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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.06361 |
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| _version_ | 1866908857224134656 |
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| author | Ding, Han Li, Yinheng Wang, Junhao Chen, Hang Guo, Doudou Zhang, Yunbai |
| author_facet | Ding, Han Li, Yinheng Wang, Junhao Chen, Hang Guo, Doudou Zhang, Yunbai |
| contents | Trading is a highly competitive task that requires a combination of strategy, knowledge, and psychological fortitude. With the recent success of large language models(LLMs), it is appealing to apply the emerging intelligence of LLM agents in this competitive arena and understanding if they can outperform professional traders. In this survey, we provide a comprehensive review of the current research on using LLMs as agents in financial trading. We summarize the common architecture used in the agent, the data inputs, and the performance of LLM trading agents in backtesting as well as the challenges presented in these research. This survey aims to provide insights into the current state of LLM-based financial trading agents and outline future research directions in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06361 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Model Agent in Financial Trading: A Survey Ding, Han Li, Yinheng Wang, Junhao Chen, Hang Guo, Doudou Zhang, Yunbai Trading and Market Microstructure Computation and Language Trading is a highly competitive task that requires a combination of strategy, knowledge, and psychological fortitude. With the recent success of large language models(LLMs), it is appealing to apply the emerging intelligence of LLM agents in this competitive arena and understanding if they can outperform professional traders. In this survey, we provide a comprehensive review of the current research on using LLMs as agents in financial trading. We summarize the common architecture used in the agent, the data inputs, and the performance of LLM trading agents in backtesting as well as the challenges presented in these research. This survey aims to provide insights into the current state of LLM-based financial trading agents and outline future research directions in this field. |
| title | Large Language Model Agent in Financial Trading: A Survey |
| topic | Trading and Market Microstructure Computation and Language |
| url | https://arxiv.org/abs/2408.06361 |