Market data wasn’t built for machines but 50 billion AI agents could become its hungriest users.
Finance is acquiring a second population. It is made of software: AI agents that monitor markets, compare scenarios, retrieve prices, prepare analysis and, where authorized, execute instructions. These agents do not show up in headcount, yet they consume data, make decisions, and increasingly move capital.
“The next 50 billion users” is a provocation that reflects what happens when every human professional is paired with hundreds of machine counterparts.
In that world, the number of market data consumers can dwarf the number of human market participants. Infrastructure needs to scale for both humans and machines.
AI agents operate continuously, query across venues in seconds, and repeat workflows at massive scale. One analyst checks a handful of prices. An agent can monitor equities, FX, commodities, futures, and digital assets simultaneously, then recheck those markets every few seconds.
This changes what “good market data” means. Agents need machine readable outputs, API delivery, cross asset coverage, real time freshness, transparent metadata for auditing, and licensing that supports programmatic use and redistribution.
Macro signals point in the same direction. Tokenized real world assets are projected to approach **$19T by 2033.** Agentic AI is projected to grow from $5.2B in 2024 to nearly $197B by 2034. Market infrastructure has to serve both trends at once: programmable assets and autonomous decision systems.
For decades, market data products were designed for humans sitting at terminals. AI agents call APIs. They require structured responses. They query repeatedly. They merge data across markets. Their outputs can influence trades, risk limits, portfolio construction, and product pricing.
When data becomes an upstream input to thousands of automated decisions, quality issues compound. A stale price propagates. An unclear symbol becomes a confident answer about the wrong instrument. A restrictive license can block production deployment. In an agentic environment, market data is not only information. It is a control surface for risk.
Pyth Terminal is the human front door to market data before integration. Users can browse more than 3,500 feeds across crypto, equities, FX, metals, and commodities, then validate behavior in context: updates, coverage and confidence.
That matters because institutions often purchase data before they can properly test it. Terminal flips that sequence. Teams see the data first, understand coverage, and decide where it belongs across trading, risk, and product systems.
Pyth Pro connects Pyth market data to AI agents through the Model Context Protocol.
Pyth Pro AI supports agent workflows that need to discover feeds, retrieve current prices, access historical prices, and request candlesticks for analysis and backtesting. Four requirements matter for autonomous finance.
First party sourcing from institutions active in price discovery.
Cross asset coverage across thousands of feeds through a single connection.
Programmatic distribution rights designed for downstream systems to consume, process, and display data to end users within their applications and workflows.
Open delivery through MCP compatible environments so data reaches the tools where agents operate.
Explore Pyth Terminal and join the next generation of finance APIs.
", "url": "https://www.pyth.network/blog/built-for-the-next-50-billion-market-data-users", "title": "Built for the next 50 billion market data users", "summary": "AI agents are becoming the next major market data users. Learn why autonomous finance needs real-time, machine-ready data.", "date_modified": "2026-09-09T00:00:00.000Z", "author": { "name": "pyth" }, "tags": [ "Education" ] }, { "id": "urn:sha256:29b3c4e600abf4de3d6135250a9acab53d65e247e740db538d3736ec8138d494", "content_html": "Stellar now hosts more than $4 billion in tokenized real-world assets. Those assets move on infrastructure that stays online around the clock. The markets they represent often do not.
That creates a specific pricing problem. A tokenized asset can remain transferable onchain while the underlying cash market is closed. Applications still need a price to value collateral, manage risk, account for vaults, and support trading.
Pyth Pro and Pyth Indices are now live on Stellar. Pyth Indices extend pricing beyond the sessions of the underlying market, while Pyth Pro gives builders access to low-latency market data across asset classes.
Pyth Indices are constructed products that provide 24/7 pricing for assets whose underlying markets follow exchange hours. The catalog includes indices for Brent, natural gas, copper, and oil, alongside single-name equity indices for AAPL, NVDA, TSLA, MSTR, GOOGL, MSFT, MU, and SPCX.
On Stellar, a perpetuals market or vault can continue marking equity-linked and commodity exposure through weekends and holidays, when the cash market is closed. Collateral values, risk controls, and portfolio accounting can continue updating instead of waiting for the next session open.
For builders that need live data across asset classes, Pyth Pro offers more than 3,500 listed feeds across equities, futures, ETFs, commodities, FX, crypto, and fixed income. The live catalog includes more than 1,000 U.S. equity feeds and more than 50 commodity and metal feeds, with delivery channels supporting updates as fast as 50 milliseconds.
Feeds are sourced directly from trading firms, exchanges, market makers, and banks contributing first-party data to Pyth. Coverage follows each market’s schedule: supported U.S. equities can run 24/5, crypto runs continuously, and commodities and FX follow their respective market sessions.
Stellar’s RWA ecosystem already shows where this infrastructure matters. Centrifuge’s deRWA launch on Stellar introduced deJTRSY and deJAAA, with Blend named as a lending and borrowing partner. As tokenized funds become composable across Stellar DeFi, continuous pricing becomes an important part of the infrastructure needed to use them as collateral and build products around them.
The same data layer can support Stellar payment applications that need live FX quotes and vaults that hold diversified, multi-asset portfolios.
Access Pyth Pro and Pyth Indices through the Pyth Terminal. Browse the feed catalog, compare Pyth prices with external sources, and start a 14-day free trial.
For integration details, see the Pyth Pro documentation for Stellar
", "url": "https://www.pyth.network/blog/pyth-pro-and-pyth-indices-bring-24-7-pricing-to-stellar%E2%80%99s-4b-rwa-ecosystem", "title": "Pyth Pro and Pyth Indices Bring 24/7 Pricing to Stellar’s $4B RWA Ecosystem", "summary": "Pyth Pro and Pyth Indices are now live on Stellar, bringing low-latency, 24/7 pricing to tokenized real-world assets and DeFi applications.", "date_modified": "2026-09-08T00:00:00.000Z", "author": { "name": "pyth" }, "tags": [ "Updates" ] }, { "id": "urn:sha256:b5cf1871c2c33ebc854d0db73e73121438caa7bdf40bc1f3344042703e49de8e", "content_html": "Tokenization is moving from financial market experiment to institutional infrastructure. That is the central signal running through Celent's recent research, including its report Tokenization Reaches the Real World, which examines the progress of real world asset tokenization through the lens of an asset manager, with money market funds identified as the most mature use case so far.
Read that work alongside Celent's research on tokenized money and treasury services and its study of investment data ecosystems, and a pattern emerges. The next phase of tokenization will be defined less by whether an asset can become digital, and more by what happens after it does. Treasury teams still need to manage liquidity. Asset managers still need to value portfolios. Risk systems still need current market inputs. Institutions still need data that can move across systems, venues, and jurisdictions without friction.
Put simply: tokenized money needs a price layer. This is where Pyth comes in, and it is worth walking through why.
The first wave of tokenization focused on the asset itself: how to represent a fund, a deposit, a security, or a payment instrument on programmable infrastructure. Institutional adoption depends on something broader than that.
A tokenized money market fund still needs valuation, liquidity monitoring, portfolio reporting, and risk controls. A tokenized deposit still needs to interact with foreign exchange markets, collateral systems, treasury platforms, and payment workflows. Turning an asset digital creates flexibility. Whether that flexibility produces real value depends on the financial processes wrapped around it.
That is why Celent's focus on money market funds carries weight. It places tokenization inside an existing institutional workflow, rather than treating it as a standalone technology project. The firm's related work on programmable money points in the same direction, identifying liquidity optimization, real time cash mobility, and FX risk management as the treasury opportunities that matter most, and arguing that programmable payments executed through smart contracts could become a genuinely decisive bank capability within five years.
The practical takeaway: tokenization earns its place when it improves how institutions manage money, assets, and risk, not before.
A treasury system can move money automatically. It still needs to know when and why to move it.
Picture a treasury engine that converts currencies the moment a liquidity threshold is crossed, a collateral system that rebalances when an asset's value shifts, or a payment instruction that fires when a defined set of market conditions is met. Every one of those workflows runs on a current price. Programmable money supplies the movement of value. Market data supplies the judgment that makes the movement intelligent, FX rates, interest rates, equity prices, commodity prices, and the other inputs that let software evaluate risk and act inside defined limits.
That creates a real design requirement for financial infrastructure. The data layer underneath programmable money has to be available in real time, usable across asset classes, transparent about its sources and confidence, compatible with automated systems, and available wherever the workflow actually runs, not just during the hours a traditional exchange happens to be open.
This is the connective tissue between tokenized money and market data infrastructure that often gets skipped over. The future of treasury isn't only about faster payment rails. It's about pairing programmable value with continuously available information about the markets surrounding that value. For banks, asset managers, custodians, and fintech providers, the opportunity is building systems that respond to market conditions as they change, and that requires a price layer built for software driven finance rather than adapted to it after the fact.
Celent's work on investment data ecosystems makes a related point worth sitting with: financial data is now widely treated as a strategic asset, yet firms face genuinely complex data value chains, and no single provider model suits every institution.
That reframes the question institutions should be asking. It is no longer simply which vendor sells a particular dataset. It's how data should be sourced, governed, distributed, consumed, and combined across an organization that increasingly runs on both conventional and blockchain based rails.
For decades, the market data supply chain has followed a familiar shape. Financial institutions and trading firms generate valuable market information. Intermediaries package and redistribute it through closed systems. Downstream users consume the result through separate contracts, terminals, feeds, and integrations, each one its own negotiation. That model was built for an earlier generation of financial technology, and the rise of tokenized assets, automated treasury, AI driven analysis, and always on markets is creating real demand for something different: market data that can move directly into applications, risk systems, portfolio tools, and programmable financial products, without every user rebuilding the same integration from scratch.
Pyth's model sources market data directly from institutional contributors and distributes it through a single layer across asset classes and geographies, rather than routing it through the usual chain of intermediaries. In a recent solution brief, the analyst firm Celent described Pyth's architecture as a single source of truth model built around three connected products: Pyth Pro for subscription based price access, Pyth Indices for continuous benchmark style products, and the Pyth Data Marketplace for distributing proprietary institutional datasets. Data flows in from more than 120 exchanges, market makers, and financial institutions, including familiar names like Fidelity Investments, Revolut, and Jane Street, and flows out through a single integration rather than dozens of separate ones.
Celent's assessment lands on a straightforward strategic choice facing data owners. They can keep treating distribution as a closed, downstream process, the way it has worked for decades, or they can participate in infrastructure built for broader reach, machine consumption, and programmable financial workflows. That isn't a traditional finance versus digital finance question. It's a question of whether the data supply chain is actually designed for how financial systems operate today.
The clearest evidence for this shift isn't theoretical. Celent's briefing on Pyth points to Hyperliquid as a working example: a venue that ran into the familiar limits of traditional market infrastructure, restricted trading hours, licensing constraints, fragmented data access, and used Pyth Pro alongside Pyth's HIP-3 infrastructure as the pricing layer for its real world asset markets. That gave Hyperliquid continuous, institutional grade pricing across equities, commodities, FX, metals, and digital assets through one framework, and Celent credits it with helping the venue evolve from a crypto native exchange into a genuinely 24/7 global markets platform.
It's a useful case study precisely because it isn't about crypto adopting crypto infrastructure. It's about a trading venue solving an ordinary market structure problem, continuous markets need continuous pricing, with a data layer designed for that reality from the start.
Market data infrastructure built for tokenized money needs to connect institutional quality with software native delivery. Based on where the research points, that comes down to four things.
Direct sourcing. The closer data sits to the institutions and venues actually forming prices, the clearer its provenance. Pyth's publisher model, sourcing directly from exchanges, market makers, banks, and trading firms rather than acquiring data through downstream vendor chains, is built around exactly this.
Cross asset coverage. Treasury and investment workflows rarely stay inside one market. They combine currencies, rates, equities, commodities, fixed income, funds, and derivatives in the same decision. Pyth's own network update from earlier this year reported more than 138 participating institutions and a catalog above 3,500 feeds, including nearly 1,900 equity feeds, evidence of a network expanding well beyond its crypto native roots into broader market data territory.
Continuous availability. Financial software increasingly runs across time zones and outside traditional market hours, and its data infrastructure has to keep up. This is the specific gap Pyth Indices was built to close, blending on-chain and off-chain inputs into 24/7 composite and single asset benchmarks, with early traction concentrated in commodities and equities.
Programmability. Data has to be usable by APIs, automated workflows, risk engines, financial applications, and AI systems, without forcing every user to rebuild the underlying distribution architecture themselves. On the institutional side, the Pyth Data Marketplace extends that same principle to proprietary datasets, giving institutions a way to distribute specialized data such as reference data, fixed income pricing, and economic indicators through one technical and commercial connection instead of building a separate pipe for every counterparty.
None of this reads as a wholesale replacement story, and it shouldn't. Traditional institutions still depend on decades deep historical time series to power risk and valuation models, and on established providers to support reconciliation, regulatory reporting, and integration with market infrastructure that remains only partially blockchain native. That's precisely why analysts covering Pyth's model tend to frame it as additive rather than replacement led: complementary to incumbents like Bloomberg, LSEG, ICE, SIX, and S&P Global, strongest where it can plug into new products, new customer segments, and new distribution rails, rather than displacing an established data estate overnight.
That framing matters because it's honest about where the real opportunity sits. The market data infrastructure built for tokenized money doesn't need to win by tearing out what already works. It needs to become difficult to leave out of the architecture of any institution building always on, tokenized, or agent enabled financial products, while continuing to strengthen the depth, historical coverage, and enterprise controls that regulated institutions will keep expecting as adoption grows.
Tokenized money needs prices to operate intelligently. Institutional data needs modern distribution to reach the systems that actually consume it. The next market data supply chain will be built around both, and the research increasingly suggests that build is already underway.
Want the full picture? This piece draws on an independent solution brief that Celent, a division of GlobalData, published on Pyth's data distribution model, including a deeper look at Pyth Pro, Pyth Indices, and the Pyth Data Marketplace, along with Celent's assessment of where the architecture still has room to grow.
To explore the product directly, visit Pyth Pro.
", "url": "https://www.pyth.network/blog/tokenized-money-needs-a-price-layer", "title": "Tokenized Money Needs a Price Layer", "summary": "A short read on Celent's latest analysis, and how Pyth is already addressing what it points to.", "date_modified": "2026-09-07T00:00:00.000Z", "author": { "name": "pyth" }, "tags": [ "News" ] }, { "id": "urn:sha256:7ea40243024e08564060a9f51ad94e24027905fead2e749d1ecfc698a34378d7", "content_html": "Cash markets do not share a clock and RWA perps don’t wait for them to agree.
August was another record month against the previous report’s published July baseline. The tracked RWA perp market generated $751.9 billion in RWA perp volume, versus $708.3 billion in July, a 6.2% increase.
The market also handled a wider range of situations. Memory equities reversed after a long run higher. Seoul paused program trading during a sharp selloff. Moderna doubled after a clinical readout. Different markets, different schedules, one place to keep trading.
The month was larger AND broader: more assets, more types of event, and more reasons to trade around the clock. Yet, still a lot of market left to build.
July was a one-way memory trade. August brought the other side of it.
The tracked memory complex — SNDK, SKHYNIX, SKHY, MU, SNXX, DRAM, and SAMSUNG — generated $327.4 billion, or 43.5% of August volume. Four of the ten most-traded assets came from the group: SNDK ranked first, SKHYNIX third, MU sixth, and SKHY tenth.
![\"rwa-august-2026-volume-by-asset-16x9@2x.png\"](\"https://framerusercontent.com/images/YdVc0al0lDlkkkJuvxFda7w2K0.png\")
On August 4, the memory trade moved higher. SanDisk rose 8%, Micron 6%, and SK hynix 4% after the companies advanced the first Open Compute Project HBF specification. On August 18, it moved lower: Micron fell 5%, SanDisk 6%, Western Digital 7%, and SK hynix 6% as Treasury yields moved higher and investors repriced the trade.
The same group drove both the rally and the selloff and kept trading through both.
Korean exposure was not a side story. SKHYNIX ranked third, KORU ninth, and SKHY tenth. Together, they generated $117.1 billion, or 15.6% of August volume.
On August 19, the KOSPI fell nearly 6% and a Korea selloff triggered a five-minute sell-side sidecar for program trading. After the close, SK hynix announced a 40 trillion won buyback plan, and the KOSPI recovered almost all of the previous session’s loss the next day.
On August 19, Moderna and Merck reported positive Phase 3 results for their personalised mRNA cancer vaccine in melanoma. Moderna’s stock rose 176.97% in the session.
Very few venues had an MRNA market live before the result; TrueCurrent was one. A few more markets followed shortly after the news broke, including TradeXYZ.
From its August 19 listing through month-end, MRNA generated $571.6 million and ranked 69th by volume. Its first two sessions produced $213.5 million combined, representing close to 40% of its total monthly volume.
The point is access to the event, not the total volume. Traders already have venues for recurring events around the MAG7 and large technology and AI companies, especially earnings. MRNA showed how that can extend to a smaller public company when a one-off clinical result drives attention. With the right risk, and market-data systems, an exchange can make the event tradable quickly, while the news is still relevant and driving volatility.
August brought a market-structure question into focus: who produces a usable price when traditional market infrastructure is closed, paused, or has not opened yet?
Douro Labs and the Hyperliquid Policy Center brought that question into the SEC’s market-structure process, arguing that the SEC should recognize qualifying independent reference prices for onchain markets where the SIP-derived NBBO is unavailable or does not reflect onchain conditions. The standard they describe rests on direct contributors, a published methodology, transparent publishers, and checks against traditional market data.
A separate SEC comment from the Hyperliquid Policy Center and trade[XYZ] used IPOPs — cash-settled pre-IPO perpetuals with no shares, voting rights, or claim on the issuer — as an example of price discovery before a public listing. The CFTC comment process raises a related question for 24/7 futures and perpetuals in energy markets, where the underlying can keep moving after U.S. futures close.
All point to the same shift: perps are bringing questions of data provenance, instrument classification and market access into policy discussions. That is directly relevant to RWA markets. These issues are directly relevant to Pyth, whose data infrastructure is used across much of the tracked volume.
August closed at $751.9 billion in tracked volume, a 6.2% increase from the previous month.
![\"rwa-august-2026-total-volume-16x9@2x.png\"](\"https://framerusercontent.com/images/gMVyZBZ0g9YBo2nan0xwMmglkjc.png\")
The market is very much still equity-led with $487.3 billion or 64.8% of the total volume. Commodities followed at $152.3 billion (20.3%), then indices at $103.9 billion (13.8%) and FX at $8.3 billion (1.1%).
![\"rwa-august-2026-asset-class-16x9@2x.png\"](\"https://framerusercontent.com/images/a85gxk9oXKTsuzfbbADToo68N8.png\")
August showcased a reshuffle behind Binance which is head and shoulders above the rest and still growing ($385.6 billion to $437.4 billion). OKX took the 2nd spot as it held its volume above $100 billion and moved from third to second, while Hyperliquid dropped sharply from July’s second-place position to $84.6 billion in August. The RWA perp volume remained top-five concentrated with over 95% of it being traded on Binance, OKX, Hyperliquid, Bitget, and Bybit.
![\"rwa-august-2026-venue-ranking-16x9@2x.png\"](\"https://framerusercontent.com/images/Zmyy06shZ57kmxnH5vl67p0XgM4.png\")
On the data provider and infrastructure front, Pyth remained the undisputed leader with over $715 billion in RWA perp volume secured, representing 96.27% of the total tracked RWA perp volume. One extra percentage point compared to July further solidifying Pyth Pro and Indices as the products powering 24/7 tradfi markets.
![\"rwa-august-2026-data-provider-16x9@2x.png\"](\"https://framerusercontent.com/images/fGznvX5CczhligKFgo11HtqZhak.png\")
All volume, listing and provider figures are drawn from Refraction Research and the RWA Markets dashboard, built by @zinnresearch.
Volume is notional traded volume across tracked perpetual venues.
Volume priced per market data provider attributes each venue-symbol pair to its stated pricing source, weighted by volume. Pairs without a confirmed source are recorded as unverified.
", "url": "https://www.pyth.network/blog/rwa-perp-report-august-2026", "title": "RWA Perp Report: August 2026", "summary": "August RWA perp volume hit $751.9B, up 6.2% from July. Memory reversed, Korea paused, MRNA listed in hours — and Pyth priced 96.27% of it.", "date_modified": "2026-09-07T00:00:00.000Z", "author": { "name": "pyth" }, "tags": [ "News" ] }, { "id": "urn:sha256:5698a969c7286f2a9a0f6b58c9b5908d5418611eb2963706a6c4b3d5882bcfe8", "content_html": "Market data is going API-first and if you can’t test it in minutes, you won’t buy it.
Finance used to sell information access as a place: a terminal. Now it is being sold as software: an API. That matters because the next generation of financial users will not all be human. AI agents will monitor markets, retrieve prices, compare scenarios, generate analysis and, where authorized, execute instructions.
They will not wait for a terminal screen. They will call a market data API. The next great investor may not have a heartbeat, but the API supporting its decisions will still need to know the price of everything.
The strongest signal in modern software is clear: buyers want to integrate, test, and ship without a sales process. In practice, an “API search” is a search for confidence: teams need to integrate quickly, validate coverage, understand update frequency, test with minimal friction, choose the right plan, and trust the data in production because that self-serve path is what turns an API from a feature into critical infrastructure.
Postman’s 2025 State of the API Report surveyed more than 5,700 developers, architects, and executives and found that 82% of organizations have adopted some level of an API first approach, 25% operate as fully API first, and 65% generate revenue from API programs. It also highlights the agent era gap: 89% of developers use generative AI, while only 24% design APIs specifically for AI agents, and 51% flag unauthorized or excessive agent calls as a top security concern.
Market data has historically been sold through contracts, bundles, and specialist processes. That model creates friction before a team can answer the only question that matters: does this data work for us.
Self service flips the sequence. Teams want to discover coverage, inspect behavior, understand the commercial model, generate credentials, and move toward integration immediately.
Pyth Terminal is built for that self serve path. It lets users explore and validate more than 3,500 feeds across crypto, equities, FX, metals, and commodities. Users can watch prices update, compare feeds, inspect publisher information, and understand coverage before committing to integration.
The Terminal is the human front door. It gives the people responsible for deploying systems a clear view of the data and the model behind it.
Agentic systems intensify the shift. AI agents can monitor dozens of markets in seconds, repeat workflows continuously, combine prices with constraints and risk models, and pass outputs into downstream applications.
When usage moves from humans to fleets of agents, the economics change. Adoption depends less on named seats and more on how deeply the API sits inside production workflows.
Pyth Pro extends Pyth into agentic workflows through the Model Context Protocol. It gives agents programmatic tools to discover feeds, retrieve latest prices, access historical prices, and request candlestick data for analysis and backtesting.
Programmable markets are the accelerant that makes the API shift inevitable. As more financial activity moves into systems that can be composed, automated, and settled by code (including tokenized real‑world assets projected to approach $19T by 2033), “getting a price” stops being an occasional, human-driven lookup and becomes a continuous machine requirement.
Every workflow, minting, lending, collateral checks, liquidation triggers, rebalancing, risk limits, NAV calculations depends on fast, reliable, programmatic access to market data. In that world, the terminal is no longer the product; it’s the onboarding layer. The real product is the API that software (and increasingly agents) can call at scale, with enough transparency and confidence to ship it into production.
Pyth Terminal helps people evaluate the data. Pyth Pro gives institutions and applications a machine-native path to the same infrastructure. Together, they point toward a financial data model built for both humans and software participants.
The next great investor may not have a heartbeat, but the API supporting its decisions will still need to know the price of everything.
Explore Pyth Terminal and get your API in seconds.
", "url": "https://www.pyth.network/blog/pyth-terminal-is-the-front-door-to-an-api-first-market", "title": "Pyth Terminal is the front door to an API-first market", "summary": "Market data is becoming API-first. Learn how Pyth Terminal and Pyth Pro give humans and AI agents programmatic access to the price of everything. ", "date_modified": "2026-09-03T00:00:00.000Z", "author": { "name": "pyth" }, "tags": [ "News" ] }, { "id": "urn:sha256:194096ff4d183856eec528f1f48e55c958b3089c3a346069d0ea7e6549947544", "content_html": "Pyth Indices are growing fast. A new batch (12) is live this week, extending continuous pricing across more of the equity market, the first Korea-listed name in the suite, and new ETF-based indices.
US equities: AMZN, META, COIN, PLTR, ORCL, NBIS, SKHY, and SNDK. Some of the most actively traded names in the market, now priced around the clock.
First Korea-listed name: Samsung. One of the most important companies in global technology and semiconductors, and the first Korea-listed name in the Pyth Indices suite.
ETF-based indices: EWY, SOXL, and SOXS. Sector and country exposure, priced continuously.
Each is a proprietary Pyth Index, built on Pyth's own price feeds and priced continuously, including the nights and weekends traditional markets are closed.
![\"3-full-coverage-1920x1080-towers.png\"](\"https://framerusercontent.com/images/ATMnQ9TPcwXmrMiYag7veI8cHw.png\")
This batch is part of a steady expansion. Since launch, Pyth Indices has grown from a single oil index into continuous pricing across oil, metals, single-name equities, thematic baskets, and now international listings and ETF-based products.
The pace reflects demand. Exchanges, platforms, and institutions want continuous pricing for a widening set of assets, and the network's position at the point where prices originate means new indices can be added across asset classes and geographies without rebuilding the underlying infrastructure each time.
The expansion is being driven by adoption. Coinbase, Kalshi, Kraken, OKX, and others are actively building on Pyth Indices, using continuous pricing to power markets that traditional infrastructure could not support. Each new index widens what exchanges can offer their users, and each new user reinforces the case for continuous pricing as the standard for modern markets.
The suite will keep growing across asset classes, geographies, and product types.
For platforms and institutions interested in building on Pyth Indices, request access here.
", "url": "https://www.pyth.network/blog/pyth-24-7-indices-expansion-amazon-meta-samsung-and-more", "title": "Pyth 24/7 Indices Expansion: Amazon, Meta, Samsung, and More", "summary": "Twelve new Pyth Indices go live this week, adding Amazon, Meta, Samsung, and three ETF-based indices to continuous around-the-clock pricing.", "date_modified": "2026-09-02T00:00:00.000Z", "author": { "name": "pyth" }, "tags": [ "News" ] }, { "id": "urn:sha256:6a044e559972445db852af2ea3aaffd49ceff9f35c3c9134dbfd68f8295ac3c1", "content_html": "Article contributed by: Douro Labs
August was Pyth’s biggest commercial month yet: Pyth overall crossed $10.4 million in annual recurring revenue, Pyth Indices reached $1.59 million in fixed annual recurring revenue (excluding variable rev share), and Pyth Terminal hit new highs with 90,850 visitors, 445,382 page views, and 2,397 completed free trials during the month.
Numbers like these are easy to read as a good month. Looked at together, they describe something more structural: the product, the commercial model, and the market around Pyth are moving in the same direction at the same time. Institutions are expanding their use of Pyth Pro, exchanges are launching continuous index products, publishers are distributing specialized datasets through the Data Marketplace, and developers are evaluating market data through an API first product experience rather than a sales conversation. That alignment, more than any single figure, is what August was really about.
Overall ARR crossed $10.4 million in August.
Gross new ARR reached approximately $2.9 million, compared with approximately $1.7 million in July.
Pyth Indices reached $1.59 million in ARR.
45 Pyth Indices are stable in the live catalog, with 14 additional index feeds marked coming soon.
Pyth Pro recorded 2,397 Pyth Pro free trials completed between August 1 and August 31.
The Terminal recorded 90,850 visitors, 134,226 sessions, and 445,382 page views during the month.
Users generated 191,526 feed view events, with 9,197 distinct feed viewers exploring 886 feeds.
![\"5a-arr-growth.png\"](\"https://framerusercontent.com/images/38Hlfz9twk0FeDB4PYh33e3AOuQ.png\")
Figures above as per the latest report from Douro Labs.
Commercial momentum came from both new subscriptions and expansion of existing accounts.
Pyth Pro delivers customizable, enterprise grade price data directly from first party publishers. Subscribers configure the feeds they need and choose update schedules that fit their applications, all delivered through standard APIs, giving developers, trading platforms, institutions, and AI systems one integration across a growing set of asset classes.
The Pyth Terminal is the front door to that product, where users explore the catalog, inspect how prices are formed, compare publisher inputs, review historical data, choose a plan, generate an API key, and start a trial, turning market data discovery into a self serve product workflow, which is exactly what shows up in the August account data above.
![\"5b-pyth-pro-feeds.png\"](\"https://framerusercontent.com/images/lsQ1D0MKBjKkNWthwraSIWmRDzI.png\")
Figures above as per the latest report from Douro Labs.
Outside of the Indices catalog, exploration in Pyth Pro tells its own story. Spot gold (XAU/USD) led the field, with WTI crude close behind across two separate contract months, WTIU6 and WTIV6 together showing that users are comparing near term and forward pricing rather than just checking a single quote. Bitcoin was the only crypto feed to break into the top five, a reminder that it still commands attention, but it's now competing directly with physical commodities for that attention rather than dominating the Terminal outright. Silver rounds out the list, echoing the metals interest that shows up even more strongly in the Indices numbers below.
Pyth Indices reached $1.59 million in live ARR by the end of August, including $1,280,250 in new ARR added during the month making it the strongest month to date, underscoring demand for always-on markets. The live catalog now includes 45 live index products, with 14 more marked coming soon, spanning US equities, oil, metals, FX, thematic baskets, and other markets designed to support pricing beyond traditional exchange hours.
Commercial demand accelerated sharply during the month, with continued expansion and adoption from OKX, Kraken, Binance, Coinbase, and MarketVector.
The significance here goes beyond any one contract. Index products give venues a way to offer continuous exposure to markets that were originally built around a closing bell, and they give Pyth a genuine product surface for supporting new market formation, backed by transparent methodology, direct data sourcing, and a single API.
![\"5d-pyth-indices.png\"](\"https://framerusercontent.com/images/EWBnDwxVzFk9Qv2sRLKA8nnkfI.png\")
Figures above as per the latest report from Douro Labs.
The gap at the top is stark. Gold and Oil indices pulled in more than four times the users of anything else in the catalog, with Silver a clear third and still well ahead of Brent and Natgas. Between them, Gold, Oil, and Silver account for the overwhelming majority of Indices activity in the Terminal this month, evidence that continuous commodity and metals pricing is currently the center of gravity for Indices demand, even as US equity and thematic products build out the rest of the catalog.
Read alongside the Pyth Pro numbers above, the pattern across the whole Terminal is consistent: Gold, Oil, and Silver indices dominated overall interest, while exploration in Pyth Pro beyond Indices was led by spot metals, WTI, Bitcoin, and futures.
The price of everything only works if the data behind it keeps growing, and August's coverage story was really a story about publishers and about how fast Pyth gets a price onto a newly listed asset. Both moved in the same direction this month.
On the publisher side, growth was steady rather than headline making: more than 138 institutions now contribute first party data to Pyth, spanning equities, crypto, FX, commodities, and metals, and August brought a significant upgrade to the Publisher Dashboard, making it easier for those institutions to see and manage their own contribution to the network. That's the less visible half of coverage. The more visible half is what happens the moment a new asset starts trading.
Pyth delivered Unitree data from the opening print, making China's first pure play humanoid robotics listing available to applications and exchanges the moment the public market opened, Pyth's second day one feed for a mainland Chinese IPO after CXMT. TrueCurrent extended that same principle beyond the technology cluster: Pyth Pro catalogs 1,024 stable US equity feeds, and TrueCurrent has now made 112 of them tradeable onchain across eight sectors, including healthcare, defense, energy, financials, and industrials.
The month also carried forward the Asian equities expansion, new commodity and metals coverage, and stronger conversion from Pyth Core customers into Pyth Pro, each one adding to the number of workflows a single integration can support.
Taken together, that's what pricing everything actually requires in practice: a publisher base broad enough to have the data in the first place, and a pipeline fast enough to put a price on it the day it starts trading, not the week after.
![\"5c-three-pillars.png\"](\"https://framerusercontent.com/images/VOBLbkKLcQdw9CNtnCDFTyuePo.png\")
Figures above as per the latest report from Douro Labs.
August made the direction of Pyth hard to miss. Commercial adoption is accelerating, expansion revenue is becoming a bigger share of growth rather than an afterthought, and the Terminal has turned market data discovery into a scalable product experience instead of a lead form. Pyth Indices is becoming a meaningful source of recurring revenue in its own right, opening up markets that need prices beyond the traditional trading day.
September will carry that momentum onto the road. Pyth contributors will be present at the major financial infrastructure events across New York, Miami, Seoul, San Francisco, London, Singapore, Copenhagen, Geneva, and Hong Kong through the rest of the year, on stage, on the exhibition floor, and in direct conversation with the institutions shaping what comes next for market data.
The common thread is access: more institutions contributing, more applications consuming, more markets priced continuously instead of closing for the night. Piece by piece, that's what the price of everything looks like in practice.
The price of everything starts in Pyth Terminal. Go explore the catalog.
", "url": "https://www.pyth.network/blog/pyth-august-2026-report-building-the-price-of-everything-layer", "title": "Pyth August 2026 Report: Building the Price of Everything Layer", "summary": "Pyth crossed $10.4M in ARR in August, with $2.9M in gross new ARR and $1.5M from Pyth Indices. Inside the numbers behind Pyth's biggest commercial month.", "date_modified": "2026-09-02T00:00:00.000Z", "author": { "name": "pyth" }, "tags": [ "News" ] }, { "id": "urn:sha256:5aaec929d78c449516d419ac0393d38dc09d04971ccde674bbb2e13b709f177c", "content_html": "Shein’s Hong Kong debut was big in every sense. For its September 1 listing, the company sold 280 million Class B shares at HK$48.56, raising approximately HK$13.6 billion in gross proceeds, about US$1.74 billion.
That scale is backed by fundamentals. Shein reported revenue of US$41.9 billion in 2025, and the deal still landed as the largest Hong Kong IPO of 2026 and the third-largest in Asia so far this year.
For markets, the headline is speed. Pyth delivered a real-time Shein feed from the opening print, giving exchanges, perpetual platforms, and prediction markets the pricing inputs to open a market on day one.
A stock that has just listed has no trading history. Any platform opening a market at the debut needs a price it can trust from the first trade, not one that arrives only once the session has settled into a range. Market data for new listings can arrive on a vendor's schedule, sometimes days or weeks after the bell, by which point the opportunity to launch a market at the debut has passed.
Shein is the third Asian listing Pyth has priced from the opening print since late July, after CXMT and Unitree, three of the region's largest offerings of 2026 inside six weeks. The same pattern runs through US listings including CRCL, FIG, CBRS, and SPCX.
Each of those feeds went live the morning the stock did. That timing is what lets a platform open a market while the listing is still the story.
Shein joins a Pyth Pro equity catalog that now spans 160 stable Asian spot-equity feeds:
Hong Kong: 107
Japan: 21
South Korea: 18
China: 14
![\"issue-17-asia-stats-v2@4x.png\"](\"https://framerusercontent.com/images/IaWys4854nb9ab9VBw0gs7yyyCk.png\")
Hong Kong went live earlier this year and is now the deepest of the four. Mainland Chinese and South Korean equities followed, including Cambricon Technologies, GigaDevice Semiconductor, and SK Hynix. Japanese equities cover Toyota, Sony, Nintendo, SoftBank, Tokyo Electron, and Advantest.
Each arrives through the same integration already delivering US equities, FX, commodities, fixed income, and crypto. Perpetual exchanges can launch Asia equity markets priced alongside crypto and US equities on the same stack. Prediction markets can build event contracts on Asian technology earnings and index movements.
Shein is live on Pyth Pro today under 00625.HK. Existing Equities and All Access subscribers can use the feed immediately. Teams looking to integrate can start at the Pyth Terminal.
Financial markets are becoming software-defined.
Prices now feed exchange engines, collateral systems, risk models, tokenized products, AI workflows, and trading venues that operate around the clock.
That shift is changing what institutions expect from market-data infrastructure: direct access, broader coverage, lower latency, transparent sourcing, and distribution that works across traditional and internet-native markets.
Pyth is building for that environment. Its model brings market participants closer to the applications that consume their data, creating a shared distribution layer for real-time pricing across asset classes.
Pyth’s current publisher page says more than 138 leading institutions are participating in this new market-data network. In its July 2026 report, Pyth Pro reported a catalog of 3,501 feeds, including 1,901 equity feeds, with 75 net-new equity feeds added during the month.
The numbers matter because coverage is what turns a data connection into a market-data layer. The more assets and markets institutions can reach through one integration, the more workflows that integration can support.
Pyth’s publisher network includes market infrastructure firms, exchanges, trading firms, fintech platforms, data specialists, and market-data and infrastructure providers.
The public publisher page highlights Cboe Global Markets, Coinbase, Revolut, and Virtu Financial among the institutions already publishing data to Pyth.
The wider network includes a growing set of names with distinct roles in financial markets. Kalshi brings regulated event markets, while Revolut contributes digital banking and digital-asset market data. Fenics brings dealer-to-dealer fixed-income data. Coinbase is building continuously priced thematic indices and exchange infrastructure, Jane Street contributes market-maker data, and SGX FX brings institutional currency pricing.
These are different institutions solving different data problems.
Together, they show how Pyth is expanding from crypto-native price feeds into a broader market-data network for financial applications, exchanges, prediction markets, risk systems, and other data-driven applications.
Kalshi is a CFTC-regulated prediction market and event exchange. In April 2026, Kalshi selected Pyth Pro as the resolution source for its Commodities Hub, a product built around event contracts tied to gold, silver, Brent crude oil, natural gas, copper, corn, soybeans, and wheat.
The problem is structural. Commodity markets trade across global venues and time zones, while many traditional pricing windows are built around markets that close overnight or on weekends.
Prediction markets that trade continuously benefit from a resolution source designed for continuous markets. Pyth Pro provides direct data access to Kalshi’s market makers and supplies pricing for contract resolution.
The earlier Pyth–Kalshi integration also made regulated event-market data available across more than 100 blockchains, extending coverage beyond asset prices into political outcomes, economic policy, sports, culture, and other events.
John Wang, Head of Crypto at Kalshi, described the infrastructure requirement this way.
“As the exchange deepens our offerings in liquid commodities, it’s important that Kalshi’s markets are backed by fast, institutional-grade data. Pyth’s price feeds are both granular and easy to consume, complementing Kalshi’s mission to make these markets accessible to a broader set of retail and institutional participants.”
Kalshi shows how Pyth’s role is expanding from pricing assets to supporting the resolution of markets built around future outcomes.
Revolut joined the Pyth ecosystem as a data publisher in January 2025. The digital banking platform contributes its proprietary digital-asset price data to Pyth, helping make that information available to applications and decentralized financial markets.
Mazen Eljundi, Revolut’s Global Business Head of Crypto, said:
“By working with Pyth to provide our reliable market data to applications, Revolut can influence digital economies by ensuring developers and users have access to the precise, real-time information they need.”
The Revolut integration is an example of the two-way movement between traditional and decentralized finance.
Financial institutions can publish data into programmable markets, while Pyth gives them a route to participate in new digital financial workflows without rebuilding the entire distribution stack.
Fenics Market Data is the exclusive data-distribution arm of BGC Group, a major interdealer broker. Fenics has started working with Pyth Pro to make its institutional OTC pricing accessible through a single integration. Fenics represents data from more than $1 trillion in daily OTC transaction volume across rates, credit, FX, commodities, and energy.
By joining the Pyth ecosystem, Fenics brings executable pricing from institutional dealer-to-dealer activity into a network accessible through a single integration.
Rich Winter, President of Market Data and Information Analytics at Fenics, said:
“By contributing our global OTC pricing to the Pyth Network, we’re supporting the creation of a more connected, efficient, and data-driven financial system that brings institutional-grade transparency to the digital asset frontier.”
Fenics illustrates why fixed income is an important expansion area for Pyth. Much of the world’s bond pricing is formed in over-the-counter markets, where data has traditionally been fragmented across dealers, venues, and specialist vendors.
Bringing those sources into programmable infrastructure makes institutional fixed-income pricing more useful to trading systems, risk engines, analytics platforms, and digital markets.
Coinbase is using Pyth at two levels: as a cross-asset pricing layer and as infrastructure for new financial products.
The Pyth Pro case study describes Coinbase using Pyth across crypto, equities, and FX, with access to more than 3,000 real-time feeds and latency below 100 milliseconds.
That pricing layer supports real-time asset pricing, collateral valuation, and liquidation infrastructure across markets.
Coinbase also launched four thematic basket indices built through the Pyth and MarketVector strategic partnership: AI10, Defense10, China10, and Tech100. Each index gives traders exposure to a market theme rather than a single company, with pricing designed to run around the clock.
The division of responsibilities is clear: MarketVector provides index methodology and governance, Pyth provides the underlying data and continuous pricing, and Coinbase provides the venue and distribution.
This is a useful example of market-data infrastructure becoming product infrastructure, the value of a data network is not limited to delivering a price feed; it can also support the creation, pricing, and distribution of new financial products.
Jane Street joined Pyth as a data provider in 2021. The firm is a quantitative trading and liquidity provider active across equities, bonds, options, ETFs, and digital assets, with offices in New York, London, Amsterdam, and Hong Kong.
Jane Street’s role connects Pyth to the market-making firms closest to live price formation. That supply-side participation is central to building data infrastructure that can serve both institutional and digital markets.
SGX FX, a wholly owned subsidiary of Singapore Exchange Group, joined Pyth as a data publisher and contributes composite pricing across 74 currency pairs and more than 40 tenors. Those rates aggregate institutional liquidity across Singapore, Tokyo, London, and New York to produce a market-neutral mid-rate that reflects activity across multiple financial centers.
Distributed through Pyth to more than 114 blockchains and 710+ applications via a single integration, SGX FX’s data gives developers, financial institutions, risk systems, and analytics platforms access to institutional-grade FX pricing in a broader digital market environment.
Jean-Philippe Male, CEO of SGX FX, said:
“Contributing this critical pricing data to the Pyth Network is a deliberate step towards accelerating real-time, decentralized finance, ensuring the ecosystem is built on a foundation of trusted, institutional-grade data.”
SGX FX demonstrates the geographic dimension of Pyth’s institutional strategy. Financial data is generated across time zones and liquidity centers. Distribution infrastructure needs to follow that reality.
Kalshi, Revolut, Fenics, Coinbase, Jane Street, SGX FX, Cboe Global Markets, Virtu Financial, Tradeweb, Euronext FX, OpenYield, Wintermute, B2C2, and Finazon each represent a different point in the market-data supply chain.
The model is simple: institutions can contribute data from the markets, venues, and systems closest to the underlying activity; Pyth distributes that data through infrastructure built for real-time applications; exchanges, protocols, risk systems, prediction markets, and financial products use it across markets.
As financial services become more automated and more connected to always-on digital markets, market data becomes a core piece of infrastructure.
The institutions participating in Pyth are helping define what that infrastructure looks like: broader, faster, more direct, and available through one integration.
The next phase of finance will be built by systems that can access the price of everything in real time. Pyth is building the market-data layer for that world.
Explore the Pyth publisher network and learn how you can join as a publisher.
", "url": "https://www.pyth.network/blog/institutions-on-pyth-are-building-the-next-market-data-layer", "title": "Institutions on Pyth Are Building the Next Market Data Layer", "summary": "How 138+ Institutions Are Building the Market Data Layer for Modern Finance", "date_modified": "2026-08-26T00:00:00.000Z", "author": { "name": "pyth" }, "tags": [ "Education" ] }, { "id": "urn:sha256:54c7e980099534c4ecbef2e8c08406c6dce33801dbd85183db63c3bbc1c171aa", "content_html": "US equity markets are making their biggest schedule change in a generation, moving toward 23 hours a day, five days a week. Onchain venues already run seven. More than $540 billion has traded on the real-world asset markets Hyperliquid hosts and Pyth prices, much of it in hours when no exchange was open.
Markets should not close. The price of everything should be published continuously, by the firms that actually set it.
Hyperliquid was built on the first conviction. Pyth was built on the second. Over the past year they have turned into one working system.
Apple does not trade on a Saturday. Neither does Nvidia, or Tesla, or the S&P 500. Their exchanges keep hours set decades ago, and outside those hours the asset has no price, no matter what is happening in the world.
The industry has decided this is worth fixing. The SEC approved Nasdaq's move to 23 hours a day, five days a week, in April, following comparable approvals for 24X and NYSE Arca (see here). NYSE Arca is cleared for 22 hours a day Monday through Thursday, and Cboe is working toward the same on EDGX (see here). The plumbing has moved with it. NSCC extended clearing to support those sessions in mid-2026, and the consolidated quotation system is expected to extend in December, running from Sunday evening through Friday evening with a short pause each night (see here).
This is the largest change to US equity market hours in a generation, yet falls short of 24/7 trading. Nasdaq's week begins with a Sunday night session and ends at the Friday close (see here). The consolidated quote will still be off on weekends and holidays. Roughly 53 hours out of every 168 remain without a national quotation.
Which is a reasonable place for the traditional system to stop. Weekend clearing, weekend corporate actions, and weekend staffing are hard problems that a five-day extension does not require anyone to solve. But an asset does not stop having a value at 8pm on Friday, and the people who want to act on that value have to go somewhere.
HIP-3 is Hyperliquid's mechanism for letting an independent team deploy its own perpetual futures market on the exchange. The team defines the market, sets the risk parameters, and chooses the price source. Hyperliquid supplies the exchange infrastructure, the order book, and the liquidity engine underneath.
That is a meaningful shift in who gets to build a market. Listing decisions that would ordinarily require an exchange's approval, a licensing negotiation, and a multi-year integration became something a competent team could ship.
Which is how Hyperliquid became the venue where real-world assets trade around the clock.
Three mechanisms, operating in different places, none of which existed in usable form three years ago.
Extend external pricing as far as it goes. Pyth publishes over 220 US equities on a 24/5 basis, sourced from firms directly involved in price formation. Overnight coverage comes through an exclusive collaboration with Blue Ocean, along with other ATSs where US equity trading continues after the primary exchanges close. That covers pre-market, regular hours, post-market, and overnight. Past the Friday close there is no underlying market left to read.
Constrain discovery where no external price exists. On weekends, price discovery happens on Hyperliquid's own order book, bounded by a mechanism called discovery bounds (designed by Trade[XYZ], whose markets account for 99% of current HIP-3 volume). The last external price sets the anchor, and price can only travel so far from it before the anchor re-sets and a new range forms. The market can move a long way over a weekend, but it moves in steps from a known starting point. Bounding the downside is what makes traders willing to take the other side at all.
Construct what cannot be observed. Pyth Indices are 24/7 products, co-developed with a regulated index administrator, including single-name US equity indices. They are constructed rather than observed, which is what allows them to run on a Sunday when no exchange is publishing anything.
Extend the real price as far as the market allows. Bound discovery where it does not. Construct a reference where nothing can be observed. Between them, an equity market on Hyperliquid has a defensible price at every hour of the week.
Weekend gap risk is a longstanding cost of equity-market structure. It comes from the trading calendar: exchanges close, while information keeps arriving.
A regulatory decision, geopolitical event, or company announcement can change the value of an equity-linked position on a Saturday morning. The holder can understand what has changed, yet has no market in which to respond until trading resumes.
Always-on onchain venues change that constraint. Participants with equity-linked exposure can act when information arrives, including while the underlying exchange is closed. The risk remains, but the period in which it must be passively absorbed becomes a market in which exposure can be managed. The cost is real even when it never appears as a line item.
This is already operating at meaningful scale. HIP-3 markets have recorded more than $540 billion in cumulative volume, with 407,000 traders and more than $4 billion in open interest. Pyth feeds provide pricing across virtually all of that activity. Those are exchange-scale figures for a market category that barely existed two years ago.
A venue took real-world assets to 24/7 trading and made it work. The pricing infrastructure underneath it held. Billions in open interest and hundreds of billions in volume have moved through markets that, by the standards of traditional market structure, should not have been priceable at all outside exchange hours.
This arrives at the same moment the traditional exchanges are extending into the night. The two developments point the same way. Nasdaq, NYSE Arca, and 24X have all concluded that a sixteen-hour trading day no longer matches demand, and each has committed capital and years of infrastructure work to changing it (see here). The frameworks governing how firms participate have not moved at the same speed, and they still assume a consolidated quotation that will be unavailable for a third of every week even after December.
On August 17, Douro Labs and Hyperliquid Policy Center filed a joint comment letter with the U.S. Securities and Exchange Commission on its proposal to rescind Rule 611 of Regulation NMS.
The letter makes that case in regulatory language. Onchain venues operate continuously while the consolidated quotation system does not, and for large stretches of every week there is no national quotation against which a trade in a security can be evaluated at all. It asks that independent price sources meeting a defined standard be recognized where the traditional quotation cannot serve, and specifies what that standard should require. First-party contributions from participants involved in price formation. Published methodology. Public publisher identities, open to audit.
Six days earlier, Douro Labs filed a separate letter with the Financial Industry Regulatory Authority, jointly with Securitize Markets, in response to FINRA's request for comment on modernizing its best execution guidance. The two are coordinated. The SEC letter asks the Commission to set expectations and direct FINRA to act. The FINRA letter proposes what that guidance should contain, element by element, for the firms that will have to comply with it. That comment period runs to September 25.
Neither filing argues from a hypothetical. The gap they describe is the one Hyperliquid's markets engineered around, and the mechanism they propose is the one already carrying volume today. The market structure question is not whether continuous trading in real-world assets can work. It is how long the institutional framework takes to catch up with the fact that it already does.
More asset classes, more teams building on HIP-3, and more of the global macro surface that has historically only been reachable during a narrow window each day.
Every one of those markets needs the same thing first, which is a price that lasts longer than the exchange behind it.
", "url": "https://www.pyth.network/blog/where-hyperliquid-and-pyth-line-up", "title": "Where Hyperliquid and Pyth Line Up", "summary": "US equities are moving to 23 hours a day, five days a week. Onchain venues run seven. How the remaining 53 hours get priced, and who is arguing about it.", "date_modified": "2026-08-24T00:00:00.000Z", "author": { "name": "pyth" }, "tags": [ "Education" ] } ] }