Lesabiavo processes financial and market flows and returns specific recommendations for action. The model takes into account risk, capital volume and frequency of available operating hours.
Every step is traceable. There are no hidden rules — the model works with publicly available market data and historical price series updated at short intervals.
The system receives price and volume data from exchanges and platforms at intervals of seconds to minutes, depending on the asset.
Data is cleaned of anomalies and reduced to a common format before entering the predictive model.
The algorithm estimates the probability of a move in a given time window and assigns a confidence level to the result.
The user receives the action, risk range and expiration date of the recommendation — without further interpretation.
An example view of the parameters that the model processes in parallel for each asset.
Each account's data is treated as financial information, not as usage statistics. Access is restricted at the session level.
Session tokens are automatically renewed and expire upon inactivity to limit risk on shared devices.
TLS 1.3 → удостоверяване → токен с ограничен живот
AES-256 → съхранение на анализи и история
Лог с времеви печат → проверка при заявка
Lesabiavo is designed for users who don't have eight hours a day to monitor markets, but want a structured approach to additional income.
Receives a short reference between courses — only assets with low time sensitivity, suitable for checking the phone.
Check: under 2 minutesSets a monthly capital limit and receives recommendations tailored to that cap instead of general market signals.
Risk control: set by the userCompares multiple assets simultaneously by forecast confidence level before allocating available funds.
Comparison: up to 5 assets in parallelLesabiavo does not publish guaranteed returns because market data does not allow for such a guarantee. Instead, we show the logic behind each recommendation and the degree of uncertainty that accompanies it.
The team maintains the model by periodically revising the input parameters and removing metrics that lose predictive value.
Learn more about the methodology
Instead of third-party recommendations, we show how each forecast is formed and under what conditions the model flags it as unreliable.
The model combines statistical analysis of price series with current liquidity data. Each prediction carries a confidence interval, not a single number.
When the inputs are incomplete or the market is unusually volatile, the system lowers the confidence level rather than hiding the uncertainty.
| Level | Criterion | Recommended action |
|---|---|---|
| Low | Stable volatility | Standard position |
| Average | Moderate deviation | Reduced position size |
| High | Sudden change in volume | Waiting for new data |
Stored data is encrypted with AES-256, and communication between the app and the server goes through TLS 1.3. Encryption keys are managed separately from the main database.
No. The platform provides analysis and a confidence level for each recommendation, but market results depend on conditions beyond the model's control. Any decision to act remains with the user.
The model works with publicly available market data — price, volume and volatility — without access to bank accounts or personal financial history unless the user explicitly provides it.
The interval depends on the asset — from a few seconds to a few minutes. Each recommendation has an expiration date, after which it is marked as outdated.
The processing of personal data follows the requirements of the GDPR. The infrastructure is maintained within the EU, with clearly defined data retention periods.
Create an account, set your capital limit and get the first referral after linking selected assets.