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David:
Please refer to our Demo #1 (01/18/2006) on our web site
under TSL Archives for
a review of Genetic Programs and
how we use them to create
Machine Designed Trading Systems.
We also discuss how we use the algorithm
to protect against curve fitting trading systems
and the significant differences between a
Genetic Algorithm and a Genetic Program.
Sincerely,
Mike Barna
President,
Trading System Lab
"TSL automatically designs Trading Systems
in a few minutes using an L-AIM-GP."
"Software that Creates Trading Systems with no coding"
www.TradingSystemLab.com
----- Original Message -----
From: "David Pyle" <dpevergreen@xxxxxxxxx>
To: <omega-list@xxxxxxxxxx>
Sent: Monday, August 24, 2009 3:11 PM
Subject: RE: [english 100%] Genetic Programs and Neurofuzzy Logic
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
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