Machine learning in trading: theory, models, practice and algo-trading - page 2740
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I once posted a table in this thread, but I don't have it handy at the moment, so I'll clarify my thought in words.
I'm relying on the concept of predictor-teacher correlation. "Linkage" is NOT the correlation or "importance" of predictors from fitting almost any MOE model. The latter reflects how often a predictor is used in an algorithm, so a large value of "importance" might be given to Saturn's rings or coffee grounds. There are packages that allow one to compute the "link" between predictor and teacher, e.g. based on information theory.
So, a word about the table I posted here.
The table contained a numerical estimate of the "link" between each predictor and teacher. Several hundred values of "connectivity" were obtained as the window moved. These values for a particular predictor varied. I calculated the mean and sd for each "connection", which allowed:
- isolate predictors that have "coupling" that is too small - noise;
- isolate predictors that have a "linkage" value that is too variable. It was possible to find predictors that have a sufficiently large value of "link" and sd less than 10%.
Once again, the problem of constructing a TC based on MO is to find predictors that have a large value of "link" and a small value of sd when the window moves. In my opinion, such predictors will ensure stability of prediction error in the future.
What packets ???
What kind of packages?
library("entropy")
classDist {caret}
That's not all, what came to mind.
I use my own algorithm - it is much faster than numerous R libraries. For example,
library("entropy")
You can just use the graphs:
Everything was posted on this thread. Everything is systematically outlined and chewed up at the code level in articles by Vladimir Perervenko
and so, where is the state?
.......
So, where's the steith?
.......
There's no counsellor.
And in R are the names of predictors, and they are the whole point.
For several years on this thread I have been calling to deal with predictors. The result is zero. And without quality predictors, the MO is meaningless.
So, where's the steith?
.......
What's the point?
it is not the object of research and the ultimate goal :-)
No counsellor.
And in R the names of the predictors, and they're the whole point.
For several years on this thread I have been calling for predictors. The results are nil. And without quality predictors, MO makes no sense.
In financial markets, the incentive is money.
please understand
If there are no financial indicators, there is no incentive.
For example, here I have the Moscow stock exchange.
(file below, the screenshot doesn't fit here for some reason, it doesn't want to)
and forex.
all at the current moment
----
elementary, Watson !!!
ahahahahahaha
in the financial markets, the incentive is money.
please understand
no financial performance, no incentive
Now I'm aware of it.
What should we do to stop fighting and unite for the sake of one goal????????
We need to be helpful to each other and not be afraid to admit mistakes, be respectful.
How so, I asked you to make a script - yes, I quote " Can you make a script in R for calculations for my sample - I will run it for the sake of the experiment. The experiment should reveal the optimal sample size. ", but this is in response to something that has already been done.
Earlier I wrote "... And how do you propose to watch in dynamics, how to realise? " - here I was asking about implementation of predictor estimation in dynamics, i.e. regular estimation by some window and it is not clear whether it is a window on each new sample or after each n samples. If you have done so, I did not understand it.
The code you posted is great, but it's just hard for me to understand what it does exactly or what it proves in essence, so I started asking additional questions. What do the two pictures with graphs mean?