基于近端强化学习的股价预测方法

股价预测一直是金融时间序列研究的热点和难点,采用一种合理有效的股价预测方法对于投资者获取高额收益回报及规避交易风险具有重要的指导意义.通过结合近端策略优化(proximal policy optimization, PPO)和强化学习(reinforcement learning, RL),将股价预测视为一个时间序列预测问题,提出一种近端强化学习的股价预测方法 (PPORL).此外,在预测方法的基础上引入股票的相对强弱性能和股票均线指标,提出一种能够自动捕捉潜在交易点的量化交易策略,期望在获取高额收益的同时降低交易过程中存在的风险.通过实验对比了长短期记忆网络(long short-term memory, LSTM)和循环神经网络(recurrent neural network, RNN)模型在上证指数(SZZS)、深证成指(SZCZ)和沪深300指数(HS300)上的预测性能和交易决策表现,并利用多种误差评估方法对预测结果进行定量分析,从而验证了PPORL在预测性能和交易决策等方面的有效性和鲁棒性.

Method of stock prices forecast based on proximal reinforcement learning

Stock prices forecast is a hot and challenging topic in financial time series research. It is of great significance for the investors in theirs stock trading, to maximize revenue and to avoid risks by adopting a reasonable and effective forecasting method. A stock prices forecast method based on proximal reinforcement learning which combines proximal policy optimization(PPO) and reinforcement learning(RL), namely PPORL, is proposed, and the forecasting process is regarded as a time series prediction problem. Furthermore, the relative strength index(RSI) and move average of five days(MA5) are also introduced working as a trading strategy, which can automatically capture potential trading points, and avoid trading risks. By comparing the prediction performance and trading decision performance with long short-term memory(LSTM) and recurrent neural network(RNN) models on the SSE composite index(SZZS), the SZSE component index(SZCZ) and the CSI300 index(HS300), and a variety of error evaluation methods are used for quantitative analysis of the prediction results, which shows the effectiveness and robustness of the PPORL in forecasting performance and trading decision.