Financial Market Trading System With a Hierarchical Coevolutionary Fuzzy Predictive Model
提出一种使用分层协同进化模糊系统（HiCEFS）预测金融时间序列的交易系统，结合价格百分比振荡器策略，在真实数据上优于买入持有及其他模型。
Financial market prediction and trading presents a challenging task that attracts great interest from researchers and investors because success may result in substantial rewards. This paper describes the application of a hierarchical coevolutionary fuzzy system called HiCEFS for predicting financial time series. A novel financial trading system using HiCEFS as a predictive model and employing a prudent trading strategy based on the price percentage oscillator (PPO) is proposed. In order to construct an accurate predictive model, a form of generic membership function named irregular shaped membership function (ISMF) is employed and a hierarchical coevolutionary genetic algorithm (HCGA) is adopted to automatically derive the ISMFs for each input feature in HiCEFS. With the accurate prediction from HiCEFS and the prudent trading strategy, the proposed system outperforms the simple buy-and-hold strategy, the trading system without prediction and the trading system with other predictive models (EFuNN, DENFIS and RSPOP) on real-world financial data.