Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment (2308.00016v1)

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 LLMs. 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.

• The paper presents a novel human-AI interaction framework that converts natural language inputs into formulaic alphas, streamlining quantitative investment research.
• It leverages high-performance computing and genetic programming to refine and backtest alpha strategies, significantly improving out-of-sample performance.
• The system provides detailed natural language explanations for generated alphas, enhancing transparency and insight into complex financial patterns.

The paper "Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment" introduces a novel approach to alpha mining by leveraging human-AI interaction through a specialized framework utilizing LLMs. It outlines the development of Alpha-GPT, a system designed to enhance the traditional paradigms of alpha mining, which typically involve manual factor synthesis and algorithmic factor mining.

Key Components of Alpha-GPT

1. Alpha-GPT Framework: The proposed framework integrates human-AI interaction where the Alpha-GPT system serves as a heuristic interface. This allows quantitative researchers to input trading ideas in natural language, which the system interprets and translates into formulaic alphas.
2. System Architecture:
   • AlphaBot Layer: This is the intermediary between the user and the algorithmic components. It includes a knowledge compiler to convert user ideas into domain-specific prompts, an LLM for generating responses, and a thought decompiler for transforming these responses into actionable configurations.
   • Algorithmic Alpha Mining Layer: This layer refines the generated seed alphas through search enhancement, evaluation, and backtesting. It utilizes genetic programming to explore and optimize the search space for higher-performing alphas.
   • Computation Acceleration: High-performance computing techniques are employed for processing large financial datasets efficiently, utilizing strategies like vectorization, streaming algorithms, and GPU acceleration.

Experimental Results

• Idea Consistency: Alpha-GPT successfully generates alpha expressions that align with the user's input trading ideas, as demonstrated through various financial patterns such as golden cross and Bollinger bands.
• Search Enhancement: The framework significantly improves out-of-sample Information Coefficient (IC) of alphas post-search enhancement, as evidenced by a substantial increase in IC values across various trading mechanics.
• Human-AI Interaction: The iterative interaction between user inputs and AI recommendations resulted in marked improvements in backtest performance, indicating the system’s adaptability to evolving user instructions.
• Alpha Explanation: The system autonomously provides comprehensive natural language explanations for the generated alphas, facilitating easier understanding and interpretation by users.

Contributions

The paper makes several notable contributions to the field of quantitative finance and AI:

• Human-AI Integration: By creating an interactive platform where LLMs mediate between quantitative researchers and computational frameworks, the paper introduces a novel paradigm that emphasizes collaboration.
• Prompt Engineering and LLM Utilization: The development of sophisticated techniques to enhance LLM outputs for domain-specific tasks is highlighted, showcasing significant progress in making AI systems contextually aware and responsive.
• Workflow Simplification: Alpha-GPT streamlines the traditionally complex alpha mining process, allowing users to focus more on high-level strategies rather than algorithmic minutiae.

In conclusion, Alpha-GPT represents a significant step forward in leveraging AI for financial applications, showcasing how interactive systems can transform traditional workflows into more efficient and effective processes. The paper underscores the potential of LLMs in enhancing the interpretability and usability of complex financial algorithms.