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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 amibroker@xxxxxxxxxxxxxxx, "whitneybroach" <WhitneyBroach@xxx>
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,
>
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