A 1,000-row, point-in-time, entity-resolved slice of Valan's global government-procurement dataset — every award linked to the awarded company's tradable security, with zero forward-bias ticker attribution. CSV, Parquet, and a reference Python loader. Download and run in under a minute.
python valan_sample_loader.py valan_sample_1k_fin_awards.parquet
investable_flag = TRUE on every row). In the full universe only ~17% of awards (12.3M of 71.9M) carry a tradable ticker — do not extrapolate coverage from this file. It demonstrates the schema, identity resolution, and point-in-time mechanics, not the live hit-rate.award_value is in local currency (AUD, BRL, CZK, EUR, HUF, PLN, USD in this slice). Never sum across currencies.ticker_as_of with pit_confirmed = TRUE — the ticker as of the award date, sourced from a genuine dated listing window. Forward-bias-free. Use this for backtests.ultimate_parent_ticker — the supplier's current ownership rollup (who owns it today), not as-of the award. Useful for screening; look-ahead present.ccgp_* sources and buyer_country='CN' excluded). Russia/Belarus excluded on/after 2022-02-24.The sample is one table. The full feed is six: fin_awards (71.9M financial-modelled awards), master_awards (71.9M text-rich awards with titles, descriptions, and source URLs), fin_tenders / master_tenders (25M open solicitations — the forward pipeline), entity_dim (312,156 resolved companies, LEI-bridged), and subcontract_graph (3.3M sub→prime edges). Daily refresh, manifest with SHA256 checksums per table, delivery as S3 parquet. Full schema: valan.io/data-dictionary.
For full-feed access or institutional enquiries: john@valan.io