AI-powered decision support
Optimize your investment decisions with AI
Ruvra Fynel analyzes real-time market data and compares outcomes with backtested strategies over decades, so you trade on patterns rather than gut feelings.
Ruvra Fynel analyzes real-time market data and compares outcomes with backtested strategies over decades, so you trade on patterns rather than gut feelings.
Ruvra Fynel combines historical data sets with ongoing market input to identify repeating patterns before they become visible in mainstream dashboards. The models are continuously updated based on new data points.
The platform has been developed for professionals who already work with portfolio management and who need a tool that documents its assumptions rather than promising results without evidence.
The model is built around two core functions: predictive analytics and risk mitigation. Both parts work on the same data base, but solve different tasks.
The model identifies pattern recognition in price movements, volume and volatility over multiple time horizons. Algorithmic precision is achieved by testing each pattern against independent periods of data before it is included in the recommendations.
Exposure is continuously calculated across positions and asset classes. As correlations increase, the model adjusts risk weights automatically, without requiring manual recalculation of the portfolio.
All signals are documented with the data period they are tested against. This makes it possible to verify why a recommendation has been made, instead of dealing with a black box.
The process is divided into four phases. Each phase is documented so that the result can be verified later.
Market data, price series and macro indicators are continuously retrieved from structured sources and cleaned of errors and outliers.
The model compares current data trends with historical patterns to assess likely outcomes.
Each signal is tested against independent historical periods before being allowed to influence a recommendation.
Validated recommendations are presented with assumptions and risk parameters, ready for manual or system-controlled action.
The following shows how the model adapts its approach to the characteristics of the asset class.
| Asset class | Typical challenge | Model approach | Monitored data point |
|---|---|---|---|
| Shares | Sudden price fluctuations on news flow | Volatility adjustment based on sector correlation | 40+ indicators per sector |
| Currency | Interest rate decisions and macroeconomic shifts | Scenario modeling against historical interest rate cycles | 12 currency pairs monitored |
| Raw materials | Supply shocks and seasonal price fluctuations | Pattern recognition over multi-year seasonal series | 8 raw material categories |
Data is encrypted during transport and in storage. Access to account data is limited to the systems that require it to perform the analysis and is logged separately from the analysis module itself.
The platform offers API access to data extraction and signals, so results can be included in own portfolio systems or reporting tools without manual export.
All strategies are tested against historical data periods that have not been included in the model's training. Prerequisites and test period are documented together with the result, so that the method can be verified.
The platform is built for users with a basic understanding of portfolio management. Recommendations are presented with prerequisites, so that the decision remains with the user.
Market data is loaded continuously throughout the trading day. Model parameters are reviewed and adjusted at a fixed cadence based on new backtest results.
Ruvra Fynel is built for professionals who demand valid results rather than loose assumptions. Create access and see how the model evaluates your current positions.