AFAIK, this will not give you much, at least it results in no better models than taking a system, making it adaptive in a more traditional way, and doing some kind of walkforward optimiation at first.Quote from prophet:
Iâve done some simple backpropagation NNs, both with the Matlab toolkit and with my own code,
My current field of interest is in using Time Recurrent NNs, sometimes generalized as Back Progragation Through Time NNs. I see a great deal of promise in this kind of network, but my overall opinion of networks for _the_system_themselves_ is very low.
I have a great idea where I think a NN would shine though...
I am surprised this would work. SVD I guess is OK for data preparation if you map the to the significant Eigenvector/Eigenvalues to act as a filter, but IMO, the data you are throwing away is just as important....along with some exotic home-brew networks based on analyzing the singular value decomposition (SVD) products.
JaAny kind of adaptive model for forecasting of prices is difficult, even when the model is fit across thousands of stocks and thousands of days.
Only the toy ones rely on price alone. AFAIK, no one has ever written a serious package that is turnkey with trading as it's target from the beginning. My guess is that the ones that have gone all the way are proprietary implementations.I never base my network models on price alone. I usually rely on system performance statistics or inter-stock money flows. I keep the networks very simple, constraining or structuring them as to specify a hypothesis. The network then figures out the details [/B]
nitro