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.
