What metrics are calculated in the provided frameworks?

The frameworks generate standard financial metrics including Sharpe Ratio, Maximum Drawdown, Annualized Volatility, and Win Rate.

Does it support walk-forward optimization?

Yes, it includes patterns for implementing walk-forward analysis to ensure strategies remain robust across different market regimes.

How does this skill help avoid look-ahead bias?

It enforces point-in-time data handling and provides specific patterns for shifting signals to ensure future data is never used during strategy execution.

Can I use this for high-frequency trading backtesting?

Yes, the skill includes event-driven patterns suitable for detailed tick-by-tick simulation as well as vectorized patterns for rapid testing of lower-frequency strategies.

Quantitative Backtesting Frameworks

Name: Quantitative Backtesting Frameworks
Author: as4584

byas4584

데이터 과학 및 ML

Builds robust, production-grade backtesting systems for trading strategies while eliminating common statistical biases.

소개

This skill provides specialized guidance for developing quantitative trading backtests, helping engineers and traders avoid critical pitfalls like look-ahead and survivorship bias. It offers implementation patterns for both high-performance vectorized backtesters and detailed event-driven engines, alongside advanced validation techniques like walk-forward analysis. Use it to build reliable infrastructure that produces accurate strategy performance estimates, realistic transaction cost models, and rigorous out-of-sample testing workflows.

주요 기능

Event-driven and vectorized backtesting implementation patterns
0 GitHub stars
Realistic transaction cost modeling including slippage and commissions
Standardized performance metrics like Sharpe ratio and Max Drawdown
Advanced walk-forward analysis and out-of-sample validation
Built-in mitigation for look-ahead, survivorship, and selection biases

사용 사례

Implementing rigorous cross-validation for quantitative financial models
Building a custom, high-performance backtesting engine in Python
Validating a new algorithmic trading strategy before live deployment

소개

주요 기능

Event-driven and vectorized backtesting implementation patterns
0 GitHub stars
Realistic transaction cost modeling including slippage and commissions
Standardized performance metrics like Sharpe ratio and Max Drawdown
Advanced walk-forward analysis and out-of-sample validation
Built-in mitigation for look-ahead, survivorship, and selection biases

사용 사례

Implementing rigorous cross-validation for quantitative financial models
Building a custom, high-performance backtesting engine in Python
Validating a new algorithmic trading strategy before live deployment