This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, Q-Learning, KNN, and regression trees and how to apply them to actual stock trading situations.
This course is composed of three mini-courses:
- Mini-course 1: Manipulating Financial Data in Python
- Mini-course 2: Computational Investing
- Mini-course 3: Machine Learning Algorithms for Trading
More information is available on the CS 7646 course website.
Note: Sample syllabi are provided for informational purposes only. For the most up-to-date information, consult the official course documentation.
To access the public version of this course's content, click here, then log into your Ed Lessons account. If you have not already created an Ed Lessons account, enter your name and email address, then click the activation link sent to your email, then revisit that link.
Before Taking This Class...
Suggested Background Knowledge
All types of students are welcome! The ML topics might be "review" for CS students, while finance parts will be review for finance students. However, even if you have experience in these topics, you will find that we consider them in a different way than you might have seen before, in particular with an eye towards implementation for trading.
If you answer "no" to the following questions, it may be beneficial to refresh your knowledge of the prerequisite material prior to taking CS 7646:
- Do you have a working knowledge of basic statistics, including probability distributions (such as normal and uniform), calculation and differences between mean, media, and mode? Do you understand the difference between geometric mean and arithmetic mean?
- Do you have strong programming skills?
Technical Requirements and Software
- Browser and connection speed: An up-to-date version of Chrome or Firefox is strongly recommended. We also support Internet Explorer 9 and the desktop versions of Internet Explorer 10 and above (not the metro versions). 2+ Mbps is recommended; the minimum requirement is 0.768 Mbps download speed.
- Operating system:
- PC: Windows XP or higher with latest updates installed
- Mac: OS X 10.6 or higher with latest updates installed
- Linux: any recent distribution that has the supported browsers installed
All Georgia Tech students are expected to uphold the Georgia Tech Academic Honor Code. This course may impose additional academic integrity stipulations; consult the official course documentation for more information.