BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting

2026-05-18 · Source: Takara TLDR - Daily AI Papers · Field: Finance & Economics — Capital Markets & Investment Management, FinTech & Digital Financial Services · Depth: Expert, medium

Summary

BacktestBench is introduced as the first large-scale benchmark for automated quantitative strategy backtesting, addressing a critical gap in evaluating Large Language Models (LLMs) for this complex financial task. The benchmark is constructed from over 6 million real market records and features 18,246 meticulously annotated question-answering pairs across four categories: metrics calculation, ticker selection, strategy selection, and parameter confirmation. Alongside the benchmark, the authors propose AutoBacktest, a multi-agent baseline system that translates natural language strategies into reproducible backtests. AutoBacktest coordinates a Summarizer for semantic factor extraction, a Retriever for validated SQL generation, and a Coder for Python backtesting implementation. Evaluations on 23 mainstream LLMs using BacktestBench, coupled with ablation studies, reveal key performance factors and emphasize the importance of grounded verification and standardized indicator representations.

Key takeaway

For research scientists developing or deploying LLMs in quantitative finance, you should utilize BacktestBench to rigorously evaluate your models' capabilities in automated strategy backtesting. This benchmark offers a standardized framework to assess performance across critical financial tasks, helping you identify model strengths and weaknesses, and guiding the development of more robust and reliable LLM-powered financial tools. Consider integrating multi-agent architectures and focusing on grounded verification to improve practical application.

Key insights

BacktestBench provides a critical benchmark for evaluating LLMs in automated quantitative trading strategy backtesting.

Principles

• Grounded verification is crucial for LLM performance.
• Standardized indicator representations improve backtesting.
• Multi-agent systems enhance complex workflow automation.

Method

AutoBacktest employs a multi-agent architecture (Summarizer, Retriever, Coder) to convert natural language trading strategies into executable Python backtests, leveraging SQL generation for data retrieval.

In practice

• Use BacktestBench to evaluate LLMs for financial tasks.
• Implement multi-agent systems for complex automation.
• Prioritize data grounding in LLM-driven financial tools.

Topics

• Quantitative Strategy Backtesting
• Large Language Models
• BacktestBench
• AutoBacktest
• Multi-Agent Systems

Code references

• safety-research/impossiblebench

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.