For those who are interested in machine learning, Sudipta posted an article on how to use SVR. SVR is a derivative of support vector machines and they are an alternative to neural networks that can be faster and more stable. I have read a number of finance papers recently that also mention a better predictive performance than NN.
One area where they can be great is for optimisation. Say you run a series of backtests with the same strategy and different parameters, SVR can learn how the strategies with these parameter sets performed and make predictions on sets that haven't been explored yet. That approach can save a lot of time finding the best configurations of a particular strategy.
But there's a lot more uses for this as well, for example detection of strategy performance degradation over time. Anyway, it's fun to play with those sorts of technology. For more background on SVM you can check out Andrew Ng's machine learning course on coursera.
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