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backtest-validator

Validate trading strategy backtests for biases and statistical significance.

Updated Mar 1, 2026
Search Tags:#validation#backtesting#quantitative finance#trading strategy#bias detection#overfitting
One-click install
npx skills add https://github.com/mqzkim/trading --skill backtest-validator-mqzkim
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: backtest-validator
Source: https://github.com/mqzkim/trading/tree/main/.agents/skills/backtest-validator
Command: npx skills add https://github.com/mqzkim/trading --skill backtest-validator-mqzkim

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill validates the integrity and reliability of backtesting results, identifying potential biases and overfitting to ensure trustworthy performance metrics.

Core Features & Use Cases

  • Bias Detection: Identifies common biases like survivorship, look-ahead, and data snooping.
  • Methodology Review: Assesses the soundness of the backtesting methodology, including walk-forward analysis and transaction cost inclusion.
  • Statistical Rigor: Verifies the statistical significance of performance metrics like Sharpe ratio and parameter sensitivity.
  • Use Case: Before deploying a trading strategy, use this Skill to rigorously audit its backtest results, ensuring it's not overly optimized to historical data and is likely to perform in live markets.

Quick Start

Use the backtest-validator skill to validate the backtest results located at the path './backtest-results.csv'.

Frequently Asked Questions about backtest-validator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I check my trading strategy backtest for overfitting and look-ahead bias?▼

To check backtesting results for overfitting and look-ahead bias, validate data integrity and methodological soundness using a comprehensive checklist that detects survivorship bias and data snooping.

What is walk-forward efficiency and how does it validate backtesting methodology?▼

Walk-forward efficiency is a methodological soundness check that assesses backtesting robustness by verifying parameter sensitivity and statistical significance across different time periods.

How do I validate the statistical significance of my Sharpe ratio in backtesting?▼

Validate Sharpe ratio statistical significance by checking performance metrics against parameter sensitivity analysis, ensuring the backtesting results are not overly optimized to historical data.

Can I audit backtesting results stored in a CSV file for survivorship bias?▼

Yes, you can audit backtesting results stored in a CSV file path to detect survivorship bias, verify data integrity, and ensure methodological soundness before deploying the trading strategy.

When should I run a backtesting validation check on my trading strategy?▼

Run backtesting validation before deploying a trading strategy to live markets, ensuring methodological soundness, statistical significance, and the absence of overfitting or data snooping biases.

What are the limitations of relying on backtesting results without parameter sensitivity analysis?▼

Without parameter sensitivity analysis and walk-forward efficiency checks, backtesting results may hide overfitting, look-ahead bias, and data snooping, leading to unreliable live market performance.

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