Machine Learning for Trading Course

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Overview

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:

Textbooks

We will use the following textbooks:

  • For Mini-course 1: Python for Finance by Yves Hilpisch [1]
  • For Mini-course 2: What Hedge Funds Really Do by Romero and Balch [2]
  • For Mini-course 3: Machine Learning by Tom Mitchell [3] (see note)

Note: The Mitchell book is expensive (as of this writing, $212) but it is also required for the OMS ML course. Also, we're working with the publisher to offer a less expensive paperback version.

Prerequisites/Co-requisites

  • Who this course is for: The course is intended for people with strong software programming experience and introductory level knowledge of investment practice. A primary prerequisite is an interest and excitement about the stock market. Here's a quiz you can take to see if you have strong programming skills compinvesti-prog-quiz
  • Who this course is not for: If you already use advanced software tools such as Mean Variance Optimizers in your regular investing practice, you will probably find that you are "overqualified" for the second part of this course (Computational Investing). However, you may find Part 1 and Part 3 interesting and challenging.
  • Software we'll use: In order to complete the programming assignments you will need to a development environment that you're comfortable with. We use Unix, but you can also work with Windows and Mac OS environments. You must download and install a set of Python modules to your computer (including NumPy, SciPy, and Pandas).

Instructor information

Announcements

  • Piazzalink to piazza
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