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 requests
import json
import numpy as np
import pytz
import datetime as dt
import ccxt

# Get the current time and timestamp
now = dt.datetime.now()
date = now.strftime("%m/%d/%Y %H:%M:%S")
print(date)

timestamp = int(time.time() * 1000)

# API keys and other configuration
API_KEY = ''
API_SECRET = ''

client = Client(API_KEY, API_SECRET)

STOP_LOSS_PERCENTAGE = -50
TAKE_PROFIT_PERCENTAGE = 100
MAX_TRADE_QUANTITY_PERCENTAGE = 100
POSITION_SIDE_SHORT = 'SELL'
POSITION_SIDE_LONG = 'BUY'
symbol = 'BTC/USDT'
quantity = 1
order_type = 'MARKET'
leverage = 100
max_trade_quantity_percentage = 1
binance_futures = ccxt.binance({
       'apiKey': '',
       'secret': '',
       'enableRateLimit': True,  # enable rate limitation
       'options': {
           'defaultType': 'future',
           'adjustForTimeDifference': True
       }
   })


# Get server time and time difference

def get_server_time(exchange):
    server_time = exchange.fetch_time()["timestamp"]
    return server_time


    # Calculate time difference between server and local machine time
server_time = get_server_time(binance_futures)
local_time = int(time.time() * 1000)
time_difference = local_time - server_time

def get_klines(symbol, interval, lookback):
    url = "https://fapi.binance.com/fapi/v1/klines"
    start_time = (dt.datetime.now() - dt.timedelta(minutes=lookback))
    end_time = dt.datetime.now()
    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'
    }
    try:
        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
    except requests.exceptions.RequestException as e:
        print(f'Error in get_klines: {e}')
        return None
df = get_klines('BTCUSDT', '1m', 44640)

def signal_generator(df):
    open = df.Open.iloc[-1]
    close = df.Close.iloc[-1]
    previous_open = df.Open.iloc[-2]
    previous_close = df.Close.iloc[-2]

    # Bearish pattern
    if (open>close and
    previous_open<previous_close and
    close<previous_open and
    open>=previous_close):
        return 'sell'

    # Bullish pattern
    elif (open<close and
    previous_open>previous_close and
    close>previous_open and
    open<=previous_close):
        return 'buy'

    # No clear pattern
    else:
        return ""

df = get_klines('BTCUSDT', '1m', 44640)
def order_execution(symbol, signal, max_trade_quantity_percentage, leverage):
    # Get current account balance
    balances = binance_futures.fetch_balance()
    usdt_balance = balances['USDT']['free']
    max_trade_quantity = round_step_size(usdt_balance * max_trade_quantity_percentage / 100, precision=3)
    max_trade_quantity = min(max_trade_quantity, 2500) # Setting a max limit of 2500 contracts to avoid high-risk trades.

    # Close any existing positions
    current_position = None
    positions = binance_futures.fapiPrivateGetPositionRisk()
    for position in positions:
        if position["symbol"] == symbol:
            current_position = position
        if current_position["positionAmt"] != 0:
            binance_futures.fapiPrivatePostOrder(
                symbol=symbol,
                side='SELL' if current_position["positionSide"] == "LONG" else 'BUY',
                type='MARKET',
                quantity=abs(float(current_position["positionAmt"])),
                positionSide=current_position["positionSide"],
                reduceOnly=True
            )
    time.sleep(1)

    # Calculate appropriate order quantity and price based on signal
    opposite_position = None
    quantity = max_trade_quantity
    if signal == 'buy':
        position_side = 'BOTH'
        opposite_position = current_position if current_position and current_position['positionSide'] == 'SHORT' else None
        order_type = FUTURE_ORDER_TYPE_TAKE_PROFIT_MARKET
        price = round_step_size(binance_futures.fetch_ticker(symbol)['askPrice'], precision=2)
        take_profit_percentage = TAKE_PROFIT_PERCENTAGE
        stop_loss_percentage = STOP_LOSS_PERCENTAGE 
    elif signal == 'sell':
        position_side = 'BOTH'
        opposite_position = current_position if current_position and current_position['positionSide'] == 'LONG' else None
        order_type = FUTURE_ORDER_TYPE_STOP_MARKET
        price = round_step_size(binance_futures.fetch_ticker(symbol)['bidPrice'], precision=2)
        take_profit_percentage = TAKE_PROFIT_PERCENTAGE
        stop_loss_percentage = STOP_LOSS_PERCENTAGE

    # Reduce quantity if opposite position exists
    if opposite_position is not None:
        if abs(opposite_position['positionAmt']) < quantity:
            quantity = abs(opposite_position['positionAmt'])

    # Set take profit and stop loss prices
    if signal == 'buy':
        take_profit_price = round_step_size(price * (1 + take_profit_percentage / 100), precision=2)
        stop_loss_price = round_step_size(price * (1 - stop_loss_percentage / 100), precision=2)
    else:
        take_profit_price = round_step_size(price * (1 - take_profit_percentage / 100), precision=2)
        stop_loss_price = round_step_size(price * (1 + stop_loss_percentage / 100), precision=2)

    # Place order
    order_params = {
        "symbol": symbol,
        "side": "BUY" if signal == "buy" else "SELL",
        "type": order_type,
        "positionSide": position_side,
        "quantity": quantity,
        "price": price,
        "stopPrice": stop_loss_price if signal == "buy" else take_profit_price,
        "reduceOnly": False,
        "newOrderRespType": "RESULT",
        "workingType": "MARK_PRICE",
        "priceProtect": False
        }
    try:
        response = binance_futures.fapiPrivatePostOrder(**order_params)
        print(f"Order details: {response}")
    except BinanceAPIException as e:
        print(f"Error in order_execution: {e}")


        while True:
            df = get_klines('BTCUSDT', '1m', 44640)
            if df is not None:
                signal = signal_generator(df)
            if signal:
                print(f"The signal time is: {dt.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} :{signal}")
            order_execution('BTCUSDT', signal, MAX_TRADE_QUANTITY_PERCENTAGE, leverage)
            df = get_klines('BTCUSDT', '1m', 44640)
            if df is not None:
                signal = signal_generator(df)
            if signal:
                print(f"The signal time is: {dt.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} :{signal}")
            order_execution('BTCUSDT', signal, MAX_TRADE_QUANTITY_PERCENTAGE, leverage)
            time.sleep(0.1)
But I getting ERROR: Traceback (most recent call last):
  File "c:\Users\Alan\.vscode\jew_bot\jew_bot\jew_bot.py", line 57, in <module>       
    server_time = get_server_time(binance_futures)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\Alan\.vscode\jew_bot\jew_bot\jew_bot.py", line 52, in get_server_time
    server_time = exchange.fetch_time()["timestamp"]
                  ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
TypeError: 'int' object is not subscriptable
Give me right code if you'll recieve any problem