Backtesting Frameworks

This framework builds event-driven and vectorized backtesting engines that handle the messy realities of strategy validation. It implements proper order execution with slippage and commission models, maintains point-in-time data integrity to prevent look-ahead bias, and includes survivorship bias checks by tracking delisted securities. The walk-forward analysis component automatically splits your data into training, validation, and test sets to catch overfitting before you deploy capital. You get equity curves, drawdown analysis, and trade-level attribution out of the box. Essential when you need to move beyond toy backtests and build something that actually reflects real trading conditions.

skills.sh installs: 17.5K
GitHub stars: 42.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add wshobson/agents --skill backtesting-frameworks --agent claude-code

Installs into .claude/skills of the current project.

Files

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