Zelborinyx continuously processes large volumes of market data from global exchanges, providing institutional investors and tech-savvy traders in the UK with signals based on quantitative models - not judgment.
The platform combines statistical preprocessing with learning models to identify patterns in large data streams that are too complex for manual analysis.
The system continuously processes price data, order book depth and structured message flows and updates its assessment of the market situation at short intervals instead of waiting for daily reports.
Each recommendation is tested against historical stress scenarios before it is issued. Position sizes are based on the measured volatility of the respective instrument.
The infrastructure is designed so that additional data sources, asset classes or portfolios can be connected without fundamental new development.
Zelborinyx relies on a multi-stage process consisting of statistical preprocessing, multivariate regression models and neural networks to identify recurring patterns in extensive data streams that are hardly noticeable in manual screening. The aim is not to predict individual price movements, but rather to systematically evaluate probabilities from which a repeatable statistical advantage can be derived.
Before release, each model version is tested on historical market periods with different levels of volatility to assess the consistency of results across different market cycles. Opinions of individual analysts play no role in the evaluation process.
Market data, macroeconomic indicators and structured news sources are brought together from different providers, cleaned and brought to a uniform time frame before they are incorporated into the modeling.
The processed data runs through an ensemble of regression models and neural networks that are trained on historical time periods and continuously checked against new, previously unseen data.
Concrete, prioritized suggestions are derived from the model results - for example regarding position size or hedging requirements - together with documentation of the underlying assumptions.
An investment team uses Zelborinyx's model outputs to hedge existing positions against short-term market volatility. The platform provides continuously updated market volatility forecasts and suggests suitable hedging instruments whose historical effectiveness in the respective market environment is documented.
Strategic planning teams in companies with a connection to the capital market use the analyzes to test budget scenarios against different market conditions. Instead of individual point estimates, you receive a range of plausible developments with the underlying assumptions, which makes internal coordination of hedging strategies easier.
The basis for the decision is the comprehensible testing of the model, not the assertion of a result.
Each strategy is tested on historical data over multiple full market cycles, including periods of increased volatility and low liquidity, to provide a realistic assessment of robustness.
The processing of new market data up to the updated model output takes place in the millisecond range, so that trading decisions are always based on a current market picture.
Multivariate regression models and neural networks are continuously checked against out-of-sample data that was not part of the training period to avoid overfitting.
A technical consultation shows which data sources and model configurations are relevant for your portfolio or strategic planning - without obligation and without sales pressure.
Please direct any questions to our team the contact page.