backtest-expertlisted

# Backtest Expert Systematic approach to backtesting trading strategies based on professional methodology that prioritizes robustness over optimistic results. ## Core Philosophy **Goal**: Find strategies that "break the least", not strategies that "profit the most" on paper. **Principle**: Add friction, stress test assumptions, and see what survives. If a strategy holds up under pessimistic conditions, it's more likely to work in live trading. ## When to Use This Skill Use this skill when: - Developing or validating systematic trading strategies - Evaluating whether a trading idea is robust enough for live implementation - Troubleshooting why a backtest might be misleading - Learning proper backtesting methodology - Avoiding common pitfalls (curve-fitting, look-ahead bias, survivorship bias) - Assessing parameter sensitivity and regime dependence - Setting realistic expectations for slippage and execution costs ## Prerequisites - Python 3.9+ (for evaluation script) - No API keys required - No external data dependencies — metrics are user-provided ## Workflow ### 1. State the Hypothesis Define the edge in one sentence. **Example**: "Stocks that gap up >3% on earnings and pull back to previous day's close within first hour provide mean-reversion opportunity." If you can't articulate the edge clearly, don't proceed to testing. ### 2. Codify Rules with Zero Discretion Define with complete specificity: - **Entry**: Exact conditions, timing, price type - **Exit**: St