Implementation:Avhz RustQuant Risk Reward

Knowledge Sources	RustQuant
Domains	Mathematics, Statistics
Last Updated	2026-02-07 19:00 GMT

Overview

Concrete tool for computing portfolio risk-reward measures provided by the RustQuant library.

Description

The PortfolioMeasures struct provides a collection of classical risk-adjusted return metrics used in portfolio performance evaluation. The struct holds the key portfolio statistics needed to compute various ratios:

Fields:

• r_p (f64) -- average return of the portfolio.
• r (f64) -- risk-free return over the same period.
• beta_p (f64) -- beta of the portfolio (systematic risk).
• sigma_p (f64) -- standard deviation of portfolio returns.
• sigma_down (f64) -- downside standard deviation (semistandard deviation).
• var (f64) -- Value-at-Risk.
• r_m (f64) -- expected market return.

Methods:

• treynors_ratio() -- Treynor (1965) ratio: (r_p - r) / beta_p. Measures excess return per unit of systematic risk.
• sharpe_ratio() -- Sharpe (1966) ratio: (r_p - r) / sigma_p. Measures excess return per unit of total risk.
• sortino_ratio() -- Sortino and Price (1994) ratio: (r_p - r) / sigma_down. Measures excess return per unit of downside risk.
• burke_ratio(drawdowns) -- Burke (1994) ratio: (r_p - r) / sum_of_squared_drawdowns. Takes a slice of drawdown values.
• return_on_var() -- Return on VaR: r_p / var.
• jensens_alpha() -- Jensen's Measure: r_p - (r + beta_p * (r_m - r)). Measures the abnormal return above the CAPM prediction.

Usage

Use these measures for portfolio performance evaluation, fund comparison, and risk management. The Sharpe ratio is the most widely used risk-adjusted performance metric. The Sortino ratio is preferred when downside risk is more relevant than total volatility. Jensen's alpha quantifies whether a portfolio manager has added value above the market benchmark.

Code Reference

Source Location

• Repository: RustQuant
• File: crates/RustQuant_math/src/risk_reward.rs
• Lines: 1-182

Signature

pub struct PortfolioMeasures {
    r_p: f64,
    r: f64,
    beta_p: f64,
    sigma_p: f64,
    sigma_down: f64,
    var: f64,
    r_m: f64,
}

impl PortfolioMeasures {
    pub fn treynors_ratio(&self) -> f64;
    pub fn sharpe_ratio(&self) -> f64;
    pub fn sortino_ratio(&self) -> f64;
    pub fn burke_ratio(&self, drawdowns: &[f64]) -> f64;
    pub fn return_on_var(&self) -> f64;
    pub fn jensens_alpha(&self) -> f64;
}

Import

use RustQuant::math::PortfolioMeasures;

I/O Contract

Inputs

Name	Type	Required	Description
r_p	f64	Yes	Average portfolio return.
r	f64	Yes	Risk-free return over the same period.
beta_p	f64	Yes	Portfolio beta (systematic risk measure).
sigma_p	f64	Yes	Standard deviation of portfolio returns.
sigma_down	f64	Yes	Downside standard deviation (semistandard deviation).
var	f64	Yes	Value-at-Risk of the portfolio.
r_m	f64	Yes	Expected market return.
drawdowns	&[f64]	For burke_ratio()	Slice of drawdown values.

Outputs

Name	Type	Description
treynors_ratio()	f64	Excess return per unit of systematic risk.
sharpe_ratio()	f64	Excess return per unit of total risk.
sortino_ratio()	f64	Excess return per unit of downside risk.
burke_ratio()	f64	Excess return per sum of squared drawdowns.
return_on_var()	f64	Portfolio return divided by VaR.
jensens_alpha()	f64	Abnormal return above CAPM prediction.

Usage Examples

use RustQuant::math::PortfolioMeasures;

let portfolio = PortfolioMeasures {
    r_p: 0.12,        // 12% average return
    r: 0.05,           // 5% risk-free rate
    beta_p: 1.2,       // portfolio beta
    sigma_p: 0.2,      // 20% standard deviation
    sigma_down: 0.1,   // 10% downside deviation
    var: 0.15,         // 15% VaR
    r_m: 0.1,          // 10% expected market return
};

// Risk-adjusted performance ratios
let treynor = portfolio.treynors_ratio();   // (0.12 - 0.05) / 1.2 = 0.0583
let sharpe = portfolio.sharpe_ratio();      // (0.12 - 0.05) / 0.2 = 0.35
let sortino = portfolio.sortino_ratio();    // (0.12 - 0.05) / 0.1 = 0.70
let ret_var = portfolio.return_on_var();    // 0.12 / 0.15 = 0.80
let alpha = portfolio.jensens_alpha();      // 0.12 - (0.05 + 1.2*(0.1-0.05)) = 0.01

// Burke ratio with drawdowns
let drawdowns = vec![0.05, 0.10, 0.20];
let burke = portfolio.burke_ratio(&drawdowns);

Related Pages

• Principle:Avhz_RustQuant_Risk_Reward_Metrics