Trouw Waardelatuur combines validated AI models with historical market data to identify low-risk entry points into the crypto market. Each strategy has been tested in advance on years of price data before being shown to you as a recommendation.
View StrategiesCrypto markets move on news, sentiment and short-term speculation. For an individual investor it is difficult to distinguish which movement is structural and which is temporary noise. However, that distinction determines the difference between a strategic entry and an impulsive purchase.
Trouw Waardelatuur works as a filter between you and that noise. The platform searches historical price and volume data for patterns that have proven to lead to more stable entry points in the past, and only shows those outcomes that meet pre-determined risk criteria.
Long series of historical data per asset, used as the basis for each model.
Identification of periods of increased risk based on previous market cycles.
Results by strategy, broken down by market condition and time frame.
Trouw Waardelatuur is built on a preference for proven methods over promises. Each model that appears on the platform is first tested against historical data over multiple market cycles before being released for use.
This approach is deliberately modest: we do not claim guaranteed returns, but provide a structured, validated way to assess risk before committing capital. This suits a target group that is learning to invest with limited assets and needs transparency over hype.
No black box: three steps that together determine whether an entry point meets the risk criteria.
The model collects historical and current price, volume and volatility data of the relevant crypto assets, supplemented with macro indicators that influenced previous price movements.
Each strategy is backtested across multiple historical periods, including bear markets, to determine how the approach behaves under different conditions.
Only scenarios that fall within predefined risk limits are shown as recommendations. Models with an unpredictable risk profile are not published.
Three components that together determine when and how you enter, based on validated data instead of market sentiment.
As soon as current market data matches a validated pattern from a backtest, you will receive a notification with the underlying reasoning: which indicators converge and within which risk margin the scenario falls.
Each strategy shows the results of previous backtests by market period, so you can assess how a model behaved during both rising and falling markets, rather than just the best-case scenario.
Based on your available capital and risk appetite, the model calculates a distribution across assets that historically led to a more stable course of action, instead of concentration in one position.
The example below is a hypothetical illustration based on backtest data from Model A, not a current promise. Past results are no guarantee for the future.
Scenario: stake of €500 based on Model A, compared to a randomly selected entry point without model guidance, over the same historical period.
Starting bet in the simulated scenario, the same for both variants.
Entry at the time that met the model's risk criteria, according to backtest data.
Entry at a randomly chosen time within the same period, without model guidance.
This example serves solely to illustrate the backtesting method and does not replace individual investment advice.
No. The models are designed to work with varying bet sizes, so that a limited available amount can also be bet within a defined risk profile.
Each model has been tested on historical data, but no model predicts the market with certainty. AI reduces risk by recognizing patterns that humans often miss; it does not completely eliminate risk. Each advice therefore states the associated risk level.
The costs are shown in advance and fully visible on the dashboard, without hidden surcharges afterwards. An up-to-date overview is available as soon as you create an account.
No hidden costs, full transparency about every model and every underlying backtest.
View dashboard