Backtesting Frameworks

When to use this skill

Use Backtesting Frameworks when an AI agent needs a reusable SKILL.md workflow for this job: Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.

When not to use it

Skip Backtesting Frameworks when the task is outside the analysis category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/backtesting-frameworks/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/backtesting-frameworks/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# Backtesting Frameworks

Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.

## When to Use This Skill

- Developing trading strategy backtests - Building backtesting infrastructure - Validating strategy performance - Avoiding common backtesting biases - Implementing walk-forward analysis - Comparing strategy alternatives

## Core Concepts

### 1. Backtesting Biases

| 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 |

### 2. Proper Backtest Structure

``` Historical Data │ ▼ ┌─────────────────────────────────────────┐ │ Training Set │ │ (Strategy Development & Optimization) │ └─────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Validation Set │ │ (Parameter Selection, No Peeking) │ └─────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Test Set │ │ (Final Performance Evaluation) │ └─────────────────────────────────────────┘ ```

### 3. Walk-Forward Analysis

``` Window 1: [Train──────][Test] Window 2: [Train──────][Test] Window 3: [Train──────][Test] Window 4: [Train──────][Test] ─────▶ Time ```

## Detailed worked examples and patterns

Detailed sections (starting with `## Implementation Patterns`) live in `references/details.md`. Read that file when the navigation summary above is insufficient.

## 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 - **Use walk-forward** - Not just train/test - **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 - **Don't optimize on full history** - Reserve test set - **Don't ignore capacity** - Market impact matters

Intended uses

• Developing trading strategy backtests
• Building backtesting infrastructure
• Validating strategy performance
• Avoiding common backtesting biases
• Implementing walk-forward analysis
• Comparing strategy alternatives