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Every recommendation can be traced in the public performance log

Clarity through data for your first investment decisions

Frei Prägentum processes market data using statistical models and translates the result into understandable signals. Artificial intelligence does not replace your own decision; it structures the information situation so that beginners can make informed decisions without prior knowledge.

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The context

Anyone who invests in capital markets for the first time will encounter an excess of opinions, price trends and news. This market noise makes it difficult to distinguish relevant signals from short-term noise - further exacerbated by a structural information asymmetry between institutional actors and private investors.

Predictive modeling

Statistical models evaluate historical price trends, balance sheet ratios and market cycles in order to identify patterns that cannot be examined at specific points. The result is an estimate of probability, not a guarantee.

Real-time analysis

Millions of data points – prices, news, trading volumes – are processed continuously. This deep learning process detects shifts earlier than manual evaluation would allow and updates signals accordingly.

Risk classification

Each recommendation is provided with a risk classification that takes into account volatility, market correlation and liquidity. In this way, it remains visible which risk confronts a possible opportunity.

Traceability

The public performance log documents every recommendation in an unchangeable manner

Once a signal is published, it becomes part of a publicly viewable protocol. Subsequent adjustments are excluded - users see the same history that is also used internally to evaluate the quality of the model.

Protocol active

Live performance indicator

Shows the current status of open and closed recommendations, each with a timestamp and initial data on which the assessment is based.

Verification process

Each recommendation is hashed and archived before publication. The community can cross-check the initial data and results at any time.

Hit rate over time

Illustrative representation of log history, not a guaranteed yield forecast.

Methodology

How raw data becomes a manageable signal

The logic behind Frei Prägentum follows a fixed process: data is filtered, weighted according to relevance and only then converted into a signal. This process is intended to reduce speculation, not replace it - the final decision remains with the person investing.

  1. Data collection

    Price data, company key figures and macroeconomic indicators are brought together from publicly available sources.

  2. Cleaning & Filtering

    Redundant or conflicting data sets are removed to keep market noise out of the model.

  3. Weighting

    Relevant factors are given weight within the model depending on their historical significance - no factor alone decides.

  4. Signal generation & logging

    The result is issued as a recommendation with risk classification and saved immediately in the public performance log.

Frei Prägentum team analyzing financial data

About Frei Prägentum

A tool for support, not a decision-making authority

Frei Prägentum sees itself as an analysis partner for people who have capital but do not easily trust traditional bank sales. Artificial intelligence takes over the analysis of large amounts of data; It remains the responsibility of the person investing to determine whether a signal fits with their own willingness to take risks.

This distribution of roles was deliberately chosen: augmentation instead of automation. Each recommendation is provided with justification and data provenance so that users can understand why a signal emerged - not just that it exists.

Start your data-driven future

No minimum investment, full transparency.

No sales pitch, no automatic account opening. You get access to the public performance log and methodology documentation.