Zcash: Analyzing why ZEC still risks a price drop toward $301

ambcryptoОпубликовано 2026-01-20Обновлено 2026-01-20

Введение

After a market crash, Zcash (ZEC) dropped to $347, extending a week-long decline and risking a further fall toward $301. Despite strong bearish pressure and a drop below key moving averages, whale activity has increased, with significant buying volume and a notable withdrawal of 8,551 ZEC ($3.12 million) from Binance. Exchange data shows eight consecutive days of negative netflow, indicating $414 million in capital inflows, which typically reduces circulating supply and supports prices. However, bearish momentum remains strong, with the Stochastic RSI deep in oversold territory, suggesting selling pressure outweighs current whale demand. A reversal would require stronger accumulation to target resistance at $390.

After the market crash, Zcash [ZEC] dropped to levels last witnessed in early December, touching a low of $335.

In fact, ZEC has posted lower lows for five straight days, underscoring strong bearish pressure. As a result, the altcoin fell below its 20, 50, and 100 short‐term EMAs.

At press time, ZEC traded at $347, down 5.98%, extending a week-long downside movement.

With ZEC dropping below its short-term MAs, investors, especially whales, have jumped into the market buying the dip.

ZEC whales are buying the dip

Since Zcash was rejected at $449 a week ago, whale activity has fallen significantly. At press time, data from TradingView showed that Whale Buy Volume peaked at 152.4k.

While whale buy activity has reduced signaling, reduced risk appetite, and increased risk aversion, they have remained active.

At the same time, the Average Buy Volume hovered around 29k, holding this level for three consecutive days. This suggested that whales have remained relatively active despite the continued market weakness.

Notably, Onchain Lens observed that a newly created wallet withdrew 8,551 ZEC, worth $3.12 million from Binance, and could withdraw more.

The wallet now holds 12.5k ZEC, worth $4.54 million, according to Arkham data. Often, when whales continue to hold on and also add more positions during a downturn, it signals strong conviction.

Zcash records $414M in capital flow

Furthermore, exchange activities have echoed this accumulation phase. According to CoinGlass data, Zcash Spot Netflow has remained in the negative zone for 8 consecutive days.

At the time of writing, Spot Netflow was -$5.87 million, indicating increased capital inflow into the asset. In fact, over this period, investors have pumped approximately $414 million into the market.

Usually, increased outflows increases scaricty, thus reducing supply in circulation, which absorbs rising selling pressure. Often, such a market setup accelerates upward strength, leading to higher prices.

Is this demand enough?

While whales continued to accumulate as ZEC dropped, downside pressure has intensified, reflecting selling by other participants.

Moreover, the downward momentum remained elevated as evidenced by Zcash’s Stochastic RSI. This momentum indicator made a bearish crossover days ago and fell to 11.96, as of writing, further deepening the oversold zone.

When this indicator reaches such levels, it signals that bears have fully overpowered bulls, taking control of the market. As a result, whale demand has been insufficient to offset selling pressure.

With bearish momentum prevailing, downside risk remains high, and ZEC could fall toward $301. However, if whale demand strengthens, Zcash may stage another rally and target immediate resistance at $390.


Final Thoughts

  • Zcash whale purchased 8,551 ZEC, worth $3.12 million rising total holdings to 12.5k ZEC worth $4.54 million.
  • ZEC dropped 5.98%, extending its long week of downward pressure and risking a drop to $300.

Связанные с этим вопросы

QWhat was the price of ZEC at the time of writing and by what percentage did it drop?

AAt press time, ZEC traded at $347, down 5.98%.

QWhat key observation did Onchain Lens make regarding whale activity for Zcash?

AOnchain Lens observed that a newly created wallet withdrew 8,551 ZEC, worth $3.12 million from Binance.

QWhat does a negative Spot Netflow value indicate for an asset like Zcash?

AA negative Spot Netflow value indicates increased capital inflow into the asset, as it is being withdrawn from exchanges, which increases scarcity.

QWhat did the Stochastic RSI level of 11.96 signal for ZEC's market momentum?

AA Stochastic RSI level of 11.96 signaled that the asset was deep in the oversold zone, indicating that bears had fully overpowered bulls and taken control of the market.

QWhat are the two potential price targets mentioned for ZEC based on the prevailing market conditions?

AIf bearish momentum prevails, ZEC could fall toward $301. If whale demand strengthens, it may rally and target immediate resistance at $390.

Похожее

It Took Me a Year to See the Bitter Truth About Agent Payments

After a year building infrastructure for the Agent economy, engaging with major players like Stripe, Visa, and Coinbase, the author shares a sobering analysis of the current state of Agent payments. The core finding is a stark lack of genuine, immediate demand across most envisioned use cases. The article breaks down four key market segments: 1. **Agent-to-Merchant (Consumer Shopping):** For most product categories (e.g., clothing, electronics), conversational AI shopping is a step backwards from visual e-commerce interfaces. While agents excel at understanding needs, they can't replace side-by-side product comparison. Real merchant interest is defensive "Agent Engine Optimization," not driven by current customer demand. Potential exists for high-frequency, low-decision purchases (like food delivery) or navigating complex store UIs, but these require massive B2C distribution channels dominated by giants like Amazon. 2. **Agent-to-API (Developer Services):** Developers already have subscriptions and billing relationships for APIs (compute, data). Prepaid balances solve micro-payment issues for low transaction volumes. A deeper structural problem is that major SaaS vendors' business models rely on enterprise contracts, resisting granular pay-per-call pricing. While protocols like MPP and x402 serve the long tail of niche services, this market is small and developers are historically low-willingness-to-pay. 3. **Agent-to-Agent:** This remains largely theoretical with minimal transaction volume. While it represents a long-term bet on a fundamentally new transaction infrastructure (sub-second, micro-penny to million-dollar, multi-party settlements), it does not constitute a present market. 4. **Agent-to-Finance:** This is the only category with existing, paying demand. Integrating AI into financial workflows (trading, portfolio management) is a natural evolution and enables new capabilities like autonomous rebalancing. However, competition favors established, regulated institutions. The "real problem" is not moving money between agents, but the broader challenge of **coordination**—orchestrating work between agents and humans, verifying outcomes, and settling results. Payment is just one component of settlement, which is itself part of coordination. Companies that solve the coordination layer will subsume payment, not the other way around. While well-funded incumbents build defensively for a long-term future, startups must find where the market is today—which, for the author's team, lies outside these four categories in an area of real, growing, and underserved activity.

marsbit7 мин. назад

It Took Me a Year to See the Bitter Truth About Agent Payments

marsbit7 мин. назад

It Took Me a Year to See the Hard Truth About Agent Payments

**Title: It Took Me a Year to See the Hard Truth About Agent Payments** Over the past year, I've worked on infrastructure for the Agent economy, engaging with major players like Stripe, Visa, Coinbase, and numerous startups. The findings reveal a stark reality: genuine, widespread demand for Agent-based payments does not yet exist. **Key Observations:** * **Agent-to-Merchant (Shopping):** The user experience for AI shopping often falls short, especially for visual product discovery. While AI excels at understanding needs, conversational interfaces can't yet replace browsing and comparing multiple products visually. Current merchant interest is largely defensive ("Agent Engine Optimization") for a future that hasn't arrived. High-frequency, low-friction purchases (like food delivery) are potential fits, but lack open APIs and face high AI inference costs. Simpler, more affordable, or cross-language interactions for complex UIs are a niche opportunity but require massive consumer distribution to scale. * **Agent-to-API (Developer Tools):** Developer payment needs for APIs (computing, data, models) are already met through subscriptions and prepaid credits. The core challenge is not payment friction but supplier economics: most large SaaS providers prefer enterprise contracts over micropayments for API calls. Protocols like MPP and x402 suit the long-tail of smaller services but cater to a developer market historically reluctant to pay for these tools. Major infrastructure needs at the top of the stack are already being addressed. * **Agent-to-Agent (Machine Commerce):** This is a long-term vision with almost no current transaction volume. While a future with high-speed, high-frequency, multi-party machine-to-machine transactions would require novel infrastructure, it remains theoretical. The market is not here yet. * **Agent-to-Finance:** This is the only category with clear, present demand. Financial professionals and DeFi users already pay for tools, and AI augmentation is a natural evolution. Autonomous AI agents can enable entirely new financial strategies. However, competition is fierce from established, regulated incumbents who can more easily layer AI onto their existing products. **The Core Insight:** Companies, especially giants with long time horizons, are building defensively for a potential future of mass machine commerce. For them, early investment is a low-cost hedge. For startups, the current market reality is different. The primary challenge isn't just moving money between agents (payments). The larger, unsolved problem is **orchestration** – coordinating work between agents and humans, verifying outcomes, and then settling. Payment is just a part of settlement, which is just a part of orchestration. Companies that solve the orchestration problem will subsume payments, not the other way around. After a year of building, we see the real, growing, and underserved market opportunity lies in this broader domain of orchestration.

链捕手31 мин. назад

It Took Me a Year to See the Hard Truth About Agent Payments

链捕手31 мин. назад

Claude Opus 4.8 Finds a $4.5 Billion Bug: The AI Era is Mass-Producing Hackers

A researcher discovered a critical "infinite mint" vulnerability in the Zcash cryptocurrency's Orchard protocol using Claude Opus 4.8, leading to a swift fix but also a 50% market drop, erasing billions in value. This incident highlights a new era where powerful, accessible AI models are dramatically lowering the barrier to finding software vulnerabilities. Previously, the security community feared specialized models like Claude Mythos Preview, capable of finding decades-old zero-day exploits. The Zcash case, however, involved a publicly available, general-purpose model. This shift makes advanced security auditing—and attack capabilities—accessible to far more people, not just experts. The mass democratization of vulnerability discovery brings a dual challenge: a flood of low-quality, AI-generated false reports that overwhelm maintainers, and the real, rapid uncovering of deep, dangerous bugs. Open-source projects, often understaffed and unfunded, are particularly vulnerable to this "attention DDoS." The article cites examples like curl shutting down its bug bounty program due to the unsustainable workload. Our perceived digital safety has often been luck, relying on the high cost and effort required to find deeply hidden flaws in complex systems, as seen with historical vulnerabilities like Heartbleed or Baron Samedit. AI changes this cost structure, effectively "mass-producing flashlights" to illuminate every corner of our codebase. While large companies operate extensive security chains involving external white-hat hackers and massive defensive operations, the global cybersecurity workforce faces a severe shortage, especially of experienced personnel capable of analyzing complex threats and coordinating fixes. The core dilemma emerges: AI makes *finding* bugs cheap and scalable, but *fixing* them remains a slow, expensive, and human-intensive process. The article concludes that AI won't destroy the internet but acts as a bright light, revealing that our digital existence is not inherently secure but is precariously maintained by ongoing human effort. The true cost in the AI era may not be discovery, but whether there will be enough people left willing and able to do the hard work of repair.

marsbit1 ч. назад

Claude Opus 4.8 Finds a $4.5 Billion Bug: The AI Era is Mass-Producing Hackers

marsbit1 ч. назад

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

"Codex Goal Mode: How to Make AI Work Continuously Toward a Specific Goal" OpenAI's Codex "goal mode" (/goal) transforms the AI from a reactive code assistant into a proactive execution agent capable of working autonomously for hours or even days to achieve a defined objective. To maximize its effectiveness, follow these key principles: 1. **Define Clear, Verifiable Exit Criteria:** The goal prompt should be a concise, measurable success condition, not a lengthy specification. Use quantifiable metrics like "reduce build time by 30%" or "achieve 100% test parity." 2. **Provide Initial Guidance and Tools:** Direct Codex toward likely problem areas and specify available tools (e.g., browsers, testing environments) to prevent it from exploring unproductive paths. 3. **Enable Progress Measurement:** Equip Codex with ways to track advancement, such as creating comparison tools for visual tasks or evaluation sets, ensuring it can gauge its own progress. 4. **Use a Realistic Execution Environment:** For tasks like performance optimization, provide access to environments that closely mimic production (e.g., similar configs, databases) to yield valid results. 5. **Be Cautious with Visual Goals:** Avoid vague "pixel-perfect" instructions. Instead, supplement visual references with functional checklists or design system specifications to prevent Codex from obsessing over minor details. 6. **Implement Progress Tracking:** For long-running tasks, have Codex commit code to draft PRs, update progress documents, or send Slack updates to maintain visibility into its work. 7. **Review and Consolidate Results:** Once the goal is met, instruct Codex to review its work, clean up ineffective experimental code, and reflect on what strategies succeeded or failed. Ultimately, using goal mode shifts the developer's role from writing prompts to managing a persistent engineering agent—defining objectives, establishing metrics, configuring environments, and conducting final reviews.

marsbit2 ч. назад

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

marsbit2 ч. назад

Торговля

Спот
Фьючерсы

Популярные статьи

Как купить ZEC

Добро пожаловать на HTX.com! Мы сделали приобретение Zcash (ZEC) простым и удобным. Следуйте нашему пошаговому руководству и отправляйтесь в свое крипто-путешествие.Шаг 1: Создайте аккаунт на HTXИспользуйте свой адрес электронной почты или номер телефона, чтобы зарегистрироваться и бесплатно создать аккаунт на HTX. Пройдите удобную регистрацию и откройте для себя весь функционал.Создать аккаунтШаг 2: Перейдите в Купить криптовалюту и выберите свой способ оплатыКредитная/Дебетовая Карта: Используйте свою карту Visa или Mastercard для мгновенной покупки Zcash (ZEC).Баланс: Используйте средства с баланса вашего аккаунта HTX для простой торговли.Третьи Лица: Мы добавили популярные способы оплаты, такие как Google Pay и Apple Pay, для повышения удобства.P2P: Торгуйте напрямую с другими пользователями на HTX.Внебиржевая Торговля (OTC): Мы предлагаем индивидуальные услуги и конкурентоспособные обменные курсы для трейдеров.Шаг 3: Хранение Zcash (ZEC)После приобретения вами Zcash (ZEC) храните их в своем аккаунте на HTX. В качестве альтернативы вы можете отправить их куда-либо с помощью перевода в блокчейне или использовать для торговли с другими криптовалютами.Шаг 4: Торговля Zcash (ZEC)С легкостью торгуйте Zcash (ZEC) на спотовом рынке HTX. Просто зайдите в свой аккаунт, выберите торговую пару, совершайте сделки и следите за ними в режиме реального времени. Мы предлагаем удобный интерфейс как для начинающих, так и для опытных трейдеров.

635 просмотров всегоОпубликовано 2024.03.29Обновлено 2026.06.02

Как купить ZEC

Обсуждения

Добро пожаловать в Сообщество HTX. Здесь вы сможете быть в курсе последних новостей о развитии платформы и получить доступ к профессиональной аналитической информации о рынке. Мнения пользователей о цене на ZEC (ZEC) представлены ниже.

活动图片