kraken-data-collection-script

import krakenex
import pandas as pd
from datetime import datetime
import time
import os
from typing import Dict, List, Optional
import logging
from huggingface_hub import HfApi, login
from io import StringIO

# 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 KrakenHuggingFaceCollector:
    """Handles data collection from Kraken and uploading to Hugging Face"""
    
    def __init__(self, kraken_key_path: str, hf_token: str, repo_id: str):
        # Initialize Kraken API
        self.kraken_api = krakenex.API()
        try:
            self.kraken_api.load_key(kraken_key_path)
            logger.info("Successfully loaded Kraken API key")
        except Exception as e:
            logger.error(f"Failed to load Kraken API key: {e}")
            raise

        # Initialize Hugging Face
        try:
            login(token=hf_token)
            self.hf_api = HfApi()
            self.repo_id = repo_id
            logger.info("Successfully logged in to Hugging Face")
        except Exception as e:
            logger.error(f"Failed to login to Hugging Face: {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.kraken_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 upload_to_huggingface(self, df: pd.DataFrame, split: str) -> None:
        """Upload DataFrame to Hugging Face as CSV"""
        try:
            # Convert DataFrame to CSV string
            csv_str = df.to_csv(index=False)
            
            # Upload to Hugging Face
            path_in_repo = f"data/{split}/kraken_trades.csv"
            
            self.hf_api.upload_file(
                path_or_fileobj=StringIO(csv_str),
                path_in_repo=path_in_repo,
                repo_id=self.repo_id,
                repo_type="dataset"
            )
            
            logger.info(f"Successfully uploaded {split} data to Hugging Face")
            
        except Exception as e:
            logger.error(f"Error uploading to Hugging Face: {e}")
            raise

    def collect_and_upload(self, split: str, num_rows: int, delay: int = 2) -> None:
        """
        Collect data and upload directly to Hugging Face
        
        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
            df = pd.DataFrame(records)
            
            # Upload to Hugging Face
            self.upload_to_huggingface(df, split)
            
            # 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 in data collection and upload: {e}")
            raise

def main():
    """Main function to run data collection and upload"""
    try:
        # Initialize collector
        collector = KrakenHuggingFaceCollector(
            kraken_key_path="kraken.key",
            hf_token="your_huggingface_token",  # Replace with your token
            repo_id="GotThatData/kraken-trading-data"  # Replace with your repo name
        )
        
        # Collect and upload 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 and uploading {split} data...")
            collector.collect_and_upload(split=split, num_rows=num_rows)
            
        logger.info("Data collection and upload completed successfully!")
        
    except Exception as e:
        logger.error(f"Fatal error: {e}")
        raise

if __name__ == "__main__":
    main()