ZCash surges on funding news, but ZEC traders shouldn’t buy yet – Here’s why!

ambcryptoPublished on 2026-03-10Last updated on 2026-03-10

Abstract

ZCash (ZEC) surged 8.17% with a 43% increase in daily trading volume following the announcement that ZCash Open Developmental Lab (ZODL) secured over $25 million in seed funding from major investors including Paradigm and a16z crypto. Despite the short-term price boost, the longer-term outlook remains cautious. The higher timeframe trend is bullish, but swing traders should note that ZEC's structure is still bearish. A key resistance level lies at $251.4, which needs to be reclaimed to shift momentum bullishly. Traders are advised to wait for a stronger bullish setup before entering positions.

ZCash [ZEC] was one of the big winners in the past 24 hours, rallying 8.17% with a 43% uptick in daily trading volume.

The gains came after the ZCash Open Developmental Lab (ZODL) announced that it had secured over $25 million in seed funding to continue building the privacy-focused ecosystem.

The funding round drew support from Paradigm, a16z crypto, Winklevoss Capital, Coinbase Ventures, among other leading angels in crypto and technology.

ZODL was founded by Josh Swihart, former CEO of Electronic Coin Company. The ECC engineering and product teams had quit ZCash in January following a governance dispute over Bootstrap.

This conflict had made it difficult to work “effectively and with integrity“, Swihart had said. After joining ZODL, the team continued to build the primary user interface for ZCash.

The Zodl wallet was one of the points of focus for the team. The self-custodial mobile wallet app allows users to hold ZEC and execute shielded transactions. According to the project, the wallet has expanded ZCash’s shielded pool by more than 400% since its launch in 2024 (then named Zashi).

Funding news sends ZCash higher

Coinalyze stats showed that the ZEC Open Interest had soared by 9% in 24 hours. This corroborated the spike in spot trading volume, showing that speculators and spot buyers were interested in ZCash.

The news release has catalyzed short-term price gains for the privacy token, but in the longer-term outlook, the retracement phase has not ended yet.

The higher timeframe trend was bullish, and the retracement from $750 to $187, though seemingly extreme, was part of the higher timeframe retracement. However, this is for investors with a multi-year horizon.

For swing traders and short-term holders, ZCash remained bearishly biased for now. The triggers for a bullish recovery have not fired yet.

What is this trigger, and how should traders prepare?

The H4 timeframe’s swing structure remained bearish after the recent lower high at $203.5 (orange) was breached.

To flip the swing structure bullishly, the $251.4 high must be reclaimed.

This was where the $250 bearish order block was also located. The sizeable supply zone overhead was a threat in the short-term, even though prices bounced nearly 10% in a day.

Buyers can wait for this area to be flipped to demand before buying.

Their patience would be rewarded with a much stronger bullish setup than what is currently seen.


Final Summary

  • The $25 million ZCash Open Development Lab seed funding news drove ZEC prices higher by nearly 10% for the day.
  • Multi-timeframe analysis gave ZEC traders and investors differing signals. The $250 and $187 levels were the pivotal ones nearby.

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

Trending Cryptos

Related Reads

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.

marsbit50m ago

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

marsbit50m ago

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.

marsbit2h ago

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

marsbit2h ago

Trading

Spot

Hot Articles

How to Buy ZEC

Welcome to HTX.com! We've made purchasing Zcash (ZEC) simple and convenient. Follow our step-by-step guide to embark on your crypto journey.Step 1: Create Your HTX AccountUse your email or phone number to sign up for a free account on HTX. Experience a hassle-free registration journey and unlock all features.Get My AccountStep 2: Go to Buy Crypto and Choose Your Payment MethodCredit/Debit Card: Use your Visa or Mastercard to buy Zcash (ZEC) instantly.Balance: Use funds from your HTX account balance to trade seamlessly.Third Parties: We've added popular payment methods such as Google Pay and Apple Pay to enhance convenience.P2P: Trade directly with other users on HTX.Over-the-Counter (OTC): We offer tailor-made services and competitive exchange rates for traders.Step 3: Store Your Zcash (ZEC)After purchasing your Zcash (ZEC), store it in your HTX account. Alternatively, you can send it elsewhere via blockchain transfer or use it to trade other cryptocurrencies.Step 4: Trade Zcash (ZEC)Easily trade Zcash (ZEC) on HTX's spot market. Simply access your account, select your trading pair, execute your trades, and monitor in real-time. We offer a user-friendly experience for both beginners and seasoned traders.

4.0k Total ViewsPublished 2024.03.29Updated 2026.06.02

How to Buy ZEC

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 ZEC (ZEC) are presented below.

活动图片