Create app.py

app.py

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+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()