✨TradingAgents: Multi-Agents LLM Financial Trading Framework

📝 Summary:
TradingAgents is a multi-agent LLM framework that simulates real-world trading firms with specialized, collaborative agents. This approach significantly improves trading performance metrics like cumulative returns and Sharpe ratio compared to baseline models.

🔹 Publication Date: Published on Dec 28, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2412.20138
• PDF: https://arxiv.org/pdf/2412.20138
• Github: https://github.com/tauricresearch/tradingagents

✨ Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList
• https://huggingface.co/spaces/Ervin2077/qiu

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#TradingAgents #MultiAgentLLM #FinancialTrading #AlgorithmicTrading #AI

✨LiveTradeBench: Seeking Real-World Alpha with Large Language Models

📝 Summary:
LiveTradeBench evaluates LLMs in live trading environments with real-time data, multi-asset portfolios, and multiple markets. It reveals that strong static benchmark scores dont predict trading success, and some LLMs can adapt to live market signals. This highlights a gap in current LLM evaluations.

🔹 Publication Date: Published on Nov 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.03628
• PDF: https://arxiv.org/pdf/2511.03628
• Project Page: https://trade-bench.live/
• Github: https://github.com/ulab-uiuc/live-trade-bench

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #AlgorithmicTrading #FinancialAI #QuantitativeFinance #AIResearch