| --- |
| title: Portfolio Optimization with OGD |
| emoji: 📈 |
| colorFrom: indigo |
| colorTo: gray |
| sdk: docker |
| app_port: 8000 |
| --- |
| |
| # Portfolio Optimizer |
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| An interactive portfolio optimization tool that uses Online Gradient Descent (OGD) to optimize asset allocation based on multiple objectives. |
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| ## Access Options in HuggingFace Spaces |
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| You can access this application in three different ways: |
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| 1. **Via Gradio Interface**: The default access method when you open the space. |
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| 2. **Via Direct Application**: For a better experience with the full UI, use one of these links: |
| - **Main Dashboard**: `/fullpage` |
| - **Education Page**: `/fullpage/education` |
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| You can append these paths to your HuggingFace space URL. |
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| 3. **Via API**: The application also exposes several API endpoints for programmatic access. |
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| ## Features |
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| - **Multi-objective Optimization**: Balance risk-adjusted returns, maximum drawdown, turnover, and portfolio concentration |
| - **Interactive UI**: Visualize portfolio performance through interactive charts |
| - **Stock Selection**: Select individual stocks or entire sectors |
| - **Educational Resources**: Learn about portfolio optimization concepts |
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| ## How It Works |
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| This application implements a robust portfolio optimization strategy using Online Gradient Descent (OGD). The optimization aims to maximize risk-adjusted returns while minimizing drawdowns, turnover, and over-concentration. |
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| - **Data Source**: Historical stock price data for S&P 500 constituents |
| - **Optimization Method**: Online Gradient Descent |
| - **Benchmarks**: Equal-weight and random portfolios for comparison |
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| ## Technical Details |
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| The application is built with: |
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| - **Backend**: Python with FastAPI |
| - **Data Processing**: pandas, NumPy |
| - **Visualization**: Chart.js |
| - **Frontend**: HTML, CSS, JavaScript |
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| ## License |
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| This project is available for educational and research purposes. |
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| ## How to Use |
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| 1. Select your desired date range and optimization parameters |
| 2. Choose stocks from the sector lists on the right |
| 3. Click "Run Allocation" to run the optimization |
| 4. View results in the interactive charts and metrics panels |
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| ## Running Locally |
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| ```bash |
| # Install dependencies |
| pip install -r requirements.txt |
| |
| # Run the application |
| python app.py |
| ``` |
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| The application will be available at http://localhost:8000 |
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