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Quant-Arena: Benchmarking LLM Agents for Quantitative Trading Research

Paper recorded by Signals 4 on 2026-10-21 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.AI · 人工智能 · first seen 2026-10-22

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 introdu

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#205 most recent of 960 cs.AI papers we have recorded · ↑ newer: The End of Consensus Research? · ↓ older: Multimodal Embodied Agents: A Unified Capability-centric Perspective f
Cite this page: Quant-Arena: Benchmarking LLM Agents for Quantitative Trading Research: the #205 most recent of 960 cs.AI papers we have recorded (as of 2026-10-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/quant-arena-benchmarking-llm-agents-for-quantitative-trading-research.html
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