Investment decisions backed by predictive models, not by intuition
Sergey Scrap analyzes the volatility of digital assets in real time and executes smart stop-losses to limit drawdown before it erodes your initial capital.
Early volatility hits hardest those who invest with limited capital
A student who sets aside part of their monthly budget for digital assets usually trades without the margin to absorb prolonged declines. Manually adjusting a stop-loss requires checking the market several times a day, which is incompatible with classes, exams and work. The usual result is a late exit, when the loss is already hard to recover.
*Estimate based on behavioral patterns observed in manual trading, not on Sergey Scrap's own data.
A stop-loss that adapts to the asset's real volatility, not to a fixed percentage
The system processes each asset's price history, volume and recent dispersion to calculate a dynamic exit threshold. When conditions change, the threshold is recalculated automatically, with no action required from the user.
Thresholds recalibrated every few minutes, depending on the asset's liquidity.
Exit threshold recalculated over rolling price and volume windows
The order is executed when the threshold is reached, without the user needing to be online
Once the portfolio's risk profile has been defined, the platform keeps watch continuously. The exit order is triggered when the calculated level is hit, eliminating the manual reaction time that often makes a loss worse.
Active monitoring even outside the usual trading hours of traditional markets.
Execution point on the simulated price curve
Preserve capital first, grow with discipline later
Drawdown protection
Limits the depth of declines before they compromise the capital available for future trades, instead of waiting for an uncertain recovery.
Automated execution
Orders are triggered according to predefined rules, without depending on the user being available to check the market at every moment of the day.
Predictive modeling
The system estimates volatility scenarios from historical and market data, providing a frame of reference before each position is set.
How each recommendation is built
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Step 1
Market data ingestion
Prices, volume and order book depth are collected from the main exchanges and updated continuously.
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Step 2
Normalization and cleaning
Data is filtered to remove one-off anomalies before feeding the model, reducing the risk of false signals.
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Step 3
Risk threshold calculation
The model estimates expected volatility and defines an exit range in line with the risk profile configured by the user.
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Step 4
Monitoring and adjustment
The threshold is reviewed on a recurring basis and adapted if market conditions change significantly.
Reference diagram: a linear flow from data capture to order execution, with a quality checkpoint between each stage.
A risk management approach, not a speculative one
Sergey Scrap was created to give those starting out with digital assets the same risk control tools that professional managers already use. Our work focuses on loss mitigation and on optimizing available capital, avoiding promises of returns that no model can guarantee.
Every decision made by the system is logged and can be reviewed, so the user understands why an exit was triggered and under what market conditions.
Start trading with a defined risk framework, not with improvised estimates
Set up your risk profile and let the system watch your positions while you focus on your priorities.
Investing in digital assets involves the risk of losing capital. Smart stop-losses reduce exposure to prolonged declines, but they do not eliminate market risk or guarantee any particular outcome. Sergey Scrap does not provide individualized financial advice.