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

2016 Spring Schedule

• [2016 Spring Schedule]

Assignments

• [MC1-Homework-1]
• [MC1-Homework-2]
• [MC1-Project-1]
• [MC1-Project-2]

• [MC2-Project-1]
• [MC2-Project-2]

• Midterm Study Guide

• [MC3-Project-1]
• [MC3-Project-2]
• [MC3-Project-3]

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 amazon.com less expensive version at mcgraw hill (optional)

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

• 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

• Mini-course 1: Two homework assignments and two programming projects.
• Mini-course 2: Two programming projects, and a midterm.
• Mini-course 3: Three programming projects (no final).

Weightings:

• MC1-Homework-1: 2.5%
• MC1-Homework-2: 2.5%
• MC1-Project-1: 5%
• MC1-Project-2: 5%
• MC2-Project-1: 15%
• MC2-Project-2: 10%
• Midterm: 20%
• MC3-Project-1: 15%
• MC3-Project-2: 10%
• MC3-Project-3: 15%

Thresholds:

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

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.

Instructor information

CAPT Tucker Balch, Ph.D. USAF (honorably discharged)

Late Policy & Absences

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.

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/content/25/absences

Legacy

• Legacy: [old schedule]