Thanks for the feedback, I have looked at the combine script. And I already have loaded the .ml files in R, I am familiar with R. In R I just have to load the ml files and create a new (per WFOCycle) list and save it. I will post the R code here, if someone needs it again.
Trades for training have normally no target, but a fixed lifetime. Instead of 0/1, you can also scale the result to an analogue 0..1 range - what is better depends on your ML algo.
I do not quite understand this approach yet but I will think about it. So far, I have tried using forecasting/regression rather than classification, because of the question: How to train an ML-Algo on a target (class) that I can not clearly define. If I had a trainer that could produce accurate train-signals, I would not need to teach ML-Algos
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I do not understand with this principle, which of the many different training signals the net should learn. And what should it learn from it. Is there somewhere more detailed information on the subject?
