SignalPlus波动率专栏(20240117):市场持续整盘,IV再度走低

Odaily星球日报Pubblicato 2024-01-17Pubblicato ultima volta 2024-01-17

Introduzione

数字货币方面仍处在整盘行情当中,BTC/ETH分别小幅上行突破43000/2600后逐渐回吐大部分涨幅,两者的隐含波动率水平也因此再度下调,BTC中前端IV已经回到50%Vol以下,处于过去三个月内偏低的水平,ETH尽管同样迎来小幅下滑。

SignalPlus波动率专栏(20240117):市场持续整盘,IV再度走低

SignalPlus波动率专栏(20240117):市场持续整盘,IV再度走低

昨日(16 JAN)美联储官员沃勒发表讲话,在承认今年降息有望的同时也淡化了市场对快速降息的预期,称今后制定政策需要更加谨慎行事,以免过度紧缩,美债收益率在日内逐渐走强,十年期收益率重回 4% 以上,报 4.06% ,两年期现报 4.281% 。美国三大股指集体收跌,纳指/标普/道指分别下跌 0.19% /0.37% /0.62% 。

SignalPlus波动率专栏(20240117):市场持续整盘,IV再度走低

Source: SignalPlus, Economic Calendar

SignalPlus波动率专栏(20240117):市场持续整盘,IV再度走低

Source: Binance & TradingView

数字货币方面仍处在整盘行情当中,BTC/ETH 分别小幅上行突破 43000/2600 后逐渐回吐大部分涨幅,两者的隐含波动率水平也因此再度下调,BTC 中前端 IV 已经回到 50% Vol 以下,处于过去三个月内偏低的水平,ETH 尽管同样迎来小幅下滑,但再度与 BTC 产生了 3 ~ 8% 左右的 Vol Premium,整体大约处在过去三个月的中位数附近。

从交易上看, 23 FEB 24 的 Call Spread 再度得到市场的青睐,代表的有 BTC 60000 vs 65000 和 ETH 2700 vs 3200 ;另外我们观察到,在当前持续了 4 天多的整盘行情下,近期的三角看涨策略也得到了关注,对 BTC 来说,交易员通过卖出 19 JAN Call 降低了买入 2 FEB Call 的成本,将押注波动发生的时间点推后至下周,ETH 同样是买 2 FEB Call,但是选择了卖出更远的 23 FEB 作为对冲。

SignalPlus波动率专栏(20240117):市场持续整盘,IV再度走低

Source: Deribit (截至 17 JAN 16: 00 UTC+ 8)

SignalPlus波动率专栏(20240117):市场持续整盘,IV再度走低

Source: SignalPlus

SignalPlus波动率专栏(20240117):市场持续整盘,IV再度走低

Source: SignalPlus

SignalPlus波动率专栏(20240117):市场持续整盘,IV再度走低

Source: Deribit Block Trade

SignalPlus波动率专栏(20240117):市场持续整盘,IV再度走低

Source: Deribit Block Trade

SignalPlus波动率专栏(20240117):市场持续整盘,IV再度走低

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Letture associate

NVIDIA CPU Advances, China's RISC-V Responds: Semiconductor Deep Dive - Part Four

NVIDIA is set to launch its new Vera AI data center CPU in China as early as August, with high pricing. While this move offers a new option, it highlights China's continued dependence on foreign-controlled Arm architecture. In response, the Chinese semiconductor industry is increasingly turning to RISC-V as a strategic alternative for achieving high-performance computing autonomy. The article explores the concept of the "impossible triangle" in CPU development—balancing prosperity, control, and autonomy—and posits that RISC-V's open-source, modular nature offers a unique path to achieving all three. While RISC-V is already dominant in embedded systems, the focus is now shifting to data centers and AI workloads. China has become a global hotspot for RISC-V development, driven by AI-driven compute demand, supply chain concerns from export controls, cost benefits of open-source, and strong policy support. Multiple Chinese companies have reportedly crossed the key performance threshold of 15 SPECint per GHz, a benchmark for entering the high-performance CPU club. Progress extends beyond single-core benchmarks. Companies are developing complete computing subsystems, including commercial-grade coherent network-on-chip (NoC) technology and server processors with up to 40 cores that strictly adhere to the RVA23 standard to ensure software compatibility. Real-world applications are emerging in areas like video transcoding and edge AI. However, significant challenges remain. The RISC-V ecosystem faces fragmentation, immature toolchains and verification processes, and gaps in single-core performance and energy efficiency compared to mature x86 and Arm architectures. The formidable software moat, epitomized by NVIDIA's CUDA, is a long-term hurdle. In conclusion, while RISC-V cannot immediately replace offerings like NVIDIA's Vera, it represents a viable long-term path for China to develop a self-sufficient, high-performance CPU ecosystem. The journey is acknowledged to be long and arduous, requiring sustained effort to overcome technical and ecosystem challenges.

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NVIDIA CPU Advances, China's RISC-V Responds: Semiconductor Deep Dive - Part Four

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My Coding Betting Dashboard is Profiting, but Polymarket is Truly Not a Good Place for 'Arbitrage'

The author built a custom monitoring dashboard for Polymarket, a prediction market platform, and tested it with $1,600, achieving over 30% returns. However, the core argument is that Polymarket is not a good venue for traditional arbitrage. The dashboard has two main sections: a "Portfolio Dashboard" for tracking active positions with key metrics like total capital, P&L, and a risk-control module using a tier system (T1, T2, T3), and an "Opportunity Watchlist" for monitoring markets. The article details a critical structural trap in binary markets: a bet with a high perceived probability of success still carries a 100% loss risk if wrong. The author's T1/T2/T3 system is designed to manage this by limiting position sizes based on conviction and time horizon, emphasizing that high confidence should not equal high concentration. A key insight is the danger of "pseudo-diversification"—betting on different markets driven by the same underlying variable. The author concludes that Polymarket offers few true low-risk, arbitrage opportunities. It is instead a high-risk environment where wins can create a false sense of mastery, leading to large losses. The platform is better viewed as a training ground for honing judgment through disciplined, framework-driven betting rather than a reliable income source. The tools help transform intuition into structured, rule-based decisions to mitigate the risk of catastrophic errors.

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My Coding Betting Dashboard is Profiting, but Polymarket is Truly Not a Good Place for 'Arbitrage'

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WeChat AI Card Hands-On Guide: Has the AI Shopping Era Arrived?

**"WeChat AI Card" Practical Test Guide: Has the Era of AI Shopping Arrived?** WeChat has officially launched the "AI Exclusive Card," a feature integrated into its Workbuddy AI assistant. This card is designed to handle payments for AI-initiated purchases. Our hands-on test reveals it's not yet a tool for fully autonomous AI shopping, but rather a controlled payment layer for AI agents. The AI Card functions as an isolated sub-wallet within WeChat Pay. Users must bind the card and transfer funds into it from their main wallet. Crucially, every transaction requires explicit user confirmation via smartphone scan; AI cannot spend autonomously. Currently accessible through the Workbuddy agent, the card targets specific digital consumption scenarios: purchasing paid content (reports, data), calling paid APIs/tools, and subscribing to services. Its design prioritizes security and control by separating funds and mandating approval for each payment. We tested a real-world scenario: ordering bubble tea via Workbuddy using a "Meituan Life Assistant" skill. The process encountered multiple hurdles: high "skill" usage costs (exceeding daily free credits), and most importantly, while a payment was successfully initiated, the AI purchased an incorrect product (a mismatched group-buy coupon instead of the desired drink). This highlights the current limitation: the **AI Card only solves the payment step**. The broader challenge lies in the **AI agent's execution chain**—accurately understanding intent, navigating third-party platforms, selecting the right product, and ensuring proper fulfillment. The payment succeeded, but the purchase failed to meet the user's need. In conclusion, the WeChat AI Exclusive Card is a cautious, early-step experiment in AI commerce. It provides a secure, user-controlled payment method for agent interactions but is not yet capable of reliable, end-to-end complex purchases. For now, it's best used for low-value, low-risk digital services with careful user verification at each step. The vision of AI handling complete shopping tasks remains a work in progress.

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WeChat AI Card Hands-On Guide: Has the AI Shopping Era Arrived?

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