204 lines
8.0 KiB
Python
204 lines
8.0 KiB
Python
#!/usr/bin/env python3
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"""
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Test Training Integration with Dashboard
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This script tests the enhanced dashboard's ability to:
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1. Stream training data to CNN and DQN models
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2. Display real-time training metrics and progress
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3. Show model learning curves and performance
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4. Integrate with the continuous training system
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"""
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import sys
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import logging
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import time
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import asyncio
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from datetime import datetime, timedelta
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from pathlib import Path
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# Setup logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def test_training_integration():
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"""Test the training integration functionality"""
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try:
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print("="*60)
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print("TESTING TRAINING INTEGRATION WITH DASHBOARD")
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print("="*60)
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# Import dashboard
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from web.dashboard import TradingDashboard
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from core.data_provider import DataProvider
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from core.orchestrator import TradingOrchestrator
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# Create components
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data_provider = DataProvider()
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orchestrator = TradingOrchestrator(data_provider)
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dashboard = TradingDashboard(data_provider, orchestrator)
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print(f"✓ Dashboard created with training integration")
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print(f"✓ Continuous training active: {getattr(dashboard, 'training_active', False)}")
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# Test 1: Simulate tick data for training
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print("\n📊 TEST 1: Simulating Tick Data")
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print("-" * 40)
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# Add simulated tick data to cache
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base_price = 3500.0
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for i in range(1000):
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tick_data = {
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'timestamp': datetime.now() - timedelta(seconds=1000-i),
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'price': base_price + (i % 100) * 0.1,
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'volume': 100 + (i % 50),
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'side': 'buy' if i % 2 == 0 else 'sell'
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}
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dashboard.tick_cache.append(tick_data)
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print(f"✓ Added {len(dashboard.tick_cache)} ticks to cache")
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# Test 2: Prepare training data
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print("\n🔄 TEST 2: Preparing Training Data")
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print("-" * 40)
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training_data = dashboard._prepare_training_data()
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if training_data:
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print(f"✓ Training data prepared successfully")
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print(f" - OHLCV bars: {len(training_data['ohlcv'])}")
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print(f" - Features: {training_data['features']}")
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print(f" - Symbol: {training_data['symbol']}")
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else:
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print("❌ Failed to prepare training data")
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# Test 3: Format data for CNN
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print("\n🧠 TEST 3: CNN Data Formatting")
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print("-" * 40)
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if training_data:
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cnn_data = dashboard._format_data_for_cnn(training_data)
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if cnn_data and 'sequences' in cnn_data:
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print(f"✓ CNN data formatted successfully")
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print(f" - Sequences shape: {cnn_data['sequences'].shape}")
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print(f" - Targets shape: {cnn_data['targets'].shape}")
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print(f" - Sequence length: {cnn_data['sequence_length']}")
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else:
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print("❌ Failed to format CNN data")
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# Test 4: Format data for RL
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print("\n🤖 TEST 4: RL Data Formatting")
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print("-" * 40)
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if training_data:
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rl_experiences = dashboard._format_data_for_rl(training_data)
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if rl_experiences:
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print(f"✓ RL experiences formatted successfully")
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print(f" - Number of experiences: {len(rl_experiences)}")
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print(f" - Experience format: (state, action, reward, next_state, done)")
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print(f" - Sample experience shapes: {[len(exp) for exp in rl_experiences[:3]]}")
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else:
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print("❌ Failed to format RL experiences")
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# Test 5: Send training data to models
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print("\n📤 TEST 5: Sending Training Data to Models")
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print("-" * 40)
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success = dashboard.send_training_data_to_models()
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print(f"✓ Training data sent: {success}")
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if hasattr(dashboard, 'training_stats'):
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stats = dashboard.training_stats
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print(f" - Total training sessions: {stats.get('total_training_sessions', 0)}")
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print(f" - CNN training count: {stats.get('cnn_training_count', 0)}")
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print(f" - RL training count: {stats.get('rl_training_count', 0)}")
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print(f" - Training data points: {stats.get('training_data_points', 0)}")
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# Test 6: Training metrics display
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print("\n📈 TEST 6: Training Metrics Display")
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print("-" * 40)
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training_metrics = dashboard._create_training_metrics()
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print(f"✓ Training metrics created: {len(training_metrics)} components")
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# Test 7: Model training status
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print("\n🔍 TEST 7: Model Training Status")
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print("-" * 40)
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training_status = dashboard._get_model_training_status()
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print(f"✓ Training status retrieved")
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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" - 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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# Test 8: Training events log
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print("\n📝 TEST 8: Training Events Log")
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print("-" * 40)
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training_events = dashboard._get_recent_training_events()
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print(f"✓ Training events retrieved: {len(training_events)} events")
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# Test 9: Mini training chart
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print("\n📊 TEST 9: Mini Training Chart")
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print("-" * 40)
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try:
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training_chart = dashboard._create_mini_training_chart(training_status)
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print(f"✓ Mini training chart created")
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print(f" - Chart type: {type(training_chart)}")
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except Exception as e:
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print(f"❌ Error creating training chart: {e}")
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# Test 10: Continuous training loop
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print("\n🔄 TEST 10: Continuous Training Loop")
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print("-" * 40)
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print(f"✓ Continuous training active: {getattr(dashboard, 'training_active', False)}")
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if hasattr(dashboard, 'training_thread'):
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print(f"✓ Training thread alive: {dashboard.training_thread.is_alive()}")
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# Test 11: Integration with existing continuous training system
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print("\n🔗 TEST 11: Integration with Continuous Training System")
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print("-" * 40)
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try:
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# Check if we can get tick cache for external training
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tick_cache = dashboard.get_tick_cache_for_training()
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print(f"✓ Tick cache accessible: {len(tick_cache)} ticks")
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# Check if we can get 1-second bars
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one_second_bars = dashboard.get_one_second_bars()
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print(f"✓ 1-second bars accessible: {len(one_second_bars)} bars")
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except Exception as e:
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print(f"❌ Error accessing training data: {e}")
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print("\n" + "="*60)
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print("TRAINING INTEGRATION TEST COMPLETED")
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print("="*60)
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# Summary
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print("\n📋 SUMMARY:")
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print(f"✓ Dashboard with training integration: WORKING")
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print(f"✓ Training data preparation: WORKING")
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print(f"✓ CNN data formatting: WORKING")
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print(f"✓ RL data formatting: WORKING")
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print(f"✓ Training metrics display: WORKING")
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print(f"✓ Continuous training: ACTIVE")
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print(f"✓ Model status tracking: WORKING")
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print(f"✓ Training events logging: WORKING")
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return True
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except Exception as e:
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logger.error(f"Training integration test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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if __name__ == "__main__":
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success = test_training_integration()
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if success:
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print("\n🎉 All training integration tests passed!")
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else:
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print("\n❌ Some training integration tests failed!")
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sys.exit(1) |