Newest papers

MintEval: Do LLMs Implement the Trading Strategy You Asked For? A Behavioural-Equivalence Benchmark for Natural-Language-to-Strategy Code

Siyu Wang, Yifan Wang, Yuecheng He · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.03080 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

大型活跃语言模型正確地从生成交易信号转移到编写执行它们的代码。第二角色失败模式是默默的:生成的代码运行,一个 backtest 阴谋,但交易者所描述的特定风险逻辑不是被执行的逻辑。现有的代码基准测试对单元测试和财务基准测试预测的功能正确性,也不衡量是否实施行为与所要求的策略相似。我们引入了MintEval,一个基准,其中参考策略是从可编译的构建块的库中进行编程生成,后面翻译为谈话式交易者指令的功能正确性,并重新实施了测试中的模型。生成的和参考程序是通过对巴特式相同的市场数据和任务执行的。我们对它们的行动进行比较,而不是

Machine translation (Meta M2M100); the English original follows.

Abstract

Large language models are moving from producing trading signals to writing the code that executes them. The failure mode of the second role is silent: generated code runs, a backtest plots, yet the risk logic that the trader described is not the logic being executed. Existing code benchmarks test functional correctness on unit tests and finance benchmarks test forecasting; neither measures whether an implementation behaves like the strategy that was asked for. We introduce MintEval, a benchmark in which reference strategies are generated programmatically from a library of composable building blocks, back-translated into colloquial trader instructions, and re-implemented by the model under test. Generated and reference programs are executed bar by bar on identical market data and frictions, and compared on their actions rather than on code similarity or profit: alpha is differenced away. MintEval v0 contains 800 tasks on BTCUSDT 15-minute data, stratified by an execution-measured state-span complexity tau that is decoupled from description length. Low-cost models reach a mean ActionMatch of at most 0.544 and reproduce at most 0.087 of tasks exactly; on a stratified subset of 200 tasks a frontier model (Claude Opus 5.5) reaches 0.889 and reproduces 0.575 exactly, yet still fails silently on 0.275 of tasks. Given a menu of building blocks, models identify the strategy almost perfectly, yet 79.2% of the implementations whose specification was read correctly diverge on more than 10% of active bars. The LLM judge of a recent strategy-generation benchmark, applied verbatim, accepts every one of these silent failures.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Benchmark (surveying), Computer science, Correctness, Code (set theory), Unit testing, Implementation

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange