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Not really, I have looked through that thread before a little bit.. a few good links but mostly just people throwing around ideas. I did find the cupsbook.pdf on that thread I believe which has some good examples of how to use a'trous wavelet technique (which is used on one of the inputs of my NN).
The NN part of my system is primarily based on the book I linked to earlier in this thread (NN control of nonlinear discrete time systems). It is a recurrent, closed-loop network.. meaning no offline training, all training is performed online in real time via convex optimization routines. Therefore it is inherently adaptive and not subject to many of the problems associated with supervised learning such as over-fitting, poor choice of sample data, etc..
Closed loop network is more difficult to implement and has its own set of challenges but in general they are explicit rather than implicit (implied from training data) so you can more easily control its mechanism of adaption and tune it the way you want.
I was thinking of toying with this again. I was wondering if you have a very basic NT framework for aforge you would share with me (just based on Close[0] is fine or whatever you have).
hi mike here the samples from https://www.codeproject.com/. For more info go in and search on this site "Neural Network". There are a lot of good examples.
for data input i use a simple loop and copy & paste from output window in a file, but you can also generate auto a csv script. but i am fool. here a dirt sample.
True, wavelets are not that useful for discretionary trading, it is very powerful for algorithmic trading due to its power of feature extraction which makes pattern recognition much easier.
I skimmed through the research paper you linked above, only …
hi mike here the samples from https://www.codeproject.com/. For more info go in and search on this site "Neural Network". There are a lot of good examples.
for data input i use a simple loop and copy & paste from output window in a file, but you can also generate auto a csv script. but i am fool. here a dirt sample.
if(CurrentBar < 48) return;
double[] _close = new double[48];
for(int i = 0, j = _close.Length; i < ma1.Length; i++) { _close[i] = Close[j];
True, wavelets are not that useful for discretionary trading, it is very powerful for algorithmic trading due to its power of feature extraction which makes pattern recognition much easier.
I skimmed through the research paper you linked above, only …
Ok, I was hoping someone had actually integrated it with Ninja already (no copy/paste). I was looking to feed it a series of variables and values and get back a prediction for a certain series.
SVM seems primarily for classification systems but it can also be used for regression. My initial impressions after getting a C# console version up and running is that real time training is not feasible. The training will have to be done offline in the background. There are sufficient methods in the C# implementation that real time predictions will be an easy implementation in Ninja from a model that is generated offline.
I have run the test console version and I feel that this is something that can be done in real time. The only problem is that what is provided at the above link converts your input into an SVD representation. There is no code showing how to take this SVD internalization to produce output. This should not be a big problem but I do not have the math skills to determine the code to reverse the SVD back into the real world. Maybe there is someone out there who can do this. Should be similar to PCA.