Cognostrix Core — a forecasting engine, not a black box.
A production-grade forecasting engine with calibrated confidence, model agreement, and transparent drivers. Available for dedicated deployment or acquisition.
What Cognostrix Core provides
- Data ingestion and preprocessing
- Feature engineering and signal generation
- Multi-model ensemble forecasting
- Confidence and model-agreement indicators
- Key-driver attribution
- Evaluation, monitoring, and reporting
- Secure delivery via dashboards and APIs
- Versioned, reproducible pipelines
The forecasting pipeline
Data ingestion
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.)
Signal engineering
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.
Ensemble forecasting
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.
Confidence & drivers
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.
Delivery
Forecasts are delivered end-of-day to dashboard and, where applicable, via REST API. Other cadences may be agreed for specific use cases.
What every forecast includes
| 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 |
How we validate
Walk-forward testing
All models evaluated using walk-forward methodology — training on past data, testing on unseen future data — to simulate real-world forecasting conditions.
Continuous re-calibration
Models are retrained on a defined cadence to adapt to changing market conditions, applying regularisation and constraints to reduce overfit risk.
Performance documentation
Serious partners receive access to historical accuracy metrics and backtest documentation under NDA, to evaluate fit-for-purpose before deployment.
Run it for your organisation — or own it outright
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.
