Data source

Data from Massive, delivered clean.

1 dataset pulled from Massive's releases, checked field by field and shipped the way you want them — daily, weekly, or hourly, your call.

  • 1 dataset
  • 1 industry
  • Real rows on request

What Datadory delivers from Massive

1
Financial Exchanges Data United States - all US exchanges · Stocks from 2003

Massive (formerly Polygon.io) Stock Market API

Pick a catch, see the rows.

Name any Massive dataset and we send real rows from it — not a screenshot of rows. 1,744 datasets. Pick your catch.

Get a sample

API, files, or your warehouse. Daily, weekly, or hourly.

Straight answers about Massive data

What does the Massive dataset include?

Five asset classes behind one schema: tick-level trades and quotes across all US exchanges, dark pools and OTC venues for 32,345+ stock tickers, OHLCV aggregates with VWAP and transaction counts, 1.67M+ option contracts, CME/CBOT/NYMEX/COMEX futures, global forex and crypto, and S&P, Dow Jones and FTSE index series - plus dividends, splits, short interest, filings and financial statements.

How far back does the history go?

By asset class: stocks from 2003, options from 2014, currencies from 2009, futures from 2017 and indices from 2023. The vintages differ on purpose and are documented separately, so a study spanning decades knows exactly which families reach back how far.

Why does every trade carry three timestamps?

Each print is stamped when the originating exchange generated it, when the consolidated feed received it, and when off-exchange volume was reported - all at nanosecond accuracy. In the captured sample the first two clocks sit roughly 460 microseconds apart. Three independent clocks on one trade are the raw material of latency and execution-quality work.

Is Massive the same thing as Polygon.io?

Same platform, new name. The catalog keeps both handles: this page files the record under Massive in Financial Exchanges & Data, while a separate telecom-tower-REIT record still titles itself Polygon.io. They document different slices of the same vendor, so join on dataset id rather than the brand string.