  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.
