Context & Problem
Deriving the theoretical value of a stock option requires complex stochastic calculus. While the Black-Scholes-Merton model provides the mathematical standard, the non-linear variables—especially the impact of implied volatility (Vega) and time decay (Theta)—are difficult to visualize without dedicated quantitative tools.
Architecture & Approach
I developed a localized financial application using Python that executes the Black-Scholes formula natively. Because computational finance tools are only as useful as they are accessible, I built a lightweight graphical user interface (GUI) using Tkinter to wrap the quantitative logic.
The application takes in real-time user parameters (underlying asset price, strike price, time to expiration, risk-free interest rate, and volatility) and immediately computes the theoretical price for both European Call and Put options.
Key Results
- Programmed the foundational stochastic partial differential equation into an optimized Python pipeline.
- Bridged the gap between raw quantitative mathematics and actionable trading insights via an interactive local dashboard.
- Demonstrated the ability to translate complex financial logic (useful for my FactSet and FINRA background) into functional software.