Machine Learning for Trading

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, KNN and regression trees and how to apply them to actual stock trading situations.

Prerequisites and Requirements: Students should have strong coding skills and some familiarity with equity markets. No finance or machine learning experience is assumed.Note that this course serves students focusing on computer science, as well as students in other majors such as industrial systems engineering, management, or math who have different experiences. 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.Programming will primarily be in Python. We will make heavy use of numerical computing libraries like NumPy and Pandas.See the for using Udacity.