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.
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.