Mare Capitanca data analysis dashboard representing real-time portfolio modelling

Predictive portfolio management, without capital lock-up

Mare Capitanca runs continuous stochastic modelling across market and macroeconomic data to produce allocation recommendations that update as conditions change. Withdrawals remain instant, because the model is built around liquidity, not around the client's patience.

Request access See the methodology

Model recalibration Continuous, not quarterly

Withdrawal processing Same working day, no notice period

Risk inputs Volatility, correlation, liquidity ratios

Static vs. adaptive

A model that recalculates exposure, rather than one that waits for a review meeting

Traditional discretionary portfolios are typically rebalanced on a fixed schedule — quarterly, or after a client call. Mare Capitanca's model ingests pricing, volatility and liquidity data continuously and adjusts weighting recommendations in response, so the portfolio reflects current conditions rather than the conditions at the last review.

For a parent managing a household, a career and a portfolio simultaneously, this removes the burden of deciding when to check in. The system does not require attention to function correctly.

No lock-up periods

Live model status

Data refresh cycleContinuous

Rebalancing triggerThreshold-based

Predictive variance trackingActive

Liquidity ratio targetMaintained at all times

Methodology

The technical sequence behind each recommendation

Every allocation decision passes through three defined stages. None are skipped, and none rely on discretionary override.

Data aggregation

Market pricing, macroeconomic indicators and historical volatility series are collected and normalised into a common structure, updated on a rolling basis rather than at fixed intervals.

Risk modelling

Stochastic simulation estimates a distribution of likely outcomes for each candidate allocation, rather than a single point forecast, giving a range of predictive variance to weigh against the client's stated risk tolerance.

Execution optimisation

The system selects the allocation that satisfies the risk constraint at the lowest estimated transaction cost, and confirms that the resulting position preserves same-day withdrawal capacity before execution.

Liquidity assurance

Why instant withdrawals do not compromise the model

How same-day withdrawals are structured

Positions are held in instruments with daily settlement cycles, and a portion of the portfolio is kept in near-cash equivalents sized to projected withdrawal demand. A withdrawal request does not require unwinding illiquid positions, because the model never allocates into them in the first place. This is a constraint applied before optimisation runs, not an exception handled afterwards.

What this means for risk management

Maintaining liquidity narrows the universe of eligible assets, which does constrain the theoretical upper bound of return. Mare Capitanca treats this as a deliberate trade-off rather than a limitation to be minimised: the model optimises within the liquid set rather than seeking liquidity as an afterthought once returns have been maximised elsewhere.

Read the full liquidity policy

Application

How the model is applied to different financial goals

The underlying engine is the same across each use case. What changes is the risk constraint and time horizon supplied to it.

Education

Education fund optimisation

Allocations are weighted toward capital preservation as the funding date approaches, with liquidity maintained throughout so a withdrawal is never delayed by notice periods tied to an academic calendar.

Horizon: long

Long-term wealth compounding

Over multi-year horizons, the model favours steady reinvestment and lower turnover, accepting higher short-term variance in exchange for compounding efficiency, while still permitting withdrawal at any point without penalty.

Horizon: short

Tactical market entry

For clients holding cash ahead of a specific opportunity or decision, the model manages interim exposure to limit downside variance while keeping the full position accessible on short notice.

About Mare Capitanca

Built for clients who want evidence, not reassurance

Mare Capitanca was built on the premise that busy, financially literate parents do not have time to monitor markets, but still want decisions grounded in data rather than intuition. The platform is designed to replace manual portfolio review with a continuously running model, and to remove the cognitive load of deciding when to act.

We are explicit about the mechanics behind each recommendation, because clients who understand the reasoning tend to trust the outcome more than those who are simply told to trust it. Every recommendation can be traced back to the data inputs and risk constraints that produced it.

Mare Capitanca analyst reviewing portfolio data and risk models

Frequently asked

Questions we are asked most often by analytically minded clients

How is client data secured?

Account data and transaction records are encrypted in transit and at rest, and access is restricted on a role basis so that model operations and client servicing use separate permission sets. We do not sell or share client data with third parties for marketing purposes.

How transparent is the underlying algorithm?

Clients receive a plain-language summary of the inputs and constraints used for their portfolio, including the risk tolerance parameters applied and the liquidity floor maintained. We do not publish the full model architecture, consistent with standard practice for proprietary quantitative systems, but the reasoning behind any given allocation is available on request.

What actually happens when I request a withdrawal?

A withdrawal request is checked against the portfolio's liquid balance, which is maintained continuously rather than calculated at the point of request. Provided the request is within that balance, funds are released for same-day processing. There is no mandatory notice period and no early-exit penalty.