Backtrader-Bench: Benchmarking LLM Agents on Algorithmic Trading with Self-Generated MCQs
A new framework that pits LLMs against executable trading logic to solve the problem of benchmark data contamination.
Evaluating LLM coding agents in algorithmic trading is difficult because static benchmarks risk data contamination and numerical backtest outputs require ground truth from actual code execution. We present Backtrader-Bench, a framework with two complementary pipelines. A determin...
ArXiv CS.CL
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Backtrader-Bench addresses the critical reliability gap in algorithmic trading by forcing LLMs to prove their reasoning through executable code rather than static memorization. This framework provides a scalable foundation for developing specialized financial agents that can navigate complex quantitative workflows with higher precision.
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