Machine Learning for Trading Course

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:

• Mini-course 1: Manipulating Financial Data in Python
• Mini-course 2: Computational Investing
• Mini-course 3: Machine Learning Algorithms for Trading

Important note

This course ramps up in difficulty towards the end. The projects in the final 1/3 of the course are challenging. Be prepared.

Instructor information

Tucker Balch, Ph.D.
Professor, Interactive Computing at Georgia Tech
CS 7646 Course Designer
CS 7646 Instructor: Spring 2016, Fall 2016

David Byrd
Research Scientist, Interactive Media Technology Center at Georgia Tech
CS 7646 Instructor: Summer 2016
CS 7646 Head TA: Spring 2016

Syllabus

• CS7646_Spring_2016
• CS7646_Summer_2016

Textbooks & Other Resources

We will use the following textbooks:

• For Mini-course 1: Python for Finance by Yves Hilpisch amazon.com (optional)
• For Mini-course 2: What Hedge Funds Really Do by Romero and Balch amazon.com (required)
• For Mini-course 3: Machine Learning by Tom Mitchell (optional)
  • Buy it for $218.00 at: amazon.com
  • Buy a paperback version for $61.78. IMPORTANT WARNINGS: 1) They only ship to the US 2) It takes them 3 weeks to print the book. If you order from outside the US they will quietly accept your money but never ship the book: less expensive version at mcgraw hill
  • Buy a paperback international version for $19.10. I am not certain about the reliability of this company: international

Other resources:

• Pandas documentation: [pandas.pydata.org]

Prerequisites/Co-requisites

All types of students are welcome! The Machine Learning 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, median and mode
• Do you understand the difference between geometric mean and arithmetic mean?
• Do you have strong programming skills? Take this quiz compinvesti-prog-quiz if you would like help determining the strength of your programming skills.

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.

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).

How to install the software: ML4T Software Installation

Logistics

• OMSCS: We will use Udacity for lecture videos.
  • Login here using your GT account: GT-Udacity Login (instruction video)
    Note: DO NOT log in using your personal Udacity account, in case you have one.
  • Go to the course on Udacity (or navigate through My Courses): https://www.udacity.com/course/viewer#!/c-ud501
• We will use T-Square for submission of code and reports: T-Square (pick appropriate course site)
• We will use Piazza for interaction and discussion: Fall 2016 Piazza forum

Grading

• A: 90% and above
• B: 80% and above
• C: 70% and above
• D: 60% and above
• F: below 60%

See semester syllabus for assignment weights.

Minimum technical requirements

• 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 recommended; at minimum 0.768 Mbps download speed.

• Hardware: A computer with at least 4GB of RAM and CPU speed of at least 2.5GHz.

• OS:
  • 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

Office hours

To be determined.

Plagiarism

In most cases I expect that all submitted code will be written by you. I will present some libraries in class that you are allowed to use (such as pandas and numpy). Otherwise, all source code, images and write-ups you provide should have been created by you alone.

Class Policies

• Official communication is by email: We use piazza for discussions, but it is not an official communications channel. All official communications to you will be sent via t-square to your official GT email address. Similarly, you should communicate important items to us by email as well.

• Student responsibilities: Be aware of the deadlines posted on the schedule. Read your GT email every day. Start work on projects even if they are not open on t-square.

• Grade contest period: After a project grade is released you have 7 days to contest the grade. After that time projects will not be reevaluated. You must have a very specific issue with a compelling argument as to why your grade is incorrect. Example compelling argument: "The TA took 10 points off because I was missing a chart, but the chart is visible on page 5." Example not compelling argument: "I think I should have gotten more points, please regrade my project."

• Grade contest process: You must enter your request for reevaluation via the online form, which is here. You will be contacted by your TA about it later on. Do not request reevaluations on piazza.

• Student responsibilities: Monitor the schedule so that you know when assignments are due. The due dates are usually set early in the semester.

• Late policy: Assignments are due at 11:55PM Eastern Time on the assignment due date. Assignments turned in after 11:55PM are considered late. Assignments may be turned in up to one day late with a 10% penalty.

• Exam scheduling: Exams will be held on specific days at specific times. If there is an emergency or other issue that requires changing the date of an exam for you, you will need to have it approved by the Dean of Students. You can apply for that here: http://www.deanofstudents.gatech.edu (under Resources -> Class Absences)

Legacy

• Legacy: [old schedule]