Corona Coniatura analyzes your business's cash flows and applies one Smart Stop-Loss: a dynamic mechanism that calculates in real time the most effective exit points to contain losses, without fixed thresholds and without continuous manual interventions.
Analyze your liquidity Discover the methodologyMany SMEs maintain cash reserves in current accounts or low-yielding instruments because active management requires time, specific skills and constant monitoring of the markets. This choice reduces the perceived risk immediately, but exposes the company to an opportunity cost that grows over time.
Traditional liquidity management often relies on periodic reviews — monthly or quarterly — that fail to capture rapid market changes. When volatility increases, the decision arrives later than the event that generated it.
The platform combines predictive analytics, optimization models and continuous monitoring to translate large volumes of market data into specific operational guidance for each business profile.
The engine aggregates market data, macroeconomic indicators and internal parameters to the company profile, normalizing them to build a coherent picture of the liquidity situation at any time.
The models estimate probabilistic scenarios of market evolution in the short and medium term, identifying the allocations that balance expected return and declared risk tolerance.
When market conditions deviate from the expected parameters, the system generates an operational signal, with the quantitative rationale that determined the alert.
Unlike a static stop-loss set only once, the Corona Coniatura mechanism continuously recalculates the optimal exit point based on the observed volatility, the correlation between assets and the time horizon set by the company.
The system establishes a starting threshold based on the declared risk profile and the current composition of liquidity.
With each significant change in market conditions, the threshold is recalculated: it narrows in phases of high volatility and widens when conditions stabilize.
The output is activated only when the quantitative parameters confirm that the drawdown risk exceeds the set tolerance, reducing false signals.
Note on operational safety. Each recalculation of the threshold and each execution is recorded with the relative quantitative rationale, so as to guarantee traceability of decisions and subsequent verifiability.
The adoption process was designed for B2B contexts, where operational continuity and clarity of steps matter more than speed of activation.
Connection to existing accounts and financial instruments through structured connectors, maintaining data segregation and access permissions defined by the company.
The engine analyzes the available history, builds the liquidity profile and proposes an initial configuration of the stop-loss threshold, subject to review by the company.
Once the configuration is approved, the system operates continuously, with periodic reports and alerts whenever conditions require a revision of the strategy.
Below are three recurring situations in which predictive liquidity management produces a measurable operational advantage compared to manual management.
Companies with collections concentrated in certain months of the year can allocate the temporary surplus following a contained risk profile, with the stop-loss threshold automatically adapting as the period of expected use of the liquidity approaches.
Funds earmarked for contingencies or future contractual obligations remain protected from sudden market changes, as the system reduces exposure when observed volatility exceeds historical reference thresholds.
When market conditions improve, the predictive engine signals time windows in which to reinvest part of the protected liquidity, while still keeping the dynamic exit mechanism active.
A technical demo allows you to see how the predictive engine processes your company's data and how the dynamic stop-loss threshold is calculated, before any operational commitment.