Oracle Data Feed

Pyth operates as an oracle network catering to diverse blockchains, protocols, and projects within the Web3 and broader cryptocurrency sector. Oracles serve as a conduit for blockchains and protocols to access data existing off-chain. For instance, while Solana can calculate the balance of SOL tokens in each account, it cannot compute the price of a SOL token, which relies on off-chain inputs like pricing data from centralized exchanges (CEXs).

Pyth's flagship product, Price Feeds, delivers nearly real-time pricing data for various assets, encompassing cryptocurrencies, commodities, and stocks. These price feeds empower third-party applications, such as lending protocols, to integrate pricing data crucial for smooth operations. In the context of a lending protocol, erroneous or faulty pricing data could accumulate bad debt and losses for the protocol's liquidity providers (LPs).

Multichain Availability

With traditional “push” oracles, each new price feed on each individual chain is its own separate deployment, and getting data on one chain about an asset on another network is more or less not possible at scale as a result.

The Pyth Network's price feeds are accessible on all Pyth-supported blockchains by default since price sourcing and aggregation occur on Pythnet, and the price updates are brought cross-chain through Wormhole. When a new Pyth Price Feed launches, it is immediately available on all supported blockchains, eliminating the need for individual deployments on each target chain. This makes Pyth an interesting oracle for launching new data feeds, as Pyth can instantly expand price exposure for an asset to dozens of blockchains.

Pyth’s on-demand model allows for new price feeds to be onboarded constantly and across countless support networks

Suppose a new asset emerges on platforms like Optimism and gains traction, with demand for it to be utilized as collateral and for borrowing purposes. In that case, Pyth can swiftly establish a data feed for it. As there are no on-chain obligations, the price feed can be promptly established off-chain at a minimal cost, with the responsibility lying on Pike to retrieve this data as required and pay accordingly.

Low Latency, High - Frequency Updates

As Pyth streams its price feed data off-chain, it isn't constrained by block time, transaction fees, or confirmations associated with on-chain data hosting. Push-based Oracle providers often limit update frequency or support only a few assets and chains due to the high costs of transacting on certain chains.

In contrast, Pyth updates each Pyth Price Feed multiple times per second. The data is streamed to Wormhole, and users can access it through a public API. Pushing every price on-chain would render frequent updates like this impractical. Push oracles typically update less frequently than block time due to the high costs of more frequent updates.

Furthermore, Pythnet streams price updates at a high frequency off-chain, enabling decentralized applications to utilize the most recent off-chain prices for every transaction. This approach yields fresher prices compared to relying on the last on-chain update pushed by an oracle.

This capability ensures that Henez Finance always has access to highly accurate price data, bolstering user confidence in managing liquidations, assessing outstanding debt health factors, and calculating loan-to-value ratios.

Reliability

Continuing from the benefit of having low latency, often networks can become congested and because push-based oracles are always pushing their data on-chain by design, it runs the risk of transactions failing (in a worst-case scenario) or paying unavoidably high transaction fees. This can result in slow to update price feeds, which can mean outdated data, compounded further by market volatility, and reduces their reliability - often when needed most.

On the other hand, Pyth’s off-chain updates are inherently not affected by blockchain congestion and better yet, as the user, you can choose specifically when to pay the higher gas fees, and to what degree. Furthermore, you’re able to automate a lot of these based on rules, whether it be to minimize expenditure on fees or predetermine how often you’ll take a new price depending on set variables.