gogo2/debug/debug_trading_activity.py

#!/usr/bin/env python3
"""
Trading Activity Diagnostic Script
Debug why no trades are happening after 6 hours
"""

import logging
import asyncio
from datetime import datetime, timedelta
import pandas as pd
import numpy as np

# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

async def diagnose_trading_system():
    """Comprehensive diagnosis of trading system"""
    logger.info("=== TRADING SYSTEM DIAGNOSTIC ===")
    
    try:
        # Import core components
        from core.config import get_config
        from core.data_provider import DataProvider
        from core.enhanced_orchestrator import EnhancedTradingOrchestrator
        
        # Initialize components
        config = get_config()
        data_provider = DataProvider()
        orchestrator = EnhancedTradingOrchestrator(
            data_provider=data_provider,
            symbols=['ETH/USDT', 'BTC/USDT'],
            enhanced_rl_training=True
        )
        
        logger.info("✅ Components initialized successfully")
        
        # 1. Check data availability
        logger.info("\n=== DATA AVAILABILITY CHECK ===")
        for symbol in ['ETH/USDT', 'BTC/USDT']:
            for timeframe in ['1m', '5m', '1h']:
                try:
                    data = data_provider.get_historical_data(symbol, timeframe, limit=10)
                    if data is not None and not data.empty:
                        logger.info(f"✅ {symbol} {timeframe}: {len(data)} bars available")
                        logger.info(f"   Last price: ${data['close'].iloc[-1]:.2f}")
                    else:
                        logger.error(f"❌ {symbol} {timeframe}: NO DATA")
                except Exception as e:
                    logger.error(f"❌ {symbol} {timeframe}: ERROR - {e}")
        
        # 2. Check model status
        logger.info("\n=== MODEL STATUS CHECK ===")
        model_status = orchestrator.get_loaded_models_status() if hasattr(orchestrator, 'get_loaded_models_status') else {}
        logger.info(f"Loaded models: {model_status}")
        
        # 3. Check confidence thresholds
        logger.info("\n=== CONFIDENCE THRESHOLD CHECK ===")
        logger.info(f"Entry threshold: {getattr(orchestrator, 'confidence_threshold_open', 'UNKNOWN')}")
        logger.info(f"Exit threshold: {getattr(orchestrator, 'confidence_threshold_close', 'UNKNOWN')}")
        logger.info(f"Config threshold: {config.orchestrator.get('confidence_threshold', 'UNKNOWN')}")
        
        # 4. Test decision making
        logger.info("\n=== DECISION MAKING TEST ===")
        try:
            decisions = await orchestrator.make_coordinated_decisions()
            logger.info(f"Generated {len(decisions)} decisions")
            
            for symbol, decision in decisions.items():
                if decision:
                    logger.info(f"✅ {symbol}: {decision.action} "
                               f"(confidence: {decision.confidence:.3f}, "
                               f"price: ${decision.price:.2f})")
                else:
                    logger.warning(f"❌ {symbol}: No decision generated")
                    
        except Exception as e:
            logger.error(f"❌ Decision making failed: {e}")
        
        # 5. Test cold start predictions
        logger.info("\n=== COLD START PREDICTIONS TEST ===")
        try:
            await orchestrator.ensure_predictions_available()
            logger.info("✅ Cold start predictions system working")
        except Exception as e:
            logger.error(f"❌ Cold start predictions failed: {e}")
        
        # 6. Check cross-asset signals
        logger.info("\n=== CROSS-ASSET SIGNALS TEST ===")
        try:
            from core.unified_data_stream import UniversalDataStream
            
            # Create mock universal stream for testing
            mock_stream = type('MockStream', (), {})()
            mock_stream.get_latest_data = lambda symbol: {'price': 2500.0 if 'ETH' in symbol else 35000.0}
            mock_stream.get_market_structure = lambda symbol: {'trend': 'NEUTRAL', 'strength': 0.5}
            mock_stream.get_cob_data = lambda symbol: {'imbalance': 0.0, 'depth': 'BALANCED'}
            
            btc_analysis = await orchestrator._analyze_btc_price_action(mock_stream)
            logger.info(f"BTC analysis result: {btc_analysis}")
            
            eth_decision = await orchestrator._make_eth_decision_from_btc_signals(
                {'signal': 'NEUTRAL', 'strength': 0.5},
                {'signal': 'NEUTRAL', 'imbalance': 0.0}
            )
            logger.info(f"ETH decision result: {eth_decision}")
            
        except Exception as e:
            logger.error(f"❌ Cross-asset signals failed: {e}")
        
        # 7. Simulate trade with lower thresholds
        logger.info("\n=== SIMULATED TRADE TEST ===")
        try:
            # Create mock prediction with low confidence
            from core.enhanced_orchestrator import EnhancedPrediction
            
            mock_prediction = EnhancedPrediction(
                model_name="TEST",
                timeframe="1m",
                action="BUY",
                confidence=0.30,  # Lower confidence
                overall_action="BUY", 
                overall_confidence=0.30,
                timeframe_predictions=[],
                reasoning="Test prediction"
            )
            
            # Test if this would generate a trade
            current_price = 2500.0
            quantity = 0.01
            
            logger.info(f"Mock prediction: {mock_prediction.action} "
                       f"(confidence: {mock_prediction.confidence:.3f})")
            
            if mock_prediction.confidence > 0.25:  # Our new lower threshold
                logger.info("✅ Would generate trade with new threshold")
            else:
                logger.warning("❌ Still below threshold")
                
        except Exception as e:
            logger.error(f"❌ Simulated trade test failed: {e}")
        
        # 8. Check RL reward functions
        logger.info("\n=== RL REWARD FUNCTION TEST ===")
        try:
            # Test reward calculation
            mock_trade = {
                'action': 'BUY',
                'confidence': 0.75,
                'price': 2500.0,
                'timestamp': datetime.now()
            }
            
            mock_outcome = {
                'net_pnl': 25.0,  # $25 profit
                'exit_price': 2525.0,
                'duration': timedelta(minutes=15)
            }
            
            mock_market_data = {
                'volatility': 0.03,
                'order_flow_direction': 'bullish',
                'order_flow_strength': 0.8
            }
            
            if hasattr(orchestrator, 'calculate_enhanced_pivot_reward'):
                reward = orchestrator.calculate_enhanced_pivot_reward(
                    mock_trade, mock_market_data, mock_outcome
                )
                logger.info(f"✅ RL reward for profitable trade: {reward:.3f}")
            else:
                logger.warning("❌ Enhanced pivot reward function not available")
                
        except Exception as e:
            logger.error(f"❌ RL reward test failed: {e}")
        
        logger.info("\n=== DIAGNOSTIC COMPLETE ===")
        logger.info("Check results above to identify trading bottlenecks")
        
    except Exception as e:
        logger.error(f"Diagnostic failed: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    asyncio.run(diagnose_trading_system())