Originally Posted by got_fx

I just thought that I should share my 5cc worth...
I've played a bit with NN's, SVM's, tree ensambles and other bizarre algs (see Flexible Neural Trees Ensemble for Stock Index (ResearchIndex)). (it works for standard problems but I did not develop it to the fullest realizing that I am spending too much time on the algorithm itself).
The key is not in the learning algorithm but rather than that in the predictors.
It took me quite a bit of wasted time to realize this. We cannot expect to find dependencies in data that has none.
One cannot predict the next bar. It's impossible and naive. We can try to capture patterns though. There was a post on Better's board stating the his approach had captured the market cycles. This was from a guy who claims to have experimented with NNs a lot (tried to find the post but couldn't, maybe it was in Russian, which I understand a bit) and I totally agree with him.
I'll give you a hint - think about MACD and try to predict the position of the next extremum (min or max) - an idea that I never really exploited meaningfully because of the little hurdles along the way (proper filtering).

I was fascinated in the past by modern machine learning algs but the truth is, once again, what we are actually trying to do. It's coming with the right approach.

BTW, libsvm is perhaps the best developed one out there but it did not give me the best results. Check mysvm and svmlight.
As for NNs, I mostly trust rbf nets as they tend to catch pockets of data very well. Those who know what a rbf is will understand. Properly configuring those (some sort of clustering first...) and training them is a pain.

I had abandoned the addiction called forex but now I am glad I found this thread.
Maybe we can make a team and really thoroughly work out the problem.
I've always believed that the solution is somewhere in the open but alas, could not find it by myself.