I used your code: import time
from binance.client import Client
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 = 'BCH/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()

order_type = 'market'
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
    ohlcv = []
    for d in data:
        timestamp = dt.datetime.fromtimestamp(d[0]/1000).strftime('%Y-%m-%d %H:%M:%S')
        ohlcv.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(ohlcv)
    df.set_index('Open time', inplace=True)
    return df
df = get_klines(symbol, interval, lookback)

def signal_generator(df):
    if df is None:
        return ''
    ema_analysis = []
    candle_analysis = []

    df['EMA5'] = df['Close'].ewm(span=5, adjust=False).mean()
    df['EMA20'] = df['Close'].ewm(span=20, adjust=False).mean()
    df['EMA100'] = df['Close'].ewm(span=100, adjust=False).mean()
    df['EMA200'] = df['Close'].ewm(span=200, adjust=False).mean()

    if (
        df['EMA5'].iloc[-1] > df['EMA20'].iloc[-1] and
        df['EMA20'].iloc[-1] > df['EMA100'].iloc[-1] and
        df['EMA100'].iloc[-1] > df['EMA200'].iloc[-1] and
        df['EMA5'].iloc[-2] < df['EMA20'].iloc[-2] and
        df['EMA20'].iloc[-2] < df['EMA100'].iloc[-2] and
        df['EMA100'].iloc[-2] < df['EMA200'].iloc[-2]
    ):
        ema_analysis.append('golden_cross')
        
    elif (
        df['EMA5'].iloc[-1] < df['EMA20'].iloc[-1] and
        df['EMA20'].iloc[-1] < df['EMA100'].iloc[-1] and
        df['EMA100'].iloc[-1] < df['EMA200'].iloc[-1] and
        df['EMA5'].iloc[-2] > df['EMA20'].iloc[-2] and
        df['EMA20'].iloc[-2] > df['EMA100'].iloc[-2] and
        df['EMA100'].iloc[-2] > df['EMA200'].iloc[-2]
    ):
        ema_analysis.append('death_cross')

    if (
        df['Close'].iloc[-1] > df['Open'].iloc[-1] and
        df['Open'].iloc[-1] > df['Low'].iloc[-1] and
        df['High'].iloc[-1] > df['Close'].iloc[-1]
    ):
        candle_analysis.append('bullish_engulfing')
    elif (
        df['Close'].iloc[-1] < df['Open'].iloc[-1] and
        df['Open'].iloc[-1] < df['High'].iloc[-1] and
        df['Low'].iloc[-1] > df['Close'].iloc[-1]
    ):
        candle_analysis.append('bearish_engulfing')

    bollinger_std = df['Close'].rolling(window=20).std()
    df['UpperBand'] = df['EMA20'] + (bollinger_std * 2)
    df['LowerBand'] = df['EMA20'] - (bollinger_std * 2)

    if (
        df['Close'].iloc[-1] > df['UpperBand'].iloc[-1] and
        df['Close'].iloc[-2] < df['UpperBand'].iloc[-2]
    ):
        candle_analysis.append('upper_band_breakout')
    elif (
        df['Close'].iloc[-1] < df['LowerBand'].iloc[-1] and
        df['Close'].iloc[-2] > df['LowerBand'].iloc[-2]
    ):
        candle_analysis.append('lower_band_breakout')

    if ('golden_cross' in ema_analysis and 'bullish_engulfing' in candle_analysis) or 'upper_band_breakout' in candle_analysis:
        return 'buy'
    elif ('death_cross' in ema_analysis and 'bearish_engulfing' in candle_analysis) or 'lower_band_breakout' in candle_analysis:
        return 'sell'
    else:
        return ''

end_time = int(time.time() * 1000)
start_time = end_time - lookback * 60 * 1000

signal = signal_generator(df)
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)
Can you please add order excution for MEXC-futures exchange based on this algorithm :if signal_generator returns buy open long position automaticaly set Stop Loss -50% , if Long position is open and signal_generator return signal to sell close long position and open Short position autoaticaly set Stop Loss -50% 
elif: 
if signal_generator returns sell open Short position automaticaly set Stop Loss -50% , if Short position is open and signal_generator return signal to buy close Short position and open Long position autoaticaly set Stop Loss -50%