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JoelLewis
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historical-risk

Quantify historical investment risk using Python volatility estimators and drawdown analysis.

164|33|Updated Feb 15, 2026
Search Tags:#risk management#portfolio analysis#historical data#VaR#volatility#drawdown
One-click install
npx skills add https://github.com/JoelLewis/finance_skills --skill historical-risk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: historical-risk
Source: https://github.com/JoelLewis/finance_skills/tree/main/plugins/wealth-management/skills/historical-risk
Command: npx skills add https://github.com/JoelLewis/finance_skills --skill historical-risk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, and includes scripts (resource) components.

What problem does it solve?

This Skill helps users understand and quantify the historical risk of an investment or portfolio by calculating various risk metrics from past performance data.

Core Features & Use Cases

  • Volatility Estimation: Calculate annualized volatility using different methods (close-to-close, Parkinson, Yang-Zhang).
  • Drawdown Analysis: Measure maximum drawdown, drawdown duration, and recovery time.
  • Downside Risk: Compute historical Value-at-Risk (VaR), downside deviation, and semi-variance.
  • Tracking Error: Assess how closely a portfolio tracks its benchmark.
  • Use Case: A portfolio manager wants to understand the worst-case historical loss for a fund over the last year. They can use this Skill to calculate the maximum drawdown and 95% historical VaR.

Quick Start

Calculate the annualized volatility and maximum drawdown for the provided daily return data.

Frequently Asked Questions about historical-risk

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate historical VaR and maximum drawdown from past return data?▼

You can calculate historical VaR and maximum drawdown by processing historical return data through Python scripts to quantify realized investment risk, measuring worst-case historical losses and peak-to-trough declines.

What is the difference between close-to-close, Parkinson, and Yang-Zhang volatility estimators?▼

These volatility estimators use different price inputs: close-to-close uses daily closing prices, Parkinson incorporates intraday high-low ranges, and Yang-Zhang combines overnight and open-to-close volatility for more comprehensive annualized volatility measurement.

How do I measure downside deviation and tracking error for a portfolio?▼

Downside deviation and tracking error are calculated by applying Python scripts to historical return data, measuring below-target volatility and the dispersion between portfolio returns and benchmark returns respectively.

Can I use numpy to quantify realized investment risk for retrospective portfolio analysis?▼

Yes, this Skill uses numpy as its dependency to run Python scripts that calculate risk metrics like historical VaR, volatility estimators, and drawdown analysis for retrospective investment portfolio assessment.

What's the best way to assess historical investment risk using Python scripts?▼

The best way is running Python scripts that compute multiple risk metrics from historical return data, including volatility estimators, drawdown analysis, historical VaR, downside deviation, and tracking error for comprehensive retrospective risk assessment.

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