CS7646-ML4T

Machine Learning for Trading

Final project for 3 different types of ML: Decision Trees, reinforcement learning, optimization.

Quantative factors X, dependant variable Y.

X1, X2, X3 -> Regression model -> Y

Drawbacks:

Cannot distinguish between

Don’t know when to exit.

Align the API.

Decision Tree could have not standardized indicators.

KNN needs to have standardized indicators.

Use vectorizing code.

Or how do we assign weight or priority in developing a strategy?

Start with 1 indicator first, see if it’s better to add more indicators.

What Hedge Funds Really Do (Book) - Required course book that is a concise source of the financial and investing material used in the course.
Investments (Book) - A comprehensive book that covers the financial and investing material used in the course.
Technical Analysis Explained (Book) - A good book that covers technical analysis. Useful as a reference for understanding indicators and manual trading strategies.
Technical Analysis of Financial Markets (Book) - Another good book that covers technical analysis. Useful as a reference for understanding indicators and manual trading strategies.
Stockcharts.com (Website) - A free and easy-to-use website that is useful for indicator education, trading strategies, and charting (using a variety of charting techniques).
Investopedia.com (Website) - A free and easy-to-use website that is useful for indicator education, news, and market simulation.
Machine Learning in Finance (Book)
Advances in Financial Machine Learning (Book)
Machine Learning for Asset Managers (Book)

Machine Learning (Book) - Tom Mitchell’s concise introduction to machine learning, including supervised and reinforcement learning. Supplemental reading in CS7646, but required reading in CS7641 (OMSCS ML course)
An Introduction to Statistical Learning (Book) - A very good introductory ML book. Many “suggested” readings shared on Ed Discussions were from this book. (The second edition - hard cover - is expected to be released this summer, with a free PDF available in early 2022.)
Elements of Statistical Learning (Book) - A more mathematical and complete introduction to ML. This book may be used in one of the courses in the OMSA program.
Foundations of Machine Learning (Book) - Good material, but one of the more theoretical treatments of ML.
Probabilistic Machine Learning: An Introduction (Book) - The long-anticipated second edition of Kevin Murphy’s Machine Learning Book. Will be a two-book series. Book 1 is camera-ready and book 2 will be released as a draft this summer. In some places, Murphy’s book is used as an alternative to Tom Mitchell’s book.
CS7641 Machine Learning (Georgia Tech OMSCS Course Videos)
CMU-601 Machine Learning (Carnegie Mellon University Course Videos)
Introduction to Machine Learning (University of Waterloo Course Videos)

Reinforcement Learning: An Introduction (Book) - This is currently the definitive RL textbook.
Grokking Deep Reinforcement Learning (Book) - Written by Miguel Morales, head TA for Georgia Tech’s CS7642 RL Course.
Reinforcement Learning (David Silver Video Lectures) - Videos and PowerPoint presentations.
CS7642 Reinforcement Learning (Georgia Tech OMSCS Course Videos)
CS234 Reinforcement Learning (Stanford University Course Videos)
RL In Finance (Stanford University - course and book DRAFT)

Deep Learning (Book) - This is the current leading Deep Learning textbook
Deep Learning Revolution (Book) - An easy-to-read book that presents Deep Learning, starting with a historical perspective and stepping forward to today.
Deep Learning (Nature Article) - A good article by Hinton and others on deep learning. Access through the Georgia Tech Library.
Grokking Deep Learning (Book) - A good book that, like ML4T, will have you implementing algorithms and models.
Foundations of Deep Reinforcement Learning (Book) - Sections 1.1 through 1.4 provide a nice introduction to reinforcement learning and our work in CS7646.
deeplearning.ai (Website) - A learning & education site by Andrew Ng.