Invest intelligently without being an expert.
Ralvero Felsuna uses AI-supported data analysis to find the optimal entry point for your portfolio - fully automated and precise.
Start analysis now
Ralvero Felsuna uses AI-supported data analysis to find the optimal entry point for your portfolio - fully automated and precise.
Start analysis nowAnyone who manages their capital themselves is confronted with a flood of news, price movements and analyst opinions every day. This phenomenon is often referred to as information overload: the amount of available data exceeds the ability to meaningfully process it in a reasonable amount of time.
The result is rarely clarity, but rather uncertainty. Volatile markets trigger emotional stress that makes rational decisions difficult. Anyone who makes decisions without structured data models in phases of strong price fluctuations tends to either sell too early or get in too late.
A real-time data model cannot completely eliminate this effect, but it provides an objective basis on which decisions can be made regardless of the day and market sentiment.
The approach is based on an extended form of dollar-cost averaging: instead of fixed intervals, the purchase time is determined based on calculated probabilities.
Millions of market signals from price data, trading volumes and macroeconomic indicators are continuously recorded and structured. This raw data forms the basis of every further calculation.
An analytical model filters out short-term market noise and identifies statistically relevant trends. The aim is to recognize patterns that indicate a favorable or unfavorable entry point.
Buy orders are only triggered when the calculated indicators show a relatively cheap entry. This combines the classic principle of regular investing with data-based fine-tuning.
Predictive models continually assess market conditions and reduce the likelihood of investing at an inopportune time. This does not involve a complete exclusion of the risk of loss.
The system monitors market data continuously, even outside normal trading hours. Investors do not have to monitor prices themselves to profit from current market conditions.
The setup requires no programming knowledge or deep financial knowledge. The user interfaces are designed to use understandable terms instead of technical jargon.
Ralvero Felsuna was developed with the aim of making complex market analysis accessible to private investors without diluting the technical depth of the underlying models.
The focus is on traceability: Every automated decision is based on documented criteria that can be traced upon request. This means investment decisions remain explainable instead of opaque.
Find out more about usRalvero Felsuna is based on mathematical probabilities, not intuition or market feel. Historical price trends, trading volumes and volatility patterns are statistically evaluated to derive probabilities for future price developments.
This calculation does not provide certainty, but rather a relative assessment: How likely is a price increase versus a price decline under the current conditions? Based on this, a decision is made as to whether a purchase order will be executed.
Illustrative representation of the probability distribution used to evaluate entry times.
Connections to external depots and data sources occur exclusively via encrypted interfaces. Access data is not stored in plain text, but is managed using established authentication procedures, as are also common in the banking environment.
The connection takes place via a step-by-step setup in which you link your existing depot via a secure interface. No prior technical knowledge is required, as the process is accompanied by understandable explanations.
The system evaluates historical and current market data to identify statistical patterns. On this basis, a probability is calculated as to whether the current time is relatively good for a purchase. It is a rule-based statistical model, not autonomous or unpredictable decision making.