leakage aware backtest auditor

The problem

Standard backtests often suffer from look-ahead bias and unrealistic cost assumptions. Most tools fail to account for the actual publication time of data, leading to inflated performance that vanishes in live trading.

What it does

• Audits feature availability by mapping every data point to its earliest possible publication timestamp.
• Enforces strict chronological separation between training and test sets to prevent data leakage.
• Calculates cost-adjusted returns by applying fractional deductions to period-specific performance.
• Generates sensitivity tables across independent walk-forward windows and parameter variants.
• Identifies missing evidence of survivorship bias and corporate-action handling.

Frameworks & tools

Python 3 for execution, JSON for data contracts, and local CLI scripts for analysis.

Why this beats prompting it yourself

General LLMs struggle with the rigorous, multi-step verification required to prove train/test isolation. This skill follows a specific audit protocol that treats source references as metadata rather than proof, forcing an evidence-based evaluation of strategy robustness.

Use cases

• Auditing a new quantitative strategy before committing capital.
• Identifying look-ahead bias in third-party backtest reports.
• Standardizing performance reporting across multiple walk-forward windows.
• Stress-testing return series against varying slippage and commission tiers.

Known limitations

Does not execute raw strategy code or fetch market data. Requires pre-computed return series and data availability timestamps as input.