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In does make sense except for the comment about randomness in markets
and the weather neither of which I agree with ...
--- In amibroker@xxxxxxxxxxxxxxx, "rhelfer123" <rhelfer99@xxx> wrote:
>
> Hi Fred,
>
> In-sample/out-of-sample testing can be performed on Monte Carlo
> Simulation, on its own. If you get a significantly different MCS
> bell curve on the out-of-sample data, I would imagine this would
> indicate the system does not function well on the OOS data. I
> usually test MCS with OOS and also known bear and bull cycle data,
> as well. This covers all bases.
>
> I have never been able to quickly identify a system that works well
> in MCS using a straight backtest first. However, every time I find
a
> system that works well in MCS, it always works well in the straight
> backtest.
>
> By "works well" I mean a system that is based in realistic real-
> world statistics. That is, testing that mirrors the true randomness
> of everyday reality.
>
> MCS was originally developed to predict where nuclear fallout would
> land, from nuclear tests in the Southwest desert of the US. The
> predictions for this MUST be very accurate. MCS was specifically
> developed for this kind of highly predictive accuracy. Weather
> patterns are just as random as the stock markets.
>
> OOS testing is specifically used for preventing curve fitting in
> backtesting for system optimization. It's a whole different
> enchilada. You can use OOS with MCS, however.
>
> I hope this makes sense.
>
> Thanks for reading,
>
> rhelfer
>
> --- In amibroker@xxxxxxxxxxxxxxx, "Fred" <ftonetti@> wrote:
> >
> > I don't think your ideas or practices nullify anything ... At
> worst
> > they supplement it ...
> >
> > While I'm not a huge fan of MCS, the methodology does provide
> > additional information over and above what a straight backtest
> > provides ...
> >
> > It's my contention however that this additional information while
> > beneficial is no substitute for out of sample testing regardless
> of
> > how it is performed.
>
> > Fred
>
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