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Very good point.
Is there a good way to gradually decrease the impact of older data on
the system during walkforward data?
The data expiring off the back end of the walkforward in sample
period has just as much impact on the optimal values as the new data
collected today.
Wouldn't it be better give more importance and less importance to
older data. Would that not reduce the system "lag" in response to
changing market conditions?
For example, last aug was a very "interesting" time. With a 1 year
in sample period, that data would be aging off now and could result
in a step change to the optimal system values.
If we could somehow use a fitness function that devalued older traded
compared to newer trades, it might make the system adapt more quickly
and avoid rapid changes due to old data aging out of the in sample
period.
--- In amibroker@xxxxxxxxxxxxxxx, "Howard B" <howardbandy@xxx> wrote:
>
> Greetings all --
>
> If the walk forward tests show poor out-of-sample results for the
entire
> out-of-sample period, that means the model is learning noise, not
signal.
> If they are good for a short period out-of-sample, then fall off,
that means
> the characteristics of the market being modeled are changing and
you need
> more frequent reoptimization -- a shorter out-of-sample time period.
>
> If you have a model that has in the past been profitable out-of-
sample, and
> it becomes unprofitable during a period of real trading, you can
reoptimize
> ahead of schedule. In fact, if you have valid system, you can
reoptimize as
> frequently as you want to. You might want to run some walk forward
tests to
> verify that this works for your system. If you have not tested
early
> reoptimizing, but do it to try to resync a system that is in a
drawdown, I
> recommend paper trading the new parameter set for a while to be
certain that
> the model just needed re-synchronizing, and was not terminally
broken.
>
> Thanks,
> Howard
>
>
>
> On Mon, Sep 8, 2008 at 2:05 PM, Mike <sfclimbers@xxx> wrote:
>
> > Sometimes it is not so much adaptation that is needed. But
rather just
> > capturing the transition of cycles.
> >
> > Consider a house located in a region that has warm summers and
cold
> > winters. The variables would be climate control (e.g. air
> > conditioner/heater). The variable values will change as the
seasons
> > unfold. To optimize comfort, we will be using increased air
> > conditioning during the summers and increased heating during the
> > winters.
> >
> > By experimenting with OOS time periods we may be able to identify
> > market cycles. If you only reoptimized your values once a year
(e.g.
> > set for average temperature for the year), your climate control
would
> > likely be generally too warm in the summers and generally too
cold in
> > the winters. But, if you reoptimized monthly, then you would
gradually
> > use increasing air conditioning leading into the summer and
conversely
> > gradually increase heating leading into the winter.
> >
> > Obviously the markets are not so perfectly cyclical as the
seasons.
> > But, the example illustrates the point.
> >
> > Mike
> >
> >
> > --- In amibroker@xxxxxxxxxxxxxxx <amibroker%
40yahoogroups.com>, "Ara
> > Kaloustian" <ara1@> wrote:
> > >
> > > WF testing seems like a logical step to take in creating a
trading
> > system. While I am not suggesting that we don't do it, I'd like to
> > clear my understanding.
> > >
> > > It seems to me that we are looking to create a system with
> > parameters that are valid "for all time". If a test is succesful
in
> > OOS period, then we can assume that we have found something that
> > remains applicable for the future - at least in this one instance.
> > >
> > > Given that the markets change all the time, the obvious
conclusion
> > for me is that we need indicators that are adaptive... and that
any
> > that do not adapt will simply not work in OOS.
> > >
> > > The issue of providing feedback from equity curve seems valid
and it
> > can provide a warning about system starting to not perform as
expected
> > ... so regardless of type of indicators used, this kind of
feedback is
> > good!.
> > >
> > > The point I am making is that there has been very little said
about
> > adaptive indicators ...
> > >
> > > maybe that is the holy grail ... and therefore untennable ...
> > >
> >
> >
> >
>
------------------------------------
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