"""
Test Continuous CNN Training

This script demonstrates how the CNN model can be trained with each new inference result
using collected data, implementing a continuous learning loop.
"""

import logging
import time
from datetime import datetime
import random
import os

from core.standardized_data_provider import StandardizedDataProvider
from core.enhanced_cnn_adapter import EnhancedCNNAdapter
from core.data_models import create_model_output

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def simulate_market_feedback(action, symbol):
    """
    Simulate market feedback for a given action
    
    In a real system, this would be replaced with actual market performance data
    
    Args:
        action: Trading action ('BUY', 'SELL', 'HOLD')
        symbol: Trading symbol
    
    Returns:
        tuple: (actual_action, reward)
    """
    # Simulate market movement (random for demonstration)
    market_direction = random.choice(['up', 'down', 'sideways'])
    
    # Determine actual best action based on market direction
    if market_direction == 'up':
        best_action = 'BUY'
    elif market_direction == 'down':
        best_action = 'SELL'
    else:
        best_action = 'HOLD'
    
    # Calculate reward based on whether the action matched the best action
    if action == best_action:
        reward = random.uniform(0.01, 0.1)  # Positive reward for correct action
    else:
        reward = random.uniform(-0.1, -0.01)  # Negative reward for incorrect action
    
    logger.info(f"Market went {market_direction}, best action was {best_action}, model chose {action}, reward: {reward:.4f}")
    
    return best_action, reward

def test_continuous_training():
    """Test continuous training of the CNN model with new inference results"""
    try:
        # Initialize data provider
        symbols = ['ETH/USDT', 'BTC/USDT']
        timeframes = ['1s', '1m', '1h', '1d']
        data_provider = StandardizedDataProvider(symbols=symbols, timeframes=timeframes)
        
        # Initialize CNN adapter
        checkpoint_dir = "models/enhanced_cnn"
        os.makedirs(checkpoint_dir, exist_ok=True)
        cnn_adapter = EnhancedCNNAdapter(checkpoint_dir=checkpoint_dir)
        
        # Load best checkpoint if available
        cnn_adapter.load_best_checkpoint()
        
        # Continuous learning loop
        num_iterations = 10
        training_frequency = 3  # Train every N iterations
        samples_collected = 0
        
        logger.info(f"Starting continuous learning loop with {num_iterations} iterations")
        
        for i in range(num_iterations):
            logger.info(f"\nIteration {i+1}/{num_iterations}")
            
            # Get standardized input data
            symbol = random.choice(symbols)
            logger.info(f"Getting data for {symbol}...")
            base_data = data_provider.get_base_data_input(symbol)
            
            if base_data is None:
                logger.warning(f"Failed to get base data input for {symbol}, skipping iteration")
                continue
            
            # Make prediction
            logger.info(f"Making prediction for {symbol}...")
            model_output = cnn_adapter.predict(base_data)
            
            # Log prediction
            action = model_output.predictions['action']
            confidence = model_output.confidence
            logger.info(f"Prediction: {action} with confidence {confidence:.4f}")
            
            # Store model output
            data_provider.store_model_output(model_output)
            
            # Simulate market feedback
            best_action, reward = simulate_market_feedback(action, symbol)
            
            # Add training sample
            logger.info(f"Adding training sample: action={best_action}, reward={reward:.4f}")
            cnn_adapter.add_training_sample(base_data, best_action, reward)
            samples_collected += 1
            
            # Train model periodically
            if (i + 1) % training_frequency == 0 and samples_collected >= 3:
                logger.info(f"Training model with {samples_collected} samples...")
                metrics = cnn_adapter.train(epochs=1)
                
                # Log training metrics
                logger.info(f"Training metrics: loss={metrics.get('loss', 0.0):.4f}, accuracy={metrics.get('accuracy', 0.0):.4f}")
            
            # Simulate time passing
            time.sleep(1)
        
        logger.info("\nContinuous learning loop completed")
        
        # Final evaluation
        logger.info("Performing final evaluation...")
        
        # Get data for evaluation
        symbol = 'ETH/USDT'
        base_data = data_provider.get_base_data_input(symbol)
        
        if base_data is not None:
            # Make prediction
            model_output = cnn_adapter.predict(base_data)
            
            # Log prediction
            action = model_output.predictions['action']
            confidence = model_output.confidence
            logger.info(f"Final prediction for {symbol}: {action} with confidence {confidence:.4f}")
            
            # Get model output manager
            output_manager = data_provider.get_model_output_manager()
            
            # Evaluate model performance
            metrics = output_manager.evaluate_model_performance(symbol, cnn_adapter.model_name)
            logger.info(f"Performance metrics: {metrics}")
        else:
            logger.warning(f"Failed to get base data input for final evaluation")
        
        logger.info("Test completed successfully")
        
    except Exception as e:
        logger.error(f"Error in test: {e}", exc_info=True)

if __name__ == "__main__":
    test_continuous_training()