import pandas as pd
from datetime import datetime

def aggregate_to_daily(input_csv, output_csv):
    """
    Load a CSV file with Bitcoin price data, aggregate to daily values, and save to a new CSV.
    
    Args:
        input_csv (str): Path to the input CSV file
        output_csv (str): Path to save the output CSV file
    """
    try:
        # Read the CSV file
        print(f"Loading data from {input_csv}...")
        df = pd.read_csv(input_csv)
        
        # Convert timestamp to datetime
        df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s')
        df.set_index('Timestamp', inplace=True)
        
        # Aggregate to daily values
        print("Aggregating data to daily values...")
        daily_df = df.resample('D').agg({
            'Open': 'first',
            'High': 'max',
            'Low': 'min',
            'Close': 'last',
            'Volume': 'sum'
        })
        
        # Reset index to make Timestamp a column
        daily_df.reset_index(inplace=True)
        
        # Save to new CSV
        print(f"Saving daily data to {output_csv}...")
        daily_df.to_csv(output_csv, index=False)
        
        print(f"Successfully processed {len(df)} records into {len(daily_df)} daily records")
        
    except Exception as e:
        print(f"Error processing data: {str(e)}")

if __name__ == "__main__":
    # Example usage
    input_file = "../data/btcusd_1-min_data.csv"  # Update this path to your input file
    output_file = "../data/btcusd_daily_data.csv"  # Update this path to your desired output file
    
    aggregate_to_daily(input_file, output_file)