gogo2/train_config.py

#!/usr/bin/env python
"""
Training Configuration for GOGO2 Trading System

This module provides a central configuration for all training scripts,
ensuring they use real market data and follow consistent practices.

Usage:
    import train_config
    config = train_config.get_config('supervised')  # or 'reinforcement' or 'hybrid'
"""

import os
import logging
import json
from datetime import datetime
from pathlib import Path

# Ensure consistent logging across all training scripts
log_dir = Path("logs")
log_dir.mkdir(exist_ok=True)
log_file = log_dir / f"training_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler(log_file),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger('training')

# Define available training types
TRAINING_TYPES = {
    'supervised': {
        'description': 'Supervised learning using CNN model',
        'script': 'train_with_realtime.py',
        'model_class': 'CNNModelPyTorch',
        'data_interface': 'MultiTimeframeDataInterface'
    },
    'reinforcement': {
        'description': 'Reinforcement learning using DQN agent',
        'script': 'train_rl_with_realtime.py',
        'model_class': 'DQNAgent',
        'data_interface': 'MultiTimeframeDataInterface'
    },
    'hybrid': {
        'description': 'Combined supervised and reinforcement learning',
        'script': 'train_hybrid.py',  # To be implemented
        'model_class': 'HybridModel',  # To be implemented
        'data_interface': 'MultiTimeframeDataInterface'
    }
}

# Default configuration
DEFAULT_CONFIG = {
    # Market data configuration
    'market_data': {
        'use_real_data_only': True,  # IMPORTANT: Only use real market data, never synthetic
        'symbol': 'BTC/USDT',
        'timeframes': ['1m', '5m', '15m'],
        'window_size': 24,
        'data_refresh_interval': 300,  # seconds
        'use_indicators': True
    },
    
    # Training parameters
    'training': {
        'max_training_time': 12 * 3600,  # seconds (12 hours)
        'checkpoint_interval': 3600,     # seconds (1 hour)
        'batch_size': 64,
        'learning_rate': 0.0001,
        'optimizer': 'adam',
        'loss_function': 'custom_pnl'    # Focus on profitability
    },
    
    # Model paths
    'paths': {
        'models_dir': 'NN/models/saved',
        'logs_dir': 'logs',
        'tensorboard_dir': 'runs'
    },
    
    # GPU configuration
    'hardware': {
        'use_gpu': True,
        'mixed_precision': True,
        'device': 'cuda' if os.environ.get('CUDA_VISIBLE_DEVICES') is not None else 'cpu'
    }
}

def get_config(training_type='supervised', custom_config=None):
    """
    Get configuration for a specific training type
    
    Args:
        training_type (str): Type of training ('supervised', 'reinforcement', or 'hybrid')
        custom_config (dict): Optional custom configuration to merge
        
    Returns:
        dict: Complete configuration
    """
    if training_type not in TRAINING_TYPES:
        raise ValueError(f"Invalid training type: {training_type}. Must be one of {list(TRAINING_TYPES.keys())}")
    
    # Start with default configuration
    config = DEFAULT_CONFIG.copy()
    
    # Add training type-specific configuration
    config['training_type'] = training_type
    config['training_info'] = TRAINING_TYPES[training_type]
    
    # Override with custom configuration if provided
    if custom_config:
        _deep_update(config, custom_config)
    
    # Validate configuration
    _validate_config(config)
    
    return config

def save_config(config, filepath=None):
    """
    Save configuration to a JSON file
    
    Args:
        config (dict): Configuration to save
        filepath (str): Path to save to (default: based on training type and timestamp)
        
    Returns:
        str: Path where configuration was saved
    """
    if filepath is None:
        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
        training_type = config.get('training_type', 'unknown')
        filepath = f"configs/training_{training_type}_{timestamp}.json"
    
    os.makedirs(os.path.dirname(filepath), exist_ok=True)
    
    with open(filepath, 'w') as f:
        json.dump(config, f, indent=2)
    
    logger.info(f"Configuration saved to {filepath}")
    return filepath

def load_config(filepath):
    """
    Load configuration from a JSON file
    
    Args:
        filepath (str): Path to load from
        
    Returns:
        dict: Loaded configuration
    """
    with open(filepath, 'r') as f:
        config = json.load(f)
    
    # Validate the loaded configuration
    _validate_config(config)
    
    logger.info(f"Configuration loaded from {filepath}")
    return config

def _deep_update(target, source):
    """
    Deep update a nested dictionary
    
    Args:
        target (dict): Target dictionary to update
        source (dict): Source dictionary with updates
        
    Returns:
        dict: Updated target dictionary
    """
    for key, value in source.items():
        if key in target and isinstance(target[key], dict) and isinstance(value, dict):
            _deep_update(target[key], value)
        else:
            target[key] = value
    return target

def _validate_config(config):
    """
    Validate configuration to ensure it follows required guidelines
    
    Args:
        config (dict): Configuration to validate
        
    Returns:
        bool: True if valid, raises exception otherwise
    """
    # Enforce real data policy
    if config.get('use_real_data_only', True) is not True:
        logger.error("POLICY VIOLATION: Real market data policy requires only using real data")
        raise ValueError("Configuration violates policy: Must use only real market data, never synthetic")
    
    # Add explicit check at the beginning of the validation function
    if 'allow_synthetic_data' in config and config['allow_synthetic_data'] is True:
        logger.error("POLICY VIOLATION: Synthetic data is not allowed under any circumstances")
        raise ValueError("Configuration violates policy: Synthetic data is explicitly forbidden")
    
    # Validate symbol
    if not config['market_data']['symbol'] or '/' not in config['market_data']['symbol']:
        raise ValueError(f"Invalid symbol format: {config['market_data']['symbol']}")
    
    # Validate timeframes
    if not config['market_data']['timeframes']:
        raise ValueError("At least one timeframe must be specified")
    
    # Ensure window size is reasonable
    if config['market_data']['window_size'] < 10 or config['market_data']['window_size'] > 500:
        raise ValueError(f"Window size out of reasonable range: {config['market_data']['window_size']}")
    
    return True

if __name__ == "__main__":
    # Show available training configurations
    print("Available Training Configurations:")
    print("=" * 40)
    for training_type, info in TRAINING_TYPES.items():
        print(f"{training_type.upper()}: {info['description']}")
    
    # Example of getting and saving a configuration
    config = get_config('supervised')
    save_config(config)
    
    print("\nDefault configuration generated and saved.")
    print(f"Log file: {log_file}")