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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

API_KEY = ‘’
API_SECRET = ‘’

client = Client(API_KEY, API_SECRET)

# Set the endpoint and parameters for the request
url = “https://fapi.binance.com/fapi/v2/account”
recv_window = 10000 # update the recv_window value

params = {
“timestamp”: int(get_server_time() * 1000),
“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
leverage = 100

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

# Get the USDT balance and calculate the max trade size based on the leverage
try:
usdt_balance = next((item for item in account_info[‘accountBalance’] if item[“asset”] == “USDT”), {“free”: 0})[‘free’]
except KeyError:
usdt_balance = 0
print(“Error: Could not retrieve USDT balance from API response.”)

max_trade_size = float(usdt_balance) * leverage

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

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…
}
})

# Load the market symbols
params = {
“timestamp”: int(get_server_time() * 1000),
“recvWindow”: recv_window
}
try:
markets = binance_futures.load_markets()
except ccxt.BaseError as e:
print(f’Error fetching markets: {e}‘)
markets = []

if symbol in markets:
print(f"{symbol} found in the market")
else:
print(f"{symbol} not found in the market")

# Get server time and time difference
def get_server_time():
ntp_client = ntplib.NTPClient()
response = ntp_client.request(‘pool.ntp.org’)
return int(response.tx_time * 1000)

def get_time_difference():
server_time = get_server_time()
local_time = int(time.time() * 1000)
time_difference = local_time - server_time
return time_difference

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):
open = df.Open.iloc[-1]
close = df.Close.iloc[-1]
previous_open = df.Open.iloc[-2]
previous_close = df.Close.iloc[-2]
# Calculate EMA indicators
ema_10 = ta.trend.EMAIndicator(df[‘Close’], window=10)
ema_50 = ta.trend.EMAIndicator(df[‘Close’], window=50)
ema_100 = ta.trend.EMAIndicator(df[‘Close’], window=100)
ema_200 = ta.trend.EMAIndicator(df[‘Close’], window=200)

# Calculate MA indicator
ma = ta.t