A hit rate carries no information about bet sizing, symmetry, or how often you trade. So turning one into a Sharpe ratio needs assumptions.

From hit rate to Sharpe starts with the simplest possible trade: equal size and independent. This gives a simple expression of a per-trade Sharpe of twice the hit-rate edge. Annualizing the per-trade Sharpe multiplies that by the square root of the number of bets, so 1,000 even-sized bets for reasonable hit rates is twice the edge times 32. Read backwards, the same relation says what accuracy a target Sharpe demands: a Sharpe-1.0 book needs a 51.6% hit rate for 100 bets.

Varying bet sizes, asymmetric payoffs, transaction costs, and correlation across simultaneous positions each pull you below that ceiling. Each is approximated in The Discounts. Taking rough numbers, like dispersion 1.0, costs 0.02 and correlation 0.2, a 54% hit rate across 1,260 trades falls from an idealized annual Sharpe of 2.85 to 1.12.

One interesting finding that I didn't appreciate was that the Sharpe ratio and the t-statistic turn out to be the same measurement: t = Sharpe × √years, so over a single year they are the same number. Statistical significance and risk-adjusted return are two readings of one quantity. From trade count to significance works through the math. Roughly 1,400 trades to reach t = 3 at a 54% hit rate, and about 2,300 if you want an 80% chance of detecting an edge that is genuinely there.

To finish, an applet that allows you to toggle all the variables (hit rate, trade count, etc.) to arrive at a Sharpe.