Sortino Ratio Calculator
The formula
What the sortino ratio tells you
Frank Sortino's fix to the Sharpe ratio: only bad volatility counts. Downside deviation measures how far and how often returns fall below your minimum acceptable return (MAR), so a strategy that lurches upward violently is not penalized.
Calculation: Sortino = (R − MAR) ÷ downside deviation. Enter your inputs and every substitution is shown below the result.
Reading the result
Because the denominator ignores upside moves, Sortino is always at least as high as Sharpe for the same data — roughly 40–60% higher for typical equities. Judge it against your own target: swap the risk-free rate for your required return and see what survives.
Rules of thumb: < 1 weak · 1–3 acceptable · 3–5 good · > 5 usually a low target rate or a short sample
Limitations and best practice
MAR sensitivity cuts both ways: the same fund gets flattering numbers against a 0% target and ugly ones against 8%. Always state the target, and remember that a short track record makes downside deviation unstable — it can miss tail events that have not happened yet.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
What is a good Sortino ratio?
When should I use Sortino instead of Sharpe?
Can Sortino be infinitely large?
Does Sortino capture fat tails?
Source & methodology
Sortino, S. & van der Meer, R. (1991) "Downside risk"; Sortino & Price (1996) "Negative asset return pricing".
Sortino Ratio: what this tool answers
You are here because you need a specific number, and you want to see how it was produced before you rely on it. There are plenty of pages about Sortino Ratio; almost none of them show the working. This one does: the formula, every input, a fully computed example, and a sensitivity analysis calculated live from this engine’s own defaults.
Return and risk arrive as a package in Sortino Ratio. A result of 0.86 means little until you ask what variance sits behind it, how it compares with the boring alternative (the companion tools above compute exactly that comparison), and whether the inputs assume the good scenario or the expected one.
A calculation like this one is worth roughly as much as a careful sentence: it forces several vague considerations into one comparable quantity. Once Sortino Ratio is on the table, the argument can stop being about impressions.
Nothing below is stock text pasted around a number: the worked example computes sortino ratio = 0.86 from this tool’s own defaults, and the sensitivity section ranks the inputs that move the result — Expected return first, at ≈18.3 % of the headline for a ten-percent error either way in that input. Read the sections in order and you will be able to reproduce every figure by hand.
Inside the Sortino Ratio calculation: formula and every input
The engine implements a single stated model: Sortino = (R − MAR) ÷ downside deviation. There is no hidden coefficient and no login-gated precision — the formula printed is the formula executed, which is also what makes the result citable.
Expected return (%) (example value 11) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. Enter it exactly as your document states it — quoted annual, monthly and effective rates are three different numbers.
Risk-free (or target) rate (%) (example value 5) is a measured number in the unit named by the label; the formula does not convert for you. Check the convention first: if the source says “6.5 % per year”, type 6.5 and let the formula do the rest.
Downside deviation (%) (example value 7) is a plain measured quantity — the unit shown in the label is the unit the formula expects. Rates are the most mis-transcribed input on any page like this; copy it from the document rather than retyping from memory.
Sortino Ratio on the example inputs: the complete substitution
Abstract formulas are hard to trust, so here is the complete substitution using the default inputs, written the way an examiner would want it. Starting figures: Expected return (%), Risk-free (or target) rate (%), Downside deviation (%). Run to the end, the sortino ratio resolves to 0.86 — Modest protection against losses on the bands this tool uses.
- Excess over minimum acceptable return = 11% − 5% = 6.00%
- Sortino = 6.00 ÷ 7 = 0.86
Excess over target — 6.00 %. This is the number experienced users quote when the headline alone would mislead.
Ratio if MAR were 0% — 1.57. If your inputs change, this is usually the first companion figure to move.
Typical Sharpe/Sortino gap — 0.39. Compare it against the main result of 0.86 before drawing conclusions.
Downside deviation needed for ratio 2 — 3.00 %. Read next to the headline, this is the figure that shows the shape of the trade-off.
Re-running with your own figures is instant; re-running by hand once is the best way to know the tool is telling you the truth.
The Sortino Ratio corridor: your result across a realistic band
The honest version of any estimate is a corridor, and this one is cheap to produce: Sortino Ratio was recomputed at 5 points along the dominant input, with every other field left untouched.
| Expected return setting | Sortino ratio |
|---|---|
| 80 % of the example value | 0.54 |
| 90 % of the example value | 0.70 |
| example inputs | 0.86 |
| 110 % of the example value | 1.01 |
| 120 % of the example value | 1.17 |
That spread — 0.54 at the low end, 1.17 at the high — is what a careful read of Sortino Ratio looks like: a band you can state out loud, with the reason for each edge. Reproduce it in the form above by editing Expected return alone; the substitution steps will follow your figures exactly.
What actually moves your Sortino Ratio
Nobody should have to guess which input matters. The engine was re-run once per field below — ten percent up, ten percent down, everything else frozen — and the results ranked by how far they moved the sortino ratio. Read it as a map of where Sortino Ratio is fragile — drawn for the example inputs, which is the map you want when deciding what to measure carefully.
- Nudge Expected return by ±10 % and Sortino Ratio lands between 0.70 and 1.01 (sensitivity ≈18.3 %)
- Downside deviation +10 % → Sortino Ratio = 0.78 · −10 % → 0.95 (the sortino ratio shifts ≈11.1 %)
- Nudge Risk-free (or target) rate by ±10 % and Sortino Ratio lands between 0.79 and 0.93 (sensitivity ≈8.3 %)
Expected return is the dominant lever here: ten percent either way moves the result by about 18.3 %, from 0.70 to 1.01. That is where a careless figure does the most damage, so check it against a source, and treat Downside deviation as the second priority (≈11.1 % per ten percent). The rest of the inputs earn only a rounding-error of attention, and that asymmetry is the practical payoff of measuring rather than asserting.
Reading the result: bands, zones and thresholds
The value above only becomes useful the moment you know what it is being measured against. The default case lands in the “Modest protection against losses” band, and bands are deliberately wide because real situations overlap. How much of that band is luck? On Expected return alone ten percent either way covers 0.70 to 1.01. The practical reading rules: compare against your own history before comparing against other people; treat band boundaries as zones, not walls — one point on the wrong side of a threshold rarely changes a real outcome; and when a companion output contradicts the headline, believe the pair.
In Investing & Markets, Sortino Ratio is one instrument in an orchestra: on its own it is a number, next to Excess over target and Ratio if MAR were 0% it becomes a diagnosis. The tools linked above exist so the headline never has to carry that weight alone.
How people get Sortino Ratio wrong
MAR sensitivity cuts both ways: the same fund gets flattering numbers against a 0% target and ugly ones against 8%. Always state the target, and remember that a short track record makes downside deviation unstable — it can miss tail events that have not happened yet. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Confusing a large number with an important one. Sortino Ratio can be dominated by one input whose absolute value looks unimpressive, which is why this page ranks influence separately from magnitude.
Attention is also misallocated between fields. Having measured this engine, we can say where it belongs: Expected return first, everything else second.
A final trap is presentation: reporting Sortino Ratio to more decimal places than the inputs support. The tool prints what the arithmetic gives; you should quote only what your least precise field justifies.
A quick check for any Sortino Ratio figure
- Record the date and the inputs alongside the number so future-you knows what was assumed.
- Confirm the example values are plausible for your own situation before replacing them.
- Put Expected return under a magnifier; the sensitivity table above is the reason why.
- Recompute once with the dominant input deliberately set low, and see whether the decision changes.
- Note the date and the inputs beside the number so the figure can be audited later.
- Confirm the answer against one independent method or published benchmark for Sortino Ratio.
A routine for decision-grade Sortino Ratio
Using a calculator well sounds trivial, but the difference between a decision-grade figure and a throwaway one lives entirely in the routine. The routine for Sortino Ratio takes a minute to learn and applies to every input above.
Step 1 — start from evidence. The inputs behind Sortino Ratio are expected return (%), risk-free (or target) rate (%), downside deviation (%). Write them down with their source next to each one; if you cannot name where a figure came from, that is the input most likely to be wrong. The point of transparency is that a reader can disagree with a specific line rather than with you.
Step 2 — enter the conservative case first. Resist the flattering scenario. Type the inputs you would defend in front of a sceptical reader, read the Sortino ratio (0.86 for the example values), and note the verdict band. A figure nobody can reproduce is a figure nobody will act on twice.
Step 3 — play with the inputs on purpose. Ten percent up, ten percent down, one field at a time. Within a few minutes you will know this calculation better than most people who quote its results. If the answer changes your behaviour, it deserves a second run with different assumptions.
Step 4 — run the scenario trio. Expected, pessimistic, optimistic. The gap between pessimistic and expected is the number to plan against; the optimistic result is useful only as a ceiling. Write the figure down before moving on; memory flatters whichever number you saw last.
Step 5 — save the story with the figure. A number without its inputs and its date is unusable within a quarter. Record 0.86, the assumptions behind it, and today’s date; your future self will be auditing a stranger’s arithmetic. Note the assumption you are least sure about; that is where the real risk lives.
Where Sortino Ratio matters in practice
Numbers earn their keep in conversations, and Sortino Ratio is no exception. In Investing & Markets discussions around Portfolio Management, this figure decides whether an option stays on the table: it converts an opinionated debate into an arithmetic one, and arithmetic is the only referee both sides accept. A result worth acting on survives having its two shakiest inputs halved.
Used properly this is a five-minute filter that saves weeks. Compute Sortino Ratio, reject what fails, and only then bring in the expensive analysis for the survivors. If the verdict changes with a ten-percent nudge, report the nudge alongside the number.
Nothing here is stored or transmitted, so the habit of checking is free: open the page, re-enter current figures, compare with the number you saved last time. Compare the calculation to the rough guess you arrived with, and ask why they differ.
Quick answers: what searchers ask about Sortino Ratio
What affects Sortino Ratio the most?
Sortino Ratio is driven above all by Expected return. Perturbing only that field by ten percent either way produced 1.01 against the 0.86 baseline — a ≈18.3 % response, while Downside deviation manages ≈11.1 %.
Which matters more: downside deviation or expected return?
By the numbers, Expected return: a ten-percent change in it moves the result ≈18.3 %, versus ≈11.1 % for Downside deviation. Spend your measuring effort in that order — precision on the weaker lever buys almost nothing.
What is a typical Sortino Ratio?
There is no honest universal average for Sortino Ratio, but there is a reference point: the defaults on this page produce 0.86 (Modest protection against losses). Replace the inputs with your own and compare — that comparison is the useful meaning of “typical”.
How do I calculate Sortino Ratio by hand?
Use the stated model — Sortino = (R − MAR) ÷ downside deviation — and substitute in the order the tool prints (it starts from Excess over minimum acceptable return = 11% − 5% = 6.00%). On the example inputs the hand route lands on 0.86, exactly what the calculator shows. A spreadsheet reproduces it in two or three cells; doing it once by hand is what makes the automated answer trustworthy.
What happens to Sortino Ratio if expected return changes by 10 %?
Try it in the form above and the printed substitution will follow you; the pre-computed answer for the defaults is 0.70 to 1.01 across the ten-percent probe, i.e. ≈18.3 % sensitivity on the sortino ratio.
Is the Sortino Ratio result of 0.86 good or bad?
Good and bad are comparisons, so the honest test is three questions: where does your own Sortino Ratio sit against the 0.86 reference above, how far would a plausible input error move it (≈18.3 % per ten percent on Expected return), and what does the alternative option compute to?
Can Sortino Ratio be negative or empty?
Yes, and that is informative rather than broken: a negative Sortino Ratio usually means the inputs encode an outflow or a shortfall, and the verdict band above labels exactly that situation instead of hiding it.
How do I calculate Sortino Ratio manually?
Write down the inputs, then follow the printed steps in order — Sortino = (R − MAR) ÷ downside deviation. A spreadsheet replicates it with two or three cells. The manual route is worth doing once to build intuition; after that the calculator saves time and eliminates transcription slips.
What is a good Sortino ratio?
Above 3 is generally strong for a personal portfolio; institutional strategies often target 1–2 over market cycles.
When should I use Sortino instead of Sharpe?
Whenever returns are lumpy or asymmetric — options sellers, crypto, concentrated holdings — because two-sided volatility badly misstates the real risk of those assets.
Does this Sortino Ratio tool store my numbers?
No. The entire calculation runs in your browser session; nothing entered above is transmitted or saved, which is the correct behaviour for sensitive Investing & Markets figures and the reason you can re-run this page as often as you like.
Key terms on this page
- Excess over target
- 6.00 % in the default Sortino Ratio calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Ratio if MAR were 0%
- 1.57 for the Sortino Ratio worked case; it sits next to the main result because it carries context the headline leaves out.
- Typical Sharpe/Sortino gap
- 0.39 on the example inputs — a companion figure printed beside the headline of Sortino Ratio so that no result is read in isolation.
- Sortino ratio
- The primary output of this tool: 0.86 for the default inputs, computed exactly from Sortino = (R − MAR) ÷ downside deviation.
- Worked example
- The step-by-step substitution printed above, so the arithmetic can be re-derived by hand or in a spreadsheet.
- Verdict band
- The plain-language label — here “Modest protection against losses” — mapping the computed figure onto experience-based ranges for Investing & Markets.
- Sensitivity (±10 %)
- How far the Sortino Ratio moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
The provenance of this Sortino Ratio calculation
The model is published arithmetic implemented exactly as written: Sortino = (R − MAR) ÷ downside deviation. Source of record: Sortino, S. & van der Meer, R. (1991) "Downside risk"; Sortino & Price (1996) "Negative asset return pricing". Any other implementation of this model should return the same figure for the same inputs. If one does not, the difference is in its assumptions — which this page prints and that one probably does not.
Treat the output as a precise answer to the question you typed: exact arithmetic, transparent formula, published source, no hidden rounding. The promise to the reader is narrow and testable: same inputs, same output, no silent rounding, and no figure presented without the formula that produced it.
After the number: what to do with it
Save the number together with its inputs and today’s date; a Sortino Ratio figure without context is unusable the moment an input changes.
This page was written for people who need one trustworthy number before their next decision, and the promise is that nothing above required trust. Bookmark the tool, re-run it whenever an input moves by more than a few percent, and keep the date with the number.