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.
Why Take This Course?
By the end of this course, you should be able to:
- Understand data structures used for algorithmic trading.
- Know how to construct software to access live equity data, assess it, and make trading decisions.
- Understand 3 popular machine learning algorithms and how to apply them to trading problems.
- Understand how to assess a machine learning algorithm's performance for time series data (stock price data).
- Know how and why data mining (machine learning) techniques fail.
- Construct a stock trading software system that uses current daily data.
- We use daily data. This is not an HFT course, but many of the concepts here are relevant.
- We don't interact (trade) directly with the market, but we will generate equity allocations that you could trade if you wanted to.