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Sharpe ratio calculator — return per unit of risk
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Short answer
Sharpe ratio = (annual return − risk-free rate) ÷ annualised volatility of returns. A strategy returning 18% with 15% volatility against a 6.5% risk-free rate has a Sharpe of (18 − 6.5) ÷ 15 = 0.77. It measures how much excess return each unit of volatility bought: below ~0.5 is weak, around 1 is solid for a retail strategy, and sustained figures much above 2 in backtests usually signal overfitting rather than brilliance.
Sharpe ratio
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Excess return
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Reading
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Reference: annualising returns and volatility
Sharpe needs return and volatility on the same annual basis. Multiply the mean by the period count and the standard deviation by its square root.
| Return frequency | Periods per year | Multiply mean by | Multiply std dev by |
|---|---|---|---|
| Daily | 252 | 252 | 15.9 |
| Weekly | 52 | 52 | 7.2 |
| Monthly | 12 | 12 | 3.5 |
| Quarterly | 4 | 4 | 2 |
Raw return is the wrong scoreboard: 25% earned with wild 40% swings is a worse strategy than 15% earned smoothly, because the volatile version is harder to size, harder to hold through drawdowns, and more likely to end you before its average materialises. The Sharpe ratio is the standard correction — excess return divided by the volatility endured to earn it.
Enter your strategy’s annual return, the risk-free rate, and the annualised standard deviation of its returns. The calculator returns the Sharpe ratio and its plain-language reading.
The inputs, done honestly
Return and volatility must cover the same period and be annualised the same way. From monthly returns, annualise the mean by ×12 and the standard deviation by ×√12; from daily returns, the convention is √252 trading days. The risk-free rate should match the currency and period — for India, a short-term government yield in the 6–7% region is the usual proxy. A backtesting engine computes all of this from the equity curve directly; by hand, the annualisation step is where most Sharpe figures quietly go wrong.
Sample length matters more than precision. A Sharpe computed over six months is mostly noise; over three-plus years spanning different regimes it starts to mean something. And costs must already be inside the returns — a before-costs Sharpe on a high-frequency strategy is fiction, since charges scale with exactly the trading that generates the volatility.
Reading Sharpe — and its blind spots
Rules of thumb for after-cost, multi-year figures: below 0.5, the return barely compensates the ride; 0.7–1.0 is respectable for a retail systematic strategy; 1.0–1.5 is genuinely good; and backtests showing 2.5+ deserve suspicion before celebration — such figures survive out-of-sample far less often than they appear in-sample. For context, buy-and-hold equity indices have historically produced long-run Sharpes in the 0.4–0.6 neighbourhood.
Sharpe’s blind spots are real: it punishes upside volatility the same as downside (the Sortino ratio corrects this), it understates the risk of strategies with rare catastrophic losses — option selling’s smooth months hide fat left tails — and it says nothing about drawdown depth or duration. Read it alongside max drawdown and Calmar, never alone. INDfolio AI reports Sharpe, Sortino, Calmar and drawdown together on every backtest for exactly this reason.
These calculators are free, run entirely in your browser, and store nothing. They produce estimates for NSE trades based on published rates and standard formulas — not investment advice, and not a substitute for your broker’s contract note. INDfolio AI builds algo trading software; the honest connection is that our backtests apply this same cost arithmetic to every simulated trade.