Hi jcl, i was also thinking that - but in the manual is still written:
Training rules and parameters at the same time only works with single assets, not with a portfolio system that contains loops with asset or algo calls.
I'm not sure what happens if i do it with set(RULES|PARAMETERS); in Asset/Algo loops ? Will it work correctly?
In our experience, a machine learning model usually deteriorates with the complexity of the objective.
I agree... But using every possible next trade return (enter on every bar/input signal) is not complex?
The best models use 0, the price difference, or the price sign for the objective. The "bad" system of your question 2 is in fact one of the best.
How is that possible? Imagine a simple Perceptron, if we change the Input, we also change the Output. In Training (by using Trade returns) we move the weights to separate our classes. We want the perceptron to fire if the input/s is(are) "in range X to Y".
But if we use Trade Returns from all possible entry's - no matter how small or big the profits are, f.i. 0.01 to 200 - trades starting from random/different Input values - aren't we confusing the TRAIN process here? What is our target? And if we use all the "next Trade returns" we train so many entry points which have no meaning? (bad or no local minima)
A Perceptron or NeuralNet is not a Support Vector Machine.
An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible.
Am I thinking wrong?
Thank you, I do not know anybody else I could ask such questions
PS: Maybe your models didn't train well (with a custom objective) because +BALANCED does not work with extreme unbalanced classes. I have seen it with that example and did the up-sampling in R.
