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The function would be a market price "generator" and thus would
produce statistically plausible price movements, but not necessarily
those of the time. I was just thinking of better ways (than
historical data) to train a neural network. You couldn't
use the function to directly predict future values because
it only produces statistically valid patterns, not necessarily
those patterns relevant at the time.
Sorry if I wasn't clear.
- John
On Mon, 18 Dec 2000, Michael Suesserott wrote:
> John,
>
> if you could define such a function, why run a Neural Net on it at all? You
> could derive anything worth knowing directly from the definition of the
> function.
>
> Regards,
>
> Michael Suesserott
>
> -----Ursprungliche Nachricht-----
> Von: John Nelson [mailto:trader@xxxxxxxxxxxxxxx]
> Gesendet: Monday, December 18, 2000 20:14
> An: pierre.orphelin
> Cc: omega-list@xxxxxxxxxx
> Betreff: Re: The Yats Group
>
>
>
> This posting provides some food for thought. If one could devise a
> function which accurately simulates the random and non-random aspects of
> markets using convenient data measures (price, volume, volatility) then it
> would be possible to train a neural net using other than historical data.
>
> Simulated data would provide a much more extensive training data set thus
> improving the NN's predictive power and avoiding curve-fitting on limited
> data.
>
> I don't believe that there is any point training a neural net, or any
> predictive system on random data since by definition, there is no
> relationship between the training data and future data.
>
> But then I don't believe that markets are entirely random.
>
> -- John
>
> __________________________________________________________
>
> John T. Nelson | John's Trading Journal
> trader@xxxxxxxxxxxxxxx | http://trader.computation.org/
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>
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John T. Nelson | John's Trading Journal
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