cleanup_1
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Dobromir Popov
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main_clean.py
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221
main_clean.py
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#!/usr/bin/env python3
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"""
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Clean Trading System - Main Entry Point
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This is the new clean entry point that demonstrates the consolidated architecture:
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- Single configuration system
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- Clean data provider
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- Modular CNN and RL components
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- Centralized orchestrator
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- Simple web dashboard
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Usage:
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python main_clean.py --mode [train|trade|web] --symbol ETH/USDT
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"""
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import asyncio
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import argparse
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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, setup_logging
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from core.data_provider import DataProvider
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from core.orchestrator import TradingOrchestrator
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logger = logging.getLogger(__name__)
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def run_data_test():
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"""Test the data provider functionality"""
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try:
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config = get_config()
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logger.info("Testing Data Provider...")
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# Test data provider
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data_provider = DataProvider(
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symbols=['ETH/USDT'],
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timeframes=['1h', '4h']
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)
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# Test historical data
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logger.info("Testing historical data fetching...")
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df = data_provider.get_historical_data('ETH/USDT', '1h', limit=100)
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if df is not None:
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logger.info(f"[SUCCESS] Historical data: {len(df)} candles loaded")
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logger.info(f" Columns: {list(df.columns)}")
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logger.info(f" Date range: {df['timestamp'].min()} to {df['timestamp'].max()}")
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else:
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logger.error("[FAILED] Failed to load historical data")
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# Test feature matrix
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logger.info("Testing feature matrix...")
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feature_matrix = data_provider.get_feature_matrix('ETH/USDT', ['1h'], window_size=20)
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if feature_matrix is not None:
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logger.info(f"[SUCCESS] Feature matrix shape: {feature_matrix.shape}")
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else:
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logger.error("[FAILED] Failed to create feature matrix")
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# Test health check
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health = data_provider.health_check()
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logger.info(f"[SUCCESS] Data provider health: {health}")
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logger.info("Data provider test completed successfully!")
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except Exception as e:
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logger.error(f"Error in data test: {e}")
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import traceback
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logger.error(traceback.format_exc())
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raise
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def run_orchestrator_test():
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"""Test the modular orchestrator system"""
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try:
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from models import get_model_registry, ModelInterface
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import numpy as np
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import torch
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logger.info("Testing Modular Orchestrator System...")
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# Test model registry
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registry = get_model_registry()
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logger.info(f"[SUCCESS] Model registry initialized with {registry.total_memory_limit_mb}MB limit")
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# Create a mock model for testing
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class MockCNNModel(ModelInterface):
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def __init__(self):
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config = {'max_memory_mb': 500} # 500MB limit
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super().__init__('MockCNN', config)
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self.model_params = torch.randn(1000, 100) # Small mock model
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def predict(self, features):
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# Mock prediction: random but consistent
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np.random.seed(42)
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action_probs = np.random.dirichlet([1, 1, 1]) # Random probabilities that sum to 1
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confidence = np.random.uniform(0.5, 0.9)
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return action_probs, confidence
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def get_memory_usage(self):
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# Estimate memory usage
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if hasattr(self, 'model_params'):
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return int(self.model_params.numel() * 4 / (1024*1024)) # 4 bytes per float, convert to MB
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return 0
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class MockRLAgent(ModelInterface):
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def __init__(self):
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config = {'max_memory_mb': 300} # 300MB limit
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super().__init__('MockRL', config)
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self.q_network = torch.randn(500, 50) # Smaller mock RL model
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def predict(self, features):
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# Mock RL prediction
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np.random.seed(123)
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action_probs = np.random.dirichlet([2, 1, 2]) # Favor BUY/SELL over HOLD
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confidence = np.random.uniform(0.6, 0.8)
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return action_probs, confidence
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def act_with_confidence(self, state):
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action_probs, confidence = self.predict(state)
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action = np.argmax(action_probs)
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return action, confidence
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def get_memory_usage(self):
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if hasattr(self, 'q_network'):
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return int(self.q_network.numel() * 4 / (1024*1024))
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return 0
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def act(self, state):
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return self.act_with_confidence(state)[0]
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def remember(self, state, action, reward, next_state, done):
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pass # Mock implementation
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def replay(self):
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return 0.0 # Mock implementation
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# Test model registration
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logger.info("Testing model registration...")
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mock_cnn = MockCNNModel()
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mock_rl = MockRLAgent()
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success1 = registry.register_model(mock_cnn)
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success2 = registry.register_model(mock_rl)
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if success1 and success2:
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logger.info("[SUCCESS] Both models registered successfully")
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else:
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logger.error(f"[FAILED] Model registration failed: CNN={success1}, RL={success2}")
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# Test memory stats
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memory_stats = registry.get_memory_stats()
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logger.info(f"[SUCCESS] Memory stats: {memory_stats}")
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# Test orchestrator
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logger.info("Testing orchestrator integration...")
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data_provider = DataProvider(symbols=['ETH/USDT'], timeframes=['1h'])
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orchestrator = TradingOrchestrator(data_provider)
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# Register models with orchestrator
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success1 = orchestrator.register_model(mock_cnn, weight=0.7)
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success2 = orchestrator.register_model(mock_rl, weight=0.3)
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if success1 and success2:
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logger.info("[SUCCESS] Models registered with orchestrator")
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else:
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logger.error(f"[FAILED] Orchestrator registration failed")
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# Test orchestrator metrics
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metrics = orchestrator.get_performance_metrics()
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logger.info(f"[SUCCESS] Orchestrator metrics: {metrics}")
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logger.info("Modular orchestrator test completed successfully!")
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except Exception as e:
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logger.error(f"Error in orchestrator test: {e}")
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import traceback
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logger.error(traceback.format_exc())
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raise
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async def main():
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"""Main entry point"""
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parser = argparse.ArgumentParser(description='Clean Trading System')
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parser.add_argument('--mode', choices=['trade', 'train', 'web', 'test', 'orchestrator'],
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default='test', help='Mode to run the system in')
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parser.add_argument('--symbol', type=str, help='Override default symbol')
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parser.add_argument('--config', type=str, default='config.yaml',
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help='Configuration file path')
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args = parser.parse_args()
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# Setup logging
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setup_logging()
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try:
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logger.info("=" * 60)
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logger.info("CLEAN TRADING SYSTEM STARTING")
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logger.info("=" * 60)
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# Run appropriate mode
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if args.mode == 'test':
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run_data_test()
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elif args.mode == 'orchestrator':
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run_orchestrator_test()
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else:
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logger.info(f"Mode '{args.mode}' not yet implemented in clean architecture")
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except KeyboardInterrupt:
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logger.info("System shutdown requested by user")
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except Exception as e:
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logger.error(f"Fatal error: {e}")
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import traceback
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logger.error(traceback.format_exc())
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return 1
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logger.info("Clean Trading System finished")
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return 0
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if __name__ == "__main__":
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sys.exit(asyncio.run(main()))
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