A whale sells 7.6K ETH – So why didn’t Ethereum break down?

ambcryptoPubblicato 2025-12-14Pubblicato ultima volta 2025-12-14

Introduzione

Despite significant selling pressure from a whale selling 7,621 ETH ($23.85M) and U.S. ETH spot ETFs recording -$19.41M in daily net outflows, Ethereum’s price remained stable around $3,129. The market absorbed the supply without a sharp decline, indicating strong underlying demand. ETH continued to trade near its realized price, a historically significant support level that often acts as a springboard for upward moves. Spot and futures data showed net outflows from exchanges, preventing liquidation cascades and maintaining stability. Key factors to watch include the realized price holding as support, MACD convergence, and ascending trendlines. A hold above support could target $3,600, while a break below $2,973 may lead to a deeper retracement.

Selling pressure built from two angles, ETFs and whale supply — it all arrived at the same price zone.

U.S. ETH spot ETFs posted -$19.41M in daily net outflows, while the market digested a whale sale of 7,621 ETH worth $23.85M near $3,129 over the past three days.

That kind of stacked supply usually forces a sharper flush, especially when traders are already lean and cautious.

Instead, Ethereum kept trading as if buyers were waiting for that supply on purpose.

Spot and Futures flow readings leant negative across multiple short windows, yet price action stayed controlled, with limited follow-through to the downside.

ETH hovering around the realized price made the message louder; sellers showed up, but demand absorbed the hit and refused to hand bears momentum.

Who’s buying all this ETH?

ETF outflows stayed orderly, pointing to rotation rather than panic. Whale transfers added supply, yet the price didn’t crack, a clear sign of absorption.

Spot and Futures data showed Ethereum [ETH] continuing to leave exchanges on a net basis.

That outflow profile avoided any liquidation cascade and kept ETH steady around the realized price, locking the market in “sell gets met” mode.

Realized Price still acts like a springboard

Ethereum’s realized price has repeatedly marked inflection points across prior cycles, with strong upside expansions following once sell pressure exhausts.

ETH now sits on that same zone again, and the current reaction matches the historical behavior, price stabilizes first, then volatility expands.

That sets up a clean read for traders.

If the realized price keeps holding while supply keeps getting absorbed, the market often shifts from distribution into accumulation. If the realized price fails, the chart typically searches for the next demand pocket.

Momentum tightens, and structure decides the next leg

Since the $2,632 sell-off, Ethereum has maintained an uptrend with higher highs and higher lows. RSI holds in the low 40s, which fits consolidation rather than a full breakdown.

On MACD, the blue line keeps moving closer to the orange signal line, and bulls need that crossover sooner rather than later to avoid additional downside bleed.

Ascending support remains the clean trigger. A bounce keeps $3,600 in play.

A loss of $2,973 weakens the setup and increases the odds of a deeper retrace toward the 50% Fibonacci zone, where buyers previously reacted.


Final Thoughts

  • Realized Price remains the battlefield where absorption either holds or fails.
  • MACD convergence and ascending support will decide whether ETH expands or bleeds.

Letture associate

Google and Amazon Simultaneously Invest Heavily in a Competitor: The Most Absurd Business Logic of the AI Era Is Becoming Reality

In a span of four days, Amazon announced an additional $25 billion investment, and Google pledged up to $40 billion—both direct competitors pouring over $65 billion into the same AI startup, Anthropic. Rather than a typical venture capital move, this signals the latest escalation in the cloud wars. The core of the deal is not equity but compute pre-orders: Anthropic must spend the majority of these funds on AWS and Google Cloud services and chips, effectively locking in massive future compute consumption. This reflects a shift in cloud market dynamics—enterprises now choose cloud providers based on which hosts the best AI models, not just price or stability. With OpenAI deeply tied to Microsoft, Anthropic’s Claude has become the only viable strategic asset for Google and Amazon to remain competitive. Anthropic’s annualized revenue has surged to $30 billion, and it is expanding into verticals like biotech, positioning itself as a cross-industry AI infrastructure layer. However, this funding comes with constraints: Anthropic’s independence is challenged as it balances two rival investors, its safety-first narrative faces pressure from regulatory scrutiny, and its path to IPO introduces new financial pressures. Globally, this accelerates a "tri-polar" closed-loop structure in AI infrastructure, with Microsoft-OpenAI, Google-Anthropic, and Amazon-Anthropic forming exclusive model-cloud alliances. In contrast, China’s landscape differs—investments like Alibaba and Tencent backing open-source model firm DeepSeek reflect a more decoupled approach, though closed-source models from major cloud providers still dominate. The $65 billion bet is ultimately about securing a seat at the table in an AI-defined future—where missing the model layer means losing the cloud war.

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Google and Amazon Simultaneously Invest Heavily in a Competitor: The Most Absurd Business Logic of the AI Era Is Becoming Reality

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Computing Power Constrained, Why Did DeepSeek-V4 Open Source?

DeepSeek-V4 has been released as a preview open-source model, featuring 1 million tokens of context length as a baseline capability—previously a premium feature locked behind enterprise paywalls by major overseas AI firms. The official announcement, however, openly acknowledges computational constraints, particularly limited service throughput for the high-end DeepSeek-V4-Pro version due to restricted high-end computing power. Rather than competing on pure scale, DeepSeek adopts a pragmatic approach that balances algorithmic innovation with hardware realities in China’s AI ecosystem. The V4-Pro model uses a highly sparse architecture with 1.6T total parameters but only activates 49B during inference. It performs strongly in agentic coding, knowledge-intensive tasks, and STEM reasoning, competing closely with top-tier closed models like Gemini Pro 3.1 and Claude Opus 4.6 in certain scenarios. A key strategic product is the Flash edition, with 284B total parameters but only 13B activated—making it cost-effective and accessible for mid- and low-tier hardware, including domestic AI chips from Huawei (Ascend), Cambricon, and Hygon. This design supports broader adoption across developers and SMEs while stimulating China's domestic semiconductor ecosystem. Despite facing talent outflow and intense competition in user traffic—with rivals like Doubao and Qianwen leading in monthly active users—DeepSeek has maintained technical momentum. The release also comes amid reports of a new funding round targeting a valuation exceeding $10 billion, potentially setting a new record in China’s LLM sector. Ultimately, DeepSeek-V4 represents a shift toward open yet realistic infrastructure development in the constrained compute landscape of Chinese AI, emphasizing engineering efficiency and domestic hardware compatibility over pure model scale.

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Computing Power Constrained, Why Did DeepSeek-V4 Open Source?

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