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Quant-Arena: Benchmarking LLM Agents for Quantitative Trading Research

Houde Qian, Zheng Jiang, WenYi Li, Chenxi Xu, Xinjie Lin, Yuzhao Peng, Ruoyu Han, Youjie Zheng, Kaisen Yang, Qinhuai Na, You Fu, Calvin Xiao · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30113 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

定量的な研究は回復的なループである:研究者は市場仮説を提案し、実行可能なシグナルとして実施し、歴史的データにテストし、保存するものを選択します。既存のベンチマークは、表式、コード、または個々の取引決定をスコアし、大規模な言語モデル(LLMs)がこのループを完了し、調査期間を超えて動作する戦略を生成するかどうかを明らかにします。我々はQuant-Arenaを導入し、実際のA株市場データの末端の定量的な研究のためのベンチマークと公共のリーダーボードを導入します。モデルは機能と株式スコアコアコードを構築し、2022～2023年にのみフィードバックを受信し、封鎖

Machine translation (Meta M2M100); the English original follows.

Abstract

Quantitative research is an iterative loop: a researcher proposes a market hypothesis, implements it as an executable signal, tests it on historical data, and decides what to keep. Existing benchmarks score formulas, code, or individual trading decisions, leaving open whether large language models (LLMs) can complete this loop and produce strategies that work beyond the research period. We introduce Quant-Arena, a benchmark and public leaderboard for end-to-end quantitative research on real A-share market data. A model constructs features and stock-scoring code, receives feedback only on 2022--2023, and freezes one strategy for assessment on the sealed year 2024. A cash-account evaluator applies trading constraints and costs, including market impact estimated from a simulated order book. Scores are dominated by realized excess return and complemented by signal quality and the diversity of features used by the strategy. We evaluate nine frontier models, among which GPT-6 Astra scores highest; yet no model beats the equal-weight market in 2024. Quant-Arena is open to submissions from any model or agent harness.

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Topics: Benchmarking, Executable, Benchmark (surveying), Computer science, Order (exchange), Algorithmic trading

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