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Hi,
The idea behind walk forward is that you select the best combination
of parameters in sample (IS), and then apply them to the next out of
sample (OOS) period.
At the end of an OOS period, that OOS period becomes part of the next
IS period and you repeat the process. Thus, the parameters to be used
for each OOS period is awlays the best ones found from optimization of
the preceding IS period.
So, in answer to your question; You do not have enough data. You must
end with an IS period such that the optimal values can be used to
enter the next OOS period.
That being said, the data you present seems incorrect, or at least
incomplete. You are showing a series of (OOS, IS, OOS) which is not
possible. How did you get the first OOS results without an IS period
from which to determine the parameter values? Did you perhaps mean to
write:
IS: 20, 4, 4 <-- gives param values for next OOS
OOS: 20, 4, 4 <-- based on preceding IS
IS: 25, 3, 6 <-- gives param values for next OOS
That being the case, then the correct values to use going forward OOS
would be (25, 3, 6) since they were the best of the immediately
preceding IS optimization.
Mike
--- In amibroker@xxxxxxxxxxxxxxx, "droskill" <droskill@xxx> wrote:
>
> Ok - so I have a system that has 3 parameters for optimization. When
> I optimized it using backtesting, I get 3 parameters:
>
> 20,3,4
>
> Now, I walk-forward optimize it, and I get:
>
> OOS: 20,4,4
> IS: 20,4,4
> OOS: 25,3,6
>
> So my question is this - they all give decent numbers for returns,
etc
> - so how would you approach figuring out which set to use?
>
> Thanks!
>
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