Active portfolio management is dynamic: new information arrives continually, and trading based on this information repeatedly changes the portfolio. With turnover controlled, the impact of today’s trading persists into the future, where it will influence future trading and commingle past and future information. Not surprisingly, important questions remain unanswered. What performance can investors reasonably expect over time? How can active managers improve their long-run performance? What are the best combinations of signals that predict imminent returns and signals that predict later returns? How much trading should be done? Active management currently delivers a multi-period product built with a single-period theory. This paper gives a solution for the time dimension of active management, bringing the theory into line with practice. The solution includes expressions for long-run active risk, turnover, and active return before and after trading costs. The paper contains the “Fundamental Law of Active Management” as a special case, and shows that actual performance typically deviates significantly from the Law’s predictions. Examples illustrate the solution, showing how to improve the long-run performance delivered to investors.