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RE: [amibroker] Re: Fitness Criteria that incorporates Walk Forward Result



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Paul,

 

Sensitivity analysis during in sample optimization is not directly related to finding a new fitness high.

 

That would be too restrictive and might well preclude finding solutions that have better fitness after being adjusted for sensitivity.

 


From: amibroker@xxxxxxxxxxxxxxx [mailto:amibroker@xxxxxxxxxxxxxxx] On Behalf Of Paul Ho
Sent: Wednesday, May 07, 2008 8:37 AM
To: amibroker@xxxxxxxxxxxxxxx
Subject: [amibroker] Re: Fitness Criteria that incorporates Walk Forward Result

 

Hi Fred
Yes, I want to use the composite fitness to compare different systems
and or use it as a feedback in deciding on different parameter sets
of the same system, This is not too dissimilar to how sensitivity
analysis is incorporated into the fitness criteria. The only
difference is that sensitivity analysis during optimization, and walk
forward is done after a new fitness high is found. Instead of using
the insample fitness as the selection criteria for the best fit
system, the composite is criteria is used to choose among the various
peak values in one system or in different systems.

What you said "the capability to automatically reoptimize when some
condition related to the performance metrics occurs during the out of
sample period i.e. MDD goes beyond some static threshold or when it
goes beyond some relationship to the same" is particularly
interesting. Because you are addressing a similar problem but using a
different method, in your case, you change the time frame and
reoptimize. In my case, I am looking at refining my fitness criteria
so I might end up in choosing a different optimized parameter set in
the same time frame.

Paul.

--- In amibroker@xxxxxxxxxps.com, Fred Tonetti <ftonetti@xx.> wrote:
>
> Paul,
>
>
>
> I understand what you are saying but I'm not sure what you do with
the
> combined fitness when you get it . Do you use it to compare
different
> systems to each other ?
>
>
>
> Personally from the perspective of multiple automated WF's I am more
> interested in . When to reoptimize .
>
>
>
> IO already has the capability to reoptimize based on:
>
>
>
> - Some static amount of time occurring during the OOS i.e.
>
>
>
> //IO: WFAuto: Rolling: 2: Weeks
>
>
>
> - or in some undefined amount of time based on some number of
long/short
> entries/exits etc i.e.
>
>
>
> //IO: WFAuto: Rolling: 2: LongEntrys
>
>
>
> What I've been playing with recently is something a little
different that is
> also based on a variable amount of time in the OOS i.e. the
capability to
> automatically reoptimize when some condition related to the
performance
> metrics occurs during the out of sample period i.e. MDD goes beyond
some
> static threshold or when it goes beyond some relationship to the
same or
> different performance metrics of in sample.
>
>
>
> For example .
>
>
>
> Assume the In Sample Performance Metrics are prefaced by IS and Out
of
> Sample Performance Metrics are prefaced by OS then one should be
able to
> write ( in terms of IO Directives )
>
> //IO: WFAuto: Rolling: Condition: OSMDD > 10 or OSMDD > 0.75 * ISMDD
>
>
>
> In reality I suspect this is what most people actually do i.e. find
some
> yardstick(s) that tell them their system is broken or about to be
broken and
> then reoptimize at that time.
>
>
>
>
>
> _____
>
> From: amibroker@xxxxxxxxxps.com [mailto:amibroker@xxxxxxxxxps.com]
On Behalf
> Of Paul Ho
> Sent: Tuesday, May 06, 2008 10:41 AM
> To: amibroker@xxxxxxxxxps.com
> Subject: [amibroker] Fitness Criteria that incorporates Walk
Forward Result
>
>
>
> Howard calls it the objective function. Fred calls it Fitness. What
I
> meant by Fitness Criteria is a mathematical function on which
fitness
> or goodness of the system is judged, and is used as an objective
> criteria to compare different systems, as a score in optimization.
>
> My currrent question is - So why not incorporate the fitness in
walk
> forward analysis into our fitness criteria? What I am talking about
> is to formalise the visual inspection process. I am not proposing
to
> use out of sample data for optimization purposes. Rather the
> parameter set that has been previously optimized is forward tested
> and a fitness is obtained and incorporated into the original
criteria
> to form a composite fitness.
>
> For example. My current composite fitness is the geometric average
of
> In sample fitness and Out of sample fitness divided by the standard
> deviation (?) of In sample and out of sample fitness.
>
> Are there anybody doing something is this area? What are your
> thoughts?
>
> If you are wondering why not use visual inspection. My plan is to
use
> the computer to do most of the work and thats why I need a fitness
> criteria.
>
> Cheers
> Paul.
>

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