rmyndharis/antigravity-skills

backtesting-frameworks

Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.

First seen Jan 23, 2026

Installation

$ npx skills add rmyndharis/antigravity-skills --skill backtesting-frameworks

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from rmyndharis/antigravity-skills · top by installs.

npx skills add rmyndharis/antigravity-skills

Browse all from rmyndharis/antigravity-skills

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 1.5K
License LICENSE
Default branch main
Open issues 0
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents antigravity

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,428 B
  • docs SUMMARY.md 271 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 37 installs

SKILL.md

Backtesting Frameworks

Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.

Use this skill when

  • Developing trading strategy backtests
  • Building backtesting infrastructure
  • Validating strategy performance and robustness
  • Avoiding common backtesting biases
  • Implementing walk-forward analysis

Do not use this skill when

  • You need live trading execution or investment advice
  • Historical data quality is unknown or incomplete
  • The task is only a quick performance summary

Instructions

  • Define hypothesis, universe, timeframe, and evaluation criteria.
  • Build point-in-time data pipelines and realistic cost models.
  • Implement event-driven simulation and execution logic.
  • Use train/validation/test splits and walk-forward testing.
  • If detailed examples are required, open resources/implementation-playbook.md.

Safety

  • Do not present backtests as guarantees of future performance.
  • Avoid providing financial or investment advice.

Resources

  • resources/implementation-playbook.md for detailed patterns and examples.