Machine learning in trading: theory, models, practice and algo-trading - page 1204
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is solved by selecting optimal weights... for example how to vary the posterior... from uniform to exponential
I'm not a proponent of "black box" models. Better when everything is transparent, with a simple "physical" meaning.
For example - we consider the a priori probability of a correction to become a reversal and then recalculate it for each certain correction in the a posteriori one depending on the time of day or the trend characteristics.
I am not a supporter of "black box" models. It is better when everything is transparent, with a simple "physical" meaning.
For example - we consider the a priori probability of a correction to become a reversal, and then we recalculate it for each specific correction to the a posteriori probability, depending on the time of day or the trend characteristics.
Unfortunately, we do not know the simple physical meaning of the Forex laws.
The simple physical meaning of the laws of forex is not known to us, unfortunately
It's about the interpretability of the model.
It's about the interpretability of the model.
then you are not in the subject of MO :) although metamodels are easily interpreted through their metrics
then you are not in the MO theme :) although metamodels are easily interpreted through their metrics
why? imho, the problem is the same as to distinguish a cat from a dog for MoD
Why? imho, the task is the same as distinguishing a cat from a dog for the MoD
Because it's like speaking different languages, in my opinion...
distinguish by signs, of course... they're chipsthen you are out of the MO theme :) although metamodels are easily interpreted through their metrics
I'm not sure it's always easy, but somehow it can be done. I believe that it is not the neural network itself, but its simplified approximation that should deal directly with trading.
Without methods of MO (you can call it "clever exploratory analysis") in our case, you can't do without it.)
I'm not sure it's always easy, but somehow it can be done. I believe that it is not the neural network itself, but its simplified approximation that should deal directly with trading.
Without methods of MO (we can call it "smart exploratory analysis") in our case we can't do without)
Now I want to add the dependence of signals on distributions to the parameters to be optimized, I have done it for the beginning, to see
If the kurtosis is higher than a certain value (you can opt for it), then a flat situation is observed, and it is possible to buy/sell with the same probability (and then fix all the wrong ones)
further on asymmetry, if there is a certain side, then the probability of the signal to buy or to sell is displaced
This is a primitive one, but this is the way we can select the targets in the optimizer
All you need to get from metrics is classification error on the test sample (to be trained on the training sample). The optimizer goes through the hyperparameters and chooses the model with the lowest error. What is non-interpretable here? It is enough to know, looking at the errors on the test data, whether such a model is capable of generalizing or not.
I just made an example of working with such bullshit
Now I want to add the dependence of signals on distributions to the optimized parameters.
And it seems that there is a dependence...
I trained "SMM" (hidden Markovian model) on returnees, divided it into 10 states and trained it without a teacher, so it divided different distributions by itself
state distributions.
And here I grouped returns by states, i.e. each row is a separate market state
Some states (1,4,6,8,9) have too few observations, so you can not take them at all
And now I will try to restore the series, that is to make a cumulative sum, if some tendency is found in some of the states - the regularity in the direction
I did a cumulative summation.
states 5 and 7 have a consistent structure, 5 is for the bay and 7 is for the village
Now I want to add to the optimized parameters the dependence of signals on distributions, I did it for the beginning, to see
If the kurtosis is higher than a certain value (you can opt for it), then a flat situation is observed, and it is possible to buy/sell with the same probability (and then to fix all the wrong ones)
further on asymmetry, if there is a certain side, then the probability of the signal to buy or to sell is displaced
This is a primitive one, but it's approximately the way the optimizer can select the targets.
Why prices and not their increments?