Machine learning in trading: theory, models, practice and algo-trading - page 581
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A new version of the library for connecting Python to MT5 has been posted. I remind the linkhttps://github.com/RandomKori/Py36MT5 But there are problems. In Visual Studio the test project works as it should, but in MT there are some unclear problems. Now the library normally works with the directory where the Python script is located. I don't know how to debug it with MT. MT is protected from the debugger. Maybe someone knows how to debug?
i understood that python support in MT5 is not planned :( only an editor of some kind
Fine :)
Yes, there are no plans. The problem I wrote about above, I solved. But that's not all. So far it is possible to run only one script on the terminal. I'll think about what to do with it.
For example, randomForest.....
The most interesting and efficient algorithm of the same breed is ada...
Fa, stop with the bullshit. Forests and boosting are different things. Give a practical application of garhs)))
How to make your machine learning model available as an API with the plumber package
https://www.r-bloggers.com/how-to-make-your-machine-learning-model-available-as-an-api-with-the-plumber-package/
Fa, stop with the bullshit. Forests and boosting are different things. Give a practical application of GARCH)))
Don't worry, different, but at the level the discussion is at .
I would like to point out that ada gives better results than rf: both more accurate and less prone to overtraining. And you should use ada, not rf.
So it's not just a matter of piling everything up.
GARCH is too complicated. So far I've made it through ARIMA, and also GARCH and distribution.
I learned that machine learning uses such a thing as feature construction. You can't get far on price alone. The feature in our case is a function of price. The question is which functions to use. Simply going through the indicators with different parameters is not an option. I am interested in materials on this subject. Google usually produces various rubbish, or rather it produces nothing on the subject. I searched on Runet. Maybe someone knows some materials on the subject.
PS. You have to start from the beginning. That's when you learned to construct signs not at random, you can move on to their selection.
I learned that machine learning uses such a thing as feature construction. You can't get far on price alone. The feature in our case is a function of price. The question is which functions to use. Simply going through the indicators with different parameters is not an option. I am interested in materials on this subject. Google usually produces various rubbish, or rather it produces nothing on the subject. I searched on Runet. Maybe someone knows some materials on the subject.
PS. You have to start from the beginning. That's when you learned to construct signs not at random, you can move on to their selection.
There are a lot of materials on this topic in this thread.
Hi!
How'd it go, did they make a superbot?
Don't worry, different, but at the level the discussion is at .
I would like to point out that ada gives better results than rf: both more accurate and less prone to overtraining. And you should use ada, not rf.
So it's not just a matter of piling everything up.
GARCH is too complicated. So far I've made my way through ARIMA, and also GARCH and distribution.
At the level at which this discussion is taking place, you didn't even know how the importance of predictors in RF is determined, slipping some nonsense about annealing and stuff without explanation (what does that have to do with anything?)
Who said where are the benches exactly for forex? why Ada and not GBM? your answers are too much fuzzy abstractions. in reality the gain will be no more than 5% with more overtraining.
Hi!
How's it going, did you make a superbot?