Savun Temorela – Visualization of data-based capital strategies
AI-powered data analysis

Scalable capital optimization through AI-powered data analysis.

Savun Temorela offers freelancers precise decision models based on historically validated strategies - automated, risk-optimized and transparent.

Start strategy analysis
Analysis basis: historical market data over several years, evaluated across different market cycles - presented as a look-back curve instead of a forecast.

The architecture of precision.

Each recommendation from Savun Temorela is based on a three-step process. The aim is to reduce decision-making variance through historical evidence instead of intuition.

01

Data aggregation (multi-source)

Market data from multiple independent sources is consolidated and checked for consistency before being incorporated into the model.

02

Pattern recognition (AI backtesting)

Models are tested against historical time periods to distinguish recurring patterns from noise and validate parameters.

03

Strategic execution

Only strategies with sufficient statistical significance are issued as recommendations for action based on the individual risk profile.

Designed for independent professionals.

Freelancers work with irregular payment receipts. Savun Temorela translates this uncertainty into structured, comprehensible decisions.

Liquidity maximization between projects

Free capital phases are systematically suggested for short to medium-term allocation based on the individual risk profile.

Save time through automated recommendations

The analysis work is eliminated. Recommendations are automatically generated and documented based on defined parameters.

Objective basis for decision-making

Recommendations are based on quantifiable metrics, not market sentiment or subjective assessment.

Structured risk management

Each strategy is tied to a defined volatility class that suits its own resilience.

Validated results instead of predictions.

Savun Temorela relies on extensive historical data sets to limit the spread of possible future outcomes for independent professionals. Forecasts in the classic sense are deliberately avoided - the focus is on the question of how comparable strategies behaved under past market conditions.

Strategy profile Backtesting period Volatility class Rebalancing
Conservative Perennial Low Quarterly
Balanced Perennial Means Monthly
Growth oriented Perennial Increased Monthly

Example representation of the reporting structure. Specific key figures are provided after an individual risk profile has been created.

Savun Temorela – Evaluation view of historical backtesting data

Two typical situations for independent professionals.

The following scenarios show how recommendations are designed depending on the individual risk profile and capital availability.

Scenario 1

Reinvestment of project fees

After payment of a project fee has been received, it is checked which portion can be tied up at short notice without endangering the liquidity reserve.

Recommendation is based on time horizon, ongoing commitments and risk class.
Scenario 2

Hedging against market fluctuations

If market volatility increases, the allocation is adjusted to keep the fluctuation range within the previously defined tolerance limit.

Adjustment is rule-based, not discretionary.

Technical and legal basics.

How is data processed within the meaning of the GDPR?

Personal data is processed exclusively to create the risk profile and recommendations. Storage and processing takes place on servers within the EU, in accordance with the requirements of the GDPR.

On what database are the models trained?

The models are trained using historical market data from multiple independent sources and are regularly checked against new data series in order to identify model deviations at an early stage.

How is integration into existing accounts carried out?

The connection takes place via standardized interfaces. No manual data entry is required; existing portfolio or account structures remain unchanged.

What is the difference between backtesting and forecasting?

Backtesting evaluates how a strategy would have performed under past market conditions. It does not provide an assurance of future results, but rather a more robust basis for evaluating strategies.

How transparent are the underlying parameters?

All relevant parameters – including time horizon, volatility class and rebalancing frequency – are disclosed in the report and can be viewed before any recommendation.

Gain data-based sovereignty.

Start optimizing your financial strategy based on AI analyzes – structured, understandable and adapted to your risk profile.

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