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---
name: backtest-parameter-sensitivity-analysis
description: Use when evaluating strategy robustness by perturbing backtest parameters
across a grid and measuring Sharpe ratio sensitivity to detect overfitting sweet
spots vs genuinely robust parameter plateaus.
domain: algorithmic-trading
subdomain: backtesting-methodology
tags:
- backtesting-methodology
- parameter-sensitivity
- overfitting-detection
- grid-search
- robustness
- sharpe-surface
brokers_frameworks:
- Parameter Sensitivity Analyzer
- Python
version: "1.0.0"
author: algo-trading-skills-contributors
license: Apache-2.0
---
## When to Use
Invoke this skill after optimizing strategy parameters. A strategy whose Sharpe jumps from 0.5 to 3.0 with a $\pm 1\%$ parameter tweak is overfit. This skill systematically perturbs parameters across a grid and measures the gradient of Sharpe ratio to distinguish fragile peaks from robust plateaus.
## Prerequisites
- Strategy with tunable parameters (e.g., lookback window, entry threshold).
- Backtest engine that accepts parameter overrides and returns performance metrics.
## Workflow
1. **Define Parameter Grid**: Specify parameter ranges and step sizes.
2. **Run Grid Sweep**: Execute backtest for each parameter combination.
3. **Compute Sensitivity Gradient**: Measure $\Delta \text{Sharpe} / \Delta \text{Param}$ across neighbors.
4. **Classify Robustness**: Flag parameters with high gradient as fragile.
> Full procedure: see `references/workflows.md`.
## Common Pitfalls
- **Single-Parameter Analysis Only**: Ignoring interaction effects between correlated parameters.
- **Too Fine Grid**: Overfitting the grid search itself.
## Verification
- Run `python scripts/test_sensitivity_analyzer.py` — 100% pass rate.
## Related Skills
- `walk-forward-validation-setup`
- `multi-year-regime-coverage-requirement`
---