Xunera Livo applies predictive models to your real-time data feeds to identify the most profitable opportunities and rule out those with an unfavorable risk/reward ratio.
Optimize my incomeEach recommendation is based on a quantitative analysis, which can be consulted in a daily report.
Our approach
Xunera Livo was designed to bring the same quantitative analysis standards used in institutional asset management to independent professionals.
The engine does not make any recommendations without prior processing of the available data: performance history, volatility of opportunities, time and access constraints. This methodological rigor remains constant, whatever the volume of activity analyzed.
Transparency
Xunera Livo does not just recommend: the platform documents the logic behind each recommendation, in a daily report accessible without intervention on our part.
Methodology
The same logic as that applied to professional portfolios, adapted to the scale of an independent activity and its time constraints.
01
The engine ingests available data on accessible opportunities — volume, history, variability — and detects significant deviations from the observed average.
02
Predictive models estimate the probability of return for each identified opportunity, taking into account market variations observed over the recent period.
03
Only opportunities whose risk/return profile exceeds a defined threshold are transmitted, with the associated supporting data.
Risk management
One of the biggest sticking points in self-employment is the month-to-month variation. Xunera Livo does not promise to remove this variability, but reduces its magnitude by filtering out the least reliable opportunities before they are presented to you.
Filtering is based on measurable criteria: frequency of similar opportunities, standard deviation of observed returns, consistency of data over the period analyzed. Scenarios deemed too unstable based on these criteria are excluded from the selection.
Frequently asked questions
The engine processes real-time data feeds relating to the opportunities available on the channels you enter, as well as their recent history. These sources are indicated in each report, with their update date.
No. A published report remains frozen in order to preserve the traceability of decisions. Any subsequent correction of data is the subject of a separate, dated note, without rewriting the initial report.
It's possible. The model favors the consistency of available data rather than isolated earning potential. A profitable but insufficiently documented opportunity can therefore be ruled out, by methodological choice.
No. Xunera Livo reduces exposure to less reliable scenarios based on observed data, but no future results can be guaranteed based on past data.
The models are recalibrated each reporting cycle, based on new available data, to reflect recent developments rather than a fixed average.