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Incorrect parameters are generally disabled for 3 reasons:
1. the study area is not of interest (e.g. excluding a number of timeframes, as the most understandable option);
Absolutely right.
Well, how do you organise the MA optimisation from the example without including the "invalid" parameters?
OK, I agree, I didn't look at it carefully. to optimise another parameter with an invalid gap cut out
OK, I agree, I didn't look at it carefully. to optimise, bring out another parameter with the invalid gap cut out
Can you give me an example?
Can I give you an example?
I agree. What about the case of timeframes?
I agree. And what to do in case of timeframes?
The same way - conversion of int inside continuous interval to timeframe. for example, create an array with all TF values and optimize by array index
For example, to create an array with all TF values and optimize by array index
Well, this looks like a crutch to me... Maybe I'm wrong, but it's almost the same thing I had to do with my starting example. Except that in this case you don't need to dump the whole array into a file beforehand...
An idea came up to sift out "invalid" parameters during genetic optimization in OnTester():
It turns out that without interfering with genetics the results of INIT_PARAMETERS_INCORRECT may not be seen.
Another question arises here: may we get "deep minus" on profit in this case?
The way I did it: a separate parameter is created and other values are substituted depending on its value.
A simple example (2 weeks ago):
There are timeframe parameters: TF_UP (Senior) and TF_DOWN (Junior), and the Senior must always be greater than the Junior, in reverse case input_incorrect comes out. In the tester there are a lot of null results and genetics "works for nothing" (though it's fast). As a result, I removed these 2 parameters and created a new parameter which has the type described below, and optimize only it, and already in the OnInit function the substitution in 2 other parameters, depending on this parameter.