Sharpe Ratio

What it does

Calculates excess return divided by volatility, with annualization and an approximate standard error.

Use it to compare return per unit of total risk.

Data you need

• Return column

Optional: Risk-free rate

Supported data: Numeric. Level: Beginner.

Formula and method

S = mean(R − Rf) / sd(R − Rf) · annual S = S × √P

The per-period risk-free rate is subtracted from each return. Mean excess return is divided by its sample standard deviation, multiplied by √P to annualize, and given an approximate standard error that allows for skewness and kurtosis.

How to read the result

Compare only over the same period and frequency.

Worked example

Illustrative run on Simulated weekly returns (teaching data, not market data). These figures are the actual output of this model on that example, and they say nothing about your own data.

• Annualized Sharpe ratio = 1.2896.
• A higher value means more excess return per unit of total risk in this sample.

Limitations

• Educational use, not investment advice
• Treats upside and downside alike

Numerical validation

Validated 14 of 14 automated checks passed, including numerical results, invalid input, missing values, small samples, constant variables, charts and the interpretation text.

Computed with python 3.13.2, numpy 2.2.5, scipy 1.14.1, pandas 2.3.3, statsmodels 0.14.4, scikit-learn 1.7.0, in your browser. A passing check confirms the listed comparison only. It does not make any result suitable for your data, and it is not a claim of equivalence with other statistical software.

Questions

How is the ratio annualized?

By multiplying by the square root of the number of periods per year.