Frizigella analyzes millions of market data points in real time and transforms them into verifiable operational recommendations, replicable through copy-trading of the algorithmic strategies with the best historical performance.
Continuous monitoring of the markets, updating of signals on an intraday basis.
The Frizigella engine applies stochastic models to time series and live streams to isolate relevant market dynamics from simple statistical noise.
The noise reduction process eliminates short-term fluctuations that do not reflect a structural change, allowing high-precision signals to emerge on which to build asset allocation decisions. The system does not replace the investor's judgment: it supports it with calibrated probabilities and alternative scenarios.
Simplified representation of the confidence levels assigned to signals on a weekly horizon.
Each module works on the same data set, but answers a different investor question: how much risk, how much scale, what execution.
Pattern recognition applied to historical volatility and cross-asset correlations identifies concentrated exposures before they translate into losses. Each recommendation includes an estimate of the downside risk consistent with the reference benchmark.
The infrastructure analyzes multiple asset classes and time horizons simultaneously, allowing you to move from a single portfolio to multiple mandates without losing granularity in signals.
Through copy-trading it is possible to automatically replicate the algorithmic strategies with the best historical performances, maintaining control over exposure limits and personalized stop thresholds.
Each recommendation is traceable back to the source of the data that generated it. This is the path we follow.
Price data, volumes, structured news and macro indicators flow into a single pipeline, normalized by frequency and quality of the source.
Neural networks and classical statistical models process the same datasets in parallel, comparing the outputs to reduce the risk of overfitting on a single approach.
The result is a recommendation contextualized to the risk profile and time horizon declared, accompanied by the quantitative reasons that generated it.
Those who operate on the markets on a daily basis evaluate above all the speed with which information becomes operational. Frizigella reduces the time between signal formation and its availability, and allows you to activate copy-trading on algorithmic strategies with the best risk-reward ratio of the selected period, without having to manually replicate each operation.
In a structured asset allocation context, the priority is the verifiability of the signal. Each recommendation is accompanied by backtesting metrics, confidence intervals and methodology documentation, so that it can be validated according to internal risk assessment processes before any decision.
Market flows are processed with an average latency in the order of seconds, which varies based on the asset class and data provider used. For high-frequency copy-trading strategies we communicate the expected execution times separately, as they also depend on the broker connected to the account.
Integration takes place via dedicated APIs towards the main brokers that support automated execution. Where direct integration is not available, the signal is still made available in an exportable format for manual insertion on the investor's platform.
Each strategy is subjected to backtesting on historical datasets and out-of-sample validation on periods not used in the training phase. Performance metrics are published alongside known limitations of the model, including market conditions where accuracy tends to decline.