Pyth Network: Compounding Advantages and New Markets

    Ryan Connor

    Key Takeaways

    • Pyth is set to benefit from numerous fundamental tailwinds, most notably, the insatiable demand for high frequency trading and price data that arises naturally as financial markets mature and complexity compounds.
    • The Pyth Network is a low friction and natural distribution channel for sophisticated crypto native and TradFi trading firms, CEXs, and DEXs to monetize their underutilized data. We see this supply-side advantage compounding as crypto gains legitimacy and de-risks in the eyes of TradFi market participants.
    • Pyth has a number of growth levers to pull, such as the introduction of increasing or dynamic fees, developing relationships with new DeFi 2.0 applications and chains, and mediating liquidation markets across chains. The latter solves a much underappreciated pain point in markets by reducing the burden of fragmentation and smart contract risk that plagues liquidators.

    Modern Markets Naturally Orient Toward Speed

    If modern financial markets can be characterized by any one concept today, it is speed. The Nasdaq Stock Exchange - the second largest exchange by volume - runs at over 1 million transactions per second, ingesting about 70 billion records daily with peak load of over 100 billion records in a single session. 

    Most of these orders are placed by market makers, HFT, and quant trading shops whose trade time horizons are measured in microseconds and nanoseconds. The scale of these operations is astounding - some shops trade so frequently that a single firm can account for a large portion of volume in even the most mature markets. US market maker Virtu, for example, has at times represented 20% of US stock market trading volume in a single session. And the need for speed in traditional markets is only increasing. In the advent of HFT, a trade signal might take place over a 3-4 second timeframe. Studies in 2015 and 2020 estimated the typical duration of a signal had fallen to around 1 to 10 microseconds - that is, 1 or 10 millionths of a second. 

    The US stock market exemplifies the complex market structures within which these sophisticated trading strategies operate, and the profit opportunities available to speed traders when prices differ ever so slightly. For example, the S&P 500 has numerous ETFs and futures products trading at different venues. If the price in Chicago varies from the price in New York, or the ETF value strays from the value of its underlying securities even for a moment, a profit opportunity exists for the fastest trader. This strategy applies to all stocks, where the entire US stock market is made up of over over 8000 stocks trading across 50 trading venues (registered exchanges and ATSs). At times, even great complexities and therefore profit opportunities exist in the fixed income, commodity, currency and derivatives markets. 

    The intersection of these dynamics results in modern financial markets where the dominant activity is latency arbitrage and high frequency trading. For example, for the FTSE 100, latency arbitrage trade represents about 22-44% of total volume for those securities, and in the US, high frequency trading has represented over 50% of total equity trading since 2013. 

    Modern financial systems have embedded incentives that encourage their major players to orient toward speed. To summarize

    • As financial markets grow and trading volumes increase, speed traders capitalize on a growing number of latency arbitrage opportunities
    • Exchanges benefit from increased transaction-based revenues that arise from speed trading, and then orient their venues and technologies toward speed traders. 
    • This activity persists due to positive externalities, as *all *traders are ultimately benefactors of the deeper liquidity and the narrower spreads that come with increasing volumes driven by faster markets. 

    In short, maturing markets are faster markets. 

    Crypto Is Getting Faster

    We’re watching these same dynamics play out in today’s maturing crypto markets, where block times and confirmation times are falling, and trading strategies are getting faster

    First, at the limit in a mature DeFi ecosystem, it’s possible that DeFi represents a comparable or even greater surface area for latency arbitrage and HFT opportunities than traditional financial markets. Above we mentioned, the US stock market has over 50 trading venues and over 8000 stocks. In crypto, there are innumerable DEXs, CEXs, perps exchanges, and over 2.4 million tokens, with thousands of new tokens being launched per day. The surface area for arbitrage and speed-oriented profit opportunities in crypto is vast and rapidly expanding.

    Today, next-gen L1s, DeFi-centric app chains, and DeFi applications are all driving improvements on speed and throughput to attract the most sophisticated and high volume traders. Prominent examples of teams pushing the limits of speed and throughput today are Solana's Firedancer clientSuiHyperliquidAptos, and Ethereum's Reth client. App chains are experimenting with novel consensus and validator designs to enable high frequency order books and transaction validation. 

    We believe this trend will continue as speed, frequency and throughput confer a number of advantages characteristics that should net improve crypto UX and increase the value of blockchain networks, because speed limits potential for sandwich attacks and improves LP experience, and higher volumes drive increased fee-generating opportunities for exchanges and blockchain validators. 

    Just as in traditional financial markets, the embedded incentives in DeFi markets orient traders and trading platforms toward speed. 


    Oracles are critical to a well-functioning DeFi ecosystem, as oracles secure 50% of TVL today and are pricing over $200 billion in DeFi derivatives volume per month. DeFi apps and the markets cannot evolve and get faster without oracles evolving with them. 

    The evolution of crypto oracles can be simplified through the lens of DeFi 1.0 and 2.0, where DeFi 1.0 was primarily focused on solving for trust, and DeFi 2.0’s primary focus is speed and throughput. 

    Chainlink’s push oracle is a prime example of an oracle built to enable DeFi 1.0. Applications on the serial processing ETH L1 with 12 second blocktimes used Chainlink’s push oracle to update prices at both large price deviation and time (“heartbeat”) thresholds, where the oracle network, favoring maximal decentralization at the expense of speed, pushed prices through multiple aggregating layers before the price could be used at the application layer, adding latency at each layer along the way. For example, Chainlink’s ETH/USD price feed on the Etheruem L1 updates at a 1 hour heartbeat and a 0.5% price interval, with the price being scraped from an aggregator like CoinGecko and then passing through three aggregating layers. 

    In stark contrast, next generation oracles are pull-based systems with sub-second pricing provided by first-party data sources, fewer aggregating layers, and a next-generation VM. This method was pioneered by Pyth Network with a similar method adopted by Chainlink’s Data Stream product.

    Pyth's Advantages

    Pyth has a number of historical and compounding advantages that we believe best position the oracle for increased adoption as chains and apps inevitably get faster and financial market complexity compounds. 

    Last Mover Advantage

    Pyth was able to watch DeFi evolve and focus on what Chainlink got wrong (push over pull) and did not prioritize (high frequency financial data), forming a wedge into the market with its next-gen oracle design. 

    As such, simple innovations like pull-based pricing and deviation bands allowed Pyth to provide a cost-effective and secure alternative to Chainlink’s legacy oracle, especially amongst DeFi 2.0 applications built on performant chains. Pyth’s last mover advantage allowed it to orient itself around speed by picking SVM off the shelf after the technology had been sufficiently de-risked. Pyth was able to benefit from the technological advancements and optimizations pioneered by some of the most respected engineers in the industry. 

    As a result, Pyth provides over 500 high frequency price feeds today at 200-400 ms blocktimes to over 340 apps and over $5 billion Total Value Secured (TVS), making it the dominant pricing oracle across many blockchains. In contrast, Chainlink today offers only 20 unique price feeds via its high frequency Data Stream product, all of which are already available on Pythnet.

    pyth kpis.png

    While Pyth doesn’t own relationships with leading trading platforms like DYDX and GMX, and has a minority market share on the Eth L1, Optimism, and Arbitrum chains, it has solidified itself as a dominant oracle on next-gen high throughput chains (Solana, Sui, Aptos, and even Base to a lesser degree), powering their most impactful applications (Jupiter perps, MarginFi, and Drift for example). Pyth has also secured big wins in the app-chain space, particularly amongst applications in or adjacent to the EVM ecosystem (Synthetix, Aevo, and SynFutures). 

    pyth daus.png

    Most notably, Pyth is winning relationships with high profile projects like Ethena. The project uses Pyth price feeds as a backup oracle today, but the market is speculating the app may use Pyth as its primary oracle if it were to launch on the SVM. As EVM-based projects increasingly migrate to the Solana ecosystem due to its accelerating adoption amongst end-users and upcoming Firedancer launch, we expect Pyth to be an obvious first choice as Chainlink seemingly continues to ignore the ecosystem

    Compounding Advantages

    Pyth has a number of compounding advantages that should catalyze increasing adoption of Pyth price feeds and drive market share gains. 

    First-party data model. Pyth incentivizes first-party sources to provide their price data on Pythnet. The network targets Web2 and Web3 traders that actually generate financial market data, not aggregate it. Exchanges (CEXs, DEXs, TradFi exchanges) and sophisticated traders can all leverage the Pyth Network for an additional revenue stream, which is particularly attractive for high frequency traders like market makers who are otherwise paying for the data they generate

    By focusing on first-party data, Pyth’s price feeds are always earliest on the market and steps ahead of any aggregators by definition. Most importantly, in markets where profit opportunities must be seized in milliseconds or less, the earlier the price, the more valuable it is, and the data generators have the earliest prices. The network has landed an impressive list of first-party TradFi data contributors, including Two Sigma, Jane Street, Cboe Global Markets, and Virtu, as well as many top-tier Web3 asset managers and exchanges. 

    Pyth’s first-party data model inherently improves the Pyth product as crypto adoption grows. As a consequence of Pyth’s first-party model, Pyth side steps the frictions typically associated with Web3 onboarding, i.e. new custodians, setting up wallets, compliance measures, etc. Pyth serves as a natural, low-friction Web3 distribution platform for TradFi, where TradFi firms can easily onboard and monetize their under-monetized data as the Web3 ecosystem continues to mature and de-risk. As crypto increases in legitimacy in the eyes of traditional financial firms, Pyth serves as a natural starting point for TradFi monetization. 

    RWAs. Pyth’s focus on first-party data uniquely positions the network for RWA price feeds, because many providers on the Pyth network today are already trading in traditional markets. These relationships limit the need for BD to onboard RWA pricing and provide a product advantage as trading firm prices will always be more timely than any aggregation layer. 

    New Growth Levers

    Higher fees & dynamic pricing. Currently, Pyth has its fees set to the lowest unit of gas per chain. This is useful for bootstrapping the demand side, but is not reflective of true market demand for prices, especially at different points in time (e.g. prices during liquidation events versus after-hours trading). Over time, we expect the Pyth DAO to introduce more dynamic pricing. While still early days, we recently saw the first ever conversation in the Pyth governance forum on fee revenue. 

    New chains & Pyth as a call option on appchains. Pyth stands out as being the most integrated oracle in crypto. Simultaneously, we’re watching new experiments proliferate, as L2s, L3s, app chains and new next-gen blockchains come to market. New chains are nearly universally focused on speed, especially in the context of financial market data, making Pyth an obvious partner. 

    Liquidation Market Mediation

    Pyth’s is uniquely positioned to solve the fragmentation and smart contract risk faced by DeFi liquidators. 

    Liquidation Fragmentation. Different DeFi protocols have their own unique interfaces for liquidations, even though the core liquidation process is essentially the same across protocols. This means that liquidators need to build bespoke integrations for each protocol they want to interact with. The lack of standardization fragments the liquidation process, increasing the integration cost and effort for liquidators. As a result, liquidators are limited in the number of protocols they can effectively support. This leads to a shortage of liquidators available for each protocol, which can threaten the timely execution of essential liquidations and impact protocol profitability. Protocols may even have to incentivize liquidators beyond the standard liquidation bonuses or run their own liquidation bots to ensure sufficient liquidator presence.

    Smart Contract Risk. When liquidators integrate with multiple DeFi protocols, they are exposed to the smart contract risks associated with each protocol, multiple standards across different liquidation interfaces mentioned above, and additional to smart contract complexities and risk on a per protocol basis. To mitigate these risks, some liquidators write separate liquidation smart contracts for each protocol. This approach isolates the funds used for liquidating positions on a specific protocol, preventing potential smart contract vulnerabilities in one protocol from affecting funds allocated to other protocols. However, this risk mitigation strategy comes at the cost of capital efficiency. By siloing funds across multiple smart contracts, liquidators cannot easily leverage their capital across different protocols within a single block, impeding capital efficiency.

    Because liquidations are reliant on external state changes delivered by oracles, oracles are first in line in the liquidation value chain and are therefore critical to the smooth functioning of liquidation markets, liquidator profitability, and application profitability. Oracles are therefore best positioned to mediate auctions for liquidation oracle prices bundled with liquidation transactions in a single interface, thereby reducing fragmentation and smart contract risk associated with liquidation markets. 

    Given its vast reach and the rapid adoption of Pyth price feeds, we believe Pyth is best positioned to mediate a single marketplace for liquidations across many blockchains and applications, which we believe could be as large as 10% of total MEV opportunities. It’s worth noting that such a marketplace can exist for any liquidation that is reliant on an oracle, i.e. liquidations in borrow/lend markets and on derivatives platforms. 

    Final Thoughts

    Pyth has a number of inherent advantages and tailwinds that will drive the network’s fundamentals as crypto markets inevitably mature. Pyth is not only positioned to take market share in financial price feeds, but has a number of growth levers it can pull to increase network and provider profitability. We expect to see strong trends in fee, providers, app integrations, Total Value Secured and Total Trading Volume over time as a result. 

    *This research report has been funded by Pyth Data Association. By providing this disclosure, we aim to ensure that the research reported in this document is conducted with objectivity and transparency. Blockworks Research makes the following disclosures: 1) Research Funding: The research reported in this document has been funded by Pyth Data Association. The sponsor may have input on the content of the report, but Blockworks Research maintains editorial control over the final report to retain data accuracy and objectivity. All published reports by Blockworks Research are reviewed by internal independent parties to prevent bias. 2) Researchers submit financial conflict of interest (FCOI) disclosures on a monthly basis that are reviewed by appropriate internal parties. Readers are advised to conduct their own independent research and seek the advice of a qualified financial advisor before making any investment decisions. *