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Seth Hobson
2026-01-19 17:07:03 -05:00
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14
plugins/quantitative-trading/skills/backtesting-frameworks/SKILL.md
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@@ -20,13 +20,13 @@ Build robust, production-grade backtesting systems that avoid common pitfalls an
### 1. Backtesting Biases
| Bias | Description | Mitigation |
|------|-------------|------------|
| **Look-ahead** | Using future information | Point-in-time data |
| Bias | Description | Mitigation |
| ---------------- | ------------------------- | ----------------------- |
| **Look-ahead** | Using future information | Point-in-time data |
| **Survivorship** | Only testing on survivors | Use delisted securities |
| **Overfitting** | Curve-fitting to history | Out-of-sample testing |
| **Selection** | Cherry-picking strategies | Pre-registration |
| **Transaction** | Ignoring trading costs | Realistic cost models |
| **Overfitting** | Curve-fitting to history | Out-of-sample testing |
| **Selection** | Cherry-picking strategies | Pre-registration |
| **Transaction** | Ignoring trading costs | Realistic cost models |
### 2. Proper Backtest Structure
@@ -641,6 +641,7 @@ def calculate_metrics(returns: pd.Series, rf_rate: float = 0.02) -> Dict[str, fl
## Best Practices
### Do's
- **Use point-in-time data** - Avoid look-ahead bias
- **Include transaction costs** - Realistic estimates
- **Test out-of-sample** - Always reserve data
@@ -648,6 +649,7 @@ def calculate_metrics(returns: pd.Series, rf_rate: float = 0.02) -> Dict[str, fl
- **Monte Carlo analysis** - Understand uncertainty
### Don'ts
- **Don't overfit** - Limit parameters
- **Don't ignore survivorship** - Include delisted
- **Don't use adjusted data carelessly** - Understand adjustments