Machine learning in trading: theory, models, practice and algo-trading - page 109
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All the talk about the gradual fading of the model on the OOS and the usefulness of this information in trade looks unconvincing without talk about the preselection of predictors.
Ato.
If the model gives wrong signals on the OOS - it is an indication of improper training, not the fact of market changes.
I agree with this. How do you analyze the signals of two grids? It is not quite clear? How do they diverge or do they move in the same way?
I bring the signals to a common scheme like this:
SELL BUY Interpretation
-1 0 sell
0 0 fence
0 1 buy
-1 1 fence
In a well-trained model the signals rarely contradict each other. Equal number of signals on the training area is not required from them, and as a rule, they are different, and this is understandable, because the market may have a prolonged global trends. But I restrict the number of signals from one grid not to exceed 2 times the number of signals from the other one. It is an empirical ratio and may be different for someone else. For example, if the trend changes from ascending to descending, the number of signals for sell increases and signals for buy start to lie, a contradiction occurs and the number of deals declines - this is a sign that a new training is required.
I bring the signals to a common scheme like this:
SELL BUY Interpretation
-1 0 sell
0 0 fence
0 1 buy
-1 1 fence
In a well-trained model the signals rarely contradict each other. Equal number of signals on the training area is not required from them, and as a rule, they are different, and this is understandable, because the market may have a prolonged global trends. But I restrict the number of signals from one grid not to exceed 2 times the number of signals from the other one. It is an empirical ratio and may be different for someone else. For example, if the trend changes from ascending to descending, the number of signals for sell increases and signals for buy start to lie, a contradiction occurs and the number of deals declines - this is a sign that a new training is required.
Yes, but not on the neuron configuration.
Andrew, apparently, is alluding to some such powerful inputs, that any model by default on them will give a good and not over-trained result.
Or maybe he is referring to something else. But I would like to get a more detailed answer.
New jPrediction 9.00 Release is out
Quote from the user manual:
"Differences between jPrediction and other machine learning software
The main difference of jPrediction is the absence of any user-defined settings, which allows you to get rid of the human factor in the form of human errors, both in the process of setting up and choosing algorithms and in the process of choosing the architecture of neural networks. The whole process of machine learning in jPrediction is fully automated and does not require any special knowledge from users or their intervention.
Functions performed by jPrediction in automatic mode
Since, from the set of models, each of which differs from any other combination of predictors, only the one that has the maximum generalizing ability is selected, the reduction (selection) of the most significant predictors is thus automatically performed."
It should be said that starting from version 8, jPrediction has no limitations on the maximum number of predictors in the training sample. Before version 8, the number of predictors in the training sample was limited to ten pieces.
Before version 8, jPrediction was single-model. That is, a sample was taken and only one single model was trained and tested on it.
Since version 8, jPrediction has become multi-model, i.e. it trains and tests many different models, on different parts of the sample, and each part contains different combinations of predictors. One of these models would give the maximum generalizability on the test part of the sample.
The problem was that if different combinations of predictors were taken, then a so-called combinatorial (from the term combinatorics) "explosion" would be obtained when the combinations are fully searched, i.e. with each additional predictor it is necessary to train and test twice as many models as without it. It is quite obvious, when the number of predictors in the sample is measured in tens and even hundreds, it becomes problematic to wait for finishing training and testing all combinations of models in reasonable time.
The problem of combinatorial "explosion" in jPrediction was solved not by going through all possible combinations, but by sequential search. The essence of the method is as follows:
Suppose we found some combination containing N predictors with maximum generalizability by trying all possible combinations of N and fewer predictors. We need to add N+1 predictor to it. For this purpose we add one by one predictors from the sample that were not included in the combination to the already found combination and measure the generalization ability for them. If in the course of such a search we found a combination with N+1 predictors whose generalizing ability exceeds the best combination of N predictors, then it will be possible to find a combination with N+2 predictors in the same way. And if they haven't found it, then it is obvious that there is no sense to search further and the algorithm of searching combinations stops at the best combination of N predictors. As a result, the algorithm of searching for combinations of predictors for the model stops much earlier, in comparison with a complete enumeration of all possible combinations. Additional saving of computational resources occurs due to the fact that the search begins with a small number of predictors in the direction of increasing this number. And the fewer predictors are needed for training, the less time and computational power it takes to build models.
That's the kind of pie.
If you're interested, the attached ZIP archive contains the manual for jPrediction 9 users in Russian in PDF format:
The new jPrediction 9.00 Release is out
Everything is fine except for one small thing: there is no comparison with other models.
I offer my services for comparison
1. You prepare an input Excel file containing predictors and target variable
2. You do the calculations
3. You send the input file to me.
4. I do the calculations using randomforest, ada, SVM
We compare.