The Pyth Network is a specialized blockchain oracle solution for latency-sensitive financial market data. It seeks to aggregate high quality, first-party data in advanced ways and publish at sub-second speeds.
In this post, we look to define financial market data and some of the nuances in specific markets where specialization proves necessary in data sourcing to ensure robust coverage.
What is Financial Market Data?
Financial market data can be simply defined as a combination of:
- Orders at which prices traders wish to trade
- The prices at which trades have just occurred
Both 1. and 2. are dependent on traders, and since traders typically match with one another at exchanges, first party data is generated by both traders and exchanges.
The Pyth Network is made up of primary source data providers and contributors of derived data publishing their data directly to the blockchain for use in the on-chain aggregate. By having a diverse group of reputable first party data providers, the Pyth Network reduces the dependency on any single data source, and thus the likelihood of a single or group of data providers attacking or manipulating the aggregate price.
The general workflow is diagrammed below. As shown, participating traders contribute their data irrespective of the exchange they’ve traded on, while some exchanges contribute their data irrespective of which traders’ orders they use. Through this combination of data from trading firms and exchanges, the Pyth Network achieves leverage in covering a large share of market activity.
Each asset class has its own share of quirks.
US Equities
The US equity market is made up of 16 exchanges and is regulated by rules such as Reg NMS that, subject to certain exceptions, require trades to occur at or inside the national best bid, best offer. Executed equity trades are then required to be reported to the consolidated tape. All equities trades are given up to a single clearing house (NSCC), which makes the trades fungible and settlement straight-forward. As a result, there is less fragmentation than the high number of exchanges may suggest, but real-time data is typically controlled by a small number of participants and expensive.
Pyth Network’s US equity data coverage today consists of a combination of primary and derived data from the orders sent to and executed on regulated exchanges (MIAX Pearl Equities, IEX) data derived from the executed trades from some of the largest traders (Virtu, GTS, Jump Trading, CTC, and Akuna) across all exchanges.
FX
The global spot FX market is largely exempt from single market regulatory oversight, and thus does not typically trade on regulated exchanges. As a result, the majority of the market trades on an “off-exchange” or OTC (over-the-counter) basis either bilaterally or via ECNs. The ECNs vary from all-to-all central limit order books with guaranteed trade execution (firm orders), to bespoke liquidity pools that allow participants to segment out anonymous counterparties based their characteristics and give market makers the ability to have a ‘last look’ or cancel trades after attempted.
While there is no central clearing house for FX, most institutional participants trade in the name of one of the 70 CLS Settlement members or prime brokers, which reduces settlement risk and allows for cross exchange margin relief. As a result, less liquid FX markets can be fragmented from 1 broker or ECN to the next, but generally not for extended periods of time.
Pyth Network’s FX data coverage today consists of firm orders and executions from both ECNs (LMAX) as well as OTC traders (Virtu, GTS, Jump Trading, CTC, and Akuna).
Crypto
The global spot crypto market is still nascent and is spread across a wide array of exchanges and OTC traders. Furthermore, there are no major prime brokers or central clearing counterparties in the spot crypto markets. As a result, traders onboard bilaterally with exchanges or counterparties with no cross-counterparty margin relief.
To add complications, it is not unusual for exchanges or brokers to halt transfers or withdrawals. This makes the crypto markets susceptible to prolonged dislocations from one exchange to the next that can last for several days or longer. For this reason, the Pyth Network reports not only the aggregate asset price but also confidence intervals reflecting levels of price uncertainty by data providers, whether they are traders or liquidity venues.
Pyth Network’s crypto data coverage today consists of orders and executions from crypto exchanges (e.g. LMAX) as well as traders (e.g. Genesis, CMS).
We can’t wait to hear what you think! You can join the Pyth Discord and Telegram, and follow us on Twitter. You can also learn more about Pyth here.
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