app.py

import requests
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
from telegram import InlineKeyboardButton, InlineKeyboardMarkup
from telegram.ext import Updater, CommandHandler, CallbackQueryHandler
import logging
from selenium import webdriver
import os

# Set up logging
logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO)
logger = logging.getLogger(__name__)

# API Keys (retrieve from environment variables or securely store them)
BINANCE_API_KEY = os.getenv('https://api.binance.com/api/v3/ticker/price?symbol={symbol}')
ALPHA_VANTAGE_API_KEY = os.getenv('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=IBM&apikey=demo')
IEX_CLOUD_API_KEY = os.getenv('https://cloud.iexapis.com/stable/stock/{symbol}/quote?token={IEX_CLOUD_API_KEY}')
FOREX_COM_API_KEY = os.getenv('https://api.forex.com/v1/quotes/{pair}?api_key={FOREX_COM_API_KEY}')

# Placeholder Binomo credentials and URLs (these should be securely managed and properly implemented)
BINOMO_USERNAME = os.getenv('BINOMO_USERNAME')
BINOMO_PASSWORD = os.getenv('BINOMO_PASSWORD')
BINOMO_URL = 'https://binomo.com/'

# Data fetching functions
def get_crypto_price_binance(symbol):
    url = f'https://api.binance.com/api/v3/ticker/price?symbol={symbol}'
    headers = {'X-MBX-APIKEY': BINANCE_API_KEY}
    response = requests.get(url, headers=headers)
    data = response.json()
    return float(data['price'])

def get_stock_price_yahoo(symbol):
    url = f'https://query1.finance.yahoo.com/v7/finance/quote?symbols={symbol}'
    response = requests.get(url)
    data = response.json()
    return float(data['quoteResponse']['result'][0]['regularMarketPrice'])

def get_crypto_price_alpha_vantage(symbol):
    url = f'https://www.alphavantage.co/query?function=CURRENCY_EXCHANGE_RATE&from_currency={symbol[:3]}&to_currency={symbol[3:]}&apikey={ALPHA_VANTAGE_API_KEY}'
    response = requests.get(url)
    data = response.json()
    return float(data['Realtime Currency Exchange Rate']['5. Exchange Rate'])

def get_stock_price_iex(symbol):
    url = f'https://cloud.iexapis.com/stable/stock/{symbol}/quote?token={IEX_CLOUD_API_KEY}'
    response = requests.get(url)
    data = response.json()
    return float(data['latestPrice'])

def get_forex_price_forex_com(pair):
    url = f'https://api.forex.com/v1/quotes/{pair}?api_key={FOREX_COM_API_KEY}'
    response = requests.get(url)
    data = response.json()
    return float(data['price'])

# Fetch historical data function
def fetch_historical_data_yahoo(symbol, start_date, end_date):
    url = f'https://query1.finance.yahoo.com/v8/finance/chart/{symbol}?period1={start_date}&period2={end_date}&interval=1d'
    response = requests.get(url)
    data = response.json()
    timestamps = data['chart']['result'][0]['timestamp']
    close_prices = data['chart']['result'][0]['indicators']['quote'][0]['close']
    return pd.DataFrame({'timestamp': timestamps, 'close': close_prices})

# Fetch and preprocess historical data
def prepare_data():
    # Example symbols and dates
    crypto_symbol = 'BTC-USD'
    stock_symbol = 'AAPL'
    start_date = '1714521600'  # Unix timestamp for May 1, 2024
    end_date = '1715990400'    # Unix timestamp for May 17, 2024

    crypto_data = fetch_historical_data_yahoo(crypto_symbol, start_date, end_date)
    stock_data = fetch_historical_data_yahoo(stock_symbol, start_date, end_date)

    # Merge data on timestamp
    merged_data = pd.merge(crypto_data, stock_data, on='timestamp', suffixes=('_crypto', '_stock'))
    X = merged_data[['close_crypto', 'close_stock']].values
    y = (merged_data['close_crypto'].shift(-1) > merged_data['close_crypto']).astype(int).values[:-1]  # Example target

    X = X[:-1]  # Align X with y
    return X, y

# Fetch and preprocess data
X, y = prepare_data()

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train the neural network
model = MLPClassifier(hidden_layer_sizes=(10,), max_iter=1000)
model.fit(X_train, y_train)

# Function to predict trade action
def predict_trade_action(data):
    return model.predict(data)

# Start command handler
def start(update, context):
    keyboard = [
        [InlineKeyboardButton("Real Account", callback_data='real_account')],
        [InlineKeyboardButton("Demo Account", callback_data='demo_account')],
        [InlineKeyboardButton("Check Balance", callback_data='check_balance')],
        [InlineKeyboardButton("Trade Options", callback_data='trade_options')]
    ]
    reply_markup = InlineKeyboardMarkup(keyboard)
    update.message.reply_text('Choose an option:', reply_markup=reply_markup)

def button(update, context):
    query = update.callback_query
    query.answer()

    if query.data == 'real_account':
        context.user_data['account'] = 'real'
        query.edit_message_text(text="Switched to Real Account")
    elif query.data == 'demo_account':
        context.user_data['account'] = 'demo'
        query.edit_message_text(text="Switched to Demo Account")
    elif query.data == 'check_balance':
        balance = check_binomo_balance(context.user_data.get('account', 'demo'))
        query.edit_message_text(text=f"Current Balance: {balance}")
    elif query.data == 'trade_options':
        keyboard = [
            [InlineKeyboardButton("Buy", callback_data='buy')],
            [InlineKeyboardButton("Sell", callback_data='sell')],
            [InlineKeyboardButton("Change Currency Pair", callback_data='change_currency_pair')]
        ]
        reply_markup = InlineKeyboardMarkup(keyboard)
        query.edit_message_text(text='Choose a trade option:', reply_markup=reply_markup)
    elif query.data in ['buy', 'sell']:
        place_trade_on_binomo(query.data)
        query.edit_message_text(text=f"Placed a {query.data} order.")
    elif query.data == 'change_currency_pair':
        query.edit_message_text(text='Please enter the currency pair (e.g., BTCUSDT):')

# Check Binomo balance (mock function)
def check_binomo_balance(account_type):
    # Mock balance for demonstration
    if account_type == 'real':
        return 1000.0  # Replace with actual API call to Binomo
    else:
        return 50000.0  # Replace with actual API call to Binomo

# Placeholder for placing a trade on Binomo
def place_trade_on_binomo(action):
    driver = webdriver.Chrome()  # Ensure you have ChromeDriver set up
    driver.get(BINOMO_URL)

    # Log in to Binomo
    driver.find_element_by_id('username').send_keys(BINOMO_USERNAME)
    driver.find_element_by_id('password').send_keys(BINOMO_PASSWORD)
    driver.find_element_by_id('login-button').click()

    # Add your own logic here to log in and navigate to the trading interface
    
    if action == 'buy':
        # Code to place a buy order
        pass
    else:
        # Code to place a sell order
        pass

    driver.quit()

# Main function to set up the bot
def main():
    # Set up the updater and dispatcher
    updater = Updater("YOUR_TELEGRAM_BOT_TOKEN", use_context=True)
    dp = updater.dispatcher

    # Add command handlers
    dp.add_handler(CommandHandler("start", start))
    dp.add_handler(CallbackQueryHandler(button))

    # Start the bot
    updater.start_polling()
    updater.idle()

if __name__ == '__main__':
    main()