Machine learning in trading: theory, models, practice and algo-trading - page 45
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Okay, that's a very good trading performance on history! Congratulations.
How to make several predictors from the ranges of one predictor? I don't understand it.
Oh it's very simple) clustering...
1) Let's take each predictor and cluster it into, say, 50 clusters (moreover clustering can and should be done in two types 1) clustering "as it is" to cluster the predictor according to numerical values and the second type 2) clustering normalized predictor to cluster it as an image) together we will get everything as human vision, we will know not only numerical "real" predictor values but also the image - curves, slopes
2) We create a table where columns are clusters, 50 clusters ---> 50 columns ---> 50 predictors, we check their importance by some algorithm and we see that out of 50 predictors only 1-5 ones are important, we leave them
3) take the next predictor, cluster them and repeat steps 1 and 2
Theoretically such selection inside the predictor should increase the recognition quality by orders of magnitude...
But there are some disadvantages
1) expensive calculations
2) if every predictor is split one by one and its contents will be evaluated separately from the contents of other predictors then it will be impossible to evaluate the correlation between the predictors it has to be solved somehow
Oh it's very simple) clustering...
1) We take each predictor and cluster it into, say, 50 clusters (clustering can and should be done in two types 1) clustering "as it is" to cluster the predictor by numerical values and the second type 2) clustering normalized predictor to cluster it as an image) in complex we get everything like human vision, we will know not only numerical "real" values of predictor but also image - curves, inclinations
2) We create a table where columns are clusters, 50 clusters ---> 50 columns ---> 50 predictors, we check their importance by some algorithm and we see that out of 50 predictors only 1-5 ones are important, we leave them
3) take the next predictor, cluster them and repeat steps 1 and 2
Theoretically such selection inside the predictor should increase the recognition quality by orders of magnitude...
But there are some disadvantages
1) expensive calculations
2) if every predictor is broken down one by one and its contents will be evaluated separately from the contents of other predictors then it will be impossible to evaluate the correlation between the predictors it has to be solved somehow
So you can try . In general, there is such a method. Construct a point chart of the output-predictor. Ideally, there will be a good dependence. But if on some segment (usually in the tails) the dependence is blurred, these observations are excluded.
What is this method called?
is there one in the r-ke?
how to solve problem no.2 ?
What is this method called?
is there one in the r-ke?
how to solve problem no.2 ?
By the way, is anyone interested in this or not, I don't get it. Do you need a trained robot that passes validation in 5 years with a profit?
Such a
I'm back from vacation. I can prepare the files and post them, and whoever needs it, can improve it for themselves.
I'm interested in how you created the robot point by point, if it's not difficult...
1) Selected the features according to your method
2) You made the model
That's all?
I'm interested in how you created the robot point by point, if it's not difficult...
1) Selected the features according to your method
2) You made the model...
and that's it?
This is a general scheme that works all the time.
I cull the features through the importance after the GBM run. And I try different numbers of selections. The machine is trained through GBM and I have tried different fitness functions. Crossvalidation is used. Its parameters also vary. And there are some more nuances.
In general, I got the result showing that more complex is not always better. On EURUSD the model uses only 5 predictors and only two crossvalidation fouls.
Very interesting neural networkhttp://gekkoquant.com/2016/05/08/evolving-neural-networks-through-augmenting-topologies-part-3-of-4/ Do you think it is possible to make it trade itself and learn from its mistakes? And if so, how, I invite to discuss.
If the developer says that the network can replace the reinforcement learning algorithm, that's promising.
Experimentation is needed. But it's an interesting topic.
If the developer says that the network can replace the reinforcement learning algorithm, that's promising.
Experimentation is needed. But it's an interesting topic.
I agree, it's interesting... But there is almost nothing really clear to me, starting from the ideology and ending with the code itself, there is a lot and many operators I don't even know
If someone could explain it all, even with elementary examples, how to use it in trading, it would be a good experiment for such inexperienced people as me