# Sortino Ratio

> The Sortino ratio measures return per unit of downside risk. Learn the formula, how to calculate downside deviation correctly, and how it compares to Sharpe.

Canonical: https://www.journalx.io/glossary/sortino-ratio

The **Sortino ratio** is a risk-adjusted return measure that only counts the volatility you don't want. Like the [Sharpe ratio](/glossary/sharpe-ratio), it divides the return a strategy earned above a benchmark by a measure of risk. The difference is the risk measure. The Sharpe ratio uses standard deviation, which treats a +5% day exactly like a −5% day. The Sortino ratio uses **downside deviation**, which only looks at returns that fell below a target you choose.

That matches how traders experience risk. Nobody loses sleep over a big winning day. The ratio is named after Frank A. Sortino, who developed downside-risk measurement in the 1980s with Brian Rom, and it is now a standard companion to the Sharpe ratio in performance reports. The [CFA Institute's overview](https://rpc.cfainstitute.org/-/media/documents/code/gips/the-sortino-ratio.pdf) covers its history and variants.

## How the Sortino Ratio Works

> Sortino ratio = (Average return − Target return) ÷ Downside deviation
>
> Downside deviation = √( Sum of squared shortfalls below the target ÷ Number of periods )

The **target return**, also called the minimum acceptable return, is the line between a result you're fine with and one you count as a loss. Traders usually set it at 0%. Fund analysts often use the risk-free rate or a required return.

A period's **shortfall** is how far its return fell below the target. A period that beat the target has a shortfall of zero. It still counts toward the number of periods, it just adds nothing to the sum. That one detail is where most Sortino calculations go wrong.

## How to Calculate the Sortino Ratio: A Worked Example

Take a year of monthly returns with a 0% target:

> +4.0%, −2.0%, +1.5%, +3.0%, −3.5%, +2.5%, +0.5%, −1.0%, +5.0%, −2.5%, +3.0%, +1.5%

1. **Average return.** The twelve months add up to 12%, so the average is 1.0% a month.
2. **Shortfalls.** Four months fell below 0%. Their squared shortfalls are 4.00, 12.25, 1.00 and 6.25, which sum to 23.5. The other eight months contribute zero.
3. **Downside deviation.** Divide by all 12 months, not the 4 losing ones: 23.5 ÷ 12 = 1.96. The square root is **1.40% a month**.
4. **Sortino ratio.** 1.0 ÷ 1.40 = 0.71 a month. Multiply by the square root of 12 (3.46) to annualize: **2.48**.

![Twelve monthly returns against a 0% target, with the four losing months counted as shortfalls and the eight winning months ignored, giving a downside deviation of 1.40% a month and a Sortino ratio of 2.48](https://assets.journalx.io/marketing/glossary/sortino-ratio/sortino-ratio-downside-months.avif)

For comparison, the standard deviation of the same twelve months is 2.72%, which gives an annualized Sharpe ratio (with a 0% risk-free rate) of 1.27. The Sortino ratio is higher because its denominator ignores the eight months that went your way. For daily data, annualize with the square root of 252 instead.

## Sortino Ratio vs Sharpe Ratio

|                     | Sharpe ratio                   | Sortino ratio                       |
| ------------------- | ------------------------------ | ----------------------------------- |
| Risk measure        | Standard deviation, all swings | Downside deviation, only shortfalls |
| Benchmark           | Risk-free rate                 | A target you choose, often 0%       |
| Big winning days    | Lower the ratio                | Ignored                             |
| On the same returns | Lower                          | Usually higher                      |
| Most useful when    | Returns are roughly symmetric  | Returns are skewed one way          |

The difference matters most when returns are lopsided. Here are two strategies with the same average daily return and the same standard deviation, so the same Sharpe ratio:

![Two return histograms with the same mean and standard deviation: Strategy A with many small losses and a few large wins has a Sharpe ratio of 1.19 and a Sortino ratio of 2.46, while Strategy B with many small wins and a few large losses has the same Sharpe ratio of 1.19 but a Sortino ratio of 1.47](https://assets.journalx.io/marketing/glossary/sortino-ratio/sortino-ratio-skew.avif)

Strategy A loses on 57% of days, but its worst day is −0.9% and its best is +4.9%. That's the shape of a trend follower, cutting losers quickly and letting a few winners run. Strategy B wins on 63% of days, but its worst day is −4.8%. That's the shape of a strategy that collects small gains and occasionally takes a large hit, like selling options.

Both have a Sharpe ratio of 1.19, because standard deviation measures how far returns spread and not which direction. The Sortino ratio separates them: 2.46 for A and 1.47 for B. Most traders would rather live with A's losing days than B's, and the Sortino ratio agrees.

With roughly bell-shaped daily returns, and both ratios measured against 0%, the Sortino ratio comes out at about 1.5 times the Sharpe ratio. Well above that, like Strategy A at 2.1 times, means your volatility is mostly upside. Close to the Sharpe ratio, like Strategy B at 1.2 times, means the losses are doing the swinging.

## Key Takeaways

- The Sortino ratio is return above a target divided by downside deviation, so only below-target returns count as risk.
- Downside deviation squares each shortfall, sums them, and divides by every period, including the winning ones.
- On the same data, the Sortino ratio is usually higher than the Sharpe ratio, so the Sharpe rules of thumb don't apply to it.
- Two strategies with identical Sharpe ratios can have Sortino ratios that are far apart when one carries large occasional losses.
- Comparing the two ratios on your own daily returns tells you which side your volatility comes from.

## Common Mistakes

**Dividing by the number of losing periods.** In the example above, dividing 23.5 by 4 instead of 12 gives a downside deviation of 2.42% and a Sortino ratio of 1.43 instead of 2.48. The fewer losing periods you have, the bigger this error gets.

**Taking the standard deviation of the losing periods.** This measures how similar your losses are to each other, not how big they are. Four straight losses of −10% have a standard deviation of zero, which would make the ratio infinite, the example Red Rock Capital uses in [Sortino: A "Sharper" Ratio](https://www.cmegroup.com/education/files/rr-sortino-a-sharper-ratio.pdf). On the example's four losing months it gives 1.04% and a Sortino ratio of 3.33.

**Mixing targets.** If you subtract the risk-free rate in the numerator, measure shortfalls against the risk-free rate too. Using 0% in one place and 4% in the other produces a number that means nothing.

**Trusting a short record.** Downside deviation rests on the losing periods only, and a quarter of weekly data might hold only a handful. As with the [Sharpe ratio](/glossary/sharpe-ratio), give the number a long record before it drives decisions.

**Treating it as a full picture of risk.** The Sortino ratio averages your shortfalls. It doesn't show how they cluster into a [drawdown](/glossary/drawdown) or how large the single worst loss was. Read it next to your maximum drawdown and your [average loss](/glossary/average-loss).

The same twelve months above produce a "Sortino ratio" of 1.43, 2.48 or 3.33 depending on how downside deviation is calculated. Only 2.48 is right. Before comparing your number with one someone else published, check how they calculated it.

## How JournalX Tracks Your Sortino Ratio Inputs

The Sortino ratio is built from your losing days, so the record of those days has to be complete. JournalX builds it from your trades, synced from your broker or imported by CSV, and rolls them up into daily P\&L, an [equity curve](/glossary/equity-curve), and drawdown, with your average loss and win rate alongside.

Filter by setup, session, or symbol to see where your downside comes from. Often it's one setup, or one time of day, doing most of the damage, and that's a far more useful finding than any single ratio. It turns "my risk-adjusted returns are low" into "these trades are the reason."