111 lines
4.5 KiB
Python
111 lines
4.5 KiB
Python
#!/usr/bin/env python3
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"""
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Test Enhanced Trading System
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Verify that both RL and CNN learning pipelines are active
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"""
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import asyncio
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import logging
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import sys
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from pathlib import Path
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# Add project root to path
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project_root = Path(__file__).parent
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sys.path.insert(0, str(project_root))
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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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from training.enhanced_cnn_trainer import EnhancedCNNTrainer
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from training.enhanced_rl_trainer import EnhancedRLTrainer
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# Setup logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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async def test_enhanced_system():
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"""Test the enhanced trading system components"""
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logger.info("Testing Enhanced Trading System...")
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try:
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# Initialize components
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config = get_config()
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data_provider = DataProvider(config)
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orchestrator = EnhancedTradingOrchestrator(data_provider)
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# Initialize trainers
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cnn_trainer = EnhancedCNNTrainer(config, orchestrator)
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rl_trainer = EnhancedRLTrainer(config, orchestrator)
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logger.info("COMPONENT STATUS:")
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logger.info(f"✓ Data Provider: {len(config.symbols)} symbols, {len(config.timeframes)} timeframes")
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logger.info(f"✓ Enhanced Orchestrator: Confidence threshold {orchestrator.confidence_threshold}")
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logger.info(f"✓ CNN Trainer: Model initialized")
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logger.info(f"✓ RL Trainer: {len(rl_trainer.agents)} agents initialized")
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# Test decision making
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logger.info("\nTesting decision making...")
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decisions_dict = await orchestrator.make_coordinated_decisions()
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decisions = [decision for decision in decisions_dict.values() if decision is not None]
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logger.info(f"✓ Generated {len(decisions)} trading decisions")
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for decision in decisions:
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logger.info(f" - {decision.action} {decision.symbol} @ ${decision.price:.2f} (conf: {decision.confidence:.1%})")
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# Test RL learning capability
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logger.info("\nTesting RL learning capability...")
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for symbol, agent in rl_trainer.agents.items():
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buffer_size = len(agent.replay_buffer)
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epsilon = agent.epsilon
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logger.info(f" - {symbol} RL Agent: Buffer={buffer_size}, Epsilon={epsilon:.3f}")
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# Test CNN training capability
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logger.info("\nTesting CNN training capability...")
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perfect_moves = orchestrator.get_perfect_moves_for_training()
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logger.info(f" - Perfect moves available: {len(perfect_moves)}")
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if len(perfect_moves) > 0:
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logger.info(" - CNN ready for training on perfect moves")
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else:
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logger.info(" - CNN waiting for perfect moves to accumulate")
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# Test configuration
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logger.info("\nTraining Configuration:")
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logger.info(f" - CNN training interval: {config.training.get('cnn_training_interval', 'N/A')} seconds")
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logger.info(f" - RL training interval: {config.training.get('rl_training_interval', 'N/A')} seconds")
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logger.info(f" - Min perfect moves for CNN: {config.training.get('min_perfect_moves', 'N/A')}")
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logger.info(f" - Min experiences for RL: {config.training.get('min_experiences', 'N/A')}")
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logger.info(f" - Continuous learning: {config.training.get('continuous_learning', False)}")
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logger.info("\n✅ Enhanced Trading System test completed successfully!")
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logger.info("LEARNING SYSTEMS STATUS:")
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logger.info("✓ RL agents ready for continuous learning from trading decisions")
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logger.info("✓ CNN trainer ready for pattern learning from perfect moves")
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logger.info("✓ Enhanced orchestrator coordinating multi-modal decisions")
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return True
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except Exception as e:
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logger.error(f"❌ 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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async def main():
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"""Main test function"""
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logger.info("🚀 Starting Enhanced Trading System Test...")
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success = await test_enhanced_system()
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if success:
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logger.info("\n🎉 All tests passed! Enhanced trading system is ready.")
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logger.info("You can now run the enhanced dashboard or main trading system.")
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else:
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logger.error("\n💥 Tests failed! Please check the configuration and try again.")
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sys.exit(1)
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if __name__ == "__main__":
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asyncio.run(main()) |