Machine learning in trading: theory, models, practice and algo-trading - page 2840
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It's the criterion that's at fault.
I can't imagine it in my head... we have a labelled dataset, we want to train as close to these labels as possible. If we take another criterion not related to them, then these labels stop mattering?
That's the point, we are moving a bit away from the kind of optimisation (learning) that is common in classical MO. We are moving towards optimisation in a broader sense (as in MT5, for example). But at the same time we want to preserve the power and flexibility of the models used in MO.
I have always been confused by the conceptual gap between MT5 optimisation and MO application. It would be nice to have options for intermediate approaches.
It is as if Fomenko does not hear what is being said. I have already said several times that the tester does not affect profitability or the ability of the TS to work profitably in the future. The tester is a tool, nothing more. An optimisation algorithm is a tool and nothing more. It is like discussing the "success" of a shovel for making money.
That's right, a conversation between the deaf and the blind.
I write that optimisation together with criteria is not necessary because financial markets are NOT stationary, and you write that I understand something about optimisation.
Success, alchemists have been converting everything into gold for several hundred years.
The point is that we are moving a bit away from the type of optimisation (training) that is accepted in classical MO. We are moving towards optimisation in a broader sense (as in MT5, for example). But at the same time we want to preserve the power and flexibility of the models used in MO.
I have always been confused by the conceptual gap between MT5 optimisation and MO application. It would be good to have possibilities for intermediate approaches.
The more adequate the evaluation criterion, the more adequate the model behaves on new data. choosing the best AO means choosing the best tool to optimise the CRITERION. It cannot be the AO's fault or the tester's fault. The criterion is at fault.
The robustness of the TS has NOTHING to do with the evaluation criteria, because the criterion is exactly one - guessed the direction of the trade or not. But the latter depends on the set and properties of predictors
That's right, a deaf man talking to a blind man.
I write that optimisation together with criteria is not necessary, because financial markets are NOT stationary, and you write that I understand something about optimisation.
You are also, in fact, doing optimisation. You have invented some criterion of "stationarity of signs" and take the signs that are optimal according to it. It's the same optimisation in history, but in profile.
The robustness of the TS has NOTHING to do with the evaluation criteria, because the criterion is exactly one - guessed the direction of the trade or not. But the latter depends on the set and properties of predictors
Here, it is absolutely necessary to invent a criterion of TS robustness and optimise according to it) Again we will get the sameoptimisation on history, but in a different profile).
There you go. I don't understand the allergy of some comrades to the word "optimisation".
Optimisation should be considered as a process of finding the best solution. the best solution of a robust model. If the model is not robust (weak evaluation criterion), then, as they say, "don't blame the mirror" (blame the optimisation).
There you go. I don't understand the allergy of some comrades to the word "optimisation".
Optimisation should be considered as a process of finding the best solution. the best solution of a robust model.
Not an exact definition, and if the search process is not in the model, then it is not optimisation? )
I, for example, create code using optimisation tools...
Optimisation is "mathematical search of unobservable parameters according to a chosen criterion of usefulness".
something like this