Fels Wertanung's smart stop loss system detects risks early and reduces drawdowns before they escalate.
Illustrative representation of the risk classification in the model.
Price movements in crypto assets occur within seconds. Those who react manually often react too late.
Fear and greed further delay important decisions. The result is unnecessarily high drawdowns and missed exit points.
Amounts of data are growing faster than a human can meaningfully evaluate them. Fels Wertanung closes this gap with ongoing, automated analysis.
Market and order book data are continuously evaluated. The system immediately detects deviations from the normal pattern.
The stop loss system dynamically adjusts exit points to the market situation. Losses are limited before they spread.
Whether one portfolio or a hundred: the analysis runs in parallel and provides consistent recommendations for each position.
Market data, order flows and volatility indicators are continuously recorded and subjected to data validation.
The AI compares current patterns with historical risk parameters and estimates the likelihood of major swings.
Based on this calculation, the system adjusts stop loss marks or issues a specific recommendation for action.
Fels Wertanung does not pursue promises of quick profits. The focus is on limiting losses.
Limiting losses, enabling profits – that is the basis of every recommendation. A controlled drawdown creates the basis for long-term stability.
Focus: Drawdown minimization instead of speculationFels Wertanung develops AI-powered analysis tools for investors who value risk control.
Each recommendation is based on verifiable data models and clearly defined risk parameters, not guesswork.
The platform is aimed at companies and investors who prefer data-based decisions. Transparency in the methodology and limitations of AI is part of the approach.
All market data is transmitted encrypted and used exclusively for analysis. It will not be passed on to third parties.
The analysis runs continuously in the background. Adjustments to the risk parameters are made based on rules, even in the event of sudden price drops, without manual intervention.
The analysis complements existing depots and wallets. The connection takes place via an interface-based integration without replacing existing processes.
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