Machine learning in trading: theory, models, practice and algo-trading - page 2073
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Why would the take point behind the extremum, because the correction could have been 50%, which means that we should take the take point around 100% from the previous interval.
,
Oh, so you almost drew the system, that I posted earlier as a report :) Only I take TP higher and do not wait till the last interval of ZZ is formed (it is a matter of settings, though).
So what? Will you throw the code? Or at least a sample.
Sampling link.
Target column "Target_100", at the end of the column with the date the next column and the last two are not used in the training.
The sample is divided into 3 parts, exam.csv is not involved in the training.
As options, an exit before an extremum (with rollover),
Exit at the crossover with the channel based on 3 extrema
There is a separate area - time series classification and related libraries like this
https://pyts.readthedocs.io/en/latest/auto_examples/transformation/plot_rocket.html
Has anyone used it?
There is a separate area - time series classification and related libraries like this
https://pyts.readthedocs.io/en/latest/auto_examples/transformation/plot_rocket.html
Has anyone used it?
You should use it, it's an interesting package. Just like a constructor.
You should use it, it's an interesting package. It's like a constructor.
I gave link to ROCKET for a reason - it's like cool feature converter. Creates a lot of uncorrelated features from original ones, improves quality of classification.
It is recommended to use it with linear models (because it makes a lot of features).
I will have to test it
I gave a link to ROCKET for a reason - it's kind of a cool feature converter. Creates a lot of uncorrelated features from original ones, increases quality of classification.
It is recommended to use it with linear models (because it makes a lot of features).
I will have to test it
Let me know about results - very interesting topic!
Creates a lot of uncorrelated features from the original ones,
regular PCA?)
regular PCA?)
no