Does it provide guidance on data annualization?

Yes, it includes specific logic for scaling ratios correctly (e.g., using the square root of time) to ensure that monthly or daily data is correctly converted to annual figures.

Can this skill help compare different investment managers?

Yes, it specifically uses the Information Ratio and M-squared (Modigliani-Modigliani) to allow for fair, risk-adjusted comparisons between different strategies and benchmarks.

Is this suitable for non-normal return distributions?

Yes, the inclusion of the Omega ratio allows for analysis of the entire return distribution, capturing skewness and kurtosis that standard ratios might miss.

How does the skill handle downside risk?

It provides dedicated metrics like the Sortino ratio and downside capture analysis, which focus specifically on 'harmful' volatility rather than total volatility.

Which financial ratios does this skill support?

The skill supports a wide range of industry-standard metrics including Sharpe, Sortino, Information, Treynor, Calmar, and Omega ratios, as well as M-squared and capture ratios.

Performance Metrics for Wealth Management

Name: Performance Metrics for Wealth Management
Author: JoelLewis

byJoelLewis

•

分析与监控

Calculates and evaluates investment performance using industry-standard risk-adjusted ratios and capture analysis.

The Performance Metrics skill empowers Claude to conduct sophisticated risk-adjusted performance evaluations for investment strategies and portfolios. It automates the calculation and interpretation of essential financial ratios such as Sharpe, Sortino, and Treynor, while providing deeper insights through Omega ratios and M-squared analysis. This skill is ideal for wealth managers and analysts who need to compare different investment products, assess manager skill relative to benchmarks, or conduct thorough downside risk analysis to determine if an investment's returns justify its volatility.

主要功能

01Full-distribution gain-loss analysis using the Omega ratio

02Statistical guidance on proper annualization and risk-free rate application

03Comparative manager evaluation via M-squared and Information Ratios

04Comprehensive risk-adjusted ratio calculations including Sharpe, Sortino, and Treynor

05Advanced downside risk and capture analysis for better loss participation insight

0618 GitHub stars

使用场景

01Assessing a manager's ability to participate in market rallies while limiting losses during downturns

02Comparing two mutual funds with different volatility profiles to identify the better risk-adjusted choice

03Evaluating hedge fund performance relative to a specific minimum acceptable return (MAR)

主要功能

01Full-distribution gain-loss analysis using the Omega ratio

02Statistical guidance on proper annualization and risk-free rate application

03Comparative manager evaluation via M-squared and Information Ratios

04Comprehensive risk-adjusted ratio calculations including Sharpe, Sortino, and Treynor

05Advanced downside risk and capture analysis for better loss participation insight

0618 GitHub stars

使用场景

01Assessing a manager's ability to participate in market rallies while limiting losses during downturns

02Comparing two mutual funds with different volatility profiles to identify the better risk-adjusted choice

03Evaluating hedge fund performance relative to a specific minimum acceptable return (MAR)