backtesting-frameworks

name: backtesting-frameworks
description: 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.

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

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