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Thank you very much Thomas. So in fact the walk-forward measures the data-mining bias in some way? I will read what you say I should read, and I will look at chapter 20 in Howard's book...
But still, so far I get the impression that if I backtested let's say cross (ma,ma...) for 2000 to 2008 and I take this best result and it is 100% CAR, then eben if OOS is 50% CAR I guess there can still be place for data-ming bias (or curve-fitting) because the optimization was done with the best result.
Anyway, if I understand correctly what you say, the OOS will ALWAYS be less than IS because the IS is optimized - that is, it will take the best-of-100 (or 200, etc.) result, and compare it with a more random result that would occure in real life. Am I wrong?
Thanks,
Louis
2008/4/16, Thomas Ludwig <Thomas.Ludwig@xxxxxx>:
Louis,
in the IS period your system is optimized, then the best values from the
optimization are used to perform a test over the IS and OOS periods.
If the OOS results are worse than the IS results, this means that the
system doesn't generalize well enough. BTW: This topic is very well
explained in chapter 20 of Howard's book. I also suggest to look at
http://www.amibroker.com/kb/2008/02/12/getting-started-with-automatic-walk-forward-optimization/ .
I must say, that walk forward testing has completely changed my way of
thinking. It's much easier to see now if a trading system is worth a
second look.
Greetings,
Thomas
> Hi,
>
> I've been experimenting with walking-forward, and I have some
> questions regarding how it works.
>
> I ran a complete random optimization or buying/selling using the
> variables I set (a MCS in fact), and systematically OOS results were
> worst than IS. I don't understand how it works, because whatever if
> the sampling is IS or OOS it is always the same variables that are in
> place.
>
> Anyone could explain how this work?
>
> Thanks,
>
> Louis
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