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MIT Crypto Startup Floating Point Group Raises $2M to Bring Prime Brokerage to Quantitative Traders

Crypto Broker Floating Point Group Announces Support For Circle's USDC on Algorand

Floating Point Group (FPG) simplifies how institutional traders deploy algorithmic systems for cryptocurrency trading. Floating Point Group provides a US-regulated single-point-of-access for sophisticated trading with a suite of algorithmic execution tools. The company closed $2 million in seed funding from AngelList & MetaStable Capital founder Naval Ravikant, pif.vc, Algorand’s CEO Steve Kokinos, BoxOne Ventures, Seabury Global Markets, and several asset management executives.

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With the investment, the startup is aggressively expanding its engineering staff and is acquiring regulatory licenses in the U.S. to broaden the jurisdictions it can serve.

“It’s becoming clear that sophisticated quantitative traders and platform developers are viewing the cryptocurrency markets as an exciting new opportunity,” said John Peurifoy, CEO of FPG. “As this market matures, we’ll continue to add critical pieces of the prime brokerage stack through innovations and strategic partnerships with other high quality groups to deliver a holistic experience that meets the technical requirements of algorithmic traders or other groups building trading applications.”

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FPG’s API enables delivery of real-time and historical market data across the most liquid exchange and non-exchange venues, the execution of trades directly on those markets using FPG’s smart order router to source liquidity, and the ability to trade on credit when funds are held with qualified custodians. To date, FPG has executed hundreds of millions of dollars of trades and is feeding market data to more than 20 organizations.

“Liquidity in cryptocurrency markets is extremely fragmented – getting best execution involves not only multiple exchanges but routing through multiple pairs on each exchange,” said Josh Felker, founder of BoxOne Ventures and previous partner at DRW. “FPG has built the most rigorous quantitative approach to this problem that I’ve seen and their performance and slippage stats are proof.”

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