AI RESEARCH
BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting
arXiv CS.CL
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ArXi:2605.17937v1 Announce Type: new 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.