Hello,
if I have, say, three variables that I want to
optimize (exhaustively) where two have a range of 100 values and one would have
a range of 10 values, this would mean
10 * 100 * 100 = 100,000 combinations
I figured that if I optimized the latter two while
keeping the first one fixed, that would take 10,000 combinations.
Afterwards, I could use the optimal parameter set
for the last two ones and optimize for the first variable, i.e. 10
steps.
Altogether, this would mean 10,100 steps as oppsoed
to 100,000 steps.
I understand that this procedure is not always
feasible. But in a case where one had for instance, a two MA crossover system
(100 steps for each MA) plus a heat parameter (10 steps), I guess this would
work.
My reasoning would be optimizing for
heat AFTER having found the "best" parameter set regarding the MA´s would
give me the highest return (or else) without the need to run thru all
theoretically possible combos.
Any thoughts on this besides using intelligent
optimization algorithm?
I´m at a point where exhaustive optimization is
taking quite a while but still would be an option if I could somewhat decrease
the number of theoretical steps.
Of course with a larger number of opt. steps,
intelligent optimization (using IO) would be the ONLY option (I´m using IO anyways but am eager to find THE best and most
robust set of variables in the system I´m observing...).
Any thoughts?
Thanks
Markus
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