Accuracy and control on every exchange, in a single dashboard.
Zenit Fondivo integrates your accounts across multiple exchanges and applies predictive models to optimize portfolio decisions, with data updated in real time.
Zenit Fondivo integrates your accounts across multiple exchanges and applies predictive models to optimize portfolio decisions, with data updated in real time.
Investors operating across multiple exchanges manually manage spreadsheets, separate notifications, and inconsistent reporting. Zenit Fondivo centralizes these sources, synchronizes balances and trades in real time, and returns a single portfolio readout, ready to be optimized.
Zenit Fondivo models process aggregate data to identify anomalies, generate signals and propose strategy adjustments, leaving the final decision up to you.
The system compares market movements to historical patterns in your portfolio and flags changes in volatility or exposure that require attention.
Data streams from connected exchanges are processed continuously, so trading signals reflect current market conditions and not static data.
In the presence of significant variations in volatility, the algorithm proposes rebalancing consistent with the set risk parameters, without continuous manual intervention.
Zenit Fondivo connects to your accounts via API keys in read-only mode or with limited permissions, according to the settings you define yourself. Data is encrypted in transit and at rest, and no credentials are shared with third parties.
Zenit Fondivo was created to respond to a concrete problem: the difficulty of consistently reading data distributed across multiple exchange platforms. Our work focuses on the quality of the input data and the transparency of the models that process it.
We do not promise returns: we provide analytical tools and operational signals that remain under the direct control of the investor.
The process of bringing exchange data to an operational recommendation follows three distinct and verifiable phases.
Balances, orders and operational history are collected from API-connected exchanges and normalized into a common format.
Normalized data powers predictive models that analyze volatility, correlations and historical patterns to generate risk and opportunity indicators.
Indicators are presented as rebalancing signals and suggestions, which the investor evaluates and confirms before each action.