What types of backtesters does this skill support?

It supports both event-driven backtesters for high-fidelity simulation of order execution and vectorized backtesters for rapid strategy iteration and large-scale optimization.

Can I customize transaction costs and slippage?

Yes, it includes implementation patterns for realistic slippage models (bps or fixed) and commission structures to ensure backtest results reflect actual net performance.

Is walk-forward optimization included?

Yes, the skill provides code patterns for both anchored and rolling walk-forward analysis to validate how a strategy might perform as market regimes change over time.

How does it handle common backtesting biases?

The skill provides architectural patterns to prevent look-ahead bias using point-in-time data, account for survivorship bias, and avoid overfitting through systematic out-of-sample testing.

Quantitative Backtesting Frameworks

Name: Quantitative Backtesting Frameworks
Author: EngineerWithAI

byEngineerWithAI

Data Science & ML

Builds production-grade backtesting systems for trading strategies while mitigating common biases and accounting for realistic market costs.

About

This skill provides specialized patterns and logic for developing robust quantitative trading backtests within Claude Code. It enables developers to construct both event-driven and vectorized backtesting architectures that handle complex factors like slippage, commissions, and look-ahead bias. By implementing industry-standard validation methods such as walk-forward analysis and out-of-sample testing, it ensures that strategy performance estimates are reliable and ready for live market transitions.

Key Features

Supports both event-driven and fast vectorized backtesting patterns
Provides automated walk-forward optimization and out-of-sample validation
Mitigates look-ahead, survivorship, and selection biases
0 GitHub stars
Implements realistic transaction cost models including slippage and commissions
Calculates key performance metrics like Sharpe ratio and Max Drawdown

Use Cases

Optimizing strategy parameters using rigorous walk-forward analysis
Building custom backtesting infrastructure with modular execution models
Developing and validating algorithmic trading strategies before deployment

About

Key Features

Supports both event-driven and fast vectorized backtesting patterns
Provides automated walk-forward optimization and out-of-sample validation
Mitigates look-ahead, survivorship, and selection biases
0 GitHub stars
Implements realistic transaction cost models including slippage and commissions
Calculates key performance metrics like Sharpe ratio and Max Drawdown

Use Cases

Optimizing strategy parameters using rigorous walk-forward analysis
Building custom backtesting infrastructure with modular execution models
Developing and validating algorithmic trading strategies before deployment