Nettare Patrimione — predictive analytics dashboard for investment decisions
Predictive analytics for private investors

Optimized decisions in real time, on real data

Nettare Patrimione processes continuously updated market data and returns a ready-to-use portfolio allocation. Initial setup takes less than 60 seconds and requires no prior financial expertise.

Start the analysis now No credit card required for initial setup
60 sec
Average setup time
1,200+
Variables analyzed
24/7
Data update

How large volumes of data become a concrete choice

The Nettare Patrimione engine collects quotes, macroeconomic indicators and market signals from multiple sources, updating them at regular intervals during trading hours. This data is distilled through statistical models into a small number of allocation scenarios.

Each scenario is evaluated with respect to three measurable parameters before being proposed to the user: these are not generic predictions, but outputs calculated on a verifiable and updated database.

From data ingestion to asset allocation, in three steps

The process is linear and traceable: each phase produces a verifiable output before moving on to the next, up to the final portfolio proposal.

Data Ingestion

The system imports market data, the declared risk profile and the investor's time horizon, normalizing them into a single format.

Risk Modelling

The models estimate expected volatility and apply risk mitigation rules consistent with the user profile.

One-Click Allocation

The asset allocation proposal is generated and can be confirmed with a single action, without additional manual steps.

Allocation proposal — example Balanced profile
Asset class Weight 30 day change
Government bond 38.00% +0.4%
Diversified equity 32.00% +2.1%
Liquidity and monetary instruments 18.00% 0.0%
Raw materials and coverage 12.00% -0.8%

How the engine works, without gray areas

Every component of the platform is documented and verifiable. There are no "closed box" steps between data input and the final proposal.

01

Machine Learning Models

Supervised algorithms trained on historical market series, periodically recalibrated to take into account new incoming data.

02

Historical backtesting

Each proposed strategy is tested on past time windows to measure its resistance in different market conditions.

03

Real-time API integration

Market data arrives via direct connections with suppliers, reducing the delay time between event and model update.

04

Algorithmic data security

User information is encrypted in transit and at rest, with access limited to only those processes necessary to calculate the allocation.

Two approaches, a single principle: strategic security

Family planning

Capital growth over a long horizon

For families planning 10-15 year goals, the system builds an allocation oriented towards gradual growth, with periodic rebalancing calculated as market conditions change.

10-15 years
Typical horizon
Quarterly
Rebalancing frequency
Declared objectiveLong-term growth
Equity exposure55.00%
Bond exposure35.00%
Reserve liquidity10.00%
Individual investor

Protection of capital from volatility

For those who have already accumulated capital and want to preserve it, the model favors low correlation assets and reduces exposure in periods of greater instability detected by market indicators.

0.31
Annual target volatility
Monthly
Risk verification
Declared objectiveCapital conservation
Equity exposure20.00%
Short-term bond50.00%
Liquidity and coverage30.00%

A team oriented towards data, not gut predictions

Nettare Patrimione was created to make analysis tools normally reserved for institutional managers accessible to families, maintaining the same discipline on data and verification of results.

Each model update is documented internally and performance metrics are tracked over time, to allow consistent comparison between different periods.

Find out more about Nettare Patrimione
Nettare Patrimione — the team working on market data analysis

Set up your wallet in less than 60 seconds

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