Glossary
Sharpe Ratio
Risk-adjusted return measure: excess return per unit of total volatility. The default benchmark for systematic strategies — and the one most often abused.
Sentivue Capital··4 min read
The Sharpe ratio measures the excess return a strategy produces per unit of total volatility. It is the most widely cited risk-adjusted return metric in systematic trading.
Formula
Sharpe = (R_p − R_f) / σ_p
- R_p — strategy return (annualized)
- R_f — risk-free rate (annualized)
- σ_p — standard deviation of strategy returns (annualized)
Annualize daily Sharpe by multiplying by √252. For monthly data use √12.
How to interpret
| Sharpe | Reading |
|---|---|
| < 0.5 | Marginal — easily explained by sample noise |
| 0.5 – 1.0 | Acceptable but unremarkable for a systematic strategy |
| 1.0 – 2.0 | Solid; survives most institutional screens |
| 2.0 – 3.0 | Strong — verify it isn't an artifact of overfitting |
| > 3.0 | Suspicious. Almost always a backtest overfitting problem unless ex-post live |
Common traps
- Annualization mismatch. Annualizing high-frequency returns inflates Sharpe by the square root of the sampling frequency. A daily Sharpe of 0.2 is not equivalent to an annual Sharpe of 0.2.
- Treating volatility as risk. Sharpe penalizes upside volatility identically to downside. For asymmetric strategies (options selling, trend following) Sortino or Calmar frequently tell a more honest story.
- Look-ahead in σ. Computing σ on full-sample data leaks future information. Use rolling or expanding-window estimates.
- Short samples. Sharpe estimates carry meaningful standard error below ~3 years of data. See statistical significance in trading.
Related
- Sortino ratio — downside-only volatility
- Calmar ratio — return ÷ max drawdown
- Sharpe vs Sortino vs Calmar — choosing the right metric