AlphaQCM: Alpha Discovery with Distributional Reinforcement Learning

Zhoufan Zhu, Ke Zhu

Keywords: Distributional Reinforcement Learning, Computational Finance, Formulaic Alpha, Quantiled Conditional Moments, Stock Trend Forecasting

Abstract: Finding synergistic formulaic alphas is very important but challenging for researchers and practitioners in finance. In this paper, we reconsider the discovery of formulaic alphas from the viewpoint of sequential decision-making, and conceptualize the entire alpha-mining process as a non-stationary and reward-sparse Markov decision process. To overcome the challenges of non-stationarity and reward-sparsity, we propose the AlphaQCM method, a novel distributional reinforcement learning method designed to search for synergistic formulaic alphas efficiently. The AlphaQCM method first learns the Q function and quantiles via a Q network and a quantile network, respectively. Then, the AlphaQCM method applies the quantiled conditional moment method to learn unbiased variance from the potentially biased quantiles. Guided by the learned Q function and variance, the AlphaQCM method navigates the non-stationarity and reward-sparsity to explore the vast search space of formulaic alphas with high efficacy. Empirical applications to real-world datasets demonstrate that our AlphaQCM method significantly outperforms its competitors, particularly when dealing with large datasets comprising numerous stocks.

Primary Area: reinforcement learning

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Submission Number: 1571