Update README.md

README.md

@@ -1,76 +1,150 @@
 ---
 license: mit
+language:
+- en
+library_name: transformers
+pipeline_tag: depth-estimation
 ---
-### Model Card for Telegram Trading Bot
-
-#### Overview
-- **Project Name:** Telegram Trading Bot
-- **Purpose:** Predict stock market prices and generate trade signals
-- **Platforms Supported:** TradingView, Forex, Coinbase, Binance, Yahoo Finance, Bloomberg
-
-#### Model Details
-- **Model Type:** Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) layers
-- **Framework Used:** TensorFlow/Keras
-- **Input Data:** Historical price data (open, high, low, close, volume) from various financial platforms
-- **Output:** Predicted price and trade signal (Buy/Sell)
-
-#### Data Sources
-- **Binance:** Real-time cryptocurrency prices
-- **Alpha Vantage:** Stock and Forex market data
-- **Yahoo Finance:** Stock prices and financial data
-- **TradingView:** Technical analysis and financial market data (Placeholder for future integration)
-- **Bloomberg:** Financial data and news (Placeholder for future integration)
-- **Coinbase:** Cryptocurrency prices (Placeholder for future integration)
-
-#### Features
-- ♦ **Real-time Data Acquisition:**
-  - Fetches latest market data from multiple platforms
-  - Supports diverse financial instruments including stocks, forex, and cryptocurrencies
-- ♦ **Data Preprocessing:**
-  - Normalizes and scales data for model input
-  - Handles missing data and ensures consistency across datasets
-- ♦ **Neural Network Model:**
-  - Utilizes LSTM layers to capture temporal dependencies in financial data
-  - Trained on historical price data to predict future prices
-- ♦ **Trade Signal Generation:**
-  - Generates Buy/Sell signals based on predicted price trends
-  - Provides actionable insights for trading on platforms like Binomo
-- ♦ **Integration with Telegram:**
-  - Responds to user commands for real-time trading signals
-  - Simple and interactive user interface through Telegram bot
-
-#### Usage
-- **Command: `/start`**
-  - Initializes the bot and provides basic instructions
-- **Command: `/signal [pair]`**
-  - Generates and returns a trade signal for the specified currency pair (default: BTCUSDT)
-
-#### Performance Metrics
-- **Evaluation Metrics:**
-  - Mean Squared Error (MSE) for regression accuracy
-  - Accuracy of trade signals (Buy/Sell) compared to actual market movements
-- **Training Data:**
-  - Historical price data from supported platforms
-- **Validation:**
-  - Split historical data into training and validation sets
-  - Evaluate model performance on unseen validation data
-
-#### Limitations and Future Work
-- ♦ **Current Limitations:**
-  - Placeholder integrations for TradingView, Bloomberg, and Coinbase
-  - Model performance highly dependent on the quality and granularity of data
-  - Limited to hourly predictions; higher frequency data may be needed for intraday trading
-- ♦ **Future Enhancements:**
-  - Complete integration with TradingView, Bloomberg, and Coinbase
-  - Experiment with different neural network architectures and hyperparameters
-  - Incorporate additional features such as sentiment analysis from news and social media
-
-#### Ethical Considerations
-- **User Discretion:** 
-  - The bot provides trade signals but users should exercise caution and perform their own analysis before making trading decisions.
-- **Data Privacy:** 
-  - Ensure secure handling of API keys and user data.
-- **Financial Risk:** 
-  - Trading involves financial risk; users should understand the risks involved and use the bot responsibly.
-
-This model card provides a comprehensive overview of the Telegram Trading Bot, highlighting its capabilities, data sources, features, and considerations for future development.
+# config.py
+BINANCE_API_KEY = 'your_binance_api_key'
+ALPHA_VANTAGE_API_KEY = 'your_alpha_vantage_api_key'
+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 = 'your_telegram_bot_api_key'
+
+# 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()