Algorithmic market analysis
Amselquarz systematically evaluates historical market data and translates the results into comprehensible strategies - suitable for beginners who want to enter crypto markets methodically instead of impulsively.
Methodology
Crypto markets generate multiple data points per day than what an individual investor can evaluate manually. This is exactly where Amselquarz comes in: Predictive models filter noise from actual market signals and check each assumption against historical data sets before it is incorporated into a recommendation.
The core of the system is algorithmic validation: each model is continuously tested on past market phases before being applied to current data. This process reduces the risk of confusing short-term trends with stable patterns — a common mistake when making purely intuitive decisions.
For beginners this means: You don't receive black box signals, but rather a derivation that can be understood at every level. Transparency about the database is just as important as the accuracy of the forecast itself.
Market, volume and volatility data from several years are recorded and cleaned up in a structured manner.
Statistical patterns are identified and formulated as hypotheses for recurring market behavior.
Each hypothesis is tested on historical data sets before being released as an active signal.
Models are regularly recalibrated in order to identify deviations between forecast and market reality at an early stage.
Backtesting results
The following values come from the back calculation of the current model version over several market cycles. They are used to classify risks, not to forecast profits.
Simulated cumulative performance of the model portfolio per quarter tested, indexed to the starting value.
All metrics are based on simulated backtests using historical data and do not take future market conditions into account. Past performance is not a reliable indicator of future results; The numbers are used exclusively to assess risk and model behavior.
Tools
Instead of individual signals, users receive a set of tools that structure decisions and reduce emotional reactions to short-term price movements.
Each position is continuously checked against current volatility indicators so that changes in risk become visible before they are reflected in the portfolio.
Rule-based alerts provide information about defined threshold values and replace spontaneous reactions with predefined decision logic.
The distribution across several positions is continuously compared with the desired risk structure and, if there are deviations, displayed transparently.
For students
Amselquarz is aimed at students and young professionals who want to approach crypto markets not on the side, but with an understanding of the underlying mechanisms. The platform not only shows a result, but also the path to get there: which data was checked, which assumption is behind it and how this assumption has behaved historically.
This approach is particularly suitable for starting out with small capital, as errors can be identified and corrected earlier through traceability instead of only becoming visible in retrospect.
View methodology documentation →
Frequently asked questions
The models are based on historical price, volume and volatility data from established crypto trading venues. Data sources are regularly checked for consistency and completeness before they are included in backtesting.
Models are recalibrated in fixed cycles to reflect structural changes in market behavior. Between cycles, the deviation between the forecast and actual development is continuously recorded.
Yes. The platform is designed so that position sizes and risk parameters can be configured independently of the capital invested, allowing for gradual entry.
No. Backtesting results show how a strategy would have performed in the past, but are not a guarantee of future performance. Crypto markets remain volatile regardless of the quality of a model.
Instead of isolated buy or sell signals, Amselquarz provides the underlying derivation including risk metrics so that users can understand the basis for the decision instead of blindly following it.
Further questions about the database or how to get started? Contact our team.
Entry
Register for access to backtesting data and methodology documentation. A reduced entry-level tariff is available for students, which is based on the typical budget during your studies.
Request accessNo long-term commitment in the first steps - entry and scope can be adjusted at any time.