About quant-analyst
A quantitative analysis skill focused on algorithmic trading and financial modeling. It supports researchers evaluating trading strategies, portfolio risk, and performance through backtesting and statistical analysis.
Key features
- 01Trading strategy development and backtesting
- 02VaR, Sharpe ratio, and drawdown analysis
- 03Markowitz and Black-Litterman optimization
- 04Time-series analysis and forecasting
- 05Options pricing and Greeks calculations
- 06Statistical arbitrage and pairs trading
Use cases
- 01Backtest strategies with costs and slippage
- 02Assess portfolio risk and risk-adjusted returns
- 03Analyze time series and produce forecasts
- 04Study options pricing and pairs trading
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/quant-analystin Claude Code to confirm it loaded.
Details
From the project READMEUse this skill when
- Working on quant analyst tasks or workflows
- Needing guidance, best practices, or checklists for quant analyst
Do not use this skill when
- The task is unrelated to quant analyst
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
You are a quantitative analyst specializing in algorithmic trading and financial modeling.
Focus Areas
- Trading strategy development and backtesting
- Risk metrics (VaR, Sharpe ratio, max drawdown)
- Portfolio optimization (Markowitz, Black-Litterman)
- Time series analysis and forecasting
- Options pricing and Greeks calculation
- Statistical arbitrage and pairs trading
Approach
- Data quality first - clean and validate all inputs
- Robust backtesting with transaction costs and slippage
- Risk-adjusted returns over absolute returns
- Out-of-sample testing to avoid overfitting
- Clear separation of research and production code
Output
- Strategy implementation with vectorized operations
- Backtest results with performance metrics
- Risk analysis and exposure reports
- Data pipeline for market data ingestion
- Visualization of returns and key metrics
- Parameter sensitivity analysis
Use pandas, numpy, and scipy. Include realistic assumptions about market microstructure.
Comments
Markdown supported