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

Paper: arXiv 2402.03755

Abstract

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.

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper focuses on a framework for self-improving LLM agents and includes empirical results on signal mining and forecasting accuracy, indicating a strong implementation focus with backtesting-ready components. While it references theoretical analysis with RL concepts like Markov Decision Processes, the mathematical exposition in the provided excerpt is not dense with advanced formulas or derivations.

Research Flowchart

  flowchart TD
  A["Research Goal: Develop Self-Improving LLM Agent for Trading"] --> B["Two-Layer Loop Framework"]
  
  B --> C{"Inner Loop"}
  C --> D["Query Agent<br>Knowledge Base"]
  D --> E["Refine Responses<br>via Prompting"]
  E --> F["Generate<br>Trading Signals"]
  
  B --> G{"Outer Loop"}
  G --> H["Backtest Signals<br>on Market Data"]
  H --> I["Update Knowledge Base<br>with New Insights"]
  I --> C
  
  F --> J["Key Findings"]
  H --> J
  
  J --> K["Provable Efficiency<br>in Optimal Behavior"]
  J --> L["Viable Financial<br>Signals Uncovered"]
  J --> M["Enhanced Financial<br>Forecast Accuracy"]

Cited References (36)

1. Reason for Future , Act for Now : A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency (2023) — link
2. What and How does In - Context Learning Learn ? Bayesian Model Averaging , Parameterization , and Generalization (2023) — link
3. An Explanation of In -context Learning as Implicit Bayesian Inference (2021) — link
4. Siren’s Song in the AI Ocean : A Survey on Hallucination in Large Language Models (2023) — link
5. Chain-of- Thought Prompting Elicits Reasoning in Large Language Models (2022) — link
6. A Survey on In -context Learning (2023) — link
7. ( More ) efficient reinforcement learning via posterior sampling (2013)
8. Bayesian optimal control of smoothly parameterized systems (2015)

0 of 36 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.