Backtest Expert
The Backtest Expert analyzes trading strategies against historical data to assess their profitability and risk, optimizing investment decisions.
Install on your platform
We auto-selected Claude Code based on this skillβs supported platforms.
Run in terminal (recommended)
claude mcp add nicepkg-backtest-expert npx -- -y @trustedskills/nicepkg-backtest-expert
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"nicepkg-backtest-expert": {
"command": "npx",
"args": [
"-y",
"@trustedskills/nicepkg-backtest-expert"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The Backtest Expert skill provides a systematic methodology for analyzing trading strategies against historical data, prioritizing robustness over simply maximizing paper profits. It guides users through a rigorous backtesting process that includes hypothesis definition, rule-based coding of strategy logic, stress testing parameters and execution conditions, and out-of-sample validation to assess real-world viability. The goal is to identify strategies that perform consistently well under pessimistic conditions, increasing the likelihood of success in live trading.
When to use it
- Developing or validating systematic trading strategies
- Evaluating a trading idea's robustness before live implementation
- Troubleshooting misleading backtest results
- Learning proper backtesting methodology and avoiding common pitfalls like curve-fitting and look-ahead bias
- Assessing how sensitive a strategy is to parameter changes and market conditions
Key capabilities
- Hypothesis Definition: Guides users in clearly articulating the trading edge.
- Rule-Based Coding: Enforces complete specificity and eliminates subjective judgment when defining entry, exit, position sizing, filters, and universe criteria.
- Stress Testing: Facilitates testing parameter sensitivity (stop loss, profit target, timing) and execution friction (slippage, order rejections).
- Time Robustness Analysis: Enables assessment of year-by-year performance and regime dependence.
- Sample Size Validation: Provides guidelines for ensuring sufficient trade volume for reliable results.
- Out-of-Sample Validation: Supports walk-forward analysis to optimize on one period and test on another.
Example prompts
- "Help me define a hypothesis for a trading strategy based on stocks gapping up on earnings."
- "What are the key considerations when stress testing my backtest for parameter sensitivity?"
- βGuide me through setting up a walk-forward analysis to validate this trading strategy.β
Tips & gotchas
- Clear Hypothesis is Key: The skill emphasizes defining your trading edge in a single, clear sentence before proceeding.
- Rule-Based Only: All aspects of the strategy (entry, exit, position sizing) must be defined with unambiguous rules β no subjective judgment allowed.
- Stress Testing is Crucial: Allocate most of your testing time to stress testing parameters and execution conditions.
Tags
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Passed automated security scans.