Sharpe, Sortino & Calmar Ratio Calculator

As a rough, widely-cited guide: below 1.0 is considered subpar, 1.0-2.0 is good, 2.0-3.0 is very good, and above 3.0 is excellent — but these thresholds depend heavily on the return period and asset class, and a Sharpe ratio computed from 12 monthly returns carries far less statistical confidence than one from 5 years of daily returns. Use the win rate significance calculator's logic as a mental model: a great-looking ratio from a short series is weaker evidence than a good-looking ratio from a long one.

Sortino's downside deviation formula has a real ambiguity in practice: some calculators divide the sum of squared downside deviations by the count of losing periods only, others by the total period count. This calculator uses the total period count (the convention in Sortino & Price's original 1994 paper), which produces a smaller, more conservative downside deviation than the losing-periods-only version — if another calculator shows a notably higher Sortino on the same data, this is almost always why.

Sharpe's standard deviation penalizes upside volatility exactly as much as downside volatility — a strategy with occasional large gains looks 'riskier' by Sharpe even though large gains are not the risk anyone actually minds. Sortino only penalizes deviations below the target (the risk-free rate here), which better matches how most people intuitively think about risk.

Calmar uses maximum drawdown instead of a standard-deviation-based measure — the single worst peak-to-trough decline in the equity curve, which is what actually determines whether an investor can psychologically or financially tolerate holding a strategy through its worst period. A strategy can have a strong Sharpe ratio with volatility spread evenly across many small moves, yet still have a brutal single drawdown that Sharpe doesn't specifically flag — Calmar does.

There's no universal cutoff, but the same statistical logic as the win rate significance calculator applies: a Sharpe ratio computed from 10-12 data points has enormous sampling uncertainty and can swing wildly with one or two outlier periods. As a practical floor, most practitioners want at least 30-36 periods (2.5-3 years of monthly data, or several months of daily data) before treating these ratios as more than a rough first look.