Overfitted Backtests

[This article was first published on Timely Portfolio, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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 about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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)