Sinner
What software are you using?
tech, I write all the vectorisers, training wrappers and modellers in Python (numPy and sciPy usually can do the maths but if not I plug the script into R) myself and use SVM-light (a free, very robust Support Vector Machine implementation for most Operating Systems) as my learning and classifying mechanism.
http://svmlight.joachims.org/
This is all an 'adaptation' of stuff I learned in CompSci at uni (now years ago) to do stuff like ranking http links for usefulness (think Google PageRank, our prof was a goog PhD) and spam filtering.
Here is a 2004 example from the scientific literature using SVM in a very very (I can't stress how very) simple model to predict (not incl magnitude) weekly direction in the Nikkei futs. It's a really good, inspiring paper though, recommended reading.
http://www.sciencedirect.com/science/article/pii/S0305054804000681
I notice this guys most successful techniques use SVM too
http://themarketpredictor.com/blog/
Hope you don't mind the hijack there canoz :/
Thats the 'aggregated decision' then...?
You know it wouldn't be difficult for me to aggregate decisions of several similar systems. The Builder has a habit of constructing very similar algorithms (makes sense on the same data) if you run it over the same data with only slight variations in the indicators or settings...I wonder if it would make sense to use several of these systems and wait until they all generated the same signal before taking a position...then test this on OOS data and see what the results are like....just thinking out load...
CanOz
Check out that adaptivetrading blog link I posted earlier, go back through the posts, it gives very useful hints as to
* Adaptive techniques
* How to value individual bots in a swarm
* Different techniques to trade the aggregate decision (hint: waiting for everyone to agree is not it, think more 'democratic' or 'representative', or net long/net short)