If you don’t know what WallStreetBets is about, that is probably for the best. If you do, this isn’t what this post is about. Instead, I am going to reflect on my assigned capstone project: Algorithmic Stock Market Trading Strategies for Individual Investors.

I was very happy when I first learned this was my assigned project. This project was my first choice for a few reasons. First, there was an outside sponsor, which seemed like a good opportunity to work with someone outside the university. Second, I thought it would be fascinating to work on a project with an objective to optimize parameters for a stock trading algorithm with a goal of obtaining better than average results. The project has exceeded my expectations so far.

Our advisor, Chester Ones, is a very interesting person. He has worked on machine learning systems for over 30 years and currently works at Levrum Data Technologies, a company which helps local municipalities more effectively manage their EMS systems. He has also been investing in the stock market for around 40 years, which means he has experienced the crashes in 1987 and 2008 and the dot.com bubble. He has also been working and developing stock trading algorithms for many years. In short, he has a wealth of hard-earned experience and knowledge.

With respect to the project, we have been directed to use QuantConnect Lean, an open-source algorithmic trading engine. This application has a relatively simple to learn command line tool that when installed and run in a docker container allows for local backtesting of trading algorithms. Successful runs are saved on a json file that contains the results of the analysis.

Our project will involve taking combining this QuantConnect tool with a multi-objective optimization algorithm to identify the optimal variables to use with the proposed trading algorithm. Right now, this project seems very challenging but I am excited by the opportunities it presents to learn how to combine different software to create a useful application.