Introduction to Algorithmic Trading – Financial Data and Modeling

MATH 585

This course explores the complexity of financial data and the challenges in modeling them.  Increasing portions of trading and investment activities are now fully automated. Many key decisions are driven by computer algorithms and models built on top of ever-larger financial data sets. Students will learn a variety of financial data sets, perform research and analysis on these data, and develop mathematical and risk management models for profitable trading and investment strategies.

David Ye received his PhD in Mathematics from Duke University.  After a career in academia he moved to finance, where he has been a chief risk officer for banking, insurance and trading firms.  He is currently the founder and CEO of TriLeaf Technologies.

Prerequisite:  Knowledge of linear algebra, probability, and a basic understanding of programing (preferably in Python).  Some understanding of finance is preferred; exposure to linear regression is also preferred.

Topics in mathematics suitable for advanced undergraduates or graduate students. Topics vary per semester. Instructor:  Dr. David Ye

n this course on the complexity of financial data and the challenges in modeling them students will learn a variety of financial data sets, perform research and analysis on these data, and develop mathematical models for profitable trading and investment strategies. Includes group projects designing algorithms in a live trading environment based on financial/mathematical theories. Industry guests will discuss real-world practices. Prerequisites: Linear Algebra (e.g., MATH 216, 218), Probability (e.g., MATH/STA 230, MATH 340/STA 231), Programing, preferably in Python (e.g., MATH 281L/260L). Preferred, but not required: Finance (e.g., MATH 581/ECON 673) and Linear Regression (e.g., STA 210/MATH 238L). Instructor: Ye
Algorithmic Trading
Curriculum Codes
  • QS
Typically Offered
Fall and/or Spring