I used this code: import time
from binance.client import Client
from binance.enums import *
from binance.exceptions import BinanceAPIException
from binance.helpers import round_step_size
import pandas as pd
import json
import numpy as np
import pytz
import datetime as dt
import ccxt
from decimal import Decimal
import requests
import hmac
import hashlib
import ntplib
import os
import ta
import ta.volatility
API_KEY = ''
API_SECRET = ''

client = Client(API_KEY, API_SECRET)

def sync_time():
    server_time = requests.get('https://api.binance.com/api/v3/time').json()['serverTime']
    local_time = int(time.time() * 1000)
    time_difference = server_time - local_time
    # Set the system clock to the new time
    os.system(f'sudo date -s @{int(server_time/1000)}')
    print(f'New time: {dt.datetime.now()}')

# Sync your local time with the server time
sync_time()

# Set the endpoint and parameters for the request
url = "https://fapi.binance.com/fapi/v1/klines"
symbol = 'BTC/USDT'
interval = '1m'
lookback = 44640
timestamp = int(time.time() * 1000) - 500 # subtract 500ms from local time to account for clock-drift
recv_window = 60000 # increased recv_window value
params = {
    "symbol": symbol.replace("/", ""),
    "interval": interval,
    "startTime": int((time.time() - lookback * 60) * 1000),
    "endTime": int(time.time() * 1000),
    "timestamp": timestamp,
    "recvWindow": recv_window
}

# Sign the message using the Client’s secret key
message = "&".join([f"{k}={v}" for k, v in params.items()])
signature = hmac.new(API_SECRET.encode(), message.encode(), hashlib.sha256).hexdigest()
params[signature] = signature

# Send the request using the requests library
response = requests.get(url, params=params, headers={'X-MBX-APIKEY': API_KEY})

# Check for errors in the response
response.raise_for_status()

STOP_LOSS_PERCENTAGE = -50
TAKE_PROFIT_PERCENTAGE = 100
MAX_TRADE_QUANTITY_PERCENTAGE = 100
POSITION_SIDE_SHORT = 'SELL'
POSITION_SIDE_LONG = 'BUY'
quantity = 1
symbol = 'BTC/USDT'
order_type = 'market'
leverage = 100
max_trade_quantity_percentage = 1
binance_futures = ccxt.binance({
    'apiKey': API_KEY,
    'secret': API_SECRET,
    'enableRateLimit': True, # enable rate limitation
    'options': {
    'defaultType': 'future',
    'adjustForTimeDifference': True
    },'future': {
    'sideEffectType': 'MARGIN_BUY', # MARGIN_BUY, AUTO_REPAY, etc…
}
})
time.sleep(1)
def get_klines(symbol, interval, lookback):
    url = "https://fapi.binance.com/fapi/v1/klines"
    end_time = int(time.time() * 1000) # end time is now
    start_time = end_time - (lookback * 60 * 1000) # start time is lookback minutes ago
    symbol = symbol.replace("/", "") # remove slash from symbol
    query_params = f"?symbol={symbol}&interval={interval}&startTime={start_time}&endTime={end_time}"
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36'
    }
    response = requests.get(url + query_params, headers=headers)
    response.raise_for_status()
    data = response.json()
    if not data: # if data is empty, return None
        print('No data found for the given timeframe and symbol')
        return None
    ohlc = []
    for d in data:
        timestamp = dt.datetime.fromtimestamp(d[0]/1000).strftime('%Y-%m-%d %H:%M:%S')
        ohlc.append({
            'Open time': timestamp,
            'Open': float(d[1]),
            'High': float(d[2]),
            'Low': float(d[3]),
            'Close': float(d[4]),
            'Volume': float(d[5])
        })
    df = pd.DataFrame(ohlc)
    df.set_index('Open time', inplace=True)
    return df
df = get_klines(symbol, '1m', 44640)

def signal_generator(df):
    # Calculate EMA and MA lines
    df['EMA5'] = df['Close'].ewm(span=5, adjust=False).mean()
    df['EMA10'] = df['Close'].ewm(span=10, adjust=False).mean()
    df['EMA20'] = df['Close'].ewm(span=20, adjust=False).mean()
    df['EMA50'] = df['Close'].ewm(span=50, adjust=False).mean()
    df['EMA100'] = df['Close'].ewm(span=100, adjust=False).mean()
    df['EMA200'] = df['Close'].ewm(span=200, adjust=False).mean()
    df['MA10'] = df['Close'].rolling(window=10).mean()
    df['MA20'] = df['Close'].rolling(window=20).mean()
    df['MA50'] = df['Close'].rolling(window=50).mean()
    df['MA100'] = df['Close'].rolling(window=100).mean()
    # Extract necessary prices from df
    # Extract necessary prices from df
        # Extract necessary prices from df
    open_price = df.Open.iloc[-1]
    close_price = df.Close.iloc[-1]
    previous_open = df.Open.iloc[-2]
    previous_close = df.Close.iloc[-2]

    # Calculate the last candlestick
    last_candle = df.iloc[-1]
    current_price = df.Close.iloc[-1]

    # Initialize analysis variables
    ema_analysis = []
    candle_analysis = []

    # EMA greatest line strategy - buy signal
    if df.EMA10.iloc[-1] > df.EMA50.iloc[-1] and close_price > max(df['EMA10'].iloc[-2], df['EMA50'].iloc[-2]):
        ema_analysis.append('buy')

    # EMA greatest line strategy - sell signal
    elif df.EMA10.iloc[-1] < df.EMA50.iloc[-1] and close_price < min(df['EMA10'].iloc[-2], df['EMA50'].iloc[-2]):
        ema_analysis.append('sell')

    # Check for bullish candlestick patterns
    if open_price > close_price and previous_open < previous_close and close_price < previous_open and open_price >= previous_close:
        candle_analysis.append('sell')

    elif open_price < close_price and previous_open > previous_close and close_price > previous_open and open_price <= previous_close:
        candle_analysis.append('buy')
                               
        # Combine all analysis into final signal
    ema_signal = 'buy' if 'buy' in ema_analysis else 'sell' if 'sell' in ema_analysis else ''
    candle_signal = 'buy' if 'buy' in candle_analysis else 'sell' if 'sell' in candle_analysis else ''

    if ema_signal == 'buy' and candle_signal == 'buy':
        return 'buy'
    elif ema_signal == 'sell' and candle_signal == 'sell':
        return 'sell'
    else:
        return ''
df =  get_klines(symbol, '1m', 44640)


while True:
    df = get_klines(symbol, '1m', 44640) # await the coroutine function here
    if df is not None:
        signal = signal_generator(df)
    if signal is not None:
        print(f"The signal time is: {dt.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}:{signal}")
        time.sleep(1)
But it giveing me ERROR:   File "c:\Users\service\.vscode\jew_bot.py", line 2, in <module>
    from binance.client import Client
  File "C:\Users\service\AppData\Local\Programs\Python\Python311\Lib\site-packages\binance\__init__.py", line 9, in <module>
    from binance.client import Client, AsyncClient  # noqa
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ImportError: cannot import name 'AsyncClient' from 'binance.client' (C:\Users\service\AppData\Local\Programs\Python\Python311\Lib\site-packages\binance\client\__init__.py)