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Hello,
I want to throw some random thoughts to the list to see if some
discussions can be stimulated.
1) With the wonderful Excel 3D macro available in file area, it
is easy to eyeball
the 'most robust or stable' area when 2 parametes are used.
How do we do if there are more parameters out there?
Is it enough just use 2 at a time and repeat?
How if the robust parameter pair we get changes each time?
How many repeat is enough?
It is very labor intensive and we get ONLY the net$ optimal,
nothing else.
The result is not quantifiable, you eye ball it, you feel
right and that is all.
Mark Johnson propsed an idea years ago, (go to Yahoo AAFT
group, you might still
be able to find it) using what he called 'hyper-sphere' to
include ALL the
parameter sets within a defined radius and then compare the
chosen statistics
amongst all the 'spheres' to find the optimal parameter set.
The idea is
same as the 3D macro, just to extend it to N parameters and
many more statistics
can ge generated and compared.
Theorerically, there can be as many hyper-spheres as your
parameter set candidates,
say after an optimization run, you have 10,000 entries, then
each entry can have
its own hyper-sphere with itself as the center point,
potentially with different
radius.
Within each sphere, all sorts of statistics can be calculated,
including net$,
and then can be compared against all other spheres for you to
decide which one
is the best.
I think it is a good idea, better still, if this idea can be
included into AB.
I would like to know if there are other opinions or ideas out
there.
2) In portfolio testing, Chuck R. brought up this question
and proposed a solution
very early on, but there are other questions.
Since resource is always limited, how if there are more
candidates than one
can act for each signal?
Current backtester gives you ALL the candidates giving
each candidate all your
equity, while portfolio tester gives you very specific
candidates regardless
how many candidates are out there because there is not
enough equity to
trade them all. You get the same candidates regardless how
many times you test
it. AB choose the candidates using its internal rules.
Even using S&R, you just artificially limit your own
candidates per the rules
defined by yourself instead of AB.
Chuck proposed using TradeSim, which uses Monte Carlo
simulation, to see more
realistic test results. I think it is a good idea to
confirm the robustness
of a system.
Even so, how about in real time trading? Do we just choose
whatever the candidates
AB or S&R chooses for us and trade them?
3) Following the idea in (2), for each system we develop,
regardless how many data
points we use, they are all history.
We tried to use as many as possible data points hoping to
cover all the
possible scenarios that might happen in the future, we use
this process
to develop 'robust' systems. Of course, there are many do
not agree with
the idea of 'using as many as possible data points', but
that is beyond
the argument here.
But, regardless, it is always single path and it is always
histoy.
Is this approach good enough?
Is this the best we can do?
I heard that synthesized data set are used to make the
data universe even larger.
I also learned that Monte Carlo simulation might have some
uses here.
Any ideas?
Thomas
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