Machine learning in trading: theory, models, practice and algo-trading - page 59
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In other words "Look at the first google link that leads to my site" :)
No one forbids you to look at other links. I did not put a link to my site at the top of google, did I? So all complaints about the fact that google indexes the site is not as you would like to, do not address to me, and to a support team of a search engine.
I found, you have a committee of two models, this is not how I understood and wrote above.
I rarely had models rise above 40-50% generalization, but after I thought about what to do with the data. What is the essence of the model obtained after the classification. On the same data I now get models no lower than 70% on average 80-90% and in the future, on unknown data errors of about 1-2 out of 10-12. It is quite enough to earn.)
Yeah, I'm trying to classify strictly buy/sell too. But how did you get the original 6 inputs, did you just take them from some known strategy? Adequate entries is one of the most important things. On the contrary, I have thousands of entries (prices and indicators over a hundred bars) and I need to sift them out leaving a couple dozen, because any model overtrains on so many inputs.
My strategy is simple. This is Thomas Demark's sequent, which gives buy and sell signals. Signals that are more profitable than 100 pips are marked with one, the rest are all zeros and at the time of the signal I save the values of the indicators set below and get a model of about 90% generalization. That's all...
You can also take the crossing of the bars as a basis for the system. I also think it should be good. So that's how it is. The main thing is to prepare the data correctly...
The last two are zetoscore models and kelly coefficient, so nothing extravagant....
My strategy is simple. This is Thomas Demark's sequent, which gives buy and sell signals. Signals that are more profitable than 100 pips are marked with one, the rest are all zeros and at the time of the signal I save the values of the indicators set below and get a model of about 90% generalization. That's all...
I.e. classification by future profitability (1 - not less than 100 points, 0 - less than 100 points), but not by signal direction? And how do you determine the direction, by the Demark's Sequence?
I.e. by the profitability classification (1 - at least 100 pips, 0 - less than 100 pips), but not by signal direction? And how do you determine the direction, by the Demark's Sequence?
The system itself gives a buy signal or sell signal, this is the direction, and the classifier already says if the signal is buying and NS says yes this is the right signal, then buy, if it says no this is not the right signal, then sell. It is the same with selling.... True or false sale, hence the conclusions ...
The system itself gives a buy signal or sell signal, this is the direction, and the classifier already says if the signal is buying and NS says yes this is the right signal, then buy, if it says no this is not the right signal, then sell. It is the same with selling.... True sale or false sale, hence the conclusions...
And it is possible to bring any input data to the output data and the system will work for some time, so he who is looking will always find :-)
And so, indeed, a small prank with the data and we have a level of generalization grows to acceptable numbers in 90%.....
And it is possible to bring any input data to the output data and the system will work for some time, so he who seeks will always find :-)
So, really, a little trick with data and we have a level of generalization grows to acceptable numbers in 90%.....