I do not know about the frogs... :)), but I do know statistics. Re-optimizing 
has no bearing on statistical relevance unless you re-optimize over your 
backtest period. Walk-forward re-optimization does not reuse backtest data.

________________________________
From: ShaggsTheStud <[email protected]>
To: [email protected]
Sent: Sat, December 4, 2010 6:05:41 PM
Subject: Re: [JBookTrader] Dynamic Parameter Optimization

Take for example two frogs who are trying to catch a fly.  Both frogs notice 
that the fly lands on a lilypad once an hour.  So both sit on the lily pad and 
wait.  One decides to chase the fly, but the fly moves much to fast for him.  
Eventually the fly makes it back to the lilypad, where the patient frog has 
been 
waiting patiently.

Let us not forget that curve fitting to the past does not guarantee results in 
the future!  Re-optimizing over short periods of time makes your results even 
less statistically relevant.




On Sat, Dec 4, 2010 at 2:22 PM, Astor <[email protected]> wrote:

Parameters may mean revert very quickly, very slowly or not at all. In case of 
the latter two, you can have some serious losses before realizing that the old 
parameters are no longer valid and re-optimization is needed. Limited 
re-optimization would allow the parameters to drift away from the old "global" 
values if the change is persistent but would not cause any harm if they mean 
revert. So it is a form of parameter monitoring and insurance. 
>
>By limited re-optimization I mean searching not the entire range of the 
>parameter values, but only the values within 5% of the most recent value. This 
>way, on any of the re-optimizations parameters will not change more than five 
>percent from the prior value, - which should not hurt even if the parameters 
>quickly mean revert. On the other hand, if there is a sustained change in 
>parameter values, after 10 or more re-optimizations the new parameters will be 
>far closer to reality than the old global ones.
>
>Perhaps the class could use a call something like:  
>addParam(FAST_PERIOD, .95*Old_FastValue, 1.05*Old_FastValue, 
>.01*Old_FastValue, 
>Old_FastValue)addParam(SLOW_PERIOD, .95*Old_SlowValue, 1.05*Old_SlowValue, 
>.01*Old_SlowValue, Old_SlowValue)
>
>This would only require evaluating 10 combinations for each parameter or 100 
>for 
>both, so it could run very fast and remain within 5% of the prior value.
>
>
________________________________
From: Eugene Kononov <[email protected]>
>To: [email protected]
>Sent: Sat, December 4, 2010 11:51:57 AM 
>
>Subject: Re: [JBookTrader] Dynamic Parameter Optimization
>
>
>
>On Sat, Dec 4, 2010 at 11:22 AM, Astor <[email protected]> wrote:
>
>Yes. This discussion thread is exactly what I am thinking.
>>

Ok, yes, nothing stops us from experimenting. One thing which makes me 
reluctant 
to put my time and efforts into coding it is what I consider a very weak 
hypothesis on which the walk-forward optimization is based. That hypothesis is 
that the best optimization parameters for the recent market period are superior 
to the more "global" optimization parameters, when applied to the future 
period. 
Markets do change, indeed, but is there any evidence that these new formations 
of behavior persist, rather than revert back to what they have been in the past?

>
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