Overfitted Backtests

October 23, 2013

(This article was first published on Timely Portfolio, and kindly contributed to R-bloggers)

It has been a while since I discussed testing for overfitting in backtests.  Since then, Marcos López de Prado and coauthors have done some very thoughtful work (see the bottom), and they even started a blog.  Their newest paper builds on discoveries they made in their earlier work, and is an absolute must-read.

Bailey, David H. and Borwein, Jonathan M. and Lopez de Prado, Marcos and Zhu, Qiji Jim

Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance (October 7, 2013)

Available at SSRN: http://ssrn.com/abstract=2308659

Translating scientific papers into code is not always easy, but I spent some time implementing some of the concepts in R, so that I can understand this more fully.  Just as a word of encouragement to others out there, I am no math genius nor have any advanced math education, so please don’t be intimidated by formulas.  Below you will see a slidify/rCharts discussion demonstrating these first steps.  I plan to research this much more thoroughly.  As always, I blog to interact, so please let me know what you are thinking.


To leave a comment for the author, please follow the link and comment on their blog: Timely Portfolio.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)