Sûretance continuously observes your markets and automatically adjusts your risk tolerance threshold based on your actual behavior, without you having to stay permanently connected.
An active portfolio generates a continuous flow of signals, often contradictory. Between two time zones and an unstable connection, this informational noise accumulates faster than it can be processed manually.
The resulting decision fatigue pushes people to react late, or to ignore relevant signals through simple cognitive saturation. It's not a problem of discipline, it's a problem of volume of data in the face of limited attention span.
Significant movements occur during your sleeping or moving hours, out of your field of vision.
Each asset generates dozens of indicators; distinguishing them manually takes time that you don't always have.
An alert received three hours after the event loses most of its arbitration value.
Sûretance does not ask you to define a risk profile once and for all. The system observes your actual decisions — validations, rejections, position adjustments — and gradually recalibrates your tolerance thresholds.
Each asset is assessed on several simultaneous dimensions: historical volatility, sector correlations, and instantaneous liquidity, rather than a single score.
Market scenarios are continuously simulated to identify exposure thresholds compatible with your objectives, without setting a rigid rule.
The model adjusts its alert thresholds with each interaction, refining its understanding of your true tolerance over the weeks.
Connecting your accounts and market sources via secure API.
Initial observation over a reference period without intervention on your part.
Progressive calibration of tolerance thresholds according to your actual reactions.
Delivery of prioritized alerts, filtered according to their real relevance to you.
These examples illustrate how the system behaves on a daily basis, without replacing a guarantee of results.
An investor based successively in Lisbon, Bangkok then Mexico holds positions in stocks, cryptoassets and short-term bonds. Sûretance aggregates these feeds and applies unified multi-vector analysis, independent of the local time zone.
Faced with a progressive deterioration of a sector indicator, the system detects a convergence of weak signals before they become visible on traditional dashboards. The alert suggests a position adjustment, with the underlying reasoning explained.
Sûretance was designed for investors and entrepreneurs whose activity does not stop at one time zone. The goal is not to multiply notifications, but to reduce their number to those that really matter.
Each component of the system — data collection, modeling, restitution — is designed to operate with intermittent connectivity and limited attention span.
Trust in an automated system is based on the clarity of its operation, not on a promise of performance.
Your portfolio and behavioral data remain hosted in France, under your control, and are never resold to third parties.
The models are audited periodically to detect possible overfitting biases linked to atypical market periods.
The architecture is based on industry-standard APIs and asynchronous synchronization, designed to tolerate frequent network outages in mobility.
The technical and logistical points most often raised by our users.
Sûretance integrates with standard APIs from leading brokers and market data providers via secure OAuth connectors. The precise list of available integrations is communicated to you when setting up your account.
The system works in asynchronous mode: analyzes continue on the server side even when your device is offline. When reconnecting, the accumulated alerts are returned in order of priority, not in chronological order.
Predictive models include explicit confidence bounds. During an out-of-norm event, the system signals a loss of reliability of its own forecasts rather than generating an unsupported recommendation.
Yes. Automatic calibration provides a starting point, adjustable at any time. Any manual modification is integrated as a new learning signal for subsequent recalibrations.
No. Portfolio and behavior data remains strictly individual and is never used to train models for other accounts.