Portfolio Optimizer: From Holdings to an Efficient Frontier

Three Ways to Solve "What Should I Own?"

The optimizer is one of the most abused tools in quant finance. Plug in trailing returns, plug in a covariance matrix, push a button, and the answer is always *wrong* in the same way — it overweights whatever recently outperformed and under-weights everything else, because the estimates are overfit to the sample. The Optimizer card doesn't hide from this; it offers three methods that each fix part of the problem: mean-variance with sample shrinkage, risk parity (return assumptions removed), and Black-Litterman (prior views + sample blend). Using all three and comparing is the discipline.

What the Optimizer Card Shows

The Portfolio Optimizer card takes your current holdings + benchmark universe and computes optimal weights under three objectives: mean-variance (max Sharpe subject to constraints), risk parity (each position contributes equally to total risk), and Black-Litterman (equilibrium blended with your views). Each result shows proposed weights, expected return, expected volatility, and tracking error vs benchmark. A trade list converts the proposed weights into actual buy/sell orders in dollar amounts, sized for your AUM.

Using It Without Fooling Yourself

Three rules keep optimizer output useful. First, never accept mean-variance output at face value — it's always overfit to the input sample. Use it as a starting point to see which direction the math wants to push, then apply judgment. Second, risk parity is more robust but mechanically overweights low-vol assets; that's a feature in most environments but a bug when vol regimes shift. Third, compare proposed weights across the three methods — positions where all three agree you should hold are higher-conviction than positions where only one suggests it.

Where It Fits

The optimizer output flows naturally into the Rebalancing card for execution planning and TCA for understanding the implementation cost of the rebalance. Cross-check optimizer proposals against Factor Analysis — if the proposal changes your factor exposure significantly, ask whether that factor bet is intentional.

Open the Portfolio Optimizer → /app/portfolio