Real-time data processing
Market feeds are continuously ingested and normalized, allowing our models to work on an updated state of the market rather than lagging data.
Nélivance combines predictive models with total liquidity of your capital, so that quantitative analysis and the availability of your funds are no longer conflicting choices.
Nélivance was built to process large volumes of market data — quotes, order books, on-chain indicators — and transform them into actionable recommendations for financial decision-makers.
Our technical team designs continuously revised statistical models so that risk parameters reflect the actual state of the market rather than a fixed assumption at the time of deployment.
Each decision proposed by Nélivance results from structured data processing, designed to remain readable and verifiable by your teams.
Market feeds are continuously ingested and normalized, allowing our models to work on an updated state of the market rather than lagging data.
Predictive analysis models estimate future volatility and adjust exposure accordingly, to limit the impact of sudden market movements.
The allocation is revised according to rules defined in advance, in a logic of optimization of return adjusted to the level of risk retained.
Unlike many institutional structures that impose lock-up periods, Nélivance does not hold your funds once a withdrawal request is initiated.
There are no lock-ups, no restricted release windows, no hidden delays. Your position remains yours, and the platform is designed to process withdrawal requests without additional manual intervention.
We do not present testimonials or performance promises. We describe how the system works, so you can assess its suitability for your profile.
Continuous aggregation of market data, order book depth and on-chain indicators from multiple sources, cleaned before processing.
Application of stochastic models to estimate the probable distribution of returns and calibrate the management of short and medium term volatility.
Orders are sized according to a weighted risk score, then executed in batches to limit the market impact on open positions.
We consider data protection as a technical prerequisite, not as a commercial argument. Each component of the platform is designed taking this constraint into account from the design phase.
Request a demo to see how the platform works on a representative dataset, before making any engagement decisions.
Request a demo