University of Illinois Urbana-Champaign The Grainger College of Engineering 8 courses 12 total credits
for the degree of Master of Science in Financial Engineering, Automated Trading Practices concentration Financial Engineering (FE) is the application of quantitative methods to the analysis of financial markets and financial products. The quantitative techniques may include Mathematics, Statistics, Computer Science, Machine Learning, Neural Nets, and generalized Data Analytics. The applications include searching for opportunities as well as deriving solutions to financial problems. All financial markets are a subject for FE, from public exchanges to private over-the-counter markets. Similarly, all financial products, from equities, bonds and derivatives to bitcoins are candidates for quantification. An overarching theme for FE involves balancing reward against techniques for the measurement, management, and mitigation of risk. Graduate Degree Programs in Financial Engineering The Master of Science in Financial Engineering (MSFE) degree program is jointly sponsored by the Department of Industrial and Enterprise Systems Engineering (ISE) in The Grainger College of Engineering and the Department of Finance in the Gies College of Business. Graduates from this program receive the MSFE degree awarded by the Graduate College. The MSFE program complements other graduate programs offered by the sponsoring departments. The Master of Science in Financial Engineering (terminal masterβs) can be completed in 18 months with an option to extend to 24 months, beginning in August each...
| Total Hours | 12 credits |
Required
| Code | Course Title | Credits |
|---|---|---|
| IE 421 | High Frequency Trading Technology | 4 |
| Total Required | 4 credits | |
Pick 1 Algo Trading Course
| Code | Course Title | Credits |
|---|---|---|
| FIN 554 | Algorithmic Trading Systems Design and Testing | 4 |
| FIN 556 | Algorithmic Market Microstructure | 4 |
| Total Pick 1 Algo Trading Course | 4 credits | |
Pick 1 Stochastic & Learning Foundations Course
| Code | Course Title | Credits |
|---|---|---|
| IE 410 | Advanced Topics in Stochastic Processes & Applications | 3-4 |
| IE 434 | Deep Learning: Mathematics and Applications | 3-4 |
| IE 518 | Queueing Systems | 4 |
| IE 531 | Algorithms for Data Analytics | 4 |
| IE 534 | Deep Learning | 4 |
| Total Pick 1 Stochastic & Learning Foundations Course | 4 credits | |
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