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 [email protected], "Dennis Daniels" <[EMAIL PROTECTED]> 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: [EMAIL PROTECTED] <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/ >
