Mynqus analyzes your variable income profile and applies backtested strategies to offer investment and liquidity recommendations adjusted to the real cycles of a self-employed professional.
An independent consultant can close a six-month contract with solid income and, three months later, face a period of no predictable billing. In that interval, decisions about where to place the surplus or how much liquidity to reserve are usually made manually, under pressure and without a historical frame of reference.
Mynqus replaces that one-time intuition with a systematic process: each recommendation is based on the verified performance of similar strategies under comparable market conditions, not on an optimistic projection.
Schematic representation of the Mynqus approach to ad hoc decision making. It does not constitute a profitability projection.
Mynqus does not generate generic suggestions. Each proposed strategy has been backtested on historical series and is adjusted using a risk model that responds to real changes in volatility.
Before proposing a strategy, the system executes it on historical market data over different time horizons. This allows its behavior to be estimated in high and low volatility scenarios, instead of being based solely on theoretical assumptions.
The recommended exposure level is not static. The model recalculates the user's risk profile based on their stated liquidity horizon and current market conditions, adjusting the suggested allocation accordingly.
The engine processes series of market data continuously to identify relevant patterns before they are reflected in conventional indicators with a longer release delay.
Each user defines their tolerance for loss and their need for short-term liquidity. The system translates these parameters into specific allocation limits, not generic recommendations.
Instead of anonymous testimonials or success stories, Mynqus lays out the logical process behind each recommendation. This allows the user to evaluate the methodology, not just the result.
The system incorporates market data, historical performance series and the financial parameters declared by the user: irregular income, current reserves and expected liquidity horizon.
The models identify correlations and recurring cycles in market data, comparing them with the behavior of strategies already backtested under similar conditions.
The result translates into a concrete proposal for liquidity allocation or management, calibrated to the user's specific risk profile, not a generic market average.
When a long contract ends with a significant remainder, the usual question is not only where to invest, but how long to keep that capital tied up. Mynqus proposes a distribution based on the liquidity horizon that the user himself defines, avoiding allocations to terms that are incompatible with his income cycle.
In non-billing months, the priority shifts from growth to preservation. Mynqus' adaptive risk model automatically reduces the suggested exposure when it detects, from the declared data, that the user is approaching a period of lower available liquidity.
The irregular income of a self-employed person usually involves installment payments and annual regularizations. Mynqus incorporates these dates as model restrictions, so that investment recommendations take into account the need for capital availability in the relevant fiscal periods.
Mynqus was developed as a data intelligence platform applied to financial decisions, with special attention to the income patterns of independent professionals. The team prioritizes historical verification of each strategy before incorporating it into the recommendation system.
The platform does not replace the user's criteria: it provides a framework of systematic analysis on which to decide with more information.
Learn moreFor a user with technical criteria, trust is not built with promises, but with verifiable explanations about how the system operates.
The data declared by the user, such as income and liquidity horizon, are used exclusively to calibrate the individual risk model. The storage infrastructure follows encryption principles in transit and at rest, and access to information is limited to the processes strictly necessary to generate the recommendations.
Backtesting consists of applying an investment strategy to historical market data to observe how it would have behaved in the past. This does not guarantee future results, but it allows us to discard strategies that have not shown consistent behavior in different economic cycles before considering them viable.
Mynqus provides data-driven analytics and predictive models as a decision support tool. Each user retains final responsibility for his or her investment decisions, and it is recommended that suggestions be compared with professional advice when the situation requires it.
The model recalculates the risk profile continuously based on market conditions and the changes that the user registers in their liquidity parameters. This is not a static evaluation done just once at the beginning.
The engine processes series of prices, volumes and volatility indicators of the instruments considered in each strategy. The goal is to detect patterns that inform recommended allocation before they are reflected in slower release indicators.
Access to Mynqus includes consultation of the performance history of each strategy from the first moment. Integration with your income data requires no additional technical configuration from the user.