When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916405290467328 |
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| author | Zhang, Chong Liu, Xinyi Zhang, Zhongmou Jin, Mingyu Li, Lingyao Wang, Zhenting Hua, Wenyue Shu, Dong Zhu, Suiyuan Jin, Xiaobo Li, Sujian Du, Mengnan Zhang, Yongfeng |
| author_facet | Zhang, Chong Liu, Xinyi Zhang, Zhongmou Jin, Mingyu Li, Lingyao Wang, Zhenting Hua, Wenyue Shu, Dong Zhu, Suiyuan Jin, Xiaobo Li, Sujian Du, Mengnan Zhang, Yongfeng |
| contents | Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fundamentals, and global events)? These factors, which frequently influence trading behaviors, are critical elements in the quest for maximizing investors' profits. Our work attempts to solve this problem through large language model based agents. We have developed a multi-agent AI system called StockAgent, driven by LLMs, designed to simulate investors' trading behaviors in response to the real stock market. The StockAgent allows users to evaluate the impact of different external factors on investor trading and to analyze trading behavior and profitability effects. Additionally, StockAgent avoids the test set leakage issue present in existing trading simulation systems based on AI Agents. Specifically, it prevents the model from leveraging prior knowledge it may have acquired related to the test data. We evaluate different LLMs under the framework of StockAgent in a stock trading environment that closely resembles real-world conditions. The experimental results demonstrate the impact of key external factors on stock market trading, including trading behavior and stock price fluctuation rules. This research explores the study of agents' free trading gaps in the context of no prior knowledge related to market data. The patterns identified through StockAgent simulations provide valuable insights for LLM-based investment advice and stock recommendation. The code is available at https://github.com/MingyuJ666/Stockagent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18957 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments Zhang, Chong Liu, Xinyi Zhang, Zhongmou Jin, Mingyu Li, Lingyao Wang, Zhenting Hua, Wenyue Shu, Dong Zhu, Suiyuan Jin, Xiaobo Li, Sujian Du, Mengnan Zhang, Yongfeng Trading and Market Microstructure Artificial Intelligence Multiagent Systems Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fundamentals, and global events)? These factors, which frequently influence trading behaviors, are critical elements in the quest for maximizing investors' profits. Our work attempts to solve this problem through large language model based agents. We have developed a multi-agent AI system called StockAgent, driven by LLMs, designed to simulate investors' trading behaviors in response to the real stock market. The StockAgent allows users to evaluate the impact of different external factors on investor trading and to analyze trading behavior and profitability effects. Additionally, StockAgent avoids the test set leakage issue present in existing trading simulation systems based on AI Agents. Specifically, it prevents the model from leveraging prior knowledge it may have acquired related to the test data. We evaluate different LLMs under the framework of StockAgent in a stock trading environment that closely resembles real-world conditions. The experimental results demonstrate the impact of key external factors on stock market trading, including trading behavior and stock price fluctuation rules. This research explores the study of agents' free trading gaps in the context of no prior knowledge related to market data. The patterns identified through StockAgent simulations provide valuable insights for LLM-based investment advice and stock recommendation. The code is available at https://github.com/MingyuJ666/Stockagent. |
| title | When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments |
| topic | Trading and Market Microstructure Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2407.18957 |