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Thanks, guys. I have IO and have looked into its sensitivity directed optimization. I do in fact use it for some things. It's a great tool.
But in this particular application, the manual method I describe with AB seems to work better. I suppose that is purely due to the way my trading system is designed. It does seem to produce fairly consistent "land masses" every month for optimum values. It's just that the 'best' values chosen by AB's WF engine are on sharp island spikes out in the deep ocean.
Thanks for the suggestions, Keith. I'm chewing over those as well. You've given me some approaches to consider.
--- In amibroker@xxxxxxxxxxxxxxx, Keith McCombs <kmccombs@xxx> wrote:
>
> Ozzy --
> I'm not sure I understand what you mean by "I then flatten the 3D
> optimization graphs to 2D for each month, and print out the six charts
> on thin sheets of paper. I stack the papers one on top of the other and
> place them on a makeshift light table. This essentially gives me
> superimposed Venn diagrams and it is easy to circle the best common area
> for the six months."
>
> But let me make a stab at it and you let me know how close I am.
> Say for example, you optimize over two parameters as in:
> small = Optimize("small", 4, 1, 10, 1);
> big = Optimize("big", 40, 10, 100, 10);
> And you optimize for CAR/MDD.
>
> Then, for each month, you produce a single chart with 10 lines, CAR/MDD
> vs 'small' for each of the 'big' values. You then overlay the 6 months
> on one another. You would have the same effect, without overlaying, if
> you plotted sixty lines for each of the 'big' values. You might plot
> all of the lines that use the same 'big' parameter the same color,
> independent of what month they are from.
>
> You may be able to automate much this. And then use your 'eyeball' for
> selecting the most promising areas?
>
> ------------------------------
>
> If you want to do less 'eyeballing', you might want to invent some
> metrics that might be based on the average value and standard error of
> the CAR/MDD for each combination of parameters over the 6 months.
> And/or you could also 'punish' each pair of parameters based on the
> value of their nearest neighbors. For example: each CAR/MD for a pair
> of small and big would be represented as CARMD[small, big]. CARMD[4,40]
> could have a new 'punished' value PCARMD[4,40] which would be reduced or
> pulled down by its nearest neighbors in both small and big directions,
> but NOT increased or pulled up by them. If you took this approach, you
> would be wise to compare your results with your 'eyeball' evaluations so
> as to increase, or reduce, confidence in either or both these attempts
> at evaluation.
>
> BTW, Fred Tonetti's IO has some provision for introducing parameter
> 'sensitivity' into the optimization system (though I don't understand
> the nitty-gritty of it).
>
> BTW2, I really appreciate your posting. New thoughts -- definitely
> worth thinking.
> -- Keith
>
>
>
>
>
> ozzyapeman wrote:
> >
> >
> > I am wondering if anyone here has already created a CBT procedure that
> > solves this problem. And if not, wondering if anyone actually would
> > volunteer to create one. It's a bit beyond my scope at the present time.
> >
> > When one does an In-Sample / Out-Of-Sample walk forward test in AB's
> > AA, the AA engine will use the "optimum" variables from the IS period
> > to use on the OOS period. But more often than not, these values are
> > not really stable.
> >
> > In a two-variable 3D optimization graph, for example, the 'best'
> > values are usually on some sharp peak somewhere. If you use these
> > values next month (i.e. for the OOS period), they can easily fall of
> > that peak and result in a big loss. One would have been better off
> > choosing values that were on a more stable plateau, even if they
> > represented a significantly lower CAR/MDD or Net Profit or whatever
> > value you are optimizing against.
> >
> > *19th Century Light-Table Method*
> >
> > What I have currently found useful is to optimize my system over six
> > individual months. I then flatten the 3D optimization graphs to 2D for
> > each month, and print out the six charts on thin sheets of paper. I
> > stack the papers one on top of the other and place them on a makeshift
> > light table. This essentially gives me superimposed Venn diagrams and
> > it is easy to circle the best common area for the six months. I then
> > choose the center of that common area, and get my X and Y coordinates
> > which corresponds to values of Profit and Stop (e.g. 30/100). When I
> > test these values on the OOS 7th month, I always end up with a
> > significant profit. I've done a lot of laborious runs like this, and
> > it always works.
> >
> > Now of course my mechanical method of printing out graphs and placing
> > them over a light is very primitive (perhaps shamefully so). In order
> > to truly validate this, I essentially need to program this method so I
> > can use it for computerized statistical validation over my historical
> > data.
> >
> > I essentially wa nt to be able to automatically:
> >
> > 1. Optimize my system on six individual months (not a single
> > six-month period)
> > 2. Find the the stable plateaus for Profit/Loss for each month,
> > that correspond to a minimum acceptable optimization level
> > 3. Compare the six stable plateaus to find the "Common Plateau"
> > 4. Find the center of the six-month Common Plateau, to obtain best
> > Profit/Loss values
> > 5. Walk-Forward to the OOS month (i.e. the 7th month) and backtest
> > with the Profit/Loss values found in step 4
> > 6. Repeat steps 1-5 for as many steps in my Walk-Forward analysis.
> >
> >
> > Of course the above is easier said than done. Or is it? Maybe there is
> > some simple way to fandangle this, that I have not thought of.
> >
> > Any suggestions on how to approach this? Or better yet, anyone feeling
> > up to the challenge of actually slapping it together as a CBT/Jscript
> > piece of coding brilliance?
> >
> > In its simplest form, it really only need to do steps 1-4
> > above,perhaps callable by a jscript. It can then easily be used with
> > AB's WF capability.
> >
>
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