Portfolio decisions are made repeatedly and each rebalancing is subject to transaction costs, turnover limits, and discrete asset selection constraints that affect future flexibility. Capturing these effects requires moving beyond single-period models to multi-period optimization. In this session, we demonstrate how multi-period portfolio problems can be formulated and solved as mixed-integer optimization models using Gurobi. We show how to model position dynamics across time, incorporate realistic discrete features such as cardinality constraints, transaction costs, and minimum trade sizes, and account for how these decisions interact across rebalancing periods in a backtesting setting. Because backtesting requires solving many related optimization problems, solver performance becomes a key practical consideration. We also share modeling best practices and practical insights for scaling multi-period mixed-integer models efficiently - Dr. Silke Horn, Senior Optimization Engineer, Gurobi.