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About this paper
Feng Tian, Flora D. Salim, Hao Xue
The University of New South Wales developed TradingGroup, a multi-agent trading system integrating specialized agents with self-reflection and an end-to-end data-synthesis pipeline for adaptive quantitative trading. It achieved superior performance against various baselines, securing globally optimal returns on several datasets, and enabled a fine-tuned Qwen3-8B model to outperform GPT-4o-mini using generated data.
Key insights
- Structured self-reflection, incorporating agent logs, performance metrics, and risk signals, is critical for LLM-based agents to continuously learn, self-correct, and optimize trading strategies.
- Automated data synthesis, guided by performance rewards, can generate high-quality, domain-specific training data from agent interactions, enabling smaller, fine-tuned LLMs to surpass larger, general-purpose models.
- Combining a multi-agent architecture with dynamic, adaptable risk management fosters a robust trading system that balances high returns with controlled risk, enhancing long-term stability.
Summary by alphaXiv.
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