Python Backtesting Framework Audit Report

What this service delivers

I perform a rigorous, evidence-based audit of your Python backtesting framework to detect critical flaws that could invalidate live trading results. Using my open-source validator and proprietary methodology, I test for look-ahead bias (via execution-time perturbation), overfitting (via effective sample size analysis), and unreproducibility (via SHA256 hash verification across environments). You receive a concise, technical PASS/FAIL report with clear root-cause explanations, reproducible steps, and actionable remediation guidance — all delivered asynchronously via secure digital channel. No black-box scoring: every finding is traceable to code, data, or configuration.

What you need to provide

Your backtesting Python codebase (repository or archive), full environment specification (Python version, key package versions like pandas, NumPy, backtrader/zipline/vnpy), and historical market data used in the test run. Note: Covers up to 3,000 lines of code (LOC). Contact me for a custom quote if your project is larger.

Python

LB

look-ahead bias auditing

RA

reproducibility audit