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Large Language Model Agent in Financial Trading: A Survey

Surveys the research landscape of LLM-based agents applied to financial trading, covering architectures, data inputs, backtesting results, and open challenges.

arXiv:2408.06361 Markdown Edit this entry

#survey #llm-agent #backtesting #literature-review

added 2026-08-15 · external

@article{llm-trading-agent-survey,
  title         = {Large Language Model Agent in Financial Trading: A Survey},
  author        = {Han Ding and Yinheng Li and Junhao Wang and Hang Chen and Doudou Guo and Yunbai Zhang},
  year          = {2024},
  eprint        = {2408.06361},
  archiveprefix = {arXiv},
  note          = {arXiv},
}

Reviews how LLM agents have been constructed for trading tasks, comparing agent architectures, the data modalities they consume, and reported backtesting performance across published systems. It catalogues the obstacles that recur in this literature and identifies directions for future research.

A good entry point into the field before reading the individual system papers indexed here.

(END)

trading [●●···] basic ai [●●···] basic programming [●····] none setup [●····] none

origin external

markets multi-asset

extended by From Deep Learning to LLMs: A Survey of AI in Quantitative Investment

athenara:~$