Machine learning in trading: theory, models, practice and algo-trading - page 3558

 
Aleksey Vyazmikin #:

I hope you realised the fallacy of the statement about the curved quantum table, and deleted for that reason.

If I am not mistaken, it is you who uses returns from mashki in your articles - I thought it will be clear and understandable.

It is possible and inside - I do not mind - I wrote long ago that it was an initial purpose of quantisation - to build models on subsamples from quantum segments (bins).

Simply I saw additional possibilities which quantisation gives and began to study them in depth.

I realised that I didn't understand anything and I don't want to go into it. Strongly invented definitions, again about some flows.

complicated communication on an empty place )

 
Maxim Dmitrievsky #:
Strongly invented definitions, it started with some streams again.

A stream of data without any sampling - this is what was meant. I don't know how not to understand it....

 
Aleksey Vyazmikin #:

Data stream without any sampling - that's what was meant. I don't know how not to understand it.....

You took a waveform, counted from it 100 in all directions and this is discretisation? Where are the returns?

 
Maxim Dmitrievsky #:
You took a fly, counted from it 100 in all directions and that's discretisation?

That's right, we have divided the range of values into intervals in this way - uniformly in increments of 100.

It is clear that at the beginning is the difference between the price (let's say closing) and the MA. We put this data stream into cells (bins) and assign them an index (serial number - as an option) instead of their own value.

Thus, the data are standardised, which makes them comparable, and quantised, which allows them to be grouped according to the similarity of the event.

 
Aleksey Vyazmikin #:

That's right, we have divided the range of values into intervals in this way - uniformly in increments of 100.

It is clear that the difference between the price (let's say closing) and MA is at the beginning. We put this data stream into cells (bins) and assign them an index (serial number - as an option) instead of their own value.

Thus, the data are standardised, which makes them comparable, and quantised, which allows them to be grouped according to the similarity of the event.


 
Andrey Dik #:

Well, the phrase "Garbage in, rubbish out" is as good as "That's just the way it is!", suitable for any occasion in life with equal success, i.e., useless. But it sounds profound.))

Yep, more assertions than connections to the marketplace

 
Ivan Butko #:

NOT rubbish will automatically make the input data - useful

Useful - working

Working - profitable



If you don't have such - then you don't know whether it is rubbish or not.

And if you have tried it and it doesn't work - that's another matter.

That's the way you need to parry it. Tried or not and what result (nature of the result)

I know how to filter out rubbish. I have repeatedly posted my approaches on this thread, not only my approaches, but also links to packages that allow filtering rubbish. And exactly in application in MO.

Moreover, the very meaning of the science of "statistical" consists in filtering rubbish.

So your reaction is nothing but surprising.

 
СанСаныч Фоменко #:

I know how to filter rubbish. I have posted my approaches many times on this thread, not only my approaches, but also links to packages that allow filtering rubbish. And exactly in application in MO.

Moreover, the very meaning of the science of "statistic" consists in filtering rubbish.

So your reaction is nothing but surprising.

You don't hear me:

Did your approaches make the forward stable?

If no - you don't know how to do it.

If yes - you are saddled with pricing and are swimming in money.


If your approach has nothing to do with forex, but with something else - tell me so, maybe I am parrying in vain and you are promoting science, not the applied thematic exploitation of this science

 
Aleksey Vyazmikin #:

Interesting for general development, but for trading it's not clear how to use it...

Any ideas?

I guess the main approach of using a teacher and a supervisor model is the main distinction here in the training phase.

Using this for creating tailored NNs for fundamental analysis opens a new path for individuals to actually adapt LLMs for specific tasks, without having to train for months or even years or rent a Datacenter for the job.


 

yeah...

I guess it's not banning/unbanning here - it's morse code :-) this is how TC (and/or moderator) gives secret signs