folder stricture reorganize
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Dobromir Popov
186
debug/debug_trading_activity.py
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186
debug/debug_trading_activity.py
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#!/usr/bin/env python3
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"""
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Trading Activity Diagnostic Script
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Debug why no trades are happening after 6 hours
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"""
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import logging
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import asyncio
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from datetime import datetime, timedelta
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import pandas as pd
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import numpy as np
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# Setup logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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async def diagnose_trading_system():
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"""Comprehensive diagnosis of trading system"""
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logger.info("=== TRADING SYSTEM DIAGNOSTIC ===")
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try:
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# Import core components
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from core.config import get_config
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from core.data_provider import DataProvider
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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# Initialize components
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config = get_config()
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data_provider = DataProvider()
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orchestrator = EnhancedTradingOrchestrator(
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data_provider=data_provider,
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symbols=['ETH/USDT', 'BTC/USDT'],
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enhanced_rl_training=True
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)
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logger.info("✅ Components initialized successfully")
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# 1. Check data availability
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logger.info("\n=== DATA AVAILABILITY CHECK ===")
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for symbol in ['ETH/USDT', 'BTC/USDT']:
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for timeframe in ['1m', '5m', '1h']:
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try:
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data = data_provider.get_historical_data(symbol, timeframe, limit=10)
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if data is not None and not data.empty:
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logger.info(f"✅ {symbol} {timeframe}: {len(data)} bars available")
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logger.info(f" Last price: ${data['close'].iloc[-1]:.2f}")
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else:
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logger.error(f"❌ {symbol} {timeframe}: NO DATA")
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except Exception as e:
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logger.error(f"❌ {symbol} {timeframe}: ERROR - {e}")
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# 2. Check model status
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logger.info("\n=== MODEL STATUS CHECK ===")
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model_status = orchestrator.get_loaded_models_status() if hasattr(orchestrator, 'get_loaded_models_status') else {}
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logger.info(f"Loaded models: {model_status}")
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# 3. Check confidence thresholds
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logger.info("\n=== CONFIDENCE THRESHOLD CHECK ===")
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logger.info(f"Entry threshold: {getattr(orchestrator, 'confidence_threshold_open', 'UNKNOWN')}")
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logger.info(f"Exit threshold: {getattr(orchestrator, 'confidence_threshold_close', 'UNKNOWN')}")
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logger.info(f"Config threshold: {config.orchestrator.get('confidence_threshold', 'UNKNOWN')}")
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# 4. Test decision making
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logger.info("\n=== DECISION MAKING TEST ===")
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try:
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decisions = await orchestrator.make_coordinated_decisions()
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logger.info(f"Generated {len(decisions)} decisions")
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for symbol, decision in decisions.items():
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if decision:
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logger.info(f"✅ {symbol}: {decision.action} "
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f"(confidence: {decision.confidence:.3f}, "
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f"price: ${decision.price:.2f})")
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else:
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logger.warning(f"❌ {symbol}: No decision generated")
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except Exception as e:
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logger.error(f"❌ Decision making failed: {e}")
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# 5. Test cold start predictions
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logger.info("\n=== COLD START PREDICTIONS TEST ===")
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try:
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await orchestrator.ensure_predictions_available()
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logger.info("✅ Cold start predictions system working")
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except Exception as e:
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logger.error(f"❌ Cold start predictions failed: {e}")
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# 6. Check cross-asset signals
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logger.info("\n=== CROSS-ASSET SIGNALS TEST ===")
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try:
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from core.unified_data_stream import UniversalDataStream
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# Create mock universal stream for testing
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mock_stream = type('MockStream', (), {})()
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mock_stream.get_latest_data = lambda symbol: {'price': 2500.0 if 'ETH' in symbol else 35000.0}
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mock_stream.get_market_structure = lambda symbol: {'trend': 'NEUTRAL', 'strength': 0.5}
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mock_stream.get_cob_data = lambda symbol: {'imbalance': 0.0, 'depth': 'BALANCED'}
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btc_analysis = await orchestrator._analyze_btc_price_action(mock_stream)
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logger.info(f"BTC analysis result: {btc_analysis}")
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eth_decision = await orchestrator._make_eth_decision_from_btc_signals(
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{'signal': 'NEUTRAL', 'strength': 0.5},
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{'signal': 'NEUTRAL', 'imbalance': 0.0}
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)
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logger.info(f"ETH decision result: {eth_decision}")
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except Exception as e:
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logger.error(f"❌ Cross-asset signals failed: {e}")
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# 7. Simulate trade with lower thresholds
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logger.info("\n=== SIMULATED TRADE TEST ===")
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try:
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# Create mock prediction with low confidence
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from core.enhanced_orchestrator import EnhancedPrediction
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mock_prediction = EnhancedPrediction(
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model_name="TEST",
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timeframe="1m",
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action="BUY",
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confidence=0.30, # Lower confidence
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overall_action="BUY",
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overall_confidence=0.30,
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timeframe_predictions=[],
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reasoning="Test prediction"
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)
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# Test if this would generate a trade
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current_price = 2500.0
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quantity = 0.01
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logger.info(f"Mock prediction: {mock_prediction.action} "
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f"(confidence: {mock_prediction.confidence:.3f})")
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if mock_prediction.confidence > 0.25: # Our new lower threshold
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logger.info("✅ Would generate trade with new threshold")
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else:
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logger.warning("❌ Still below threshold")
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except Exception as e:
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logger.error(f"❌ Simulated trade test failed: {e}")
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# 8. Check RL reward functions
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logger.info("\n=== RL REWARD FUNCTION TEST ===")
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try:
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# Test reward calculation
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mock_trade = {
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'action': 'BUY',
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'confidence': 0.75,
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'price': 2500.0,
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'timestamp': datetime.now()
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}
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mock_outcome = {
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'net_pnl': 25.0, # $25 profit
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'exit_price': 2525.0,
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'duration': timedelta(minutes=15)
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}
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mock_market_data = {
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'volatility': 0.03,
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'order_flow_direction': 'bullish',
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'order_flow_strength': 0.8
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}
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if hasattr(orchestrator, 'calculate_enhanced_pivot_reward'):
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reward = orchestrator.calculate_enhanced_pivot_reward(
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mock_trade, mock_market_data, mock_outcome
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)
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logger.info(f"✅ RL reward for profitable trade: {reward:.3f}")
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else:
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logger.warning("❌ Enhanced pivot reward function not available")
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except Exception as e:
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logger.error(f"❌ RL reward test failed: {e}")
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logger.info("\n=== DIAGNOSTIC COMPLETE ===")
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logger.info("Check results above to identify trading bottlenecks")
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except Exception as e:
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logger.error(f"Diagnostic failed: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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asyncio.run(diagnose_trading_system())
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