Sharpe Ratio vs Sortino Ratio — Which One Tells You More?

The Sharpe ratio vs Sortino ratio comparison comes down to how each defines risk. Sharpe divides return by total volatility, so upside swings count against a model just as much as downside ones. Sortino divides return by downside volatility only, so a model is penalised for losses rather than for large gains.

What the Sharpe ratio measures

The Sharpe ratio takes a model's average return, subtracts a risk-free baseline, and divides by the standard deviation of returns. The result tells you how much return was earned for each unit of variability. A higher figure means a smoother ride for the same reward.

The weakness is that standard deviation treats every deviation alike. A model that occasionally produces an unusually large winning trade sees its volatility rise, and its Sharpe ratio fall, even though nothing about that outcome is unwelcome. For a deeper treatment, see the article on the Sharpe ratio.

What the Sortino ratio changes

The Sortino ratio keeps the numerator but replaces the denominator. Instead of the full standard deviation, it uses downside deviation: the spread of returns below a chosen target, usually zero. Gains above the target do not count as risk at all.

That adjustment suits trading, where nobody complains about being surprised on the upside. It also means the Sortino ratio is more informative for models with asymmetric returns, such as trend-following designs that take many small losses and a few large wins. The Sortino ratio article covers the calculation in more depth.

When the two ratios disagree

For a model with symmetric, well-behaved returns, the two ratios give broadly similar rankings. The interesting cases are the ones where they diverge.

If a model has a much higher Sortino ratio than Sharpe ratio, its volatility is mostly on the upside. That is usually a good sign, typical of a design with a favourable payoff shape. If the Sortino ratio is barely better than the Sharpe ratio, or lower, the volatility is dominated by losses, and the model's downside is the thing to examine.

Neither outcome is a verdict on its own. The gap is a prompt to look at how returns are distributed, including the skew and the size of the worst losses.

Reading both ratios in darwintIQ

On the darwintIQ dashboard both ratios are shown among the public metrics, so you can compare them directly for any model. Because every model is evaluated on the same rolling four-hour window, the figures are comparable between models and refresh as the window moves.

A practical habit is to read the two together with drawdown and the Calmar ratio. Sharpe and Sortino describe the everyday texture of returns; drawdown and Calmar describe the worst case. A model that looks good on all four has earned its ranking from several angles rather than one.

Be careful with short samples. Both ratios are statistical estimates, and with few trades they swing widely. A high Sharpe or Sortino ratio on a small number of trades is weak evidence, which is why the trade count belongs beside them.

Which one should you use?

Neither replaces the other. The Sharpe ratio is the more familiar benchmark and is useful for comparing against conventional strategies. The Sortino ratio is usually the better lens for trading models with uneven payoffs, because it asks the question traders actually care about: how bad do the bad outcomes get?

If you can only look at one, choose the Sortino ratio for asymmetric designs and the Sharpe ratio for those with symmetric returns. Better still, look at both and treat any large gap between them as information.

A worked intuition

Take two models with the same average return. The first makes small, steady gains and small, steady losses. The second makes many small losses and occasional large wins. Their Sharpe ratios may look similar or even favour the first, because the second has higher overall volatility. Their Sortino ratios will usually favour the second, since its volatility comes from gains. Neither number is wrong; they answer different questions.

Final thoughts

The Sharpe ratio vs Sortino ratio question is really a question about what you count as risk. Sharpe counts all variability; Sortino counts only the part that costs money. Used together, and checked against drawdown and sample size, they give a clearer picture of a model's quality than either gives alone.