The 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.
It was divided into five main projects: assessing a portfolio, instance / ensemble learners, market simulators, trading learners, and reinforcement learning.
Date: December 2014
Course: CS6601 Machine Learning for Trading
Skills: Pandas, Python, Linear Regression, Q-Learning, KNN, Regression Trees