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
La recherche quantitative est une boucle itérative: un chercheur propose une hypothèse de marché, la met en œuvre en tant que signal exécutable, la teste sur les données historiques et décide de ce qu'il faut garder. Les référentiels existants marquent des formules, du code ou des décisions de trading individuelles, laissant ouvert si les grands modèles linguistiques (LLM) peuvent compléter cette boucle et produire des stratégies qui fonctionnent au-delà de la période de recherche. Nous introduisons Quant-Arena, un référentiel et un tableau de bord public pour la recherche quantitative de bout en bout sur les données du marché réel A-share. Un modèle construit des fonctionnalités et un code de notation d'actions, reçoit des commentaires seulement sur 2022-2023 et
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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