---
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.