In-Person event
Future Alpha 2026
March 31 - April 1, 2026
9:00 AM
New York Marriott, Brooklyn Bridge
Join Gurobi at Future Alpha 2026 at the New York Marriott, Brooklyn Bridge.
Gurobi showcases its advanced optimization technology to the quantitative finance community, highlighting how mathematical optimization powers smarter portfolio construction, risk management, and data-driven investment strategies.
📍 Visit Our Booth: Meet our experts and see how Gurobi’s powerful mathematical optimization solver can enhance your financial models.
🎤 Don’t Miss Our Speaking Session: Dr. Silke Horn will share insights on Portfolio optimization and backtesting with discrete constraints.
Tuesday, March 31 – 2:20 – 2:40 PM
Multi-Period Portfolio Optimization with Discrete Decisions
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.
🌎 Location: New York Marriott, Brooklyn Bridge
Join Gurobi at Future Alpha 2026 at the New York Marriott, Brooklyn Bridge.
Gurobi showcases its advanced optimization technology to the quantitative finance community, highlighting how mathematical optimization powers smarter portfolio construction, risk management, and data-driven investment strategies.
📍 Visit Our Booth: Meet our experts and see how Gurobi’s powerful mathematical optimization solver can enhance your financial models.
🎤 Don’t Miss Our Speaking Session: Dr. Silke Horn will share insights on Portfolio optimization and backtesting with discrete constraints.
Tuesday, March 31 – 2:20 – 2:40 PM
Multi-Period Portfolio Optimization with Discrete Decisions
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.
🌎 Location: New York Marriott, Brooklyn Bridge
Join Gurobi at Future Alpha 2026 at the New York Marriott, Brooklyn Bridge.
Gurobi showcases its advanced optimization technology to the quantitative finance community, highlighting how mathematical optimization powers smarter portfolio construction, risk management, and data-driven investment strategies.
📍 Visit Our Booth: Meet our experts and see how Gurobi’s powerful mathematical optimization solver can enhance your financial models.
🎤 Don’t Miss Our Speaking Session: Dr. Silke Horn will share insights on Portfolio optimization and backtesting with discrete constraints.
Tuesday, March 31 – 2:20 – 2:40 PM
Multi-Period Portfolio Optimization with Discrete Decisions
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.
🌎 Location: New York Marriott, Brooklyn Bridge
