309 lines
10 KiB
Python
309 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Test Training Script for AI Trading Models
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This script tests the training functionality of our CNN and RL models
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and demonstrates the learning capabilities.
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"""
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import logging
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import sys
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import asyncio
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from pathlib import Path
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from datetime import datetime, timedelta
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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 setup_logging
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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 models import get_model_registry, CNNModelWrapper, RLAgentWrapper
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# Setup logging
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setup_logging()
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logger = logging.getLogger(__name__)
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def test_model_loading():
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"""Test that models load correctly"""
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logger.info("=== TESTING MODEL LOADING ===")
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try:
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# Get model registry
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registry = get_model_registry()
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# Check loaded models
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logger.info(f"Loaded models: {list(registry.models.keys())}")
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# Test each model
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for name, model in registry.models.items():
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logger.info(f"Testing {name} model...")
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# Test prediction
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import numpy as np
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test_features = np.random.random((20, 5)) # 20 timesteps, 5 features
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try:
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predictions, confidence = model.predict(test_features)
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logger.info(f" ✅ {name} prediction: {predictions} (confidence: {confidence:.3f})")
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except Exception as e:
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logger.error(f" ❌ {name} prediction failed: {e}")
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# Memory stats
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stats = registry.get_memory_stats()
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logger.info(f"Memory usage: {stats['total_used_mb']:.1f}MB / {stats['total_limit_mb']:.1f}MB")
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return True
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except Exception as e:
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logger.error(f"Model loading test failed: {e}")
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return False
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async def test_orchestrator_integration():
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"""Test orchestrator integration with models"""
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logger.info("=== TESTING ORCHESTRATOR INTEGRATION ===")
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try:
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# Initialize components
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data_provider = DataProvider()
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orchestrator = EnhancedTradingOrchestrator(data_provider)
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# Test coordinated decisions
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logger.info("Testing coordinated decision making...")
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decisions = await orchestrator.make_coordinated_decisions()
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if 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} (confidence: {decision.confidence:.3f})")
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else:
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logger.info(f" ⏸️ {symbol}: No decision (waiting)")
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else:
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logger.warning(" ❌ No decisions made")
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# Test RL evaluation
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logger.info("Testing RL evaluation...")
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await orchestrator.evaluate_actions_with_rl()
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return True
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except Exception as e:
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logger.error(f"Orchestrator integration test failed: {e}")
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return False
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def test_rl_learning():
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"""Test RL learning functionality"""
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logger.info("=== TESTING RL LEARNING ===")
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try:
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registry = get_model_registry()
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rl_agent = registry.get_model('RL')
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if not rl_agent:
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logger.error("RL agent not found")
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return False
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# Simulate some experiences
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import numpy as np
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logger.info("Simulating trading experiences...")
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for i in range(50):
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state = np.random.random(10)
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action = np.random.randint(0, 3)
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reward = np.random.uniform(-0.1, 0.1) # Random P&L
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next_state = np.random.random(10)
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done = False
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# Store experience
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rl_agent.remember(state, action, reward, next_state, done)
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logger.info(f"Stored {len(rl_agent.experience_buffer)} experiences")
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# Test replay training
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logger.info("Testing replay training...")
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loss = rl_agent.replay()
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if loss is not None:
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logger.info(f" ✅ Training loss: {loss:.4f}")
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else:
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logger.info(" ⏸️ Not enough experiences for training")
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return True
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except Exception as e:
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logger.error(f"RL learning test failed: {e}")
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return False
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def test_cnn_training():
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"""Test CNN training functionality"""
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logger.info("=== TESTING CNN TRAINING ===")
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try:
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registry = get_model_registry()
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cnn_model = registry.get_model('CNN')
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if not cnn_model:
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logger.error("CNN model not found")
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return False
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# Test training with mock perfect moves
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training_data = {
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'perfect_moves': [],
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'market_data': {},
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'symbols': ['ETH/USDT', 'BTC/USDT'],
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'timeframes': ['1m', '1h']
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}
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# Mock some perfect moves
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for i in range(10):
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perfect_move = {
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'symbol': 'ETH/USDT',
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'timeframe': '1m',
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'timestamp': datetime.now() - timedelta(hours=i),
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'optimal_action': 'BUY' if i % 2 == 0 else 'SELL',
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'confidence_should_have_been': 0.8 + i * 0.01,
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'actual_outcome': 0.02 if i % 2 == 0 else -0.015
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}
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training_data['perfect_moves'].append(perfect_move)
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logger.info(f"Testing training with {len(training_data['perfect_moves'])} perfect moves...")
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# Test training
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result = cnn_model.train(training_data)
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if result and result.get('status') == 'training_simulated':
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logger.info(f" ✅ Training completed: {result}")
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else:
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logger.warning(f" ⚠️ Training result: {result}")
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return True
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except Exception as e:
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logger.error(f"CNN training test failed: {e}")
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return False
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def test_prediction_tracking():
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"""Test prediction tracking and learning feedback"""
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logger.info("=== TESTING PREDICTION TRACKING ===")
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try:
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# Initialize components
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data_provider = DataProvider()
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orchestrator = EnhancedTradingOrchestrator(data_provider)
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# Get some market data for testing
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test_data = data_provider.get_historical_data('ETH/USDT', '1m', limit=100)
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if test_data is None or test_data.empty:
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logger.warning("No market data available for testing")
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return True
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logger.info(f"Testing with {len(test_data)} candles of ETH/USDT 1m data")
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# Simulate some predictions and outcomes
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correct_predictions = 0
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total_predictions = 0
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for i in range(min(10, len(test_data) - 5)):
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# Get a slice of data
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current_data = test_data.iloc[i:i+20]
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future_data = test_data.iloc[i+20:i+25]
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if len(current_data) < 20 or len(future_data) < 5:
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continue
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# Make prediction
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current_price = current_data['close'].iloc[-1]
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future_price = future_data['close'].iloc[-1]
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actual_change = (future_price - current_price) / current_price
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# Simulate model prediction
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predicted_action = 'BUY' if actual_change > 0.001 else 'SELL' if actual_change < -0.001 else 'HOLD'
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# Check if prediction was correct
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if predicted_action == 'BUY' and actual_change > 0:
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correct_predictions += 1
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logger.info(f" ✅ Correct BUY prediction: {actual_change:.4f}")
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elif predicted_action == 'SELL' and actual_change < 0:
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correct_predictions += 1
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logger.info(f" ✅ Correct SELL prediction: {actual_change:.4f}")
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elif predicted_action == 'HOLD' and abs(actual_change) < 0.001:
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correct_predictions += 1
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logger.info(f" ✅ Correct HOLD prediction: {actual_change:.4f}")
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else:
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logger.info(f" ❌ Wrong {predicted_action} prediction: {actual_change:.4f}")
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total_predictions += 1
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if total_predictions > 0:
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accuracy = correct_predictions / total_predictions
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logger.info(f"Prediction accuracy: {accuracy:.1%} ({correct_predictions}/{total_predictions})")
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return True
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except Exception as e:
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logger.error(f"Prediction tracking test failed: {e}")
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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 AI TRADING MODEL TESTS")
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logger.info("Testing model loading, training, and learning capabilities")
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tests = [
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("Model Loading", test_model_loading),
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("Orchestrator Integration", test_orchestrator_integration),
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("RL Learning", test_rl_learning),
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("CNN Training", test_cnn_training),
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("Prediction Tracking", test_prediction_tracking)
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]
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results = {}
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for test_name, test_func in tests:
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logger.info(f"\n{'='*50}")
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logger.info(f"Running: {test_name}")
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logger.info(f"{'='*50}")
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try:
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if asyncio.iscoroutinefunction(test_func):
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result = await test_func()
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else:
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result = test_func()
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results[test_name] = result
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if result:
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logger.info(f"✅ {test_name}: PASSED")
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else:
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logger.error(f"❌ {test_name}: FAILED")
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except Exception as e:
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logger.error(f"❌ {test_name}: ERROR - {e}")
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results[test_name] = False
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# Summary
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logger.info(f"\n{'='*50}")
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logger.info("TEST SUMMARY")
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logger.info(f"{'='*50}")
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passed = sum(1 for result in results.values() if result)
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total = len(results)
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for test_name, result in results.items():
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status = "✅ PASSED" if result else "❌ FAILED"
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logger.info(f"{test_name}: {status}")
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logger.info(f"\nOverall: {passed}/{total} tests passed ({passed/total:.1%})")
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if passed == total:
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logger.info("🎉 All tests passed! The AI trading system is working correctly.")
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else:
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logger.warning(f"⚠️ {total-passed} tests failed. Please check the logs above.")
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return 0 if passed == total else 1
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if __name__ == "__main__":
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exit_code = asyncio.run(main())
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sys.exit(exit_code) |