Dogecoin And XRP Open Interest Crash To 2024 Levels, Here Are The Figures

bitcoinistPublicado a 2026-02-26Actualizado a 2026-02-26

Resumen

According to Coinglass data, open interest for Dogecoin and XRP has crashed to levels last seen in late 2024. Dogecoin's open interest has fallen below $1 billion, a threshold it had consistently held above since late 2024, and is now valued at $992.65 million. This decline is attributed to a loss of major price support and reflects a 3.11% drop in the last 24 hours, with significant deleveraging on exchanges like BingX (-24.75%). Similarly, XRP's open interest has returned to late November 2024 levels, now standing at $2.27 billion after a 0.61% daily decrease. The Chicago Mercantile Exchange (CME) holds the largest share of XRP open contracts. The overall crash in derivatives markets for both assets is a result of slower capital inflows and extended outflows across the crypto market, effectively erasing over a year of position buildup.

Open interest in the derivatives markets for Dogecoin and XRP has fallen back to levels last seen in 2024, according to data from Coinglass. Slower capital inflows into the broader crypto market and extended outflows have weighed on the price action of these cryptocurrencies, and the impact is now visible in their futures markets, where investor positioning has been scaled back.

Dogecoin’s open interest, for one, is now below $1 billion, while XRP’s figure is now back to late November 2024 territory, effectively erasing over a year of position buildup in the futures market.

Dogecoin Open Interest Falls Below $1 Billion

Data from Coinglass shows that Dogecoin’s total open interest currently stands at 10.63 billion DOGE across multiple exchanges. Based on the current price action of Dogecoin, this total open interest is valued at $992.65 million.

Interestingly, this is a return of Dogecoin’s open interest to sub-$1 billion levels in USD terms, something not seen since October 2024. Since late 2024, Dogecoin’s open interest has consistently held above the $1 billion mark, even during periods of price consolidation. However, this is not the case anymore in February 2026. Most of this can be attributed to the fact that Dogecoin has lost major price support levels since the beginning of 2026.

Source: Chart from Coinglass

A breakdown of exchange data shows that Binance holds 2.09 billion DOGE in open interest, worth approximately $195 million, accounting for 19.64% of the total. Gate leads in USD terms with about $228.99 million in open positions, representing 23.06% of the market share. OKX follows with $99.74 million, while Bybit holds $86.52 million.

In the past 24 hours, the total Dogecoin open interest across exchanges is down 3.11%, reflecting continued deleveraging. Some exchanges have seen more declines, with Gate down 13.83% and BingX down 24.75% over the same period.

XRP Open Interest Returns To Late November 2024 Levels

XRP’s open interest has also suffered the same fate as Dogecoin, with total open contracts now standing at 1.65 billion XRP, valued at $2.27 billion. This brings XRP’s derivatives exposure back to levels last seen in late November 2024, when the XRP open interest was hovering just below $2.5 billion.

On a 24-hour basis, total XRP open interest is down 0.61%. The Chicago Mercantile Exchange (CME) currently leads with 378.89 million XRP in open contracts, valued at $519.11 million. Binance comes second with 339.57 million XRP worth $465.17 million, accounting for 20.52% of open interest.

Other notable positions include Bybit with $225.82 million and Gate with $200.67 million in open contracts. However, some exchanges have seen sharp daily declines, including Gate, which is down 17.24% over the past 24 hours, and BingX, which is down 31.19%.

DOGE trading at $0.09 on the 1D chart | Source: DOGEUSDT on Tradingview.com

Preguntas relacionadas

QAccording to the article, what has happened to the open interest for Dogecoin and XRP in the derivatives markets?

AThe open interest for both Dogecoin and XRP has crashed to levels last seen in 2024.

QWhat is the current value of Dogecoin's total open interest in USD, and what significant threshold has it fallen below?

ADogecoin's total open interest is valued at $992.65 million, which is below the $1 billion threshold for the first time since October 2024.

QWhich exchange holds the largest share of XRP open interest in terms of value, and how much is it worth?

AThe Chicago Mercantile Exchange (CME) holds the largest share of XRP open interest, valued at $519.11 million.

QWhat is cited as a major reason for the decline in Dogecoin's open interest and price action since the beginning of 2026?

AA major reason is that Dogecoin has lost major price support levels since the beginning of 2026.

QHow much has the total XRP open interest changed in the last 24 hours, and which exchanges saw the sharpest daily declines?

AThe total XRP open interest is down 0.61% in the last 24 hours. Gate saw a decline of 17.24% and BingX saw a sharp decline of 31.19%.

Lecturas Relacionadas

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbitHace 1 hora(s)

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbitHace 1 hora(s)

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbitHace 1 hora(s)

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbitHace 1 hora(s)

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbitHace 1 hora(s)

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbitHace 1 hora(s)

Trading

Spot
活动图片