Alphas are pivotal in providing signals for quantitative trading. The industry highly values the discovery of formulaic alphas for their interpretability and ease of analysis, compared with the expressive yet overfitting-prone black-box alphas. In this work, we focus on discovering formulaic alphas. Prior studies on automatically generating a collection of formulaic alphas were mostly based on genetic programming (GP), which is known to suffer from the problems of being sensitive to the initial population, converting to local optima, and slow computation speed. Recent efforts employing deep reinforcement learning (DRL) for alpha discovery have not fully addressed key practical considerations such as alpha correlations and validity, which are crucial for their effectiveness. In this work, we propose a novel framework for alpha discovery using DRL by formulating the alpha discovery process as program construction. Our agent, $\text{Alpha}^2$, assembles an alpha program optimized for an evaluation metric. A search algorithm guided by DRL navigates through the search space based on value estimates for potential alpha outcomes. The evaluation metric encourages both the performance and the diversity of alphas for a better final trading strategy. Our formulation of searching alphas also brings the advantage of pre-calculation dimensional analysis, ensuring the logical soundness of alphas, and pruning the vast search space to a large extent. Empirical experiments on real-world stock markets demonstrates $\text{Alpha}^2$'s capability to identify a diverse set of logical and effective alphas, which significantly improves the performance of the final trading strategy. The code of our method is available at https://github.com/x35f/alpha2.

翻译：阿尔法因子在量化交易中为信号提供至关重要。相较于表达能力虽强但易过拟合的黑箱阿尔法因子，业界高度重视公式化阿尔法因子的发现，因其具有可解释性强且易于分析的优势。本文聚焦于公式化阿尔法因子的发现。先前关于自动生成公式化阿尔法因子集合的研究大多基于遗传编程（GP），而GP方法普遍存在对初始种群敏感、易陷入局部最优以及计算速度缓慢等问题。近期利用深度强化学习（DRL）进行阿尔法因子发现的研究尚未充分解决诸如阿尔法因子相关性与有效性等关键实际考量，而这些因素对其实际效用至关重要。本文提出了一种基于DRL的阿尔法因子发现新框架，将阿尔法发现过程形式化为程序构建任务。我们的智能体Alpha²通过组装针对评估指标优化的阿尔法程序。一种由DRL引导的搜索算法基于对潜在阿尔法结果的价值估计在搜索空间中进行导航。该评估指标同时鼓励阿尔法因子的性能与多样性，以构建更优的最终交易策略。我们提出的阿尔法搜索框架还带来了预计算量纲分析的优势，确保了阿尔法因子的逻辑合理性，并在很大程度上对庞大搜索空间进行了剪枝。在真实股票市场上的实证实验表明，Alpha²能够识别出一系列多样化的逻辑有效阿尔法因子，显著提升了最终交易策略的表现。本方法代码公开于 https://github.com/x35f/alpha2。