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
