Crescânio Equity analyzes large volumes of market data in real time and applies historically tested predictive models, so that each recommendation is based on evidence and not on a hunch.
The Crescânio Equity model processes historical series and real-time data to identify relevant patterns before they become visible on conventional charts.
The models cross-reference the price, volume and volatility of thousands of assets to identify patterns that have historically repeated before relevant market movements.
Each signal is accompanied by exposure parameters and simulated loss scenarios, so that the decision takes into account risk before potential return.
The same analysis structure covers everything from an individual trader's portfolio to the volume of operations at an institutional desk, without loss of depth.
No recommendation reaches your screen without going through this process. The transparency of the methodology is what sustains confidence in the result.
Quotes, volume, news and macroeconomic indicators are collected continuously from multiple market sources.
Algorithms identify correlations and recurring behaviors in different time windows, from intraday to year-long history.
Each identified pattern is tested against historical data to measure its performance before any practical application.
Only patterns validated by backtesting generate a signal, delivered with context, estimated deadline and associated risk level.
Illustrative representation of the historical adherence of patterns tested in different time windows. Past results do not guarantee future performance.
Each strategy is built to support intelligent strategic decisions, within a level of exposure defined by the investor himself.
Signals concentrated in more liquid assets, with stricter volatility filters and lower frequency of operations.
It combines trend and reversal signals, distributing exposure across different asset classes throughout the day.
Models adjusted for short-term intraday operations, with greater tolerance to oscillations and high volume of signals.
Crescânio Equity was born from the realization that most market data intelligence is restricted to institutional tables, while individual traders continue to make decisions with fragmented tools.
Our job is to translate large volumes of data into clear, tested and documented signals, so that decision-making depends on methodology and not on time available to analyze spreadsheets.
We believe that financial decisions improve when access to data analysis is no longer the privilege of a few.
Processing occurs in continuous cycles of seconds to a few minutes, depending on the type of strategy and the granularity of the data analyzed. High-frequency intraday strategies utilize shorter update windows.
Yes. The platform offers integration via API for consuming signals and performance data, allowing the final execution to occur in the environment already used by the trader.
Signals are recommendations based on statistical models and historical backtesting. The final execution decision remains under the control of the user, who can adjust risk parameters before taking action.
Account and usage data is stored in a segregated environment per customer, with encryption in transit and at rest. Market data used in the models is treated in an aggregated and anonymized form.
Yes. Backtesting incorporates transaction cost and price slippage estimates so that simulated performance approximates actual execution conditions.
Request access to the platform and see how Crescânio Equity models organize signals, risk and performance history into a single dashboard.
Request AccessDocumented methodology. Data treated with encryption. Final decision always under user control.