After completing the C++ Programming for Financial Engineering course, I was eager to build on my knowledge and take my skills to the next level. My goal was to simplify and modularize my approach to option pricing, making the code more accessible and flexible while still retaining the computational rigor required in financial engineering. Moreover, I knew I needed to get my Python skills back into good shape for the start of my MFE program. This ambition led to the creation of the Python Option Pricing & Hedging Tool.
This project allowed me to translate the concepts I worked on in C++ into Python, a language known for its versatility and ease of use. By leveraging Python’s Streamlit library and the Black-Scholes framework, I was able to create an intuitive and GUI that simplifies the complexities of option pricing. The modular design of the tool ensures that each component—whether it’s the pricing models, strategy overlays, or hedging techniques—can be easily adapted or expanded upon as needed.
One of the key features of this tool is the Strike Price vs. Volatility Analysis. In the world of options trading, the interplay between strike price and volatility is a critical factor that influences pricing decisions. This feature allows users to visually explore how varying strike prices and volatility levels affect option prices in the Black-Scholes framework, offering a deeper understanding of the market dynamics at play.
In addition to this, I’ve incorporated trading strategy overlays. These overlays provide users with the ability to visualize the payoffs of various trading strategies, such as covered calls, bull spreads, butterflies, and more. The visualizations and the heat maps help the user get a better understanding of what these positions actually look like and the intentions behind them.
To ensure a comprehensive approach to risk management, I also developed a section on optimal hedging techniques. By utilizing Black-Scholes parameters, the tool calculates the neutral (0) positions for delta, gamma, vega, rho, and theta hedging. This feature is particularly valuable for those looking to mitigate risk effectively in their trading portfolios, and can be built upon in the future to auto provide these for an entire portfolio.
Drawing on the online Q/KDB+ courses at KX Academy, I integrated a live Q server into the tool, enabling real-time storage and retrieval of user inputs and option prices. This not only enhances the tool’s functionality but opens the door to a potential high-frequency data input and constant graphical monitoring of option positions through time using KDB+.
Finally, I need to give credit to some folks who inspired me to make this:
The idea for a Streamlit application came from CodingJesus on YouTube, who worked with one of his clients to develop the call-put heatmap project idea. I ran by my idea with his client, Prudhvi Reddy, before creating this to make sure he was alright with me using his work as inspiration. Do check out his content and LinkedIn, I believe he has another Streamlit application on VaR for those of you interested in risk applications.
The video to the project idea is below, it may help those of you who are interested in this come up with unique applications and additions.
This project allowed me to translate the concepts I worked on in C++ into Python, a language known for its versatility and ease of use. By leveraging Python’s Streamlit library and the Black-Scholes framework, I was able to create an intuitive and GUI that simplifies the complexities of option pricing. The modular design of the tool ensures that each component—whether it’s the pricing models, strategy overlays, or hedging techniques—can be easily adapted or expanded upon as needed.
One of the key features of this tool is the Strike Price vs. Volatility Analysis. In the world of options trading, the interplay between strike price and volatility is a critical factor that influences pricing decisions. This feature allows users to visually explore how varying strike prices and volatility levels affect option prices in the Black-Scholes framework, offering a deeper understanding of the market dynamics at play.
In addition to this, I’ve incorporated trading strategy overlays. These overlays provide users with the ability to visualize the payoffs of various trading strategies, such as covered calls, bull spreads, butterflies, and more. The visualizations and the heat maps help the user get a better understanding of what these positions actually look like and the intentions behind them.
To ensure a comprehensive approach to risk management, I also developed a section on optimal hedging techniques. By utilizing Black-Scholes parameters, the tool calculates the neutral (0) positions for delta, gamma, vega, rho, and theta hedging. This feature is particularly valuable for those looking to mitigate risk effectively in their trading portfolios, and can be built upon in the future to auto provide these for an entire portfolio.
Drawing on the online Q/KDB+ courses at KX Academy, I integrated a live Q server into the tool, enabling real-time storage and retrieval of user inputs and option prices. This not only enhances the tool’s functionality but opens the door to a potential high-frequency data input and constant graphical monitoring of option positions through time using KDB+.
Finally, I need to give credit to some folks who inspired me to make this:
The idea for a Streamlit application came from CodingJesus on YouTube, who worked with one of his clients to develop the call-put heatmap project idea. I ran by my idea with his client, Prudhvi Reddy, before creating this to make sure he was alright with me using his work as inspiration. Do check out his content and LinkedIn, I believe he has another Streamlit application on VaR for those of you interested in risk applications.
The video to the project idea is below, it may help those of you who are interested in this come up with unique applications and additions.
