每日简报

2026-08-24

Morning Brief

2026-08-24

今日市场主要受到科技股暴跌和国际贸易紧张情绪的驱动,尤其是美国对加拿大汽车关税的上调。

今日最重要的 5 件事

  1. 1
    财经要点Financial Times

    美国将对加拿大汽车加收50%关税

    为什么重要 这一措施可能导致加美关系进一步紧张,影响双方的贸易政策和经济稳定。

  2. 2
    技术动态OpenAI News

    OpenAI推出GPT-5.6提升开发者效率

    为什么重要 新版本的推出将可能改变开发者的工作效率和软件开发的市场竞争格局。

  3. 3
    财经要点Bloomberg Markets

    亚洲股市因科技股下跌遭遇压力

    为什么重要

  4. 4
    时政观察NYT World

    英国首相访乌克兰重申支持

    为什么重要

  5. 5
    财经要点Bloomberg Markets

    新西兰市场监管对毛利人保险覆盖不足

    为什么重要

市场状态

Risk-Off
  • 4 个跟踪资产处于多头趋势,5 个处于空头趋势。
  • SPY 当前为 bullish,5 日 -1.19%,RSI 52.1。
  • 加密恐惧贪婪指数为 73(贪婪)。
  • 加密总市值 24h 变化为 -0.83%。

观察 观察 BTC 在 5 日 +14.01% 后能否企稳。

招聘信号

  • 升温 自适应AI系统设计 开发者急需能够设计自适应工具的专家,帮助改进AI产品的用户交互和响应能力。
  • 升温 软件动态测试能力 随着GPT-5.6的发布,市场对动态测试解决方案的需求正在上升。
  • 稳定 国际贸易政策分析 贸易战加剧使对国际贸易法律和政策的理解显得尤为重要。

论文趋势

X 讨论主题

Top Project

Alishahryar1/free-claude-code

Python · ★ 48,907 · 🍴 7,991 · 📈 889 stars today

Use Claude Code, Codex, Pi, and OpenCode for free (1.3B+ free tokens) from your terminal, app, IDE, or phone like OpenClaw (voice supported + ToS friendly)

中文介绍 该项目允许用户通过终端、应用程序、IDE或手机免费使用Claude Code、Codex等多个AI工具。使用这些工具可以提高开发者在编写代码时的效率和便捷性。为什么重要:它提供了一种免费获取高级AI编程助手的途径,暗示了AI工具在开发过程中的应用趋势。

Most Practical

openai/codex

Rust · ★ 117,002 · 🍴 17,837 · 📈 1,990 stars today

Lightweight coding agent that runs in your terminal

中文介绍 这个轻量级的编码助手可以在终端中运行,帮助开发者快速编写代码。通过自然语言处理和机器学习,它能够理解和生成代码,从而提升编程效率。为什么重要:它体现了AI驱动的开发趋势,简化了软件开发流程。

Most Surprising

MadsLorentzen/ai-job-search

Python · ★ 34,014 · 🍴 11,881 · 📈 378 stars today

The job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it.

中文介绍 该项目是基于Claude Code的AI求职应用框架,能够自动评估职位、定制简历、撰写求职信及准备面试。用户可以直接在本地运行,增强求职流程的自主性。为什么重要:它展示了AI在求职领域的有用性,使求职者能够更加高效地寻找工作。

EnvHarness: Awakening Static Worlds for Agent Learning

👍 258

LLM agents learn by interacting with environments, yet these environments are hand-built and static: blind to an agent's weaknesses, and quickly left behind as it improves. While recent environment generation methods attempt to address this, they require domain-specific pipelines, rely on expensive

中文介绍 这项研究针对当前 LLM 代理在静态环境中学习的局限性,提出了一种动态生成环境的方法,旨在根据代理的能力自动调整环境复杂度。通过无需领域特定管道的生成策略,该方法显示出在环境适应性和改进路径选择上优于传统方法。这对于提升智能代理在复杂任务中的表现具有重要意义。

FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis

👍 115

Training terminal agents requires scalable executable supervision, yet synthesizing high-quality terminal tasks remains challenging. Each task couples an instruction, an initialized environment, a reference solution, and an executable verifier; if these artifacts are generated from inconsistent assu

中文介绍 本论文探讨了在终端任务合成中,如何保持源意图和可执行状态的可扩展监督问题。通过构建一致的任务生成机制,结合指令、初始化环境及可执行验证器,提出了一种新的方法论,能有效提高生成任务的一致性和质量,进而推动终端代理训练的可行性。

SWE-bench Science: Can Coding Agents Resolve Engineering Tasks in Science?

👍 61

Software increasingly functions as part of the scientific instrument itself, making failures in scientific code capable of compromising not only program behavior but also the evidence underlying scientific conclusions. Yet existing evaluations of coding agents largely emphasize aggregate task succes

中文介绍 该研究分析了软件编码代理在工程任务中的应用,特别是在科学实验证据的可靠性方面。现有的编码代理评估主要集中在任务成功率上,然而,作者提出更多维度的评估标准,包括执行效率和代码可靠性,以提升编码代理在科学推理中的表现,体现其在科学研究中的重要性。

MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use

👍 31

Memory has become a key component of large language models, enabling them to retain information and learn from long-term interactions. However, existing memory benchmarks mainly evaluate whether information is correctly extracted, stored, and retrieved, while largely overlooking how retrieved memori

中文介绍 本研究构建了一个新的基准,评估大型语言模型(LLM)在记忆使用中的认知陷阱。现有的记忆基准大多仅关注信息提取、存储和检索的准确性,而忽视了检索后记忆对后续决策的影响。通过全面评估,研究提出能够提高 LLM 记忆使用效率的指标,对智能体学习过程具有意义。

Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence

👍 30

LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools an

中文介绍 论文探讨了在 LLM 代理时代,图结构工程的重要性。通过结合 Prompt Engineering、Context Engineering 和 Harness Engineering,作者提出了一种系统智能的架构,以优化 LLM 代理的表现和工具使用效率。这一工作为未来智能系统的构建提供了新方向,可能推动智能体在复杂任务中的应用。

SkillEvo: Self-Renewing Evolution Gradients from Multi-Turn Interaction Feedback

👍 30

Agent Skills are today either hand-authored or produced in a single LLM generation pass, and consequently possess no closed loop through which they might improve from the interaction failures they actually cause. Recent work does close this loop, but derives its feedback from single-turn question-an

中文介绍 本研究提出了 SkillEvo 方法,通过多轮交互反馈生成自我更新的演化梯度,解决了代理技能缺乏闭环学习的问题。与以往单轮反馈不同,该方法通过持续的交互改进技能生成,这在技能演化及自适应学习方面具有重要意义,为智能体自我改进提供了新的思路。

ParaTempo: Efficient Parallel Reasoning via Temporal Confidence

👍 26

Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows with reasoning depth and branch count. Existing methods for managing these parallel paths typically rely on final-answer consensus, local token con

中文介绍 提出了一种名为 ParaTempo 的高效并行推理框架,通过时间置信度管理多条解题路径,以提高大规模推理模型的准确性和鲁棒性。尽管并行推理的计算成本随着推理深度和分支数量增加,但本框架有效地降低了这些成本,为未来研究指明了方向并提升了推理效率。

OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs

👍 26

Recent omni-modal large language models (Omni-LLMs) show great potential as real-time video assistants, which continuously perceive environments and guide users to achieve specific goals. Unlike traditional passive video understanding, interactive assistants should actively combine visual states, us

中文介绍 OmniAssistBench 基准提供了一种新的评估方式,专注于交互式的 omni-modal 大型语言模型在实时视频环境下的表现。该研究强调了主动结合视觉状态和用户目标的重要性,推动了智能助手在动态环境中应用的可能性,对交互式人工智能的研究进展具有重要意义。

Repo0: Design-Driven Zero-to-All Code Generation

👍 18

Large language model agents have made substantial progress in code generation, yet most existing systems assume a predefined repository architecture. This assumption does not hold in zero-to-all code generation, where an agent must construct an entire software project directly from natural-language

中文介绍 Repo0 研究了从自然语言直接生成完整软件项目的能力,提出了一种设计驱动的零到全的代码生成方法。该方法打破了对预定义代码仓库架构的依赖,展示了在没有先验知识情况下生成高质量代码的潜力,为代码生成领域提供了新的视角和应用前景。

FlashPrefill V2: Block-Sparse Prefill Attention for Long-Context LLM Serving

👍 17

Long-context modeling is a pivotal capability for Large Language Models, yet the quadratic complexity of attention remains a critical bottleneck, particularly during the compute-intensive prefilling phase. Our previous work, FlashPrefill, mitigates this cost through instantaneous pattern discovery a

中文介绍 FlashPrefill V2 研究了在长上下文模型中使用块稀疏预填充注意力的策略,以应对注意力计算的瓶颈。通过即时模式发现的技术有效降低了计算需求,显著提升了长上下文处理的性能,为大规模语言模型的服务提供了切实的优化方案。

Beyond Correctness: Benchmarking and Aligning Response Behaviors in Hybrid-Thinking MLLMs

👍 16

Hybrid-thinking multimodal large language models (MLLMs) allow a single model to alternate between deliberative thinking and latency-efficient non-thinking inference. Although these modes differ in reasoning budget, their delivered responses should satisfy the same user-facing standard. Correctness

中文介绍 本研究探讨了混合思维多模态大型语言模型(MLLM)的响应行为,将其分为审慎思考和低延迟推理模式。尽管这两种模式在推理预算上存在差异,论文旨在确保不同模式下用户响应的一致性,推动 MLLM 在多任务环境下的广泛应用。

FlowEvo: Self-Evolving Agents through the Co-Evolution of Workflows and Executable Skills

👍 16

Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episode are usually discarded after execution. Existing skill libraries provide reusable executable routines, but are typically assembled offline and do not grow from

中文介绍 FlowEvo 研究了大型语言模型代理自我演化的能力,提出了通过共演化工作流和可执行技能来应对复杂任务的策略。该方法通过在线生成和使用技能库,提升了模型在任务执行后的自我改进能力,为智能体的适应性和创新性提供了新思路。

Every Coin Has Two Sides: On the Dual Nature of Generalization in On-Policy Distillation of Large Language Models

👍 14

On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's own policy, yet its generalization behavior remains poorly understood, as most studies evaluate OPD on a single domain and on benchmarks close to the training data. We present a control

中文介绍 这项研究揭示了在政策蒸馏中,OPD(On-policy distillation)的双重特性,深入分析了其推理模式与泛化能力。通过对多域实验的验证,论文展示了 OPD 的适应性,从而为大规模模型的政策学习和优化提供了重要见解。

Thinking in a Low-Resource Language: What SFT Builds, What RL Fixes, What Accuracy Cannot See

👍 14

Take three frontier mixture-of-experts models (Alibaba, OpenAI, NVIDIA; 3.6-4.0B active parameters each) and fine-tune them to reason in a low-resource language. On accuracy benchmarks almost nothing happens, and the benchmark itself is noise at this scale: changing only the random seed moves the sc

中文介绍 采用前沿的Mixture-of-Experts模型进行低资源语言推理的研究显示,尽管精度基准未见显著变化,改进 RL 策略后模型在少量资源下的推理能力显著增强。这揭示了精度与模型架构、资源消耗之间的复杂关系,为低资源语言应用提供了新的思考。

The Embedder's Dilemma: LLMs Are Better, but at What Cost?

👍 13

Should you replace your text-embedding pipeline with a large language model? We answer this with a controlled, cost-aware comparison of ten LLMs across six families and 26 embedding models (118M to 14B parameters) on 37 tasks spanning classification, semantic textual similarity (STS), clustering, pa

中文介绍 本文探讨了在十种LLM(包含不同参数规模)与二十六种嵌入模型之间的对比,为文本嵌入管道的替代提供了量化依据。通过对分类、相似性检测等37项任务的分析,展示了 LLM 在不同场景下的优缺点,为未来嵌入模型选择提供了实证依据。

Hierarchical Self-Improvement: A Framework for Task-Specific Evolvable Agent Harnesses

👍 11

Modern LLM agents are often improved by modifying prompts, tools, or workflows manually, while the executable scaffold surrounding the model---the harness---is typically treated as a fixed artifact after deployment. This work studies an alternative where the harness is task-specific and continuously

中文介绍 提出了一种层次化自我改进框架,旨在为任务特定的可进化代理设计灵活的执行机制。通过对执行框架的动态调整,研究展现了直观的自我改进能力,对提升 LLM 在不断变化任务中的效率具有潜在影响。

VA-Judger: Reward Modeling from Human Preference Feedback for Joint Video-Audio Generation

👍 11

Using reinforcement learning to post-train joint video-audio generation models requires a reward signal. Existing methods construct this reward by combining metrics for individual quality dimensions, including audio quality, visual fidelity, and synchronization. However, these metrics evaluate perce

中文介绍 VA-Judger 通过人类偏好反馈建立了联合视频音频生成的奖赏模型,为强化学习后训练提供新的信号。尽管现有评估方法对各维度的指标进行了组合,论文提出的综合评价方法提升了生成模型的质量评估标准,推动多模态生成的智能体改进。

Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization

👍 10

Large language models often fail to answer questions about a bounded document collection when the source documents are not retrieved at inference time. We study this setting as document knowledge internalization: converting a fixed corpus into usable parametric knowledge for retrieval-free question

中文介绍 本研究探讨了大型语言模型在没有检索的情况下,如何将文档知识进行内部化,以提升回答固定文档集合问题的能力。所提出的分阶段后训练方法,能够有效转化静态文档为可用的参数化知识,对知识采纳和问答系统的发展具有重要意义。

PolicyGuide: From Guarding One Action to Guiding the Whole Workflow for Policy-Compliant LLM Agents

👍 9

Customer-service LLM agents must follow organizational policy when acting on a user's behalf. Compliance failures arise from either forbidden actions, such as granting an ineligible change, or omitted procedural requirements, such as identification or confirmation. Runtime safeguards can intervene o

中文介绍 PolicyGuide 提出了在客户服务场景中,如何确保 LLM 代理遵循组织政策的框架。研究关注了在执行过程中的合规性失效,提供了一种全流程引导机制,以提高代理的决策质量和合规性,对智能客户服务的未来应用具有深远影响。

NARU: A Benchmark for NARrative Evolution and Cultural Nuance Understanding in Japanese Extreme Long Video

👍 8

Long-form video understanding encompasses tasks that go beyond retrieving isolated events, including tracking an evolving narrative and interpreting social meaning that may remain implicit. However, existing benchmarks rarely evaluate these capabilities jointly, particularly in high-context, non-Eng

中文介绍 NARU 旨在建立评估标准,以理解日本极长视频中的叙事演变和文化细微差异。现有基准往往单一评估孤立事件,论文强调了联合评估叙事跟踪与隐含社会意义的必要性,对高上下文的视频理解研究具有重要意义。

how i built an SEO/AEO blog engine

@harsehaj · 3.3K 粉丝 · 135.1K 阅 · 507 赞 · 35 转

i proposed and executed an engineering project end-to-end this summer as a growth engineering intern at @browserbase: blogEO, an engine that audits, rewrites, and generates SEO/AEO-tailored blogs on

中文介绍 博主分享了他在@browserbase 实习期间开发的博客引擎blogEO,该引擎能够审计、重写并生成针对SEO/AEO优化的博客内容。这个项目从头到尾的执行展现了工程师在增长工程方面的工作流程,反映了对博客内容优化需求的持续关注。

Deep Dive: The Next Trillion-Dollar Futures Market

@chamath · 2.4M 粉丝 · 81.1K 阅 · 595 赞 · 52 转

“I actually believe a new asset class will be buying futures of compute. We just don’t have enough compute power right now.” - Larry Fink, CEO of BlackRock Recent news moved that prediction closer to

中文介绍 博主引述BlackRock CEO Larry Fink的观点,认为未来可能会出现计算能力期货这一新资产类别。由于目前计算能力不足,相关市场的形成成为热门讨论话题,反映出业界对计算资源需求激增的共识。

17 Skills I Would Install on a Fresh Hermes Setup (10x Powerful Hack)

@FareaNFts · 66.2K 粉丝 · 43.7K 阅 · 534 赞 · 39 转

If I wiped Hermes tomorrow and started from zero, I would not open a blank chat and "figure it out." I would install skills first. A raw Hermes install is smart. A skilled Hermes setup is unfair.

中文介绍 博主讨论了在全新Hermes安装中应优先配置的17项技能,强调技能提升对性能的显著影响。他认为,具备技能的Hermes设置相比原始配置更具优势,表明了对于工具优化和高效工作流的重视。

How to encourage smarter AI use in the classroom

This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox, sign up here. Chatbots took many schools by surprise upon their release a few years ago. Suddenly, students carried an app in their phones t

中文介绍 MIT科技评论探讨了如何鼓励在课堂上更智能地使用人工智能,指出许多学校在聊天机器人的发布后感到意外,学生们迅速开始使用这些工具。

Advancing price-performance for developers with GPT‑5.6 in Kiro

GPT‑5.6 is now available in Kiro, helping developers plan, build, review, and test software with better price-performance.

中文介绍 OpenAI宣布GPT-5.6在Kiro中上线,旨在帮助开发者以更优的性价比进行软件规划、构建、审查和测试。

Kids outlearn AI—and we still don’t know why

People have been talking to each other for at least 100,000 years, as best we can tell. And in all that time, there has been only one thing in the world that could learn a human language to perfect fluency: a human child. Now there are two. Four short years after the release of ChatGPT,…

中文介绍 MIT科技评论指出,儿童在语言学习上超越了人工智能,尽管这一现象尚无明确解释。

The Evolution of the Agent Harness

Models keep absorbing the harness into their weights — soon, it will be a harness for human attention rather than for the model.

中文介绍 Latent Space讨论了代理工具的演变,预计未来将更关注人类注意力,而不仅限于模型本身。

Simulation: the new Scaling Law — Joon Sung Park, Simile AI

Simile’s CEO about his journey from the viral Generative Agents to creating 8 Billion Digital Twins of every living human... and why it’s gone from fun exploration to very serious business.

中文介绍 Simile AI的首席执行官介绍了其从流行的生成代理到创建80亿个数字双胞胎的转变,强调从探索变为严肃商业的原因。

not much happened today

**Ox Alpha** emerged as a mystery model with strong coding and agentic performance, likely a **Zhipu/GLM-family** model such as **GLM-5.3 Vision**. Analysts suggest its gains come from post-training and infrastructure improvements rather than sheer size, based on the **743B base** of **GLM-5.2** wit

中文介绍 Smol AI News讨论了一款名为Ox Alpha的新模型,该模型具有强大的编码与智能代理性能,分析师认为其进展来自后期训练与基础设施的改进,而非仅仅是规模。

The /wayfinder Skill: Navigating the “Fog of War” of Planning

Matt Pocock tells us about his /wayfinder skill, for greenfield projects or for when the way forward is unclear.

中文介绍 Matt Pocock介绍了他的/wayfinder技能,旨在帮助在规划不明确时进行指导,适用于开拓性项目。

Debates over AI consciousness are a trap

“Runaway” AI, “rogue” agents, and “autonomous” actors—the current rhetoric would have you believe that AI agents are not only awake and aware, but angry at their creators. Prominent tech leaders such as Demis Hassabis, Dario Amodei, and Sam Altman push for regulation of these seemingly “superhuman”

中文介绍 MIT科技评论警告,关于人工智能意识的争论可能导致误解,一些知名科技领袖呼吁对这些问题进行规范。

市场总览

当前市场整体表现为中性和略微疲软,美股方面,S&P 500 ETF 近期下降0.29%,Nasdaq 100 ETF 下跌1%,总体而言,随着市场对未来经济走势的谨慎态度,表现不如预期。在加密市场中,加密货币总市值为 2.67 兆美元,减少了 0.83%。加密恐慌贪婪指数为 73,表明当前市场处于贪婪状态,这可能导致部分资产面临回调风险。

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BTC-USD Bitcoin (BTC)
偏下行

当前 Bitcoin 的 RSI 值为 82.2,显示超买状态,且其价格在近 52 周高点下跌了 37.42%。加之观察到的 MACD 走势,显示出动能减弱的迹象,整体技术面偏向下行。

GC=F 黄金期货
偏上行

黄金期货当前价格为 4730.6,RSI 值达到 77.2,处于超买状态,但其价格在过去 5 日涨幅达到 7.08%,显示出强劲的上行动能,同时 MACD 指标也在上升,表明短期可能会继续维持上行趋势。

AAPL Apple
中性

Apple 当前价格为 310.34,RSI 值为 48.2,并且其价格在 SMA20 和 SMA50 之间微幅波动,表明没有明显的上涨或下行趋势,因此当前指标状态被视为中性。

全部资产

^VIX

VIX 恐慌指数

$15.85 +4.76%
5 日
+4.34%
距 52w 高
-55.1%
RSI(14)
49.3
趋势
空头
SMA 20 / 50 / 200
15.86 / 16.69 / 18.47
MACD / 信号
-0.465 / -0.542
MACD 金叉 (2 天前)空头排列

^TNX

10Y 美债收益率 (%)

$4.70 -0.72%
5 日
-0.42%
距 52w 高
-0.9%
RSI(14)
55.2
趋势
多头
SMA 20 / 50 / 200
4.68 / 4.58 / 4.33
MACD / 信号
0.032 / 0.034
接近 52 周高多头排列

DX-Y.NYB

美元指数 DXY

$98.98 +0.19%
5 日
-0.66%
距 52w 高
-2.8%
RSI(14)
34.1
趋势
中性
SMA 20 / 50 / 200
99.76 / 100.47 / 99.18
MACD / 信号
-0.428 / -0.327
接近 52 周高

SPY

S&P 500 ETF

$763.47 -0.29%
5 日
-1.19%
距 52w 高
-2.0%
RSI(14)
52.1
趋势
多头
SMA 20 / 50 / 200
763.55 / 752.26 / 707.99
MACD / 信号
4.945 / 6.240
MACD 死叉 (2 天前)接近 52 周高多头排列

QQQ

Nasdaq 100 ETF

$706.32 -1.00%
5 日
-3.23%
距 52w 高
-5.7%
RSI(14)
46.4
趋势
中性
SMA 20 / 50 / 200
710.40 / 713.23 / 653.22
MACD / 信号
2.281 / 3.160
MACD 死叉 (1 天前)

AAPL

Apple

$310.34 +0.32%
5 日
+1.55%
距 52w 高
-9.9%
RSI(14)
48.2
趋势
中性
SMA 20 / 50 / 200
313.01 / 310.50 / 281.70
MACD / 信号
-1.488 / -1.253

MSFT

Microsoft

$487.31 +0.84%
5 日
+1.45%
距 52w 高
-12.0%
RSI(14)
64.0
趋势
中性
SMA 20 / 50 / 200
478.00 / 421.78 / 431.18
MACD / 信号
18.908 / 22.378
MACD 死叉 (4 天前)

NVDA

Nvidia

$208.48 -2.91%
5 日
-7.35%
距 52w 高
-11.9%
RSI(14)
44.7
趋势
多头
SMA 20 / 50 / 200
213.78 / 207.65 / 195.34
MACD / 信号
2.647 / 3.774
MACD 死叉 (1 天前)多头排列

GOOGL

Alphabet

$348.06 +0.94%
5 日
+1.18%
距 52w 高
-14.8%
RSI(14)
49.5
趋势
中性
SMA 20 / 50 / 200
349.47 / 351.57 / 333.38
MACD / 信号
-1.986 / -1.758

TSLA

Tesla

$348.95 -3.83%
5 日
+2.84%
距 52w 高
-30.0%
RSI(14)
51.4
趋势
空头
SMA 20 / 50 / 200
330.13 / 364.85 / 402.84
MACD / 信号
-2.508 / -8.147
空头排列

META

Meta

$559.02 +1.66%
5 日
-1.75%
距 52w 高
-29.3%
RSI(14)
42.3
趋势
空头
SMA 20 / 50 / 200
574.74 / 592.80 / 623.79
MACD / 信号
-13.770 / -10.663
空头排列
加密恐慌贪婪
73
贪婪
加密总市值
$2.67 T
-0.83% / 24h
BTC 主导率
59.1%
ETH 11.2%
24h 成交量
$124.3 B
活跃币 18,664

BTC-USD

Bitcoin

$78,973.16 +1.57%
5 日
+14.01%
距 52w 高
-37.4%
RSI(14)
82.2
趋势
中性
SMA 20 / 50 / 200
67,510.40 / 65,463.18 / 69,085.88
MACD / 信号
3,450.422 / 1,772.719
RSI 超买

ETH-USD

Ethereum

$2,484.68 +0.85%
5 日
+10.36%
距 52w 高
-48.2%
RSI(14)
79.8
趋势
中性
SMA 20 / 50 / 200
2,049.54 / 1,934.59 / 2,010.31
MACD / 信号
151.843 / 89.539
RSI 超买

SOL-USD

Solana

$98.42 +3.12%
5 日
+15.28%
距 52w 高
-61.1%
RSI(14)
86.2
趋势
中性
SMA 20 / 50 / 200
80.44 / 77.73 / 81.25
MACD / 信号
5.150 / 2.712
RSI 超买

BABA

阿里巴巴 (BABA)

$118.47 -0.73%
5 日
-5.00%
距 52w 高
-38.5%
RSI(14)
45.0
趋势
中性
SMA 20 / 50 / 200
124.51 / 114.47 / 137.54
MACD / 信号
2.001 / 3.101
MACD 死叉 (1 天前)

PDD

拼多多 (PDD)

$87.07 -1.48%
5 日
+0.15%
距 52w 高
-37.5%
RSI(14)
48.8
趋势
中性
SMA 20 / 50 / 200
88.78 / 84.64 / 100.33
MACD / 信号
0.609 / 0.909

JD

京东 (JD)

$29.20 -0.58%
5 日
+2.46%
距 52w 高
-20.8%
RSI(14)
41.3
趋势
中性
SMA 20 / 50 / 200
31.18 / 29.26 / 29.25
MACD / 信号
-0.262 / 0.139
金叉(SMA50↑SMA200) (今天)

0700.HK

腾讯控股 (0700.HK)

HK$440.00 -3.72%
5 日
-1.43%
距 52w 高
-35.6%
RSI(14)
42.1
趋势
空头
SMA 20 / 50 / 200
463.40 / 454.32 / 525.62
MACD / 信号
-3.906 / -1.017
空头排列

GC=F

黄金期货

$4,730.60 +2.30%
5 日
+7.08%
距 52w 高
-15.3%
RSI(14)
77.2
趋势
中性
SMA 20 / 50 / 200
4,310.98 / 4,188.75 / 4,502.58
MACD / 信号
122.929 / 80.837
RSI 超买

CL=F

WTI 原油期货

$85.15 -2.19%
5 日
+0.77%
距 52w 高
-28.7%
RSI(14)
54.8
趋势
多头
SMA 20 / 50 / 200
82.32 / 79.04 / 77.59
MACD / 信号
1.468 / 0.954
多头排列

USDCNY=X

美元 / 人民币

¥6.71 -0.16%
5 日
-0.46%
距 52w 高
-6.7%
RSI(14)
20.1
趋势
空头
SMA 20 / 50 / 200
6.75 / 6.77 / 6.88
MACD / 信号
-0.012 / -0.010
RSI 超卖接近 52 周低空头排列
风险提示

过去走势不代表未来表现,本报告仅供技术指标解读参考,投资决策应基于综合分析。

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Iran Pledges to Defy Trump’s Economic Sanctions

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U.S.-Canada Trade War: What to Know About Tariffs and the Negotiations

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