What is a good Sharpe ratio for crypto?

Short answer: A common answer to “what is a good Sharpe ratio?” is above 1, but that is a convention, not a pass mark. For crypto, the number depends on return frequency, annualization method, costs, record length, and whether the returns are live. A short or heavily selected track record can make a high ratio look more certain than it is.

What is a good Sharpe ratio?

The common rule of thumb calls a Sharpe above 1 acceptable to good, above 2 very good, and 3 or more excellent. These bands are widely repeated, including by Investopedia, 2026, but they’re conventions, not scientific cutoffs that separate safe from risky strategies.

Reported annualized Sharpe	Common label	Your next check
Below 1	Below the usual “acceptable” threshold	Check whether return exceeded its benchmark and whether the sample period was unusually weak
1 to 1.99	Acceptable to good	Confirm costs, frequency, and the length of the record
2 to 2.99	Very good	Ask how leverage, short sample length, and strategy selection affected the result
3 or higher	Excellent	Request the full return series and independent evidence before treating it as repeatable

The Sharpe ratio compares excess return with the variation in returns. A larger value means more measured return for each unit of measured volatility over that sample. It doesn’t tell you how much money you can lose, how long a recovery may take, or whether the result will continue.

So treat “good” as a comparison question: good against what? Compare the same market, period, return frequency, annualization convention, leverage, and costs. A crypto strategy with daily observations should not be ranked against a monthly fund figure or a stock strategy using a different calendar without first reconciling those inputs.

How do you calculate a Sharpe ratio for crypto?

For a return series, calculate the average return above a matching risk-free rate, then divide by the standard deviation of those excess returns. The ratio is usually annualized by multiplying a daily ratio by the square root of the number of daily periods in a year — but that shortcut assumes returns behave independently through time (Sharpe, 1994; Lo, 2002).

For an illustrative crypto convention, use 365 daily observations: annualized Sharpe = daily Sharpe × √365. An equity series with 252 trading days often uses daily Sharpe × √252. If the daily returns were identical, the √365 convention would print a Sharpe about 20% higher. That difference comes from the calendar convention, not better trading. Lo shows that serial correlation can make simple annualization inaccurate, so don’t use the square-root rule as if it were a law.

The table uses one synthetic, seeded 365-day series. It assumes a zero daily risk-free rate and zero Sortino target to keep the arithmetic visible. It’s an example, not a market result or forecast.

Measure	Calculation used	Result
Sharpe, annualized on 365 days	Mean daily return ÷ sample standard deviation × √365	0.82
Sharpe, annualized on 252 days	Same daily data × √252	0.68
Sortino, annualized on 365 days	Mean daily return ÷ downside deviation × √365	1.20
Calmar	Annualized geometric return ÷ maximum drawdown	1.44

The example’s compounded return was 11.77% and its maximum drawdown was 8.19%. In the Sortino calculation, downside deviation counts returns below the chosen target; here that target is zero. The ratio therefore ignores positive volatility in its denominator. Calmar uses the largest peak-to-trough fall instead of day-to-day variation. We used the full synthetic year for both annualized return and drawdown. CFA Institute, 2012 notes that the selected target and calculation method matter; Fidelity, n.d. divides annualized return by maximum drawdown.

When should you use the Sortino ratio or Calmar ratio instead?

Use the ratio whose risk question matches yours, then inspect the underlying returns and drawdown as well. No single number captures every way a strategy can lose money.

Measure	What counts as risk	Useful question	What it leaves out
Sharpe	Upside and downside variation	How much excess return came with each unit of total volatility?	The size and duration of the worst fall; unusual tail losses can be hidden by an average
Sortino	Variation below a chosen target return	How uneven were returns below the minimum result I care about?	Losses below the target may be rare but severe; target and denominator conventions affect the value
Calmar	Maximum peak-to-trough drawdown	How much annualized return came with the worst observed fall from a prior peak?	Other drawdowns and how often they occurred; it depends heavily on the selected window

A low Sharpe and high Sortino can mean the series had substantial positive swings that Sharpe also penalized. A high Calmar can still hide a long recovery or a different risk that did not appear in the sample. Read how drawdown is measured alongside any Calmar figure.

How much can you trust a short-record Sharpe?

A Sharpe is an estimate from a finite set of returns, so it has sampling error. Lo derives standard errors and confidence intervals under stated return-process assumptions. Under independent, identically distributed, approximately normal returns, a useful large-sample standard error for a per-period estimate is roughly √((1 + 0.5 × SR²) / n), where n is the number of observations. The table converts that approximation to an annualized daily Sharpe using 365 observations per year.

Assume the reported annualized Sharpe is 1.5 in each row. The intervals below are approximate 95% ranges around that estimate, computed as estimate ± 1.96 standard errors. They’re not a forecast or a guarantee. The simple formula assumes independent, normal daily returns. Crypto returns may be serially correlated, skewed, or fat-tailed, so this interval can understate uncertainty (Lo, 2002).

Record	Daily observations	Approx. annualized standard error	Approx. 95% interval around Sharpe 1.5
1 year	365	1.00	−0.46 to 3.46
2 years	730	0.71	0.11 to 2.89
5 years	1,825	0.45	0.62 to 2.38

Even under the simplifying assumptions, one year leaves a very wide range. A point estimate alone doesn’t show how precise it is. If the returns are smoothed or serially correlated, ask for an adjustment that accounts for that dependence. Lo’s hedge-fund example found annual Sharpe estimates could be overstated by as much as 65% when serial correlation was ignored (Lo, 2002). That was an example from hedge funds, not a universal correction for crypto.

A second issue is selection. If someone tested many parameter settings or strategies and shows only the best one, the winner may partly reflect luck. Bailey and López de Prado’s Deflated Sharpe Ratio adjusts for the number of trials, sample length, and non-normal returns (Bailey and López de Prado, 2014). Ask how many variants were tried, including discarded ones. A single attractive result without that count is incomplete evidence.

What should you check before trusting a crypto track record?

Ask for enough detail to reproduce the number and understand the risks behind it:

• Period and frequency: What exact start and end dates are covered? Are returns daily, weekly, or monthly, and are missing periods visible?
• Return basis: Are returns time-weighted or based on account cash flows? Is the figure before or after trading fees, funding, slippage, and any management or performance fee? Compare fee treatment consistently; our maker and taker fee guide explains one exchange trading cost.
• Live or simulated: Which months are actual trades, and which are backtest or paper results? For a backtest, ask when the rules were chosen and whether the reported period was held out from development.
• Risk alongside return: Request the equity curve, maximum drawdown, longest recovery, and leverage used. Check that return and drawdown cover the same dates and account value series.
• Selection history: How many strategies, parameter sets, coins, or date windows were tested before this result was selected? Were losing variants retained in the record?
• Verification: Can you compare the return series with dated account statements, an independently viewable record, or another source that you can check? A screenshot of a single ratio isn’t enough to reconstruct it.

If you’re reviewing a pooled investment rather than trading a strategy yourself, check that the provider explains how your own share value is recorded separately from deposits and withdrawals. We run Cointrapper, a private trading pool on Binance; our guide to how a participant’s share is tracked explains the pool index and position value. That accounting explanation doesn’t validate a performance claim. You should still ask for the period, fee basis, risk figures, and source behind any Sharpe you are shown.

A ratio is a summary of one historical return series. Compare like with like, check the inputs, and treat a short or selected record as uncertain. Crypto investment is high risk and you can lose your entire investment; past performance does not guarantee future results. This is not individual investment advice.