A production-grade forecasting engine with calibrated confidence, model agreement, and transparent drivers. Available for dedicated deployment or acquisition.
Cognostrix Core ingests price data, macro indicators, sentiment metrics, and other features from reputable third-party providers. Pipelines run automated quality checks and normalisation before downstream processing. (Data sources and availability vary by asset class.)
Derived features — momentum measures, volatility signals, economic regime classifications, and more — are computed using systematic, rule-based, and statistical transformations. Every transformation is versioned for reproducibility.
Multiple model families are trained and blended using walk-forward evaluation. Model weights adjust over time to reflect evolving conditions, subject to constraints that help prevent overfitting.
Each forecast is accompanied by a calibrated confidence indicator and a 'driver view' — providing transparency into which factors (momentum, macro regime, sentiment, etc.) are influencing the output.
Forecasts are delivered end-of-day to dashboard and, where applicable, via REST API. Other cadences may be agreed for specific use cases.
| Direction | Expected bias over the chosen horizon |
|---|---|
| Expected move | Forecasted change over that horizon (where available) |
| Confidence | Calibrated indicator based on historical behaviour (not a guarantee) |
| Model agreement | When models align vs. disagree |
| Key drivers | The most influential factors behind the forecast |
All models evaluated using walk-forward methodology — training on past data, testing on unseen future data — to simulate real-world forecasting conditions.
Models are retrained on a defined cadence to adapt to changing market conditions, applying regularisation and constraints to reduce overfit risk.
Serious partners receive access to historical accuracy metrics and backtest documentation under NDA, to evaluate fit-for-purpose before deployment.
Cognostrix Core is available for dedicated deployment — run for your organisation against your instruments — or acquisition of the engine and its infrastructure. Pipelines cover ingestion, feature engineering, ensemble forecasting, evaluation, and delivery via dashboard or API.
