Mean-variance was a beautiful theory. Your constraints are real.
Textbook optimization assumes stable correlations, normal returns, and portfolios with no history. The Asset Allocation engine proposes allocations under your actual constraints, explains its reasoning, and leaves the decision with you.
Constraints, scenarios, regimes.
Constraints as first-class inputs
Mandate limits, position caps, liquidity tiers, turnover budgets, tax lots, exclusion lists. The engine optimizes inside the rules you live under, not toward an elegant portfolio you cannot hold.
Scenario analysis, not point estimates
Every proposal is examined across historical stress periods, hypothetical shocks, and paths your team defines. You see how it behaves when the assumptions are wrong, before you commit.
Regime awareness
Correlations and volatilities are not constants. The engine conditions its inputs on the current market regime and shows you which regime assumptions drive the proposal. A calm-market allocation should never be mistaken for an all-weather one.
No black-box rebalances.
An allocation you cannot explain to your investment committee is not an allocation you can use. Every proposal arrives as an argument. Your team interrogates it, adjusts it, and approves it.
Annotated changes
Each weight change carries the constraint or input that drove it.
Side-by-side comparison
Against your current allocation and any alternative your team proposes.
Committee-ready output
The objective, the binding constraints, the scenario behavior, and what changed since the last review, in one reviewable document.
Decision log
Proposals, amendments, and approvals, with the evidence behind each.
Allocators, funds, and every team that answers to a committee.
Institutional investors
Policy portfolios reviewed continuously against mandate and regime, not annually against a memo.
Hedge funds
Allocation across strategies and books with the same constraint discipline as a single portfolio.
Quantitative researchWealth managers
Household-level proposals that respect suitability, tax lots, and each client’s written mandate.
An optimizer is only as honest as its inputs. The engine reads positions, prices, and exposures from the same reconciled layer your risk and reporting run on. No stale extracts, no parallel books.
Drakkar Data EngineTest it against your real constraints.
Bring your mandate, your limits, plus a portfolio you know well. In a demo we will run a proposal under your constraints and walk through every line of its reasoning.