{"slug": "backtrader-bench-benchmarking-llm-agents-on-algorithmic-trading-with-self-mcqs", "title": "Backtrader-Bench: Benchmarking LLM Agents on Algorithmic Trading with Self-Generated MCQs", "summary": "Researchers introduced Backtrader-Bench, a framework for benchmarking LLM coding agents on algorithmic trading using self-generated multiple-choice questions, achieving 90.0% accuracy for tool-augmented agents (GPT-5.5 and Opus 4.7) versus 73.0% for the best no-tools baselines. The framework includes deterministic and generator-solver pipelines to mitigate data contamination and produce training data for reinforcement learning.", "body_md": "arXiv:2608.11232v1 Announce Type: new\nAbstract: 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 deterministic multiple-choice question (MCQ) pipeline generates questions from backtest configurations across five trading strategies, 33 templates, and three difficulty tiers, with an independent checker that re-derives every answer. A generator-solver filtering pipeline autonomously mines harder questions: a generator writes questions verified by executable code, converts them to MCQs, and discards any that a no-tool solver can answer without code execution. We evaluate 11 models without tools (10 runs each) and four with-tools configurations on a 30-question curated set. Tool-augmented agents reach 90.0% accuracy in a single pass (GPT-5.5 and Opus 4.7), outperforming the best no-tools baselines (73.0%, averaged over 10 runs) by 17 percentage points. On 38 separately mined questions, no-tools accuracy drops further, with half the models falling to roughly random-chance level (25%). Beyond evaluation, the scalable MCQ infrastructure is designed to produce a training corpus for reinforcement learning, with the ultimate goal of building a specialized agent for quantitative trading workflows.", "url": "https://wpnews.pro/news/backtrader-bench-benchmarking-llm-agents-on-algorithmic-trading-with-self-mcqs", "canonical_source": "https://arxiv.org/abs/2608.11232", "published_at": "2026-08-13 04:00:00+00:00", "updated_at": "2026-08-13 04:11:04.479874+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": ["Backtrader-Bench", "GPT-5.5", "Opus 4.7"], "alternates": {"html": "https://wpnews.pro/news/backtrader-bench-benchmarking-llm-agents-on-algorithmic-trading-with-self-mcqs", "markdown": "https://wpnews.pro/news/backtrader-bench-benchmarking-llm-agents-on-algorithmic-trading-with-self-mcqs.md", "text": "https://wpnews.pro/news/backtrader-bench-benchmarking-llm-agents-on-algorithmic-trading-with-self-mcqs.txt", "jsonld": "https://wpnews.pro/news/backtrader-bench-benchmarking-llm-agents-on-algorithmic-trading-with-self-mcqs.jsonld"}}