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Pyth Just Got the Keys to NYSE Market Data Oracle blockchain platform Pyth Network (@PythNetwork) has become an authorized distributor of NYSE (@NYSE) equities market data. The data will be used in decentralized applications, fintech companies, and trading algorithms. This deal will enable the transmission of top-of-book data for all equities listed on five NYSE Group stock exchanges: NYSE, NYSE American, NYSE Arca, NYSE National, and NYSE Texas. The data will be provided through the Pyth Data Marketplace via the NYSE BQT product feed.
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Pyth Network’s paid data business has grown 8x since January @PythNetwork, the market data network behind solana:HZ1JovNiVvGrGNiiYvEozEVgZ58xaU3RKwX8eACQBCt3, closed October with $13.67M in annual recurring revenue, up 18.9% from $11.49M in September. That figure, the yearly value of its paid subscriptions, stood at about $1.7M in January. October brought $2.49M in new yearly revenue, and 44.6% of it came from existing customers spending more. Pyth now counts 332 paying accounts, 63 of them new in October, while revenue from its index products jumped 64% to $2.88M.
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Pyth Network (@PythNetwork) ends a strong month, with growth in revenue, products, and usage. The following are just a few of the developments that have taken place: October Recap: - Annualized paid subscription run rate (ARR) of $13.67 million, up 18.9% from September. - Pyth Terminal saw 4,451 sign-ups, up 37% from last month, with 124,705 unique visitors. - solana:HZ1JovNiVvGrGNiiYvEozEVgZ58xaU3RKwX8eACQBCt3 Indices increased from 50 to 77 stable feeds, including an intraday U.S. Treasury curve across seven maturities. - Corporate action data for supported U.S. equities, including dividends and stock splits, made available through the Pyth Intelligence API. Other notable feats: [Oct. 13] Pyth tested data as the market-data layer for Exa's agentic search. [Oct. 15] Pyth Pro MCP Server 1.1 released. Improved feed discovery by AI agents and access to historical price information. [Oct. 21] Four Korean equity feeds, such as SK Hynix and Samsung Electronics, started publishing through Korea's post-market session. [Oct. 22] Pyth joined the WFIC 2026 extended trading hours panel and ran out-of-home ads in Miami, New York, London, and Copenhagen.
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Pyth Pro now carries 14 US Treasury indices across 7 maturities from 2 to 30 years, and Messari says 33% of monthly revenue goes to the PYTH Reserve, which buys PYTH on the open market Full BSCN Daily episode:
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Pyth Network adds US Treasury yields to its paid data service @PythNetwork has added 14 US Treasury indices to Pyth Pro, its subscription market data service, covering seven terms from two to 30 years with a yield feed and a pricing feed for each. Pyth also widened its commodity futures coverage and added more round-the-clock crypto price feeds Pyth launched Pyth Pro in September 2025 to sell institutional-grade data across crypto, stocks, bonds, commodities and currencies. The service draws on prices published directly by trading firms, exchanges and banks, and solana:HZ1JovNiVvGrGNiiYvEozEVgZ58xaU3RKwX8eACQBCt3 holders benefit from network revenue through the PYTH Reserve.
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Pyth Network says round-the-clock stock trading is already here @PythNetwork, the solana:HZ1JovNiVvGrGNiiYvEozEVgZ58xaU3RKwX8eACQBCt3 oracle network, said its price data powered more than $2T in trading volume on stocks, commodities, and currencies across offshore derivatives venues in the third quarter. About $1.3T of that came from equity perps. Pyth argues these venues help U.S. exchanges rather than compete with them, since market makers on those venues hedge their stock exposure on U.S. exchanges, especially overnight.
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Python is generally available on Vercel. • Run existing frameworks & libraries with CPython • Use Vercel infrastructure with Python SDKs • Develop, preview, and deploy your entire app
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Python 3.15.0 stable ships today
python workers... workers previews... cf just like me, a birthday should be 2 weeks
Python allowed for rapid prototyping of our platform to support all of OpenAI's product use cases. But making Python work at this scale meant tackling challenges in event-loop management and connection pooling.
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