Real-time data analysis for more informed investment decisions
Quarfina continuously processes more than 500 trading pairs, applying predictive modeling to support portfolio optimization and reduce the burden of cognitive bias in market decisions.
Quarfina continuously processes more than 500 trading pairs, applying predictive modeling to support portfolio optimization and reduce the burden of cognitive bias in market decisions.
The Quarfina infrastructure monitors market volume that exceeds manual monitoring capacity, crossing price, volume and volatility data between multiple assets in parallel.
This breadth makes it possible to identify correlations and divergences between pairs that, in isolation, would not be evident — a relevant factor for portfolio managers with distributed positions.
Explore the Platform
Quarfina was born to bridge the gap between the growing volume of market data and the human ability to interpret it accurately under time pressure.
We work with day traders and institutional investors who are looking for a consistent analytical process — not guaranteed predictions, but a more solid and repeatable decision basis.
Get to know the team and methodologyThe system does not replace investor discretion — it organizes information systematically, signaling patterns that decision fatigue or excessive exposure to news tend to obscure.
Prices, volumes and volatility indicators are collected in short, normalized intervals to allow direct comparison between assets with different characteristics.
Models identify recurring statistical patterns and estimate probabilities of movement, without assigning certainty to future scenarios — the result is a confidence interval, not an absolute prediction.
Each signal generated is accompanied by suggested exposure parameters, aligned with risk management practices, to avoid impulsive decisions based on isolated movements.
The same analytical data is presented differently, depending on the time horizon and the objective of the person making the decision.
Real-time alerts on relevant volume or price variations in specific pairs, allowing reaction within short market windows.
Rebalancing recommendations based on correlations between assets, with the aim of reducing unintentional risk concentration.
Simulations that support medium-term business and investment decisions, contextualizing market data with sectoral trends.
The system identifies significant statistical deviations and notifies them immediately, without requiring constant manual monitoring of the graphs.
Each forecast is accompanied by an explicit degree of confidence, allowing the exposure to be calibrated according to the real uncertainty of the scenario.
Periodic synthesis of market behavior across 500+ pairs, useful for reviewing investment theses and decision committees.
Confidence in an analysis system depends on understanding what goes in and what comes out of it. We describe the process without oversimplification.
Market sources are linked via price and volume data APIs, with integrity checking at each reception cycle to rule out inconsistent readings.
The normalized data feeds the predictive models, which run in parallel for the 500+ pairs, generating updated indicators at short intervals.
Each signal is compared against the model's historical performance before it is presented, and signals outside the defined confidence limits are discarded.
A demo allows you to observe how data from 500+ pairs is processed and presented, before any commitment.