Trading decisions based on verifiable data, not intuition
Ruzite Faguhi processes large volumes of market data in real time and translates that information into concrete recommendations. Each suggested operation is recorded, so that the model can be evaluated with facts and not promises.
- Real time analysis
- Continuous processing of market data, without manual delays.
- Public record
- Suggested operations documented with date and result.
- Multi-market models
- Coverage of different assets under the same analysis engine.
Results verified in a public registry
We do not publish selected averages. Each recommendation generated by the model goes into a log with timestamp, asset and result, including operations that did not work.
Public record format. The real values are displayed within the platform, ordered chronologically and without subsequent editing.
The registry groups operations by week and separates successes from errors in independent columns, without averages that hide individual results.
Any user with access to the platform can review the complete history of recommendations, filter by asset and compare the behavior of the model in different periods of volatility.
The goal of the registry is not to show consistent performance, but to allow each trader to evaluate the consistency of the model with their own criteria.
How the model turns data into recommendations
The Ruzite Faguhi engine combines historical series, trading volume and intraday volatility to identify patterns that manual analysis hardly detects at the same speed.
Big data analysis
The system processes market flows in real time, without depending on manual updates or daily closings.
Exposure risk reduction
Each recommendation includes an estimate of the associated risk, calculated before suggesting entry or exit from a position.
Scalable recommendations
The same analysis engine is applied to different capital volumes, adjusting the size of the suggested position according to the defined risk profile.
Analyze. Predict. Optimize.
From raw data to a concrete decision
The process is divided into three fixed stages. Each stage produces a verifiable output before moving on to the next.
Market data capture
The system receives prices, volume and market depth directly from connected sources, without manual intervention in this phase.
Predictive modeling
The models compare the current state with similar historical patterns and calculate the probability of different price scenarios.
Operational recommendation
The result is translated into a specific recommendation: asset, address, suggested size and associated risk level.
Two market scenarios, two ways to apply the analysis
Risk management in volatile markets
When volatility increases, the model reduces the suggested position size and widens the safety margins. The recommendation is updated every scan cycle, not once a day.
Construction of medium and long-term strategy
For longer horizon positions, the system weighs multi-week trends against daily noise, prioritizing signals that remain stable over different time frames.
Optimize your strategy today with data, not assumptions
Activate access to Ruzite Faguhi and review the public trading log before defining how to integrate it into your daily trading.