Black-Scholes modeling and visualizing tool

A fully functional black-scholes model written in python that is interactable and includes a heatmap for calls and puts

This project was made as a personal project for myself as I have a love for mathematics and investing. I think it really provides value to myself as a student because it is a good beginning to show what I can do, not just in quantitative finance, but with data science tools too. Everything was written in python as my languge of choice for data science applications.

It can do the following

• Automatically update and calculate the call and put premium price based on the changing values on the sidebar
• automatically update the heatmap which shows the different call and put premium cost at different volatilitys and asset prices, with the rest of the variables based on the chosen ones above

It uses the following tools

• Streamlit to create a user interface and publish the program online
• Seaborn and Matplotlib together to create my heatmap visuals
• Scipy to do statistical computations within the call and put calculations

How to access

• To access this project online, head to the link options-erik.streamlit.app