About this Course

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.

Course Cost
Free
Timeline
Approx. 4 months
Skill Level
Intermediate
Included in Course
  • Icon course 01 3edf6b45629a2e8f1b490e1fb1516899e98b3b30db721466e83b1a1c16e237b1 Rich Learning Content

  • Icon course 04 2edd94a12ef9e5f0ebe04f6c9f6ae2c89e5efba5fd0b703c60f65837f8b54430 Interactive Quizzes

  • Icon course 02 2d90171a3a467a7d4613c7c615f15093d7402c66f2cf9a5ab4bcf11a4958aa33 Taught by Industry Pros

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Join the Path to Greatness

This free course is your first step towards a new career with the Machine Learning Engineer Nanodegree Program.

Free Course

Machine Learning for Trading

by Georgia Institute of Technology

Enhance your skill set and boost your hirability through innovative, independent learning.

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Course Leads

  • Tucker Balch
    Tucker Balch

    Instructor

  • Arpan Chakraborty
    Arpan Chakraborty

    Instructor

What You Will Learn

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

Each mini-course consists of about 7-10 short lessons. Assignments and projects are interleaved.

Fall 2015 OMS students: There will be two tests - one midterm after mini-course 2, and one final exam.

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 Technology Requirements for using Udacity.

Why Take This Course

By the end of this course, you should be able to:

  • Understand data structures used for algorithmic trading.
  • Know how to construct software to access live equity data, assess it, and make trading decisions.
  • Understand 3 popular machine learning algorithms and how to apply them to trading problems.
  • Understand how to assess a machine learning algorithm's performance for time series data (stock price data).
  • Know how and why data mining (machine learning) techniques fail.
  • Construct a stock trading software system that uses current daily data.

Some limitations/constraints:

  • We use daily data. This is not an HFT course, but many of the concepts here are relevant.
  • We don't interact (trade) directly with the market, but we will generate equity allocations that you could trade if you wanted to.
What do I get?
  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals

Thanks for your interest!

We'll be in touch soon.

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