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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
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