Fruverje applies predictive, rules-based analysis to historical equity, gilt and inflation data, giving retirees and private investors a clearer view of drawdown risk before capital is committed.
Request Methodology OverviewEvery recommendation produced by Fruverje can be traced back through five stages of processing. Nothing is presented as a single probability score without the underlying data being shown.
Historical UK equity, gilt and inflation series are normalised and checked for survivorship bias before any model is permitted to run against them.
Periods of recorded volatility, including rate shocks and gilt repricing events, are isolated and replayed against the allocation under review.
Correlation patterns between asset classes are projected forward using constrained statistical models, bounded by historical precedent rather than speculative extrapolation.
Each proposed portfolio is tested against its worst historical twelve-month drawdown, establishing a realistic threshold for capital preservation planning.
Results are compiled into a written methodology report that states the data range, assumptions and limitations used, rather than a single headline figure.
Four capabilities underpin every report, each designed to make risk visible rather than to obscure it behind a composite score.
Forward-looking correlation analysis across equities, gilts and cash equivalents, recalculated on a monthly cycle as new data arrives.
Portfolio sensitivity is scored against recorded UK downturns, including the 2008 equity correction and the 2022 gilt repricing episode.
Cross-asset correlation matrices are maintained against four decades of recorded market behaviour and refreshed as new data is published.
Rebalancing is recommended only once deviation from target allocation exceeds a defined tolerance band, limiting unnecessary turnover.
Underlying series are drawn from publicly recorded UK equity indices, gilt yield data and ONS inflation figures. Each dataset is timestamped and version-controlled, so a report can always be traced back to the data available on the date it was produced.
When the underlying allocation logic is tested across rolling ten-year windows since the mid-1980s, it has generally shown lower average drawdown than a simple sixty-forty benchmark during the periods examined. A backtest describes how a strategy would have performed historically; it does not guarantee future results, and every report states the date range and data source used.
Client data is encrypted at rest and in transit. No personal financial information is used to adjust model parameters, and historical market data is kept separate from individual client records.
For illustration only. Reflects the format of data presented in a full methodology report, not live figures.
Fruverje does not claim to forecast short-term market movements. The platform exists to make the historical behaviour of UK asset classes legible, so that an allocation decision can be weighed against evidence rather than sentiment.
Reports are written for people who want to understand the reasoning behind a recommendation, not simply receive one. Every figure is shown alongside the data range and assumptions used to produce it.
This is intended for investors who are already comfortable with the language of risk management and want that language applied with discipline to their own circumstances.
Four situations commonly bring investors to Fruverje. Each is addressed through the same backtested methodology, applied to a different question.
An equity-heavy portfolio approaching retirement is exposed to sequence-of-return risk: a downturn in the final years before drawdown can disproportionately reduce lifetime income.
Retirees drawing a regular income face uncertainty over whether a given withdrawal rate remains sustainable against inflation and market volatility.
Market volatility often prompts reactive decisions driven by sentiment rather than evidence, which can lock in losses at the worst possible time.
Portfolios concentrated in UK equities and gilts can carry hidden correlation risk that only becomes visible during a shared downturn.
Request a written overview of the backtesting process and underlying data sources, or arrange a technical walkthrough of the dashboard. There is no obligation to proceed afterwards.