Create README.md

README.md

@@ -1,13 +1,105 @@
 ---
-title: Crypto Trading
-emoji: 💻
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 5.9.1
-app_file: app.py
-pinned: false
 license: mit
+sdk: gradio
+colorFrom: red
+colorTo: green
 ---
+# Cryptocurrency Price Predictor
+
+An advanced cryptocurrency price prediction application using LSTM neural networks and technical indicators.
+
+## Features
+
+- Real-time cryptocurrency price prediction
+- Technical indicator analysis
+- Interactive visualization
+- Model confidence scoring
+- Performance metrics (RMSE, MAPE, R²)
+- Volatility analysis
+- Pre-trained model support
+- GPU acceleration (when available)
+
+## Requirements
+
+- Python 3.8+
+- CUDA-capable GPU (optional, for faster training)
+
+## Installation
+
+1. Clone the repository:
+```bash
+git clone https://github.com/yourusername/crypto-predictor.git
+cd crypto-predictor
+```
+
+2. Create a virtual environment (recommended):
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+
+1. Start the application:
+```bash
+python crypto_predictor.py
+```
+
+2. Open your web browser and navigate to:
+```
+http://localhost:7860
+```
+
+3. Enter a cryptocurrency symbol (e.g., BTC, ETH) and adjust the parameters:
+   - Historical Days: Amount of historical data to use
+   - Lookback Period: Number of days to look back for predictions
+
+4. Click "Generate Analysis" to view predictions and analysis
+
+## Model Details
+
+The prediction model uses:
+- LSTM (Long Short-Term Memory) neural network
+- Technical indicators including RSI, MACD, and moving averages
+- Batch normalization
+- Dropout regularization
+- Early stopping
+- Learning rate scheduling
+
+## Directory Structure
+
+```
+crypto-predictor/
+├── crypto_predictor.py
+├── requirements.txt
+├── README.md
+├── models/            # Saved model states
+├── venv/             # Virtual environment
+└── crypto_predictor.log
+```
+
+## Performance Metrics
+
+The application provides several metrics:
+- RMSE (Root Mean Square Error)
+- MAPE (Mean Absolute Percentage Error)
+- R² Score (Coefficient of Determination)
+- Prediction Confidence
+- Price Volatility
+
+## Contributing
+
+Feel free to submit issues, fork the repository, and create pull requests for any improvements.
+
+## License
+
+MIT License - feel free to use this project for any purpose.
+
+## Disclaimer
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This application is for educational purposes only. Cryptocurrency trading involves substantial risk of loss and is not suitable for every investor. Do not trade with money you cannot afford to lose.