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Tomasz,
I have one question regarding the DLL vs the Source code that you
provided for the CMAES engine. I am almost embarrassed about how
ignorant I am, but I will ask anyway.
The source code appears to be the standard source provided by the
original author. Is the DLL you provided the exact compilation of
this source, or is there something else that is AB specific?
The reason I ask, is that I would like to run this engine directly
from an AFL formula, providing my own iteration data instead of the
built-in back tester. And of course I would like to be able to
interface to a compiled DLL directly without all the additional work
of changing and debugging the large source.
Before, when I wrote my first DLL, I used your example that set up the
required definitions for the AFL variables. Would I need to modify
the source to do that again to use it with AFL, or has that already
been taken care of with the compiled DLL?
I am pretty sure I know the answer to this and I won't like it.
So, if not, is there a shortcut I could take, like interfacing AFL to
jscript to DLL to make the job easier and less error prone?
I also know for a fact that I am not the only one who is interested in
doing this at this time.
Thanks for your guidance.
Best regards,
Dennis
On Jun 27, 2008, at 8:46 AM, Tomasz Janeczko wrote:
> Hello,
>
> With the version 5.13.0 BETA AmiBroker is the first backtesting/
> optimization platform
> featuring state-of-the-art optimization algorithm called "Covariance
> Matrix Adaptation Evolutionary Strategy"
> that is is outperforming other evolutionary methods (genetic, PSO,
> DE, etc).
>
> For scientific background see:
> http://www.bionik.tu-berlin.de/user/niko/cmaesintro.html
> According to scientific benchmarks outperforms nine other, most
> popular evolutionary strategies
> (like PSO, Genetic and Differential evolution).
> http://www.bionik.tu-berlin.de/user/niko/cec2005.html
>
> The plugin (CMAE.DLL) comes with FULL SOURCE CODE (inside "ADK"
> folder, the source code is released under GPL terms)
>
> The CMAE.DLL plugin implements "Global" variant of search with
> several restarts with increasing population size
>
> By default number of runs (or restarts) is set to 5.
> It is advised to leave the default number of restarts.
> You may vary it using OptimizerSetOption("Runs", N ) call, where N
> should be in range 1..10.
> Specifying more than 10 runs is not recommended, although possible.
> Note that each run uses TWICE the size of population of previous run
> so it grows exponentially.
> Therefore with 10 runs you end up with population 2^10 greater (1024
> times) than the first run.
>
> There is another parameter "MaxEval". The default value is ZERO
> which means that plugin will automatically calculate MaxEval
> required. It is advised to NOT to define MaxEval by yourself as
> default works fine.
>
> The algorithm is smart enough to minimize the number of evaluations
> required and it converges VERY fast to solution point, so
> usually it finds solutions way faster than other strategies.
>
> It is normal that the plugin will skip some evaluations steps, if it
> detects that solution was found, therefore you should not be
> surprised that optimization progress bar may move very fast at some
> points. The plugin also has ability to increase number of steps
> over initially estimated value if it is needed to find the solution.
> Due to its adaptive nature, the "estimated time left" and/or
> "number of steps" displayed by the progress dialog is only "best
> guess at the time" and may vary during optimization course.
>
> To use CMA-ES optimizer, you just need to add one line to your code:
>
> OptimizerSetEngine("cmae");
>
> This will run the optimization with default settings which are fine
> for most cases.
>
> It should be noted, as it is the case with many continouos-space
> search algorithms, that decreasing "step" parameter in Optimize()
> funciton calls does not significantly affect optimization times. The
> only thing that matters is the problem "dimension", i.e. the
> number of different parameters (number of optimize function calls).
> The number of "steps" per parameter can be set without affecting
> the optimization time, so use the finest resolution you want. In
> theory the algorithm should be able to find solution in at most
> 900*(N+3)*(N+3) backtests where "N" is the dimension. In practice it
> converges a LOT faster. For example the solution in 3 (N=3)
> dimensional parameter space (say 100*100*100 = 1 million exhaustive
> steps) can be found in as few as 500-900 CMA-ES steps.
>
>
> Best regards,
> Tomasz Janeczko
> amibroker.com
>
>
> ------------------------------------
>
> Please note that this group is for discussion between users only.
>
> To get support from AmiBroker please send an e-mail directly to
> SUPPORT {at} amibroker.com
>
> For NEW RELEASE ANNOUNCEMENTS and other news always check DEVLOG:
> http://www.amibroker.com/devlog/
>
> For other support material please check also:
> http://www.amibroker.com/support.html
> Yahoo! Groups Links
>
>
>
------------------------------------
Please note that this group is for discussion between users only.
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