Built to keep capital protected, not just invested.
Dunelshove combines predictive analytics with dynamic stop-loss logic so investors can hold positions with discipline, not guesswork.
Started from a simple frustration
Most portfolio tools are built to track performance after the fact. They tell you what happened, not what to do about it. Dunelshove was created to close that gap — to give investors a system that watches positions continuously and reacts to risk in real time, rather than waiting for a quarterly review or a manual check-in.
That starting point shaped everything that followed: a product focused less on dashboards for their own sake, and more on the single question that matters most to anyone holding capital in the market — how much am I exposed to lose, right now?
What guides how we build
Every feature in Dunelshove is weighed against the same set of principles before it ships.
Capital preservation first
We design for downside protection before we design for upside potential. Stop-loss logic is treated as a core feature, not an afterthought.
Clarity over noise
Predictive signals are only useful if they're understandable. We favour clear, actionable output over dashboards crowded with vanity metrics.
Continuous oversight
Markets don't pause, so neither does our monitoring. The system is built to watch positions persistently, not on a fixed schedule.
Honest about risk
We don't claim to remove risk from investing — only to help manage it more systematically than manual oversight allows.
Built for real decisions
Every tool is shaped around the moments where investors actually need support: entry, exit, and everything that happens in between.
Iterative, not static
Our models and thresholds are refined continuously as we learn more about how markets and user portfolios behave.
From idea to safeguard
A consistent process sits behind every analytical tool we release.
Research the risk pattern
We study how specific types of losses tend to form — the conditions, timing, and warning signs — before writing a single line of logic.
Model and test
Predictive models are built and stress-tested against historical and simulated scenarios to understand where they hold up and where they don't.
Translate into action
Raw signals are converted into clear thresholds and stop-loss behaviour that investors can actually use without needing a data science background.
Monitor and refine
Once live, performance is reviewed on an ongoing basis so that models stay aligned with how markets continue to evolve.
A focused group behind the platform
Dunelshove is built by a small, cross-disciplinary team spanning quantitative research, software engineering, and investment risk management.
Quantitative Analysis
Responsible for the predictive models that power forecasting and risk scoring across the platform.
Platform & Infrastructure
Builds and maintains the systems that monitor positions continuously and execute stop-loss logic reliably.
Investment Oversight
Brings practical market and portfolio management experience to how thresholds and alerts are designed.