backtest-validator

Name: backtest-validator
Availability: InStock
Author: gtnix

Validate backtest metrics and detect anomalies across strategy runs.

Updated Jun 24, 2025

Search Tags:#validation #metrics #anomaly-detection #backtest #sanity-check #fitness-evaluation

Ready to run

npx skills add https://github.com/gtnix/quant_b3_backtest --skill backtest-validator

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Please help me install this Agent Skill.
Skill: backtest-validator
Source: https://github.com/gtnix/quant_b3_backtest/tree/main/.cursor/skills/backtest-validator
Command: npx skills add https://github.com/gtnix/quant_b3_backtest --skill backtest-validator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Validate backtest results to ensure metric integrity and detect anomalies.

Core Features & Use Cases

Detects zero-trade outcomes and invalidates such runs to prevent misleading insights.
Checks key metrics against defined clamp ranges (Sharpe, Sortino, Calmar, Profit Factor) and flags suspicious values for review.
Performs end-to-end validation within the backtesting framework by cross-referencing fitness evaluation and SIMD metric calculations to ensure net-of-cost accuracy.

Quick Start

Execute a validation pass on the latest backtest results to ensure metrics are sane and compliant.

Frequently Asked Questions about backtest-validator

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

FAQPage Schema

How do I validate backtest metrics for accuracy and detect anomalies?▼

You validate backtest metrics by enforcing hard constraints like zero-trade invalidation, metric clamping for Sharpe and Sortino, and net-of-cost checks to detect extreme values and unintended artifacts. This ensures metric integrity across post-backtest analysis.

Why does my backtest show extreme Sharpe or Profit Factor values?▼

Extreme Sharpe or Profit Factor values occur when backtest metrics are not clamped within defined ranges. A validation pass flags these suspicious metric values for review to prevent misleading insights during fitness evaluation.

How do I invalidate zero-trade backtest runs automatically?▼

You invalidate zero-trade backtest runs by applying a hard constraint that automatically flags and rejects such outcomes. This prevents misleading insights from entering the fitness evaluation pipeline.

Does backtest validation check net-of-cost accuracy across fitness evaluation?▼

Yes, backtest validation performs end-to-end checks by cross-referencing fitness evaluation and SIMD metric calculations. This ensures net-of-cost accuracy and confirms that metrics are sane and compliant.

What is the best way to run a sanity check on my backtesting framework results?▼

The best way to run a sanity check is to execute a validation pass on your latest backtest results. This enforces metric clamping and catches unintended artifacts across your strategies.

When should I not rely on automated backtest validation?▼

You should not rely solely on automated backtest validation when your strategy logic requires custom anomaly detection beyond defined metric clamp ranges, as the validation focuses on standard fitness evaluation constraints like zero-trade invalidation.