SignalPlus波动率专栏(20241008):健康的回调

Odaily星球日报Published on 2024-10-08Last updated on 2024-10-08

Abstract

美国最高法院驳回了Battle Born Investment关于价值44亿美元比特币的所有权请求...

SignalPlus波动率专栏(20241008):健康的回调

SignalPlus波动率专栏(20241008):健康的回调

美国最高法院驳回了 Battle Born Investment 关于价值 44 亿美元比特币的所有权请求,使得美国政府能够继续出售这批来自丝绸之路市场的被查封的数字资产,这则新闻带来的负面情绪和地缘政治层面紧张情绪抵消了市场的乐观情绪,使得币价日内下探 2% 挑战 62 k 支撑位以及随之而来的实际波动率飙升。

尽管如此,有分析认为这波行情对 BTC 来说或许是一次健康的回调,投资者在高位展现出的避险情绪让币价得以暂时退回到 60 k 上方的支撑区间,减少了未来突然下跌的可能性,整体的乐观情绪被扭回中性,整段隐含波动率水平都被大幅下修,而且前端曲线异常平坦,磨平了 11 OCT 本该由 CPI 带来的不确定性,为看好经济事件波动的交易员提供了建仓短期日历价差的机会。

SignalPlus波动率专栏(20241008):健康的回调

Source: TradingView

SignalPlus波动率专栏(20241008):健康的回调

Source: Deribit (截至 2 MAY 16: 00 UTC+ 8)

SignalPlus波动率专栏(20241008):健康的回调

Source: SignalPlus

隐含波动率整体下跌

SignalPlus波动率专栏(20241008):健康的回调

Source: SignalPlus

RV 上涨但 IV 下跌

SignalPlus波动率专栏(20241008):健康的回调

您可在 t.signalplus.com 使用 SignalPlus 交易风向标功能,获取更多实时加密资讯。如果想即时收到我们的更新,欢迎关注我们的推特账号@SignalPlusCN,或者加入我们的微信群(添加小助手微信:SignalPlus 123)、Telegram 群以及 Discord 社群,和更多朋友一起交流互动。

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Founder of Baixing.com: The Notion That Large Language Models Will Devour Everything, I Believe Half of It

marsbit25m ago

Founder of Baixing.com: I Only Half Believe in the Notion that Large Language Models Devour Everything

The founder of Baixing Wang states that while large language models (LLMs) are an extremely important foundational technology—akin to electricity or the internet—he only "half believes" the notion that they will "consume everything." He argues that LLMs provide a base layer of intelligence, but real-world value and transformation come from integrating this intelligence into specific applications and devices designed for particular scenarios—like how electricity powers various appliances from washing machines to TVs. He agrees LLMs will likely consume or replace a significant portion of existing rule-based, workflow-driven software (e.g., many SaaS systems, CRMs), as these are precisely what LLMs excel at handling. However, numerous other elements—such as customer data, execution capabilities (e.g., booking a flight), trust, and physical-world interactions—will not be consumed. Wang emphasizes that after LLMs absorb certain software layers, they will open up a much larger space for innovation: new types of "streaming" software with less rigid interfaces, where fixed rules are managed by AI. This next wave of applications built on top of the stable LLM foundation is where the true mainstream opportunity lies. He cautions against the short-sightedness of declaring any technology as all-consuming, drawing parallels to past premature predictions about internet giants monopolizing the web. The key is to find opportunities within the areas LLMs do transform.

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