The effectiveness of a trend-following rule is determined by the shape of its return weights, making it possible to use analytical and numerical methods to identify optimal trading rules. This chapter explores trading rules that optimize individual properties—accuracy, responsiveness, and smoothness—as well as those that achieve the best tradeoffs between two or all three properties. By systematically examining the impact of return weight structures, we uncover trading rules that strike an optimal balance between these competing objectives. The findings reveal that while no single rule is universally superior, certain weight configurations significantly enhance trend-following performance depending on the specific tradeoff desired. This analysis provides a structured framework for designing and evaluating trading rules beyond traditional moving average-based approaches.