One of the first books I read when I began studying the markets a few years ago was David Aronson’s Evidence Based Technical Analysis. The engineer in me was attracted to the ‘Evidence Based’ part of the title. This was soon after I had digested a trading book that claimed a basis in chaos theory, the link to which actually turned out to be non-existent. Apparently using complex-sounding terms in the title of a trading book lends some measure of credibility. Anyway, Evidence Based Technical Analysis is largely a justification of a scientific approach to trading, including a method for rigorous assessment of the presence of data mining bias in backtest results. There is also a compelling discussion based in cognitive psychology of the reasons that some traders turn away from objective methods and embrace subjective beliefs. I find this area fascinating.
Readers of this blog will know that I am very interested in using machine learning to profit from the markets. Imagine my delight when I discovered that David Aronson had co-authored a new book with Timothy Masters titled Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments – which I will herein refer to as SSML. I quickly devoured the book and have used it as a handy reference ever since. While it is intended as a companion to Aronson’s (free) software platform for strategy development, it contains numerous practical tips for any machine learning practitioner and I’ve implemented most of his ideas in R.
I used SSML to guide my early forays into machine learning for trading, and this series describes some of those early experiments. While a detailed review of everything I learned from SSML and all the research it inspired is a bit voluminous to relate in detail, what follows is an account of what I found to be some of the more significant and practical learnings that I encountered along the way.
This post will focus on feature engineering and also introduce the data mining approach. The next post will focus on algorithm selection and ensemble methods for combining the predictions of numerous learners.