¿Cómo se protegen los datos de la cuenta y de operativa?

Los datos se almacenan cifrados en reposo y en tránsito mediante protocolos estándar del sector. El acceso a credenciales de conexión con brókers se gestiona mediante tokens de permisos limitados, revocables en cualquier momento desde la cuenta del usuario.

¿Es compatible con los brókers y plataformas que ya utilizo?

Zavavo Ferino se integra mediante API con los proveedores que exponen interfaces de trading estándar del mercado. La compatibilidad exacta depende del bróker, y se puede verificar antes de activar cualquier ejecución automática.

¿Qué tan fiables son los resultados de backtesting mostrados?

Los resultados de backtesting se calculan sobre datos históricos segmentados por periodos fuera de muestra, para reducir el sobreajuste. Se indica siempre el tamaño de la muestra y el intervalo de confianza asociado a cada métrica reportada.

Zavavo Ferino predictive analytics dashboard showing real-time market data streams

Financial intelligence platform

Real-Time Decision Optimization through Predictive AI

Zavavo Ferino processes millions of market data points per second and converts them into statistically based entry and exit signals. Traders can also replicate, through copy-trading, the AI strategies with the best verified performance within the platform.

Request demo See how it works

The dashboard visualizes correlations between thousands of assets simultaneously, highlighting price anomalies and statistical confidence levels for each signal generated by the model.

The Market Challenge

The speed of the markets exceeds the capacity of manual analysis

Today's markets generate micro price variations in millisecond intervals. A human analyst, no matter how prepared, needs seconds or hours to interpret a comparable volume of information, and that delay translates into missed opportunities or late entries.

1 Fatigue and emotional bias alter repeated decisions during high volatility sessions.
2 Manual analysis of multiple correlated assets consumes time that reduces the execution window.
3 The absence of an auditable record of criteria makes it difficult to review why a particular decision was made.

Zavavo Ferino team analyzing financial data models on screen

Artificial Intelligence Engine

Three technical pillars support the analysis engine

Each component operates independently and auditably, allowing its contribution to a final recommendation to be reviewed.

01 — MODELING

Predictive modeling

Deep learning models trained on historical series and book order data, calibrated to estimate price movement probabilities in different time windows.

02 — ARBITRATION

Risk arbitrage

Continuous comparison between correlated assets to identify temporary price divergences, with user-configurable exposure limits before any execution.

03 — INFRASTRUCTURE

Scalable infrastructure

Distributed processing with ultra-low latency between data ingestion and signal generation, designed to maintain performance under peak market volumes.

Methodology

From data to recommendation, in three traceable steps

Each stage is recorded, so the reasoning behind a signal can be reviewed at any time.

Data ingestion

Market data, order depth and macroeconomic variables are collected from standardized sources, with synchronized timestamps to avoid mismatches between series.

Model analysis

The engine evaluates historical patterns and current conditions, generating a confidence score for each signal along with the variables that most influenced the result.

Implementation support

The recommendation is presented with its statistical justification and associated risk level, leaving the final decision to execute or not in the hands of the user.

Use Cases

The same engine, applied to different investment profiles

Parameter settings vary depending on the user's time horizon and risk tolerance.

Day Trading Institutional Risk Portfolio Rebalancing

Intraday trading

The model identifies volume anomalies and microtrends in minute windows, pinpointing entry and exit points with an explicit confidence level for each trade.

Detection of range breaks with prior statistical validation.
Divergence alerts between price and volume in real time.
Copy-trading option on verified best performing intraday strategies.

Institutional risk management

For teams managing larger portfolios, the system calculates aggregate exposure by asset and sector, flagging concentrations that exceed internally defined limits.

Simulation of stress scenarios on current positions.
Correlation reports between assets to avoid hidden risk.
Auditable record of each alert generated and its justification.

Portfolio rebalancing

The engine compares current allocation against user-defined targets and suggests incremental adjustments that minimize fiscal impact and transaction costs.

Calculation of deviation from the target allocation.
Proposals for gradual adjustment instead of abrupt changes.
History of previous rebalances with comparable results.

Frequently Asked Questions

Technical answers to the most common questions

How is account and trading data protected?

Data is stored encrypted at rest and in transit using industry standard protocols. Access to connection credentials with brokers is managed through limited permission tokens, revocable at any time from the user's account.

Is it compatible with the brokers and platforms I already use?

Zavavo Ferino integrates via API with providers that expose market-standard trading interfaces. Exact compatibility depends on the broker, and can be verified before activating any auto-execution.

How reliable are the backtesting results shown?

Backtesting results are calculated on historical data segmented by out-of-sample periods, to reduce overfitting. The sample size and the confidence interval associated with each reported metric are always indicated.