Casa Hernández combines predictive analytics and artificial intelligence with an automated stop-loss system that protects small portfolios from the first trade. Designed for those who start investing while studying and cannot monitor the market at all hours.
Start nowThe crypto market operates 24 hours a day, seven days a week. For a student managing classes, work, and a tight budget, it's impossible to follow every price movement. The emotional reaction to a sudden drop—selling late, buying on impulse—is the main cause of avoidable losses in small portfolios.
Most traditional stop-losses are triggered after the price has already fallen. The Casa Hernández engine analyzes historical drawdown patterns and market signals in real time to anticipate a drawdown before it consolidates, adjusting the exit point dynamically instead of setting it once.
The system receives prices, volume and market depth in real time from connected sources, without delays or partial sampling.
The models assign a risk score to each position, comparing current behavior to past decline patterns.
When the risk score exceeds the defined threshold, an exit strategy is executed without waiting for manual confirmation from the user.
The graph is a conceptual representation of the expected effect of the system, not a projection of profitability or a guarantee of future results.
Casa Hernández was created to give students and profiles with reduced initial capital access to analysis tools that normally require constant market monitoring. Our work focuses on risk models, not short-term price predictions.
Every system design decision prioritizes capital conservation over the aggressive pursuit of profitability. It is a management tool, not a buying signal system.
The connection is made using API keys with permissions restricted to reading and executing protection orders. In no case are withdrawal permissions requested, and credentials are stored encrypted.
The cost structure is displayed in full before any account is activated. No additional commissions are applied on trades executed by the stop-loss system.
The models are trained with historical price and volume series from different market cycles, including periods of high volatility, to recognize patterns prior to significant falls.