Quant-Arena Benchmarks LLM Agents on Out-of-Sample A-Share Trading Strategies
Houde Qian · hf · 2026-10-23
A new benchmark, Quant-Arena, has been released on Hugging Face for evaluating LLM agents on end-to-end quantitative trading research.
- Models must build trading strategies from historical A-share data, then are judged on whether those strategies stay profitable in a sealed future year.
- The emphasis is end-to-end: not just code generation or commentary, but whether the full research pipeline yields strategies that hold up out of sample.
- The A-share setting makes it an agent evaluation in a financial time-series domain.
Related event: Quant-Arena benchmark: only 4 of 9 frontier LLMs profit on A-shares(3 posts)→
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