Lyganiusis AI analyzes market data in real time and provides daily, overview reports. The system operates independently of your physical location, and each recommendation is traceable to its original data.
Each report Lyganiusis AI generates follows a fixed four-step process. This structure is designed to remain tractable, allowing a user to trace any recommendation to the underlying data.
Raw market data is collected from public exchanges and licensed financial feeders, without adjustment of the underlying figures.
Incoming data is structured and compared against historical patterns to identify anomalies and missing points.
Statistical models calculate probabilities for different market scenarios, based on correlation and volatility over time.
Findings are converted into annotated, readable reports intended for human verification prior to any decision.
The platform is built as an open structure: each output can be traced back to the original data source from which it is calculated. This approach avoids the "black box" problem common to automated investment systems.
Users accessing the technical documentation can examine the exact formulas and thresholds used in each step.
At the heart of Lyganiusis AI's offering is transparency: a daily report showing what has changed, and why.
The dashboard provides a single screen of positions, risk exposure and report status, laid out according to a fixed grid structure that remains consistent across devices and screen sizes.
Because reports are automatically generated, physical location is irrelevant for access. A user in Cape Town, Bali or Lisbon receives the same data on the same schedule, with the same level of detail.
Risk is not eliminated, but measured, ranked and explained before it influences a decision.
Predictive models estimate the probability of different outcomes by combining historical volatility, correlation between assets and macroeconomic indicators into a single risk profile.
Exposure is spread across asset classes according to a correlation matrix. This prevents a single market move from affecting the entire portfolio at once.
Capital preservation — low-volatility instruments that serve as the basis for the portfolio.
Growth allocation — balanced exposure based on medium-term forecasts.
Tactical positions — limited, time-bound exposure to higher-volatility opportunities, subject to tighter thresholds.
Three examples of how automated analysis is put into practice by users who frequently change locations.
A user moving across time zones receives an alert when volatility exceeds a pre-set threshold, even while flightless or online.
The system monitors return-generating positions and suggests an adjusted allocation weekly based on changing risk-return ratios.
Earnings in different currencies and jurisdictions are converted to a common baseline, so that performance remains comparable across borders.
Answers to questions frequently asked by data-conscious users.
The models produce probabilities, not certainties. Each report shows the confidence interval of a forecast, so the user can judge the reliability for themselves before making a decision.
Data is restricted to the account owner and the system's processing layers. No data is sold or shared with third parties for marketing purposes.
Yes. Each recommendation is a suggestion, not an automatic transaction. Users can adjust thresholds or override recommendations without interrupting the system's operation.
The system uses public market data and licensed financial feeds. Where a source has limited historical depth, this is indicated in the report.
Transparency statement: all data sources are listed in the technical documentation, including the update frequency and any known limitations of each feeder.
There is no obligation to accept recommendations. A sample report shows exactly how data is collected, processed and presented, so you can judge for yourself whether the methodology fits your approach.