AI-Driven Risk Intelligence
Fundara Invest continuously reads market volatility and your own trading behaviour, then recalibrates exposure in real time. The model does not guess your appetite for risk once at sign-up; it re-measures it with every decision you make.
Illustrative Risk Band Calibration
Sample output for demonstration purposes; actual bands are generated per account.
The Signal Problem
A typical trading day produces thousands of price ticks, headlines, and sentiment shifts. Most of this is noise — information with little bearing on the decision in front of you. The remainder, the signal, is often buried underneath it, and the ratio between the two deteriorates further during periods of volatility, precisely when decisions matter most.
The second problem is internal, not external. Fatigue, recency bias, and loss aversion quietly distort judgement, even among experienced traders. These are well-documented cognitive biases, and no amount of discipline fully removes them, because they operate below conscious awareness.
Fundara Invest is built to sit between you and that noise: an objective observer that applies the same quantifiable criteria to every data point, regardless of how the last trade felt.
The Mechanism
The engine does not simply recommend instruments. Its primary function is to manage the volatility of your existing portfolio against a threshold that it learns from your behaviour, and adjusts as that behaviour changes.
Price series, order-flow data, and macro indicators are pulled into a rolling window, refreshed continuously rather than at fixed intervals.
An ensemble of time-series and volatility models estimates probable near-term price dispersion, not a single forecast but a distribution of plausible outcomes.
Your historical responses to drawdown and gains are used to infer a working tolerance level, updated with each new session rather than fixed at onboarding.
Position sizing and hedging recommendations are recalculated as conditions shift, keeping realised volatility within your inferred comfort range.
The underlying approach draws on stochastic volatility modelling and Bayesian updating: each new observation adjusts prior estimates rather than replacing them outright, which keeps the model stable during short-lived spikes in noise.
Applied Logic
The engine behaves differently depending on what a position is meant to achieve. Below are the underlying decision logics, not projected returns.
Scenario One — Dynamic Hedging
When two previously uncorrelated holdings begin to move together during a volatility spike, diversification benefit erodes quickly. The engine detects this correlation drift and proposes a partial hedge sized to restore the original risk profile, rather than closing the position outright.
Correlation Monitor
Scenario Two — Alpha Generation
Pattern recognition models flag instances where historical price structures preceded favourable risk-adjusted moves, filtered through your current exposure limits. The signal is scored, not guaranteed, and sized according to your available risk budget rather than conviction alone.
Pattern Confidence Score
Scenario Three — Capital Preservation
When realised volatility exceeds your inferred threshold for a sustained period, the engine incrementally reduces net exposure rather than exiting all at once, limiting the risk of selling into a temporary dislocation while still capping further drawdown.
Drawdown Protocol
Process Transparency
We would rather explain how the system works than ask you to take it on faith. The three areas below cover where data originates, how models are tested, and how your information is handled.
Market data is drawn from licensed exchange feeds and public macroeconomic releases, aggregated into a unified time series before any modelling begins. Gaps and anomalies are flagged rather than silently interpolated.
Licensed FeedsPredictive models are stress-tested against historical periods of elevated volatility before deployment, and performance is reviewed on a rolling basis rather than validated once and left unchecked.
Continuous ReviewAccount and portfolio data is protected with end-to-end encryption in transit and at rest, with infrastructure practices aligned to SOC2 compliance standards for data handling and access control.
SOC2-AlignedEarly access is being rolled out in limited cohorts so that the risk-adaptation engine can be tuned against real account behaviour before wider release. Joining the list does not commit you to anything beyond being notified.