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Re: [amibroker] Re: Corr Matrix via Gfx functions



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Thanks a lot Nicolas. It will help me to get started in a different direction ...
BTW if you know the book of David Aronson - "Evidence Based TA" - my remark about randomness and TA is where EBTA is all about. I have mailed 10 time series of stocks to several technical analysts around the world. Most of them in the Belenux. Question was to select the 4 correct stocks. 6 were home made stocks with a randomizer. Of the 49 answers I got the result was that only 1 analyst could select the correct combination of 4 stocks. He could not tell me why he selected these stocks. Most of them or 38 had zero correct stocks, 5 only 1 correct, 4 had 2 correct stocks and 1 had 3 correct stocks selected. Yes Aronson makes a point about TA and more in particular subjective TA
in his book ...
 
Regards, Ton.
 
----- Original Message -----
Sent: Tuesday, May 29, 2007 5:46 PM
Subject: [amibroker] Re: Corr Matrix via Gfx functions


> Hi Nicolas. Thanks and that's more or less where I am. I have an
>article here from Chaitin based on the compression criterion for
>randomness. He claims that financial time series are more or less ALL
>random. 'Only one series in 1.000 can be compressed'. Meaning that in
>practice you can find only 1 non-random time series in 1.000. I just
>cannot believe this because if that's true what about technical
>analysis ? So I am missing something ...

You make a good point, if this is true, then TA would be pointless. I
would say it all depends on the timescale, at small timescales (1mn -
5mn) there are for sure recurrent patterns. The whole problem is to
find ones that are tradable despite slippage and delays : )

> Anyway, you are saying that you did some testing on the NAS100
stocks with the Lempel-Ziv algorithm (?). Do you have any code for me
or an URL where I can find it ?

Unfortunately, I cannot give the code (part of a C++ library). You may
implement one of the algorithms described in
http://www.snl-e.salk.edu/publications/Kennel-2005.pdf
The one on page 1571 (Kontoyiannis et al) is known to be good.

Alternatively, you may export your time series to a txt file and
compress it using gzip/pkzip. Then you do the same for a large number
of random time series (1000) and compute the ratio of the size of the
files (your_time_series/average_random_time_series), it will give you
a rough idea about how far you are from randomness .. but never
explain that to a physicist : ) (see for instance
http://cscs.umich.edu/~crshalizi/notebooks/cep-gzip.html
)

Regards,

Nicolas

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