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 strateg...
Best use case
backtesting-frameworks is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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 strateg...
Teams using backtesting-frameworks should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
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 | Not specified | 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 strateg...
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.
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