binance_api/client.py

from binance.client import Client
import pandas as pd
from datetime import datetime

class BinanceClient:
    def __init__(self, api_key, api_secret):
        """Initialize the Binance client with user's API key and secret."""
        self.client = Client(api_key, api_secret)

    def fetch_historical_prices(self, symbol, interval, days):
        """Fetch historical prices for a given symbol and interval.

        Args:
            symbol (str): The cryptocurrency symbol, e.g., 'BTCUSDT'.
            interval (str): The candlestick chart intervals.
            days (int): Number of days back to fetch data for.

        Returns:
            pd.DataFrame: A DataFrame with columns: date, open, high, low, close, volume.
        """
        # Calculate the timestamp for 'days' days ago
        end_time = datetime.utcnow()
        start_str = (end_time - pd.Timedelta(days=days)).strftime('%d %b %Y %H:%M:%S')
        
        # Fetch historical candlestick data from Binance
        candles = self.client.get_historical_klines(symbol, interval, start_str)

        # Create a DataFrame from the fetched data
        df = pd.DataFrame(candles, columns=['date', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_asset_volume', 'number_of_trades', 'taker_buy_base_asset_volume', 'taker_buy_quote_asset_volume', 'ignore'])
        
        # Convert timestamp to datetime and adjust columns
        df['date'] = pd.to_datetime(df['date'], unit='ms')
        df = df[['date', 'open', 'high', 'low', 'close', 'volume']].copy()
        
        # Convert columns to the appropriate data type
        df['open'] = df['open'].astype(float)
        df['high'] = df['high'].astype(float)
        df['low'] = df['low'].astype(float)
        df['close'] = df['close'].astype(float)
        df['volume'] = df['volume'].astype(float)

        return df