Paper: arXiv 2502.01992

Abstract

In response to Task II of the FinRL Challenge at ACM ICAIF 2024, this study proposes a novel prompt framework for fine-tuning large language models (LLM) with Reinforcement Learning from Market Feedback (RLMF). Our framework incorporates market-specific features and short-term price dynamics to generate more precise trading signals. Traditional LLMs, while competent in sentiment analysis, lack contextual alignment for financial market applications. To bridge this gap, we fine-tune the LLaMA-3.2-3B-Instruct model using a custom RLMF prompt design that integrates historical market data and reward-based feedback. Our evaluation shows that this RLMF-tuned framework outperforms baseline methods in signal consistency and achieving tighter trading outcomes; awarded as winner of Task II. You can find the code for this project on GitHub.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses standard deep learning and reinforcement learning concepts without advanced mathematical derivations, yet it presents a complete experimental pipeline with dataset splits, performance metrics, and a GitHub code link.

Research Flowchart

  flowchart TD
  A["Research Goal: Improve LLM-Engineered<br>Trading Signals Challenge"] --> B["Data Input: Historical<br>Market Data & Features"]
  B --> C["Methodology: RLMF Prompt Framework<br>RL from Market Feedback"]
  C --> D["Computational Process:<br>Fine-tune LLaMA-3.2-3B-Instruct"]
  D --> E["Output: Precise<br>Trading Signals"]
  E --> F["Findings: Winner of Task II<br>Superior Consistency & Outcomes"]