A trader shows a Sharpe of 2.0 over six months. On such a short window, luck alone can explain that number. The real question isn't whether your Sharpe looks good, but whether six months of data is enough to prove it isn't noise.

Most traders dramatically underestimate how much data is needed. The standard error of the Sharpe ratio is approximately 1/√T, where T is the number of independent observations (Lo, 2002). Confidence grows painfully slowly — and this is the optimistic version, built on assumptions real markets routinely violate.

Sample Size Determines What You Can Conclude

Assume for a moment that the data is clean. The number of independent observations determines how tight the confidence interval around any metric can be. The key word is independent. We return to what that actually means below. More observations means less uncertainty, but confidence grows slowly, as √T.

Years of Daily Data Needed to Reject Luck

Given an observed live Sharpe ratio, how many years of continuous daily data are needed to reject H₀: Sharpe = 0? The IID columns assume independent returns. The realistic column accounts for the violations above.

A Sharpe of 0.5 is common among consistently profitable traders, but proving it requires over a decade of continuous data. For most strategies, multiply the IID estimate by 2–4x.

What a Clean Sharpe Can Hide

Even with enough data and honest collection, the Sharpe ratio can paint a misleading picture. Three structural issues sit outside what standard significance tests capture.

Rejecting Sharpe = 0 Is the Wrong Question

Even the table above tests the wrong hypothesis. Rejecting Sharpe = 0 means proving you're better than cash. That's an extremely low bar. The real question an allocator asks is: does this Sharpe exceed a meaningful threshold?

If the cost of capital is equivalent to a Sharpe of 0.3, or if the relevant benchmark delivers a Sharpe of 0.5, the null hypothesis should be H₀: SR ≤ 0.5, not H₀: SR = 0. Testing against a non-zero threshold requires far more data, because you're trying to detect a smaller effect size.

Bailey & López de Prado (2012, 2014) formalized this with the Probabilistic Sharpe Ratio (PSR) and the Deflated Sharpe Ratio (DSR). The DSR adjusts for:

• Non-normality of returns (skewness and kurtosis)
• The number of strategies tried before selecting this one (multiple testing)
• Track record length
• A non-zero benchmark Sharpe ratio

Apply the DSR to most track records posted on social media (typically 6–18 months of data, with no accounting for how many strategies were tried) and the vast majority fail to achieve statistical significance. The apparent Sharpe of 1.5 or 2.0 deflates to something statistically indistinguishable from the benchmark.

What This Means in Practice

• Short track records are statistically meaningless. A 6-month record cannot distinguish skill from variance, period. Even after a year, you can only tell if the Sharpe is likely positive, not whether it's good enough to allocate to.
• Daily observations are non-negotiable. Monthly data gives 12 points per year, daily gives 250. This is why institutional verification uses daily equity snapshots.
• Continuity must be enforced, not self-reported. Any gap in the equity curve reduces the effective sample size and can hide the worst drawdowns. A track record with holes is not a track record.
• Data must be collected independently at the source. Cherry-picked windows, exported spreadsheets, and screenshots invalidate any significance test before it begins.
• A clean Sharpe is necessary but not sufficient. Gain concentration, hidden non-linear risks, and correlated trades can make a track record look more robust than it is. Significance tests only work if the inputs are structurally honest.
• The Deflated Sharpe Ratio is the proper framework. It accounts for multiple testing, non-normality, and a real benchmark. These three adjustments collapse most publicly shared track records.