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131791 https://hdl.handle.net/2286/R.I.56201 http://rightsstatements.org/vocab/InC/1.0/ 2020-05 36 pages eng Maheshwari, Nicholas Leo Balasooriya, Janaka Hoffman, David Dean, W.P. Carey School of Business Computer Science and Engineering Program Computer Science and Engineering Program Barrett, The Honors College Text My thesis is an exploration on the principles of algorithmic trading. I was introduced to the world of algorithmic trading in the Summer of 2018 when I got an internship at a startup trading firm called Helios Machine Intelligence. At HeliosMI, my job was to model algorithms for their in-house developed platform (in Java and C#). I learned how to model several different strategies, but I didn’t understand how, or more importantly, why these strategies worked. In the Spring of 2019 when I first began planning my thesis, I initially planned on recreating and optimizing HeliosMI’s trading platform. It was after reading a few books over the summer, namely; The Man Who Solved the Market by Gregory Zuckerman, Algorithmic Trading by Ernie Chan, and A Random Walk Down Wall Street by Burton Gordon Malkiel, that I realized that I was much more interested in learning the fundamentals of algorithmic trading, so I decided to make this the new focus of my thesis. At HeliosMI, we tested strategies against the historical data of stocks using an application called QuantConnect. This application is easy-to-use, cheap (even offering a free tier) and provides plenty of documentation with an active community forum, making it the obvious choice as the platform for my thesis research. Throughout my research I focused on exploring high-frequency trading algorithms, mainly because these are the types of algorithms that are employed at Wall Street hedge funds, and also the type I worked on at HeliosMI. I developed three distinct algorithms throughout my research; a momentum based strategy, a mean reversion based strategy, and a preferred time of day based strategy. In my thesis report, I go in depth on each of these strategies, as well as discuss the history of algorithmic trading, and explore some future research aspirations. Algorithmic Trading QuantConnect Stocks High-frequency trading Stock Trading Quantified: An Exploration of Algorithmic Trading Principles using QuantConnect