Providing University of Chicago Students With Real-Life Quantitative Trading Experience

At QuantConnect, we’re always interested in how our users are deploying our institutional-grade technology across a variety of scenarios. With Organizations, QuantConnect is more flexible and customizable than ever before, allowing you to scale up and down your resource use as your team grows.

University of Chicago Masters of Computer Science students can apply their programming skills to finance by taking Dylan Hall’s “Applied Financial Technology” class near the end of their program completion. Dylan has served as a lecturer for the MCS program since 2018. 

Students enter with Python skills and take the class to learn how to apply those skills to finance. To start the eleven-week course, students are asked to choose two stocks they are interested in following for the quarter, and then are broken into groups so they can observe the range of stock performance. They begin with technical analysis and other concepts that are easier for computer science students to initially grasp. 

Next, they transition into value analysis and begin to pull in financial data. After they build this foundation and discuss theories and models such as the Fama/French factors (originally researched at the University of Chicago), they begin learning to build algorithms. 

Using QuantConnect’s research environment, students look back at historical data and identify trends in their stocks to find an indicator. This is where, Dylan said, they begin to backtest strategies using the lessons from previous weeks. After discussing behavior analysis and momentum, they have a “trading day” where students build an algorithm to trade their stocks. They dive into the LEAN framework, as students are taken step-by-step through universe selection, alpha selection, portfolio construction, and risk overlays. After building their algorithms, the groups turn in their best performing algorithm for Dylan to backtest and ultimately see if it performs well out-of-sample through the end of the course. 

Dylan, who had previously used Quantopian, migrated to QuantConnect in October. Both Dylan and his class easily transferred their materials over to QuantConnect using the migrator tool and quickly grasped the fundamentals of LEAN. The LEAN framework is especially beneficial for Dylan’s students, he says, as it gives them a systemic approach to producing well-performing algorithms. 

“It was just incredible to be able to take them from researching and using data all the way up to implementation and backtesting their algorithms,” he said.

Following their transition to QuantConnect, Dylan also found the community forum was one of the most helpful tools for his class. 

“When I have students reach out to me, I can usually take the question they ask and search it on the forum and get a solid answer for them,” Dylan said. 

As a lecturer equipped with those forum responses, he’s able to help his students implement some particularly unconventional ideas. 

Dylan said for his students, who are usually just starting out in quantitative finance, the concept of QuantConnect’s Alpha Streams marketplace also helped bridge the gap for some students who viewed trading stocks as an unattainable career. 

“The typical thought is that to work in finance you have to have a lot of money. It would discourage students who don’t necessarily know that’s an option for them because it seems like something only people in New York in suits with $200 haircuts do,” Dylan said. “Alpha Streams tells my students that even if you don’t have a lot of money to invest, if you can do the research you can sell the signal on QuantConnect. It really democratizes finance.” 


By: Lexie

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