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Around-the-Clock Trading Gets a Boost as Kalshi Joins Forces with Pyth to Launch 24/7 Commodity Prediction Platforms

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Around-the-Clock Trading Gets a Boost as Kalshi Joins Forces with Pyth to Launch 24/7 Commodity Prediction Platforms

Table of Contents Prediction marketplace Kalshi has tapped blockchain oracle provider Pyth Network to deliver pricing information for its Commodities Hub, which went live in April 2026. The partnership designates Pyth as the authoritative data source for settling event-based contracts linked to commodity valuations. BREAKING: @Kalshi, the first CFTC-regulated prediction market exchange in the US, has selected Pyth Pro as the exclusive data layer for its commodities markets. Gold. Silver. Oil. Natural gas. Copper. Corn. Soybeans. Wheat. Here's why it matters 🧵 pic.twitter.com/Q78FEpiink — Pyth Network 🔮 (@PythNetwork) April 22, 2026 The newly established Commodities Hub enables participants to trade yes-or-no contracts on various assets, including gold, silver, crude oil, copper, lithium, and soybeans. Market participants predict whether each commodity will close above or below a predetermined price threshold. Pyth aggregates live pricing information from a network exceeding 125 financial institutions, encompassing trading venues and professional market makers. This data stream operates continuously without interruption. Kalshi’s crypto division head, John Wang, explained the platform required rapid, institutional-quality data streams to support its expanding commodity market portfolio. Wang emphasized that Pyth’s infrastructure serves both individual traders and institutional clients effectively. Mike Cahill, CEO of Douro Labs (Pyth’s research and development entity), highlighted that commodity valuations respond constantly to global political developments. Cahill stressed that market participants require ongoing price transparency, particularly during hours when conventional exchanges remain shuttered. Established commodity trading venues such as the Chicago Mercantile Exchange maintain weekday-only schedules. Prediction platforms and cryptocurrency-based services are bridging this accessibility gap through uninterrupted market availability. Competing platform Polymarket revealed its own Pyth Network integration for commodity-based markets during early April. Polymarket simultaneously utilizes Chainlink for oracle services. Both platforms are vying for market dominance through strategic data partnerships and escalating valuations. Kalshi secured a $22 billion valuation during its March funding round. Polymarket is presently seeking investment at a $15 billion company valuation. Notably, Kalshi’s highest-volume oil market, which processes approximately $4 million in trading activity, relies on Intercontinental Exchange (ICE) data for contract settlement instead of Pyth. Pyth Network has additionally rolled out capabilities allowing financial institutions to publish and commercialize proprietary data feeds across numerous blockchain ecosystems. Kalshi operates under US Commodity Futures Trading Commission oversight as a designated contract market. This regulatory status grants federal authorization for derivatives market operations. State-level authorities have challenged certain activities, contending that specific prediction market offerings resemble unauthorized wagering operations. The US Department of Justice alongside the CFTC recently petitioned a federal court to prevent Arizona from enforcing state gambling regulations against Kalshi. Senators Adam Schiff and John Curtis have proposed the “Prediction Markets Are Gambling Act” targeting sports-related betting on prediction platforms, currently the sector’s most rapidly expanding category. International jurisdictions are implementing restrictive measures as well. Argentina is pursuing initiatives to completely prohibit access to prediction market platforms. Following Kalshi’s partnership announcement, Pyth Network’s PYTH token climbed more than 6% to reach $0.048. Discover top-performing stocks in AI, Crypto, and Technology with expert analysis.