Portfolio Theory

objective

Apply portfolio theory to derive robust allocation principles and theoretical risk-return diagnostics.

workflow

1. •define investable universe, return assumptions, and covariance model.
2. •derive efficient frontier and tangency portfolios under constraints.
3. •analyze diversification benefit and marginal risk contribution.
4. •compare equilibrium-implied returns to observed risk premia.
5. •release only when theory outputs are reconciled with implementation limits.

required diagnostics

• •efficient-frontier stability under parameter perturbation.
• •marginal contribution to risk by asset and factor.
• •concentration and diversification diagnostics.
• •implied-versus-realized risk-premium divergence.
• •constraint shadow-price interpretation.

risk controls

• •enforce covariance regularization and robustness checks.
• •enforce limits on unstable frontier points.
• •enforce interpretation checks before production use.

outputs

• •run python scripts/portfolio_theory_diagnostics.py input.csv --output diagnostics.json and keep the json artifact.
• •write an implementation memo using references/portfolio-theory-playbook.md with assumptions, tests, limits, and rollout plan.

resources

• •use scripts/portfolio_theory_diagnostics.py for deterministic diagnostics.
• •use references/portfolio-theory-playbook.md for the domain checklist and delivery structure.