For day traders and quantitative investors
Vast Rijpendom's model observes your orders, position size and response to drawdowns, and continuously calibrates the system's risk tolerance accordingly. No fixed profile, but an allocation logic that moves with the market and your behavior.
The overview below shows the signals and quality indicators that are continuously monitored, from processing speed to the spread around each prediction.
Average processing time per market update, measured over the last thousand ticks.
Bandwidth around the point forecast under current market volatility.
Internal consistency score based on cross-validation over recent market regimes.
Number of instruments currently within the model's monitoring range.
View illustrating the processing speed of the platform. This is not current trading advice and does not reflect live market positions.
The system starts with a conservative base setting and then builds a risk profile from your actual decisions: position size, holding time and the way you respond to losses. Every change in behavior is processed as new evidence, not as an exception to an established model.
Dynamic allocation means that the allocation between positions shifts once the model determines that your risk tolerance has changed, for example after a series of losses or increased volatility. The final control remains with you: the model proposes limits, you confirm or change them.
The table below compares manual analysis with Vast Rijpendom's model, based on an example scenario with historical market data.
| Method | Error Reduction Rate | Time to Insight |
|---|---|---|
| Manual analysis | Reference value | 45–90 min per cycle |
| Fixed Maturity AI | Up to 20% lower deviation margin* | Under 2 minutes |
Error Reduction Rate measures the decrease in deviation between predicted and actual outcome, compared to a manual estimate. Time-to-Insight measures the time between new market data becoming available and a detailed recommendation.
* Based on a simulated comparison with historical market data; no guarantee of future results. Data sources used: public market data and, if provided, your own transaction history. Model results are not audited externally.
The integration is designed for technical users who prefer to work via an API rather than manual configuration screens.
Connect your broker or data environment via a REST endpoint or web socket feed. A sandbox environment is available directly via /v1/sandbox.
Define the frameworks within which the model may operate: maximum position size, permitted instruments and hard stop limits.
Connect a webhook to your own systems or have signals transferred directly to your order execution via /v1/webhooks.
Technical questions we receive most often from traders who want to understand the model before deploying it.
Transaction data is stored encrypted and only used to calibrate your risk profile. Data is not shared with third parties for marketing purposes and remains accessible for export at your request.
Each recommendation is shown with the underlying factors that led to the signal, including the weighted contribution of volatility, momentum and your own risk parameters. You not only see the outcome, but also the reasoning behind it.
Processing time is typically between 10 and 25 milliseconds, depending on the complexity of the instrument and the load on the feed at the time.
Yes. Every allocation change stays within the limits you set, and you can pause or roll back the automation at any time.
A better calibrated risk profile usually translates into less emotional decisions and more consistent execution of your own strategy, not a replacement for it.