Traditional genetic programming (GP) often struggles in stock alpha factor discovery due to its vast search space, overwhelming computational burden, and sporadic effective alphas. We find that GP performs better when focusing on promising regions rather than random searching. This paper proposes a new GP framework with carefully chosen initialization and structural constraints to enhance search performance and improve the interpretability of the alpha factors. This approach is motivated by and mimics the alpha searching practice and aims to boost the efficiency of such a process. Analysis of 2020-2024 Chinese stock market data shows that our method yields superior out-of-sample prediction results and higher portfolio returns than the benchmark.

翻译：传统遗传规划（GP）在股票阿尔法因子挖掘中常面临搜索空间巨大、计算负担过重以及有效因子稀疏等挑战。我们发现，当GP聚焦于有潜力的搜索区域而非随机搜索时，其表现更优。本文提出一种新的GP框架，通过精心设计的初始化和结构约束来提升搜索性能，并增强阿尔法因子的可解释性。该方法受阿尔因子搜索实践的启发并模拟该过程，旨在提升此类过程的效率。基于2020-2024年中国股市数据的分析表明，相较于基准方法，本方法能产生更优的样本外预测结果及更高的投资组合收益。