Sharpe Ratio Calculator
Annualise a strategy's risk-adjusted return from its periodic mean return, volatility and the risk-free rate.
Quick answer: The Sharpe ratio measures excess return per unit of total volatility. This tool takes the mean and standard deviation of your periodic returns, subtracts the per-period risk-free rate from the mean, divides by the standard deviation, and scales the result by the square root of the number of periods per year to give an annualised figure. Higher is better; it rewards steady returns and penalises volatility.
How to use it
Enter the mean and standard deviation of your returns for one period (for daily data use daily figures) and the number of such periods in a year (about 252 trading days). The annual risk-free rate is converted to a per-period rate before subtraction. The output is the annualised Sharpe ratio. Convention: the risk-free rate is divided by periods per year to match the period of your returns.
Formula
Sharpe = ( ( Mean − Risk-free ÷ Periods ) ÷ Std deviation ) × √Periods
Mean and Std deviation are per-period percentages; the annual risk-free rate is divided by periods per year to bring it to the same period. Percentage units cancel in the ratio.
Limitations of the Sharpe Ratio Calculator
The Sharpe Ratio Calculator is a teaching aid, not a live risk system. It does not model the following:
- Selection and multiple-testing bias — a Sharpe chosen as the best of many trials is inflated; the deflated Sharpe ratio corrects for this
- Non-normal returns; Sharpe penalises upside and downside volatility equally and understates tail risk from skewed or fat-tailed distributions
- Autocorrelation and illiquidity, which can bias the volatility estimate downward and overstate Sharpe
- The choice of risk-free rate and annualisation factor, both of which change the number
Frequently asked questions
What is a good backtested Sharpe ratio?
As a loose convention, below 1 is modest, 1 to 2 respectable and above 2 excellent — but only for honest, out-of-sample, cost-inclusive returns. A high in-sample Sharpe produced by searching many configurations is not the same thing and is usually inflated.
Should I apply a deflated Sharpe to this calculator's result?
It adjusts an observed Sharpe for the number of strategy configurations you tested, the track-record length and the skew and kurtosis of returns, yielding the probability that the true Sharpe exceeds zero. It corrects the upward bias created by keeping the best of many backtests.
Why does a backtested Sharpe fall out-of-sample and after costs?
Because costs lower the mean return, and out-of-sample data removes the fit-to-noise that flattered the in-sample figure. The effect is largest for high-turnover strategies, so a gross in-sample Sharpe can look far better than the achievable net, out-of-sample number.
Why can an option-selling backtest show a misleadingly high Sharpe?
Because it books many small premiums and rarely realises its tail, so standard deviation understates the true risk. The Sharpe looks excellent until a single gap event delivers the catastrophic loss the volatility estimate never captured — a classic reason a smooth backtest lies.
How does autocorrelation distort a backtested Sharpe?
The square-root-of-time annualisation assumes returns are independent. Positive autocorrelation makes it understate volatility and overstate the annual Sharpe; negative autocorrelation does the reverse. Trend and mean-reversion backtests routinely violate the independence assumption.
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