Thanks to all. Last year QT posted a simple Ami method that is similar to *MCP + random entry* as a benchmark.
OT: Statistics #103009 BrianB2 --- In [email protected], "whitneybroach" <[EMAIL PROTECTED]> wrote: > > 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, >
