Dowthriftry Platform
Investment decisions backed by predictive models, not intuition
Dowthriftry analyses digital asset volatility in real time and executes smart stop-losses to limit drawdown before it erodes your initial capital.
Initial volatility has the greatest impact on investors with limited capital.
A student who allocates part of their monthly budget to digital assets usually trades without enough buffer to withstand prolonged declines. Manually adjusting a stop-loss means checking the market several times a day, which does not fit well with classes, exams and work. The usual outcome is exiting too late, when the loss is already difficult to recover.
*Estimate based on behavioural patterns observed in manual trading, not on Dowthriftry's own data.
A stop-loss that adjusts to the asset’s actual volatility, rather than a fixed percentage.
The system analyses each asset’s price history, volume, and recent dispersion to calculate a dynamic exit threshold. When conditions change, the threshold is automatically recalculated, with no action needed from the user.
Thresholds are recalibrated every few minutes based on the asset’s liquidity.
Exit threshold recalculated using rolling price and volume windows
The order is executed once the threshold is reached, without the user needing to be online.
Once the portfolio’s risk profile is defined, the platform monitors it continuously. The exit order is triggered when the calculated level is reached, removing the manual response time that often worsens a loss.
Active monitoring, even outside the regular trading hours of traditional markets.
Execution point along the simulated price curve
Preserve capital first, then grow with discipline
Drawdown protection
Limits how far declines can go before they compromise the capital available for future trades, rather than waiting for an uncertain recovery.
Automated execution
Orders are triggered based on predefined rules, without relying on the user to be available to monitor the market at all times.
Predictive modeling
The system estimates volatility scenarios using historical and market data, providing a frame of reference before each position is established.
How each recommendation is created
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Step 1
Market data ingestion
Prices, volume, and order book depth are sourced 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 it is fed into 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 aligned with the risk profile set by the user.
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Step 4
Monitoring and adjustment
The threshold is reviewed regularly and adjusted if market conditions change significantly.
Reference diagram: A linear workflow from data capture to order execution, with a quality checkpoint between each stage.
A risk-management approach, not a speculative one
Dowthriftry was created to give people starting out with digital assets access to the same risk control tools used by professional managers. Our work focuses on mitigating losses and optimising available capital, without making return promises that no model can guarantee.
Every decision the system makes is logged and available for review, so the user understands why an exit was triggered and the market conditions at the time.
Start trading with a defined risk framework, not ad hoc estimates.
Set up your risk profile and let the system monitor your positions while you focus on your priorities.
Investing in digital assets carries the risk of capital loss. Smart stop-losses reduce exposure to prolonged declines, but they do not eliminate market risk or guarantee any particular outcome. Dowthriftry does not provide personalised financial advice.