Providing University At Buffalo Students With Real-Life Quantitative Trading Experience

At QuantConnect, we’re always interested in how our users are deploying our institutional-grade technology. 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.

Dr. Zhen Liu is an Adjunct Assistant Professor of Teaching (Computer Science and Engineering) and Adjunct Lecturer (Economics) at the University at Buffalo and teaches “Computational Investment.” While the class is an elective open to all majors, it mainly serves students from computer science, finance, economics, engineering, and math disciplines. 

The focus of the class is to build the bridge between computation and finance for students who may be familiar with programming, but are not familiar with quantitative finance. Liu — who has a strong interest in engineering and programming — builds the curriculum with his co-instructor, a former stock broker and business owner with an interest in investing. During the year, they examine financial market data, explain concepts such as options, technical analysis and financial decision making, discuss strategy development, and code algorithms. Students are encouraged to first manually apply a strategy to an existing portfolio of stocks, so that they have hands-on experience about how the strategy works. Meanwhile, they are required to participate in team projects to either build software prototypes, design and evaluate strategies, or study real financial data about an event. Liu, a long-time user of QuantConnect, said this is where QuantConnect comes into play for his students. 

QuantConnect is introduced to them as a data source to use for their team projects. Liu said that the availability of options data, which is hard for a student to access and manipulate due to its size and complexity, is especially helpful for those with an interest in studying options trading strategies. He also said the availability of high-frequency data is essential to study advanced algorithms that use the information of market microstructure. All of them can be used  for the projects proposed by students and the findings are presented and reviewed at the end of the semester. 

“You can only get daily data with most of the simple API’s. Access to high frequency data helps to build more sophisticated measures about market activities.” 

Liu also cites the Alpha Streams marketplace as one of the best features for his class, as it demonstrates to students the new way of creating value with their skills and ideas, and that an alternative career in quantitative finance is attainable.  

“It shows my students that individuals without big institutions behind them can also have the power in terms of making high-quality financial decisions. By sharing within a team and across teams, we try to cultivate interdisciplinary problem-solving skills and communication skills. These are in-line with our mission statement in teaching this course.”

Lexie Robinson

By: Lexie Robinson

16.12.2020