MATH 585
Introduction to Algorithmic Trading: Financial Data and Modeling
David Ye
Description from DukeHub
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).
Skills
- programmingtitle: "algorithmic"
- data and statisticsdescription: "modeling" and 3 more
- financedescription: "financial" and 4 more
AI-resilient: 0 of 5
- No: Physical, lab or field work
- No: Live work with people
- No: Judgment someone is accountable for
- No: A craft you practice with your hands or body
- No: Small, in-person format
The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works
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