The variability and low signal-to-noise ratio in financial data, combined
with the necessity for interpretability, make the alpha factor mining workflow
a crucial component of quantitative investment. Transitioning from early manual
extraction to genetic programming, the most advanced approach in this domain
currently employs reinforcement learning to mine a set of combination factors
with fixed weights. However, the performance of resultant alpha factors
exhibits inconsistency, and the inflexibility of fixed factor weights proves
insufficient in adapting to the dynamic nature of financial markets. To address
this issue, this paper proposes a two-stage formulaic alpha generating
framework AlphaForge, for alpha factor mining and factor combination. This
framework employs a generative-predictive neural network to generate factors,
leveraging the robust spatial exploration capabilities inherent in deep
learning while concurrently preserving diversity. The combination model within
the framework incorporates the temporal performance of factors for selection
and dynamically adjusts the weights assigned to each component alpha factor.
Experiments conducted on real-world datasets demonstrate that our proposed
model outperforms contemporary benchmarks in formulaic alpha factor mining.
Furthermore, our model exhibits a notable enhancement in portfolio returns
within the realm of quantitative investment.

本研究提出了一种基于两阶段的公式化 α 因子生成框架 AlphaForge，用于 α 因子挖掘和因子组合。该框架利用生成性 - 预测性神经网络生成因子，并结合因子的时间性能进行选择和动态调整权重，实现了在量化投资领域中超越现有基准的效果。

AlphaForge：一个挖掘和动态组合公式化 Alpha 因子的框架

AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha  Factors

Mining of formulaic alpha factors refers to the process of discovering and
developing specific factors or indicators (referred to as alpha factors) for
quantitative trading in stock market. To efficiently discover alpha factors in
vast search space, reinforcement learning (RL) is commonly employed. This paper
proposes a method to enhance existing alpha factor mining approaches by
expanding a search space and utilizing pretrained formulaic alpha set as
initial seed values to generate synergistic formulaic alpha. We employ
information coefficient (IC) and rank information coefficient (Rank IC) as
performance evaluation metrics for the model. Using CSI300 market data, we
conducted real investment simulations and observed significant performance
improvement compared to existing techniques.

通过扩展搜索空间和利用预训练的公式化 Alpha 集作为初始种子值以生成协同公式化 Alpha，本文提出了一种增强现有 Alpha 因子挖掘方法的方法，并以强化学习作为高效发现股票市场量化交易中已知指标（称为 Alpha 因子）的过程。我们使用信息系数（IC）和排名信息系数（Rank IC）作为模型的性能评估指标。通过使用 CSI300 市场数据进行实际投资模拟，我们观察到与现有技术相比的显著性能改善。