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NO ... IO does NOT utilize nn's ... It uses two types of
artificially intelligent optimization techniques ... a genetic
algorithm ( Differential Evolution ) ... and Particle Swarm.
IO will try to meet all goals simultaneously not in multiple steps
or processes i.e. a multi-objective optimization.
--- In amibroker@xxxxxxxxxxxxxxx, "Dennis Daniels"
<dennisgdaniels@xxx> wrote:
>
> Hello,
>
> Nice subject whitneybroach.
> I would like to open it to another field, the one of the multi-
objective
> optimizitation.
>
> Actually random walk, monte carlo can be made "raw", so you
extract all
> statitstic from each run, and finally you can look for the best
solution for
>
> several objective at the same time... but this is too too and...
too ...
> long time consuming if you have complex system with many
parameters.
> MPC can be good, but I think it is long time computation too ?
>
> We got IO too (it use neural networks if i am right), thanks Fred,
it can
> have several goals, but i don't know how it works inside... are
all the
> goals reached during different optimization process (several
> single-objective optimization) or is it real multi-objective
optimization ?
>
> Here is a good articles on multi-objective optimization wich
review several
> methods (for gentics, but same can be used for trading i guess) :
> http://www.calresco <http://www.calresco.org/lucas/pmo.htm>
> .org/lucas/pmo.htm
>
> My question is, are their some people here who try to use some of
those
> another technics of non linear optimisation with multi-objective.
>
> Cheers,
> Mich.
>
> ----- Original Message -----
> From: whitneybroach
> To: amibroker@xxxxxxxxx <mailto:amibroker%40yahoogroups.com> ps.com
> Sent: Friday, March 16, 2007 5:12 AM
> Subject: [amibroker] Detecting data mining bias with modified
Monte Carlo
> procedure
>
> 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.evidence
<http://www.evidencebasedta.com/MonteDoc12.15.06.pdf>
> basedta.com/MonteDoc12.15.06.pdf>.
>
> Aronson's book site, including a link to Amazon, is:
> <http://www.evidence <http://www.evidencebasedta.com> basedta.com>.
> Separately, I'm looking forward to
> the imminent books from Howard
> <http://www.quantita <http://www.quantitativetradingsystems.com/>
> tivetradingsystems.com/> and Ralph Vince
> <http://tinyurl. <http://tinyurl.com/2os2p7> 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,
>
> __________________________________________________________
> Découvrez le Blog heroic Fantaisy d'Eragon!
> http://eragon- <http://eragon-heroic-fantasy.spaces.live.com/>
> heroic-fantasy.spaces.live.com/
>
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