Norvik Praral uses AI models tested against years of historical market data, so you can see how a strategy might have behaved before you commit a single pound.
Begin your first analysisMarkets move on thousands of inputs at once — rate decisions, earnings reports, sentiment shifts, global events. For someone without a finance background, separating a meaningful signal from background noise is genuinely difficult.
Most new investors don't lack intelligence. They lack a clear way to test an idea before acting on it. Norvik Praral was built to close that gap, turning historical market data into a working proof ground for your strategy.
We don't ask you to trust a black box. We show you the data behind every recommendation, and how it would have performed in the past.
Our model doesn't guess. It studies how similar market conditions played out historically, then shows you the range of plausible outcomes before you decide.
The system collects pricing, volume, and macroeconomic data across multiple decades, cleaning and structuring it so comparisons between periods are consistent and fair.
Patterns from historical conditions are matched against current market behaviour, generating a probability-weighted view of likely near-term scenarios — not a single fixed forecast.
Before any recommendation reaches you, it is run against real historical scenarios to see how it would have performed, including during downturns, so the risk profile is visible upfront.
Every output is built around your own risk tolerance and time horizon, giving you a measured basis for decisions rather than a prediction dressed up as certainty.
Each strategy carries a plain-language risk score derived from its historical volatility and drawdown, so you understand what you're weighing up, not just what you might gain.
When live market conditions start to diverge from the backtested assumptions behind your strategy, you're notified, so adjustments stay timely rather than reactive.
Allocations are shaped around your stated goals and capacity for loss, then stress-tested against historical periods of volatility before being presented to you.
Decisions are supported by evidence from comparable past scenarios, replacing guesswork with a documented rationale you can review at any time.
Consider a scenario where sector rotation data began shifting several weeks before broader market commentary caught up. In a historical backtest of this kind of pattern, our model flagged the early-stage shift based on comparable conditions from past cycles, well before the move became widely visible.
The point isn't that the model predicted the future with certainty. It identified a pattern consistent with prior shifts, and surfaced it early enough for a human decision-maker to review and act on if they chose to.
Illustrative backtest only, based on historical data. Past performance in simulated scenarios does not guarantee future results. Predictive models support human judgement; they do not replace it.
Your account data is encrypted in transit and at rest, and access to any linked financial information is permission-based and limited to what's needed to generate your analysis. We do not sell personal data to third parties.
No. The platform is built to be useful whether you're analysing a modest first portfolio or a larger one. Backtesting and risk scoring work the same way regardless of the amount you're considering.
The model compares current market conditions against historically similar periods and ranks strategies by how they performed in those contexts, including during downturns. Every recommendation is accompanied by the historical reasoning behind it, so you can review it rather than take it on faith.
There's no obligation to invest anything. Run a backtest, review the reasoning, and decide at your own pace whether the strategy fits your goals.
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