  Financial trading aims to build profitable strategies to make wise investment
decisions in the financial market. It has attracted interests in the machine
learning community for a long time. This paper proposes to trade financial
assets automatically using feature preprocessing skills and Recurrent
Reinforcement Learning (RRL) algorithm. The strategy starts from technical
indicators extracted from assets' market information. Then these technical
indicators are preprocessed by Principal Component Analysis (PCA) and Discrete
Wavelet Transform (DWT) and eventually inputted to the RRL algorithm to do the
trading. The extensive empirical evidence shows that the proposed strategy is
not only effective and robust in its performance, but also can mitigate the
drawbacks underlying the initial trading using RRL.
