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Why transparent trading improves execution for whales Throughout Hyperliquid’s growth, skeptics questioned the platform's ability to scale liquidity. These concerns have been resolved now that Hyperliquid is one of the most liquid venues globally. With Hyperliquid’s adoption by some of the largest traders in crypto, discussion has shifted to concerns around transparent trading. Many believe that whales on Hyperliquid are: 1) frontrun as they enter their position 2) hunted because their liquidation and stop prices are public These concerns are natural, but the opposite is actually true: for most whales, transparent trading improves execution compared to private venues. The high level argument is that markets are efficient machines that convert information into fair prices and liquidity. By trading publicly on Hyperliquid, whales give market makers more opportunity to provide liquidity to their flow, resulting in better execution. Billion dollar positions can have better execution on Hyperliquid than on centralized exchanges. This post covers a complex line of reasoning, so it may be more compelling to start with a real-world example from tradfi to demonstrate this universal principle. After all, actions speak louder than words. Example Consider the largest tradfi ETFs in the world that need to rebalance daily. Examples include leveraged ETFs that increase positions when prices move favorably and decrease positions in the other direction. These funds manage hundreds of billions of dollars in AUM. Many of these funds choose to execute on the closing auction of the exchanges. In many ways this is a more extreme version of whales trading publicly on Hyperliquid: 1. These funds’ positions are known almost exactly by the public. This is true on Hyperliquid as well. 2. These funds follow a precise strategy that is public. This is not true on Hyperliquid. Whales can trade however they want. 3. These funds trade predictably every day, often in massive size. This is not true on Hyperliquid. Whales can trade whenever they want. 4. The closing auction gives ample opportunity for other participants to react to the ETFs’ flows. This is not true on Hyperliquid, where trading is continuous and immediate. Despite these points, these ETF managers opt into a Hyperliquid-like transparency. These funds have full flexibility to make their flows private, but proactively choose to broadcast their intentions and trades. Why? History of transparency in electronic markets A complementary example is the history of electronic markets. As summarized above, markets are efficient machines that convert information into fair prices and liquidity. In particular, electronic trading was a step-function innovation for financial markets in the early 2000s. Prior trading occurred largely in trading pits, where execution quality was often inconsistent and spreads wider. With the advent of programmatic matching engines transparently enforcing price-time priority, spreads compressed and liquidity improved for end users. Public order books allowed market forces to incorporate supply and demand information into fairer prices and deeper liquidity. The spectrum of information Order books are classified by their information granularity. Note that L0 and L4 are not standard terminology, but are included here as natural extensions of the spectrum. L0: No book information (e.g. dark pools) L1: Best bid and offer L2: Levels of the book with price, total size of level, and optionally number of orders in the level L3: Individual anonymized orders with time, price and size. Some fields including sender are private L4 (Hyperliquid): Individual orders with complete parity between private and public information Each new level of order book granularity offers dramatically improved information for participants to incorporate into their models. Tradfi venues stop at L3, but Hyperliquid advances to L4. Part of this is necessity, as blockchains are transparent and verifiable by nature. However, I argue that this is a feature, not a bug. Zooming out, the tradeoff between privacy and market efficiency spans the full spectrum from L0 to L4 books. On this scale, L3 books can be viewed as an arbitrary compromise, not necessarily optimal. The main argument against L4 books is that some strategy operators prefer privacy. Perhaps there is some alpha in the strategy that is revealed by the order placement. However, it’s easy to underestimate the sheer talent and effort going into the industry of quantitative finance, which backs out much of these flows despite anonymized data. It’s difficult to enter a substantial position over time without leaking that information to sophisticated participants. As an aside, I believe financial privacy should be an individual right. I look forward to blockchains implementing privacy primitives in a thoughtful way in the coming years. However, it's important not to conflate privacy and execution. Rather than hand-in-hand concepts, they are independently important concepts that can be at odds. How market makers react to information One might argue that some privacy is still strictly beneficial. But privacy is far from free due to its tradeoff with execution: toxic flow can commingle with non-toxic taker flow, worsening execution for all participants. Toxic flow can be defined as trades where one side immediately regrets making the trade, where the timescale of "immediate" defines the timescale of the toxicity. One common example is sophisticated takers who have the fastest line of communication between two venues running toxic arbitrage taker strategies. Market makers lose money providing liquidity to these actors. The main job of a market maker is to provide liquidity to non-toxic flow while avoiding toxic flow as much as possible. On transparent venues, market makers can categorize participants by toxicity and selectively size up to provide as a non-toxic participant executes. As a result, a whale can quickly scale into a large position faster than on anonymized venues. Summary Finally returning to the example of ETF rebalancing, I imagine the conclusion of rigorous experimentation confirmed the points above. Addressing the specific subpoints in the introduction: 1) A transparent venue does not lead to more frontrunning than private venues. Rather, traders with consistently negative short term markouts benefit by broadcasting their autocorrelated flow directly to the market. Transparent venues offer a provable way for every user to benefit from this feature. 2) Liquidations and stops are not “hunted” on transparent venues more than on private venues. Attempts to push the price on a transparent venue are met with counterparties more confident to take the mean reversion trade. If a trader wants to trade massive size, one of the best things to do is tell the world beforehand. Though counterintuitive, the more information that is out there, the better the execution. On Hyperliquid, these transparent labels exist at the protocol level for every order. This enables a unique opportunity to scale liquidity and execution for traders of all sizes.

Most tech giants in the 2000s built their infrastructure and product as one entangled unit. Amazon had the foresight to separate out AWS as an API layer, of which Amazon retail was the first of many users. Today, AWS generates more profit than all of Amazon's other business lines combined. Hyperliquid is built with the same philosophy. Housing all of finance requires thoughtfully designed, open financial primitives. Each primitive should obey the Unix principle of "Do one thing and do it well." Talented builders then have the foundation to chain these together to create magical applications. HyperCore borrowing is an example to highlight this philosophy in action. Most other platforms implement portfolio margin by marking an account's collateral to market value with an LTV haircut, creating borrowed assets without an explicit lender. This system is simpler to implement, but misses a golden opportunity for composability. Hyperliquid instead begins with a borrow/lend protocol on HyperCore. Every borrowed asset is sourced from a supplier, so risk is isolated within the borrow/lend primitive instead of platform-wide. HyperCore's portfolio margin system is implemented as an orchestration layer that composes borrow/lend, with other primitives such as perps, spot, and outcome trading. This decomposition has several nice corollaries: 1. Today's announcement of manual borrowing is not a new feature, but simply an extension of the underlying primitive. Borrowers on day one have access to 400M and growing of supplied liquidity. 2. Portfolio margin users earn interest on their idle stablecoin collateral. This is not a new feature, but a natural byproduct of composing trading with lending. 3. System safety is easier to reason about when perp and borrow/lend margining are independent. In the same way that math theorems almost prove themselves when the right abstractions are defined, composable designs just feel right.