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For systems with a single parameter this is of course fine ...
For systems with more then 2 two parameters where the 3d plots are no
longer of great use it's a simple matter to generate random data
points in the +/- n% range of what appears to be the optimum value to
see how sensitive or not the parameter values actually are i.e.
Original System where the default values are assumed to be optimum
but sensitivity is unknown ...
X = Optimize("X", 10, 1, 20, 1);
Y = Optimize("Y", 100, 1, 200, 1);
Z = Optimize("Z", 1000, 1, 2000, 1);
Sensitivity Tester for +/- 10%
Senstivity = Optimize("Sensitivity", 1, 1, 5000, 1);
X = Round((Random() - 0.5) * 2.99) + 10;
Y = Round((Random() - 0.5) * 20.99) + 100;
Z = Round((Random() - 0.5) * 200.99) + 1000;
The results based on whatever fitness metric interests you can be
observed and sorted in the output of optimization and/or can be
exported for further statistical analysis (distribution) in Excel.
--- In amibroker@xxxxxxxxxxxxxxx, "Steve Dugas" <sjdugas@xxxx> wrote:
> Hi Natasha,
>
> Here is the entire message intact, with thanks to Herman.
>
> Steve
>
> -------------------------------------------------------------------
>
> In addition to using 3D surface maps to evaluate the Robustness of
two or more parameters you may consider assessing the Robustness and
across-market dependency of single parameters over a Watch list.
>
> You can do this by superimposing equity lines for different
parameter values to see whether they track over time (fan-out) . This
is a far better method for evaluating systems and finding stocks than
picking a high equity from the Optimization result table. If the
equity ratios remain approximately constant your parameters are more
Robust than if the Equities do not track.
>
> In my example I use Steve Karnish's CMO5 system (symmetrical
triggers). To test this method on your own trading system you should
substitute your own code inside the curly brackets of the System()
function and substitute the variable named "TestParameter" for the
parameter you like to evaluate. Here is a typical chart showing
robust behavior:
>
>
>
> Here is a chart for a system that may be over optimized and lacks
robustness:
>
>
>
>
> Things to look for:
>
> 1) Over-optimization: High profits but Equity lines move all over
the place.
> 2) Robustness: Equity lines "fanout" keeping the system profitable
under most TestParameter values
> 3) Synchronized dips; if all equities dip you can create an Equity-
composite and use it to qualify signals.
> 4) Stepping through the stocks in you workspace you can observe
stock-synchronization
> 5) Is the optimum value optimum over time?
> 6) Straight equity lines
>
> Happy trading,
>
> Herman
>
>
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> ----- Original Message -----
> From: Natasha !!
> To: amibroker@xxxxxxxxxxxxxxx
> Sent: Monday, April 04, 2005 1:02 PM
> Subject: [amibroker] Optimizing & Robustness of single parameters
>
>
> Hi,
> Just going over the archieves and i dug out a mail i didnt
quite well understand:
> Since the attachments are not stored in the usergoups any graph
to illustrate the same would be much obliged;
>
> Thanks.
>
> -- N !!
>
>
> In add From: "Herman van den Bergen" <psytek@>
> Date: Thu Sep 4, 2003 9:44 am
> Subject: Optimizing & Robustness of single parameters ition to
using 3D surface maps to evaluate the Robustness of two or more
parameters you may consider assessing the Robustness and across-
market dependency of single parameters over a Watch list.
>
> You can do this by superimposing equity lines for different
parameter values to see whether they track over time (fan-out) . This
is a far better method for evaluating systems and finding stocks than
picking a high equity from the Optimization result table. If the
equity ratios remain approximately constant your parameters are more
Robust than if the Equities do not track.
>
> In my example I use Steve Karnish's CMO5 system (symmetrical
triggers). To test this method on your own trading system you should
substitute your own code inside the curly brackets of the System()
function and substitute the variable named "TestParameter" for the
parameter you like to evaluate. Here is a typical chart showing
robust behavior:
>
> Chart for the above and for the below
explanation.Thanks ???
>
> Here is a chart for a system that may be over optimized and lacks
robustness:
>
>
>
> Things to look for:
>
> 1) Over-optimization: High profits but Equity lines move all over
the place.
> 2) Robustness: Equity lines "fanout" keeping the system
profitable under most TestParameter values
> 3) Synchronized dips; if all equities dip you can create an
Equity-composite and use it to qualify signals.
> 4) Stepping through the stocks in you workspace you can observe
stock-synchronization
> 5) Is the optimum value optimum over time?
> 6) Straight equity lines
>
> Happy trading,
>
> Herman
>
>
>
>
> Warm regards,
> Natasha !
>
>
>
>
> --------------------------------------------------------------------
----------
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>
>
>
>
>
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