59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
#!/usr/bin/env python3
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"""
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Test script to check training status functionality
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"""
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import logging
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logging.basicConfig(level=logging.INFO)
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print("Testing training status functionality...")
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try:
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from web.scalping_dashboard import create_scalping_dashboard
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from core.data_provider import DataProvider
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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print("✅ Imports successful")
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# Create components
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data_provider = DataProvider()
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orchestrator = EnhancedTradingOrchestrator(data_provider)
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dashboard = create_scalping_dashboard(data_provider, orchestrator)
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print("✅ Dashboard created successfully")
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# Test training status
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training_status = dashboard._get_model_training_status()
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print("\n📊 Training Status:")
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print(f"CNN Status: {training_status['cnn']['status']}")
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print(f"CNN Accuracy: {training_status['cnn']['accuracy']:.1%}")
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print(f"CNN Loss: {training_status['cnn']['loss']:.4f}")
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print(f"CNN Epochs: {training_status['cnn']['epochs']}")
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print(f"RL Status: {training_status['rl']['status']}")
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print(f"RL Win Rate: {training_status['rl']['win_rate']:.1%}")
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print(f"RL Episodes: {training_status['rl']['episodes']}")
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print(f"RL Memory: {training_status['rl']['memory_size']}")
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# Test extrema stats
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if hasattr(orchestrator, 'get_extrema_stats'):
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extrema_stats = orchestrator.get_extrema_stats()
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print(f"\n🎯 Extrema Stats:")
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print(f"Total extrema detected: {extrema_stats.get('total_extrema_detected', 0)}")
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print(f"Training queue size: {extrema_stats.get('training_queue_size', 0)}")
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print("✅ Extrema stats available")
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else:
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print("❌ Extrema stats not available")
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# Test tick cache
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print(f"\n📈 Training Data:")
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print(f"Tick cache size: {len(dashboard.tick_cache)}")
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print(f"1s bars cache size: {len(dashboard.one_second_bars)}")
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print(f"Streaming status: {dashboard.is_streaming}")
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print("\n✅ All tests completed successfully!")
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except Exception as e:
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print(f"❌ Error: {e}")
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import traceback
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traceback.print_exc() |