Replace manual tracking of charts and news with a system that continuously processes market data and flags relevant patterns before they become evident to the human eye.
An investor who manually monitors news, social networks, trading volumes and technical indicators is exposed to a volume of information that exceeds human processing capacity. We estimate that around 90% of this information is redundant or irrelevant to price movement in the short and medium term.
CoraTechUp applies statistical filtering models to isolate this signal, presenting only those patterns with relevant historical correlation with subsequent price movements.
The process is divided into three sequential steps, each with a specific technical purpose and without undocumented "black box" intervention.
Continuous collection of on-chain data, trading volumes, market depth and textual content from public sources, processed by Natural Language Processing (NLP) to identify sentiment and emerging topics.
Neural networks trained on time series identify correlation patterns between aggregated data and historical price movements, generating probabilities associated with different market scenarios.
The results are translated into concrete recommendations — such as adjusting exposure or setting risk limits — that the investor evaluates and approves before any execution.
Every morning, the user receives a report with the signals identified the previous day, the associated statistical confidence level and the technical justification for the recommendation. There are no "black box" algorithms: each suggestion can be traced back to the data that originated it.
Suggested rebalancing based on correlations between assets, avoiding excessive concentration of risk in a single crypto sector.
Early identification of market conditions associated with sharp declines, allowing positions to be adjusted before risk materializes.
Detection of emerging macro trends by analyzing volume of discussion and tone of communication in relevant public sources.
Before any model is put into production, it is subjected to a backtesting process over multiple market cycles, including periods of high volatility and low liquidity.
The models are recalibrated every 24 hours with new liquidity and market volume data, avoiding adjustments to recent conditions.
Each version of the model is validated against historical data before being applied to real-time decisions, recording observed success rates.
Access to account information and reporting history is protected by dedicated authentication, with data stored in a segregated environment.
CoraTechUp was built for investors and managers who prefer informed decisions over promises of returns. The system presents analysis and recommendation; The final execution decision always remains with the user. This separation between suggestion and action is deliberate and reflects the way quantitative management teams operate in regulated markets.
The technical team responsible for maintaining the models periodically reviews predictive performance, adjusting parameters whenever the market structure changes significantly.
Access a free initial analysis of your portfolio, including identification of risk concentration and relevant signals detected in the last 30 market days.
Access the PlatformNo credit card required to start the analysis.