Bonus Programm combines predictive modeling with real-time decision optimization. Data clusters of market and trading data are continuously evaluated to produce signals based on historical validation rather than intuition.
Each signal goes through four stages before it is released as a basis for decision-making. This process reduces the dependence on individual opinions and makes the logic behind each recommendation understandable.
Structured and unstructured market data is continuously imported and checked for consistency before it enters the model pipeline.
Algorithms identify recurring data clusters and correlations that would not be detectable by human analysis at this speed.
Each identified pattern is tested against historical time periods to check hit rate and risk parameters under different market conditions.
Only after validation has been passed is a signal with a confidence value and risk classification transferred to the decision interface.
Each function is aimed at a specific decision problem: what could happen, how big is the risk and what is happening right now.
Predictive modeling assesses the probabilities of price movements based on historical data clusters and provides confidence intervals instead of blanket forecasts.
Position sizes and risk parameters are automatically adjusted based on volatility and drawdown history to systematically limit capital losses.
A running dashboard shows signal status, market changes and portfolio impact without the need for manual evaluation.
Every recommendation that Bonus Programm makes is based on a documented backtesting history. Instead of opinions or short-term trends, the system relies on objective data clusters that have been examined over multiple market cycles.
The methodology is deliberately kept comprehensible: every signal can be traced back to the underlying test runs, including the market conditions under which it was created.
The platform is used both for fine-tuning existing portfolios and for building new, automated sources of income.
An investor with an existing portfolio uses signal attribution to rebalance positions based on current risk parameters. The backtesting history provides an objective basis for reallocation decisions that would otherwise have to be checked manually and in a time-consuming manner.
An employed professional with a limited time budget sets up automated signal notifications to make structured decisions on a part-time basis. The preparatory modeling work is no longer necessary because the signals are already based on validated data clusters.
Answers to questions that typically arise before deciding on a data-driven platform.
Market data is continuously read in and processed every minute. The exact latency depends on the specific data source, but is in the low single-digit minutes range for all supported markets.
Retraining takes place in regular cycles based on newly available market data. In addition, if there are significant market changes, an unscheduled validation run is initiated to maintain model accuracy.
Risk parameters are based on historical volatility, drawdown behavior and individually stored upper limits. Users can adjust these parameters within defined limits without changing the underlying model logic.
Models that do not meet the defined minimum criteria in backtesting will not be released for signal generation. They remain in the development environment until a revalidation is passed.
The platform processes publicly available market data as well as structured historical time series. A detailed overview of the data sources used is available via the contact page.