Zurela Kivel processes your market data in real time, calculates allocation scenarios according to your risk profile and delivers an immediately usable recommendation, without spreadsheets or manual processing.
Configure my walletThe process is sequential and timed: each step automatically triggers the next one as soon as the required data is available.
Import your existing positions via API or file, or start with a blank portfolio.
Define your risk tolerance, investment horizon and target asset classes.
The engine cross-references your parameters with real-time market feeds and generates several allocation scenarios.
You compare the proposed scenarios and validate the one that corresponds to your strategy.
Each module directly feeds into the final recommendation; no indicator is displayed without impacting the proposed allocation.
| Capacity | Function |
|---|---|
| Real-time risk analysis | Monitors position volatility and triggers an alert if the defined threshold is exceeded. |
| Predictive modeling | Projects multiple return trajectories from multi-variable statistical models. |
| Allocation Optimization | Recalculates the optimal allocation between asset classes according to the validated risk profile. |
| Anomaly Detection | Identifies market deviations that may affect portfolio performance. |
The engine applies diversification rules and exposure thresholds by asset class, recalculated with each market update. The goal is to limit concentration, not to predict a guaranteed return.
Market feeds are continuously ingested, normalized and then compared to current positions. Any significant variation triggers a new allocation calculation cycle.
Market intelligence is only valuable if it translates into action. These three modules directly link analysis to allocation.
The recommendations cover several asset classes to allow real diversification rather than concentration on a single market.
Yield projections are based on statistical models recalibrated every cycle, not a fixed estimate communicated once and for all.
Each allocation recommendation is accompanied by a summary of the associated return/risk trade-offs, for a reasoned decision.
The objective is that each recommendation can be explained a posteriori to an analyst or an auditor.
Market data comes from public financial feeds and regulated API connections. No sensitive personal data is required to generate an allocation recommendation.
The models are continuously compared to historical market data in order to monitor the evolution of their error rate over time.
When a significant discrepancy is detected between the forecast and the observed market, the relevant model parameters are adjusted before the next recommendation cycle.
Transmitted data is encrypted in transit and at rest. The processing infrastructures are hosted within the European Union, within the framework of compliance with the GDPR.
Integration is done via API or file import. Existing positions are integrated into the allocation calculation as soon as they are imported, without manual re-entry.
The models are recalibrated as soon as a significant discrepancy is detected between their forecasts and observed market data, in addition to systematic periodic monitoring.
No. It serves as a basis for rapid decisions; final validation and execution remain under your control.