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Thanks Fred - very nice idea!
FWIW, I just realized that Herman sent the code for his graphs in a
follow-up message to the original. Here it is for anyone that might be
interested.
Steve
----- Original Message -----
From: "Fred" <ftonetti@xxxxxxxxxxxxx>
To: <amibroker@xxxxxxxxxxxxxxx>
Sent: Monday, April 04, 2005 9:22 PM
Subject: [amibroker] Re: Optimizing & Robustness of single parameters
>
>
> 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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