backtesting-frameworks
Framework for building accurate trading backtests that account for biases and transaction costs.
Install
mkdir -p .claude/skills/backtesting-frameworks-agent-skills-hub && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/11694" && unzip -o skill.zip -d .claude/skills/backtesting-frameworks-agent-skills-hub && rm skill.zipInstalls to .claude/skills/backtesting-frameworks-agent-skills-hub
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Develop trading strategy backtests
- →Build backtesting infrastructure
- →Validate strategy performance and reliable
- →Avoid common backtesting biases like look-ahead and survivorship
- →Implement walk-forward analysis for strategy evaluation
- →Model realistic transaction costs in simulations
How it works
The skill guides the user through defining strategy parameters, building data pipelines, implementing event-driven simulations, and using validation techniques like train/test splits to create reliable backtesting systems.
Inputs & outputs
When to use backtesting-frameworks
- →Develop a trading strategy backtest
- →Validate backtesting infrastructure
- →Calculate strategy performance bias
- →Implement event-driven simulations
About this skill
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.mdfor detailed patterns and examples.
When not to use it
- →When live trading execution or investment advice is needed
- →When historical data quality is unknown or incomplete
- →When only a quick performance summary is required
Limitations
- →Does not provide live trading execution
- →Does not offer investment advice
- →Requires known and complete historical data
How it compares
This skill focuses on building production-grade backtesting systems that explicitly address biases and cost modeling, providing a more rigorous validation than simple historical performance checks.
Compared to similar skills
backtesting-frameworks side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| backtesting-frameworks (this skill) | 0 | 6mo | No flags | Advanced |
| backtesting-frameworks | 17 | 2mo | No flags | Advanced |
| miniqmt-skill | 0 | 6mo | Review | Intermediate |
| quant-analyst | 103 | 2mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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