Optimization Constraints

Optimization constraints are rules imposed during portfolio construction to ensure practical, diversified, and risk-aware allocations. Without constraints, mathematical optimizers may produce extreme or unrealistic portfolios. Common constraints include maximum position sizes, sector caps, turnover limits, liquidity thresholds, and beta targets. These guardrails prevent excessive concentration and unintended exposures. Constraints also reflect real-world considerations such as regulatory requirements and execution feasibility. For example, limiting allocation to illiquid stocks helps reduce slippage and market impact. In quantitative investing, constraints shape strategy behavior as much as signals do. Two portfolios built from identical rankings can look very different depending on constraint design. Well-calibrated constraints strike a balance. Too loose, and portfolios become unstable. Too tight, and alpha potential is suppressed. Constraints are not static. They are refined through backtesting and live experience, evolving as market conditions and strategy objectives change. Optimization constraints transform theoretical models into investable portfolios.