Have been here for a long time, but just start to write something recently. Thanks people in Quantopian for creating such a good community.
Recently, I read the tutorial of applying machine learning method. Here is the link: https://www.quantopian.com/posts/machine-learning-on-quantopian-part-3-building-an-algorithm
Tomas includes many fundamental factor and I want to write something using technical data. Here is a simple and easy demo. It can be easily changed to other machine learning model, such as tree model in sklearn. Hope it will help someone.
From research environment, the factor learned by ridge regression has IC around 0.007 and from return analysis, it looks like it will achieve position returns. However, in algorithm environment, the performance is not good at all, no matter how I change the parameters and frequency of rebalance. It looks like slippage is one cause, but I don't understand why.
I have searched several post of machine learning methods in forum, but they don't perform well. I think it is a promising method, but currently I didn't find a good result.
