While reading David Aronson's book _Evidence-based Technical
Analysis_, I stumbled across a modified Monte Carlo permutation
(MCP) procedure that compensates for data mining bias, assuming that
the "best" permutation of rules was not selected with a directed search.
>From Aronson's perspective, this is good news. He views data mining
as a useful procedure in the discovery phase of research. Plus, MCP
does not require out-of-sample data. Thus it is possible to use more
data for mining and still minimize data mining bias in test results.
The likely result: fewer false positives for systems that are
worthless, and fewer false negatives for systems that are valuable.
The paper with discussion and C# code is here:
<http://www.evidencebasedta.com/MonteDoc12.15.06.pdf>.
Aronson's book site, including a link to Amazon, is:
<http://www.evidencebasedta.com>. Separately, I'm looking forward to
the imminent books from Howard
<http://www.quantitativetradingsystems.com/> and Ralph Vince
<http://tinyurl.com/2os2p7>.
Not being a user of IO (or other AB add-ons), I have no idea if this
MCP approach is already being used in the AB community. It looks
interesting to me. MCP appears to require market data and trade data
from every run, not simply the trade data. That suggests to me that
an AB add-on, rather than a completely external program, would be a
more straightforward implementation.
Aronson also refers to a patented boostrap procedure that accomplishes
much the same thing, White's Reality Check, named for Halbert White,
the patent holder. Apparently WRC is not available commercially.
Best,