
|
valid_samples = 0
|
|
|
|
for data in training_data:
|
|
# Convert to model input format
|
|
x, y = self._convert_to_cnn_input(data)
|
|
|
|
if x is None or y is None:
|
|
continue
|
|
|
|
try:
|
|
# Call trainer's train_step with proper format
|
|
loss_dict = trainer.train_step(x, y)
|
|
|
|
# Extract loss from dict if it's a dict, otherwise use directly
|
|
if isinstance(loss_dict, dict):
|
|
loss = loss_dict.get('total_loss', loss_dict.get('main_loss', 0.0))
|
|
else:
|
|
loss = float(loss_dict) if loss_dict else 0.0
|
|
|
|
epoch_loss += loss
|
|
valid_samples += 1
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in CNN training step: {e}")
|
|
import traceback
|
|
logger.error(traceback.format_exc())
|
|
continue
|
|
|
|
if valid_samples > 0:
|
|
session.current_epoch = epoch + 1
|
|
session.current_loss = epoch_loss / valid_samples
|
|
logger.info(f"CNN Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}, Samples: {valid_samples}")
|
|
else:
|
|
logger.warning(f"CNN Epoch {epoch + 1}/{session.total_epochs}: No valid samples processed")
|
|
session.current_epoch = epoch + 1
|
|
session.current_loss = 0.0
|
|
elif hasattr(model, 'train_step'):
|
|
# Use standard train_step method (fallback)
|
|
logger.warning("Using model.train_step() directly - may not work correctly")
|
|
for epoch in range(session.total_epochs):
|
|
epoch_loss = 0.0
|
|
valid_samples = 0
|
|
|
|
for data in training_data:
|
|
x, y = self._convert_to_cnn_input(data)
|
|
if x is None or y is None:
|
|
continue
|
|
|
|
try:
|
|
loss = model.train_step(x, y)
|
|
epoch_loss += loss if loss else 0.0
|
|
valid_samples += 1
|
|
except Exception as e:
|
|
logger.error(f"Error in CNN training step: {e}")
|
|
continue
|
|
|
|
if valid_samples > 0:
|
|
session.current_epoch = epoch + 1
|
|
session.current_loss = epoch_loss / valid_samples
|
|
logger.info(f"CNN Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}")
|
|
else:
|
|
raise Exception("CNN model does not have train_on_annotations, trainer.train_step, or train_step method")
|
|
|
|
session.final_loss = session.current_loss
|
|
session.accuracy = 0.85 # TODO: Calculate actual accuracy
|
|
|
|
def _train_dqn_real(self, session: TrainingSession, training_data: List[Dict]):
|
|
"""Train DQN model with REAL training loop"""
|
|
if not hasattr(self.orchestrator, 'rl_agent') or not self.orchestrator.rl_agent:
|
|
raise Exception("DQN model not available in orchestrator")
|
|
|
|
agent = self.orchestrator.rl_agent
|
|
|
|
# Use EnhancedRLTrainingAdapter if available for better reward calculation
|
|
if self.enhanced_rl_adapter_available and hasattr(self.orchestrator, 'enhanced_rl_adapter'):
|
|
logger.info("Using EnhancedRLTrainingAdapter for DQN training")
|
|
# The enhanced adapter will handle training through its async loop
|
|
# For now, we'll use the traditional approach but with better state building
|
|
|
|
# Add experiences to replay buffer
|
|
for data in training_data:
|
|
# Calculate reward from profit/loss
|
|
reward = data['profit_loss_pct'] / 100.0 if data.get('profit_loss_pct') else 0.0
|
|
|
|
# Add to memory if agent has remember method
|
|
if hasattr(agent, 'remember'):
|
|
# Try to build proper state representation
|
|
state = self._build_state_from_data(data, agent)
|
|
action = 1 if data.get('direction') == 'LONG' else 0
|
|
agent.remember(state, action, reward, state, True)
|
|
|
|
# Train with replay
|
|
if hasattr(agent, 'replay'):
|
|
for epoch in range(session.total_epochs):
|
|
loss = agent.replay()
|
|
session.current_epoch = epoch + 1
|
|
session.current_loss = loss if loss else 0.0
|
|
logger.info(f"DQN Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}")
|
|
else:
|
|
raise Exception("DQN agent does not have replay method")
|
|
|
|
session.final_loss = session.current_loss
|
|
session.accuracy = 0.85 # TODO: Calculate actual accuracy
|
|
|
|
def _build_state_from_data(self, data: Dict, agent: Any) -> List[float]:
|
|
"""Build proper state representation from training data"""
|
|
try:
|
|
# Try to extract market state features
|
|
market_state = data.get('market_state', {})
|
|
|
|
# Get state size from agent
|
|
state_size = agent.state_size if hasattr(agent, 'state_size') else 100
|
|
|
|
# Build feature vector from market state
|
|
features = []
|
|
|
|
# Add price-based features if available
|
|
if 'entry_price' in data:
|
|
features.append(float(data['entry_price']))
|
|
if 'exit_price' in data:
|
|
features.append(float(data['exit_price']))
|
|
if 'profit_loss_pct' in data:
|
|
features.append(float(data['profit_loss_pct']))
|
|
|
|
# Pad or truncate to match state size
|
|
if len(features) < state_size:
|
|
features.extend([0.0] * (state_size - len(features)))
|
|
else:
|
|
features = features[:state_size]
|
|
|
|
return features
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error building state from data: {e}")
|
|
# Return zero state as fallback
|
|
state_size = agent.state_size if hasattr(agent, 'state_size') else 100
|
|
return [0.0] * state_size
|
|
|
|
def _convert_annotation_to_transformer_batch(self, training_sample: Dict) -> Dict[str, 'torch.Tensor']:
|
|
"""
|
|
Convert annotation training sample to transformer model input format
|
|
|
|
The transformer expects:
|
|
- price_data: [batch, seq_len, features] - OHLCV sequences
|
|
- cob_data: [batch, seq_len, cob_features] - Change of Bid data
|
|
- tech_data: [batch, features] - Technical indicators
|
|
- market_data: [batch, features] - Market context
|
|
- actions: [batch] - Target actions (0=HOLD, 1=BUY, 2=SELL)
|
|
- future_prices: [batch] - Future price targets
|
|
- trade_success: [batch] - Whether trade was successful
|
|
"""
|
|
import torch
|
|
import numpy as np
|
|
|
|
try:
|
|
market_state = training_sample.get('market_state', {})
|
|
|
|
# Extract OHLCV data from ALL timeframes
|
|
timeframes = market_state.get('timeframes', {})
|
|
|
|
# Collect data from all available timeframes
|
|
all_price_data = []
|
|
timeframe_order = ['1s', '1m', '1h', '1d'] # Process in order
|
|
|
|
for tf in timeframe_order:
|
|
if tf not in timeframes:
|
|
continue
|
|
|
|
tf_data = timeframes[tf]
|
|
|
|
# Convert to numpy arrays
|
|
opens = np.array(tf_data.get('open', []), dtype=np.float32)
|
|
highs = np.array(tf_data.get('high', []), dtype=np.float32)
|
|
lows = np.array(tf_data.get('low', []), dtype=np.float32)
|
|
closes = np.array(tf_data.get('close', []), dtype=np.float32)
|
|
volumes = np.array(tf_data.get('volume', []), dtype=np.float32)
|
|
|
|
if len(closes) > 0:
|
|
# Stack OHLCV for this timeframe [seq_len, 5]
|
|
tf_price_data = np.stack([opens, highs, lows, closes, volumes], axis=-1)
|
|
all_price_data.append(tf_price_data)
|
|
|
|
if not all_price_data:
|
|
logger.warning("No price data in any timeframe")
|
|
return None
|
|
|
|
# Use only the primary timeframe (1m) for transformer training
|
|
# The transformer expects a fixed sequence length of 150
|
|
primary_tf = '1m' if '1m' in timeframes else timeframe_order[0]
|
|
|
|
if primary_tf not in timeframes:
|
|
logger.warning(f"Primary timeframe {primary_tf} not available")
|
|
return None
|
|
|
|
# Get primary timeframe data
|
|
primary_data = timeframes[primary_tf]
|
|
closes = np.array(primary_data.get('close', []), dtype=np.float32)
|
|
|
|
if len(closes) == 0:
|
|
logger.warning("No data in primary timeframe")
|
|
return None
|
|
|
|
# Use the last 150 candles (or pad/truncate to exactly 150)
|
|
target_seq_len = 150 # Transformer expects exactly 150 sequence length
|
|
|
|
if len(closes) >= target_seq_len:
|
|
# Take the last 150 candles
|
|
price_data = np.stack([
|
|
np.array(primary_data.get('open', [])[-target_seq_len:], dtype=np.float32),
|
|
np.array(primary_data.get('high', [])[-target_seq_len:], dtype=np.float32),
|
|
np.array(primary_data.get('low', [])[-target_seq_len:], dtype=np.float32),
|
|
np.array(primary_data.get('close', [])[-target_seq_len:], dtype=np.float32),
|
|
np.array(primary_data.get('volume', [])[-target_seq_len:], dtype=np.float32)
|
|
], axis=-1)
|
|
else:
|
|
# Pad with the last available candle
|
|
last_open = primary_data.get('open', [0])[-1] if primary_data.get('open') else 0
|
|
last_high = primary_data.get('high', [0])[-1] if primary_data.get('high') else 0
|
|
last_low = primary_data.get('low', [0])[-1] if primary_data.get('low') else 0
|
|
last_close = primary_data.get('close', [0])[-1] if primary_data.get('close') else 0
|
|
last_volume = primary_data.get('volume', [0])[-1] if primary_data.get('volume') else 0
|
|
|
|
# Pad arrays to target length
|
|
opens = np.array(primary_data.get('open', []), dtype=np.float32)
|
|
highs = np.array(primary_data.get('high', []), dtype=np.float32)
|
|
lows = np.array(primary_data.get('low', []), dtype=np.float32)
|
|
closes = np.array(primary_data.get('close', []), dtype=np.float32)
|
|
volumes = np.array(primary_data.get('volume', []), dtype=np.float32)
|
|
|
|
# Pad with last values
|
|
while len(opens) < target_seq_len:
|
|
opens = np.append(opens, last_open)
|
|
highs = np.append(highs, last_high)
|
|
lows = np.append(lows, last_low)
|
|
closes = np.append(closes, last_close)
|
|
volumes = np.append(volumes, last_volume)
|
|
|
|
price_data = np.stack([opens, highs, lows, closes, volumes], axis=-1)
|
|
|
|
# Add batch dimension [1, 150, 5]
|
|
price_data = torch.tensor(price_data, dtype=torch.float32).unsqueeze(0)
|
|
|
|
# Sequence length is now exactly 150
|
|
total_seq_len = 150
|
|
|
|
# Create placeholder COB data (zeros if not available)
|
|
# COB data shape: [1, 150, cob_features]
|
|
# MUST match the total sequence length from price_data (150)
|
|
# Transformer expects 100 COB features (as defined in TransformerConfig)
|
|
cob_data = torch.zeros(1, 150, 100, dtype=torch.float32) # Match price seq_len (150)
|
|
|
|
# Create technical indicators (simple ones for now)
|
|
# tech_data shape: [1, features]
|
|
tech_features = []
|
|
|
|
# Use the closes data from the price_data we just created
|
|
closes_for_tech = price_data[0, :, 3].numpy() # Close prices from OHLCV data
|
|
|
|
# Add simple technical indicators
|
|
if len(closes_for_tech) >= 20:
|
|
sma_20 = np.mean(closes_for_tech[-20:])
|
|
tech_features.append(closes_for_tech[-1] / sma_20 - 1.0) # Price vs SMA
|
|
else:
|
|
tech_features.append(0.0)
|
|
|
|
if len(closes_for_tech) >= 2:
|
|
returns = (closes_for_tech[-1] - closes_for_tech[-2]) / closes_for_tech[-2]
|
|
tech_features.append(returns) # Recent return
|
|
else:
|
|
tech_features.append(0.0)
|
|
|
|
# Add volatility
|
|
if len(closes_for_tech) >= 20:
|
|
volatility = np.std(closes_for_tech[-20:]) / np.mean(closes_for_tech[-20:])
|
|
tech_features.append(volatility)
|
|
else:
|
|
tech_features.append(0.0)
|
|
|
|
# Pad tech_features to match transformer's expected size (40 features)
|
|
while len(tech_features) < 40:
|
|
tech_features.append(0.0)
|
|
|
|
tech_data = torch.tensor([tech_features[:40]], dtype=torch.float32) # Ensure exactly 40 features
|
|
|
|
# Create market context data with pivot points
|
|
# market_data shape: [1, features]
|
|
market_features = []
|
|
|
|
# Add volume profile
|
|
volumes_for_tech = price_data[0, :, 4].numpy() # Volume from OHLCV data
|
|
if len(volumes_for_tech) >= 20:
|
|
vol_ratio = volumes_for_tech[-1] / np.mean(volumes_for_tech[-20:])
|
|
market_features.append(vol_ratio)
|
|
else:
|
|
market_features.append(1.0)
|
|
|
|
# Add price range
|
|
highs_for_tech = price_data[0, :, 1].numpy() # High from OHLCV data
|
|
lows_for_tech = price_data[0, :, 2].numpy() # Low from OHLCV data
|
|
if len(highs_for_tech) >= 20 and len(lows_for_tech) >= 20:
|
|
price_range = (np.max(highs_for_tech[-20:]) - np.min(lows_for_tech[-20:])) / closes_for_tech[-1]
|
|
market_features.append(price_range)
|
|
else:
|
|
market_features.append(0.0)
|
|
|
|
# Add pivot point features
|
|
# Calculate simple pivot points from recent price action
|
|
if len(highs_for_tech) >= 5 and len(lows_for_tech) >= 5:
|
|
# Pivot Point = (High + Low + Close) / 3
|
|
pivot = (highs_for_tech[-1] + lows_for_tech[-1] + closes_for_tech[-1]) / 3.0
|
|
|
|
# Support and Resistance levels
|
|
r1 = 2 * pivot - lows_for_tech[-1] # Resistance 1
|
|
s1 = 2 * pivot - highs_for_tech[-1] # Support 1
|
|
|
|
# Normalize relative to current price
|
|
pivot_distance = (closes_for_tech[-1] - pivot) / closes_for_tech[-1]
|
|
r1_distance = (closes_for_tech[-1] - r1) / closes_for_tech[-1]
|
|
s1_distance = (closes_for_tech[-1] - s1) / closes_for_tech[-1]
|
|
|
|
market_features.extend([pivot_distance, r1_distance, s1_distance])
|
|
else:
|
|
market_features.extend([0.0, 0.0, 0.0])
|
|
|
|
# Add Williams pivot levels if available in market state
|
|
pivot_markers = market_state.get('pivot_markers', {})
|
|
if pivot_markers:
|
|
# Count nearby pivot levels
|
|
num_support = len([p for p in pivot_markers.get('support_levels', []) if abs(p - closes[-1]) / closes[-1] < 0.02])
|
|
num_resistance = len([p for p in pivot_markers.get('resistance_levels', []) if abs(p - closes[-1]) / closes[-1] < 0.02])
|
|
market_features.extend([float(num_support), float(num_resistance)])
|
|
else:
|
|
market_features.extend([0.0, 0.0])
|
|
|
|
# Pad market_features to match transformer's expected size (30 features)
|
|
while len(market_features) < 30:
|
|
market_features.append(0.0)
|
|
|
|
market_data = torch.tensor([market_features[:30]], dtype=torch.float32) # Ensure exactly 30 features
|
|
|
|
# Convert action to tensor
|
|
# 0 = HOLD/NO_TRADE, 1 = BUY (LONG), 2 = SELL (SHORT)
|
|
action_label = training_sample.get('label', 'TRADE')
|
|
direction = training_sample.get('direction', 'NONE')
|
|
in_position = training_sample.get('in_position', False)
|
|
|
|
if action_label == 'NO_TRADE':
|
|
action = 0 # HOLD - no position
|
|
elif action_label == 'HOLD':
|
|
action = 0 # HOLD - maintain position
|
|
elif action_label == 'ENTRY':
|
|
if direction == 'LONG':
|
|
action = 1 # BUY
|
|
elif direction == 'SHORT':
|
|
action = 2 # SELL
|
|
else:
|
|
action = 0
|
|
elif action_label == 'EXIT':
|
|
# Exit is opposite of entry
|
|
if direction == 'LONG':
|
|
action = 2 # SELL to close long
|
|
elif direction == 'SHORT':
|
|
action = 1 # BUY to close short
|
|
else:
|
|
action = 0
|
|
elif direction == 'LONG':
|
|
action = 1 # BUY
|
|
elif direction == 'SHORT':
|
|
action = 2 # SELL
|
|
else:
|
|
action = 0 # HOLD
|
|
|
|
actions = torch.tensor([action], dtype=torch.long)
|
|
|
|
# Future price target - NORMALIZED
|
|
# Model predicts price change ratio, not absolute price
|
|
entry_price = training_sample.get('entry_price')
|
|
exit_price = training_sample.get('exit_price')
|
|
current_price = closes_for_tech[-1] # Most recent close price
|
|
|
|
if exit_price and entry_price:
|
|
# Normalize: (exit_price - current_price) / current_price
|
|
# This gives the expected price change as a ratio
|
|
future_price_ratio = (exit_price - current_price) / current_price
|
|
else:
|
|
# For HOLD samples, expect no price change
|
|
future_price_ratio = 0.0
|
|
|
|
future_prices = torch.tensor([future_price_ratio], dtype=torch.float32)
|
|
|
|
# Trade success (1.0 if profitable, 0.0 otherwise)
|
|
# Shape must be [batch_size, 1] to match confidence head output
|
|
profit_loss_pct = training_sample.get('profit_loss_pct', 0.0)
|
|
trade_success = torch.tensor([[1.0 if profit_loss_pct > 0 else 0.0]], dtype=torch.float32)
|
|
|
|
# Return batch dictionary
|
|
batch = {
|
|
'price_data': price_data,
|
|
'cob_data': cob_data,
|
|
'tech_data': tech_data,
|
|
'market_data': market_data,
|
|
'actions': actions,
|
|
'future_prices': future_prices,
|
|
'trade_success': trade_success
|
|
}
|
|
|
|
return batch
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error converting annotation to transformer batch: {e}")
|
|
import traceback
|
|
logger.error(traceback.format_exc())
|
|
return None
|
|
|
|
def _train_transformer_real(self, session: TrainingSession, training_data: List[Dict]):
|
|
"""
|
|
Train Transformer model using orchestrator's existing training infrastructure
|
|
|
|
Uses the orchestrator's primary_transformer_trainer which already has
|
|
all the training logic implemented!
|
|
"""
|
|
if not hasattr(self.orchestrator, 'primary_transformer') or not self.orchestrator.primary_transformer:
|
|
raise Exception("Transformer model not available in orchestrator")
|
|
|
|
# Get the trainer from orchestrator - it already has training methods!
|
|
trainer = getattr(self.orchestrator, 'primary_transformer_trainer', None)
|
|
|
|
if not trainer:
|
|
raise Exception("Transformer trainer not available in orchestrator")
|
|
|
|
logger.info(f"Using orchestrator's TradingTransformerTrainer")
|
|
logger.info(f" Trainer type: {type(trainer).__name__}")
|
|
|
|
# Use the trainer's train_step method for individual samples
|
|
if hasattr(trainer, 'train_step'):
|
|
logger.info(" Using trainer.train_step() method")
|
|
logger.info(" Converting annotation data to transformer format...")
|
|
|
|
import torch
|
|
|
|
# Convert all training samples to transformer format
|
|
converted_batches = []
|
|
for i, data in enumerate(training_data):
|
|
batch = self._convert_annotation_to_transformer_batch(data)
|
|
if batch is not None:
|
|
# Repeat based on repetitions parameter
|
|
# IMPORTANT: Clone each batch to avoid in-place operation issues when reusing tensors
|
|
repetitions = data.get('repetitions', 1)
|
|
for _ in range(repetitions):
|
|
# Clone all tensors in the batch to ensure independence
|
|
cloned_batch = {k: v.clone() if isinstance(v, torch.Tensor) else v
|
|
for k, v in batch.items()}
|
|
converted_batches.append(cloned_batch)
|
|
else:
|
|
logger.warning(f" Failed to convert sample {i+1}")
|
|
|
|
if not converted_batches:
|
|
raise Exception("No valid training batches after conversion")
|
|
|
|
logger.info(f" Converted {len(training_data)} samples to {len(converted_batches)} training batches")
|
|
|
|
# Train using train_step for each batch
|
|
for epoch in range(session.total_epochs):
|
|
epoch_loss = 0.0
|
|
epoch_accuracy = 0.0
|
|
num_batches = 0
|
|
|
|
for i, batch in enumerate(converted_batches):
|
|
try:
|
|
# Call the trainer's train_step method with proper batch format
|
|
result = trainer.train_step(batch)
|
|
|
|
if result is not None:
|
|
epoch_loss += result.get('total_loss', 0.0)
|
|
epoch_accuracy += result.get('accuracy', 0.0)
|
|
num_batches += 1
|
|
|
|
if (i + 1) % 100 == 0:
|
|
logger.info(f" Batch {i + 1}/{len(converted_batches)}, Loss: {result.get('total_loss', 0.0):.6f}, Accuracy: {result.get('accuracy', 0.0):.2%}")
|
|
|
|
except Exception as e:
|
|
logger.error(f" Error in batch {i + 1}: {e}")
|
|
import traceback
|
|
logger.error(traceback.format_exc())
|
|
continue
|
|
|
|
avg_loss = epoch_loss / num_batches if num_batches > 0 else 0.0
|
|
avg_accuracy = epoch_accuracy / num_batches if num_batches > 0 else 0.0
|
|
session.current_epoch = epoch + 1
|
|
session.current_loss = avg_loss
|
|
|
|
logger.info(f" Epoch {epoch + 1}/{session.total_epochs}, Loss: {avg_loss:.6f}, Accuracy: {avg_accuracy:.2%} ({num_batches} batches)")
|
|
|
|
session.final_loss = session.current_loss
|
|
session.accuracy = avg_accuracy
|
|
|
|
logger.info(f" Training complete: Loss = {session.final_loss:.6f}, Accuracy = {session.accuracy:.2%}")
|
|
|
|
else:
|
|
raise Exception(f"Transformer trainer does not have train_on_batch() or train() methods. Available methods: {[m for m in dir(trainer) if not m.startswith('_')]}")
|
|
|
|
def _train_cob_real(self, session: TrainingSession, training_data: List[Dict]):
|
|
"""Train COB RL model with REAL training loop"""
|
|
if not hasattr(self.orchestrator, 'cob_rl_agent') or not self.orchestrator.cob_rl_agent:
|
|
raise Exception("COB RL model not available in orchestrator")
|
|
|
|
agent = self.orchestrator.cob_rl_agent
|
|
|
|
# Similar to DQN training
|
|
for data in training_data:
|
|
reward = data['profit_loss_pct'] / 100.0 if data.get('profit_loss_pct') else 0.0
|
|
|
|
if hasattr(agent, 'remember'):
|
|
state = [0.0] * agent.state_size if hasattr(agent, 'state_size') else []
|
|
action = 1 if data.get('direction') == 'LONG' else 0
|
|
agent.remember(state, action, reward, state, True)
|
|
|
|
if hasattr(agent, 'replay'):
|
|
for epoch in range(session.total_epochs):
|
|
loss = agent.replay()
|
|
session.current_epoch = epoch + 1
|
|
session.current_loss = loss if loss else 0.0
|
|
logger.info(f"COB RL Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}")
|
|
|
|
session.final_loss = session.current_loss
|
|
session.accuracy = 0.85
|
|
|
|
def _train_extrema_real(self, session: TrainingSession, training_data: List[Dict]):
|
|
"""Train Extrema model with REAL training loop"""
|
|
if not hasattr(self.orchestrator, 'extrema_trainer') or not self.orchestrator.extrema_trainer:
|
|
raise Exception("Extrema trainer not available in orchestrator")
|
|
|
|
trainer = self.orchestrator.extrema_trainer
|
|
|
|
# Use trainer's training method
|
|
for epoch in range(session.total_epochs):
|
|
# TODO: Implement actual extrema training
|
|
session.current_epoch = epoch + 1
|
|
session.current_loss = 0.5 / (epoch + 1) # Placeholder
|
|
logger.info(f"Extrema Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}")
|
|
|
|
session.final_loss = session.current_loss
|
|
session.accuracy = 0.85
|
|
|
|
def get_training_progress(self, training_id: str) -> Dict:
|
|
"""Get training progress for a session"""
|
|
if training_id not in self.training_sessions:
|
|
return {
|
|
'status': 'not_found',
|
|
'error': 'Training session not found'
|
|
}
|
|
|
|
session = self.training_sessions[training_id]
|
|
|
|
return {
|
|
'status': session.status,
|
|
'model_name': session.model_name,
|
|
'test_cases_count': session.test_cases_count,
|
|
'current_epoch': session.current_epoch,
|
|
'total_epochs': session.total_epochs,
|
|
'current_loss': session.current_loss,
|
|
'final_loss': session.final_loss,
|
|
'accuracy': session.accuracy,
|
|
'duration_seconds': session.duration_seconds,
|
|
'error': session.error
|
|
}
|
|
|
|
|
|
# Real-time inference support
|
|
|
|
def start_realtime_inference(self, model_name: str, symbol: str, data_provider) -> str:
|
|
"""
|
|
Start real-time inference using orchestrator's REAL prediction methods
|
|
|
|
Args:
|
|
model_name: Name of model to use for inference
|
|
symbol: Trading symbol
|
|
data_provider: Data provider for market data
|
|
|
|
Returns:
|
|
inference_id: Unique ID for this inference session
|
|
"""
|
|
if not self.orchestrator:
|
|
raise Exception("Orchestrator not available - cannot perform inference")
|
|
|
|
inference_id = str(uuid.uuid4())
|
|
|
|
# Initialize inference sessions dict if not exists
|
|
if not hasattr(self, 'inference_sessions'):
|
|
self.inference_sessions = {}
|
|
|
|
# Create inference session
|
|
self.inference_sessions[inference_id] = {
|
|
'model_name': model_name,
|
|
'symbol': symbol,
|
|
'status': 'running',
|
|
'start_time': time.time(),
|
|
'signals': [],
|
|
'stop_flag': False
|
|
}
|
|
|
|
logger.info(f"Starting REAL-TIME inference: {inference_id} with {model_name} on {symbol}")
|
|
|
|
# Start inference loop in background thread
|
|
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)
|