Alternative data datasets

Browse by provider, category, marketplace, access, delivery and license

Showing 1–9 of9 results for “alternative-data”
DataYes
china-market-alternative-data

Non-traditional Chinese financial datasets include capital movement patterns and broader macroeconomic signals.

Estimates, Events & Fund Flows $ View
Sandalwood
china-alternative-data

Non-traditional datasets from China reflect online retail activity, transportation trends, and real estate signals.

Credit & Lending $ View
Consumer Edge
Consumer In-Store & Ecommerce Receipt Transaction Data | CPG

An aggregated U.S. alternative-data feed of CPG-labeled point-of-sale transactions curated under the CE Basketview Signal banner.

Email Receipts Free View
DerivedFunction01
sec-filings-snippets

DerivedFunction01's sec-filings-snippets is a public-records dataset of short text excerpts drawn from U.S. SEC filings, released on Hugging Face in Parquet format. It is sized for language-model fill-mask work and is not positioned as a commercial alternative-data product.

Public Records & Filings Free View
UBS Evidence Lab
proprietary-evidence-research

Curated datasets produced by UBS Evidence Lab through its in-house research across numerous alternative-data subject areas.

Weather & Agriculture $ View
wetinkdata
us-insider-trading-form4-sample

Cleaned, quality-flagged insider transactions and buying clusters, Jan 2024

Card & Transactions Free View
Yehoon
patent-spec-xml

patent-spec-xml is a dataset of patent specification documents in XML format, published by Yehoon and licensed under unspecified terms. It is catalogued on the alternative-data directory as a public-records resource.

Public Records & Filings Free View
ZipLime
congress-trading

Normalized stock-trade disclosures filed by U.S. House and Senate members under the STOCK Act, regenerated on a schedule by the repository's own pipeline rather than curated manually.

Public Records & Filings Free View
ZipLime
insider-trading

ZipLime US Insider Trading Disclosures (PIT) [!CAUTION] Use knowledge_date, not transaction_date, when backtesting. transaction_date says when a trade occurred; knowledge_date says when the filing became observable through EDGAR. Using the former as the signal date introduces look-ahead bias. visible = trades.filter(pl.col("knowledge_date") <= simulation_time) This dataset normalizes corporate-ins

Public Records & Filings Free View