Original title: 《Oracles and the New Frontier for Application-Owned Orderflow Auctions》

Original author: Shayou Sengupta, Multicoin Capital

Original translation: 0xjs, Golden Finance

We are pleased to have recently invested in Pyth Network, the leading first-party data oracle in the crypto space.

The implicit premise behind traditional oracles in cryptocurrency is that all data, including financial data, is freely available and accessible to on-chain contracts. Therefore, oracles only need to incentivize supply-side network contributors to collect and aggregate this data, reach consensus on it, and bring it to the chain. While this approach may work for widely available public data sets, such as weather data or election results, it is generally not suitable for latency-sensitive data, such as financial data. For latency-sensitive data, large market participants (e.g., HFT firms, market makers, and equity order book exchanges) are actually superior data sources than third-party aggregators because they create (rather than just scrape) the data and therefore have inherently higher quality, lower latency data.

Pyth’s oracle design follows the argument that first-party financial data is not inherently open; rather, it is proprietary to its creators. Rather than being aggregated, financial data is generated through open market transactions across a broad range of CeFi exchange venues, which, along with 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, forex, commodities, interest rate assets, etc.), 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 compared to oracles that only update on an arbitrary, periodic basis. It also structurally reduces the cost of user agreements and applications because they do not 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.

As a company, we are very interested in oracles because they are a foundational primitive for crypto application development and act as a bridge between off-chain and on-chain state. 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 our research, Pyth’s model is currently best positioned to capture this opportunity and pave the way for protocols and applications to unlock new revenue streams through oracle extractable value (OEV).

Introduction to Oracle Extractable Value (OEV)

To recap, miner extractable value (MEV) is largely a misnomer. Today, it loosely refers to the profits that validators and stakers derive from arbitrage or liquidation opportunities arising from transaction reordering that exploits temporary state inconsistencies. In many cases, MEV arises when there is a difference between the price represented by an application and the price represented by a canonically accurate external off-chain state. Oracle Extractable Value (OEV) is a subset of MEV where applications rely on oracle updates from arbitrageurs or liquidators to exploit such state inconsistencies.

By bringing external data (such as open market prices) on-chain, oracles naturally infiltrate valuable blockspace. This creates profitable windows for arbitrage and liquidations between states, and provides oracles themselves with the opportunity to enter the MEV lifecycle (either directly or through auction dynamics) and capture some of the MEV generated from price updates.

In a push-based oracle system, competition for trading space after an oracle update is intense. In a pull-based oracle system, applications have more autonomy over how they choose to incorporate updates into their applications, therefore giving them greater control over the MEV extraction and/or redistribution system.

Let’s look at two examples of state transitions that provide MEV opportunities: one where OEV does not exist, and one where OEV does.

1. MEV (independent of the 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 buy order against a constant AMM exchange, causing the quote to be inconsistent with the external price, a bot can capture MEV by correcting the spread 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 application state to a canonical off-chain state after an oracle imports the updated state on-chain. For example, a MEV bot on a lending protocol may choose to liquidate an account that is underwater after an adverse price movement 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 their users’ MEV and efficiently redistribute the remaining value to users or internalize it themselves. Today, many developers believe 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 enormous technical, operational, and interoperability complexities. The first proper 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-friendly trades aggregated by the application, and the demand side consists of MEV bots or market makers that seek to insert or reorder these trades in their favor. 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 launch their own order flow auctions 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 should, executing liquidations would typically require a token swap after collateral is seized to complete the arbitrage, and they may reject 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 emergent MEV is to outsource the auction via the Global Order Flow Auction (GOFA). Pyth is structurally positioned to run OFAs directly for all the applications it supports, as these applications already rely on Pyth’s oracle updates to keep their systems functional. Pyth thus has access to high-value blockspace across a wide range 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 brings more liquidity: MEV bots are more likely to be takers of bundled order flow across multiple applications (due to atomic composability), and more applications are incentivized to participate when there are more competitive takers (submitting higher bids, which translate directly into revenue).

A New Frontier of OEV for Specialized Applications

OEV represents a novel approach to capturing value for oracles and applications. The OFA run by the oracle directly delivers 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.

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