Alternative data turned into tradable signal.
Governed and compliant from day one.
A scalable ML and NLP pipeline mines alternative data for market signals, feeding predictive models that sharpen risk management and portfolio performance, with governance and compliance built into every stage.
processing and analysis
text data
and timeliness
less manual work
Client
A European hedge fund running quantitative, data-driven investment strategies.
Goal
Build out an alternative data team and back it with machine learning and big data infrastructure, so the fund could uncover new investment signals and sharpen its decision-making.
The data existed. The infrastructure to use it didn’t.
The fund wanted an edge from alternative data, the unstructured, high-volume information sitting outside conventional market feeds. But its existing trading systems weren’t built to take it in. Diverse alternative data sources had to be integrated into infrastructure never designed for them, at volumes that were large and high-frequency.
Any model built on top of that data had to earn its place in a regulated environment. It needed to be accurate and reliable, but interpretable enough for the fund to trust and defend its calls under financial regulation, with compliance holding steady as the trading approach changed underneath it.
Three constraints were non-negotiable:
- Diverse alternative data, no infrastructure built to take it in
- Large, high-frequency datasets moving at trading speed
- Predictive models had to stay accurate, reliable and explainable
An alternative data function, built from the infrastructure up.
The engagement started with the plumbing: scalable machine learning and big data infrastructure designed to support advanced trading strategies, and automated data pipelines to handle ingestion, processing and analysis in real time, so alternative data could move from raw feed to usable signal without manual intervention at every step.
Governance wasn’t bolted on afterwards. It was built alongside the models, so the fund could trust every signal it traded on.
On top of that, NLP techniques were applied to pull insight out of unstructured alternative data, text that conventional trading systems couldn’t read. Predictive models were developed specifically to turn that into market signals feeding directly into investment decisions.
None of it ran unchecked. Model governance, validation and monitoring practices were established alongside the models themselves, so accuracy and reliability held up under real trading conditions, and the fund stayed inside financial regulation as its data-driven approach expanded.
From raw alternative data to a trading edge.
The fund now runs an alternative data operation that didn’t exist before, one that reads unstructured data most competitors can’t use, turns it into signal fast enough to trade on, and stays governed and compliant as it scales. That combination is what positions it as a leader in AI-driven investing, not just an adopter of it.
* Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.
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*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.