Turning futures market data into predictive trading signals

A FinTech business working with Cloud Combinator on AWS. The client is anonymised at their request.

The challenge had three focus areas, each a prerequisite for a trustworthy signal.

We built the pipeline in clear stages so each layer could be validated before the next was added, and so the client's engineers could follow every design decision.

The engagement delivered a complete, documented training pipeline in the client's own AWS environment, meeting the acceptance criteria set out at the start: successful data ingestion and feature extraction, a trained TFT predictive model, and a validated handover to the client's technical team.

• Reliable data foundation — A S3-to-Glue batch ingestion path cleans, normalises and partitions raw market data into a consistent, training-ready shape.
• Predictive features on demand — Managed Flink computes Basis Z-Score, Tick-Delta and EWMA Volatility features into a reusable time series, ready for repeated training runs.
• A model held to a standard — The TFT model is trained on GPU compute and validated and stress tested against defined 2023 to 2025 periods, with an explicit AUC above 0.70 acceptance goal.
• Signals ready to consume — The pipeline produces mock trading signals in JSON, structured for interpretation by NinjaTrader.
• Handover — A runbook, reference architecture and knowledge-transfer session left the client able to run, monitor and iterate on the pipeline independently.

With the pipeline proven, the client's roadmap points to scaling through 2026, moving from batch validation towards live data feeds and larger datasets on additional GPU instances, to support the third-party provider subscription signal service. These are the client's forward-looking projections rather than delivered outcomes.