import krakenex
import pandas as pd
from datetime import datetime
import time
import os
from typing import Dict, List, Optional
import logging

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('kraken_data_collection.log'),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger(__name__)

class KrakenDataCollector:
    """Handles data collection from Kraken API"""
    
    def __init__(self, api_key_path: str):
        self.api = krakenex.API()
        try:
            self.api.load_key(api_key_path)
            logger.info("Successfully loaded Kraken API key")
        except Exception as e:
            logger.error(f"Failed to load API key: {e}")
            raise
        
        # Trading pairs to collect data for
        self.pairs = [
            "XXBTZUSD",  # Bitcoin
            "XETHZUSD",  # Ethereum
            "XXRPZUSD",  # Ripple
            "ADAUSD",    # Cardano
            "DOGEUSD",   # Dogecoin
            "BNBUSD",    # Binance Coin
            "SOLUSD",    # Solana
            "DOTUSD",    # Polkadot
            "MATICUSD",  # Polygon
            "LTCUSD"     # Litecoin
        ]

    def fetch_ticker_data(self, pair: str) -> Optional[Dict]:
        """Fetch ticker data for a single pair"""
        try:
            response = self.api.query_public('Ticker', {'pair': pair})
            
            if 'error' in response and response['error']:
                logger.error(f"Kraken API error for {pair}: {response['error']}")
                return None
                
            data = response['result']
            pair_data = list(data.values())[0]
            
            return {
                'timestamp': datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S'),
                'pair': pair,
                'price': float(pair_data['c'][0]),    # Last trade closed price
                'volume': float(pair_data['v'][0]),   # 24h volume
                'bid': float(pair_data['b'][0]),      # Best bid
                'ask': float(pair_data['a'][0]),      # Best ask
                'low': float(pair_data['l'][0]),      # 24h low
                'high': float(pair_data['h'][0]),     # 24h high
                'vwap': float(pair_data['p'][0]),     # 24h VWAP
                'trades': int(pair_data['t'][0])      # Number of trades
            }
            
        except Exception as e:
            logger.error(f"Error fetching data for {pair}: {e}")
            return None

    def create_data_directories(self) -> None:
        """Create directory structure for data storage"""
        for split in ['training', 'validation', 'test']:
            directory = f'data/{split}'
            if not os.path.exists(directory):
                os.makedirs(directory)
                logger.info(f"Created directory: {directory}")

    def save_data_to_csv(self, split: str, num_rows: int, delay: int = 2) -> None:
        """
        Collect and save data for all pairs
        
        Args:
            split: Data split type ('training', 'validation', 'test')
            num_rows: Number of data points to collect per pair
            delay: Delay between API calls in seconds
        """
        try:
            records = []
            
            for i in range(num_rows):
                logger.info(f"Collecting row {i+1}/{num_rows}")
                
                for pair in self.pairs:
                    record = self.fetch_ticker_data(pair)
                    if record:
                        records.append(record)
                        
                if i < num_rows - 1:  # Don't sleep after last iteration
                    time.sleep(delay)  # Respect API rate limits
            
            # Create DataFrame and save to CSV
            df = pd.DataFrame(records)
            file_path = f"data/{split}/kraken_trades.csv"
            
            # Create directory if it doesn't exist
            os.makedirs(os.path.dirname(file_path), exist_ok=True)
            
            # Save data
            df.to_csv(file_path, index=False)
            logger.info(f"Successfully saved {len(records)} records to {file_path}")
            
            # Print data summary
            logger.info("\nData Summary:")
            logger.info(f"Total records: {len(records)}")
            logger.info(f"Pairs collected: {len(df['pair'].unique())}")
            logger.info(f"Time range: {df['timestamp'].min()} to {df['timestamp'].max()}")
            
        except Exception as e:
            logger.error(f"Error saving data: {e}")
            raise

def main():
    """Main function to run data collection"""
    try:
        # Initialize collector
        collector = KrakenDataCollector("kraken.key")
        
        # Create directory structure
        collector.create_data_directories()
        
        # Collect data for each split
        splits_config = {
            'training': 1000,    # 1000 rows for training
            'validation': 200,   # 200 rows for validation
            'test': 200         # 200 rows for test
        }
        
        for split, num_rows in splits_config.items():
            logger.info(f"\nCollecting {split} data...")
            collector.save_data_to_csv(split=split, num_rows=num_rows)
            
        logger.info("Data collection completed successfully!")
        
    except Exception as e:
        logger.error(f"Fatal error in data collection: {e}")
        raise

if __name__ == "__main__":
    main()