Ethereum hits 2-month low: Analyzing if ETH can reclaim $3k

ambcryptoPublished on 2026-01-30Last updated on 2026-01-30

Abstract

Ethereum (ETH) dropped to a two-month low of $2,681, declining 8.2% amid a broader crypto market sell-off. Long liquidations surged to $242 million, with one prominent trader losing $2 million. Despite the downturn, whale activity intensified: one purchased 20,000 ETH worth $56 million, while another opened an $18 million leveraged long position. Exchange outflows exceeded inflows, indicating aggressive accumulation. However, momentum indicators like RSI (35) and DMI (13) remain bearish, suggesting potential further downside to $2.5k. A recovery to $3k depends on sustained whale demand absorbing sell pressure.

The broader crypto market experienced a significant sell-off, losing more than $170 billion in total market value. Altcoins, especially Ethereum [ETH], were hit the hardest, and ETH fell to a low of $2,681, levels last seen in November 2025.

At press time, ETH traded at $2,714, down 8.2% on the daily charts, extending its long-week downtrend. With ETH dropping to a low of $2.6k, whale activity on both the spot and futures markets intensified.

Ethereum total long liquidation hits $242M

After Ethereum dropped to a low of $2.6k, investors holding long positions saw massive liquidations. In fact, long liquidations jumped to $242.4 million, at press time, adding $175 million from the day earlier.

Amid this soaring liquidations, a prominent ETH trader, MachiBigBrother, was fully liquidated on his 25x long position. The liquidation resulted in Machi recording a $2 million loss, bringing total losses to over $25.8 million.

Despite this liquidation, Machibigbrother returned to the market and took another long position. The whale deposited $144,573 in USDC into Hyperliquid, adding to his ETH positions.

Another whale returned after two years of dormancy, sold 699 ETH for $1.87 million, and deposited it into Hyperliquid, according to Onchain Lens. The whale then opened an ETH long position with 20x leverage, valued at $18 million.

With whales entering the market after such a slip, this indicates confidence, as they expect the correction to be short-lived.

A whale adds $56M amid the buying the dip spree

On the spot side, as ETH prices dropped, Ethereum whales rushed into the market to buy the dip.

According to Onchain Lens, a whale purchased an additional 20,000 ETH for $56.03 million. This brings the whale’s holdings to 110,154 ETH, valued at $311.26 million in staking.

The continued accumulation indicates whales’ conviction and suggests that they perceive the current conditions as ideal for strategic positioning.

Furthermore, exchange activity further echoed this buying-the-dip spree. According to CoinGlass data, $2.34 billion in ETH flowed out of exchanges, compared to the $2.19 billion in inflows, as of writing.

As a result, the Spot Netflow dropped 967% to $146.3 million, a clear sign of aggressive spot accumulation. Usually, a higher outflow tends to increase scarcity, thereby accelerating upward momentum, a prelude to price recovery.

Is ETH at risk of further slip?

Ethereum’s massive liquidations after the market crash further exacerbated downward pressure on the market. As a result, the altcoin’s Relative Strength Index (RSI) fell deeper into the bearish territory, dropping to 35 at press time.

At the same time, its Directional Movement Index (DMI) dropped to 13. further validating the downward momentum. When these momentum indicators drop to such low levels, they signal sellers’ dominance in the market.

Thus, although whales bought the dip and others opened long positions, these demand-side activities have failed to drive a trend reversal in Ethereum.

Therefore, prevailing market conditions indicate further losses for ETH. If the trend persists, ETH could drop again towards $2.5k.

However, if whales continue to buy the dip and absorb the sell pressure, the market will clear recent losses and reclaim $3k.


Final Thoughts

  • Ethereum declined 8.2% to a two-month low of $2,681, as long liquidations jumped to $242 million.
  • ETH whale bought the dip, adding 20,000 ETH worth $56.03 million.

Trending Cryptos

Related Questions

QWhat was the lowest price Ethereum (ETH) dropped to during the recent sell-off, and what was its price at press time?

AEthereum dropped to a low of $2,681, and at press time, it was trading at $2,714.

QHow much was the total long liquidation for Ethereum, and what was the increase from the day before?

AThe total long liquidation for Ethereum was $242.4 million, which was an increase of $175 million from the day before.

QWhat significant action did a prominent whale take on the spot market after the price drop?

AA whale purchased an additional 20,000 ETH for $56.03 million, increasing their total holdings to 110,154 ETH valued at $311.26 million in staking.

QWhat do the RSI and DMI indicators suggest about the current market momentum for Ethereum?

AEthereum's RSI fell to 35, and its DMI dropped to 13, both indicating bearish territory and seller dominance in the market, validating the downward momentum.

QAccording to the article, what are the two potential price scenarios for Ethereum if the current trend persists?

AIf the trend persists, ETH could drop again towards $2.5k. However, if whales continue to buy the dip and absorb the sell pressure, the market could clear recent losses and reclaim $3k.

Related Reads

Former CFTC Chairman, Circle President Tarbert: Preaching Long-Termism While Cashing Out $30 Million Himself

Former CFTC Chairman and Circle President Heath Tarbert has consistently advocated for a long-term vision in public, urging patience from investors as Circle’s stock price has fallen significantly from its peak. However, it has been revealed that since Circle’s IPO, Tarbert has continuously sold his CRCL shares through pre-arranged trading plans, cashing out approximately $30 million, without making any public market purchases. This contrast between his public messaging and personal actions has drawn criticism. Tarbert joined Circle in July 2023 as Chief Legal Officer, leveraging his regulatory experience to help guide the company through its IPO and expansion. Despite promoting stablecoins as long-term infrastructure, he established a 10b5-1 trading plan just before Circle went public, leading to substantial stock sales over the following year. In March 2026, he initiated another plan to sell more shares. His career trajectory highlights a pattern of moving between high-level regulatory roles and influential positions in the financial sector. After resigning as CFTC Chairman in early 2021, he joined Citadel Securities as Chief Legal Officer just 27 days later, during a period of intense regulatory scrutiny for the firm. He later joined Circle, aiding its efforts to navigate regulatory challenges for its public listing. While Tarbert's expertise in policy and compliance is valuable to companies like Circle, his actions—advocating long-term confidence while personally divesting—raise questions about the alignment between his public statements and his private financial decisions, leaving investors who followed his advice to bear the market risks.

marsbit9m ago

Former CFTC Chairman, Circle President Tarbert: Preaching Long-Termism While Cashing Out $30 Million Himself

marsbit9m ago

Gate Research Institute: The 'Wall Street-ization' Wave of Crypto Financial Products – Competition or Integration?

The article titled "Gate Research Institute: Are Crypto Financial Products Sparking a 'Wall Street' Wave—Competition or Convergence?" explores the evolving relationship between the crypto ecosystem and traditional finance (TradFi). The piece begins by reflecting on Bitcoin's original 2009 vision of decentralization, disintermediation, and moving away from banks. It then contrasts this with the 2024 landscape, where key crypto assets like Bitcoin are increasingly held through Wall Street products like ETFs issued by giants like BlackRock. The article questions whether this signifies that TradFi is systematically taking over the rights to issue, price, custody, and distribute crypto financial assets. The core argument is that this is not a zero-sum takeover but rather a bidirectional convergence where each side addresses the other's weaknesses. Crypto offers 24/7 global markets, programmable settlement, and open access but lacks compliant channels, institutional-grade custody, deep fiat liquidity, and mainstream distribution. TradFi possesses these but is constrained by legacy systems, limited operating hours, and slow settlement. Two primary convergence paths are highlighted: * **Path A (CEX to TradFi):** Exemplified by Gate, which has progressed from offering tokenized stocks and CFDs to providing direct, real stock trading (US, Hong Kong, South Korea) within its platform, using USDT. * **Path B (TradFi to Crypto):** Exemplified by Robinhood, which has integrated crypto trading, acquired exchanges like Bitstamp, and is moving traditional assets like stocks onto the blockchain via tokenization and its own Layer 2. Both paths are ultimately competing to become the next-generation, unified financial account—a "super account" where users can seamlessly trade cryptocurrencies, stocks, ETFs, RWA (Real World Assets), and tokenized treasury products in one interface. The growth of RWA and tokenized treasuries (e.g., BlackRock's BUIDL) is presented as the asset-layer fusion, providing stable, yield-bearing assets on-chain and acting as a bridge between the two worlds. In conclusion, the "Wall Street-ization" of crypto is framed as a mutual transformation. Decentralized ideals persist in the protocol layer, while at the application layer, a more efficient, global, and accessible unified capital market is emerging from this convergence. The future competition lies not between crypto exchanges and stockbrokers, but between platforms vying to offer the most comprehensive asset coverage, liquidity, and user experience within a single account.

marsbit13m ago

Gate Research Institute: The 'Wall Street-ization' Wave of Crypto Financial Products – Competition or Integration?

marsbit13m ago

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbit18m ago

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbit18m ago

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

Feeding "Noise" to AI Can Improve Performance: A Method Enables Positive Transfer from Noise This work, Semi-Supervised Noise Adaptation (SSNA), introduces a Noise Adaptation Framework (NAF) that challenges traditional transfer learning. Instead of requiring a labeled source domain of real data (e.g., images, text), NAF uses randomly generated Gaussian noise as the source. For a target task with C classes, it constructs C noise clusters by sampling from Gaussian distributions. Although this synthetic noise contains no semantic meaning, NAF trains it to form a discriminative class structure in a shared representation space—clustering same-class noise and separating different classes. The key is aligning this learned structure from the noise domain to the real, sparsely labeled target domain. A small number of target labels are still essential to establish the correspondence between noise clusters and actual classes. The training objective combines: 1) supervised loss on the few labeled target samples, 2) classification loss for the noise to build its structure, and 3) a distribution alignment loss (using Negative Domain Similarity) to minimize the gap between the noise and target domains in the shared space. Experiments show significant gains in few-label settings. With just 4 labels per class, NAF with a ResNet-18 backbone improves accuracy over a standard supervised baseline (ERM) by +12.35% on CIFAR-10, +7.61% on CIFAR-100, +4.38% on DTD-47, and +2.74% on Caltech-101. It also benefits fine-grained datasets and scales to ImageNet-1K (with 100 labels/class) and text classification (AG News). NAF can be integrated into existing semi-supervised methods like FixMatch for further gains. Ablation studies confirm the transferred benefit comes from the discriminative structure of the noise, not randomness itself. Collapsing all noise into a single point causes negative transfer, while increasing separation between noise cluster centers improves performance. The amount of noise per class is less critical once a basic structure forms. In conclusion, this work demonstrates that for positive transfer, the semantic content of source data may not be necessary. What can be effectively transferred is the *organizational structure* of categories within a representation space. This offers a promising alternative for scenarios where real source data is unavailable due to privacy, copyright, or procurement constraints.

marsbit19m ago

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

marsbit19m 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