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Code-With-Shanmukh / repository
Developed a real-time cryptocurrency price prediction system using Python. The project fetches live crypto market data using CCXT, preprocesses it, and applies an LSTM model to predict future prices. A Tkinter-based GUI visualizes actual vs. predicted prices and provides alerts for significant price drops.
📈 Crypto Price Prediction Tool 🚀 Overview This project is a real-time cryptocurrency price prediction tool using LSTM (Long Short-Term Memory) neural networks. It fetches live market data from multiple exchanges via CCXT, preprocesses it, trains an LSTM model, and provides real-time price predictions with alerts for significant drops. The tool also features a GUI-based visualization built with Tkinter and Matplotlib.
🛠️ Features ✅ Real-time data fetching using CCXT ✅ Predicts future crypto prices using LSTM ✅ Supports multiple timeframes (1m, 5m, 15m, 1h, etc.) ✅ Graphical visualization of actual vs. predicted prices ✅ Alert system for significant price drops ✅ Dynamic INR conversion for local currency insights
📌 Requirements Ensure you have the following installed: pip install ccxt pandas numpy matplotlib scikit-learn tensorflow
🔧 How to Run
Clone the repository:
git clone https://github.com/Code-With-Shanmukh/CryptoPredictionTool.git
cd CryptoPredictionTool
Run the script: python CryptoPredictionTool.py
Enter user inputs when prompted: Timeframe (e.g., 1m, 5m, 1h, 1d): Crypto symbol (e.g., BTC, ETH): Exchange name (e.g., Binance, Bitget, KuCoin, MEXC):
📊 How It Works Fetches real-time OHLCV data (Open, High, Low, Close, Volume) using CCXT. Preprocesses data (normalization, feature engineering). Trains an LSTM model using past 60 timestamps. Predicts the next closing price every few seconds. Displays real-time graph updates with actual and predicted prices.
🖥️ GUI Preview The application launches a Tkinter GUI showing live price updates and predictions:
Blue line → Actual prices Red line → Predicted prices Red warning → Significant price drop detected 📌 Future Enhancements 🔹 Multi-crypto portfolio tracking 🔹 Advanced deep learning architectures 🔹 Web-based version using Flask/Django
🤝 Contributing Pull requests are welcome! If you find an issue, open a GitHub issue.
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