An evolutionary trend reversion model for stock trading rule discovery

Quantitative investment (QI) is certainly a hot topic in big data analysis. For knowledge discovery in huge, complex and nonlinear stock market data, the eXtended Classifier Systems (XCS) is quite suitable because of the excellent learning and explicit expression abilities derived from its intrinsic techniques that include classification rule mining, evolutionary learning and reinforcement learning. This paper presents an Evolutionary Trend Reversion Model (eTrendRev), which is based on the proposed XCS with learn mode (XCSL) and trend-reversion strategy. The eTrendRev is highlighted in three aspects: (1) the explicit rules generated by XCSL are more understandable than black-box models, such as neural networks, thus can provide justifiable knowledge to guide trading; (2) the original pure explore mode of XCS is substituted by the proposed learn mode, which is shown in this study to perform better and is more stable; (3) a variety of trend-reversion strategies are integrated and made dynamic through evolutionary learning. For model evaluation, experiments were carried out on the historical data of the Shanghai Composite Index and the NASDAQ Composite Index, and back-testing results indicate that eTrendRev can produce higher return with lower risk and recognize significant market turning points in a timely fashion. This study also confirms the profitability of using sole trend-reversion indicators in machine learning-based QI model.