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I don't understand how it works at all.
I also do not understand and saw the difference in use, so I abandoned it after 2-3 hours of study)
I too did not understand and saw the difference in use, that's why I abandoned it after 2-3 hours of study)
let's say, we can multiply Hidden matrix values by current value, e.g. return, and get a class label (i.e. hidden state). But then it turns out to be the simplest classifier.
and to input also normalized values 0:1
I don't know what's the trick :)
The method is very reminiscent of a nonparametric naive Bayesian classifier. There is no target. If two states are used in the model, they well distinguish a downtrend and an uptrend (example EURUSD-H4). The simplest implementation of the indicator in R is done in a few lines (depmixS4 package). Trade signals and results generated by them fully correspond to the observation area where the simulation was performed. It is interesting to see how the probability state curves will change in the real market when new data arrives.
The method is very reminiscent of a nonparametric naive Bayesian classifier. There is no target. If two states are used in the model, they well distinguish a downtrend and an uptrend (example EURUSD-H4). The simplest implementation of the indicator in R is done in a few lines (depmixS4 package). Trade signals and results generated by them fully correspond to the observation area where the simulation was performed. It is interesting to see how the probability state curves will change in the real market when new data arrives.
Yeah, on new data it's interesting. So far we haven't found any simple and understandable code in C, that I could use mql to write the library and not bother. I don't have any easy to understand C code for mql libc and don't bother with it.
it would be good as a debugging indicator.
As a breakdown indicator would have been fineRight on. And we would help you determine the minimums, highs and where to close anyway.
Right on. And we can help you determine the minimums, maximums, and where to close.
You have to study the math with God's help, it's dangerous to just use packages mindlessly.
But yes, it's a simple Bayesian grid, a probabilistic one.
You have to learn the math with God's help, it's dangerous to just use packages mindlessly.
But yes, it's a simple Bayesian grid, a probabilistic one.
Can you make a similar one? I wonder how it works in real time. And if you add a channel to this thing, it'll take a second.
Can you make one of these? I wonder how it works in real time. And a channel to attach to this thing, we can do it in a second.
I've been reading for days, poking around the packages. I can't yet, I need more mana.
The method is very reminiscent of a nonparametric naive Bayesian classifier. There is no target. If two states are used in the model, they well distinguish a downtrend and an uptrend (example EURUSD-H4). The simplest implementation of the indicator in R is done in a few lines (depmixS4 package). Trade signals and results generated by them fully correspond to the observation area where the simulation was performed. It's interesting to see how the probability curves of states will change in the real market when new data arrives.
I don't know why, but your indicator looks like MACD truncated by some band-pass filter, add MACD for comparison
The trading signals and the results generated by them fully correspond to the observation area where the simulation took place.
It is possible to make any machine a grail on training. And in general it's been said much about the fact that little depends on the choice of classification/regression method, as well as with "indicators" which, by the way, can also be hardly called an MO (if it's optimized).