Create app.py

app.py

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+BINANCE_API_KEY = 'https://api.binance.com/api/v3/ticker/price?symbol='
+ALPHA_VANTAGE_API_KEY = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=IBM&apikey=demo'
+YAHOO_FINANCE_API_KEY = 'your_yahoo_finance_api_key'
+TRADING_VIEW_API_KEY = 'your_trading_view_api_key'
+BINOMO_API_KEY = 'your_binomo_api_key'
+TELEGRAM_BOT_API_KEY = '7074501421:AAEOoCMvXxr_T6sYIXAvHIDr515pLZ_ERtE'
+
+# data_acquisition.py
+import requests
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense, Dropout
+from telegram.ext import Updater, CommandHandler
+
+def fetch_binance_data(pair):
+    url = f"https://api.binance.com/api/v3/klines?symbol={pair}&interval=1h"
+    response = requests.get(url)
+    data = response.json()
+    df = pd.DataFrame(data, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_asset_volume', 'number_of_trades', 'taker_buy_base_asset_volume', 'taker_buy_quote_asset_volume', 'ignore'])
+    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+    return df[['timestamp', 'open', 'high', 'low', 'close', 'volume']]
+
+def fetch_alpha_vantage_data(pair):
+    symbol = pair.split("USDT")[0]  # Assuming pair like BTCUSDT
+    url = f"https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=60min&apikey={ALPHA_VANTAGE_API_KEY}"
+    response = requests.get(url)
+    data = response.json()
+    time_series_key = 'Time Series (60min)'
+    if time_series_key not in data:
+        raise ValueError(f"Error fetching data from Alpha Vantage: {data}")
+    df = pd.DataFrame(data[time_series_key]).T
+    df.columns = ['open', 'high', 'low', 'close', 'volume']
+    df.index = pd.to_datetime(df.index)
+    return df.reset_index().rename(columns={'index': 'timestamp'})
+
+def fetch_yahoo_finance_data(pair):
+    url = f"https://yfapi.net/v8/finance/chart/{pair}?interval=60m"
+    headers = {'x-api-key': YAHOO_FINANCE_API_KEY}
+    response = requests.get(url, headers=headers)
+    data = response.json()
+    timestamps = data['chart']['result'][0]['timestamp']
+    ohlc = data['chart']['result'][0]['indicators']['quote'][0]
+    df = pd.DataFrame({
+        'timestamp': pd.to_datetime(timestamps, unit='s'), 
+        'open': ohlc['open'], 
+        'high': ohlc['high'], 
+        'low': ohlc['low'], 
+        'close': ohlc['close'], 
+        'volume': ohlc['volume']
+    })
+    return df
+
+def fetch_trading_view_data(pair):
+    # Placeholder for TradingView API data fetching
+    raise NotImplementedError("TradingView API integration not implemented.")
+
+def fetch_binomo_data(pair):
+    # Placeholder for Binomo API data fetching
+    raise NotImplementedError("Binomo API integration not implemented.")
+
+def get_combined_data(pair):
+    df_binance = fetch_binance_data(pair)
+    df_alpha = fetch_alpha_vantage_data(pair)
+    df_yahoo = fetch_yahoo_finance_data(pair)
+    # Merge dataframes on timestamp
+    df = pd.merge(df_binance, df_alpha, on='timestamp', suffixes=('_binance', '_alpha'))
+    df = pd.merge(df, df_yahoo, on='timestamp', suffixes=('', '_yahoo'))
+    # Drop any redundant columns or handle conflicts
+    return df
+
+def preprocess_data(df):
+    df = df.dropna()
+    scaler = StandardScaler()
+    scaled_data = scaler.fit_transform(df[['open', 'high', 'low', 'close', 'volume']])
+    return scaled_data, scaler
+
+def create_dataset(data, time_step=60):
+    X, Y = [], []
+    for i in range(len(data) - time_step - 1):
+        a = data[i:(i + time_step), :]
+        X.append(a)
+        Y.append(data[i + time_step, 3])  # Assuming 'close' price is the target
+    return np.array(X), np.array(Y)
+
+def build_model(input_shape):
+    model = Sequential()
+    model.add(LSTM(50, return_sequences=True, input_shape=input_shape))
+    model.add(LSTM(50, return_sequences=False))
+    model.add(Dropout(0.2))
+    model.add(Dense(25))
+    model.add(Dense(1))
+    model.compile(optimizer='adam', loss='mean_squared_error')
+    return model
+
+def train_model(df):
+    data, scaler = preprocess_data(df)
+    X, Y = create_dataset(data)
+    X_train, Y_train = X[:int(len(X) * 0.8)], Y[:int(len(Y) * 0.8)]
+    X_val, Y_val = X[int(len(X) * 0.8):], Y[int(len(Y) * 0.8):]
+    model = build_model((X_train.shape[1], X_train.shape[2]))
+    model.fit(X_train, Y_train, validation_data=(X_val, Y_val), epochs=20, batch_size=32)
+    return model, scaler
+
+def generate_signal(pair):
+    df = get_combined_data(pair)
+    model, scaler = train_model(df)
+    recent_data = df.tail(60).drop(columns=['timestamp'])
+    scaled_recent_data = scaler.transform(recent_data)
+    prediction = model.predict(np.expand_dims(scaled_recent_data, axis=0))
+    last_close = df['close'].iloc[-1]
+    if prediction > last_close:
+        return "Buy"
+    else:
+        return "Sell"
+
+def start(update, context):
+    context.bot.send_message(chat_id=update.effective_chat.id, text="I'm a trading bot, how can I help you today?")
+
+def signal(update, context):
+    pair = context.args[0] if context.args else 'BTCUSDT'
+    try:
+        trade_signal = generate_signal(pair)
+        context.bot.send_message(chat_id=update.effective_chat.id, text=f"Trade Signal for {pair}: {trade_signal}")
+    except Exception as e:
+        context.bot.send_message(chat_id=update.effective_chat.id, text=f"Error: {e}")
+
+def main():
+    updater = Updater(token=TELEGRAM_BOT_API_KEY, use_context=True)
+    dispatcher = updater.dispatcher
+
+    start_handler = CommandHandler('start', start)
+    signal_handler = CommandHandler('signal', signal)
+
+    dispatcher.add_handler(start_handler)
+    dispatcher.add_handler(signal_handler)
+
+    updater.start_polling()
+
+if __name__ == '__main__':
+    main()