Built by people who were tired of cleaning data.

Daniel spent years working alongside trading desks and on market-structure research. On every project, most of the calendar went to the data before any of it went to the question. One venue reports timestamps in milliseconds and another in microseconds. One calls a perpetual BTCUSDT, another BTC-USDT-SWAP, a third BTC. A capture connection can lose messages during volatile minutes, and most vendors fill the hole quietly so nobody can tell where it was.

Each firm we talked to had solved this on its own, usually with a pair of engineers and a folder of venue-specific parsers that nobody wanted to own. Those parsers need rework every time an exchange ships a new API version. Research built on top of them inherits every silent gap and every clock mismatch.

Finance Data Cloud exists so that work happens once, with the gaps written down. We record each venue from two regions, merge the streams by the venue's own sequence numbers and publish a gaps table for anything both regions missed. Every message carries the exchange timestamp and our capture timestamp in nanoseconds, so latency can be measured from the data itself.

The first customers we are building for are quant funds, market makers and research groups who already know what good data looks like and would like to stop producing it themselves.