---
title: "AlphaQT-Bench — paper by Luo et al. (2026)"
description: "The authors propose AlphaQT-Bench, a diagnostic benchmark of instruction-grounded tasks. 31 references."
authors: ["Sichun Luo", "Yi Huang", "Shichang Meng", "Fengyuan Liu", "Mukai Li", "Qinghua Yao", "Zefa Hu", "Junlan Feng", "Qi Liu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.138/"
doi: "10.18653/v1/2026.findings-acl.138"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/luo-2026-alphaqt-bench-diagnosing-gap-between/"
---
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# AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMs

**Authors:** [Sichun Luo](https://md.research.elamur.ai/free/en/authors/sichun-luo/), [Yi Huang](https://md.research.elamur.ai/free/en/authors/yi-huang/), Shichang Meng, Fengyuan Liu, [Mukai Li](https://md.research.elamur.ai/free/en/authors/mukai-li/), Qinghua Yao, Zefa Hu, [Junlan Feng](https://md.research.elamur.ai/free/en/authors/junlan-feng/) and [Qi Liu](https://md.research.elamur.ai/free/en/authors/qi-liu/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.138/) · [PDF](https://aclanthology.org/2026.findings-acl.138.pdf) · [DOI 10.18653/v1/2026.findings-acl.138](https://doi.org/10.18653/v1/2026.findings-acl.138) · [OpenAlex](https://openalex.org/W7166834389)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
LLMs increasingly write executable Python code for alpha mining rather than symbolic factor formulas. This adds expressiveness but also hidden risks: look-ahead bias (using future data) and bugs in stateful logic. Existing alpha benchmarks mostly score backtest profitability, which can reward leaked information or punish sound code because of market noise. The authors propose AlphaQT-Bench, a diagnostic benchmark of instruction-grounded tasks. In each task, a model turns a natural-language specification into a function that maps OHLCV data to a time-aligned signal series. Tasks span three difficulty levels. Evaluation has four layers:

- whether the code runs;
- causality safety, checked by a dynamic truncation test;
- numerical agreement with expert-written Golden Code;
- structural compliance, meaning loop-free vectorized implementation.

Across 12 LLMs, many models produce runnable code yet fail causal, functional or structural checks, and the gap widens as tasks get harder. The authors conclude that models act more like approximate retrievers of indicator templates than true reasoners. The benchmark matters because it audits process reliability rather than profit, which is safer for LLM-based quant pipelines.

## Key points
- Four evaluation layers: Executability, Causality Safety (dynamic truncation), Functional Accuracy versus Golden Code, and Structural Compliance (vectorization analysis); 12 LLMs were tested.
- Semantic misalignment in stateful logic (e.g., RSI, Money Flow Index, Wilder's Smoothing) causes almost 60% of Level 3 errors.
- Observed failure modes include:
  - **Safety over-alignment:** Llama-3.1-70B adds a needless shift "to avoid look-ahead bias".
  - **Complexity trap:** GPT-5-Mini builds a manual cumulative-sum OLS that yields NaNs.
  - **`ewm` misconfiguration:** wrong `adjust` settings in `ewm`.
- One failure pattern appeared in about 15% of failure cases for Llama-3.1-70B and DeepSeek-V3, mostly in the Risk and Reversion categories.
- Limitations: Python only, single-turn generation, and possible rejection of valid alternative implementations when financial definitions are ambiguous.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Sichun Luo, Yi Huang, Shichang Meng, Fengyuan Liu, Mukai Li, Qinghua Yao, Zefa Hu, Junlan Feng and Qi Liu. 2026. AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMs. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{luo2026alphaqt,
  title = {{AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMs}},
  author = {Sichun Luo, Yi Huang, Shichang Meng, Fengyuan Liu, Mukai Li, Qinghua Yao, Zefa Hu, Junlan Feng and Qi Liu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.138},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.138}
}
```

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*Updated 1 November 2026*