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Home»Blockchain»Pyth Network Publishes Real-Time Prices for 100 Major ETFs
Blockchain

Pyth Network Publishes Real-Time Prices for 100 Major ETFs

NBTCBy NBTC23/06/2025No Comments2 Mins Read
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Pyth Network has launched real-time price feeds for over 100 major exchange-traded funds, including products from Blackrock, State Street, and Vanguard, with onchain access to data representing more than $8 trillion in assets.

New Pyth Integration Brings Live ETF Prices to DeFi Builders

Pyth Network has introduced real-time exchange-traded fund (ETF) price feeds, enabling blockchain developers and financial platforms to access live pricing for some of the world’s most heavily traded funds.

According to the announcement shared with Bitcoin.com News, the integration covers more than 100 ETFs managed by major financial firms, including Blackrock, State Street and Vanguard.

The team explained that the move brings ETF pricing data onchain at scale, providing direct, real-time prices from global trading venues. This stands in contrast to synthetic or mirrored tokens often used in decentralized finance (DeFi) to approximate ETF exposure.

According to Pyth Network, the new data set spans a broad array of ETFs across asset classes such as equities, global indices, commodities, and fixed income. It also includes strategies based on dividends, sector exposure, and growth or value styles.

The network’s data is sourced from global financial institutions and trading platforms, aiming to reduce reliance on costly traditional market data providers. Douro Labs CEO Mike Cahill, a contributor to the network, said the current data ecosystem is structurally exclusionary, citing ICE’s $1.4 billion in annual data revenue.

“Nasdaq’s data revenue more than doubled over the past decade, while ICE pulled in $1.4 billion from data and connectivity services in 2023 alone,” Cahill remarked.

By making ETF data publicly accessible onchain, Pyth suggested that developers can build financial tools that more accurately reflect real-world market conditions. Potential use cases include trading applications, treasury tools, and products tailored to specific investment strategies.

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