Backtrader-Bench: Benchmarking LLM Agents on Algorithmic Trading with Self-Generated MCQs
A new evaluation framework, Backtrader-Bench, has been unveiled to enhance the assessment of large language model (LLM) coding agents in algorithmic trading. Published in an arXiv paper (2608.11232), it addresses the shortcomings of static benchmarks that can misrepresent performance. The framework comprises two components: a multiple-choice question pipeline that draws from five trading strategies and various templates, and a generator-solver pipeline, designed to create challenging questions requiring code execution for answers. In testing, tool-assisted models, including GPT-5.5 and Opus 4.7, achieved an impressive accuracy rate of 90.0%, indicating a significant advancement in evaluation methodologies.
Key facts
- Backtrader-Bench is a new benchmark for evaluating LLM coding agents in algorithmic trading.
- It uses two pipelines: deterministic MCQ generation and generator-solver filtering.
- The MCQ pipeline covers five trading strategies, 33 templates, and three difficulty tiers.
- An independent checker re-derives every answer in the deterministic pipeline.
- The generator-solver filtering pipeline mines harder questions by discarding those solvable without code execution.
- 11 models without tools (10 runs each) and four with-tools configurations were evaluated.
- Tool-augmented agents reached 90.0% accuracy in a single pass.
- GPT-5.5 and Opus 4.7 achieved the highest accuracy among tested models.
- The benchmark aims to reduce data contamination and ensure ground truth from code execution.
- The paper is available on arXiv with ID 2608.11232.
Entities
Institutions
- arXiv