New Pyth Data Provider: Two Sigma Securities

Pyth Network
2 min readOct 28, 2021

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We are thrilled to welcome Two Sigma Securities, a systematic market-maker founded in 2009, within the Pyth network to enable transformative innovations in the financial marketplace and the DeFi landscape with high-quality, real-time market data.

Who is Two Sigma Securities?

  • Two Sigma Securities (TSS) is a systematic liquidity provider and trading business. Founded in 2009, its goal is to bring together the brightest minds in quantitative research, engineering, and trading to make global financial markets more efficient
  • TSS trades globally across asset classes, leveraging its high-performance trading system to regularly execute over a billion shares per day.

Two Sigma Securities will provide its US equities pricing information to the Pyth Network, and explore contributing data for other asset classes as the network scales.

“Two Sigma Securities understands the importance of having access to high-quality, real-time market data to guide trading decisions. We are looking forward to working with the Pyth team and community to solve a long-standing challenge in DeFi,” said Chris Marty, CEO of Two Sigma Securities. “Building foundational infrastructure for DeFi to grow will enable transformative innovations in the financial marketplace — values that Two Sigma Securities holds as fundamental to our business.”

We look forward to further coordinating with amazing partners to establish a digital marketplace of institutional grade, high fidelity financial data to power any financial dApps regardless of their blockchain base and offer the best data to the world.

Two Sigma Securities Website: www.twosigma.com/businesses/securities/

Pyth Network: https://pyth.network/

Press Release: https://www.prnewswire.com/news-releases/two-sigma-securities-joins-the-pyth-network-301411249.html

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Pyth Network
Pyth Network

Written by Pyth Network

Smarter data for smarter contracts. Pyth is designed to bring real-world data on-chain on a sub-second timescale.

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