multiAI Summary Pending
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.
231 stars
Installation
Claude Code / Cursor / Codex
$curl -o ~/.claude/skills/backtesting-frameworks/SKILL.md --create-dirs "https://raw.githubusercontent.com/aiskillstore/marketplace/main/skills/sickn33/backtesting-frameworks/SKILL.md"
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/backtesting-frameworks/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How backtesting-frameworks Compares
| Feature / Agent | backtesting-frameworks | Standard Approach |
|---|---|---|
| Platform Support | multi | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Which AI agents support this skill?
This skill is compatible with multi.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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.