Dzoskaruma Real-time data analysis for portfolio decisions
Predictive analysis for traders

From raw data to an actionable portfolio strategy in 60 seconds

Manual chart analysis costs time and misses patterns in millisecond data. Dzoskaruma connects to your trading interface and delivers quantified recommendations before the next chart is loaded.

Automate your portfolio now

How the analytics engine supports decisions

  • Real-time processing

    Price data, order book depth and volatility indicators are continuously evaluated and not queried periodically.

  • Pattern recognition

    The model compares current price formations with historical data sets and highlights statistically relevant repetitions.

  • Emotionless execution

    Recommendations are based on defined thresholds - not on daily form or market sentiment.

Dzoskaruma working environment for data analysis and model development

A model that reveals its assumptions

Dzoskaruma is designed for market participants who want to make decisions based on data without being data scientists themselves. The underlying models evaluate publicly available market data, historical price trends and order book information.

Instead of blanket buy signals, the system provides comprehensible key figures on risk, probability and expected fluctuation range for each position. Each recommendation can be traced back to the underlying parameters.

Three steps to the first analysis

01

Connect interface

Connect a depot or broker via a secure interface – without code.

02

Define parameters

Define risk profile, target return and trading frequency in just a few fields.

03

Start AI optimization

Model calculates scenarios and delivers prioritized recommendations in real time.

Informed decisions instead of gut feeling

Volatility cannot be eliminated, but it can be classified. The model weights positions based on historical fluctuation ranges and current correlations between asset classes in order to make cluster risks visible at an early stage.

Instead of issuing a single forecast, Dzoskaruma compares several scenarios with each probability of occurrence. The decision as to which risk is acceptable remains with you - the model provides the basis for this.

Scalable infrastructure for real-time data analysis

The meaningfulness of a model depends on the quality and timeliness of its data. The following points describe how Dzoskaruma handles this.

Data sources

Market data from multiple liquidity sources

Price, volume and order book data are brought together from multiple sources and checked for consistency before being incorporated into the models.

Predictive models

Continuously retrained procedures

Models are regularly readjusted based on new market data to reduce distortions caused by outdated patterns.

Scalability

Parallel evaluation of several depots

The infrastructure is designed to serve individual users and larger portfolios with the same update frequency.

Update

Continuous rather than periodic analysis

Calculations run continuously in the background, so recommendations are not based on outdated daily closing prices.

Ready for data-driven leadership?

Connect the interface, set the parameters, start the analysis – the entire setup process is completed in under 60 seconds.

Test Dzoskaruma