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

TL;DR

AlphaForge introduces a neural network-based framework for mining and dynamically combining formulaic alpha factors, significantly improving performance and adaptability in quantitative investment strategies.

Contribution

The paper presents a novel two-stage framework employing deep learning for generating and adaptively combining alpha factors, addressing limitations of fixed-weight methods.

Findings

Outperforms existing benchmarks in alpha factor mining

Enhances portfolio returns in real-world investments

Demonstrates robustness and adaptability in dynamic markets

Abstract

The complexity of financial data, characterized by its variability and low signal-to-noise ratio, necessitates advanced methods in quantitative investment that prioritize both performance and interpretability.Transitioning from early manual extraction to genetic programming, the most advanced approach in the alpha factor mining 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 and real money investment.