backtest-expert
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Why use this skill?
Master systematic backtesting with expert guidance. Learn to stress test, validate, and build robust trading strategies with this professional framework.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/veeramanikandanr48/backtest-expertWhat This Skill Does
The backtest-expert skill provides a rigorous framework for developing and validating quantitative trading strategies. It serves as a methodology coach, moving you away from 'curve-fitting' and towards building robust systems that survive live market conditions. The skill implements a professional-grade workflow that emphasizes hypothesis definition, zero-discretion coding, extensive stress testing, and pessimistic performance modeling. It focuses on the principle of 'beating ideas to death'—challenging every assumption until only strategies that demonstrate genuine, persistent edges remain.
Installation
To install this skill, use the following command in your OpenClaw terminal:
clawhub install openclaw/skills/skills/veeramanikandanr48/backtest-expert
Use Cases
- Algorithm Development: Translating a trading idea into a strict, rule-based logic flow without subjective bias.
- Robustness Testing: Determining if a strategy's success is due to a persistent market edge or accidental parameter optimization.
- Performance Auditing: Analyzing existing backtest results to identify 'red flags' such as look-ahead bias, survivorship bias, or reliance on outlier trades.
- Regime Sensitivity Analysis: Evaluating how a strategy performs across diverse market conditions, including high-volatility crashes and extended bull runs.
- Execution Modeling: Integrating realistic slippage, commission costs, and latency factors to set achievable expectations for live trading.
Example Prompts
- "I have a mean-reversion strategy for stocks, but the results look too good to be true. Can you help me perform a parameter sensitivity analysis to see if it's overfitted?"
- "How should I adjust my backtesting logic to account for worst-case slippage and realistic execution fees on low-liquidity assets?"
- "My strategy performs well over a 10-year period, but it fails in the last two years. How can I diagnose if this is due to a structural market shift or poor rule design?"
Tips & Limitations
- Always start with a hypothesis: Do not test data blindly. If you cannot explain the 'why' behind the edge, the strategy is likely noise.
- Prioritize sample size: A strategy with only 20 trades is statistically insignificant. Aim for at least 100-200 trades for high confidence.
- Embrace pessimism: If your strategy stops being profitable after adding conservative slippage and commissions, it is not ready for production.
- Limitation: This skill is a consultant for logic and methodology; it does not execute live trades or pull live market data directly from your brokerage.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-veeramanikandanr48-backtest-expert": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
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