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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

Japanese

大規模なブロックのアクティブモデルは、正確に取引シグナルを生成し、それを実行するコードを書くことに移行しています。第2の役割の失敗モードは静かです:生成されたコードが実行され、バックテストの計画は、しかし、トレーダーが正確に識別したリスク論理は、論理が実行されていません。既存のコードベンチマークは、ユニットテストと金融ベンチマークのテスト予測で機能的正確性をテストし、テスト中のモデルによって再実行されていません。実行が要求された戦略と同様であるかどうかを測定しません。我々はMintEvalを導入し、リファレンス戦略がプログラム的に構成され、コンポー

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.

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

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