What is a good Omega ratio?

Any value above 1 at a threshold means returns exceeded that threshold by more in total than they fell short of it. Beyond that, there is no universal cutoff: Omega depends on the threshold and on the data frequency, so compare values only for the same threshold, frequency and period.

What is the difference between the Omega ratio and the Sharpe ratio?

The Sharpe ratio summarizes returns by their mean and standard deviation, which describe a bell curve fully but miss skew and fat tails. Omega uses every return at its full size against a threshold, so two portfolios with the same mean and standard deviation but different tails get different Omega values.

Which threshold does the Omega ratio use?

The return below which an outcome counts as a failure for the investor: zero for not losing money, a Treasury-bill rate for beating cash, or a required return for a pension or spending plan. Keating and Shadwick intended Omega to be read as a curve across thresholds; MarketHeist reports it at 0 only.

Is the Omega ratio the same as the gain-loss ratio?

At a threshold of zero, yes: total gains over total losses. Some authors define a gain-loss ratio with averages over the up and down periods separately, which divides by different counts and gives a different number.

What is the Omega ratio? Formula, threshold and an example

By Jeffrey Lin · Published 2026-11-08 · Updated 2026-11-08 · Data through Sep 2026

On this page

Key takeaways

The Omega ratio compares the gains above a chosen threshold with the shortfalls below it, in total: how far the good months cleared the line against how far the bad months missed it.
Above 1 means the returns have beaten the threshold by more, in total, than they fell short of it; with the threshold set at the average return, Omega is exactly 1.
Unlike the Sharpe ratio, it makes no assumption about the shape of returns; unlike the Sortino ratio, it counts each shortfall at its plain size instead of giving large shortfalls extra weight.
The threshold is part of the answer. MarketHeist uses 0% a month, so Omega measures gains against losses, not against cash, and favours steady low-volatility holdings when interest rates are high.
MarketHeist computes Omega on monthly total returns and does not convert it to a yearly figure; in the backtester it works out the same as the profit factor, total gains against total losses.

Definition

The Omega ratio is the probability-weighted sum of returns above a threshold divided by the probability-weighted sum of shortfalls below it. Introduced by Con Keating and William Shadwick in 2002, it uses the whole return distribution rather than its mean and standard deviation, so skew and fat tails enter it directly.

Also known as: Omega, Omega function, Keating Shadwick Omega, gain-loss ratio, Omega performance measure, Omega vs Sharpe ratio.

How it works

Pick a threshold, the return below which a period counts as a disappointment: 0%, an interest rate, or a required return. Add up how far each period's return rose above it, and separately how far each fell below it. Omega is the first total divided by the second. On monthly data at a threshold of 0%, it says how many dollars the up months added, in total, for each dollar the down months took away.

Con Keating and William Shadwick introduced it in 2002 as a function of the threshold rather than a single number: draw the cumulative distribution of returns, and Omega at each threshold is the area above the curve to the right of the threshold divided by the area below it to the left. Every return enters at its full size, so a distribution with a long left tail scores lower than a symmetric one with the same mean and standard deviation. Read across many thresholds, the function describes the whole distribution.

Kazemi, Schneeweis and Gupta (2004) showed that Omega minus 1 equals the average return in excess of the threshold divided by the average shortfall below it. That puts Omega beside the Sharpe and Sortino ratios: the same numerator, with the standard deviation in the Sharpe ratio, the downside deviation (the square root of the average squared shortfall) in the Sortino ratio, and the average shortfall itself in Omega. Squaring makes the Sortino ratio punish a few large losses harder than Omega does.

Omega is like a shop's ledger that sets every refund against every sale: it does not care whether the refunds came as many small ones or one large one, only how they add up. The Sortino ratio is the same ledger with large refunds counted extra.

Two features make Omega easy to misread. It is not annualized and depends on the data frequency: daily returns sit closer to the threshold than monthly ones, so the same strategy shows a daily Omega much nearer 1 than its monthly Omega. And periods with a return exactly at the threshold, such as months a strategy spends in cash earning nothing, count in neither total; they leave Omega unchanged while they lower the Sharpe and Sortino ratios, which average over every period.

Formula

Omega function (Keating and Shadwick 2002)

Ω (L) = \frac{\int_{L}^{b} (1 - F (x)) d x}{\int_{a}^{L} F (x) d x} = \frac{E [\max (R - L, 0)]}{E [\max (L - R, 0)]}

Ω(L): Omega at threshold L
L: threshold return per period (MarketHeist: 0 per month)
F(x): cumulative distribution function of the period return R
a, b: the lowest and highest possible returns
E[max(R − L, 0)]: expected gain above the threshold
E[max(L − R, 0)]: expected shortfall below the threshold

The two forms are equal: integrating the distribution function by parts turns each area into an expected gain or shortfall.

As MarketHeist computes it (monthly returns, threshold 0)

Ω = \frac{\sum_{t = 1}^{N} \max (r_{t}, 0)}{\sum_{t = 1}^{N} \max (- r_{t}, 0)}

rₜ: total return of month t, dividends reinvested
N: number of months

Infinite when no month lost money. A month at exactly 0 adds to neither sum.

Omega beside the Sharpe and Sortino ratios (Kazemi, Schneeweis and Gupta 2004)

Ω (L) - 1 = \frac{μ - L}{E [\max (L - R, 0)]}, Sortino = \frac{μ - L}{\sqrt{E [\max {(L - R, 0)}^{2}]}}

μ: mean period return
μ − L: average return in excess of the threshold, the shared numerator
E[max(L − R, 0)]: average shortfall below the threshold (first lower partial moment)
√E[max(L − R, 0)²]: downside deviation (square root of the second lower partial moment)

The Sortino ratio on this line is per period; MarketHeist annualizes it by √12 and uses L = 0. Months exactly at the threshold leave Omega unchanged and multiply the per-period Sortino ratio by √(1 − z), z being their share of all months.

Worked example

Time-series momentum on the S&P 500 (hold the index while its trailing 12-month return is positive, otherwise cash earning nothing, decided monthly) and intermediate Treasury bonds (IEF), 500 months from Feb 1985 to Sep 2026.

At MarketHeist's threshold of 0% a month, Omega is a ratio of totals. The trend strategy's up months added up to 2.09 times what its down months took away; for the Treasury fund the figure was 1.99.

The Sharpe ratio ranks them the other way round, 0.92 for the Treasury fund against 0.88 for the strategy, and the Sortino ratio by a wider margin, 1.59 against 1.40. The strategy grew faster, 10.6% a year against 5.5%, but each of these ratios measures return per unit of something, not return itself.

Two features of the strategy's returns explain the split. It sat in cash, at exactly zero, in many months: those months add nothing to either of Omega's totals, but they count among the months the Sharpe and Sortino ratios average over, which pulls both down. And its down months were lumpier: a skew of -0.66 and an excess kurtosis of 4.21, against -0.06 and 0.73 for the bonds. The Sortino ratio squares each shortfall, so a handful of big down months weighs heavily on it and much less on Omega.

The threshold matters as much as the returns. At a threshold equal to the Treasury-bill rate instead of 0%, every month the strategy spent in cash would count as a shortfall, and much of the bond fund's monthly return would sit below the bar too. The ranking at one threshold says nothing about the ranking at another; Keating and Shadwick's point was to read Omega across thresholds.

Data through Sep 2026. Reproduce id 8358d824cfa66cc7 (this guide, its lab setup and the data month).

Interactive lab

Omega at a 0% monthly threshold beside the Sharpe and Sortino ratios, skew and kurtosis, for both picks over the same months. Switch portfolios to find pairs the three ratios rank differently.

Annualized Sharpe and Sortino ratios of monthly returns. Feb 1985 to Sep 2026.

Probability that the true Sharpe ratio exceeds 0.00 as the record grows. The dashed line marks 95%.

Measure	Time-Series Momentum — S&P 500	IEF
Sharpe ratio	0.88	0.92
Sortino ratio	1.40	1.59
Probabilistic Sharpe ratio (against 0.00)	100.0%	100.0%
Skewness of monthly returns	-0.66	-0.06
Excess kurtosis of monthly returns	4.21	0.73
Downside deviation (annualized)	7.8%	3.5%
Volatility (annualized)	12.5%	6.0%
Average return (mean monthly return x 12)	10.9%	5.6%
CAGR	10.6%	5.5%
Volatility drag (annualized)	0.8%	0.2%
Omega ratio (threshold 0%)	2.09	1.99
Months in the window	500	500

Risk-free rate 0, 12 periods a year. The probabilistic Sharpe ratio is the probability that the true Sharpe ratio exceeds the benchmark, given the record's length, skewness and kurtosis. Volatility drag: 12 times the arithmetic mean monthly return minus the geometric mean monthly return. Omega ratio: the sum of the monthly gains over the sum of the monthly losses, at a threshold of 0% a month. Feb 1985 to Sep 2026.

How MarketHeist computes it

Every choice an implementation has to make, as the code makes it today. The page is checked against the code each time it is built.

Choice	How it is computed	In the code
Data	Monthly total returns (dividends reinvested). Funds younger than the period shown are extended backward with older funds, spliced on returns, not prices.
Window	The worked example and the lab use the common window of the two portfolios compared, Feb 1985 to Sep 2026 (500 months), ending at the data month of this page. Another pick in the lab is read over its own common window.
Portfolio streams	A fund is its proxy-extended monthly return stream. A built-in portfolio is read as the MarketHeist MCP reads it: its composed stream with its own rebalancing rule and overlay. A custom mix is rebalanced yearly.
Ratios	Risk-free rate 0, 12 periods a year. Sharpe: mean of the monthly returns over their sample standard deviation, times the square root of 12. Sortino: the same mean over the downside deviation, the root of the average squared negative return over all N months. The probabilistic Sharpe ratio uses the per-period Sharpe ratio, the skewness and the kurtosis of the monthly returns; the benchmark Sharpe ratio is divided by the square root of 12 first.
Omega ratio	The sum of (return − threshold) over the periods above the threshold divided by the sum of (threshold − return) over the periods below it. Periods exactly at the threshold count in neither sum. Infinite when no period is below the threshold.	`omega`packages/shared/engine/metrics.ts
Threshold	0 per period (0% a month on this page and in the portfolio engine). No risk-free rate or required return is used.
Frequency	Monthly total returns, dividends reinvested, not annualized. The backtester computes Omega on the returns of its bars (daily, weekly or monthly), so its daily figures sit much closer to 1 than monthly ones.	`runBacktest`packages/shared/engine/backtest.ts
Same number as the per-bar profit factor	At a threshold of 0, the formula is identical to the backtester's profit factor, which is computed per bar rather than per trade; the two columns of its metrics table show the same value for the same returns.	`profitFactor`packages/shared/engine/metrics.ts
Portfolio engine	The portfolio engine keeps an identical copy of the function, so the app, the MCP tools and this page apply the same formula.	`omega`packages/portfolio/engine/metrics.ts
Engine functions	The functions the portfolio app and the MarketHeist MCP tools run, called here on the same monthly returns.	`sharpe`packages/shared/engine/metrics.ts`sortino`packages/shared/engine/metrics.ts`probabilisticSharpeRatio`packages/shared/engine/metrics.ts`skewness`packages/shared/engine/metrics.ts`excessKurtosis`packages/shared/engine/metrics.ts

Omega ratio compared with its neighbours

Omega and the ratios built on the same numerator
Ratio	Denominator	How a large loss counts	Convention on MarketHeist
Omega ratio	Average shortfall below the threshold	In proportion to its size	Threshold 0, monthly, not annualized
Sortino ratio	Downside deviation below the target	In proportion to its size squared	Target 0, divides by all N, √12
Sharpe ratio	Standard deviation of all returns	Squared, and large gains count too	Risk-free rate 0, sample standard deviation, √12
Profit factor (per bar)	Sum of losing periods	In proportion to its size	Equal to Omega at 0 on the same returns
Calmar ratio	Maximum drawdown	Only the single deepest decline counts	CAGR over the absolute maximum drawdown

Limitations

The threshold changes the answer and can reverse a ranking. A single Omega figure without its threshold and data frequency cannot be compared with another.
It ignores the order of returns. Two series with the same monthly returns in a different order have the same Omega and very different drawdowns.
Months at the threshold are invisible to it, so a strategy that spends long stretches in cash looks the same as one that was invested only in its active months.
Like every ratio estimated from history, it has sampling error, and a few extreme months can move it. No confidence band is computed for it here.
Long histories are extended backward with older funds or indexes where a fund is younger than the window, so early months approximate the fund rather than reproduce it.

Frequently asked questions

What is a good Omega ratio?: Any value above 1 at a threshold means returns exceeded that threshold by more in total than they fell short of it. Beyond that, there is no universal cutoff: Omega depends on the threshold and on the data frequency, so compare values only for the same threshold, frequency and period.
What is the difference between the Omega ratio and the Sharpe ratio?: The Sharpe ratio summarizes returns by their mean and standard deviation, which describe a bell curve fully but miss skew and fat tails. Omega uses every return at its full size against a threshold, so two portfolios with the same mean and standard deviation but different tails get different Omega values.
Which threshold does the Omega ratio use?: The return below which an outcome counts as a failure for the investor: zero for not losing money, a Treasury-bill rate for beating cash, or a required return for a pension or spending plan. Keating and Shadwick intended Omega to be read as a curve across thresholds; MarketHeist reports it at 0 only.
Is the Omega ratio the same as the gain-loss ratio?: At a threshold of zero, yes: total gains over total losses. Some authors define a gain-loss ratio with averages over the up and down periods separately, which divides by different counts and gives a different number.

References

Keating, C. and Shadwick, W. F. (2002) A Universal Performance Measure. Journal of Performance Measurement 6(3). Introduces the Omega function.
Kazemi, H., Schneeweis, T. and Gupta, R. (2004) Omega as a Performance Measure. Journal of Performance Measurement 8(3). Omega minus 1 as excess return over the first lower partial moment.
Sortino, F. A. and Price, L. N. (1994) Performance Measurement in a Downside Risk Framework. Journal of Investing 3(3). The downside-deviation ratio Omega is often compared with.
Bernardo, A. E. and Ledoit, O. (2000) Gain, Loss, and Asset Pricing. Journal of Political Economy 108(1). The gain-loss ratio at a zero threshold.

Try it on your own holdings

Open the S&P 500 time-series momentum strategy →

Or ask an assistant connected to the MarketHeist MCP server (https://marketheist.io/mcp), which computes the same figures:

Using MarketHeist.io, compute the Omega ratio at a 0% monthly threshold, the Sharpe ratio and the Sortino ratio of S&amp;P 500 time-series momentum and of IEF over the same months, and explain why they rank the two differently.

Ask an AI assistant

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The prompt opens in a new tab, already filled in. An assistant without the MarketHeist connector answers from its own knowledge, not from the figures on this page.

Historical simulation, not advice. Computed by the MarketHeist portfolio engine from monthly total returns, data through Sep 2026. Markdown version of this page: omega-ratio.md.