Does it handle risk management?

Yes, the skill specializes in calculating risk metrics such as Value at Risk (VaR), Sharpe ratios, and drawdowns to ensure strategies are evaluated on a risk-adjusted basis.

Is it suitable for options trading?

Absolutely. It includes capabilities for options pricing, Greeks calculation, and volatility analysis to support derivative-based strategies.

What libraries does the Quant Analyst skill use?

This skill primarily utilizes Python’s data science stack, specifically pandas, numpy, and scipy, to perform high-performance vectorized operations.

Can I use this skill for live trading?

While it generates production-ready code for strategy logic and data pipelines, it is designed for research and modeling; users should integrate it with their own brokerage APIs for live execution.

Quantitative Financial Analyst

Name: Quantitative Financial Analyst
Author: sickn33

bysickn33

•

15,684

•

Data Science & ML

Builds sophisticated financial models, backtests trading strategies, and implements risk management metrics using vectorized operations.

The Quantitative Financial Analyst skill transforms Claude into a specialized expert for algorithmic trading and financial engineering. It provides a robust framework for developing trading strategies, performing out-of-sample backtesting, and calculating critical risk-adjusted performance metrics like the Sharpe ratio and Value at Risk (VaR). By prioritizing data quality and realistic market assumptions, this skill helps users build production-ready pipelines for portfolio optimization, statistical arbitrage, and time series forecasting while ensuring clear separation between research and implementation.

Key Features

01Algorithmic trading strategy development and vectorized backtesting

02Time series forecasting and statistical arbitrage modeling

03Advanced risk analysis including VaR, Sharpe ratio, and Max Drawdown

0415,684 GitHub stars

05Portfolio optimization using Markowitz and Black-Litterman models

06Automated data cleaning and market data ingestion pipelines

Use Cases

01Backtesting a momentum-based trading strategy with realistic slippage and transaction costs

02Optimizing a multi-asset portfolio for maximum risk-adjusted returns

03Developing an options pricing engine with Greeks calculation and sensitivity analysis

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sickn33/antigravity-awesome-skills quant-analyst

For use in Claude.ai and ChatGPT

Download Skill