Data intelligence applied to investment

Data-driven investment decisions, reviewed by a community that audits its own results

Semilla Rentecto processes high volumes of market data in real time and translates it into risk allocation recommendations. The goal is not to eliminate volatility, but to reduce the time a family spends managing it.

Access performance analysis See how it works
24/7
Continuous data ingestion
< 15 min
Estimated weekly dedication
Public
Searchable performance log
From spreadsheet to predictive model

Why manual analysis loses effectiveness as data volume grows

An individual investor can review a limited number of variables per week. A predictive model, on the other hand, constantly recalculates correlations and risk signals. Consequently, the gap between both approaches widens the greater the amount of information available.

Approach Comparison: Manual Analysis vs. Semilla Rentecto Model
Dimension Manual analysis Predictive model
Review Frequency Weekly or monthly, subject to availability Continuous, based on live data streams
Variables considered Limited by the analyst's time Multiple data series combined
emotional bias Present in specific decisions Reduced using previously defined rules
Traceability Depends on own records Systematically recorded and searchable

Decision dispersion versus target risk range

Manual management
Model Semilla Rentecto

Illustrative representation of the variability observed between individual decisions and model-adjusted decisions within the same declared risk profile.

System architecture

Three components that support each recommendation generated

The system combines data ingestion, predictive modeling and exposure control. Each component operates independently, allowing its operation to be audited separately.

01

Real-time data ingestion

The system incorporates market data, macroeconomic indicators and trading volumes at regular intervals, without manual intervention.

What does it contribute?

Prevents recommendations from being based on outdated information, a common problem in manual portfolio tracking.

02

Predictive engine

The models identify historical patterns and project risk-adjusted return scenarios, updating their parameters as new data arrives.

What does it contribute?

Based on time series and not on point predictions, which allows distinguishing persistent signals from statistical noise.

03

Risk Management (Risk Shield)

A set of rules automatically limits exposure when volatility exceeds user-defined thresholds.

What does it contribute?

Therefore, capital protection does not depend on the user's speed of reaction to sudden market movements.

Verification, not promises

A public performance record instead of testimonials

Semilla Rentecto publishes the history of model decisions along with the data that originated them. Any registered user can consult this history, which allows the result to be contrasted with the declared methodology.

Semilla Rentecto: Team reviewing performance record and predictive model methodology

Applied methodology

Each model decision is documented along with the supporting variables and the target risk range of the corresponding profile. Unfavorable results are not removed from the historical record.

Model parameters and source data quality are reviewed in quarterly cycles. The conclusions of each review are incorporated into the public record.

Operation without daily active management

A flow designed for those who have little time available

The system is designed to operate under parameters defined only once, with occasional adjustments when family circumstances change.

  1. Definition of the risk profile

    The user establishes the time horizon, the level of acceptable exposure and the financial objectives of the family.

  2. Setting automation thresholds

    The limits are set beyond which the system can rebalance the portfolio without requiring manual confirmation.

  3. Continuous model monitoring

    The predictive engine analyzes incoming data and makes adjustments within previously approved margins.

  4. Notification of relevant events

    The user receives a periodic summary and specific alerts only when a critical threshold defined by the user is exceeded.

Automation thresholds

Configurable by level of volatility, percentage of deviation from the target or age of the last manual review.

Notification frequency

Available in daily, weekly or monthly format, plus immediate alerts reserved for events that exceed the configured risk threshold.

Frequently asked questions

Direct answers to the most common questions before starting

Where does the data used by the model come from?

The system combines market sources, public macroeconomic indicators and trading volume data. Each source is identified in the methodology record accessible from the user's account.

What happens if the model is wrong in a specific period?

Periods of underperformance remain visible in the public record, along with subsequent analysis of the causes. Unfavorable results are not hidden.

Can I modify the risk thresholds once they are configured?

Yes. Thresholds can be adjusted at any time. The change is applied starting with the next review cycle, not retroactively.

What level of security is the account data?

Access to the account and linked financial data is protected through individual authentication and encryption at storage. The technical detail is described in the methodology document available after registration.

Is it necessary to have advanced financial knowledge?

It is not a requirement. The initial setup is based on questions about time horizon and risk tolerance, not complex technical terminology.

Review the performance log before deciding if Semilla Rentecto fits your profile

Access to the demo includes the model's decision history, the applied methodology and the available configuration parameters. No permanence commitment is required to consult it.

Start optimization Consult the methodology document