The post Will Saylor’s MicroStrategy Join the S&P 500? THIS Bitcoin Price Level Will Decide appeared first on Coinpedia Fintech News Michael Saylor’s MicroStrategy, now Strategy, may be days away from a historic Wall Street milestone but the gatekeeper, once again, is Bitcoin. According to financial analyst Jeff Walton, there’s a 91% chance Strategy qualifies for the S&P 500 index by the end of Q2. Yes, of course there’s a catch. Bitcoin must stay above $95,240 until June 30. That leaves just days for the market to hold steady and the odds, as of now, are on Saylor’s side. More on this below! The $95K Cutoff That Could Make or Break Strategy To join the S&P 500, a company needs to show cumulative positive earnings across the last four quarters. That’s where Bitcoin comes in. Strategy holds 592,345 BTC, the largest stash among any public company. Since January, those holdings are marked to market value under the ASU 2023-08 accounting rule, meaning every price move flows straight into net income. Walton breaks it down simply: “If [Bitcoin] drops below that, Strategy… will not have the earnings in Q2 be more than the last three quarters combined.” In other words – a single sharp dip could wipe out their shot. 91% chance of $MSTR qualifying for S&P in 6 days https://t.co/uGkzAuTQ2Y — Jeff Walton (@PunterJeff) June 24, 2025 History Is (Mostly) on Strategy’s Side But how likely is a double-digit crash over six days? Not very, says Walton, who pulled historical data on Bitcoin’s short-term volatility since 2014. Out of all six-day periods since then, BTC has dropped more than 10% only 8.7% of the time. That means 91.3% of the time, it holds the line – roughly matching Walton’s forecast for Strategy’s odds. Those chances only improve by the day. Over five days: 92.4% chance of no 10% drop. With just one day left? 97.6%. Geopolitics, Volatility, and a Race to the Finish Still, it’s not smooth sailing. Over the weekend, rising tensions between Iran and Israel briefly pushed BTC below $100K – the lowest since early May. At press time, it’s bounced back to around $106,200. The point here is that this is crypto and anything can still happen, so don’t pull out your party hats just yet. Another Big Win for Crypto? If Strategy makes it in, it would become the second crypto-linked firm to join the S&P 500 this year, following Coinbase in May. For the crypto industry, it’s another step toward mainstream acceptance. “It cements the legitimacy of an entire asset class,” said Meryem Habibi, Chief Revenue Officer at Bitpace. Strategy is already in the Nasdaq-100 as of late 2024. An S&P 500 entry would take that credibility one level higher and once again prove that Saylor’s Bitcoin gamble is truly paying off. The countdown is on.
The cryptocurrency market is currently experiencing heightened volatility, particularly with Bitcoin (BTC) fluctuating dramatically. Recently, the price dipped below $99,000 before rebounding to over $106,000 within a span of just 24 hours. Bullish Bitcoin Setup Amid Geopolitical Tensions In a recent post on social media platform X (formerly Twitter), analyst Cyclop suggested that despite the current market conditions, BTC exhibits a bullish setup reminiscent of the patterns seen in March 2020. The analyst noted that Bitcoin appears to be mirroring its past movements, with a brief dip followed by a rally for both BTC and altcoins. Cyclop drew parallels between the ongoing geopolitical tensions involving Israel, Iran, and the US and the market dynamics observed during the COVID-19 crash. Related Reading: Dogecoin About To Explode? ‘Don’t Send It Too Hard,’ Analyst Warns While acknowledging that geopolitical strife and global market panic are distinct issues, he pointed out that both scenarios resulted in sharp but temporary sell-offs followed by swift recoveries. According to Cyclop, the current market setup displays similar characteristics: widespread fear, a risk-off sentiment among investors, and global uncertainty. He emphasized the importance of understanding the timing of resolution to these tensions, suggesting that for a robust rally, several bullish catalysts are necessary to alleviate market uncertainty. He identified three key factors: potential interest rate cuts, a ceasefire between Iran and Israel, and Bitcoin holding crucial support levels. $120,000 By Year-End? Recently, a ceasefire was declared following 12 days of intense conflict between Iran and Israel. In a notable public statement, President Donald Trump criticized both nations, suggesting that their actions were misguided. This period of relative calm is seen as a positive indicator for the market. Cyclop highlighted that maintaining the $100,000 level for Bitcoin was crucial, and the cryptocurrency has successfully broken through the $106,000 barrier, signaling further growth. Furthermore, Ethereum (ETH) has also shown signs of a quick recovery alongside Bitcoin with its price nearing the key $2,500 level. Cyclop advised investors not to attempt to time the market perfectly, as reversals can often feel unsettling and uncertain. Related Reading: Ethereum Bounces Hard After Support Bluff, A False Alarm Or Fresh Rally? Looking ahead, Cyclop anticipates a consolidation phase for Bitcoin within the $102,000 to $106,000 range, with expectations of a breakout that could push BTC to an all-time high of around $120,000 by November or December of this year. As of this writing, Bitcoin is trading at $106,500 per coin. Despite ongoing economic uncertainties, the market’s leading cryptocurrency has seen a 75% increase year-to-date. However, Bitcoin is still trading nearly 5% below its record high of $111,800, which was reached on May 23. The most important resistance level is $110,200, which has prevented a new price discovery phase for Bitcoin on two occasions. Featured image from DALL-E, chart from TradingView.com
BitcoinWorld Multimodal AI Data: How Eventual’s Daft Unlocks the Future of Unstructured Processing In the rapidly evolving landscape of artificial intelligence, where data is the new gold, a crucial challenge has emerged: effectively managing and processing the vast oceans of unstructured data that power advanced AI applications . For those immersed in the world of blockchain and decentralized technologies, understanding the foundational infrastructure that drives innovation is key. This is where the compelling story of Eventual, and its groundbreaking open-source engine Daft, begins – born from a real-world dilemma at Lyft’s autonomous vehicle program, a problem that mirrors the complexities faced across many data-intensive industries, including those leveraging decentralized data solutions. The Genesis of a Data Revolution: Understanding the Unstructured Data Challenge The journey of Eventual’s Daft engine began not in a venture capital boardroom, but in the trenches of Lyft’s ambitious autonomous vehicle program. Founders Sammy Sidhu and Jay Chia, then software engineers at Lyft, encountered a significant hurdle: how to efficiently process the colossal and diverse streams of data generated by self-driving cars. Imagine the sheer volume and variety: 3D scans mapping environments, high-resolution photos, sensor data, spoken commands, and even text logs. This is not your typical neatly organized spreadsheet data; this is raw, complex, and highly varied unstructured data . The problem was clear: there was no single, unified tool capable of understanding and processing all these different types of data simultaneously and in one centralized location. Engineers were forced into a painstaking, often unreliable process of cobbling together various open-source tools. This fragmented approach led to significant inefficiencies. Sidhu, now Eventual’s CEO, recalled a frustrating reality: “We had all these brilliant PhDs, brilliant folks across the industry, working on autonomous vehicles but they’re spending like 80% of their time working on infrastructure rather than building their core application.” This substantial time sink on infrastructure, particularly around data management, highlighted a critical gap in the existing toolset. This challenge wasn’t unique to Lyft; it was a systemic issue waiting to be addressed, especially with the impending explosion of AI. The need for a robust data processing solution that could handle multimodal AI data – information from multiple sources like text, images, audio, and video – was becoming increasingly apparent. Eventual Daft: A Python-Native Solution for Multimodal AI Recognizing the profound nature of this problem, Sidhu and Chia initially helped develop an internal multimodal data processing tool for Lyft. The true validation of their idea came when Sidhu explored other career opportunities. Consistently, interviewers would inquire about the possibility of building a similar data solution for their own companies. This widespread demand solidified the concept for Eventual. Eventual’s core offering is Daft, a Python-native open-source data processing engine. Daft is meticulously designed for speed and efficiency across various modalities, from text and audio to video and beyond. Sidhu articulates an ambitious goal for Daft: to achieve a similar transformative impact on unstructured data infrastructure as SQL had on tabular datasets in the past. This vision speaks to a fundamental shift in how organizations will interact with and leverage their most complex data assets. The company’s foresight is particularly striking. Eventual was founded in early 2022, nearly a year before ChatGPT’s public release truly ignited global awareness of the vast potential and inherent data infrastructure gaps in generative AI. The initial launch of Daft’s open-source version in 2022 was a precursor to the massive surge in demand. As Sidhu noted, “The explosion of ChatGPT, what we saw is just a lot of other folks who are then building AI applications with different types of modalities. Then everyone started kind of like using things like images and documents and videos in their applications. And that’s kind of where we saw, usage just increased dramatically.” This surge unequivocally validated Eventual’s early bet on multimodal data processing. To illustrate the stark contrast, consider the shift Daft enables: Feature Before Eventual Daft (Typical Legacy Approach) With Eventual Daft (Modern Multimodal Processing) Data Types Handled Often siloed; requires separate tools for text, images, audio. Unified processing for text, audio, video, 3D scans, etc. Processing Method Manual integration of disparate open-source libraries; custom scripts. Python-native, purpose-built engine for speed and scale. Engineer Focus Up to 80% time on infrastructure setup and maintenance. Primary focus on building core AI applications and innovation. Reliability & Speed Prone to breakage, slow due to integration overhead. High reliability, designed for rapid, efficient processing. Scalability Challenging to scale efficiently with increasing data volume/variety. Built for enterprise-grade scalability across diverse modalities. Why Multimodal AI is Driving Unprecedented Data Demand The rise of multimodal AI is not just a technological trend; it’s a fundamental shift in how AI interacts with and understands the world. Humans perceive reality through multiple senses – sight, sound, touch, taste, smell – and the next generation of AI aims to mimic this comprehensive understanding. This ambition inherently demands the ability to process data from various modalities simultaneously. The statistics underscore the urgency of this need. According to management consulting firm MarketsandMarkets, the multimodal AI industry is projected to grow at a staggering 35% compound annual growth rate (CAGR) between 2023 and 2028. This explosive growth signifies a burgeoning market ripe for innovative infrastructure solutions. The sheer volume of data being generated is equally astounding: “Annual data generation is up 1,000x over the past 20 years and 90% of the world’s data was generated in the past two years,” explained Astasia Myers, a general partner at Felicis. She further emphasized, “according to IDC, the vast majority of data is unstructured.” This confluence of rapid data generation and the increasing dominance of unstructured formats creates an undeniable imperative for advanced data processing engines like Daft. Myers aptly summarizes the situation: “Daft fits into this huge macro trend of generative AI being built around text, image, video, and voice. You need a multimodal-native data processing engine.” Without such an engine, the promise of truly intelligent, versatile AI applications remains bottlenecked by the inability to efficiently ingest, clean, and transform the very data that fuels them. Powering AI Applications Across Diverse Industries While the initial spark for Eventual’s innovation came from the demanding autonomous vehicle sector, the utility of efficient multimodal data processing extends far beyond self-driving cars. Numerous other industries grapple with similar challenges of managing and extracting value from complex, unstructured datasets. Consider the robotics industry, where robots need to process visual input, audio commands, and sensor data to navigate and interact with their environments. Retail technology relies on understanding customer behavior through video analytics, text reviews, and audio interactions. Healthcare is another prime example, dealing with medical images (X-rays, MRIs), patient notes, audio recordings of consultations, and even biometric data. Each of these sectors, and many more, stands to benefit immensely from a unified, high-performance engine for unstructured data . Eventual’s growing customer base reflects this broad applicability, with notable names like Amazon, CloudKitchens, and Together AI already leveraging their solution. These companies represent diverse use cases, from e-commerce giants processing vast amounts of product imagery and customer feedback to food tech innovators managing complex logistics and AI model developers building the next generation of intelligent systems. The ability to streamline their data processing pipelines empowers them to build smarter, scale faster, and connect deeper with their data. Fueling Growth: Eventual’s Strategic Fundraising and Future Vision The rapid validation of Eventual’s solution in the market has translated into significant investor confidence. The company successfully closed two rounds of funding within an impressive eight-month period. The first was a $7.5 million seed round led by CRV, providing the initial capital to scale their operations and further develop Daft. More recently, Eventual secured a substantial $20 million Series A round, led by Felicis, with key participation from Microsoft’s M12 venture fund and Citi. This latest infusion of capital is earmarked for crucial strategic initiatives. A significant portion will go towards bulking up Eventual’s open-source offering, ensuring that the Daft community continues to thrive and innovate. Simultaneously, the company is gearing up to launch an enterprise product in the third quarter. This commercial offering will provide advanced features and support, enabling customers to build sophisticated AI applications directly off their processed multimodal data, moving from raw information to actionable intelligence seamlessly. Astasia Myers of Felicis shared her perspective on why Eventual stood out in a competitive landscape. She discovered the company through a market mapping exercise specifically aimed at identifying data infrastructure capable of supporting the burgeoning number of multimodal AI models. Eventual’s unique position as a first mover in this space, coupled with the founders’ direct, firsthand experience with the data processing problem at Lyft, made them a compelling investment. Myers emphasized that Eventual is not just solving a niche problem but addressing a fundamental and rapidly expanding need within the AI ecosystem. The strategic investments from industry giants like Microsoft’s M12 further underscore the perceived importance and potential of Eventual’s technology in shaping the future of AI infrastructure. Navigating the Future of Unstructured Data Processing The path forward for unstructured data processing, especially in the context of advanced AI, is dynamic and promises to be increasingly crowded. However, Eventual’s early entry and the robust design of Daft position them strongly. Their commitment to an open-source core, combined with a forthcoming enterprise product, creates a powerful dual strategy, appealing to both the developer community and large-scale commercial entities. The fundamental benefit Eventual brings is liberating highly skilled engineers and data scientists from the tedious and complex task of infrastructure plumbing. By providing a reliable, efficient, and unified engine for multimodal data, Eventual allows these brilliant minds to dedicate their time to what they do best: building groundbreaking AI applications . This shift from infrastructure maintenance to core innovation is a game-changer for any organization aiming to leverage the full potential of modern AI. For businesses grappling with mounting volumes of diverse data, Eventual offers actionable insights. Instead of patching together disparate tools and spending valuable resources on data wrangling, companies can adopt a purpose-built solution designed for the complexities of multimodal information. This not only enhances efficiency and reliability but also accelerates the development cycle for AI-powered products and services. The future of AI hinges on effective data management, and Eventual’s Daft is poised to be a pivotal player in that evolution. Eventual’s journey, from a pressing problem at Lyft to a leading solution in the burgeoning multimodal AI space, is a testament to the power of addressing real-world pain points with innovative technology. Their Daft engine represents a significant leap forward in data processing for unstructured data , empowering a new generation of AI applications across diverse industries. As the world continues to generate vast amounts of complex data, Eventual stands ready to provide the crucial infrastructure needed to transform this data into actionable intelligence, unlocking unprecedented possibilities for AI’s future. To learn more about the latest AI market trends, explore our article on key developments shaping AI models features, institutional adoption. This post Multimodal AI Data: How Eventual’s Daft Unlocks the Future of Unstructured Processing first appeared on BitcoinWorld and is written by Editorial Team
Japan's FSA noted that there were more than 12 million domestic crypto accounts as of January 2025, holding assets worth $34 billion.
Done.AI, a publicly listed firm, has announced a strategic expansion by incorporating blockchain infrastructure assessment into its AI-driven financial platform. This move underscores the company’s commitment to enhancing its technological
On-chain arbitrage bots are useful, but not as safe as you'd expect them to be
Ground-Breaking Presale Opportunity Anso Finance’s presale is generating significant interest, offering tokens at a fraction of their potential value. Stage 1 price: $0.00355, with an expected listing price of $0.03. Early investors can earn 10x returns just by participating in the presale. In addition, staking rewards are set to be highly attractive, with early-bird staking bonuses for the first three months, including dynamic APYs reaching up to 50% for committed investors. Anso’s tiered staking program ensures long-term value for those who lock up their tokens. Real-World Utility: Not Just a Speculative Asset Unlike speculative assets, Anso Finance is rooted in real-world applications. The upcoming Anso Card will enable seamless spending of $ANSO tokens for everyday purchases through Apple Pay and Google Pay, offering a real use-case beyond just holding a token. Anso’s ecosystem also includes crypto-backed loans, savings (staking), and asset tokenization, allowing users to invest in real estate and luxury goods by owning tokenized shares of these assets. This Real-World Asset (RWA) integration makes Anso a true utility coin, unlike XRP, which serves primarily as a payment bridge for financial institutions and Solana, which is more focused on high-speed DeFi transactions but lacks a fully integrated, user-focused ecosystem. Anso, on the other hand, allows everyday users to earn rewards, make payments, and invest in real-world assets – all within one ecosystem. Security and Transparency Anso Finance is built with investor protection as a priority. Key security measures include locked liquidity to prevent rug pulls, an immutable smart contract ensuring no changes to tokenomics, and multi-signature governance for fund control. These efforts ensure a transparent, secure platform that builds trust within its community. With open-source code and third-party audits, Anso guarantees that no hidden surprises will undermine its growth. XRP and Solana: Great, but Anso Has a Unique Advantage XRP is a payment-focused network with limited adoption for retail users. While it excels in cross-border payments, it doesn’t provide significant opportunities for everyday use or passive income like Anso does with staking and real-world applications. Solana, although renowned for high throughput and low fees, is mainly focused on the tech infrastructure. Anso, built on Solana, leverages its speed and scalability but adds a user-driven ecosystem designed for real-world financial services payments, staking, and tokenized assets creating a deeper value proposition. Anso also offers significant early-stage growth potential: while XRP and Solana have already reached substantial market caps, Anso remains a micro-cap poised to capture the attention of investors as it develops its platform. Conclusion: A Bullish Outlook for Anso Finance Anso Finance isn’t just another crypto project. It’s a comprehensive financial ecosystem built for real-world utility, security, and long-term growth. With a promising presale, strong staking rewards, and groundbreaking real-world applications such as the Anso Card and RWA integration, Anso is positioned to outperform giants like XRP and Solana in the coming years. Retail investors looking for a utility-driven, secure, and high-growth cryptocurrency should pay attention to Anso Finance as it emerges as a major player in the next crypto bull run. Links & Contacts: Https://ansofinance.com Telegram: @Ansofinance X: Https://x.com/ansofinance Documentation: Docs.ansofinance.com Disclaimer: This is a sponsored press release and is for informational purposes only. It does not reflect the views of Crypto Daily, nor is it intended to be used as legal, tax, investment, or financial advice.
BitcoinWorld Google Gemini Robotics: How On-Device AI Revolutionizes Autonomous Robot Control In the rapidly evolving world of technology, where decentralization meets cutting-edge innovation, the intersection of AI and robotics is creating unprecedented possibilities. While much of the AI conversation revolves around cloud-based models, a quiet revolution is brewing at the edge. Imagine a future where robots operate with unparalleled autonomy, making decisions and executing complex tasks without a constant internet connection. This isn’t science fiction anymore; it’s the groundbreaking reality ushered in by Google DeepMind’s latest advancement: the Google Gemini Robotics On-Device model. For those tracking the pulse of innovation, from blockchain breakthroughs to AI marvels, this development signals a pivotal shift in how we conceive and interact with intelligent machines. The Dawn of Autonomous AI: Google Gemini Robotics Takes Center Stage Google DeepMind, a pioneer in artificial intelligence research, has once again pushed the boundaries with its new Google Gemini Robotics On-Device model. Building upon the foundational Gemini Robotics model released earlier this year, this iteration marks a significant leap forward by enabling AI to run directly on robotic hardware. This local execution capability is a game-changer, removing the dependency on cloud servers and ushering in an era of truly autonomous robotic operations. The implications are vast, impacting everything from industrial automation to personal assistance robots, making them more reliable, responsive, and secure. This innovation highlights Google’s commitment to advancing practical AI applications that can function independently of constant network access. Unleashing Potential: What is Google Gemini Robotics On-Device? So, what exactly makes Google Gemini Robotics On-Device so remarkable? At its core, this new language model empowers robots to perform intricate tasks locally. This means the robot itself processes information and executes commands, significantly reducing latency and enhancing operational reliability, especially in environments with limited or no internet connectivity. Developers can interact with and fine-tune the model using natural language prompts, simplifying the programming process and opening up new avenues for customization. Google’s benchmarks indicate that this On-Device AI model performs at a level comparable to its cloud-based predecessor, a testament to its efficiency and power. Furthermore, it reportedly outperforms other on-device models in general benchmarks, solidifying its position as a frontrunner in the field. This capability allows robots to learn and adapt more quickly, responding to dynamic environments with greater agility and precision. How Does On-Device AI Transform Robot Control? The true power of On-Device AI lies in its ability to fundamentally transform robot control . By integrating the AI model directly onto the robot, decision-making becomes instantaneous. Consider the practical benefits: Reduced Latency: Commands are processed milliseconds faster, crucial for precision tasks and dynamic environments where immediate reactions are vital. Enhanced Privacy and Security: Data processing occurs locally, minimizing the need to send sensitive information to the cloud, which is particularly important for sensitive applications. Offline Capability: Robots can operate reliably in remote locations or during network outages, ensuring continuous productivity and mission completion. Adaptive Learning: The model can be fine-tuned on the fly using natural language, allowing for rapid adaptation to new tasks or environmental changes without extensive reprogramming. Google DeepMind demonstrated these capabilities with robots successfully performing complex actions like unzipping bags and folding clothes. While initially trained for ALOHA robots, the model’s adaptability was showcased by its successful implementation on a bi-arm Franka FR3 robot and Apptronik’s Apollo humanoid robot. The Franka FR3, for instance, tackled novel scenarios and objects, including industrial assembly tasks on a moving belt, proving the model’s robust generalization abilities. This level of autonomous control opens doors for robots to take on increasingly complex and varied roles in our daily lives and industries. Real-World Applications and the Future of AI in Robotics The implications of advanced AI in Robotics are profound, extending far beyond the factory floor. With models like Gemini Robotics On-Device, we are witnessing the dawn of a new era for automated systems. Imagine: Logistics and Warehousing: Robots autonomously managing inventory, sorting packages, and navigating complex warehouse layouts without constant human oversight or internet dependency, leading to unprecedented efficiency. Healthcare: Robotic assistants performing delicate tasks in operating rooms or aiding patients in their homes, with critical data processed securely on-device, enhancing patient care and safety. Disaster Response: Robots deployed in hazardous environments, making real-time decisions to navigate debris, search for survivors, or handle dangerous materials, even in communication-blackout zones, protecting human lives. Personal Robotics: More intelligent and responsive home robots capable of performing a wider range of chores and interactions, adapting to unique household needs and preferences. To further empower developers, Google DeepMind is also releasing a Gemini Robotics SDK. This Software Development Kit allows developers to train robots on new tasks by showing them 50 to 100 demonstrations within the MuJoCo physics simulator. This democratizes robot training, enabling rapid prototyping and deployment of new robotic functionalities across various sectors. The Broader Landscape: DeepMind AI and the Robotics Ecosystem Google’s advancements are part of a larger, exciting trend within the tech industry, highlighting the growing convergence of advanced DeepMind AI and robotics. Several other major players and innovative startups are also making significant strides in this domain: Nvidia: Actively building a platform designed to create foundational models specifically for humanoid robots, emphasizing simulation and real-world transfer learning. Hugging Face: Known for its open-source contributions in natural language processing, Hugging Face is expanding into robotics by developing open models and datasets, and even working on physical robots, fostering a collaborative development environment. RLWRLD (Mirae Asset-backed Korean startup): Focused on creating foundational models for robots, aiming to provide a universal AI layer for various robotic applications, driving standardization and accessibility. This competitive yet collaborative environment is accelerating innovation, pushing the boundaries of what robots can achieve. The drive towards more intelligent, autonomous, and adaptable robots is a shared vision, and Google DeepMind’s Gemini Robotics On-Device is a significant milestone on this journey. As these technologies mature, they promise to redefine industries, enhance human capabilities, and create a future where intelligent machines seamlessly integrate into our lives. The release of Google DeepMind’s Gemini Robotics On-Device model represents a monumental leap forward for AI in robotics. By enabling powerful language models to run locally on robots, Google is not just improving efficiency; it’s fundamentally reshaping the potential for autonomous systems. From enhanced privacy and security to unparalleled reliability in disconnected environments, the benefits are clear. As developers gain access to tools like the Gemini Robotics SDK, we can expect an explosion of innovation in robot capabilities. This groundbreaking development, alongside the efforts of other industry leaders, is propelling us towards a future where intelligent robots are not only more capable but also more accessible and integrated into the fabric of our society. The era of truly smart, self-sufficient robots is here, promising to transform our world in profound and exciting ways. To learn more about the latest AI market trends, explore our article on key developments shaping AI Models features. This post Google Gemini Robotics: How On-Device AI Revolutionizes Autonomous Robot Control first appeared on BitcoinWorld and is written by Editorial Team
XRP’s holder ratio experienced a notable decline from 5% to 2.42% in the first half of 2025, yet it continues to outperform Solana’s 1.76% holder ratio as of May 2025.
Cryptocurrency exchange OKX has announced that it will remove three different perpetual futures contracts from its platform on June 27, 2025, in an effort to provide its users with a safer and more stable trading experience. OKX to Shut Down Perpetual Futures Contracts on Three Crypto Assets on June 27 This decision is based on reasons such as low liquidity and increased market risk. Futures Contracts to be Removed The removal will take place as of 16:00 on June 27, 2025 and will cover the following contracts: SANDUSD Perpetual Futures Contract ALGOUSD Perpetual Futures Contract TONUSD Perpetual Futures Contract During the removal process: All open transactions will be closed automatically by the system. All open orders on the order board will be canceled. Contracts will be delivered using the arithmetic average of OKX index prices in the last hour before closing. If price manipulation is detected during this one-hour period, OKX stated that it may readjust the delivery price to reasonable levels. No additional fees or funding charges will be charged at the time of delivery. Additionally, users with open positions over 10,000 USDT at the time of delivery will not be able to transfer assets between accounts for 30 minutes. This restriction was introduced to ensure that transactions are completed safely. Recommendations and Additional Information for Users OKX warned users with leveraged positions to close positions or reduce leverage in advance. It was stated that risk control should be carried out against such sudden price movements. All historical order and transaction records for the removed contracts will later be available for download via the “Order Center” on the desktop version. OKX stated that it may also change the limit price rules according to market conditions. The company emphasized that it will continue to make continuous improvements in order to provide better service to its users. *This is not investment advice. Continue Reading: Bitcoin Exchange OKX Announces It Will Delist Three Altcoins From Futures! Here Are the Details