Financial Math is the application of math, statistics, and programming within the finance industry. This course for current undergraduate and post-baccalaureate students in Quantitative Portfolio Management and Algorithmic Trading provides a rigorous introduction to modern applications in Financial Math through an interdisciplinary curriculum delivered via remote instruction by lecturers and industry experts affiliated with the Financial Math MS program at UChicago.
Financial markets have become increasingly complex, requiring specialized skills to effectively predict opportunities for profit and manage risk. Demand has grown steadily for people who can understand, enhance, and develop complex mathematical models. During the course, you’ll have a chance to learn more about careers in Quant Finance through presentations by our Career Development Office team. We can’t wait to help you explore the exciting world of quantitative finance!
These classes will be delivered via remote instruction.
Course Information
Course Description
This course teaches quantitative finance and algorithmic trading with an approach that emphasizes computation and application. The first half of the course focuses on designing, coding, and testing automated trading strategies in Python, with particular consideration to market models, infrastructure, and order execution. The second half of the course builds on this by covering case studies in quantitative investment that illustrate key issues in allocation, attribution, and risk management. Students will have the chance to learn classic models as well as more modern, computational approaches, all illustrated with application.
Academic Prerequisites
Applicants from any academic major are welcome! We are particularly interested in students with no previous background in finance who are interested in exploring Quantitative Finance as a career option.
Statistics, math, finance and Python programming will be featured. Familiarity in some of these areas is helpful, but there are not strict prerequisites. Some experience in regression and programming is highly recommended, but the course is accessible to motivated students still new to some of these areas.
Schedule
Quantitative Portfolio Management and Algorithmic Trading (FINM 25000 91)
- Synchronous class meets Mondays, 6:00pm - 9:00pm Central, June 10 - August 9.
- This class will be delivered via remote instruction.
Instructional Team
Mark Hendricks
Mark Hendricks is the Associate Director of the Master in Financial Mathematics where he helps manage all aspects of the program. His industry experience includes quantitative research for a hedge fund, Racon Capital. He has also done consulting work in finance, (asset management, corporate, real estate,) and data analysis (retail and pharmaceuticals.)
Mark has taught courses, reviews, and workshops at the graduate level for Financial Math, the Booth School of Business, and the Department of Economics. Among other things, he has significant experience teaching portfolio management, dynamic asset pricing, corporate valuation, and statistical estimation. Mark’s courses emphasize active learning with application and data.
As a Ph.D. candidate for Financial Economics at the University of Chicago’s Booth School and Department of Economics, Mark won awards including a Stevanovich Fellowship and Lee Prize. Mark holds an M.A. in economics and a B.S. in Mathematics.
Sebastien Donadio
Sebastien Donadio is currently Chief Technology Officer at Tradair. There he is in charge of leading the technology team. He has a wide variety of professional experience, including being the head of software engineering at HC Technologies, quantitative trading strategy software developer at Sun Trading, partner at AienTech, high-frequency trading hedge fund, working as technological leader in creating operating system for the Department of Defense. He also has research experience with Bull, and an IT Credit Risk Manager with Société Générale while in France.
Sebastien has taught various computer science courses for the past fifteen years. This time was spent between the University of Versailles, Columbia University, University of Chicago, NYU. Courses included: Computer Architecture, Parallel Architecture, Operating System, Machine Learning, Advanced Programming, Real-time Smart Systems, Advanced Financial Computing.