Can This Toolkit Enhance Your Financial Analysis and Options Pricing Skills?
This project employs the Black-Scholes model to calculate option prices using five key inputs: underlying asset price, strike price, time to expiration, volatility, and the risk-free interest rate. By compiling C++ code into WebAssembly, the model achieves an execution time of just 9 microseconds.
Black Scholes Model Parameters
Model Name
Current Asset Price
Strike Price
Time to Maturity (Years)
Volatility
Risk-Free Interest Rate
Heatmap Parameters
Min Spot Price
Max Spot Price
Min Volatility
Max Volatility
Call Price Heatmap
Put Price Heatmap
Call Price
$10.45
Put Price
$5.57
Project Description
Technology Stack
Outline
This project leverages the Black-Scholes model to calculate option prices based on inputs such as underlying asset price, strike price, time to expiration, volatility, and the risk-free interest rate. The core model is implemented in C++ and compiled into WebAssembly using Emscripten and CMake. The WebAssembly module is integrated into a React application via a JavaScript wrapper, enabling high-performance computations. A heatmap visualizes how call and put prices fluctuate based on volatility and strike price.
Performance Metric
The key performance metric is model execution time.
Project Conclusion
Results
The project successfully demonstrates real-time option pricing using the Black-Scholes model. It visualizes price sensitivity through a dynamic heatmap, offering insights into how volatility and strike price affect option values. Notably, the application runs efficiently on mobile devices, with real-time UI updates and responsive performance.
Future Work
In future work, the project will expand its capabilities by incorporating additional option pricing models, such as binomial models and Monte Carlo simulations. Integration of live market data will enable real-time input updates, enhancing the system's responsiveness to current market conditions. To improve the user experience, the platform will offer more customizable visualizations and a wider array of analytical tools, empowering users with greater flexibility in analysis. Performance optimization will also be a priority, focusing on handling larger datasets and more complex financial scenarios with increased efficiency.