Schlüssur Equity continuously analyzes market data and automatically adjusts risk parameters to open positions. Capital protection runs in the background, without manual monitoring.
The module processes tick data, order book depth and volatility indices in a continuous cycle. From this data it calculates risk parameters for each open position. If volatility changes, the system automatically adjusts position size recommendations. Every adjustment is recorded and remains traceable.
Each recommendation is based on multiple independent data sources. Missing or inconsistent data is flagged before it is included in the analysis.
Captures price movements, volume and spreads across multiple trading venues.
Analyzes liquidity at multiple price levels to identify slippage risks early.
Detects phases of increased fluctuation and adjusts risk thresholds accordingly.
Calculates dependencies between open positions to avoid cluster risks.
Compares current patterns to historical data sets for ongoing model validation.
Connects to popular broker and data providers via REST and WebSocket interfaces.
Market data, order book depth and volatility metrics flow continuously into the system. External APIs provide additional reference data for validation.
The model calculates a risk score per position and identifies deviations from defined parameters. Each result receives a confidence value.
The system provides concrete recommendations for position sizing. Execution remains in the trader's control.
Schlüssur Equity develops analysis infrastructure for traders and quantitative analysts who want to make decisions based on data. The focus is on comprehensible risk parameters instead of forecasts with high hit rate claims.
Each model is tested against historical market phases before going live. Changes to risk thresholds are logged and can be checked afterwards.
An analysis cycle usually takes less than 20 milliseconds. The actual latency depends on the quality and connection of the respective data source.
The system connects via standardized REST and WebSocket interfaces. For a complete list of compatible providers, see technical onboarding.
The models are trained using historical market data over multiple market cycles and regularly validated against current data. Training intervals depend on observed market volatility.
No. The system provides recommendations and risk parameters. Order execution remains the responsibility of the user unless a separate broker connection with execution rights has been configured.
Incoming data is compared against reference sources. The system automatically flags deviations above a defined threshold for manual inspection.
Talk to us about your trading infrastructure. We will show you how to adjust the risk parameters using realistic example data.