ZCash short-term momentum solidifies: Here’s what you can expect this week

ambcryptoPublicado em 2026-02-16Última atualização em 2026-02-16

Resumo

ZCash (ZEC) has rebounded from a key long-term support level at $187.9, showing strong short-term momentum with a 9.88% price increase and 25% rise in trading volume. The cryptocurrency has reclaimed the $300 resistance level as support, but faces significant supply zones between $365 and $460. Technical indicators suggest potential overbought conditions and a possible correction, though the $300 level may now act as support. Liquidation maps indicate higher liquidation leverage around $342-$360, making an upward move more likely than a drop below $300 in the near term. However, Bitcoin's rejection at $70k could negatively impact ZEC's trajectory. A move toward $360 appears probable unless Bitcoin faces strong selling pressure.

ZCash [ZEC] rebounded from a key long-term support level at $187.9. In a recent report, AMBCrypto explored how the long-term bias of ZEC might not be as bearish as feared.

The weekly swing structure has held, and there was hope.

A recovery might not be imminent. With Bitcoin’s [BTC] bearish woes, ZEC might enter a deeper consolidation and fall below $187 in the coming weeks.

But, for now, the short-term momentum was bullish. According to CoinMarketCap, ZCash has rallied 9.88% in the past 24 hours with a 25% increase in daily trading volume.

The $300 resistance has been overcome, but the main threats overhead were at $365-$460.

What to expect from the ZEC price action this week

Earlier, the importance of the $365-$450 supply zone was explained using the 1-day chart. Zooming into the lower timeframe price charts only reinforced this view.

Using the H4 bearish impulse move to $184 and its Fibonacci retracement levels, we can see why $320 and $357 are key local resistances. To the south, there were imbalances in this timeframe at $300, $260, and $240.

The OBV has made new highs for the month of February, but the RSI and Stochastic RSI showed overbought conditions. This indicated a potential correction ahead.

The upcoming price dip might not extend as deep as $240. This was because of the $300 level, which was a local resistance that the price rocketed past and left behind an imbalance, or fair value gap, in the process.

ZEC will likely respect this and continue higher, although a few hours of consolidation could ensue.

Another compelling reason why ZEC will likely run higher before correcting was seen in the liquidation map. The $342 and $360 regions had considerable cumulative short liquidation leverage.

Since price is attracted to liquidity, a move higher seemed more likely in the coming days than a drop below $300.

At the same time, traders shouldn’t FOMO into long positions. Bitcoin tested the $70k resistance in recent hours and could see rejection over the next 24 hours. If it does, it can drag ZEC lower.

Final Summary

The ZEC bulls have reclaimed the $300 former local resistance as support, at least for now.
If Bitcoin does not see strong selling pressure in the near term, a ZEC move to $360 is more likely than a breakdown below $300.

Disclaimer: The information presented does not constitute financial, investment, trading, or other types of advice and is solely the writer’s opinion.

Criptomoedas em alta

CitreaCTR

wrapped stUSDTWSTUSDT

Velodrome FinanceVELODROME

BrevisBREV

PancakeSwapCAKE

JUSTJST

Perguntas relacionadas

QWhat key long-term support level did ZCash (ZEC) rebound from according to the article?

AZCash rebounded from the key long-term support level at $187.9.

QWhat is the main threat to ZEC's price in the $365-$460 range?

AThe main threats overhead for ZEC were the supply zone in the $365-$460 range.

QBased on the liquidation map, why is a move towards $342 and $360 considered likely for ZEC?

AThe $342 and $360 regions had considerable cumulative short liquidation leverage, and since price is attracted to liquidity, a move higher to these levels is more likely.

QWhat potential risk to ZEC's price movement does Bitcoin's (BTC) price action pose?

AIf Bitcoin tests the $70k resistance and sees a rejection, it could drag ZEC's price lower.

QWhat do the RSI and Stochastic RSI indicators suggest about ZEC's current market condition?

AThe RSI and Stochastic RSI showed overbought conditions, indicating a potential correction ahead for ZEC.

Leituras Relacionadas

Upbit Rebalances 864B SHIB In Internal Wallet Move

South Korean cryptocurrency exchange Upbit moved a total of 864 billion SHIB (worth approximately $4 million) between its internal wallet addresses. The activity, involving transfers of 384 billion SHIB outbound and 480 billion SHIB inbound, occurred following a 36% SHIB price rally. On-chain data indicates this was a routine internal wallet rebalancing operation by the exchange, not a sell-off or liquidation event. The article cautions that while large on-chain transfers are highly visible, they require proper context and wallet labeling to interpret correctly. In this case, the movement between known Upbit addresses does not inherently signal a change in market supply or selling pressure for SHIB.

bitcoinistHá 7m

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinistHá 7m

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbitHá 7m

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbitHá 7m

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

The International Conference on Learning Representations (ICLR) 2027 has introduced a new submission rule limiting authors to a maximum of 20 papers per conference. This policy, announced due to a 68% surge in submissions for ICLR 2026 that strained the review system, aims to ensure fair and informative peer review. Google DeepMind researcher Dan Roy responded with a sarcastic petition on X, calling the 20-paper cap "absurd" and claiming it would slow AI progress. His post mockingly argued that AI agents can now produce incremental research and that large language models (LLMs) already handle all reviewing, so output should be maximized. This satire critiques the broader trend of AI's role in academia, referencing a 2025 study that found about 21% of ICLR 2026 reviews were likely fully AI-generated. Roy's underlying point questions whether simply capping submissions addresses the systemic issue of AI potentially flooding conferences with agent-written papers and AI-assisted reviews. The debate highlights tensions between managing submission volume and maintaining research quality as AI tools become pervasive.

marsbitHá 12m

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

marsbitHá 12m

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

A security vulnerability in Coldcard hardware wallets led to the theft of 1,367.05 BTC (approx. $89 million) from 4,585 addresses, according to Galaxy Research. The flaw, caused by insufficient randomness in seed phrase generation for specific firmware versions, triggered a massive migration of funds as users moved assets to new, secure wallets. This event caused the largest on-chain movement of small Bitcoin transactions since the FTX collapse, distorting key market indicators like active addresses and exchange deposits, and significantly dampening market sentiment. The incident also highlighted challenges in cybersecurity investigations, as researchers reported that safety guardrails on mainstream U.S. AI models hindered the analysis of attack patterns, forcing them to use an open-source Chinese AI model for forensic work. This underscores a dilemma in balancing AI security restrictions with the need for rapid response in tracing stolen crypto assets.

marsbitHá 57m

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

marsbitHá 57m

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

The AI boom is facing an unexpected bottleneck: a severe shortage of skilled construction workers and electricians. As tech giants like Meta, OpenAI, and Alphabet race to build massive data centers—such as OpenAI's $16 billion "Stargate" project—they are hitting a critical labor wall. The U.S. needs an estimated 130,000 more electricians, 240,000 construction workers, and 150,000 supervisors by 2030 for AI infrastructure alone, but tens of thousands of electrician jobs go unfilled each year. While AI companies offer high premiums, with electricians earning up to $280,000 annually, worker scarcity still causes massive losses—delays on a single project can cost $14.2 million per month. The complexity of building AI data centers, which require immense power (equivalent to powering hundreds of thousands of homes), sophisticated electrical systems, and advanced liquid cooling solutions, demands highly skilled technicians who are in short supply. To combat this, companies are investing heavily in training. Meta has committed $115 million to a free training school offering tuition, housing, and stipends, targeting 5,000 new workers. OpenAI is partnering with unions to secure skilled labor. These efforts are paying off, with a significant rise in Gen Z interest in trade schools over college. However, the power demands are staggering. AI data centers are driving a rapid surge in electricity consumption, projected to account for up to 12% of U.S. power use by 2028 and raising costs for consumers. Furthermore, the construction boom is project-based, leading to a potential future glut of trained workers once building peaks, which could depress wages industry-wide. The race for AI supremacy now depends as much on skilled hands as on advanced chips.

marsbitHá 1h

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

marsbitHá 1h

Trading

Spot

Artigos em Destaque

Como comprar ZEC

Bem-vindo à HTX.com!Tornámos a compra de Zcash (ZEC) simples e conveniente.Segue o nosso guia passo a passo para iniciar a tua jornada no mundo das criptos.Passo 1: cria a tua conta HTXUtiliza o teu e-mail ou número de telefone para te inscreveres numa conta gratuita na HTX.Desfruta de um processo de inscrição sem complicações e desbloqueia todas as funcionalidades.Obter a minha contaPasso 2: vai para Comprar Cripto e escolhe o teu método de pagamentoCartão de crédito/débito: usa o teu visa ou mastercard para comprar Zcash (ZEC) instantaneamente.Saldo: usa os fundos da tua conta HTX para transacionar sem problemas.Terceiros: adicionamos métodos de pagamento populares, como Google Pay e Apple Pay, para aumentar a conveniência.P2P: transaciona diretamente com outros utilizadores na HTX.Mercado de balcão (OTC): oferecemos serviços personalizados e taxas de câmbio competitivas para os traders.Passo 3: armazena teu Zcash (ZEC)Depois de comprar o teu Zcash (ZEC), armazena-o na tua conta HTX.Alternativamente, podes enviá-lo para outro lugar através de transferência blockchain ou usá-lo para transacionar outras criptomoedas.Passo 4: transaciona Zcash (ZEC)Transaciona facilmente Zcash (ZEC) no mercado à vista da HTX.Acede simplesmente à tua conta, seleciona o teu par de trading, executa as tuas transações e monitoriza em tempo real.Oferecemos uma experiência de fácil utilização tanto para principiantes como para traders experientes.

396 Visualizações TotaisPublicado em {updateTime}Atualizado em 2026.06.02

Discussões

Bem-vindo à Comunidade HTX. Aqui, pode manter-se informado sobre os mais recentes desenvolvimentos da plataforma e obter acesso a análises profissionais de mercado. As opiniões dos utilizadores sobre o preço de ZEC (ZEC) são apresentadas abaixo.