Pyth’s oracle design follows the argument that first-party financial data is not inherently open; rather, it is proprietary to its creators. Financial data is generated through open market transactions across a wide range of CeFi exchange venues, rather than aggregated, and these venues and the groups that trade on them most frequently are the best sources of data. Therefore, Pyth works directly with first-party data partners (market makers, trading desks, exchanges, etc.) rather than third-party aggregators to provide direct, low-latency price updates on-chain.
Pyth first launched in 2021 and has since partnered with CBOE, Wintermute, Two Sigma, Cumberland, and 90 other market makers, exchanges, and other first-party data partners. Today, Pyth provides mid-market prices and trusted ranges for over 400 stocks (e.g. BTC, TSLA, EUR/USD, cryptocurrencies, stocks, FX, commodities, interest rate assets, and more) and provides high-fidelity data to over 45 different public chains while securing over $1.7B in value across some of the largest protocols in crypto, including MarginFi, Drift, Helium, Jupiter, Synthetix, and Hashflow, as well as 90 others.
In addition to pioneering the first-party data contributor model in the cryptocurrency space, Pyth has also pioneered a pull-based price publishing model. Instead of constantly pushing data to the chain at some defined interval (e.g., every time there is a 50 basis point price deviation, or every hour like an oracle like Chainlink provides data), Pyth allows smart contracts to pull precise data when they need it. This is a completely new design that produces newer, more accurate prices than oracles that only update on an arbitrary, periodic basis. It also structurally reduces the cost of user agreements and applications because they don't need to constantly pay for unnecessary updates. This design also allows Pyth to inherently expand asset and public chain coverage faster because the pull mechanism eliminates the need for separate oracle deployments. For example, applications built on Base and Mantle are able to integrate Pyth immediately because Pyth does not require any custom code to be written.
They are foundational primitives for crypto application development and act as a bridge between off-chain and on-chain states. Their primary job is to keep prices consistent across liquidity venues; however, behind the scenes, there is a huge design space to capture and redistribute the value of emergent state transitions. In research, Pyth’s model is currently best suited to capture this opportunity and pave the way for protocols and applications to unlock new revenue streams through oracle extractable value (OEV).
Two examples of MEV opportunities based on state transitions: one where OEV does not exist and one where OEV does.
1. MEV (independent of oracle): Application state is either organic or decoupled from external state through some on-chain operation. For example, if a whale trader executes a large number of buy orders against a constant AMM exchange, causing the quote to be inconsistent with the external price, the bot can capture MEV by correcting the difference and closing the arbitrage without directly using the protocol that needs to be updated.
2. OEV (Oracle-dependent): Price changes in external markets create profitable opportunities to restore the application state to the canonical off-chain state after the oracle imports the updated state on-chain. For example, a MEV bot on a lending protocol may choose to liquidate an underwater account after an adverse price move on a price discovery centralized exchange.
We classify OEV as the latter, where oracle updates trigger opportunities for value capture. Today, the activity of generating OEV disproportionately benefits validators and stakers at the expense of their users, i.e., liquidity providers. If protocols and applications can capture more OEV, they can redistribute these profits to incentivize and reward user loyalty. Ultimately, the ability to align OEV with users makes user protocols more competitive. Application design for MEV capture is difficult. All applications want to minimize users’ MEV and efficiently redistribute the remaining value to users or internalize it themselves. Today, many developers believe that the only way to achieve this is to deploy their protocol as a standalone application chain with the goal of accumulating value for their native token via MEV, but this comes with huge technical, operational, and interoperability complexities. The first and correct solution to internalize MEV is to conduct an Order Flow Auction (OFA). OFA facilitates a market where the supply side consists of a batch of MEV-prone trades aggregated by applications, and the demand side consists of MEV bots or market makers that seek to insert or reorder these trades in a way that favors them. The proceeds from the auction go directly to the application and represent the share of net MEV that the application can capture on its own.
Implementing OEV capture
A seemingly intuitive approach would be for applications to initiate their own auctions for order flow and realize profits from bids for block space around oracle updates. However, this would require significant effort. Each application controls a finite amount of order flow, and OFAs are fundamentally markets that rely on deep liquidity on both the maker (user trade batches) and taker (MEV bots). Application-specific OFAs would fragment liquidity and limit atomic composability (e.g., if MEV bots cannot guarantee that both legs of the strategy happen exactly as they do, then executing liquidations typically requires a token swap after collateral is seized to complete the arbitrage, and they may refuse this opportunity entirely). The operational and social overhead of configuring application-specific OFAs may be too high to justify building an in-house solution.
A better path to capture urgent MEV is to outsource the auction via the Global Order Flow Auction (GOFA). Pyth is structurally positioned to run OFA directly for all the applications it supports, as these applications already rely on Pyth's oracle updates to keep their systems functional. As a result, Pyth has access to high-value blockspace across a large number of applications, and the natural next step is to commoditize the complement by intervening in the blockspace around oracle updates (i.e. the portion of the block that extracts MEV).
Rather than every application reinventing the wheel, oracle-run GOFAs leverage natural economies of scale. Deep liquidity leads to more liquidity: MEV bots are more likely to be takers of bundled order flows across multiple applications (due to atomic composability), and more applications are incentivized to participate when there are more competitive takers (submitting higher bids, which translates directly into revenue).
Professional Applications New Frontiers for OEV
OEV represents a novel approach to capturing value for oracles and applications. The OFA run by the oracle directly passes the emerging value of OEV to the application, allowing the application to reap the benefits of owning its own OFA without any of the overhead. As a neutral third party in the exchange of order flow between applications and MEV bots, Pyth can choose to charge service fees to either party, introducing a new revenue stream for the network without compromising the neutrality of the ecosystem. We are excited about new mechanisms that can more tightly capture MEV directly at the application layer. #BTC