5亿ETF资金出逃VS 5900万现货扫货,ETH多空博弈指向关键支撑

ambcryptoPublished on 2025-11-24Last updated on 2025-11-24

Abstract

但如果跌破这个价位……ETF资金流出、结构恶化以及价格下方巨大的缺口都会迅速产生影响。下方几乎没有支撑,只有一片空白。

以太坊最近的下跌并没有吓跑大钱包。恰恰相反,它反而吸引了更多大钱包!

一个与 Bitmine 有关联的地址购买了数百万美元的以太坊 [ETH],尽管分析师警告称,该资产目前正处于其最后一个主要支撑位,之后将面临一个陡峭的空穴。

还有 Tom Lee,他的估值模型现在显示 ETH 的价值可能在 12,000 美元到 62,500 美元之间——这个范围如此之大,感觉他好像想让每个人都满意。

接下来发生的事情不仅会考验以太坊的价格水平,还会考验它的信徒。

这些数字引发了热议。

Tom Lee公布了以太坊的最新数据,这些数据令人无法忽视。两者之间的差距如此之大,几乎像是对所有人信心的一次压力测试。

他的模型显示,如果以太坊价格简单地跟随其长期ETH/BTC平均价格走势,那么其“公允价值”约为12,000美元。如果市场价格回落到2021年的水平,那么这个数字将跃升至21,800美元。

在最乐观的情况下——以太坊成为核心结算基础设施——估值将飙升至 62,500 美元。

所有这些都与如今约 2800 美元的价格显得格格不入。

鲸鱼喜欢恐惧!

一个与 Bitmine关联的钱包刚刚进行了一笔大额买入,以大约 2750 美元的价格买入了 21537 个 ETH(约合 5917 万美元),而散户交易员则在恐慌性抛售中趁低吸纳。

它类似于我们在比特币 [BTC]中看到的 MicroStrategy 式积累策略,但这次是针对以太坊的。

即使社交媒体上充斥着对鲸鱼崩溃的担忧,鲸鱼的活动也不会受到影响!

未平仓合约总额稳定在154.6亿美元左右,因此市场并未出现恐慌。没有出现大规模杠杆释放、强制清算潮,也没有出现抛售潮。

如果交易员真的感到恐慌,未平仓合约量(OI)应该会大幅下降,但事实并非如此。资金量略微为正,为0.0053,因此交易员倾向于做多,但并未过于激进。

这种情况通常出现在市场下跌后企稳阶段。市场可能看起来摇摇欲坠,但实力雄厚的投资者正在入场。

ETF资金流动转为负值

根据 SoSoValue 最新的每周数据显示,以太坊 ETF 目前已录得约 5 亿美元的净流出,这是几个月来最大的回调之一。

与此同时,总净资产已从近期高点下滑,因此 ETF 投资者正在减少投资,而不是增加投资。

有趣的是,尽管 ETF 资金流动转为负值,但大型投资者却在现货市场购买数百万枚 ETH。

一方面,受监管的ETF投资者正在撤离,这可能是对价格疲软和宏观因素的反应。另一方面,直接从市场买入的大户似乎并不在意!

最后支撑

这一次,以太坊几乎没有下跌空间。从图表上看,ETH 目前正处于其最终的结构性底部,也就是支撑 2022 年至 2025 年整个价格区间的区域。

在之前的周期(2016-2018 年和 2018-2021 年)中,每当以太坊跌破这个价位,价格都会迅速下跌,因为下方几乎没有支撑。这就是为什么分析师将此刻称为“悬崖”。

从结构上看,确实如此。

更令人担忧的是K线走势。卖方展现出强大的实力,成交量不断攀升也印证了这一点。价格正在最不该走弱的地方走弱。

但市场情绪与图表并不相符。巨鲸正在买入,群体心理也转向看涨。ETF资金流出和现货资金吸纳方向却截然相反。

这是一种奇怪的组合,问题就在这里——市场情绪乐观,但结构却不乐观。如果以太坊跌破这个价位,下一个支撑位就不是“略低一些”,而是会低得多。

这就是悬崖。

接下来会发生什么?

如果这种支撑位能够守住,一切都将改变。巨鲸的大量买入开始显得明智,Bitmine 的逢低买入成为一个信号,而那些长期公允价值模型也突然变得更加可信。

但如果跌破这个价位……ETF资金流出、结构恶化以及价格下方巨大的缺口都会迅速产生影响。下方几乎没有支撑,只有一片空白。

下一步行动将决定一切。

Trending Cryptos

Related Reads

In Just 11 Days, Claude Rewrote Millions of Lines of Code, an Epic AI Engineering Feat Sparks Fury

In just 11 days, Bun's founder Jarred Sumner used Anthropic's Claude AI models to rewrite its million lines of code from Zig to Rust. This move sparked significant controversy, particularly from Zig's creator, Andrew Kelley, who publicly criticized Sumner's engineering practices and the decision to use AI for such a massive rewrite. Bun, a high-performance JavaScript/TypeScript runtime and rival to Node.js, was originally written in Zig. After Anthropic acquired Bun, the team encountered persistent stability and memory safety bugs in the Zig codebase. These issues, combined with Zig's strict policy against LLM-generated code, led to the decision to rewrite in Rust. The rewrite was executed using Claude AI tools at an estimated API cost of $165,000, dramatically reducing the expected time and financial cost. Andrew Kelley's response was scathing. He blamed the original bugs on poor engineering habits, calling Bun's Zig code a collection of "hacks on top of hacks." He expressed relief that Bun was no longer associated with Zig, fearing it would misrepresent the language and attract low-quality, AI-generated contributions. The tech community is divided; some view Kelley's critique as unprofessional, while others see it as a defense of engineering integrity. A major concern about the AI-driven rewrite is the resulting code quality. The translation from Zig left approximately 27,000 lines of unsafe Rust code, raising fears about long-term maintainability and technical debt. The debate centers on whether this project is a milestone in AI-assisted development or a future maintenance nightmare.

marsbit32m ago

In Just 11 Days, Claude Rewrote Millions of Lines of Code, an Epic AI Engineering Feat Sparks Fury

marsbit32m ago

From Auto Finance to Bitcoin to AI Engines: An Analysis of Cango's 'What Not to Do' Strategy

From Auto Finance to Bitcoin and Now AI: Cango's "What Not to Do" Strategy Cango, a Chinese auto finance platform that went public on the NYSE in 2018, is undergoing its third major transformation. After selling its entire auto business in 2024, it pivoted to become a large-scale Bitcoin miner, acquiring 50 exahash of mining rigs from Bitmain. However, its true goal was never Bitcoin, but owning and controlling energy infrastructure. Now, Cango is pivoting again. While most listed Bitcoin miners are leasing power to giant hyperscalers for AI training clusters, Cango is taking the opposite path. It has launched an AI inference subsidiary called EcoHash, focusing not on training but on distributed inference. The company's strategy hinges on the insight that over 70% of mining industry power is controlled by small, independent sites (10-50 MW), which are too small for hyperscalers but ideal for low-latency AI inference. Cango aims to partner with these small operators, providing the AI technology, customers, and financing through its EcoLink software layer, which can distribute workloads across sites for reliability. Cango maintains a hybrid model, running roughly 31.7 EH/s of Bitcoin mining for cash flow while aggressively cleaning its balance sheet—slashing long-term debt by 94.5% to $30.6 million and raising $75 million for its AI venture. Its first AI deployment will be at a 50 MW site in Georgia. The strategy faces skepticism, given the high costs of converting mining sites and the potential for an AI bubble. However, Cango's leadership believes discipline around "what not to do"—avoiding direct competition with hyperscalers in training—positions it to capture the long-tail demand for distributed AI inference power.

Foresight News49m ago

From Auto Finance to Bitcoin to AI Engines: An Analysis of Cango's 'What Not to Do' Strategy

Foresight News49m ago

Strategy's Bitcoin Sales Cap Far Exceeds $1.25 Billion: A Detail the Market Overlooked

The article discusses how MicroStrategy's potential Bitcoin sales go far beyond the announced $1.25 billion "reserve-building capacity." It clarifies a key distinction in the company's "BTC Monetization Program": selling Bitcoin to *build* a new dollar reserve (the $1.25B cap) versus selling to *replenish* the existing USD Reserve after it's used for expenses like preferred share dividends. The recent $216M BTC sale for dividend payments was a "replenishment," leaving the headline $1.25B building quota untouched. The plan actually outlines three potential funding pools from BTC sales: 1) Building the reserve ($1.25B cap), 2) Covering preferred share/ debt costs (no specified cap), and 3) Funding buyback programs (up to $20B). This means the structured sales potential exceeds $30 billion, not including uncapped replenishment sales. The piece argues this marks MicroStrategy's shift from a passive "buy-and-hold" Bitcoin proxy to an actively managed entity using BTC as a balance-sheet tool to manage its complex capital structure (common stock, preferred shares, debt, reserve). This creates new dynamics and potential conflicts, as actions benefiting one part (e.g., selling BTC to pay dividends) may pressure another (e.g., undermining the "never sell" narrative). Investors must now parse the company's specific terminology ("build" vs. "replenish") to understand the true scope of future BTC sales, which is significantly larger than the market initially perceived.

marsbit55m ago

Strategy's Bitcoin Sales Cap Far Exceeds $1.25 Billion: A Detail the Market Overlooked

marsbit55m ago

Goldman Sachs Report Deconstructs the Competitive Landscape of China's AI Large Models: Who Will Be the Long-Term Winner?

Goldman Sachs analyzes China's AI large language model (LLM) landscape, identifying key players and a strategic shift towards efficiency and global expansion. The report highlights that Chinese open-source/open-weight models are closing the performance gap with top global proprietary models at significantly lower cost, driven by architectural innovations like MoE. This enables a "two-tier" market: a high-end segment (e.g., GLM5.2, Qwen3.7 Max) with pricing at ~$1 per million tokens, and a low-end, price-sensitive global segment. Open-source strategies aid adoption but limit monetization, as deployments via third-party platforms (e.g., AWS Bedrock, Alibaba Cloud) may not generate direct revenue for model creators. The industry is thus moving towards "open-weight + community license" models with revenue-sharing to improve unit economics. Internationally, the focus is shifting from "token maximization" to ROI-driven enterprise adoption, particularly in non-U.S. markets. Major cloud platforms are integrating Chinese models (e.g., DeepSeek, MiniMax). Using a competitive framework based on pricing power, cost advantage, and financial strength, Goldman Sachs identifies **Zhipu AI** and **DeepSeek** as leaders in foundational text models, and **ByteDance** (with Seedance) leading in multimodal/video generation. **MiniMax** and **Kuaishou** are also rated favorably. The firm forecasts China's AI model API/subscription revenue growing from ~RMB 35bn (2026E) to RMB 879bn by 2030.

marsbit55m ago

Goldman Sachs Report Deconstructs the Competitive Landscape of China's AI Large Models: Who Will Be the Long-Term Winner?

marsbit55m ago

Trading

Spot

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of ETH (ETH) are presented below.

活动图片