Machine learning in trading: theory, models, practice and algo-trading - page 1513

 
Andrey Dik:

The point of this action is to isolate stable patterns (or whatever you want to call them), and they are stable because they work on different BPs, my timid experiments in this area show that this is in principle possible... and as a consequence the robustness increases (decreasing the degree of fitting)

is trained first on one VR, then it is further trained on another VR. Which implementations you need to look at, may be different

I also artificially add noise to features, sometimes it improves the results.

Have a look at importance sampling, it's also interesting.

more meta learning

 
Kesha Rutov:

I would call it "drawdown learning", or "drawdown learning", for importance, we are waiting for an article on "drawdown learning" by Pereverenko or Denisenko, with advanced OOP (>5 inheritance depth), 90% acuracy and the same (equal) ratio of profit to drawdown in the test, or as in the good old days without any test, all on Lern and with martin, a pure exponent)))

Kesha, stop smoking that crap. Otherwise you'll really become ...

Where do these idiots come from?

 
Kesha Rutov:

Urgent to write an article on"drawdown learning".

Transferlearning is when selected (usually the first 1-2 layers) neurons/layers trained on one dataset or algorithm are used in another grid as a part, it is used for example for stylization of pictures.

You do not need to give references on topics you do not understand anything. Jumped on the top, picked up the terms and you think you have become an expert. Chatterbox, yes, but not an expert.

 
Maxim Dmitrievsky:

no snotty ice

Vladimir Perervenko:

No need to give references on topics in which you do not understand anything. You jumped up and down, picked up terms, and you think you've become an expert. A chatterbox, yes, but not an expert.

Well, of course not a "specialist", otherwise why should I hang around here, all hope for you and Maxim Denisenko, for "experts", I am waiting when you will write an "article" onthe training, and even better to back it up with a scam signal, something like dopamine from Maxim Denisenko

 
Kesha Rutov:

Of course I am not a "specialist", otherwise why should I hang around here, all my hopes are on you and Maksim Denisenko, the "specialists", I am waiting for you to write an "article" on the learning process, and even better to back it up with a scam signal, something like dopamine from Maxim Denisenko

As for the RL signal, you should read articles and books where RL "experts" are trying to apply it to financial time series, and you'll see that dopamine is the best thing that can be useful in this subject

and i don't care if you like it or not, they forgot to ask you

at least write a simple reinforce without 3rd party libraries, i'll laugh

cocksucker

 
Maxim Dmitrievsky:

cockerel

And it will happen to you too, when LIFE will take you into turn, nothing is eternal under the moon, sooner or later your "golden cage" will collapse and then...

 
Kesha Rutov:

And it will happen to you too, when LIFE will take you in a turn, nothing is eternal under the moon, sooner or later your "golden cage" will collapse and then...

Life took you by the breasts and you're crying like a girl, what does the subject of MO have to do with it? Go to psychologists, there are plenty of them on the forum. I can only send you a message, I do not have a fine sense of tact

 
Maxim Dmitrievsky:

is trained first on one VR, then it is further trained on the other VR. What specific implementations you need to look at, may be different

I also artificially add noise to features, sometimes it improves the results.

Have a look at importance sampling, it's also interesting.

more meta learning

No... I use one, other, third BPs as equal in training, let's see what comes out of it

 
Andrey Dik:

No... One, the other, the third BPs are used as equal in training, let's see what comes out of it

i also tried several bots at once in my bot, i did not see any improvement... i have a specific thing, it's on its own mind

interesting research on maximum entropy i saw today, i liked how to use entropy for inputs determination (part 2 of the article)

What's missing in mine, apparently. I even came up with almost the same thing, but couldn't articulate it. And it's kind of backed up by theory.

It also shows that different markets are predicted differently, so if it's all in one pile... I don't know

https://robotwealth.com/shannon-entropy/

Shannon Entropy: A Genius Gambler's Guide to Market Randomness - Robot Wealth
Shannon Entropy: A Genius Gambler's Guide to Market Randomness - Robot Wealth
  • robotwealth.com
Before you commit your precious time to read this blog post, I need to warn you that this is one of those posts that market nerds like myself will get a kick out of, but which probably won’t add much of practical value to your trading. The purpose of this post is to scratch the surface of the markets from an information theoretic perspective...
 
Maxim Dmitrievsky:

I have a peculiar thing, it's on its own mind... I have an interesting research on maximum entropy.

interesting study on maximum entropy i saw today, i liked how to use entropy for inputs determination (part 2 of the article)

What's missing in mine, apparently. I even came up with almost the same thing, but couldn't articulate it. And it's kind of backed up by theory.

It also shows that different markets are predicted differently, so if it's all in one pile... I don't know

https://robotwealth.com/shannon-entropy/

Here is another interesting material about entropy, so to say the author explains it with his fingers

https://habr.com/ru/post/171759/


I can't find something about decision trees, some fragments of information are on the net, I need something in the form of literature

Энтропия и деревья принятия решений
Энтропия и деревья принятия решений
  • habr.com
Деревья принятия решений являются удобным инструментом в тех случаях, когда требуется не просто классифицировать данные, но ещё и объяснить почему тот или иной объект отнесён к какому-либо классу. Давайте сначала, для полноты картины, рассмотрим природу энтропии и некоторые её свойства. Затем, на простом примере, увидим каким образом...
Reason: