Volatility Surface Explorer

A web application to compute and visualize the implied volatility (IV) surface for options using the Black-Scholes model. The backend fetches option chain data and computes IVs, while the frontend displays the IV surface using Streamlit and Plotly.

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Features

• Fetch option chain data (calls and puts) from financial APIs using yfinance.
• Calculate implied volatilities for options using the Black-Scholes model.
• Display a 3D surface of implied volatilities for selected tickers, strikes, and expiries.
• Identify option premium inefficiencies by analyzing the IV surface.

Project Structure

vol_surface_explorer
├── backend
│   ├── src
│   │   ├── run.py              # Flask app to serve the backend API
│   │   ├── fetch_data.py       # Fetch option chain data using yfinance
│   │   ├── black_scholes.py    # Calculate implied volatility using the Black-Scholes model
│   │   └── __init__.py         # Optional: Can leave empty
├── frontend
│   └── app.py                  # Streamlit app for the UI to display the IV surface
├── .env                        # Store API keys and secrets (not included in version control)
├── README.md                   # This file
├── requirements.txt            # Required Python packages
├── .gitignore                  # Ignoring sensitive files like .env and unnecessary caches
└── calls.csv                   # Optional: Example option data for testing

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Installation - MacOS Users

Local Environment Setup

First, download the repository.

Clone the repository

git clone https://github.com/alshodiev/vol_surface_explorer.git
cd vol_surface_explorer

Now, create the environment.

python -m venv vol_explorer
source vol_explorer/bin/activate    # For MacOS/Linux

Install Dependencies and Set up Environment Variables

# .env file
POLYGON_API_KEY=your_polygon_api_key_here

Running the Application

Start the Backend

cd backend/src
python run.py

The backend should run on http://127.0.0.1:5000 but the port number can still be changed if other servers are running on port=5000

Start the Frontend

cd frontend
streamlit run app.py

Future Steps

• Add support for advanced option pricing models (e.g., Heston, SABR).
• Enable real-time data fetching and updates.
• Introduce user-defined IV caps and filters for better surface control.