Free data, real rigour.
You don't need an institutional data budget to build institutional-grade analysis. You need an honest transformation layer.
February 2026
Every time we propose a quantitative modelling engagement, someone asks whether the client needs a Bloomberg terminal or a paid data feed. Almost every time, the answer is no.
What free data actually covers
Public APIs now expose most of what a serious research desk uses. On-chain metrics, macro indicators, market fundamentals, regulatory filings. Coverage is not the problem. Coverage has not been the problem for years.
Where the work actually lives
The work is not getting the data. The work is making scattered, inconsistent feeds usable. Different rate limits. Missing fields. Noisy time series. Incompatible units. The first third of every modelling engagement is just bringing the data into a shape the model can touch.
The transformation layer is not a footnote
The difference between rigorous analysis and hobby analysis is the transformation layer. Z-scoring, bounded mapping, windowed normalisation, sample-size flags. This is the layer that stops a composite score lying politely because one input silently drowned out the others.
What institutional-grade actually means
Institutional-grade is not what data you paid for. It is whether your model can survive an audit. Whether every input is documented. Whether every transformation is reproducible. Whether every weight choice has a defensible rationale.
Budgets are not the constraint most people think they are. Discipline is. Build the transformation layer honestly and free public data is enough to ship a model that holds up in front of a challenging audience.