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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
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