Portfolio Optimization

Portfolio Optimization

Optimizers amplify the noise in their inputs, and the noisiest input in finance is the expected return. This skill therefore optimizes risk structure only — and always asks the question most optimizers dodge: did the clever method beat equal weight out-of-sample?

Principles

1. No expected-return inputs. Sample means are noise; MVO on sample means is noise squared. The methods here (inverse vol, min variance, ERC risk parity, HRP) need only the covariance structure. If the user brings views, the honest framing is scenario analysis, not a mean-variance frontier.
2. Equal weight is the benchmark, not the strawman. The compare mode walk-forwards every method against EW; optimizer_no_edge is a first-class finding, common in practice, and worth telling the user plainly.
3. Estimation error is disclosed, not hidden. Covariance is shrunk toward an identity target when the panel is short relative to breadth, and the report states the intensity. T < 2N earns a wide-error-bars note.
4. Weights describe risk, not conviction. The disclosure lines say so and must be surfaced.

Workflow

1. Assemble the return matrix (shared dates, per-asset periodic returns) and any constraints (max weight per asset). Confirm the frequency.
2. Start with the comparison, not a single method: python scripts/optimize_portfolio.py --returns returns.csv --method compare — walk-forward OOS vol/Sharpe/maxDD per method vs equal weight.
3. Pick the method the comparison actually supports. Defaults when the comparison is inconclusive: hrp for structured books (clusters visible), erc when the user wants balanced risk, equal when nothing beats it.
4. Produce weights: --method hrp --max-weight 0.25 --json weights.json. Report weights, portfolio vol, effective N, and per-asset risk contributions together — a weight table without risk contributions hides exactly what the user needs to see.
5. Hand the resulting book to risk-assessment for the full profile (concentration, tails, stress) before anyone acts on it.
6. Rebalancing advice must state turnover implications — weights that change 10% a month feed the cost ladder in backtest-validation.

Guardrails

• No mean-variance frontiers from sample means; no implied return forecasts.
• optimizer_no_edge findings are reported, not suppressed.
• Shrinkage intensity and estimation-error notes are part of the deliverable.
• Weights are research output, not individualized investment advice — the disclosure lines ship with every result.
• Live execution is out of scope; the strongest recommendation is a paper rebalance reviewed by the user.