CardinQuota processes market data in real time and compares it with strategies tested on time series, to quantify the risk before each allocation.
Model running on historical and current market data. Each strategy is accompanied by a past performance report, before activation.
A continuous flow of prices, volumes and news does not automatically produce a better decision. We need a process that filters the noise and quantifies the residual risk.
Efficiency metric: the generation time of the analysis report is proportional to the number of strategies selected and is communicated before activation.
CardinQuota does not generate isolated predictions. Combine statistical models, on-chain data and traditional market data into a single, continuously updated analysis stream.
Each recommendation is accompanied by its confidence interval and the backtesting history on which it was built. The goal is to reduce decision risk, not promise a return.
Four technical components work together to transform raw data into operational guidance.
Statistical models trained on time series of price, volume and volatility, updated with current market data at regular intervals.
Relevant changes in market data are reported when they exceed the thresholds defined in the selected strategy.
Each recommendation includes an estimate of the expected volatility and the maximum historical drawdown recorded by the corresponding strategy.
The allocation parameters are adapted to the time horizon and risk tolerance indicated during the configuration phase.
Before being proposed, each strategy goes through a verification process based on historical data, documented and repeatable.
Historical price and volume series over multi-year periods, coming from public market sources.
Definition of the entry and exit parameters of the strategy based on the risk objectives.
Applying the strategy to distinct market periods, including down cycles and phases of high volatility.
Verification of results on data not used in the model construction phase, to limit overfitting.
Delivery of a report with historical performance, maximum drawdown and simulated market conditions.
The historical data used in backtesting is not changed after the simulation. Each report indicates the period analyzed and the reference market conditions, including phases of negative performance.
Illustrative graph. It does not represent actual performance or guarantee future results.
The same predictive analytics engine supports different needs, from managing a personal portfolio to business planning.
Allocation between digital assets guided by defined risk thresholds and strategies already tested on historical data, with periodic reports on current exposure.
Comparison of multiple backtesting strategies to select the allocation most consistent with the client's risk mandate.
Assessment of exposure to digital assets in corporate liquidity management, supported by documented historical scenarios.
In all cases, the objective remains the same: to reduce the margin of uncertainty before capital allocation, not to promise a specific return.
The data provided during the configuration phase are used exclusively to calibrate the required analysis strategies. Access to internal systems is regulated by permissions differentiated by user.
Each model reports a confidence interval calculated on historical validation data. No model eliminates market risk; makes it measurable.
No. Backtesting shows how a strategy would have performed on historical data. Future market conditions may differ from those simulated.
Historical series of price, volume and volatility over multi-year periods, including both positive and negative market phases, to avoid partial selection of data.
Upon request, it is possible to receive details of the data and parameters used for the simulation of a specific strategy, to support the independent verification.
Request a preliminary analysis based on the parameters of your portfolio or treasury strategy. You will receive a report with the backtesting history of compatible strategies.