Machine learning in trading: theory, models, practice and algo-trading - page 110
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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 less predictors you need for training, the less time and computing power needed to build models.
Hello Yuri !
There are questions )) about the sequential search ...
Let's say we have 10 predictors
1, 2, 3, 4, 5, 6, 7, 8, 9, 10
the green group of predictors is that group which has shown the best generalizing ability exactly to this group will be added other predictors N+1
the red group, it is the group which has shown itself a little bit worse, than the green group, and it(the red group) already will not take part in the tests, all tests are already focused on the green group
Question: what if after all the trials with other predictors one by one N+1 it turns out that in the end the red group has more generalizing ability, is it also quite real, or am I misunderstanding something ???? please explain
All is well except for a trifle: there is no comparison with other models.
Comparison.
All is well except for a trifle: there is no comparison with other models.
I offer my services in 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.
No need to go far, here is a file with forex market quotes, the average generalization ability when training with reshetov predictor from 70% to 80%. Nusssss...... Waiting for your result.
P.s. Rename the file to ksv
No need to go far, here is a file with forex market quotes, the average generalization ability when training with Reshetov's predictor from 70% to 80%. Nusssssss...... Waiting for your result.
P.s. Rename the file to ksv
Couldn't it be packaged?
Can't we see the result? Actually generalizability in learning is about nothing.
Supported... Just take quotes as data and not some irises.
Couldn't it be packaged?
Can't we see the result? Actually, generalizability in learning is about nothing.
I couldn't agree more.
No need to go far, here is a file with forex market quotes, the average generalization ability when training with reshetov predictor from 70% to 80%. Nusssssss...... Waiting for your result.
P.s. Rename the file to csv
what is that? 71 observations?
how did you even check the total capacity?
What's that? 71 observations?
how did you even check the general ability?
And you keep trying to curb the market on minutes for 5 years?????? These 71 observations, two weeks of trading on 5 minutes if anything...... And only buying. So go for it..... Or are you deflated?
If to speak without the European politeness, you write full nonsense...
Give me two normal files, at least 500 observations each, as well as the results of the program.
Average generalization ability of Reshetov's predictor is from 70% to 80%.
I already told you, this metric is useless.
The data is randomly divided into 2 roughly equal parts, then the model is trained on the first part only, and tested on both at once. A generalizability of ~75% means that the model at the end correctly predicts 75% of all the examples in the file.
There are several ways the model can reach 75%:
1) The model has trained to an accuracy of 100% on the data that was used for training, and failed at all on the new data from the second part of the file, where it got 50% (the same as flipping a coin). The average would be exactly 75%. This is a very bad scenario, and it will be bad in the trade.
2) The model is trained to 75% accuracy on the training data and shows the same 75% on the test data, that is 75% on average again. In this situation, this is the best scenario, there is a chance to earn something.
3) Any intermediate option between these two.
Your version is probably closer to the first one. You have to rely a lot on luck in order to trade with such a result, I assume that you have not lost your deposit only thanks to the indicator that serves as your main signal (sequent, or whatever). I suspect that an Expert Advisor based on this one indicator will give you the same result as the indicator + jPrediction.