Alternative data turned into trading signal.
Sharper, faster investment decisions.

Unstructured data sat outside the investment process.

The firm’s trading strategies ran on structured market data. Alternative data – the unstructured, textual signals sitting outside conventional feeds – had no route into the decision-making process because no infrastructure existed to bring it in.

Fixing that meant more than a one-off model. It required machine learning infrastructure built to handle large datasets efficiently, and predictive models robust enough to sit alongside strategies the firm already relied on.

Three constraints were non-negotiable:

• Alternative data sat outside the investment process
• No infrastructure existed to handle it at scale
• Predictive models had to earn their place

An alternative data team, built from the infrastructure up.

We designed and built the machine learning and big data infrastructure needed to support advanced analytics – the foundation an alternative data capability runs on.

On top of it, we initiated projects using Natural Language Processing and machine learning to extract insight from unstructured data sources: the text and signals that structured pipelines miss entirely.

Predictive models and data pipelines followed, built to feed more accurate and timely data into the firm’s investment decisions.

Alternative data became a genuine trading edge.

None of this replaced the firm’s existing strategies – it gave them a new input. Alternative data, machine learning and NLP together turned unstructured information into a working part of the investment process, not a research curiosity sitting outside it.