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backtest-diagnose

Diagnose and resolve runtime errors, logic bugs, and data issues in backtests.

2|Updated May 13, 2026
Search Tags:#data validation#diagnosis#backtesting#algorithm analysis#code fixes
One-click install
npx skills add https://github.com/thanhtai040805/AI_Invest --skill backtest-diagnose-thanhtai040805
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: backtest-diagnose
Source: https://github.com/thanhtai040805/AI_Invest/tree/main/ai-engine/app/domain/services/quant/skills_data/backtest-diagnose
Command: npx skills add https://github.com/thanhtai040805/AI_Invest --skill backtest-diagnose-thanhtai040805

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework to diagnose and resolve issues in backtests, identifying root causes and facilitating quick fixes.

Core Features & Use Cases

  • Diagnostic Workflow: Follow a structured approach to identify issues such as missing dependencies, DataFrame errors, or data issues.
  • Error Taxonomy: Classify errors into categories such as Runtime Errors, Logic Bugs, and Data Errors.
  • Fixing Principles: Follow best practices for applying fixes, ensuring precision and avoiding overhauls.
  • Post-Fix Validation: Rigorously test fixes with a set of validation rules before considering the problem resolved.

Quick Start

Use the 'backtest-diagnose' skill to diagnose the cause of your failed backtest by providing the required input files.

Frequently Asked Questions about backtest-diagnose

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

FAQPage Schema
How do I diagnose and fix backtest failures caused by data or code errors?▼

To diagnose backtest failures, you classify errors into runtime, logic, or data categories, identify root causes like missing dependencies, and apply precise code fixes followed by post-repair validation.

Why does my backtest execution fail with DataFrame errors?▼

Backtest execution fails with DataFrame errors due to data issues or missing dependencies. A structured diagnostic workflow identifies the specific root cause to facilitate quick and robust code fixes.

What is the best way to resolve logic bugs in algorithm backtesting?▼

The best way to resolve logic bugs is using an error taxonomy to classify them, followed by applying targeted fixing principles that ensure precision and avoid unnecessary algorithm overhauls.

How do I validate backtest fixes before considering the problem resolved?▼

You validate backtest fixes by rigorously testing the corrected code against a set of post-fix validation rules, ensuring the restored backtest functionality operates without suboptimal behavior.

Can I use a diagnostic workflow to fix suboptimal backtests without rewriting the algorithm?▼

Yes, the diagnostic workflow applies fixing principles that ensure precision and explicitly avoid overhauls, allowing you to resolve suboptimal backtests while maintaining the original algorithm structure.

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