AutoAlpha：一种挖掘量化投资中阿尔法因子的高效分层进化算法
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment
摘要：
多因子模型是定量投资中应用广泛的模型。多因子模型的成功在很大程度上取决于模型中使用的α因子的有效性。本文提出了一种新的进化算法AutoAlpha，自动生成有效的公式化阿尔法从海量的股票数据集。具体来说，首先，我们发现了一个固有的模式公式化的阿尔法，并提出了一个层次结构，快速定位有前途的部分空间搜索。然后，我们提出了一种新的质量多样性搜索的基础上的主成分分析（PCA-QD），以引导搜索远离良好的探索空间，以获得更理想的结果。其次，我们利用热启动方法和替换方法来防止早熟收敛问题。基于公式化的阿尔法，我们发现，我们提出了一个集成学习排名模型生成的投资组合。在中国股票市场上的回测以及与多个基准的比较进一步证明了AutoAlpha在挖掘公式化阿尔法值用于定量交易方面的有效性。
英文摘要：
The multi-factor model is a widely used model in quantitative investment. The success of a multi-factor model is largely determined by the effectiveness of the alpha factors used in the model. This paper proposes a new evolutionary algorithm called AutoAlpha to automatically generate effective formulaic alphas from massive stock datasets. Specifically, first we discover an inherent pattern of the formulaic alphas and propose a hierarchical structure to quickly locate the promising part of space for search. Then we propose a new Quality Diversity search based on the Principal Component Analysis (PCA-QD) to guide the search away from the well-explored space for more desirable results. Next, we utilize the warm start method and the replacement method to prevent the premature convergence problem. Based on the formulaic alphas we discover, we propose an ensemble learning-to-rank model for generating the portfolio. The backtests in the Chinese stock market and the comparisons with several baselines further demonstrate the effectiveness of AutoAlpha in mining formulaic alphas for quantitative trading.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
