Superstar VIX: Customized Volatility Index (CVI)

Welcome to Project Superstar VIX, a financial dashboard built with Streamlit. This mini app calculates a Customized Volatility Index (CVI) for high-performing "Superstar" stocks in both the Republic of Korea (KR) and the United States (US) markets.

🚀 Deployment

This application is designed to be hosted on Streamlit Community Cloud.

🛠 Features

• Real-time CVI Calculation: Using a Recursive EWMA model ($\lambda=0.94$).
• Stateful Engine: SQLite-backed state management for efficient incremental updates.
• Robust Covariance: Implements Ledoit-Wolf Shrinkage for stable correlation matrices.
• Hierarchical Visualization: Sector-based Treemaps and Risk-Return Profile scatterplots.
• Standardized Comparison: Fixed Y-axis (0-100) for direct cross-market comparison.

📊 Methodology

1. Volatility (EWMA)

Individual stock volatility is calculated using the Recursive Exponentially Weighted Moving Average formula: $$\sigma_{i,t}^2 = \lambda \sigma_{i,t-1}^2 + (1 - \lambda) r_{i,t-1}^2$$

2. Aggregation

The index is aggregated using Market Capitalization weights ($w_t$): $$\sigma_{port, t} = \sqrt{w_t^T (D_t R_t D_t) w_t}$$ Where $R_t$ is the Ledoit-Wolf shrunk correlation matrix.

💻 Tech Stack

• Framework: Streamlit
• Data: FinanceDataReader (KR), yfinance (US)
• Math/Stats: Pandas, NumPy, Scikit-learn (Ledoit-Wolf)
• Viz: Plotly

⚙️ Local Setup

1. Clone the repository:

   git clone https://github.com/your-username/lab-superstar-vix.git
   cd lab-superstar-vix

2. Create and activate a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the app:

   streamlit run app.py

📄 License

MIT License. Feel free to use and modify for your own research.