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QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model

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arxiv 2402.03755 v1 pith:YFCAI2WW submitted 2024-02-06 cs.AI q-fin.CP

Saizhuo Wang , Hang Yuan , Lionel M. Ni , Jian Guo This is my paper · ORCID
classification cs.AIq-fin.CP
keywords agentbaseknowledgeloopquantagentagentsautonomouschallenge
verification ladder T0 review T1 audit T2 compute T3 formal
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Autonomous agents based on Large Language Models (LLMs) that devise plans and tackle real-world challenges have gained prominence.However, tailoring these agents for specialized domains like quantitative investment remains a formidable task. The core challenge involves efficiently building and integrating a domain-specific knowledge base for the agent's learning process. This paper introduces a principled framework to address this challenge, comprising a two-layer loop.In the inner loop, the agent refines its responses by drawing from its knowledge base, while in the outer loop, these responses are tested in real-world scenarios to automatically enhance the knowledge base with new insights.We demonstrate that our approach enables the agent to progressively approximate optimal behavior with provable efficiency.Furthermore, we instantiate this framework through an autonomous agent for mining trading signals named QuantAgent. Empirical results showcase QuantAgent's capability in uncovering viable financial signals and enhancing the accuracy of financial forecasts.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism

    q-fin.TR 2025-08 reject novelty 6.0 of 10

    An LLM trading system that selects agents through an internal contest scored by a zero-intelligence trader and LightGBM predictions reports 52.8% returns and Sharpe 3.12 on A-shares over six months.

  2. BizFinBench.v2: Towards Reliable LLMs in Finance via Real-User Data and Offline/Online Bilingual Evaluation

    cs.AI 2026-01 conditional novelty 5.0 of 10

    A new benchmark built on real user queries from Chinese and U.S. equity markets shows leading LLMs still far below financial experts (61.5% vs 84.8%).

  3. Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning

    cs.CL 2025-07 conditional novelty 5.0 of 10

    Agentar-Fin-R1, an 8B and 32B financial LLM family, reports top scores on FinEval, FinanceIQ, and a new Finova benchmark while keeping general reasoning near its Qwen3 base.

  4. WebCryptoAgent: Agentic Crypto Trading with Web Informatics

    cs.CV 2026-01 reject novelty 4.0 of 10

    A two-tier, memory-augmented LLM trading agent is described, but its experiments only compare LLM backbones with/without memory and do not support the claimed improvements.

  5. Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews

    cs.AI 2025-02 conditional novelty 4.0 of 10

    A CrewAI-based multi-agent system with human oversight built financial models and carried out model risk management checks on three public credit datasets, with results comparable to AutoML and Kaggle baselines.

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