|
PureBytes Links
Trading Reference Links
|
Thank you Pierre,
Actually I was asking about Genetic Programs not Genetic Algorithims but I appreciate your patient explanation.
In my opinion you and others on this list are quite brilliant!
Sincerely,
Dave Pyle
--- On Mon, 8/24/09, Pierre Orphelin <pierre.orphelin@xxxxxxx> wrote:
> From: Pierre Orphelin <pierre.orphelin@xxxxxxx>
> Subject: RE: [english 100%] Genetic Programs and Neurofuzzy Logic
> To: omega-list@xxxxxxxxxx
> Date: Monday, August 24, 2009, 2:14 PM
> Genetic algorithms (GA)
> are methods of
> evolving optimization of
> rules, parameters.
> They speed up the trial
> and error optimization process
> by zillions vs
> human coding trial and erro testing.
> In this sense they are in the top
> notch optimization category, the
> drawback being that they can find
> solutions everywhere on any data
> set.
> Of course, the problem soon arise
> with unseen data, where the
> dazzling solutions miserably fail,
> excepted by chance.
>
> Neurofuzzy logic is a mathematical based
> method of encoding / decoding
> the information, that work better
> when the raw info are not
> as
> crisp as expected ( market data are
> a good example of unclear
> data,
> and most of human activities
> share this. In this sense, fuzzy logic
> is a privilegiate way to encode /
> decode uncertain data for further
> easier treatment).
>
> We do not use GA
> methods to build the neurofuzzy
> rules because of
> the overfitting drawback above.
> So we do not
> have shining results,
> but the out of sample real
> time data will have better
> chance to
> look like to the out of sample
> data used during the
> development test
> phase
>
> Anyway, one cannot compare GA
> and fuzzy logic since they
> do not
> belong to the same category.
> The first one is a learning
> method, the second is a method
> to encode
> the information and the decision to
> be learnt. I would be like asking
> if GA is more powerful than Bollinger
> bands what would not make
> sense, they only
> have here in common the fact
> that they are used in
> trading, regardless to their results.
>
> One of the most difficult part to
> understand with technical analysis
> is the part of illusion that
> is in it. GA will improve this part
> where neurofuzzy is less
> prone to that
> (because fuzzy logic
> implies a logic in it through
> the decision tree, what is less obvious
> with the usual Neural nets evolved
> by GA.
>
> The perfect solution will never exist
> anyway. There is no method
> leading to that.
> One may be happy with an
> approximation that understand most
> of the
> obvious trends and do not lose
> too much money on noisy
> data. It
> could seem simple to understand,
> but it's one of the most
> difficult
> task to make it for real.
>
> Neurofuzzy systems have absolutely
> NO predicting value. They just
> attempt to follow the trend
> as close as possible in any circumstance,
> what is sufficient to make money (
> to figure it out, think to a
> centered moving average, but updated
> to the last bar).
> You may see
> them here as a kind
> of sophisticated denoising tool
> applied to the market data serie
> while maintaining the minimum lag (
> by comparison, the centered moving
> average has no lag and is a perfect
> trading system unfortunately for past data
> only).
>
> Because there is no other
> neurofuzzy trading program over
> there
> (unlike GA stuff, widely spread in
> the US), you will have some
> difficulties to get some
> valid feedback information...
>
> Anyway you could check soon
> the real time results that we will soon
> post on the web site.
> If they are good, there is
> probably something valid behind all
> of
> this.
>
> And if I was sure was not the
> case, do you think that I would take
> the risk to publish such
> results after 20 years in this business ?
>
> Sincerely,
>
> Pierre Orphelin
> www.sirtrade.com
>
> Disclaimer: No affiliation with any GA program
> since 1994.
>
>
>
> -----Message d'origine-----
> De : David Pyle [mailto:dpevergreen@xxxxxxxxx]
>
> Envoyé : lundi 24 août 2009 20:49
> À : omega-list@xxxxxxxxxx
> Objet : [english 100%] Genetic Programs and Neurofuzzy
> Logic
>
> Dear List,
>
> I would like to ask what I hope are fair questions
> regarding genetic
> programs and neurofuzzy systems. If I am too simplistic in
> my understanding,
> I hope you will take the time to help me understand.
>
> Here goes.. Are these types of programs super optimizated
> systems or
> distinct systems? Aren't these just brute optimizations,
> how do they have an
> advantage going forward? Don't genetic programs and
> neurofuzzy systems have
> a danger of curve fitting also? In other words if they
> don't have predictive
> features then aren't they just really good at curve
> fitting?
>
> Thanks for allowing me to ask honest questions?
>
> Dave Pyle
>
>
>
>
>
|