论文标题
自动α：用于定量投资中采矿α因子的有效层次进化算法
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment
论文作者
论文摘要
多因素模型是定量投资中广泛使用的模型。多因素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。本文提出了一种称为Autoalpha的新的进化算法，以自动从大量库存数据集中产生有效的配方式alpha。具体而言，首先，我们发现了公式化alpha的固有模式，并提出了层次结构，以迅速找到搜索空间的有希望的部分。然后，我们根据主要组件分析（PCA-QD）提出了一个新的质量多样性搜索，以指导搜索从探索良好的空间中，以获得更理想的结果。接下来，我们利用温暖的开始方法和替换方法来防止过早收敛问题。基于我们发现的公式化Alpha，我们提出了一个集合学习模型来生成投资组合。中国股票市场的回测以及与几个基线的比较进一步证明了自动α在采矿公式化alpha在定量交易中的有效性。
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.
