FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading

• URL: http://arxiv.org/abs/2502.11433v3
• Date: Wed, 19 Feb 2025 03:40:56 GMT
• Title: FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading
• Authors: Guojun Xiong, Zhiyang Deng, Keyi Wang, Yupeng Cao, Haohang Li, Yangyang Yu, Xueqing Peng, Mingquan Lin, Kaleb E Smith, Xiao-Yang Liu, Jimin Huang, Sophia Ananiadou, Qianqian Xie,
• Abstract summary: Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities. We propose textscFLAG-Trader, a unified architecture integrating linguistic processing (via LLMs) with gradient-driven reinforcement learning (RL) policy optimization.
• Score: 28.57263158928989
• License:
• Abstract: Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks. However, they often struggle with multi-step, goal-oriented scenarios in interactive financial markets, such as trading, where complex agentic approaches are required to improve decision-making. To address this, we propose \textsc{FLAG-Trader}, a unified architecture integrating linguistic processing (via LLMs) with gradient-driven reinforcement learning (RL) policy optimization, in which a partially fine-tuned LLM acts as the policy network, leveraging pre-trained knowledge while adapting to the financial domain through parameter-efficient fine-tuning. Through policy gradient optimization driven by trading rewards, our framework not only enhances LLM performance in trading but also improves results on other financial-domain tasks. We present extensive empirical evidence to validate these enhancements.

Related papers

• Demystifying Domain-adaptive Post-training for Financial LLMs [79.581577578952]
  FINDAP is a systematic and fine-grained investigation into domain adaptive post-training of large language models (LLMs) Our approach consists of four key components: FinCap, FinRec, FinTrain and FinEval. The resulting model, Llama-Fin, achieves state-of-the-art performance across a wide range of financial tasks.
  arXiv  Detail & Related papers  (2025-01-09T04:26:15Z)
• Enhancing Financial Domain Adaptation of Language Models via Model Augmentation [2.9960693856871545]
  This study demonstrates the effectiveness of Composition to Augment Language Models (CALM) in adapting to the financial domain. We developed a CALM to enhance the financial performance of an LLM with strong response capabilities.
  arXiv  Detail & Related papers  (2024-11-14T07:28:09Z)
• FinVision: A Multi-Agent Framework for Stock Market Prediction [0.0]
  This research introduces a multi-modal multi-agent system designed specifically for financial trading tasks. A key feature of our approach is the integration of a reflection module, which conducts analyses of historical trading signals and their outcomes.
  arXiv  Detail & Related papers  (2024-10-29T06:02:28Z)
• Automate Strategy Finding with LLM in Quant investment [4.46212317245124]
  We propose a novel framework for quantitative stock investment in portfolio management and alpha mining. This paper proposes a framework where large language models (LLMs) mine alpha factors from multimodal financial data. Experiments on the Chinese stock markets demonstrate that this framework significantly outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-09-10T07:42:28Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration [70.09561665520043]
  We propose a novel framework for multi-agent collaboration that introduces Reinforced Advantage feedback (ReAd) for efficient self-refinement of plans. We provide theoretical analysis by extending advantage-weighted regression in reinforcement learning to multi-agent systems. Experiments on Over-AI and a difficult variant of RoCoBench show that ReAd surpasses baselines in success rate, and also significantly decreases the interaction steps of agents.
  arXiv  Detail & Related papers  (2024-05-23T08:33:19Z)
• Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning [79.38140606606126]
  We propose an algorithmic framework that fine-tunes vision-language models (VLMs) with reinforcement learning (RL) Our framework provides a task description and then prompts the VLM to generate chain-of-thought (CoT) reasoning. We demonstrate that our proposed framework enhances the decision-making capabilities of VLM agents across various tasks.
  arXiv  Detail & Related papers  (2024-05-16T17:50:19Z)
• FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design [11.913409501633616]
  textscFinMem is a novel LLM-based agent framework devised for financial decision-making. textscFinMem's memory module aligns closely with the cognitive structure of human traders, offering robust interpretability. This framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions.
  arXiv  Detail & Related papers  (2023-11-23T00:24:40Z)
• Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
  Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents. Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes. We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
  arXiv  Detail & Related papers  (2023-09-30T07:11:39Z)
• Large Language Models in Finance: A Survey [12.243277149505364]
  Large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance. Recent advances in large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance.
  arXiv  Detail & Related papers  (2023-09-28T06:04:04Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)

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.