Options Volatility Surface Cleaner & Interpolator

A quantitative finance tool built by Belous Ivan a 10th-grade student to construct arbitrage-free implied volatility surfaces from real market data.

This project fetches live options data, cleans noise and static arbitrage violations, and builds a smooth, interpolatable volatility surface — a core component in derivatives pricing used by hedge funds and investment banks.

🔍 Features

• ✅ Real-time data from Yahoo Finance (SPY, QQQ, and more)
• ✅ Implied volatility calculation via Black-Scholes inversion
• ✅ Arbitrage cleaning: removes violations of monotonicity and convexity
• ✅ Surface interpolation across strikes and expirations
• ✅ Cross-asset comparison: SPY vs QQQ term structure analysis
• ✅ Multiple visualizations:
  • Static plots (PNG)
  • Time-evolution animation (GIF)
  • Fully interactive 3D surface (HTML with Plotly)

📂 Project Structure

• ├── core/ # Core quant engine
• │ ├── init.py
• │ └── vol_surface.py # Black-Scholes, IV solver, cleaner
• ├── compare_assets.py # Compares SPY and QQQ volatility
• ├── animate_surface.py # Generates GIF of surface evolution
• ├── interactive_plot.py # Creates browser-based 3D plot
• ├── run_all.py # One-click pipeline execution
• ├── plots/ # Output visualizations
• │ ├── compare_SPY_QQQ.png
• │ ├── vol_surface_evolution.gif
• │ └── vol_surface_interactive.html ← Open in browser!
• └── requirements.txt # Dependencies

▶️ How to Run

1. Install dependencies:

   pip install -r requirements.txt

2. Run the full pipeline: bash python run_all.py
3. Explore results :

• Open plots/vol_surface_interactive.html in your browser
• View animation: plots/vol_surface_evolution.gif
• Analyze CSV data in data/ (optional)
• 💡 No internet? The tool automatically falls back to realistic simulated data.

🎓 Academic Context

• This project was developed as part of independent research into market microstructure and derivatives pricing. It demonstrates:

• Understanding of option pricing theory (Black-Scholes, implied volatility)

• Ability to implement arbitrage constraints (no butterfly arbitrage)

• Proficiency in Python for quantitative finance (NumPy, SciPy, Pandas, Plotly)

• Skills in data engineering and scientific visualization

• Potential extensions include SVI parametrization, local volatility calibration, and integration with Russian market data (MOEX).

Built with curiosity • For students, by a student. Belous Ivan