Polymarket Launches Traditional Asset Markets Using Pyth Pro Data
Polymarket Launches Traditional Asset Markets Using Pyth Pro Data
🎯 Polymarket goes TradFi
Polymarket has integrated Pyth Pro to power its new traditional asset markets, enabling prediction markets on major equity index ETFs, commodities, and single-name US equities.
How it works:
- Pyth Pro delivers real-time price data via WebSocket
- Polymarket samples data every second and publishes a live chart showing the "price to beat"
- Traders see exactly where the market stands relative to their position in real time
Why Pyth Pro: When millions of dollars are at stake, Polymarket chose Pyth Pro for its accurate, transparent, and tamper-resistant data quality. The integration gives traders institutional-grade price feeds for traditional assets within the prediction market platform.
This marks a significant expansion for Polymarket beyond crypto-native markets, bringing traditional finance assets into its prediction market ecosystem.
Pyth's equity feeds are built differently: 1. First-party data from institutional publishers (market makers, exchanges, trading firms) 2. Accuracy benchmarked against the primary exchange, not the cheapest licensing venue 3. Separate publisher sets curated for each of the four
What Pyth Pro X delivers: → 3000+ price feeds across crypto, equities, FX, indices → 96%+ accuracy vs. NBBO → Sub-100ms end-to-end latency → 24/5 equity coverage for 50+ U.S. stocks → New market launches in days, not months One integration. One price layer.
BREAKING: @Polymarket just went live with traditional asset markets, powered by Pyth Pro 🔮 Major equity index ETFs, commodities, single-name US equities up/down + daily close contracts. All relying on Pyth data. Here's how it works and why it matters 🧵
Integrate close to 3000 price feeds with Pyth Pro: pyth.network/price-feeds
Exchanges are standardizing on a new price layer with the launch of Pyth Pro X.
Introducing Pyth Pro X. Institutional market data built for exchanges. One subscription. Every asset class. No exchange fees, no redistribution barriers, no vendor lock-in. Powering @coinbase, @cryptocom, @tradexyz, @Dreamcash, @bitget, @BitMEX, @LMAX, and others.
More on Pyth Pro X here: pyth.network/blog/introduci…
Pyth Pro for Agents is live now. Read the full breakdown: pyth.network/blog/pyth-pro-…
Introducing Pyth Pro X. Institutional market data built for exchanges. One subscription. Every asset class. No exchange fees, no redistribution barriers, no vendor lock-in. Powering @coinbase, @cryptocom, @tradexyz, @Dreamcash, @bitget, @BitMEX, @LMAX, and others.
When millions of dollars are on the line, data quality is everything. The price source has to be accurate, transparent, and tamper-resistant. Polymarket chose Pyth Pro.
Pyth Pro delivers real-time price data via WebSocket. Polymarket samples it every second and publishes a live chart: the "price to beat." Traders see exactly where the market stands relative to their position, in real time, powered by Pyth data.
ICYMI: @CryptoEconomyEN covers the launch of Pyth Pro for AI Agents 🤖 3,000+ institutional price feeds. One integration. Built for agents operating in autonomous financial environments. crypto-economy.com/pyth-unveils-a…
💼 New Pyth Pro User: 360X 360X, the regulated digital securities trading venue, is now a Pyth Pro user—integrating real-time market data to power non-binding spot prices for stablecoin settlement on order entry.
The exchange data stack was built for a different era — gated access, opaque pricing, slow innovation. Pyth Pro X replaces that with a single price layer designed for global, always-on markets. The price of everything, everywhere.
Learn more about Polymarket’s integration with Pyth Pro here: pyth.network/blog/polymarke…
Pyth Network Launches Real-Time Data Terminal for Market Verification
Pyth Network has launched **Pyth Terminal**, a new platform for exploring oracle data feeds in real-time. **Key Features:** - Access to live data feeds updating every second - Direct verification of market data - Available at [pythdata.app/explore](https://pythdata.app/explore) The terminal emphasizes transparency with the principle "Don't trust. Verify," allowing users to independently confirm the accuracy of price feeds. This builds on Pyth's existing infrastructure that provides real-time data for over 2,500 assets across various markets. The launch gives developers and traders a direct window into the oracle network's data streams, supporting the verification of information that powers decentralized applications and smart contracts.
Major Launch Brings 100+ Tokenized Stocks and ETFs Onchain
A significant expansion of tokenized traditional assets is launching, bringing equity index ETFs, commodities, and single-name US stocks onchain. **Assets at Launch:** - Major equity index ETFs - Commodities: Gold, Silver, WTI, Natural Gas - Single-name US equities: TSLA, NVDA, PLTR, MSFT - Daily up/down markets and daily close contracts The platform will feature **100+ assets on day one**, with plans to expand to over a thousand by year end. Pricing data comes from an institutional network that has secured over $2.7T in transaction volume across onchain and offchain markets.
🤖 Pyth Pro Launches Agent Integration Across Major AI Platforms
Pyth Network has released **Pyth Pro for Agents**, making its oracle services accessible across multiple AI development platforms. **Available on:** - Claude MCP registry - Cursor plugin directory - Glama - Solana AI ecosystem - npm as [@pyth-network/mcp-server](https://www.npmjs.com/package/@pyth-network/mcp-server) - [GitHub repository](https://github.com/pyth-network/pyth-crosschain/tree/main/apps/mcp) The integration allows AI agents to access Pyth's oracle data directly through these platforms, expanding the network's reach into the growing AI agent ecosystem.
How 24/7 Markets Find Price Without Anchors
**The Challenge of Price Discovery** Bootstrapping a new market presents a fundamental challenge: establishing price without existing reference points or anchors. **Building 24/7 Market Infrastructure** At DAS, @mdomcahill explained how continuous markets like oil achieve price discovery by: - Aggregating signals from traditional trading venues - Incorporating data from emerging platforms - Synthesizing derivatives market information This multi-source approach helps create reliable pricing in markets that operate around the clock, where no single venue provides complete price information.