Create binance_api/client.py

binance_api/client.py

@@ -0,0 +1,42 @@
+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