Performance Metrics
This one handles all the standard risk-adjusted performance metrics you'd need for comparing funds or strategies. It covers Sharpe, Sortino, Information Ratio, Treynor, Calmar, Omega, and upside/downside capture ratios, plus M-squared. The formulas are straightforward and it includes annualization guidance, which matters because getting the sqrt(12) wrong on monthly Sharpe is a classic mistake. The worked examples walk through actual calculations and there's a nice comparison showing when Sortino trumps Sharpe for downside-focused investors. Use this when you need to evaluate whether a manager's returns justify the risk they took, or when someone asks if a fund is worth its volatility. The math is clean and the pitfalls section catches the usual errors.
- skills.sh installs
- 746
- GitHub stars
- 224
- last updated
- Aug 21, 2026
Install
$ npx -y skills add joellewis/finance_skills --skill performance-metrics --agent claude-codeInstalls into .claude/skills of the current project.
Files
Performance Metrics
Core Concepts
Sharpe Ratio
The most widely used risk-adjusted performance measure. It divides excess return (over the risk-free rate) by total volatility.
SR = (R_p - R_f) / sigma_p
- R_p: annualized portfolio return
- R_f: annualized risk-free rate
- sigma_p: annualized portfolio volatility (standard deviation of returns)
A higher Sharpe ratio indicates more return per unit of total risk. Typical benchmarks: SR < 0.5 is poor, 0.5-1.0 is acceptable, > 1.0 is strong, > 2.0 is exceptional.
Annualization: If computed from monthly data, SR_annual = SR_monthly * sqrt(12).
Sortino Ratio
Replaces total volatility with downside deviation, penalizing only harmful volatility (returns below a Minimum Acceptable Return).
Sortino = (R_p - R_f) / sigma_downside
where sigma_downside = sqrt((1/n) * sum(min(R_i - MAR, 0)^2)).
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- Repository
- github.com/joellewis/finance_skills
- Categories
- Debugging, Finance & Trading
- First seen
- Jun 3, 2026
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