加密货币市场周度赢家与输家 – M、ZEC、STORY、JUP

ambcryptoPublished on 2025-12-14Last updated on 2025-12-14

Abstract

本周加密货币市场经历剧烈波动。比特币(BTC)小幅上涨,以太坊(ETH)反弹强劲,或预示山寨币季节临近。MemeCore(M)以42%涨幅领涨,周线转阳但面临2美元关键阻力;Merlin Chain(MERL)涨33%,需突破0.5美元阻力位延续涨势;Zcash(ZEC)仍在探底。下跌方面,Story(IP)跌10%,周线七连阴;Jupiter(JUP)跌9.17%,恐跌破0.2美元支撑;The Graph(GRT)跌9%,但底部支撑相对稳固。其他如LGTC、AIOT等跌幅超50%。市场整体呈现高波动性,需谨慎操作。

本周加密货币市场考验了投资者的耐心。

尽管利率下调,比特币 [BTC] 仍表现稳健。以太坊 [ETH] 展现出更强劲的反弹,暗示山寨币季节的早期迹象。在此背景下,少数山寨币以两位数的巨大涨幅领跑榜单。

周度赢家

MemeCore [M] — 以 meme 为重点的 L1 强势反弹领涨

MemeCore [M] 以 42% 的涨幅位居周度榜首。其结构显示出早期的 FOMO(错失恐惧症)信号,M 在经历了连续三周下跌和残酷的 60% 回撤后,首次收出周线阳烛。

放大来看,这波上涨将价格推回至 11 月底的区间,这自然引发了关于后续走势的疑问。近 50% 的周涨幅可能显得沉重,尤其是在动能冷却的情况下。

话虽如此,技术指标尚未过度拉伸。周线 RSI 位于 60 附近,表明这波上涨尚未进入超买区域。如果动能得以维持,M 可能准备向 2 美元水平回推。

然而,空头正在悄然回归。在日内下跌 6.97% 后,卖家似乎正大力施压于 1.90 美元至 2.00 美元的上方供应区,这是自 11 月底以来 M 未能攻克的关键阻力位。

如果多头处理得当并守住防线,空头挤压(short squeeze)可能再次成为可能,清晰的 2 美元突破近在眼前。如若不然,此处的拒绝很可能将价格回落以重新测试下方支撑,因为动能消退。

Merlin Chain [MERL] — 比特币 L2 需突破阻力位以延续涨势

Merlin Chain [MERL] 是周度第二大赢家,从 0.35 美元开盘价飙升 33%。在经历了八周的横盘整理后,周线图显示出一个教科书式的整理后突破形态。

支撑这一走势的是,链上数据看起来相当稳健。正如 AMBCrypto 所指出的,MERL 的两位数涨幅得到了不断增长的 HODLer(长期持有者)基础的支持,显示出早期的 FOMO 信号。在这种形态下,动能看起来仍然具有建设性。

话虽如此,0.50 美元的阻力位是需要关注的关键水平。

干净利落地突破该水平对于涨势的延续至关重要。随着基本面因素的配合,在阻力位附近出现空头陷阱(bear trap)的可能性越来越大。总体而言,如果多头跟进,MERL 将成为一个强劲的短期动量交易标的。

Zcash [ZEC] — 隐私代币仍在寻底中

周度输家

Story [IP] — Layer-1 代币抹去了其之前所有的周度涨幅

Story [IP] 领跌本周输家榜单,下跌 10%。在风险规避的市场环境中,这种跌幅通常看起来尚可管理,尤其是在 L1 公链普遍面临链上资金流枯竭的情况下。

但 IP 的图表讲述了一个更为严峻的故事。周线结构坚定看跌,自 10 月中旬跌破 6 美元以来,已连续收出第七根阴烛,显示出明显的投降迹象。

由此看来,下探 1 美元水平是非常有可能的。

从技术角度来看,RSI 位于 38 左右,尚未进入深度超卖区域,这表明仍有下行空间。在空头掌控局面的情况下,目前很难看到多头能组织起有效的防御。

Jupiter [JUP] — 基于 Solana 的 DEX 未能守住关键支撑位

Jupiter [JUP] 位列周度第二大输家,从 0.22 美元开盘价下跌 9.17%。与 IP 类似,JUP 正经历持续的资金外流,目前较其 11 月底 0.44 美元的高点下跌了约 50%。

那么,卖压缓解了吗?并非如此。周线图仍显示由空头控制的结构,多头屡次未能守住关键支撑——这表明卖家仍在持续施压。

即使在 11 月底出现短暂的 4% 反弹后,多头也未能将 0.25 美元转化为支撑位。那次拒绝将价格推低,在这种形态下,如果空头继续掌控,JUP 接下来仍面临跌破 0.20 美元的高风险。

The Graph [GRT] — 数据协议显示空头明显控盘

The Graph [GRT] 在周度输家中排名第三,下跌 9%。虽然 GRT 和 JUP 都承受着看跌压力并难以守住支撑,但 GRT 的图表看起来仍稍具建设性。

自第三季度以来,多头已进行了两次明确的筑底尝试。第一次在 0.08 美元附近,第二次在 0.07 美元附近。每次空头都将价格打压下去,将 GRT 拖入更深的回调。

话虽如此,与 JUP 不同,买家仍在逢低防守,这给了 GRT 微弱的优势。如果这种情况持续下去,接下来可能是更沉重的吸筹阶段。目前,成交量是需要关注的关键指标。

其他显著输家

在更广泛的市场中,下行波动性打击严重。

Legacy Token [LGTC] 以暴跌 66% 领跌输家榜,其次是 OKZOO [AIOT] 下跌 64%,Pieverse [PIEVERSE] 随着动能急剧冷却而下跌 52%。

结论

本周市场如坐过山车。大涨、急跌、动作不停。一如既往,保持敏锐,做好自己的研究,并聪明地进行交易。


最终思考

  • MemeCore [M]、Merlin Chain [MERL]、Zcash [ZEC] 本周涨幅领先。
  • Story [IP]、Jupiter [JUP]、The Graph [GRT] 出现显著下跌。

Trending Cryptos

Related Questions

Q本周加密货币市场中,涨幅最大的代币是哪个?

AMemeCore [M] 以42%的涨幅位居本周涨幅榜首。

QMerlin Chain [MERL] 需要突破哪个关键阻力位才能延续涨势?

AMerlin Chain [MERL] 需要突破0.50美元的关键阻力位才能延续涨势。

Q本周跌幅最大的三个主要代币是哪些?

A本周跌幅最大的三个主要代币是 Story [IP]、Jupiter [JUP] 和 The Graph [GRT]。

Q根据文章,哪个隐私币仍在寻找底部?

AZcash [ZEC] 仍在寻找底部。

Q文章中提到比特币和以太坊本周表现如何?

A尽管有降息,比特币(BTC)表现尚可,而以太坊(ETH)显示出更强的复苏迹象,暗示了山寨币季节的早期信号。

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Agent S: The Future of Autonomous Interaction in Web3 Introduction In the ever-evolving landscape of Web3 and cryptocurrency, innovations are constantly redefining how individuals interact with digital platforms. One such pioneering project, Agent S, promises to revolutionise human-computer interaction through its open agentic framework. By paving the way for autonomous interactions, Agent S aims to simplify complex tasks, offering transformative applications in artificial intelligence (AI). This detailed exploration will delve into the project's intricacies, its unique features, and the implications for the cryptocurrency domain. What is Agent S? Agent S stands as a groundbreaking open agentic framework, specifically designed to tackle three fundamental challenges in the automation of computer tasks: Acquiring Domain-Specific Knowledge: The framework intelligently learns from various external knowledge sources and internal experiences. This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

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What is AGENT S

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