I´ll look it up.

Thanks, man.

Cheers

Markus
  ----- Original Message ----- 
  From: j0etr4der 
  To: [email protected] 
  Sent: Tuesday, February 09, 2010 8:10 PM
  Subject: [amibroker] Re: Saving on optimization time


    
  Hi,

  Assuming that the width of the range in more significant than the resolution, 
you might try something like this:

  heat = optimize( "heat", 5, 1, 10, 1 );
  fast = optimize( "fast", 25, 1, 50, 1 ) * 2;
  slow = optimize( "slow", 50, 25, 75, 1 ) * 2; 

  25,000 vs 100,000 combinations.

  I've had good success with CMAE, you might try it out.

  OptimizerSetEngine("cmae");

  It's pretty much self-configuring, so don't worry about the parameters. If 
you are unfamiliar with CMAE, there's a fair amount of good info in Help.

  --- In [email protected], "Markus Witzler" <funny...@...> wrote:
  >
  > 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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