Where human intuition meets algorithmic execution
Valsontezza analyzes markets in real time and transforms large volumes of data into concrete operational indications, designed for those who want to invest methodically and not on impulse.
Valsontezza analyzes markets in real time and transforms large volumes of data into concrete operational indications, designed for those who want to invest methodically and not on impulse.
Those who trade on a daily basis are well aware of the tension between wanting to jump into a movement straight away and doubting whether it's the wrong time. This emotional swing often leads to early entries into rallies and premature exits into downturns, with a cumulative effect that weighs on returns over time.
Valsontezza does not eliminate market uncertainty, which remains structural. Instead, he proposes a method for managing it: distributing inputs according to a calculated logic, rather than according to the mood of the moment.
Classic DCA distributes capital at fixed intervals, ignoring context. Valsontezza applies the same principle of discipline, but calibrates each entry based on updated market signals, not the calendar alone.
The engine collects price, volume and volatility data over multiple time periods to build an updated snapshot of the observed instrument.
A predictive model estimates the time windows in which the relationship between risk and exposure is most favorable, according to parameters that the user can calibrate.
Purchases are divided into tranches, carried out according to calculated logic and not according to the instinct of the moment, reducing the impact of impulsive decisions.
Each strategy shows a synthetic indicator of the level of risk assumed compared to the allocated capital, updated at each rebalancing cycle. The user defines the maximum acceptable threshold before starting the automation.
Market data streams are continuously ingested and normalized. The system updates its estimates at each significant change in price or volume, without waiting for session closures or periodic reports.
This approach reduces the delay between the market event and the strategy's reaction, a critical factor for those operating on short horizons.
The operational indications are not generic: they take into account the declared time horizon, the risk tolerance set and the current composition of the connected portfolio.
The model does not promise guaranteed results. It provides a quantitative basis on which the user always maintains the final decision to confirm or modify.
We prefer to show how the engine works rather than rely on third-party statements. Here are the three pillars on which the process is based.
Price and volume data comes from standard market feeds, with provenance tracking for each instrument analyzed. No internal or unverifiable sources enter into the calculation.
Predictive metrics are recalibrated at regular intervals based on observed performance, with a documented historical versioning and comparison process.
Access credentials to the connected brokers are managed via encrypted connections and permissions limited only to necessary operations, without storing unsolicited sensitive data.
Valsontezza was born from the idea that automation must support the investor's judgment, not replace it with promises of returns. Our job consists of translating large quantities of data into understandable indications, always leaving the final decision to the user.
We work with investors who already know the markets and are looking for a tool of algorithmic precision, not a logical shortcut.
Find out more about Valsontezza
Valsontezza connects via API to a select number of brokers and exchanges that support secure programmatic connections. The exact list of available integrations is communicated during the activation phase, based on your geographical area and the tools you intend to operate.
The model does not provide precise price forecasts, but probabilistic estimates on favorable entry windows. Accuracy is monitored by comparing simulated performance with real historical performance, and parameters are adjusted when deviation exceeds predefined thresholds.
Yes. You can set the maximum exposure threshold, the frequency of purchase tranches and the time horizon of the strategy. These settings can be changed at any time and do not require programming skills.
It depends on the mode chosen. You can set automatic execution within the configured limits, or request manual confirmation for each operation proposed by the engine before it is sent to the broker.
The engine uses public market data — price, volume and historical volatility — along with portfolio parameters that the user voluntarily plugs in. No unverifiable sources or data not relevant to the financial decision are used.
Consider whether a calculated and distributed approach suits your way of investing, without immediate commitment and with full control over the parameters.