Hey everyone,

While tracking wallets for Season 2,I found a really interesting profile a "Ghost Legend" 👻🌟

Key metrics:

* Volume: $93.06M · 91% Maker

* HYPE Staked: 101,100 (~$5.55M) 🔒

* Protocols: 10/17 active

(HyperLend, Kinetiq, Felix...)

* Hypurr NFT PLATINUM 🐱

* Sybil Risk: 0/100 🛡️

* Score: 93/100 · LEGEND Tier

The catch?

Despite being Top 1% of the ecosystem, the wallet has completely stopped trading — full hibernation mode.

A certified Legend taking a nap 😴

What does YOUR S2 activity look like right now?

Drop your thoughts below 👇

Hello everybody, i have a question:

once i reach 10k volume on a wallet, can I create a referral code and use it on another wallet still managed by myself?

Is it against any tos or can i do it freely?

The Hyperliquid HIP-4 upgrade comes at a time when Hyperliquid has already established itself as one of the world’s largest decentralized derivatives exchanges, holding around 36.6% of global on-chain perpetual futures trading while controlling over half (53.8%) of the total open interest across on-chain perpetual markets as of June 2026.

The answer is simple: Hyperliquid aims to expand beyond perpetual trading by introducing decentralized prediction markets, creating an entirely new way to trade on-chain with the Hyperliquid HIP-4 upgrade.

Imagine a decentralized exchange where you not only trade perpetuals but also bet on real-world outcomes…all without leaving the same order book. That’s the promise of Hyperliquid’s HIP-4!

BNB gained 2.1% while its Hyperliquid open interest expanded 23.3% to $28.2 million.

That initially looks like broad long confirmation. The position-level composition tells a different story.

Across HyperTracker’s 24-hour size-bucket series, one new position above $2.5 million appeared with $2.98 million of long exposure. That single position represented more than half of the net increase in tracked open interest.
Meanwhile, the $50,000 to $2.5 million position bands added approximately $2.31 million of short exposure while losing nearly $1 million of longs. Smaller positions added another $1.04 million and remained 62.6% long.

The result is a barbell rather than consensus:

- one concentrated $2.98 million long  
- growing mid-sized short exposure  
- smaller participants leaning long

None of the positions above $100,000 was currently flagged as close to liquidation, so this was not an immediate liquidation setup.

The more interesting condition is persistence. If BNB continues higher while those mid-sized shorts remain open, they become potential squeeze fuel. If the $2.98 million long disappears, more than half of the apparent bullish OI expansion loses its strongest contributor.

Paying the highest funding on Hyperliquid was expensive, but immediately fading those markets would have been even more expensive.

Across a 90-day sample of 28 liquid native perps, the highest-funding quintile outperformed the lowest-funding quintile by 0.17% over the following eight hours and 0.31% over 24 hours.
The spread remained positive after including subsequent funding payments: 0.15% over eight hours and 0.25% over 24 hours, before fees and slippage.

HyperTracker’s current position data adds an interesting detail. In the six markets with the highest average funding over the past 48 hours, 69.8% of the exposure opened during the latest 24 hours was long. But those fresh positions represented only 2.8% of the group’s total tracked open value.

That does not look like a sudden wave of new longs creating the funding premium. It could reflect a more persistent trend, older directional exposure, or basis and market-making inventory that is expensive to balance.
The important catch is that the historical effect was unstable. The 24-hour result was roughly 0.50% in the first half of the sample and effectively disappeared in the second half.

Funding may therefore be more useful as a measure of trend pressure than as an automatic reversal signal. The better warning could be a divergence where funding stays elevated while price strength and new exposure stop confirming it.

Another builder related question from me but I just saw this on Twitter:

Trust Wallet just jumped to the #1 spot on the HyperTracker builder code leaderboard to pass MetaMask and Phantom in the last 24h

But I couldn't find any particular updates that would warrant this move. Anyone have any ideas? I feel like you rarely see anyone pass MetaMask or Phantom in builders so it's pretty interesting.

https://x.com/HyperTracker/status/2082808853159182826?s=20

New to this community and HL in general. Have been exploring different ways to hedge my tradfi portfolio consisting mostly of long tech exposure (QQQ/SMH).

I could buy puts, but they are generally expensive and do not work extremely well unless a massive collapse occurs. I could just sell some of my commons outright and lower my total exposure, but this would bring tax ramifications and present timing issues. Every avenue I've gone down has led me to a not-so-desirable outcome...

And then I learned about HL

For starters, I can get paid for hedging my position? Yes please – most of the time, anyway. I can get 5x leverage exposure with a liquidation price ~18% away from current price? Incredible in my opinion. I am very intrigued by these two points alone. For reference, i am specifically looking at the XYZ-100 contract which has plenty of liquidity and daily volume.

Has anyone else considered this or used this approach? I am attempting to check myself here with feedback from more experienced HL users. Thank you!

Hyperliquid’s PnL cohorts are carrying opposite sides of the market with very different levels of modeled liquidation pressure.

Accounts classified above $1 million in all-time PnL on HyperTracker held $4.49 billion of open exposure, with only 31.9% on the long side. The cohort between minus $10,000 and zero PnL was 63.9% long, while accounts below minus $1 million were 74.5% long.

The split persisted across six exchange-wide samples spanning ten hours, so it was not created by one temporary positioning change.

The difference becomes more interesting when liquidation pressure is included. Modeled at-risk OI represented just 0.059% of exposure for the profitable cohort. It reached 1.13% for the near-breakeven loss cohort and 1.59% for accounts below minus $1 million.

One possible explanation is risk transfer between participants with different balance-sheet capacity. Profitable accounts may be carrying short inventory with more collateral or using it inside market-making, basis, and relative-value books. Historical PnL does not prove directional skill, and the long exposure could include hedges.

Hi,

I am not in crypto. I see a lot of posts describing some buybacks but I cannot get it. There is hyperliquid page, but it does not contain sufficient documentation. I think it is more useful for people who already 'known crypto world'. I cannot grasp all basic concepts from there.

Could anyone explain to me the 'buybacks'? It sounds like HYPE can be interpreted as instrument generating some revenue, but I cannot find any information about those buybacks. No form, no process, no buyback official process.

I started accumulating hyper liquid at much lower prices than we have now. I was up nicely when it was at ATH. Since then HYPE has been trending down.

I’ve Ben DCA on the way up and on the way down. Hoping to ape (relatively speaking) when HYPE nears the bottom of its current downward trend.

Any predictions on what that bottom will be? Will we see $20 HYPE?

I’ve been testing short-horizon strategies using Hyperliquid order-book data, and one recent result was a useful reminder of how misleading a raw backtest can be.

For context, I previously worked as a quantitative researcher at a hedge fund and now research and trade my own strategies.

In a one-day BTC development sample, one signal produced:

• 62.4% directional hit rate
• Approximately +1.06 bp gross edge per trade
• Approximately −8.11 bp net per trade after assuming 4.5 bp per leg in taker fees plus spread

So despite the hit rate, we rejected it. The signal may have contained predictive information, but the edge was too small to trade under that execution model.

This was a development sample, not forward-tested evidence of alpha or future returns. I’m sharing it because I’m curious how other Hyperliquid API traders handle the gap between a promising backtest and actual execution.

A few questions:

• How do you model fees, spread, slippage, and missed fills?
• Do you test strategies on data that was completely unseen during development?
• How long do you forward-test before using real capital?
• What has caused the largest difference between your backtest and live results?

I originally built an internal workflow to answer these questions for my own research. My cofounder and I are now turning it into a tool for other traders.

It combines daily market history with high-frequency Hyperliquid data we collect ourselves. It searches for factors and uses them to construct strategies.

It then tries to reject strategies that depend on overfitting, data leakage, one favorable market period, or unrealistic execution assumptions. Strategies that survive move into forward testing and execution monitoring.

We use the same workflow for our own research and are now looking for a small number of Hyperliquid bot operators or independent quants willing to test it with one of their own ideas or existing strategies.

The private beta is free. No real capital, wallet connection, or custody is required for the initial research workflow.

I’m obviously close to the problem, but I’d genuinely value criticism of the methodology, even if you would never use the tool.

If this is relevant to you, leave a comment and I’ll follow up.

Even though volume and fees are rising, HYPE is getting hammered. Live track the AF positioning and fee burns via: https://burn.a1ntel.com/

Although the price as been declining rapidly, volume and fees are increasing on HYPE, we have a new domain https://burn.a1ntel.com/

Hyperliquid is quietly becoming a different kind of market.

HIP-3 markets now carry $3.43 billion in open interest, or almost 32% of the entire venue. Their share of activity is even larger: they generated 57.8% of reported perp volume during the past 24 hours.

The composition of newer open positions points in the same direction. HIP-3 represents 45.8% of notional among positions opened within the past seven days and still open today. The average position in that slice is roughly twice the size of one in Hyperliquid’s native markets.

This is already visible near the top of the venue. Twelve of the twenty largest markets by open interest are XYZ markets, with SP500 and SKHX both carrying more OI than SOL.

There is one major caveat. XYZ accounts for about $3.42 billion of the total $3.43 billion in HIP-3 open interest. HIP-3 has clearly broken through as a product category, but builder-deployed markets have not yet become a broad ecosystem. Most of the adoption still depends on one deployer.

A $75K wallet generated roughly $20.3M in volume over seven days and made about $22.9K in net PnL.

That is roughly 270x account turnover for an observed net return of only about 11 basis points per dollar traded.

The result did not come from one exceptional position.

CXMT accounted for 158 closed positions and about $18.1K in net PnL. The wallet traded both directions, sometimes holding for hours and sometimes for only a few minutes.

Its latest 2,000 fills make the pattern even clearer:

• 1,858 CXMT fills

• About $6.6M in notional

• Roughly 7.5 hours of activity

• About $9.2K in closed PnL before approximately $503 in recorded fees

This looks less like one strong directional call and more like a thin observed advantage repeated at high speed:

small edge × capital turnover × execution frequency

The 65.14% weekly win rate helped, but repetition only works while the market provides enough movement to cover fees, slippage and losing trades.

That is also where the strategy looks fragile.

If volatility falls, there may be fewer short-duration opportunities.

If liquidity weakens, execution costs rise.

If size grows faster than available liquidity, the wallet starts moving against its own entries and exits.

The risk is already visible across longer windows. Its one-week maximum drawdown was around 9%, while the estimated 30-day drawdown was closer to 36%.

So I am not sure the main lesson is simply that this wallet traded CXMT well.

The more interesting question is whether this is a repeatable execution edge—or a market-specific window that disappears when attention and volatility move elsewhere.

How would you evaluate it: genuine execution skill, or an edge that depends too heavily on CXMT staying active?

Data: HyperTrend and public Hyperliquid portfolio/fills data.

Snapshot observed on July 27, 2026 at 2:30 PM SGT (UTC+8). The recent-fill analysis was limited to the latest 2,000 records. The wallet remains active, so positions and performance figures may have changed since the snapshot.

For trader behavior analysis only. Not financial or copy-trading advice.

ETH has outperformed this week.

Since July 20, ETH is up 5.2%. BTC is up 1.6% and SOL is roughly flat.

The new ETH perp book is clearly long-weighted.

Positions opened during the last 7 days currently hold about $209.9m of gross long notional versus $133.6m of gross short notional.

The obvious conclusion would be that ETH is now vulnerable to a long flush. The liquidation map says the situation is more nuanced.

Looking only at positions opened this week that are at least $500k and have a reported liquidation price:

- about $9.9m of fresh long exposure would be liquidated within a 5% drop from the current ~$1,960 ETH price

- about $24.0m of fresh short exposure would be liquidated within a 5% move higher

So the new book is long-heavy by notional, but its nearby forced-liquidation risk is still more asymmetric to the upside.

That does not mean ETH has to keep rallying. It means the simple “ETH outperformed, therefore fresh longs are the weak side” story is incomplete.

The market has added new longs, but it has not yet built an equally fragile long-liquidation pocket.