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Raxelo Vunizo Real-time data visualization of financial markets
Real-time analysis

Real-time analysis for 500+ trading pairs

Raxelo Vunizo processes market data continuously and without delay through manual intermediate steps. Predictive models recognize patterns in price trends that are easily overlooked when viewed intuitively, thereby reducing the influence of emotional decisions on one's own investment strategy.

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The illustration shows a section of the data streams that are continuously collected from more than 500 trading pairs and processed by the Raxelo Vunizo models.

Market coverage

The breadth of the market at a glance

If you want to base decisions on data, you first need enough data. Raxelo Vunizo monitors a wide range of trading pairs simultaneously - from classic forex pairs to digital assets - and maintains this observation without interruption. This creates the foundation on which the predictive models are built.

500+

Trading pairs are monitored in parallel, including forex, commodities and digital assets.

Real time

Price data flows continuously into the models, without delay due to manual checking steps.

24/7

Market monitoring runs continuously, even outside the traditional trading hours of individual stock exchanges.

Multi-level

Each signal goes through several levels of review before it becomes visible as a recommendation.

Methodology

How data becomes decisions

Artificial intelligence is not used at Raxelo Vunizo to obscure complexity, but to make it manageable. Models process significantly larger amounts of data than a single analyst could review in a reasonable amount of time, and they do so without the fatigue or emotional bias that can influence human decisions over long periods of time.

  1. Data collection – Price data from more than 500 trading pairs is continuously collected and cleaned.
  2. Model training – historical patterns and current market data are incorporated into the predictive models.
  3. Pattern recognition – the models identify probabilities for certain price movements.
  4. Testing and issuing – Signals are checked for plausibility before they are displayed as a recommendation.
Raxelo Vunizo team reviewing model results and market data
How it works in detail

Risk management and predictive models

  • Predictive models

    Probabilities instead of predictions

    The Raxelo Vunizo models calculate probabilities of occurrence for certain price developments based on historical and current data. They do not provide guarantees, but rather a reasoned assessment based on significantly more data points than manual analysis could process in the same time.

  • Risk management

    Volatility-related position sizes

    Recommendations take into account the current volatility of the respective trading pair. If this increases sharply, the proposed position size adjusts accordingly in order to keep the ratio of opportunity and risk within a comprehensible range.

  • Transparency

    Comprehensible signal basis

    Each generated signal is documented with the underlying data points and the model logic used. This makes it possible to understand which factors led to a particular assessment instead of accepting it as a mere expense.

“Models do not replace a decision – they provide the basis on which an informed decision becomes possible.”

Methodology of Raxelo Vunizo
Use cases

Strategic decisions in specific market situations

Transparency

Questions about database and model accuracy

How is data quality ensured?

Incoming price data is checked for plausibility before processing. Conspicuous outliers or gaps in the data transmission are marked and are not included in the model calculation without being checked.

How accurate are the predictive models?

The models provide probability information, not reliable predictions. Your hit rate varies depending on the market phase and trading pair, as volatility and liquidity are constantly changing.

Will historical data or real-time data be used?

Both. Historical data is used to train the models, while real-time data is continuously incorporated into the current signal calculation. Both data sources are documented separately.

Who checks the automatically generated signals?

Before a signal is displayed, it goes through an automated plausibility check. Conspicuous or contradictory signals are marked instead of being output unchecked.

How is model uncertainty dealt with?

Each recommendation contains information about the underlying database. This makes it possible to assess whether a signal is based on broad or limited data.

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