QuantEval: A Benchmark for Financial Quantitative Tasks in Large Language Models

TL;DR

QuantEval is a comprehensive benchmark for assessing large language models in financial quantitative tasks, integrating knowledge, reasoning, and strategy coding with realistic backtesting to evaluate practical trading performance.

Contribution

This paper introduces QuantEval, a novel benchmark that combines traditional QA with backtesting for strategy evaluation, filling a gap in financial LLM assessment methods.

Findings

State-of-the-art LLMs lag behind human experts in reasoning and strategy coding.

Fine-tuning and reinforcement learning improve LLM performance on quantitative finance tasks.

QuantEval's backtesting framework enables realistic evaluation of trading strategies.

Abstract

Large Language Models (LLMs) have shown strong capabilities across many domains, yet their evaluation in financial quantitative tasks remains fragmented and mostly limited to knowledge-centric question answering. We introduce QuantEval, a benchmark that evaluates LLMs across three essential dimensions of quantitative finance: knowledge-based QA, quantitative mathematical reasoning, and quantitative strategy coding. Unlike prior financial benchmarks, QuantEval integrates a CTA-style backtesting framework that executes model-generated strategies and evaluates them using financial performance metrics, enabling a more realistic assessment of quantitative coding ability. We evaluate some state-of-the-art open-source and proprietary LLMs and observe substantial gaps to human experts, particularly in reasoning and strategy coding. Finally, we conduct large-scale supervised fine-tuning and reinforcement learning experiments on domain-aligned data, demonstrating consistent improvements. We hope QuantEval will facilitate research on LLMs' quantitative finance capabilities and accelerate their practical adoption in real-world trading workflows. We additionally release the full deterministic backtesting configuration (asset universe, cost model, and metric definitions) to ensure strict reproducibility.