Data Research: How Big Is the Liquidity Gap Between Hyperliquid and CME Crude Oil?

_Original Author / _Castle Labs

Compiled by / Odaily Planet Daily Golem（@web 3_golem）

Editor’s note: This article systematically studies the differences in crude oil futures contract trading data between Hyperliquid and the CME during weekday and weekend sessions, and draws several important conclusions. At present, Hyperliquid indeed cannot match the CME on absolute metrics such as liquidity depth or slippage; overall liquidity is below 1%, which is consistent with the fact that the main user base of RWA trading platforms is still crypto-native retail traders.

What makes Hyperliquid different is that during weekend sessions, the trading size of crude oil contracts on Hyperliquid increases noticeably.This suggests that besides retail traders with speculative demand on weekends, traders who want to obtain crude oil trading exposure before Monday and perform hedging also trade on Hyperliquid.Moreover, this trend is becoming more and more obvious, meaning Hyperliquid already has price discovery capabilities for large commodities.

However, for institutional investors, Hyperliquid’s high trading costs compared with the CME remain the main obstacle to its expansion in the large-commodities trading space. If Hyperliquid does not improve its ability to handle institutional-level orders early on, then it can only remain a temporary trading venue for traditional traders on weekends, ultimately becoming only a small supplement to the traditional financial landscape.

Research methodology and data sources

This analysis evaluates the microstructure of the crude oil market through two studies, covering both weekday and weekend markets. It uses per-trade execution data from two trading venues: Hyperliquid’s xyz:CL perpetual contract and the Chicago Mercantile Exchange (CME) contract CLJ6 (April 2026 NYMEX WTI crude oil futures).

CME data comes from Databento’s trading data source. This data captures per-trade execution data, not order book snapshots. Therefore, all CME depth and slippage estimates are based on actual traded volumes, not quoted depth. Hyperliquid data comes from Hyperliquid’s publicly available S3 database, which contains complete on-chain trade records.

Therefore, the analysis of both trading venues is based on actual traded volume. All depth data represents visible liquidity—i.e., the traded volume within specific basis-point ranges around the VWAP mid-price over a 5-minute window—rather than the full resting depth in the order book.

Study period and market background

The study period is from February 27, 2026 to March 16, 2026. This coincides with the geopolitical turmoil that followed Iran’s attacks on February 28, 2026.

• Pre-attack market close: the last CME trading day before the attack.
• Monday open: reopening pressure was huge; the CME stock gapped up significantly, while the Hyperliquid xyz:CL market was constrained by discovery boundary limits.
• The following weekends: because oil prices remained high, market volatility kept crude oil trading volume on the Hyperliquid platform high.

xyz:CL went live in early 2026, meaning these three weekends of observation cover the early maturity stage of the Hyperliquid market. The trends observed—including improvements in liquidity depth, increased trading volume, and growth in user count—partly reflect market maturity. But we believe that, in terms of absolute metrics such as liquidity depth or slippage, on-chain exchanges cannot yet compare to traditional exchanges.

Our research goal is to track directional trends: whether the spread between the two is shrinking, how fast it is shrinking, and under what conditions it shrinks.

Data analysis

The data analysis is divided into two parts by time period:

• Weekday session: covers a complete three-week period, comparing the depth, slippage, and the premium/discount of trades executed on Hyperliquid versus CME during their overlapping active periods. For Hyperliquid, we also analyze its funding rates throughout the entire period.
• Weekend session: within a given time period, encompassing three weekends, we analyze price discovery and the deviation in Hyperliquid relative to the CME opening price.

Weekday session data analysis

This analysis covers a complete three-week period, focusing on the time periods during which both exchanges are simultaneously active.

Liquidity depth is measured as the dollar trading volume within basis-point ranges of VWAP mid-price ±2, ±3, and ±5 in each 5-minute interval, and is aggregated as the median across all weekday intervals. As noted above, this reflects traded volume within the interval, not resting quoted depth. This method may understate the liquidity depth of CME and Hyperliquid.

Execution slippage is estimated using a synthetic order book constructed by sorting trades by execution price. Within each 5-minute time interval, the observed taker-side executions are ordered by ascending price (simulating sell orders walking the book), and then the sell-side orders are walked in sequence until reaching the target order size. The arrival price is set as the lowest execution price within that time period (representing the best sell price when the order arrives). Slippage is calculated as the difference between the execution volume-weighted average price (VWAP) and the arrival price, expressed in basis points. This method is applied to incremental order sizes ranging from $10k to $1M.

Hyperliquid–CME basis during weekday sessions: tracks the signed price difference between the Hyperliquid mid-price and the CME latest price within all 5-minute windows during weekday sessions. This reflects any structural premium or discount of Hyperliquid relative to the CME reference price during active periods. The Hyperliquid mid-price is derived from the volume-weighted average price (VWAP) of trades within each 5-minute trading window, not from real-time order book quotes.

Hyperliquid funding rates are quoted hourly, with the funding rate expressed in basis points per hour.

Weekend session data analysis

This analysis focuses on three different CME weekend trading halts:

• W1: February 28 to March 1, 2026
• W2: March 7 to March 8, 2026
• W3: March 14 to March 15, 2026

In W1 and W2, Hyperliquid perpetual contracts are constrained, so the mark price cannot exceed the “price range boundary” (DB). When oracle prices are frozen (e.g., when the main reference market (CME) is closed and external price data sources stop updating), the protocol effectively limits price discovery to a narrow range.

For each weekend window, we report key metrics for Hyperliquid xyz:CL, including price, traded volume, and number of trades. To measure deviation from the Monday opening spread, for each weekend we measure the price gap between Hyperliquid and CME at three reference points:

• 3 hours before CME reopens
• 1 hour before CME reopens
• At CME open (T=0)

All spreads are expressed in basis points. A positive value means Hyperliquid is above the CME opening price, while a negative value means a discount.

Quantitative analysis

This section first conducts analysis comparing the liquidity conditions of the Hyperliquid xyz:CL and NYMEX CLJ6 crude oil markets during overlapping weekday periods.

Liquidity depth: less than 1% of CME

There is no doubt that on-chain exchanges have liquidity conditions that are fundamentally different from the CME. Hyperliquid’s average liquidity depth for CL is less than 1% of CLJ6, and liquidity depth is consistent across price levels (109x at ±5 bps). In the ±2 bps interval around the midpoint price, CME’s executable depth is $19.0 million, while Hyperliquid’s is only $152k—an 125x difference.

Given Hyperliquid CL market’s novelty and the different target user groups, this result is not surprising. The main value of an on-chain exchange is to provide a permissionless trading channel for users who are traditionally excluded by institutions such as the CME.

However, as weekend trading volume on DEXs like Hyperliquid grows, perceptions of these platforms are starting to shift. Institutional investors’ interest in hedging positions during non-trading hours is increasing. Therefore, for Hyperliquid it is becoming increasingly important to cultivate a market environment suitable for both traditional investors and retail users.

For retail traders with trading notional of $10k, this cost difference is negligible. But for institutional investors with trading notional exceeding $1M, on-chain trading costs for CL (and most other markets) remain difficult to bear.

In fact, these inherent differences in the user base are reflected in the median trade sizes during the overlapping active periods of these markets.

The 166x difference in median trade size (90,450 vs. 543) most clearly proves that the user segments served by these trading venues are fundamentally different. CLJ6’s median trade size is comparable to a standard crude oil futures contract (at current prices, nominal value is about $94k), while Hyperliquid’s median trade size is $543, reflecting leverage-directional bets made by crypto-native retail traders.

We expect that as these markets become increasingly legitimized in the eyes of more traditional investors and capital shifts to on-chain venues, the median trade size in Hyperliquid’s commodities market will reach an inflection point.

To further distinguish between different trade sizes, we ran order simulations with maximum order sizes ranging from $10k to $1M.

For a $10k order, CLJ6 traders experience no slippage, which matches expectations, while Hyperliquid users’ median execution slippage is below 1 basis point—0.77 basis points. The gap shows up at a $100k order size, where Hyperliquid users’ slippage rises to 4.33 basis points, approaching the 5-bps threshold, while CME CLJ6 has no slippage.

Notably, this is higher than CLJ6’s median trade size of $90,450.

At a $1M trade size, Hyperliquid’s 15.4 bps is about 20x CME’s 0.79 bps, confirming that this venue currently does not have the capability to handle institutional-level orders. Given Hyperliquid’s average trade size, the platform could provide equally high-quality service without generating slippage entirely.

Significant slippage on CLJ6 orders starts to appear around trade sizes of roughly $500k, affecting execution.

When we extend the order size analysis to weekends, slippage decreases across all order sizes, especially for $100k and $1M order sizes, indicating that the market has matured. Over the three weeks analyzed, the slippage decline for simulated orders is as follows:

• $10k: -16%
• $100k: -75%
• $1M: -86.9%

Funding rates

CL funding rates fluctuate more during CME’s close-to-close sessions, but fluctuate less during the delivery window. This helps us reveal the market’s internal pricing dynamics during non-trading periods. Weekend openness means the CL market can leverage internal price discovery mechanisms (supported by DB and other risk-reduction mechanisms). Therefore, funding rates are expected to be more volatile, as highlighted in the section below.

During active trading sessions, Hyperliquid’s xyz:CL and CME’s CLJ6 move closely together, but as oil prices rise, a structural discount emerges and widens. This is very likely caused by funding-rate pressure stemming from accumulated long positions. During weekends, with CME closed, Hyperliquid’s price discovery is further constrained by the price-range mechanism (DB). In the absence of a real-time reference market, this mechanism limits the extent of mark price fluctuations.

Weekend session analysis separately: Hyperliquid already has price discovery capability

These three weekends demonstrate the rapid maturation of the Hyperliquid market:

W1: February 28 to March 1, 2026 (Iran attack event)

Prices on Hyperliquid rose from around $67.29 on CME to about $70.80, accounting for about 45% of Monday’s eventual gap up to $75 (+1146 bps).

It is especially important to note that, due to the ±5% price range constraint mechanism (DB) for trade.xyz mentioned above, price discovery this weekend was limited. This explains why the curve in the charts is relatively smooth, and why a Monday gap up occurs. Even so, in the first second after the paired data was released, the gap between Hyperliquid xyz:CL ($73.89) and CME CLJ6 ($75) remained within 1.5%.

This is not a “mistake” or a “failure,” but rather risk protection achieved through market design. Therefore, from a data perspective, the correlation is lowest on the first weekend; it nonetheless shows that xyz:CL responded to the initial impact of the Iranian airstrikes, while also recognizing the importance of DB as a weekend price discovery mechanism—especially for an emerging market.

W2: March 7 to March 8, 2026

The second weekend is the real test, because xyz:CL touched the upper boundary price of the range near the market close. CLJ6’s opening price was $98 (up 737 bps from the $91.27 close), while xyz:CL peaked at about $95.83, capturing only 68% of the upside.

In the second weekend, xyz:CL captured the market move better and was closer to CME’s opening price than the previous weekend.

W3: March 14 to March 15, 2026

The third weekend’s data indicates that, in a comparatively calmer market environment, Hyperliquid can more reliably predict the final direction of CME’s opening**.**

Weekend xyz:CL and CLJ6 show their best convergence this time: up 226 bps versus CME’s close, slightly above 62 bps versus Monday’s opening price. CLJ6’s Friday close was $99.31 and its opening price was $100.93 (+163 bps), while xyz:CL’s opening price was $101.56.

Overall, these three snapshots show structural changes in the xyz:CL market on the Hyperliquid platform: the market transitions from an emerging market constrained by DB price discovery (weekend 1 and weekend 2) to one with progressively freer price discovery, accompanied by overshooting and pullbacks (weekend 3).

When analyzing the price deviation errors of CME opening across different time segments before the opening (3 hours, 1 hour, 0 hours), W3 data is the most reliable. In the first two weekends, the xyz:CL market was influenced by DB. In W3, the xyz:CL errors 3 hours and 1 hour before CME’s open are approximately +70 and -139 bps, indicating that its price discovery capability is better than the earlier weekends analyzed.

Other metrics

We also provide other metrics from the weekend summary analysis, including trading volume, total number of trades, and average trade size. These metrics vary across weekends and have been increasing continuously for several weekends.

Over the three weeks, xyz:CL’s total trading volume grew from $31 million to over $1 billion, reflecting an increase in the number of users and the market’s eventual maturity.

In addition, the total number of trades increased from 26k trades in the first weekend to over 700k trades in the third weekend.

Notably, the average trade size on weekends actually rose from the median mentioned earlier to $534. The same upward trend was observed across all three weekends, which may indicate that more institutional capital is flowing into the market.

The first weekend’s average trade size was $1,199, rising to over $1,500 by the third weekend.

This may suggest that the user base using the platform on weekends is different: fewer retail users and more traders who need to obtain crude oil trading exposure before Monday. Therefore, weekend trading is more aligned with hedging demand rather than speculation.