Algorithmic optimization of family capital
Courcef combines DCA (Dollar-Cost Averaging) smoothing with predictive models to distribute your investments over time, without requiring you to monitor the markets on a daily basis.
Dashboard overview
Our approach
Courcef was designed for families who wish to enhance their capital over ten to twenty years without becoming financial market specialists. The method is based on two simple principles: distributing inputs over time and adjusting the schedule through data analysis.
Concretely, a predictive analysis engine processes continuous market time series and identifies windows where the risk/opportunity ratio is statistically more favorable, without ever promising a guaranteed return.
The observation
Investing capital all at once exposes you to a timing risk: a market drop just after entry can have a lasting impact on performance and the level of stress felt. Conversely, waiting for the “perfect moment” often leads to never investing.
DCA smoothing distributes the investment into several regular tranches. Courcef refines this method by adjusting the size and timing of tranches based on volatility indicators, rather than following a fixed schedule blind to market conditions.
Exposure relative to timing risk, with identical investment horizon. Simplified illustration, excluding fees and taxes.
The methodology
Each component of the system has a specific role in reducing timing risk and preserving investment discipline over the long term.
The engine continuously processes market data (volatility, volumes, correlations) to estimate the probability of favorable entry conditions over a short horizon.
Investment tranches are executed according to a schedule adjusted by the algorithm, without manual intervention and without emotional decisions at the time of the order.
Distribution over time reduces the impact of a poorly positioned entry and mechanically smoothes the average acquisition price over the duration of the plan.
Each plan is accompanied by projections based on historical scenarios, presented with their assumptions and limitations, without promise of performance.
Operation
The transparency of the process allows you to understand every execution decision, without having to interpret technical indicators on your own.
Feeds of prices, volumes and volatility indicators are collected and normalized in real time from multiple market sources.
A statistical model evaluates the probability of favorable conditions over the following days, based on patterns observed in historical data.
The size and timing of each tranche are adjusted within the limits of the defined plan, never deviating from the overall planned amount.
The allocation is reviewed periodically to remain aligned with the horizon and risk profile initially declared.
Use cases
A plan of regular payments over fifteen to twenty years, with installments adjusted according to market volatility rather than according to a fixed schedule. The objective is to reduce the gap between the average entry price and market troughs, without imposing active surveillance on the household.
The projections incorporate historical favorable and unfavorable scenarios, in order to present a realistic range rather than a single value.
Example of retirement plan
For capital intended to finance studies in eight to twelve years, the system favors a gradual reduction of risk as the maturity approaches, by tightening exposure to the most volatile assets.
Parents maintain monthly visibility on the current allocation, without having to arbitrate between asset classes themselves.
Example of education fund
Technical questions
Personal and financial data is encrypted during transmission and storage. Account access is based on strong authentication, and order execution flows are separated from analysis systems to limit points of failure.
The model is based on volatility indicators and statistical patterns observed over a long period. It is not intended to predict a precise price, but to estimate a relative probability of favorable entry conditions, within the limits inherent to any market modeling.
Applicable fees (service management, order execution, possible third-party brokerage fees) are detailed in the contractual documentation before any plan is put in place, so that each household can assess the impact on net performance.
Liquidity depends on the investment vehicles chosen within the plan. Withdrawal conditions, processing times and possible penalties linked to certain media are specified when defining the investment profile.
An initial analysis makes it possible to define an amount, a horizon and a risk profile, before any automated plan is implemented. No obligation to commit at this stage.