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
양적 연구는 반복적 인 루프입니다 : 연구자는 시장 가설을 제안하고 실행 가능한 신호로 구현하고, 역사적 데이터에 테스트하고, 무엇을 유지해야하는지 결정합니다. 기존 벤치마크 점수 수식, 코드 또는 개별 거래 결정, 대형 언어 모델 (LLM)이이 루프를 완료하고 연구 기간을 넘어 작동하는 전략을 생산할 수 있는지 여부를 열어 놓습니다. 우리는 Quant-Arena, 진짜 A-share 시장 데이터에 대한 최종 정량 연구를위한 벤치마크 및 공공 리더 보드를 소개합니다. 모델은 기능과 주식 점수 코드를 구축하고 2022-2023에만 피드백을 받고 2024 년에 평가를위한 하나의 전략을 냉각합니다. 현금 계좌 평가자는 시뮬레이션
Machine translation (Meta M2M100); the English original follows.
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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