---
title: numeraire-dataset
---

# numeraire-dataset

Open, reproducible **data loaders and builders** for the [numeraire](https://numeraire.py-numeraire.org/)
research framework. This package ships **code, not data**: it fetches public (and, with your own
credentials, licensed) sources and cleans them into tidy, **point-in-time** tables the framework
consumes — so the cleaning is auditable and re-runnable, and no licensed data is ever redistributed
inside a wheel.

```python
from numeraire_dataset import load_ff_factors, load_gw_view

ff = load_ff_factors()                       # tidy frame: date, mkt_excess, smb, hml, risk_free
view, vintage = load_gw_view(start_date="1926-07-01", end_date="2020-12-31")
# feed `view` straight into numeraire's backtest; `vintage` stamps the run's provenance
```

The `*_view` helpers return a numeraire {class}`~numeraire.core.data.TimeSeriesView` together with a
`data_vintage` stamp, so a downstream result can always trace back to the exact snapshot it was built
from.

## Two layers

Sources
: [tidyfinance](https://github.com/tidy-finance/py-tidyfinance)-backed loaders for standard sources
  (Fama–French, Goyal–Welch, and more) — thin functions returning tidy frames, with an optional
  point-in-time view helper that bridges into numeraire.

Builders
: self-built ETL for what tidyfinance does not cover — a **vintage-aware FRED-MD** builder
  (reference period × vintage × series, with stationarity transforms applied at build time) and,
  with your own credentials, WRDS panels.

## Where to go next

- {doc}`installation` — install and the optional extras.
- {doc}`user-guide` — the sources loaders and the FRED-MD builder, end to end.
- {doc}`data-zones-design` — the raw → clean → view lifecycle behind reproducible provenance.
- {doc}`api` — the full API reference.

```{toctree}
:hidden:

installation
user-guide
data-zones-design
api
changelog
```