Mining of formulaic alpha factors refers to the process of discovering and
developing specific factors or indicators (referred to as alpha factors) for
quantitative trading in stock market. To efficiently discover alpha factors in
vast search space, reinforcement learning (RL) is commonly employed. This paper
proposes a method to enhance existing alpha factor mining approaches by
expanding a search space and utilizing pretrained formulaic alpha set as
initial seed values to generate synergistic formulaic alpha. We employ
information coefficient (IC) and rank information coefficient (Rank IC) as
performance evaluation metrics for the model. Using CSI300 market data, we
conducted real investment simulations and observed significant performance
improvement compared to existing techniques.

通过扩展搜索空间和利用预训练的公式化 Alpha 集作为初始种子值以生成协同公式化 Alpha，本文提出了一种增强现有 Alpha 因子挖掘方法的方法，并以强化学习作为高效发现股票市场量化交易中已知指标（称为 Alpha 因子）的过程。我们使用信息系数（IC）和排名信息系数（Rank IC）作为模型的性能评估指标。通过使用 CSI300 市场数据进行实际投资模拟，我们观察到与现有技术相比的显著性能改善。