Introduction to Algorithmic Trading – Financial Data and Modeling

Catalog description

In 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).