Machine learning in trading: theory, models, practice and algo-trading - page 2690
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Of course it needs to be clarified. For simple models like logistic regression, tree models, etc. it is probably possible. But I am talking about serious models for both TC and tabular data. These two areas have now become very divided and specialised. For tabular data, which are mainly used in machine learning, TabNet(paper, implementations (py) 1, 2, 3) is very promising. And a lot of other packages that give great results. Here is a list of what I have researched and partially use.
Not all of them are used mainly because of machine power limitations and personal preferences. For me training and optimisation for more than an hour is not interesting.
I don't think it will be possible to transfer these models to MCL. And here we can't do without creating infrastructure for linking MKL<->Python.
This is a bit of a digression, but the topic is important to me.
The main idea, I repeat: every developer, whether he is a freelancer, a marketer or a forex/crypto/stock trader, has his "favourite" language and his "favourite" bicycles with crutches to it. We need to share experience of use, not argue what is better. And even more so not to vang about the future of the API.
Good luck to everyone.
Tabular data != time series as tables, they are different things after all
Of course, tabular data and time series are different things.
And you cannot argue with it.
Especially hilarious are series of articles like neural network is simple. As an example of programming on MKL - good, but for practice - zero. Well, this is already grumbling.
Good luck to everyone
Of course tabular data and time series are different things.
You can't argue with that.
Especially hilarious is the series of articles like neural network is simple. As an example of programming on MKL - good, but for practice - zero. Well, this is already a grumble.
Good luck to everyone
Read it if you haven't seen it yet.
there's a ranking of classifiers
https://www.timeseriesclassification.com
As far as I remember, neural networks were not in the top of the listTabNet is highly dependent on the dataset and the selected features
Sometimes almost no difference with other classifiers
So I would like more specificity when pushing to apply something. Is this classifier that much superior.
https://arxiv.org/pdf/1908.07442.pdf
I have tried similar architectures for synthesising new data. All neural networks performed worse than GMM on forex time series (less plausible). Whereas on simple tabular data they worked well. I don't remember if there was Tabnet there.
That's why I wrote a clarification that tabular data != time series in the form of tables, the results will be worse.What are you talking about. Crypto exchanges (specifically Binance) provides...
I thought about poking my nose in the bibles too, but I thought about it and I'm sorry for the time...
When you ironise, look first at what you have done yourself... and in fact nothing, you have turned packages in R.
You can throw packages at each other on another forum, I guess nothing will change in terms of development of MO in trading.
When using R, the product is so cool that it's a shame to sell it 😁
I totally agree that the final product (for mt5) should consist of ex5 file without any integrations and (preferably) without additional files. The history of getting it is not so important - the main thing is that it should work (or be sold).
Using R makes a product so cool it's just a shame to sell it 😁
I totally agree that the final product (for mt5) should consist of ex5 file without any integrations and (preferably) without additional files. The history of getting it is not so important - the main thing is that it should work (or be sold).
I agree and I think this is the right way. Everything should be in the exe, it should not pull up anything. Otherwise it is not a product for sales.
I agree and I think this is the right way. Everything should be in the exe, it should not pull up anything. Otherwise it is not a product for sales.
Tell that to amazon and google. That they do not build their business correctly and their infrastructure is wrong :-)
Tell that to amazon and google. That they have the wrong business and the wrong infrastructure :-)