<p>Many trading teams operating on Sui and Hyperliquid may not know how much on-chain data latency is costing them. Not because they are making bad decisions. Not because their strategies are flawed. Because the infrastructure baseline they are measuring against was never fast enough to begin with.</p><p>When every team in your market is working from the same delayed data feed, the cost of that delay becomes invisible. There is no benchmark to reveal it. No P&L line that says “latency loss.” The opportunity simply does not appear, and the team moves on, assuming the strategy underperformed.</p><p>This is the hidden cost of on-chain data latency. And on chains with sub-second finality like Sui and Hyperliquid, it is larger than most teams realize.</p><h2 id="what-on-chain-data-latency-actually-means">What on-chain data latency actually means</h2><p>On-chain data latency is the gap between when something happens on the network and when your systems see it.</p><p>It sounds simple. In practice, it compounds across every layer of public infrastructure. A transaction is processed by a validator. Before it reaches your system, it has to propagate through the network, reach a public checkpoint or RPC endpoint, pass through shared infrastructure serving hundreds of other clients, and finally arrive at your stack. Each hop adds delay. Shared infrastructure adds queuing. Rate limits add throttling.</p><p>The result is that by the time your system sees the data, the network has moved on. Other teams have already acted. The window you were trying to trade is closed.</p><p>On Ethereum, where block times are measured in seconds, this gap is inconvenient but manageable. On Sui and Hyperliquid, where block times are measured in hundreds of milliseconds, the math changes entirely. A latency gap of 150 to 170 ms is not a rounding error on a chain that finalizes every 200 to 400 ms. It is the difference between seeing a state change before and after the next block.</p><h2 id="the-baseline-problem">The baseline problem</h2><p>The reason most teams do not notice this cost is straightforward: everyone is using the same infrastructure.</p><p>When trading teams and market makers are all consuming data from the same public endpoints, on-chain data latency becomes a shared condition rather than a competitive disadvantage. No individual team feels the pain acutely because no individual team has a faster alternative to compare against.</p><p>This is the baseline problem. The loss is real, but it is diffuse. It shows up as strategies that should work in theory but underperform in practice. It shows up as fill rates that are slightly worse than expected. It shows up as opportunities that seem to close just before your orders land.</p><p>Teams attribute these outcomes to market conditions, strategy parameters, and execution quality. Rarely to data infrastructure. Because the data infrastructure question was never asked.</p><p>The question only gets asked when a team benchmarks against a faster feed and sees the gap directly.</p><figure class="kg-card kg-image-card kg-card-hascaption"><img src="https://p2p.org/economy/content/images/2026/06/syncro_data_stream_baseline_problem.jpg" class="kg-image" alt="Two scenarios side by side. Left: all trading teams consuming from the same public endpoint with no competitive edge visible. Right: one team connected directly to the validator, receiving data before the others, still on the public endpoint, with the latency gap clearly visible." loading="lazy" width="1600" height="900" srcset="https://p2p.org/economy/content/images/size/w600/2026/06/syncro_data_stream_baseline_problem.jpg 600w, https://p2p.org/economy/content/images/size/w1000/2026/06/syncro_data_stream_baseline_problem.jpg 1000w, https://p2p.org/economy/content/images/2026/06/syncro_data_stream_baseline_problem.jpg 1600w" sizes="(min-width: 720px) 720px"><figcaption><i><em class="italic" style="white-space: pre-wrap;">The cost of on-chain data latency is invisible when every team is on the same baseline. It only becomes measurable when one team has faster access for comparison.</em></i></figcaption></figure><h2 id="what-the-gap-looks-like-in-practice">What the gap looks like in practice</h2><p>On Sui, transaction events surface at public checkpoints after the network has processed and propagated them. A team consuming data from a public RPC is seeing the network state as it was, not as it is. On a chain where validators process transactions in single-digit milliseconds at the certificate processing stage, the gap between what a validator knows and what a public endpoint delivers is measured in tens of milliseconds. That is enough time for multiple state changes to occur.</p><p><em>On Hyperliquid, the dynamic is sharper. The public API delivers order book data at approximately 260 ms</em>, with snapshots only, rate-limited to 100 requests per minute*. For a market maker or quant fund trying to model counterparty flow, that feed is not just slow. It is structurally limited in important ways. Snapshot-based delivery without user attribution makes it difficult to conduct entire classes of signal research on public infrastructure.*</p><p>The teams that have moved to validator-speed real-time blockchain data streams on these networks are not just faster. They are operating with a fundamentally different information set.</p><h2 id="why-this-is-a-business-problem-not-a-technical-problem">Why this is a business problem, not a technical problem</h2><p>On-chain data latency is easy to frame as an infrastructure concern. For execution-critical teams, it is a bottom-line concern.</p><p>For MEV searchers on Sui, being 15 ms* behind the fastest available feed means running strategies against a state that has already been acted on. Every search that resolves to a closed opportunity is a search that costs gas and returns nothing. The latency is not a technical inefficiency. It is a direct cost per failed search.</p><p>For market makers on Hyperliquid, quoting on stale orderbook data means setting spreads that do not reflect current conditions. A market maker quoting on data that is 200 ms* old on a venue that moves in 100 ms* intervals is not providing liquidity. They are subsidising better-informed counterparties with tighter access to the same data.</p><p>For arbitrage desks operating across pairs or venues, the window for a viable round-trip closes as soon as faster participants act on the same signal. On-chain data latency determines whether you see that signal in time to act, or whether you see it after the round-trip is already unviable. In each case, the latency cost is not a line item. It is embedded in the gap between theoretical and realised returns. It is hard to surface without a faster point of comparison.</p><h2 id="when-the-cost-becomes-visible">When the cost becomes visible</h2><p>The cost of on-chain data latency only becomes visible through comparison. And the comparison only becomes possible when a faster alternative exists and is accessible.</p><p>For most of the history of on-chain trading on Sui and Hyperliquid, accessible, documented, validator-speed data feeds have been hard to come by, particularly for teams without institutional-scale infrastructure budgets. The barrier to entry was high enough that most teams never made the comparison.</p><p>That is changing. Validator-speed real-time blockchain data streams are now available at flat monthly pricing, with free trials designed to make comparisons easy. The benchmark is the product. Run it alongside your existing feed. Measure the gap. Decide whether the edge is worth the cost.</p><p>For most execution-critical teams, the answer becomes clear quickly.</p><h2 id="the-infrastructure-principle-behind-the-edge">The infrastructure principle behind the edge</h2><p>The latency advantage of validator-speed data comes from one architectural decision: sourcing data at the point of origin rather than consuming it downstream.</p><p>Public endpoints are downstream consumers of validator output. They receive data after it has propagated through the network, been confirmed, and been made available to shared infrastructure. The delay is structural. It cannot be optimised away by tuning polling intervals or upgrading RPC tiers. It is inherent to the architecture.</p><p>A real-time blockchain data stream sourced directly from an active validator node eliminates that structural delay. On Sui, this means surfacing transaction events at certificate processing, before public checkpoints. On Hyperliquid, this means reading order flow data directly from disk files on private Sentry infrastructure that peers with the validator over a private network, before block data propagates publicly.</p><p>The result is not incremental improvement on the same architecture. It is a different position entirely in the data delivery chain.</p><h2 id="what-to-do-with-this">What to do with this</h2><p>If your team is operating on Sui or Hyperliquid and has never benchmarked your data feed against validator-speed delivery, the first step is straightforward: run the comparison.</p><p>Syncro Data Stream by P2P.org is a real-time blockchain data stream for Sui and Hyperliquid, built directly on P2P.org’s active validator infrastructure. New clients receive a one-week free trial to validate latency and data quality against their existing setup. No credit card required.</p><p>The trial is designed to answer one question: how much latency is your current feed adding, and does it matter for how you operate?</p><p>Check the full technical documentation for Sui Data Stream <a href="https://docs.p2p.org/docs/syncro-data-sui-overview?ref=p2p.org" rel="noreferrer">here</a> and Hyperliquid <a href="https://docs.p2p.org/docs/syncro-data-hyperliquid-overview?ref=p2p.org" rel="noreferrer">here</a>.</p><p>To benchmark Syncro Data Stream for Sui against your existing feed, visit the <a href="https://www.p2p.org/products/syncro-sui-transaction-data-stream?ref=p2p.org" rel="noreferrer">Syncro Data Stream Sui page</a>.</p><p>To benchmark Syncro Data Stream for Hyperliquid against your existing feed, visit the <a href="https://www.p2p.org/products/syncro-hyperliquid-data-stream?ref=p2p.org" rel="noreferrer">Syncro Data Stream Hyperliquid page</a>.</p><p>For a full overview of what Syncro Data Stream delivers and how it is built, read the <a href="https://p2p.org/economy/syncro-data-stream-real-time-blockchain-data-stream/" rel="noreferrer">launch's product introduction post</a>.</p><hr><h2 id="about-p2porg">About P2P.org</h2><p>P2P.org has operated blockchain infrastructure since 2018 across 40+ proof-of-stake networks, serving 190+ institutional partners. Syncro is P2P.org’s crypto trading infrastructure product line, built on active validator nodes across Solana, Sui, and Hyperliquid.</p><hr><h2 id="disclaimer">Disclaimer</h2><p>This material is provided for informational purposes only and does not constitute investment, financial, legal, or tax advice. P2P.org accepts no liability for any actions taken based on it. Latency and performance figures referenced are estimates based on internal benchmarks and may vary depending on network conditions, geography, and client infrastructure. Past performance is not indicative of future results.</p>
from p2p validator
<p>Today, <a href="http://p2p.org/?ref=p2p.org">P2P.org</a> launches Syncro Data Stream: a real-time blockchain data stream for Sui and Hyperliquid, built directly on P2P.org's active validator infrastructure.</p><p>Syncro Data Stream is designed for latency-sensitive teams where on-chain data latency directly affects trading performance. Trading teams, market makers, quant funds, and arbitrage desks operating on Sui or Hyperliquid now have access to validator-speed data delivery through a dedicated WebSocket endpoint, at flat monthly pricing with a one-week free trial.</p><p>For trading teams that have been making do with shared public endpoints and checkpoint-based feeds, this changes what is available at a documented, accessible price point.</p><figure class="kg-card kg-image-card kg-card-hascaption"><img src="https://p2p.org/economy/content/images/2026/05/syncro-data-stream-data-path-diagram.jpg" class="kg-image" alt="Diagram showing two data delivery paths from a validator node: the Syncro Data Stream path via private Sentry and dedicated WebSocket, and the public path via network gossip and shared RPC endpoint" loading="lazy" width="1600" height="900" srcset="https://p2p.org/economy/content/images/size/w600/2026/05/syncro-data-stream-data-path-diagram.jpg 600w, https://p2p.org/economy/content/images/size/w1000/2026/05/syncro-data-stream-data-path-diagram.jpg 1000w, https://p2p.org/economy/content/images/2026/05/syncro-data-stream-data-path-diagram.jpg 1600w" sizes="(min-width: 720px) 720px"><figcaption><i><em class="italic" style="white-space: pre-wrap;">Syncro Data Stream sources data at the validator and delivers it to clients before it reaches public infrastructure. The public path adds network propagation and shared endpoint delays on top.</em></i></figcaption></figure><h2 id="what-is-syncro-data-stream">What is Syncro Data Stream?</h2><p>Syncro Data Stream is a real-time blockchain data stream sourced directly from P2P.org's active validators. Unlike shared public endpoints and standard RPC providers, Syncro Data Stream delivers on-chain data before it propagates to public infrastructure, at the earliest point of network availability.</p><p>The product launches on two networks simultaneously, Sui Network and Hyperliquid Network.</p><figure class="kg-card kg-video-card kg-width-regular kg-card-hascaption" data-kg-thumbnail="https://p2p.org/economy/content/media/2026/05/p2p-org-syncro-data-stream-Sui-Hyperliquid-promotional-video_thumb.jpg" data-kg-custom-thumbnail=""> <div class="kg-video-container"> <video src="https://p2p.org/economy/content/media/2026/05/p2p-org-syncro-data-stream-Sui-Hyperliquid-promotional-video.mp4" poster="https://img.spacergif.org/v1/1920x1080/0a/spacer.png" width="1920" height="1080" playsinline="" preload="metadata" style="background: transparent url('https://p2p.org/economy/content/media/2026/05/p2p-org-syncro-data-stream-Sui-Hyperliquid-promotional-video_thumb.jpg') 50% 50% / cover no-repeat;"></video> <div class="kg-video-overlay"> <button class="kg-video-large-play-icon" aria-label="Play video"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"> <path d="M23.14 10.608 2.253.164A1.559 1.559 0 0 0 0 1.557v20.887a1.558 1.558 0 0 0 2.253 1.392L23.14 13.393a1.557 1.557 0 0 0 0-2.785Z"></path> </svg> </button> </div> <div class="kg-video-player-container"> <div class="kg-video-player"> <button class="kg-video-play-icon" aria-label="Play video"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"> <path d="M23.14 10.608 2.253.164A1.559 1.559 0 0 0 0 1.557v20.887a1.558 1.558 0 0 0 2.253 1.392L23.14 13.393a1.557 1.557 0 0 0 0-2.785Z"></path> </svg> </button> <button class="kg-video-pause-icon kg-video-hide" aria-label="Pause video"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"> <rect x="3" y="1" width="7" height="22" rx="1.5" ry="1.5"></rect> <rect x="14" y="1" width="7" height="22" rx="1.5" ry="1.5"></rect> </svg> </button> <span class="kg-video-current-time">0:00</span> <div class="kg-video-time"> /<span class="kg-video-duration">0:58</span> </div> <input type="range" class="kg-video-seek-slider" max="100" value="0"> <button class="kg-video-playback-rate" aria-label="Adjust playback speed">1×</button> <button class="kg-video-unmute-icon" aria-label="Unmute"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"> <path d="M15.189 2.021a9.728 9.728 0 0 0-7.924 4.85.249.249 0 0 1-.221.133H5.25a3 3 0 0 0-3 3v2a3 3 0 0 0 3 3h1.794a.249.249 0 0 1 .221.133 9.73 9.73 0 0 0 7.924 4.85h.06a1 1 0 0 0 1-1V3.02a1 1 0 0 0-1.06-.998Z"></path> </svg> </button> <button class="kg-video-mute-icon kg-video-hide" aria-label="Mute"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"> <path d="M16.177 4.3a.248.248 0 0 0 .073-.176v-1.1a1 1 0 0 0-1.061-1 9.728 9.728 0 0 0-7.924 4.85.249.249 0 0 1-.221.133H5.25a3 3 0 0 0-3 3v2a3 3 0 0 0 3 3h.114a.251.251 0 0 0 .177-.073ZM23.707 1.706A1 1 0 0 0 22.293.292l-22 22a1 1 0 0 0 0 1.414l.009.009a1 1 0 0 0 1.405-.009l6.63-6.631A.251.251 0 0 1 8.515 17a.245.245 0 0 1 .177.075 10.081 10.081 0 0 0 6.5 2.92 1 1 0 0 0 1.061-1V9.266a.247.247 0 0 1 .073-.176Z"></path> </svg> </button> <input type="range" class="kg-video-volume-slider" max="100" value="100"> </div> </div> </div> <figcaption><p dir="ltr"><span style="white-space: pre-wrap;">Syncro Data Stream delivers real-time blockchain data for Sui and Hyperliquid, sourced directly from P2P.org's active validator nodes before it reaches any public endpoint.</span></p></figcaption> </figure><h3 id="syncro-data-stream-for-sui-network">Syncro Data Stream for Sui Network</h3><p>The stream delivers Sui transaction events at certificate processing, before transactions reach public checkpoints and RPC feeds. This is the stage at which the validator has processed the transaction, but before it has been confirmed and published to the broader network. Each client receives a WebSocket endpoint with isolated credentials and IP-based access controls, providing real-time data streaming optimised for execution speed.</p><h3 id="syncro-data-stream-for-hyperliquid-network">Syncro Data Stream for Hyperliquid Network</h3><p>The stream delivers full Hyperliquid order flow from P2P.org's active validator and private sentry nodes, within milliseconds of block creation. Every order across every asset: open, modify, cancel, and fill, with side, price, quantity, status, order ID, and user attribution. Block events, system metrics, and heartbeat data arrive on a dedicated channel, keeping operational signals out of the market data path. Per-asset subscriptions or full firehose, with WebSocket JSON or ESP binary delivery.</p><p>Both products are available at $2,000 per month each, with a one-week free trial for new clients.</p><h2 id="why-on-chain-data-latency-matters">Why on-chain data latency matters</h2><p>For most applications, receiving transaction data a few hundred milliseconds after it hits public infrastructure is acceptable. For latency-sensitive teams, it is not.</p><p>On-chain data latency is the gap between when something happens on the network and when your systems see it. For trading teams, that gap determines whether an opportunity is still open or already taken. For market makers, it determines whether a quote reflects the current state or stale state. For arbitrage desks, it determines whether a price discrepancy still exists by the time an order reaches the book.</p><p>Public APIs and shared RPC endpoints introduce on-chain data latency in two ways. First, data has to propagate from the validator through the network before it reaches a public endpoint. Second, shared infrastructure adds queuing and rate limiting that compound the delay under load. The result is that by the time your systems see the data, multiple other teams have already acted on it.</p><p>This is not a marginal problem. On chains with sub-second finality like Sui and Hyperliquid, where block times are measured in hundreds of milliseconds, even a 5 ms latency gap relative to the fastest available feed can be meaningful. In these environments, opportunities can open and close within milliseconds</p><p>Syncro Data Stream reduces that gap to a single validator-to-client hop, delivering data before it touches any public infrastructure.</p><h2 id="built-on-active-validator-infrastructure">Built on active validator infrastructure</h2><p>The differentiator for Syncro Data Stream is not just speed. It is where the data originates.</p><p>P2P.org operates active validators on both Sui and Hyperliquid, giving us direct access to network data at the infrastructure level. For Sui, our data stream is integrated with our validator operations, allowing us to surface transaction events earlier than standard public data sources.</p><p>For Hyperliquid, we use a dedicated low-latency data delivery setup within our private infrastructure, designed to reduce unnecessary overhead and provide clients with timely access to block and transaction data.</p><p>P2P.org is not a trading firm. Syncro Data Stream is a read-only data stream. P2P.org has no visibility into client strategies, positions, execution logic, or customer data. For teams evaluating infrastructure providers that also trade, this distinction matters.</p><h2 id="who-syncro-data-stream-is-for">Who Syncro Data Stream is for</h2><p>Syncro Data Stream is built for teams that have outgrown shared public infrastructure and need dedicated, validator-speed data delivery.</p><h3 id="high-frequency-and-systematic-traders">High-frequency and systematic traders</h3><p>For directional and arbitrage teams, execution quality depends on latency. Public endpoint latency puts teams at a structural disadvantage relative to teams with faster access. Syncro Data Stream is designed to help close that gap, providing a low-latency baseline that supports tighter entry and exit timing across Sui and Hyperliquid.</p><h3 id="market-making-and-liquidity-provision">Market making and liquidity provision</h3><p>Relying on delayed data leads to adverse selection. Syncro Data Stream delivers validator-speed order flow with granular user attribution, allowing market makers to maintain tighter spreads, manage inventory risk more effectively, and meaningfully shift the information baseline for teams that need to quote accurately under fast-moving conditions.</p><h3 id="quantitative-research-and-alpha-generation">Quantitative research and alpha generation</h3><p>Aggregated feeds mask market microstructure. Syncro Data Stream for Hyperliquid provides the complete, non-summarised order flow across every asset with persistent user IDs. This high-fidelity stream can enable modelling approaches that are difficult to achieve on snapshot-based feeds, supporting predictive signal research and counterparty analysis.</p><p>For Hyperliquid specifically, Syncro Data Stream is positioned as an open, documented, validator-speed offering with transparent pricing at $2,000 per month and a one-week free trial, designed for teams that need accessible, production-ready sentry-level data.</p><h2 id="part-of-the-syncro-infrastructure-product-line">Part of the Syncro infrastructure product line</h2><p>Syncro Data Stream joins Syncro Sender, P2P.org's Solana transaction landing service, as part of the Syncro infrastructure line.</p><p>Syncro Sender routes Solana transactions through P2P.org's staked validator connections and multi-path delivery to reach the block leader before traffic coming through public RPCs. It is already in production with leading trading teams.</p><p>Syncro Data Stream and Syncro Sender address two sides of the same problem: getting your systems closer to the chain than shared public infrastructure allows. Sender handles the execution side on Solana. Syncro Data Stream handles the data side on Sui and Hyperliquid. For teams operating across multiple networks, both products run on the same <a href="http://p2p.org/?ref=p2p.org">P2P.org</a> validator infrastructure and follow the same dedicated endpoint model.</p><h2 id="getting-started">Getting started</h2><p>Both Syncro Data Stream products are available now. Provisioning is straightforward: share your IP for allowlisting, receive your dedicated WebSocket endpoint and credentials, and connect your systems. Most teams are live within days of signing.</p><p>New clients receive a one-week free trial to validate integration, latency, and data quality against their existing setup. No credit card required.</p><p>Full technical documentation is available at <a href="https://docs.p2p.org/?ref=p2p.org">docs.p2p.org</a>.</p><p>To get started with Syncro Data Stream for Sui, visit the <a href="https://www.p2p.org/products/syncro-sui-transaction-data-stream?ref=p2p.org" rel="noreferrer">Syncro Data Stream Sui page</a>.</p><p>To get started with Syncro Data Stream for Hyperliquid, visit the <a href="https://www.p2p.org/products/syncro-hyperliquid-data-stream?ref=p2p.org" rel="noreferrer">Syncro Data Stream Hyperliquid page</a>.</p><hr><h2 id="about-p2porg">About P2P.org</h2><p>P2P.org has operated blockchain infrastructure since 2018 across dozens of proof-of-stake networks, serving a broad base of institutional partners. Syncro is P2P.org's crypto trading infrastructure product line, built on the same validator infrastructure that powers our staking business.</p><hr><h2 id="disclaimer"><em>Disclaimer</em></h2><p>This material is provided for informational purposes only and does not constitute investment, financial, legal, or tax advice. <a href="http://p2p.org/?ref=p2p.org">P2P.org</a> accepts no liability for any actions taken based on it. Latency and performance figures referenced are estimates based on internal benchmarks and may vary depending on network conditions, geography, and client infrastructure. Past performance is not indicative of future results.</p>
from p2p validator
<p>On January 25th, 2023, P2P joined the Sui network testnet wave-2. We had also participated in the first wave of the testnet with the goal of understanding the node operation nuances of the Sui blockchain. The second testnet was a game played by validators and delegators to understand the network economics and staking specifics.</p><h3 id="high-level-overview-of-sui-economics-specifics">High-level overview of Sui economics specifics</h3><p>Sui is a layer one blockchain focused on scalability, user and developer experience, and utilizes its own variant of the Move programming language. It allows for consensus to be forgotten in simple use cases, and transaction processing is parallelized.</p><p><em>In this article, we will briefly cover two elements of the Sui economy:</em></p><ul><li>Storage Fund</li><li>Reference Gas Price Mechanism</li></ul><p>Let’s start with the <strong>storage fund</strong>. It plays the role of a compensation buffer to reward validators for on-chain data storage. It is evenly staked to validators and a portion of staking rewards get reinvested back into the fund. This parameter influences the overall reward calculation. It starts relatively low in the beginning but its share steadily increases slowly along with the storage growth.</p><p>Another element influencing the validator rewards is a <strong>Reference Gas Price [RGP]</strong>. It represents the minimum price at which validators are willing to process transactions. For users, it plays the role of a guide when propagating transactions ensuring that gas prices close to the reference price will be executed appropriately. Every epoch, validators submit their quotes for RGP and the protocol chooses the 2/3's percentile by stake as the epoch reference price.</p><p><strong>Staking rewards</strong> consist of stake subsidies and gas revenue. Subsidies are known for each epoch. For testnet-2 epoch, subsidies were equal to 0,01% * total stake (mainnet subsidies will differ from wave-2) making them relatively low compared to gas revenue. Validators could calculate expected gas revenue based on the selected RGP and on-chain activity. Each participant received random costs and had to select RGP to remain profitable but at a reasonable level to improve UX and maintain lower transaction costs for users.</p><h3 id="testnet-overview">Testnet overview</h3><p>The Sui team has prepared a dashboard to track the progress of the validators during the testnet If validators selected an RGP that was equal to or lower than the final epoch RGP, they received a bonus point. Points were also assigned for maintaining positive profitability during the epoch. The score rules were changed after the 6th epoch to switch the behavior of participants during the game, so some data will be split in two groups: The first group from epoch 2 to epoch 6, and the second group from epoch 7 to epoch 31. The first two epochs were experimental and were not counted and we excluded them from the analysis.</p><p>Validator shares and costs varied during the testnet, resulting in different capabilities to remain profitable and get into the ⅔ percentile in RGP voting leading to a semi-random points distribution. The analytics below can bring some clarity to the actual activity of participants during the testnet. <br><br><em>Important note, validators could have different goals and strategies for the game, and the data below should not be taken as a representation of participation quality. We set the following goals for the testnet:</em></p><ul><li>To align with Sui economics specifics</li><li>To understand the RGP selection process and build a reliable model to calculate it</li></ul><p>We decided to prioritize the calculation precision and set our RGP quotes for each epoch with a targeted profitability margin of 5-15%. Our goal was to strike a balance between maintaining profitability without overpricing the user experience. It’s important to note that the logic for the mainnet may differ from that of the testnet.</p><h3 id="wave-2-analytics">Wave-2 Analytics</h3><p>The economic conditions during the testnet changed every epoch, requiring validators to adjust their strategies in order to remain profitable. Out of the 41 validators, only 17 changed their RGP for 80% of the epochs during the testnet.</p><figure class="kg-card kg-image-card"><img src="https://lh6.googleusercontent.com/Kz0Q0-DgbF35ujQVJbD2hJ083ZFVCJ6HzY7JjfBd5sOgr3J5roudMxqoFNfpFMZobviLue1kpd_mbU2G2ebHCyPrTMdO1PttWC8dPs5dOw9ZtxLYO6QDxizl4W-ryEAfUZtM5M8HH4pvN18x48Vw8tE" class="kg-image" alt loading="lazy" width="615" height="351"></figure><p>Profitability was an important part of the assessment, and the following breakdown shows the number of epochs in which each validator had positive profits. However, it is important to note that validators may have had different goals for the testnet. Nonetheless, over 50% of participants were able to make a profit in 80% of the epochs.</p><figure class="kg-card kg-image-card"><img src="https://lh6.googleusercontent.com/DBwnNdG7x-WMqH9NevDtILzT8muOnKQmAA1vi00V3Olo_x9nC713a98Au4BYljCHAqJ-Ey-QovfX9OhRbk3tAswqGlhvwPS-O3YXS6LhQ2nmAzoCGNEsoJ-5ToA1_AQduBzBEDNbhZ2AFFbPOuv6M7k" class="kg-image" alt loading="lazy" width="624" height="296"></figure><p>This chart shows the total margin of validators during the testnet. We can observe changes in behavior after the rule change that stopped punishing validators for not being in the ⅔ percentile with their RGP quotes.</p><figure class="kg-card kg-image-card kg-card-hascaption"><img src="https://lh4.googleusercontent.com/MNJK1YAe8-t2MqRNbiAK-PqKKofoEi02oNgVaCcVdIivrJN8m7fmeuy5rw3PKfFqZeavV5wibb-7ewuWHHxCO1KRh_hBlAzh4K2KRNvTGmaDqTJzSSIivrA4bP4HrmuHdAXxY59N1zKqbG2jvi93YOY" class="kg-image" alt loading="lazy" width="624" height="304"><figcaption>Epochs 2-6</figcaption></figure><p>Epoch 7-31 resulted in a more selective approach that takes profit into consideration.</p><figure class="kg-card kg-image-card kg-card-hascaption"><img src="https://lh3.googleusercontent.com/P1bFZ02CXinViO1MTRo4xpJFGiJbf6io0xZX2Uz26PPDUAbCx9BjTKDnmTvV3TkobVWkxKrs3yFfLKH3KDkB_wrUseLcaPqrOSxR4D9-OctEFIjTDVQr_oddggTDX4qTECOVJ7ZNKotFUdj-KZ76rh8" class="kg-image" alt loading="lazy" width="624" height="304"><figcaption>Epochs 7-31</figcaption></figure><p>In the following plots, we will display the standard deviation of each validator, which indicates how stable and precise their margin was. A lower standard deviation indicates more precise and consistent RGP quotes. For instance, if a validator quoted around 10% margin most of the time, their deviation would be low.</p><figure class="kg-card kg-image-card kg-card-hascaption"><img src="https://lh5.googleusercontent.com/wn2t8VuyRxjTSWm8oBEYyNClUV-2WRc2jWuPrjFr27lGjFs0zqJmctTxbk65mpOcC1Db6SWJMD772ok_U5ZTCMlFUgplSQg6ghdFGsWzDtXDHuNBxkxuuIJtTecFg86wE59lfDVg0I0symK6pJhwxVQ" class="kg-image" alt loading="lazy" width="624" height="315"><figcaption>Epochs 2-6</figcaption></figure><figure class="kg-card kg-image-card kg-card-hascaption"><img src="https://lh3.googleusercontent.com/sofwFnKTNhtRXpRCAKN2bdZBsJUsOOCpkZ_Rsql67YEMFuChDmYKIfOvPZ0o6iFYiPoRwkWq-OMOsznP5SUVOgvehZ68yboAda2HJXpoGDaVIClwEJfyuRAmlRoFWtOYwDFcyMizoslIhFd9_VCn3TU" class="kg-image" alt loading="lazy" width="624" height="272"><figcaption>Epochs 7-31</figcaption></figure><p>Another way to represent margin statistics is through the mean, total and median margin parameters, which also provide insights into validator behavior. This chart excludes extreme values that deviate from the average by more than 2.5 standard deviations. If all three parameters are similar, it indicates that a participant successfully targeted and maintained the margin. For instance, the mean, total and median parameters for P2P vary from 8 to 11 percent, falling within the selected 5-15 percent range that we aimed for internally beforehand.</p><figure class="kg-card kg-image-card"><img src="https://lh3.googleusercontent.com/zYeUw2njbpfDchttyajDZN5o_z0yS6SuhDXBHSdDCqhIQuhJb0RBZcNjzXf5ZoEFLlU-XNfNADzqUD4j5pk4q_WD9rieY2NG7Kc8WJ7yvf_BclWRA-O_FWYI99ZZdN1MvfQMuhAJ-zf918yo-3auryU" class="kg-image" alt loading="lazy" width="624" height="268"></figure><p><em>Feel free to explore the </em><a href="https://docs.google.com/spreadsheets/d/1XAe7rFMQ2I9BZHNiJHbcMZvgRXdu9KcGKXPfQo84Yus/edit?usp=sharing&ref=p2p.org"><em>spreadsheet with the data</em></a><em> and graphs provided in the article.</em></p><p>For a more detailed picture we have compiled a heatmap showing the deviation from each validator’s optimal RGP for each epoch. We assumed that the validator's optimal RGP is calculated based on a random margin value within the 5-15% interval. We can infer that In the second part of the game, validators moved towards profitability and calculated their RGP more precisely.</p><figure class="kg-card kg-image-card"><img src="https://lh4.googleusercontent.com/rmUHootLT_PQVFlI-hVLXMDAbtiYWtVGUIiMbvWZiNS1B1n6cn5vtI4Rr2wS3gx-d6MDlQlhV1oRv3inaytTRf_fHexlbeyskVzNPJKmv9QVeGUKKXTYRvPgbp1JqUqjGL6mka5tL_BW_4zYK2Kvj2c" class="kg-image" alt loading="lazy" width="624" height="573"></figure><h3 id="summary">Summary</h3><p>The Sui testnet-2 was an intriguing experiment that contributed to a better understanding of staking economics and network specifics.</p><ul><li>Validators were able to experiment with different calculations to develop their own strategies during the game</li><li>The main purpose of RGP is to maintain a balance between validator profitability and end-user experience</li><li>Using an optimal margin level for calculating RGP may be an effective way to achieve both goals</li><li>Calculating RGP involves multiple parameters that can fluctuate and may be difficult to predict in advance leaving room for modeling improvements over time</li><li>Participating in a testnet is an excellent opportunity to familiarise with network specifics and prepare for mainnet launch.</li></ul><p><em>If you’re interested in staking or in launching a white-label validator with us, feel free to express interest on p2p.org. We’ll keep you informed about key milestones and help you get onboarded to the mainnet from the very beginning.</em></p><hr><p><em>Special thanks to Alexey Toporov for aggregating the data and creating the charts for the article.</em></p><hr><h3 id="about-p2p-validator">About P2P Validator</h3><p><a href="https://p2p.org/?ref=p2p.org">P2P Validator</a> is a world-leading staking provider with the best industry security practices AAA SR-rating and proven expertise. We provide comprehensive due diligence on digital assets and offer only top-notch staking opportunities. At the time of the latest update, more than 1 billion USD value is staked with P2P Validator by over 40,000 delegators across 40+ networks. We have successfully participated in Sui testnet-1 & testnet-2 to become the most comprehensive partner for staking and branded node maintenance.</p>
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