Ok thanks for the information, maybe you can add this feature in the future
...
So for me the only way to get this running is to do it in separate for loops?
... train all portfolio components separately and combine the resulting .c files. The Combine.c script can be used as a template for automatically combining parameters and rules from different assets or algos. Machine learning models (.ml files) can be combined with an appropriate R function.
I'll later look into Combine.c and i can do the R part, no problem.
When i use separate for loops, i can use every option? For WFO nothing changes?
1. Parameters depend on rules.
2. Rules depend on parameters.
3. Rules and parameters affect each other.
And yes, you're confusing the training process when you use stops and then train trade returns. Train the system only with plain returns or with the price curve or other fundamental data, but not with artificial values generated with stops or other algorithms.
I have to think about it
thanks
with the price curve = regression?
But one question comes up again, in neural.train() you cast every positive return to 1 and everything else to 0.
How do you define the targets/close of your trades? What am I missing here?
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