Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Finance

Battle Count: 72

Highly relevant to quantitative trading. The paper directly addresses a critical methodological flaw (lookahead bias) that can invalidate backtested trading strategies. The alpha decay metric provides a practical diagnostic tool for evaluating whether LLM-based trading signals are genuine or artifacts of training data contamination. The 'Scaling Paradox' finding (larger standard models perform worse out-of-sample due to stronger memorized priors) has direct implications for model selection in production trading systems. The benchmark framework is immediately applicable to validating any LLM-based trading agent before deployment.

Moderate complexity. The benchmark leverages the existing AI Hedge Fund framework (open-source, 45k+ GitHub stars) with multi-agent architecture. Setting up the evaluation requires configuring LLM API access, implementing the dual-period backtesting protocol, and computing alpha metrics. The quantitative baselines are straightforward. However, accessing proprietary Pitinf models requires commercial licensing. The core evaluation logic (monthly rebalancing, alpha calculation, decay analysis) is relatively simple to implement.

Code is available on GitHub (https://github.com/benstaf/lookaheadbench). The AI Hedge Fund framework is open-source. However, the Pitinf models (Pitinf-Small, Pitinf-Medium, Pitinf-Large) are proprietary commercial models from PiT-Inference, limiting full reproducibility. Standard models (Llama 3.1, DeepSeek 3.2) are open-source. The evaluation uses only 5 stocks and 2 periods, which is a proof-of-concept scope.