AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay

• URL: http://arxiv.org/abs/2502.16789v1
• Date: Mon, 24 Feb 2025 02:56:46 GMT
• Title: AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay
• Authors: Ziyi Tang, Zechuan Chen, Jiarui Yang, Jiayao Mai, Yongsen Zheng, Keze Wang, Jinrui Chen, Liang Lin,
• Abstract summary: We propose AlphaAgent, an autonomous framework that integrates Large Language Models with ad hoc regularizations for mining decay-resistant alpha factors.<n>AlphaAgent consistently delivers significant alpha in Chinese CSI 500 and US S&P 500 markets over the past four years.<n> Notably, AlphaAgent showcases remarkable resistance to alpha decay, elevating the potential for yielding powerful factors.
• Score: 43.50447460231601
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Alpha mining, a critical component in quantitative investment, focuses on discovering predictive signals for future asset returns in increasingly complex financial markets. However, the pervasive issue of alpha decay, where factors lose their predictive power over time, poses a significant challenge for alpha mining. Traditional methods like genetic programming face rapid alpha decay from overfitting and complexity, while approaches driven by Large Language Models (LLMs), despite their promise, often rely too heavily on existing knowledge, creating homogeneous factors that worsen crowding and accelerate decay. To address this challenge, we propose AlphaAgent, an autonomous framework that effectively integrates LLM agents with ad hoc regularizations for mining decay-resistant alpha factors. AlphaAgent employs three key mechanisms: (i) originality enforcement through a similarity measure based on abstract syntax trees (ASTs) against existing alphas, (ii) hypothesis-factor alignment via LLM-evaluated semantic consistency between market hypotheses and generated factors, and (iii) complexity control via AST-based structural constraints, preventing over-engineered constructions that are prone to overfitting. These mechanisms collectively guide the alpha generation process to balance originality, financial rationale, and adaptability to evolving market conditions, mitigating the risk of alpha decay. Extensive evaluations show that AlphaAgent outperforms traditional and LLM-based methods in mitigating alpha decay across bull and bear markets, consistently delivering significant alpha in Chinese CSI 500 and US S&P 500 markets over the past four years. Notably, AlphaAgent showcases remarkable resistance to alpha decay, elevating the potential for yielding powerful factors.

Related papers

• Agentic Reinforced Policy Optimization [66.96989268893932]
  Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn reasoning tasks.<n>Current RL algorithms inadequately balance the models' intrinsic long-horizon reasoning capabilities and their proficiency in multi-turn tool interactions.<n>We propose Agentic Reinforced Policy Optimization (ARPO), a novel agentic RL algorithm tailored for training multi-turn LLM-based agents.
  arXiv  Detail & Related papers  (2025-07-26T07:53:11Z)
• Safety Devolution in AI Agents [56.482973617087254]
  This study investigates how expanding retrieval access affects model reliability, bias propagation, and harmful content generation.<n>Retrieval-augmented agents built on aligned LLMs often behave more unsafely than uncensored models without retrieval.<n>These findings underscore the need for robust mitigation strategies to ensure fairness and reliability in retrieval-augmented and increasingly autonomous AI systems.
  arXiv  Detail & Related papers  (2025-05-20T11:21:40Z)
• Navigating the Alpha Jungle: An LLM-Powered MCTS Framework for Formulaic Factor Mining [8.53606484300001]
  This paper introduces a novel framework that integrates Large Language Models (LLMs) with Monte Carlo Tree Search (MCTS)<n>A key innovation is the guidance of MCTS exploration by rich, quantitative feedback from financial backtesting of each candidate factor.<n> Experimental results on real-world stock market data demonstrate that our LLM-based framework outperforms existing methods by mining alphas with superior predictive accuracy and trading performance.
  arXiv  Detail & Related papers  (2025-05-16T11:14:17Z)
• Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios. Our research identifies emerging behaviours of strategic AI agents, which tend to adopt more "pessimistic" stances than pure game-theoretic agents.
  arXiv  Detail & Related papers  (2025-04-11T15:41:21Z)
• Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception [58.62352010928591]
  Large language models (LLMs) exhibit impressive performance across diverse tasks but often struggle to accurately gauge their knowledge boundaries. This paper explores leveraging LLMs' internal states to enhance their perception of knowledge boundaries from efficiency and risk perspectives.
  arXiv  Detail & Related papers  (2025-02-17T11:11:09Z)
• Adversarial Reasoning at Jailbreaking Time [49.70772424278124]
  We develop an adversarial reasoning approach to automatic jailbreaking via test-time computation. Our approach introduces a new paradigm in understanding LLM vulnerabilities, laying the foundation for the development of more robust and trustworthy AI systems.
  arXiv  Detail & Related papers  (2025-02-03T18:59:01Z)
• QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors with Variance-bounded REINFORCE [5.560011325936085]
  The goal of alpha factor mining is to discover indicative signals of investment opportunities from the historical financial market data of assets. Recently, a promising framework is proposed for generating formulaic alpha factors using deep reinforcement learning.
  arXiv  Detail & Related papers  (2024-09-08T15:57:58Z)
• AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors [14.80394452270726]
  This paper proposes a two-stage alpha generating framework AlphaForge, for alpha factor mining and factor combination.<n> Experiments conducted on real-world datasets demonstrate that our proposed model outperforms contemporary benchmarks in formulaic alpha factor mining.
  arXiv  Detail & Related papers  (2024-06-26T14:34:37Z)
• Agent Alignment in Evolving Social Norms [65.45423591744434]
  We propose an evolutionary framework for agent evolution and alignment, named EvolutionaryAgent. In an environment where social norms continuously evolve, agents better adapted to the current social norms will have a higher probability of survival and proliferation. We show that EvolutionaryAgent can align progressively better with the evolving social norms while maintaining its proficiency in general tasks.
  arXiv  Detail & Related papers  (2024-01-09T15:44:44Z)
• Synergistic Formulaic Alpha Generation for Quantitative Trading based on Reinforcement Learning [1.3194391758295114]
  This paper proposes a method to enhance existing alpha factor mining approaches by expanding a search space. We employ information coefficient (IC) and rank information coefficient (Rank IC) as performance evaluation metrics for the model.
  arXiv  Detail & Related papers  (2024-01-05T08:49:13Z)
• How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation [46.42384207122049]
  We design SimulateBench to evaluate the believability of large language models (LLMs) when simulating human behaviors. Based on SimulateBench, we evaluate the performances of 10 widely used LLMs when simulating characters.
  arXiv  Detail & Related papers  (2023-12-28T16:51:11Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning [20.589583396095225]
  We propose a new alpha-mining framework that prioritizes mining a synergistic set of alphas. We show that our framework is able to achieve higher returns compared to previous approaches.
  arXiv  Detail & Related papers  (2023-05-25T13:41:07Z)
• AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment [16.27557073668891]
  We introduce a new class of alphas to model scalar, vector, and matrix features. The new alphas predict returns with high accuracy and can be mined into a weakly correlated set. We propose a novel alpha mining framework based on AutoML, called AlphaEvolve, to generate the new alphas.
  arXiv  Detail & Related papers  (2021-03-30T09:28:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.