Portfolio Optimizer
Design allocations with explicit objectives, constraints, and walk-forward context before promoting them into portfolio research.
Portfolio Optimizer is where an allocation idea becomes a candidate set of weights under a chosen objective and constraint set.
This page covers the optimization methods available, what constraints you can set, and how to validate the resulting weights.
The biggest risk is treating optimized weights as truth instead of as outputs of specific assumptions. Expected returns, covariance windows, and constraints all materially change the result.
Use the optimizer to generate a candidate allocation, then test that allocation in Portfolio Backtest under the same assumptions that matter to implementation.
Should I optimize directly on a short sample window?
Usually not without caution. Short windows make optimizer outputs more brittle, which is why walk-forward validation and explicit constraints matter.