Recurrent reinforcement learning (RRL) was a technique to tune financial trading systems for the purpose of utility maximization. The RRL technique is a stochastic gradient ascent algorithm which continuously optimizes a utility measure by using new market information. Although, in most discussions on RRL, the market information usually comprises a series of lagged price returns. The basic RRL trading system is designed to trade a single-asset with a two-position action (long/short), which is produced by using linear combinations of returns and a tanh function.

Profitability and stability are two particularly important factors in a financial trading system. In this study, we use the Sharpe ratio to measure the profitability and we calculate the Sharpe ratio using daily returns. It should be noted that the trading performance of RRL-type trading systems relates directly to the initialization of signal parameters. Therefore, stability refers to the consistency of the Sharpe ratios recorded from independent restarts of the trading system.

nclick="viewGallery_GalleryManager.GoToPrev();"> nclick="viewGallery_GalleryManager.GoToPrev();">

ta itemprop="image" content="https://c.mql5.com/31/279/self-learning-screen-3894.png" />

nclick="viewGallery_GalleryManager.GoTonext();"> nclick="viewGallery_GalleryManager.GoTonext();">