notebook-auditor

What problem does it solve?

Auditing Jupyter notebooks in quantitative finance projects to verify runnability, data integrity, model validity, and metric trustworthiness, preventing hidden state and leakage from skewing conclusions.

Core Features & Use Cases

• Rigorous, evidence-backed audits of notebook experiments across forecasting, PINN vs non-PINN comparisons, and dissertation workflows.
• Structured workflow aligned with a published audit specification, producing a dated report scaffold and actionable patch plans.
• Use cases include validating notebook reproducibility, checking temporal alignment and data preprocessing, and auditing reported results for integrity.

Quick Start

Run the audit tool on a notebook to generate a dated audit report scaffold.