Zoomex Launches ZoomexStocks: Trade Global Equities With USDT + Limited-Time Fee Rebate Campaign

TheNewsCryptoPublished on 2026-04-15Last updated on 2026-04-15

Abstract

Zoomex has launched ZoomexStocks, a new feature enabling users to trade global equities using USDT without a traditional brokerage account. Initially offering 12 U.S. assets—including tech stocks like Apple and NVIDIA, indices, and crypto-related equities—the platform allows trading with as little as 5 USDT. It eliminates the need for fiat deposits or account switching, providing a unified experience for crypto users. Prices mirror real-market data, and trading is available 24/7. A limited-time campaign offers up to 100 USDT in fee rebates to new users. ZoomexStocks aims to simplify multi-asset portfolio management within a single platform.

Crypto trading platform Zoomex today officially announced the launch of ZoomexStocks, enabling users to trade global equity assets directly using USDT—without the need for a traditional brokerage account.

At launch, 12 major U.S. equity-related assets are available, covering leading tech stocks, core indices, and crypto-related equities, including Apple, Tesla, and NVIDIA. Users can start trading with as little as 5 USDT.

To celebrate the launch, Zoomex is introducing a limited-time trading fee rebate campaign, offering up to 100 USDT in rebates to further lower the barrier to entry.

Breaking Traditional Barriers: A Stock Trading Experience Designed for Crypto Users

ZoomexStocks introduces a new way to access equity markets—distinct from traditional brokerage systems—allowing users to manage both crypto and stock exposure within a single account:

• No brokerage account required — trade directly with an existing Zoomex account

• No fiat deposits needed — supports USDT / USDC trading

• Simplified workflow — no platform switching or cross-border transfers

This product is purpose-built for crypto-native users, enabling frictionless access to global markets.

Three Core Asset Categories

The initial launch includes three categories to support diverse trading strategies:

Tech Stocks

Apple (AAPLx), Tesla (TSLAx), Alphabet (GOOGLx), NVIDIA (NVDAx), Meta (METAx), Amazon (AMZNx)

Index Assets

Nasdaq (QQQx), S&P 500 (SPYx)

Crypto-Related Stocks

MicroStrategy (MSTRx), Robinhood (HOODx), Circle (CRCLx), Coinbase (COINx)

With a unified account, users can seamlessly manage cross-asset allocation and strategy execution within a single platform.

Transparent Pricing & Liquidity Design

ZoomexStocks uses a price-mirroring mechanism based on real market data, referencing major exchanges such as Nasdaq and NYSE:

• Real-time price synchronization to minimize deviation

• Profit and loss calculated based on price movements

• Buy and sell anytime for enhanced liquidity

Note: ZoomexStocks provides exposure to the price performance of underlying assets and does not represent direct ownership of equities.

24/7 Trading: Beyond Traditional Market Hours

Unlike traditional stock markets, ZoomexStocks supports 24/7 trading, allowing users to:

• Position ahead of weekends

• React instantly to macro or industry news

• Dynamically hedge between crypto and equity assets

This model offers greater flexibility and aligns with the always-on nature of crypto markets.

Limited-Time Trading Fee Rebate Campaign

To encourage users to explore the new product, Zoomex is launching a promotional campaign:

• 100% rebate on stock token trading fees during the campaign

• Maximum rebate per user: 100 USDT

• Total prize pool: 50,000 USDT

• Rewards distributed within 7 working days after the campaign ends

Users must register for the campaign to qualify.

👉 Join now:

https://www.zoomex.com/en/alpha

A Zoomex product lead commented:

“ZoomexStocks is not about replicating traditional brokerages—it’s about offering crypto users a more intuitive way to access global assets.”

“By lowering barriers and simplifying the process, we aim to enable users to manage multi-asset portfolios within a single platform.”

For more information about Zoomex US stock-related assets, pleasevisit

About ZOOMEX

Founded in 2021, Zoomex is a global cryptocurrency trading platform with over 3 million users across more than 35 countries and regions, offering 700+ trading pairs. Guided by its core values of “Simple × User-Friendly × Fast,” Zoomex is also committed to the principles of fairness, integrity, and transparency, delivering a high-performance, low-barrier, and trustworthy trading experience.

Powered by a high-performance matching engine and transparent asset and order displays, Zoomex ensures consistent trade execution and fully traceable results. This approach reduces information asymmetry and allows users to clearly understand their asset status and every trading outcome. While prioritizing speed and efficiency, the platform continues to optimize product structure and overall user experience with robust risk management in place.

As an official partner of the Haas F1 Team, Zoomex brings the same focus on speed, precision, and reliable rule execution from the racetrack to trading. In addition, Zoomex has established a global exclusive brand ambassador partnership with world-class goalkeeper Emiliano Martínez. His professionalism, discipline, and consistency further reinforce Zoomex’s commitment to fair trading and long-term user trust.

In terms of security and compliance, Zoomex holds regulatory licenses including Canada MSB, U.S. MSB, U.S. NFA, and Australia AUSTRAC, and has successfully passed security audits conducted by blockchain security firm Hacken. Operating within a compliant framework while offering flexible identity verification options and an open trading system, Zoomex is building a trading environment that is simpler, more transparent, more secure, and more accessible for users worldwide.

For more info: ZOOMEX Website | X | Telegram | Discord

Disclaimer: TheNewsCrypto does not endorse any content on this page. The content depicted in this Press Release does not represent any investment advice. TheNewsCrypto recommends our readers to make decisions based on their own research. TheNewsCrypto is not accountable for any damage or loss related to content, products, or services stated in this Press Release.

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.

marsbit12m ago

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

marsbit12m ago

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36m ago

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.

marsbit1h ago

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

marsbit1h ago

Ethereum Price Prediction: ETH Could Double While Cardano (ADA) and This Newcomer Token Near 500% Rally

As the cryptocurrency market anticipates a potential bull cycle, investor focus is shifting toward projects with high return potential. Ethereum (ETH), currently at approximately $2,014, is highlighted for its long-term value, with predictions suggesting a possible 2x increase to over $4,000. Cardano (ADA), trading around $0.2329, remains a choice for its research-driven development and scalability focus, though some seek higher upside elsewhere. Significant attention is drawn to Little Pepe (LILPEPE), a new Layer 2 Ethereum-compatible token priced at $0.0022 in its presale stage, which has raised over $28.19 million. The project aims to build a dedicated blockchain for meme communities, offering lower costs and faster transactions. Backers speculate a potential 500% gain, driven by strong presale demand, planned ecosystem features like staking and a launchpad, and active community engagement through giveaways. While Ethereum and Cardano are viewed as foundational investments, LILPEPE represents a newer, high-growth opportunity for investors targeting the meme coin sector.

TheNewsCrypto1h ago

Ethereum Price Prediction: ETH Could Double While Cardano (ADA) and This Newcomer Token Near 500% Rally

TheNewsCrypto1h ago

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.

marsbit2h ago

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

marsbit2h ago

Trading

Spot

Futures