Sharpe Ratio Calculator

The Sharpe ratio is the most widely used metric for evaluating risk-adjusted investment performance. Developed by Nobel laureate William Sharpe, it measures how much excess return, above the risk-free rate, you receive for each unit of volatility you accept. A portfolio returning 10 percent with a standard deviation of 8 percent and a risk-free rate of 2 percent has a Sharpe ratio of 1.0. A ratio above 1 is generally considered good, above 2 is very good, and above 3 is excellent. Comparing Sharpe ratios allows you to evaluate whether a higher-returning portfolio is genuinely better or simply taking more risk to deliver the same risk-adjusted result.

Enter your portfolio's annual return, the current risk-free rate (typically a short-term government bond yield) and the standard deviation of your portfolio returns. The calculator produces the Sharpe ratio and categorises it against standard benchmarks. If you are comparing two portfolios, the one with the higher Sharpe ratio delivers more return per unit of risk, it is the more efficient portfolio regardless of which has the higher absolute return.

Evaluating a fund manager's performance by assessing whether returns are the result of skill and efficient risk management or simply higher risk exposure.
Comparing two investment strategies or portfolios with different return and volatility profiles to identify the more efficient one.
Assessing your own portfolio's risk-adjusted performance relative to a benchmark index.
When constructing a portfolio, identifying which combination of assets produces the highest Sharpe ratio, the efficient frontier.
Before increasing allocation to a high-return asset, to check whether it improves or degrades the portfolio's overall Sharpe ratio.

Sharpe Ratio: Excess return per unit of risk (standard deviation). A higher ratio indicates more efficient risk-adjusted performance. Above 1.0 is good; above 2.0 is excellent.

Risk-Free Rate: The return available on a zero-risk investment, typically short-term government bonds. The Sharpe ratio measures excess return above this baseline.

Standard Deviation: The statistical measure of return variability. A higher standard deviation means returns fluctuate more widely, more volatility, more risk.

Excess Return: Portfolio return minus the risk-free rate. This is the return you actually earn above what you could get from a completely safe investment.

The Sharpe ratio has important limitations that are frequently overlooked. It assumes returns are normally distributed, but many investment strategies have negatively skewed return distributions, they earn small steady gains but occasionally suffer large losses. Such strategies can show artificially high Sharpe ratios during calm periods. Always use the Sharpe ratio alongside other risk metrics rather than in isolation, and be cautious of strategies with very high ratios that seem too good to be true.

Use the Sortino Ratio Calculator for a better risk-adjusted measure when your portfolio has asymmetric downside risk. The Portfolio Standard Deviation Calculator will compute your volatility input. The Expected Return Calculator and Beta Calculator provide additional context for full risk-return portfolio analysis.

Frequently Asked Questions

A Sharpe ratio above 1.0 is generally considered good, meaning you earn more than one unit of excess return for each unit of risk. Above 2.0 is very good; above 3.0 is exceptional and often indicates either a genuinely superior strategy or measurement issues such as using too short a data period. Global equity indices have historically delivered Sharpe ratios of 0.3 to 0.5 over long periods. Hedge funds targeting Sharpe ratios above 1.0 are pursuing what most active managers cannot consistently sustain. When comparing funds or strategies, always use Sharpe ratios calculated over the same time period and benchmark.

Systematic risk, also called market risk, affects all investments and cannot be eliminated through diversification. Economic recessions, interest rate changes and geopolitical events are systematic risks. Unsystematic risk, also called idiosyncratic or specific risk, affects individual companies or sectors and can be reduced through diversification. Holding 20 to 30 uncorrelated stocks eliminates most unsystematic risk. The key insight from modern portfolio theory is that only systematic risk is compensated by higher expected returns, investors are not rewarded for taking unsystematic risk that could have been diversified away.

Value at Risk (VaR) estimates the maximum loss over a given period at a specified confidence level, for example, a 95 percent one-day VaR of €50,000 means you expect to lose no more than €50,000 on 95 out of 100 trading days. The critical limitation is that VaR says nothing about losses beyond the confidence threshold, the 5 percent of days not covered. In financial crises, losses frequently far exceed VaR estimates because the assumptions of normal return distributions break down precisely when tail risks materialise. VaR is best used as one risk metric among several, not as a standalone measure of portfolio risk.

A beta of 1.5 means your portfolio or investment moves 1.5 times as much as the market, in both directions. If the market falls 20 percent, a 1.5 beta portfolio would be expected to fall approximately 30 percent. High-beta portfolios outperform in strong bull markets and underperform significantly in bear markets. A beta below 1 indicates less sensitivity to market movements, defensive sectors like utilities and consumer staples typically have betas of 0.5 to 0.8. Beta is calculated from historical data and assumes the future relationship with the market will mirror the past, which is not always the case, particularly when a company's business model changes significantly.

CAPM remains the most widely taught and used framework for estimating the required return on an investment, despite well-documented empirical limitations. The model predicts that expected return equals the risk-free rate plus beta times the market risk premium. In practice, factors beyond beta, such as size, value, momentum and profitability, have been shown to explain additional return variation that CAPM misses. The Fama-French multi-factor models extend CAPM to capture these additional risk factors. For practical corporate finance and investment analysis, CAPM is used as a starting point and supplemented with judgment and sector-specific knowledge rather than applied mechanically.