metadata
license: mit
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.