Stephen Coley

Background

About Stephen Coley

Former actor turned aspiring algorithmic trader. I started my undergraduate studies intending to become a Hollywood actor, but a year in I developed a passion for options trading. I focused on spreads, seeking to understand everything I could. I ventured into finance, taking courses in Finance and Accounting and attending BYU's Finance Society, but I quickly realized I wanted to be more technical and systematic in my research and trading. I took a break from school to serve as a volunteer for my church, and when I came back I decided to study Data Science. I excelled in the program, and was invited to lead a Data Science consulting project for the Research and Business Development Center, co-lead a project for Data Science Society, and guide students through coursework in the Data Science Lab. I earned my school's top merit scholarship all years of attendance and finished with a 3.93/4.0 GPA. After conversations with alumni who worked in etrading, I chose to prepare for a Master's in Quantitative Finance. I catered my degree to this goal, focusing on mathematical and statistics heavy coursework. During this time, I also completed personal projects using machine learning to forecast equities and cryptocurrency returns. I was able to go further in depth during my internship as a Quantitative Analyst at DeepQDigital, a high-frequency digital assets hedge fund. During this internship I trained a gradient boosted algorithm to predict short-term returns in cryptocurrencies, used SHAP values and dependence plots to explain model predictions, and presented findings to C-Suite executives. After graduation, I was admitted to the Master's in Financial Mathematics at the University of Chicago. Here I've been able to deepen my experience with Quantitative Research through courses like Portfolio Management, Options Pricing, Stochastic Processes, and Computing for Finance in Python. I've continued to use Python throughout my courses for analysis, statistics, machine learning and object oriented programming. Notably, I completed a project building a high-mid frequency trading algorithm that pulls data every minute, adds features, passes the normalized data to a machine learning algorithm, and translates the resulting prediction into an order that is sent through the broker's API. Outside of work, I enjoy weightlifting, competitive video games (Overwatch, Starcraft, Apex Legends, Marvel Rivals), watching movies and enjoying music.