What's a good machine learning algorithm for low frequency trading?

Question

I'm trying to train an algorithm to copy some of the top traders on various forex social trading sites. The problem is that the traders only trade around say 10 times per month so even if I only look at minute resolution numbers that's .02% of the time [ 10/(60*24*30)*100 ].

I've tried using random forest and it gives an error rate of around 2% which is unacceptable and from what I've read most machine learning algorithms have similar errors rates.

Does anyone know of a better approach?

Answer 1

Random forests, GBM or even the newer and fancier xgboost are not the best candidates for binary classification (predicting ups and down) of stocks predictions or forex trading or at least not as the main algorithm. The reason is that, for this particular problem, they require a huge amount of trees (and tree depth in case of GBM or xgboost) to obtain reasonable accuracy (Breiman suggested using at least 5000 trees and to "not be stingy" and in fact his main ML paper on RF he used 50,000 trees per run).

However, some quants use random forests as feature selectors while others use it to generate new features. It all depends on the characteristics of the data.

I would suggest you read this question and answers on quant.stackexchange where people discuss what methods are the best and when to use them, among them ISOMAP, Laplacian eigenmaps, ANNs, swarm optimization.

Check out the machine-learning tag on the same site, there you might find information related to your particular dataset.