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I use nnet and it appears to work reasonably 'out of the box' as an
algorithm but you get better performance if you play with the
parameters to tune it.<br>
<br>
svm appears to work best when you select Gaussian so I'm not sure
about your assumption.<br>
<br>
All these algorithms have similar R calls so its not to difficult to
try lots of them out. Try then out of the box first up.<br>
<br>
good luck.<br>
<br>
<div class="moz-signature">Stephen Choularton Ph.D., FIoD<br>
<br>
9999 2226<br>
0413 545 182<br>
<br>
</div>
<br>
On 23/08/2011 4:51 PM, Gentil Homme wrote:
<blockquote
cite="mid:CAM9XErAi+SP=Jf08MkjBr_7xnZOSxEjq9Mziq=fgBhQe3TsxMA@mail.gmail.com"
type="cite">I didn't know about the theorem but it seems
reasonable to believe that some techniques are more appropriate
than others for modelling/predicting financial data.<br>
It should be because of their nature : non gaussian, non linear,
non stationary, ...<br>
<br>
I think it's like the usual technical indicators (MACD,
Stochastic, etc ... ) which are more or less suitable depending on
the market conditions.<br>
<br>
What would be your recommended R package for NNs, as there are
different possible architecture : GRNN, PNN, SOM ... (see <a
moz-do-not-send="true"
href="http://en.wikipedia.org/wiki/NeuroSolutions">http://en.wikipedia.org/wiki/NeuroSolutions</a>)<br>
<br>
Before trying many solutions, maybe it's worth to have some
discussion ... there can be another mighty theorem we should all
know :-)<br>
<br>
Best Rgds,<br>
<br>
Pierre<br>
<br>
<br>
<br>
<div class="gmail_quote">2011/8/23 Stephen Choularton <span
dir="ltr"><<a moz-do-not-send="true"
href="mailto:stephen@organicfoodmarkets.com.au">stephen@organicfoodmarkets.com.au</a>></span><br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt
0.8ex; border-left: 1px solid rgb(204, 204, 204);
padding-left: 1ex;">I think that Gentil should be aware of the
/No Free Lunch Theorem/ (Duda et al., 2001, Wolpert and
Macready, 1997). There are no context-independent or
usage-independent reasons to favor one machine learning
algorithm over another. If one performs better than another,
it is owing to its better fit to the particular problem, not
its general superiority. If you wish to use these techniques
try lots of them: certainly neural networks and support
vector machines, but also try some of the ensemble techniques
such as bagging, boosting and random forest. You can even try
the statisticians favorite, logistic regression. They are all
available in R.<br>
<font color="#888888">
<br>
Stephen Choularton Ph.D., FIoD</font>
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<div class="h5"><br>
<br>
<br>
On 23/08/2011 12:14 AM, Brian G. Peterson wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt
0.8ex; border-left: 1px solid rgb(204, 204, 204);
padding-left: 1ex;">
On Mon, 2011-08-22 at 10:11 +0200, Gentil Homme wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt
0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204);
padding-left: 1ex;">
I just send out this post in order to share within
r-sig-finance any<br>
possible experience, advice, ... about NNs or SVMs
with R.<br>
</blockquote>
It seems that you're asking us to share with you, and
not sharing much<br>
yourself in return.<br>
<br>
Perhaps you could answer your own questions in this
thread with the<br>
things you are trying?<br>
<br>
SVM's have been discussed on this list many times,
please search the<br>
list archives.<br>
<br>
This blog has covered this topic:<br>
<a moz-do-not-send="true"
href="http://www.aphysicistinwallstreet.com/"
target="_blank">http://www.aphysicistinwallstreet.com/</a><br>
<br>
Also, there are a few books on machine learning that use
R.<br>
<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt
0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204);
padding-left: 1ex;">
Several good records have been published in the
litterature using these<br>
techniques for financial trading strategies.<br>
</blockquote>
Which ones? References?<br>
<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt
0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204);
padding-left: 1ex;">
There are also commercial packages (expensive !) which
seem to have achieved<br>
good results.<br>
</blockquote>
Which packages? References again?<br>
<br>
Note that neural network strategies are very likely to
create look-ahead<br>
bias as you develop and test them. You try something,
fail, and try<br>
again on the same data. Unless you are very careful to
reserve a 'pure'<br>
set of instruments and dates that you won't *ever* touch
until you think<br>
you have a 'good' machine learning system, you're at
pretty serious risk<br>
of introducing your look-ahead knowledge into the
system. While this is<br>
true to one degree or another in any quantitative
strategy development,<br>
I think it is a particular risk in self-adaptive machine
learning<br>
methodologies.<br>
<br>
<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt
0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204);
padding-left: 1ex;">
So I feel it could be nice to share within this group
about the following<br>
subjects :<br>
<br>
- experience using the R packages<br>
- data pre-processing before feeding the NNs
(technical indicators,<br>
wavelets, EMDs, ....)<br>
- which type of NNs are suitable<br>
- how to build and train them<br>
- etc ...<br>
<br>
Thanks to all for sharing within the R community<br>
</blockquote>
Now, your turn. Bring the community up to date with
your research so<br>
far.<br>
<br>
Regards,<br>
<br>
- Brian<br>
<br>
</blockquote>
<br>
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