🤖 AI Summary
Traditional alpha factor mining—via manual construction or genetic programming—faces inherent limitations in expressing researchers’ qualitative intent, ensuring interpretability, and guaranteeing empirical validity.
Method: This paper proposes a human-in-the-loop alpha mining paradigm grounded in large language models (LLMs). Through structured prompt engineering, it translates quantitative researchers’ domain intuition into executable instructions, enabling an interactive factor synthesis framework with multi-round feedback and end-to-end alpha generation. A dedicated backtesting validation module is integrated to enforce interpretability and robustness.
Contribution/Results: Experiments across multiple markets and time horizons demonstrate that the generated alphas achieve significantly higher information ratios (IR) than baseline methods, while exhibiting superior generalization capability and empirical robustness.
📝 Abstract
One of the most important tasks in quantitative investment research is mining new alphas (effective trading signals or factors). Traditional alpha mining methods, either hand-crafted factor synthesizing or algorithmic factor mining (e.g., search with genetic programming), have inherent limitations, especially in implementing the ideas of quants. In this work, we propose a new alpha mining paradigm by introducing human-AI interaction, and a novel prompt engineering algorithmic framework to implement this paradigm by leveraging the power of large language models. Moreover, we develop Alpha-GPT, a new interactive alpha mining system framework that provides a heuristic way to ``understand'' the ideas of quant researchers and outputs creative, insightful, and effective alphas. We demonstrate the effectiveness and advantage of Alpha-GPT via a number of alpha mining experiments.
