Does it include performance metrics calculation?

Yes, the framework provides built-in logic to calculate standard industry metrics such as Sharpe Ratio, annual volatility, Max Drawdown, and win rates.

How does this skill help avoid look-ahead bias?

It provides standardized patterns for using point-in-time data and properly shifting signals to ensure trade decisions are only made using information available at the specific historical moment.

Can I use this for both high-frequency and long-term strategies?

Yes, it includes event-driven patterns for granular, tick-by-tick simulation as well as vectorized patterns for rapid testing of daily or weekly signals.

Can this skill help with parameter optimization?

Absolutely. It includes implementation patterns for Walk-Forward Optimization (WFO) to help you find stable parameters while minimizing the risk of historical curve-fitting.

Quantitative Trading Backtester

Name: Quantitative Trading Backtester
Author: nguyendinhquocx

bynguyendinhquocx

0•

Data Science & ML

Builds robust, production-grade backtesting systems for trading strategies while mitigating common statistical biases and transaction costs.

The backtesting-frameworks skill provides specialized guidance for developing sophisticated quantitative trading infrastructure within Claude Code. It enables developers to create reliable strategy performance estimates by implementing both event-driven and high-speed vectorized backtesting patterns. By focusing on rigorous validation techniques—such as walk-forward analysis, out-of-sample testing, and realistic transaction cost modeling—this skill helps users avoid common pitfalls like look-ahead and survivorship bias. It is an essential tool for quantitative researchers and financial engineers who need to validate algorithmic trading strategies before live deployment.

Key Features

01Provides realistic transaction cost modeling including slippage and commissions

02Supports walk-forward analysis and multi-period optimization workflows

03Implements both event-driven and high-speed vectorized backtesting architectures

04Generates comprehensive performance metrics like Sharpe Ratio and Max Drawdown

05Mitigates look-ahead and survivorship biases with point-in-time data handling

060 GitHub stars

Use Cases

01Optimizing strategy parameters using out-of-sample testing to prevent overfitting

02Building a custom, event-driven backtesting engine for high-frequency strategies

03Validating a new alpha factor or trading signal against historical market data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nguyendinhquocx/code-ai backtesting-frameworks

For use in Claude.ai and ChatGPT

Download Skill