TradingGroup: A Multi-Agent Trading System with Self-Reflection and Data-Synthesis

2025-08-24

AI Summary

Existing LLM-driven multi-agent financial trading systems suffer from insufficient agent coordination, absence of structured self-reflection mechanisms, and scarcity of high-quality domain-specific training data. To address these challenges, this paper proposes a self-reflective, multi-agent trading framework with automated domain data synthesis capabilities. Specifically, we design a collaborative decision-making mechanism based on signal fusion and dynamic risk management; introduce self-reflection prompting and trading-style adaptation modules to enable cross-scenario experience transfer; and construct an end-to-end domain data synthesis pipeline that generates high-fidelity, labeled data incorporating market states and agent decisions. Backtesting on five real-world stock datasets demonstrates that our system significantly outperforms rule-based, machine learning, reinforcement learning, and state-of-the-art LLM-based baselines—validating its superiority in decision quality, market adaptability, and agent coordination.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Recent advancements in large language models (LLMs) have enabled powerful agent-based applications in finance, particularly for sentiment analysis, financial report comprehension, and stock forecasting. However, existing systems often lack inter-agent coordination, structured self-reflection, and access to high-quality, domain-specific post-training data such as data from trading activities including both market conditions and agent decisions. These data are crucial for agents to understand the market dynamics, improve the quality of decision-making and promote effective coordination. We introduce TradingGroup, a multi-agent trading system designed to address these limitations through a self-reflective architecture and an end-to-end data-synthesis pipeline. TradingGroup consists of specialized agents for news sentiment analysis, financial report interpretation, stock trend forecasting, trading style adaptation, and a trading decision making agent that merges all signals and style preferences to produce buy, sell or hold decisions. Specifically, we design self-reflection mechanisms for the stock forecasting, style, and decision-making agents to distill past successes and failures for similar reasoning in analogous future scenarios and a dynamic risk-management model to offer configurable dynamic stop-loss and take-profit mechanisms. In addition, TradingGroup embeds an automated data-synthesis and annotation pipeline that generates high-quality post-training data for further improving the agent performance through post-training. Our backtesting experiments across five real-world stock datasets demonstrate TradingGroup's superior performance over rule-based, machine learning, reinforcement learning, and existing LLM-based trading strategies.

Problem

• Addresses lack of inter-agent coordination in financial trading systems
• Solves insufficient structured self-reflection for trading decision improvement
• Provides high-quality domain-specific post-training data synthesis pipeline

Innovation

• Multi-agent system with self-reflection mechanisms
• Automated data-synthesis pipeline for post-training
• Dynamic risk-management model with configurable controls