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Fred's Intelligent Optimizer (IO) in the file section, is an add-on
product that provides a non-exhaustive optimization capability for
systems with many parameters. One of the features of IO is the
sensitivity adjusted optimization. It has a way to avoid the peaks by
also testing the surrounding data points in the N-dimensional solution
space. IO let's you control how much of your precious CPU time is
spent on sensitivity testing. It also produces graphs of the
sensitivity of the overall solution as well as the sensitivity of each
parameter by itself.
-Steve
--- In amibroker@xxxxxxxxxxxxxxx, "Tomasz Lutelmowski"
<tomasz.lutelmowski@xxx> wrote:
>
> Hello,
>
> I found optimizing with one or two parameters is quite easy. For
example, if
> I want to optimize two parameters I look at 3D optimization graph,
then look
> for large areas ("high level islands" but ignoring peaks at the same
time)
> where my optimization target is relatively high, and use coordinates of
> middle of these islands as optimized parameters. Optimizing this way
I hope
> to be on safe side and not over-adjust system parameters.
>
> I suppose that there is a way of not taking this results at face
value, and
> maybe it is the only way of optimizing systems with more than 2
parameters.
> I think derivatives and convex function might be somehow applicable,
but I
> have no clue of how to implement it. Especially if I want to use
BackTester
> results lists as input, not the function. So my question is - do you
know
> any method of reliable estimation of optimization parameters for systems
> with 3-5 of them ?
>
> Best regards,
> Tomek
>
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