BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting
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Computer Science > Computation and Language
Title:BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting
Abstract:Quantitative backtesting is essential for evaluating trading strategies but remains hampered by high technical barriers and limited scalability. While Large Language Models (LLMs) offer a transformative path to automate this complex, interdisciplinary workflow through advanced code generation, tool usage, and agentic planning, the practical realization is significantly challenged by the current lack of a large-scale benchmark dedicated to automated quantitative backtesting, which hinders progress in this field. To bridge this critical gap, we introduce BacktestBench, the first large-scale benchmark for automated quantitative backtesting. Built from over 6 million real market records, it comprises 18,246 meticulously annotated question-answering pairs across four task categories: metrics calculation, ticker selection, strategy selection, and parameter confirmation. We also propose AutoBacktest, a robust multi-agent baseline that translates natural language strategies into reproducible backtests by coordinating a Summarizer for semantic factor extraction, a Retriever for validated SQL generation, and a Coder for Python backtesting implementation. Our evaluation on 23 mainstream LLMs, complemented by targeted ablations, identifies key factors that influence end-to-end performance and highlights the importance of grounded verification and standardized indicator representations.
| Comments: | This paper has been accepted by KDD 2026 (Datasets and Benchmarks Track) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.17937 [cs.CL] |
| (or arXiv:2605.17937v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.17937
arXiv-issued DOI via DataCite (pending registration)
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