金融周度展望_finance-weekly-outlook
以下为本文档的中文说明finance-weekly-outlook 是一个生成式金融周度展望技能旨在为下一个交易周输出基于可靠数据源的、高概率的看多和看空行业及个股分析报告同时覆盖美股和中国 A 股两大市场。该技能位于完整的金融分析流水线的末端daily-finance 到 finance-core-analysis 再到 finance-explosive-article 最后到 finance-weekly-outlook是这一系列上游分析的最终输出产物。其核心用途是为投资者提供结构化的、逻辑严密的、数据驱动的周度交易计划和风险提示。该技能的核心特点包括系统性分析框架——采用严格的七步分析流程建立事实基础宏观流动性、风险偏好、资金流向、行业行为、预期与拥挤度、催化剂、判断宏观定位进攻/防守仓位上限、选择多空行业要求行业级证据而非单一股票走势、制定个股操盘计划包含入场条件、分批建仓、止损止盈、持仓决策、TradingAgents 内部评审模拟基本面分析师、技术分析师、多头/空头研究员等七个角色辩论、验证后保存、输出完整报告。严格的数据标准——要求所有数据来源权威可靠央行、统计局、交易所、监管机构、上市公司财报、路透/彭博/FT/WSJ 等主流财经媒体不使用自媒体和未经验证的社交帖子。数据标签清晰标注为已确认实际数据、市场预期/共识、模型/分析师估计和未独立验证。中文输出——报告完全以中文撰写包含执行摘要、数据仪表盘、行业多空矩阵、美股操盘计划、A 股操盘计划、情景推演、下周关键日历和数据来源等完整章节。合规边界——明确声明所有内容基于公开信息不构成个性化投资建议每个交易建议必须包含前提假设和证伪信号。该技能适用于对美股和 A 股有综合投资需求的分析师和交易者。Finance Weekly OutlookOverviewGenerate a Markdown weekly outlook that identifies high-probability bullish and bearish industries and stocks for the next trading week, covering both US equities and China A-shares. The report must combine upstream finance articles, fresh authoritative data, market-behavior validation, expectation-gap reasoning, scenario falsification, and explicit trading action plans.This skill is downstream of:daily-finance - finance-core-analysis - finance-explosive-article - finance-weekly-outlookIt can also run independently when upstream files are absent, but never invent missing data.Required InputsPrefer the newest available files in the current project’smarkdown/directory:markdown/daily-finance-YYYY-MM-DD.mdmarkdown/finance-core-analysis-YYYY-MM-DD.mdmarkdown/finance-explosive-article-YYYY-MM-DD.mdIf multiple dates exist, use the most recent trading-relevant date unless the user specifies a date. Preserve upstream facts and sources, but re-check any number used in the final thesis.Resolve the forecast window explicitly:For US markets, use the next US trading week.For A-shares, use the next China trading week.If holidays make the US and China windows different, show both date ranges in the header.If the user asks for “next week” during an active session, use the next full trading week unless they ask for the remaining days of the current week.If upstream files are absent, run independently with fresh data and state上游文件未使用in the report metadata.Mandatory External Data StepAlways access current external data before writing a current weekly outlook. Use primary/authoritative sources where possible:Official: central banks, statistics agencies, exchanges, regulators, listed-company filings, investor relations, earnings releasesMarket data: exchange pages, ETF/fund issuer data, CBOE/FRED/Treasury/Nasdaq/NYSE, HKEX, SSE/SZSE, CSRC/PBOC, Wind/Eastmoney/同花顺 only when official data is unavailableNews: Reuters, Bloomberg, FT, WSJ, 财新, 第一财经, 财经, 21世纪经济报道, 经济观察报Readreferences/data-checklist.mdwhen planning the data pull. Readreferences/tradingagents-method.mdwhen structuring the reasoning and risk review.Do not rely on self-media, unverified social posts, headlines without source traceability, or a single isolated stock example to support an industry conclusion.Current data must be no older than the latest completed trading session unless a source publishes less frequently. Label stale indicators and avoid using them as decisive evidence.Workflow1. Build the Fact BaseExtract upstream facts from daily/core/explosive articles, then add fresh data:Macro liquidity: Fed/PBOC signal, US 10Y, China 10Y, DXY, SHIBOR/DR007, Treasury/TGA or Fed balance-sheet context when relevantRisk appetite: VIX, put/call, market breadth, new highs/new lows, major index trendCapital flow: US sector ETF flow when available, A-share northbound/southbound if available, margin financing, main-board/STAR/ChiNext turnover structureSector behavior: weekly relative strength vs SP 500 and CSI 300, volume confirmation, support/resistance, breakout/failureExpectations and crowding: earnings revisions, revenue/EPS consensus direction, valuation percentile if available, fund positioning/crowdedness proxiesCatalysts: earnings calendar, macro data calendar, policy events, product/order/legal/regulatory eventsTag data clearly:【实】confirmed actual data【预】market expectation or consensus【估】model/analyst estimate【待】reported but not independently verified; avoid using as core evidenceProduce an internal evidence matrix before selecting sectors:MarketConstraintIndicatorLatest value/statusSourceDecision impactUSRates/liquidity/riske.g. 10Y, DXY, VIXvalue datesourcesupports/weakens/neutralChina ALiquidity/policy/breadthe.g. DR007, turnover, marginvalue datesourcesupports/weakens/neutral2. Decide Macro PositioningState whether next week should be偏进攻,中性偏进攻,中性偏防守, or偏防守.Use this chain:宏观约束 - 资金行为 - 风格偏好 - 仓位上限 - 证伪信号Do not say “risk appetite improves” without naming the observable variable that improved.Positioning must include:Gross exposure ceiling, expressed as a range, not a false precision point.Conditions for increasing exposure.Conditions for cutting exposure.One correlated-risk warning if several trades depend on the same macro factor.3. Select Bullish and Bearish IndustriesCover both US equities and A-shares. Output at least:2-4 bullish industries across the two markets2-4 bearish industries across the two marketsAt least one bullish and one bearish view for each market unless data quality makes this unsafe; if so, explain whyFor each industry, require:Industry-level evidence, not only a single stock moveMarket-behavior validation: price, volume, relative strength, breadth, or fund flowCatalyst and expectation-gap judgmentAssumptions and what changes if assumptions failCrowding/overpricing risk checkAvoid hindsight logic:Do not mark a current hot theme bullish merely because it is hot.Do not mark a falling industry bearish merely because it fell.Ask: “what is not yet priced for the coming week?”Use confidence labels:高: thesis has macro support, industry confirmation, catalyst, and clear invalidation.中: thesis has 2-3 supports but one important uncertainty.低: watchlist only; do not force a trading plan.4. Select Stocks and Trading PlansFor each market, provide a small number of stock calls. Prefer quality over quantity:US equities: 2-4 stocks total, including bullish and bearish/avoid candidatesA-shares: 2-4 stocks total, including bullish and bearish/avoid candidatesEach stock must include:Name, ticker/code, marketDirection: buy/add/hold/reduce/sell/avoid/watchThesis with 2-4 evidence pointsThree-factor score: earnings expectation, valuation/sentiment, liquidity/risk premiumExpectation-gap quadrant:Good news high expectation risk of “priced in”Good news low expectation bullish catalystBad news high expectation bearish shockBad news low expectation possible exhaustion/reversalConcrete action plan:Entry trigger or buy zoneStaged position sizingStop-loss or invalidation levelTake-profit or reduction planHold/sell/buy decision for existing positionsDaily tracking indicators and action timetableIf precise price levels are unavailable from reliable data, use technical conditions instead of fabricated numbers, such as “daily close above prior 20-day high with volume above 20-day average”.Trading action rules:Do not give abuy/add/sellaction unless entry, invalidation, sizing, and review date are all present.Usewatch/avoid/holdwhen data quality is insufficient or the setup lacks confirmation.Position sizing must be bounded by thesis confidence and correlated exposure; avoid all-in or certainty language.For existing positions, distinguish持有观察,减仓, and止损退出conditions.5. Run TradingAgents-Style Internal ReviewBefore finalizing, simulate these roles in writing or internally:Fundamental analyst: earnings, balance sheet, valuation, guidanceNews/policy analyst: catalysts, official data, regulatory and geopolitical eventsTechnical/market analyst: trend, volume, breadth, support/resistanceBull researcher: best case and upside pathBear researcher: strongest counterargument and downside pathTrader: translate thesis into action rulesRisk manager: position sizing, invalidation, drawdown, correlated exposuresUse structured findings rather than long dialogue. The final article should show the result of the debate through assumptions, alternatives, and risk controls.For each final stock call, include the review result in compact form:多头证据 / 空头反驳 / 最终处理 / 证伪信号6. Validate Before SavingCheck:Every major number has a source.Dates, units, direction, actual vs expected, intraday vs close are correct.US and A-share coverage is explicit.Bullish and bearish views are both present.Industry calls have industry-level evidence.Stock calls include clear trading actions, not vague “关注”.Each key thesis has assumptions and falsification signals.The report includes charts/tables/Mermaid diagrams.The article ends with a disclaimer.If a claim cannot be verified, weaken it, label it, or remove it.Legal and suitability boundary:Write actions as scenario-based public-information plans, not personalized advice.Do not imply guaranteed returns, target certainty, or suitability for all investors.Keep the disclaimer, but do not use it to justify unsupported precision.Output StructureSave the final Markdown to:markdown/finance-weekly-outlook-YYYY-MM-DD.mdUse the report date, createmarkdown/if needed, and write UTF-8.Required structure:# 未来一周中美股多空展望YYYY-MM-DD 至 YYYY-MM-DD 执行摘要... ## 一、结论先行下周仓位与主线 Include: forecast window, report date, upstream files used, macro positioning, gross exposure ceiling, top bullish/bearish themes. ## 二、数据仪表盘 Include tables and at least one Mermaid diagram showing: 触发变量 - 数据验证 - 预期差 - 交易计划 - 证伪信号 ## 三、行业多空矩阵 ## 四、美股个股操盘计划 ## 五、A股个股操盘计划 ## 六、情景推演前提、备选观点、证伪信号 ## 七、下周关键日历与跟踪清单 ## 八、数据来源 ## 免责声明 本文仅供参考不构成投资建议。市场有风险投资需谨慎。文中观点基于公开信息、特定前提假设和当时可得数据未来可能因宏观政策、流动性、财报、监管、地缘政治和市场情绪变化而失效。任何买入、卖出、持有或仓位安排都不应被视为个性化投资建议投资者应结合自身风险承受能力独立决策。Writing RulesWrite in Chinese unless the user requests another language.Be direct, structured, and source-backed.Use “不是A而是B” only when it exposes a real mechanism mismatch.Do not overstate probability; use “基准情景/备选情景/尾部风险” instead of false certainty.Give explicit actions for individual stocks, but keep the disclaimer and assumption boundaries clear.Prefer concise tables over long prose where decisions need comparison.

相关新闻

一文读懂汽车CAN总线 —— 从原理到故障诊断

一文读懂汽车CAN总线 —— 从原理到故障诊断

一、CAN总线是什么CAN(Controller Area Network,控制器局域网)是一种串行通信总线标准,最初由德国BOSCH公司于1980年代开发,用于解决汽车内部电子控制单元(ECU)之间的数据交换问题。在CAN出现之…

2026/7/24 13:27:15 阅读更多 →
企业AI知识库:八大行业如何落地?

企业AI知识库:八大行业如何落地?

企业AI知识库:八大行业如何落地? 当你的公司有几千份文档,员工找个资料要翻半小时,你会怎么做?买个大模型把所有文件喂进去?先别急——如果你的文件里有客户的银行流水、患者的病历、核心工艺的配方参数&a…

2026/7/21 23:18:59 阅读更多 →
【Linux网络】深入理解Linux IO多路复用:从本质到select服务器实战

【Linux网络】深入理解Linux IO多路复用:从本质到select服务器实战

🔥草莓熊Lotso:个人主页 ❄️个人专栏: 《C知识分享》 《Linux 入门到实践:零基础也能懂》 ✨生活是默默的坚持,毅力是永久的享受! 🎬 博主简介: 文章目录前言一. 多路转接的本质:把…

2026/7/21 23:18:59 阅读更多 →

最新新闻

OpenCV实战:图像拼接、自动判分与目标提取技术解析

OpenCV实战:图像拼接、自动判分与目标提取技术解析

1. 项目概述计算机视觉技术正在深刻改变我们处理图像信息的方式。作为一名长期从事视觉算法开发的工程师,我发现OpenCV这个开源库在解决实际问题时展现出惊人的灵活性。今天我想通过三个典型场景——图像拼接、试卷自动判分和目标提取,带大家深入理解计算…

2026/7/24 13:27:39 阅读更多 →
大模型在输变配电工程评审中的落地实践——金曲AI评审技术方案解析

大模型在输变配电工程评审中的落地实践——金曲AI评审技术方案解析

在输变配电工程建设领域,传统人工评审模式存在流程繁琐、参数核对量大、规则适配性弱、人为误差率高等痛点。随着行业数字化转型推进,依托大语言模型赋能工程评审全流程,成为解决行业评审效率低、专业性参差不齐问题的关键方向。本文基于Deep…

2026/7/24 13:27:39 阅读更多 →
露易丝·海的诗歌18

露易丝·海的诗歌18

我带着爱意看待过去在我所处的无穷无尽的生命中,一切都完美、完整、完全。我们每一个人,包括我自己,都以对我们来说意义非凡的方式体验着生命的富足和充实。现在,我带着爱意看待过去,选择从过去的经历中学习。没有对错…

2026/7/24 13:27:39 阅读更多 →
细粒度动作质量评估:从技术原理到应用实践

细粒度动作质量评估:从技术原理到应用实践

1. 项目概述:重新定义动作评估的维度 "以人为中心的细粒度动作质量评估"这个课题第一次引起我的注意是在三年前的一次康复训练项目中。当时我们团队需要为术后患者设计一套居家康复动作监测系统,但市面上所有动作识别方案都只能判断"是否…

2026/7/24 13:27:39 阅读更多 →
Anthropic 机器人实验:Agent 能力为何取决于接口抽象层

Anthropic 机器人实验:Agent 能力为何取决于接口抽象层

谈机器人 Agent 时,开发团队很容易先问“该选哪个大模型”。Anthropic 7 月 9 日发布的机器人研究给出了一个更接近工程现实的答案:同一个模型看起来强不强,很大程度取决于它通过什么接口接触物理世界。让模型直接输出关节力矩、让它写控制器…

2026/7/24 13:27:39 阅读更多 →
深入解析SAR ADC典型特性:以ADS8584S为例的高精度数据采集设计指南

深入解析SAR ADC典型特性:以ADS8584S为例的高精度数据采集设计指南

1. 项目概述:为什么我们需要深入理解一颗ADC的“典型特性”?在工业自动化、高端测试仪器或者精密医疗设备的设计中,我们常常会面对一个核心挑战:如何将现实世界中连续变化的物理信号(比如电机电流、传感器电压、生物电…

2026/7/24 13:26:39 阅读更多 →

日新闻

用Highcharts 创建可拖拽三维散点立方体3D图表

用Highcharts 创建可拖拽三维散点立方体3D图表

该案例基于Highcharts scatter3d 三维散点图实现空间立方体散点可视化,核心特色:三维 X/Y/Z 三轴空间,所有散点分布在 0~10 立方体空间内;散点使用径向渐变实现立体 3D 圆球质感;支持鼠标 / 触屏拖拽画布,…

2026/7/24 0:00:29 阅读更多 →
AppCertDlls:进程创建路径上的 DLL 入口

AppCertDlls:进程创建路径上的 DLL 入口

AppCertDlls:进程创建路径上的 DLL 入口 AppCertDlls 位于 HKLM\System\CurrentControlSet\Control\Session Manager\AppCertDlls。本文的程序功能是只读列出这个键在 64 位和 32 位注册表视图中的全部值,并显示每条值的来源、名称、类型和可安全显示的数…

2026/7/24 0:00:29 阅读更多 →
我的编程之路:第一篇博客

我的编程之路:第一篇博客

大家好,我是一名编程初学者,同时这也是我编程学习之路上的第一篇博客。在这里,我想要向大家介绍我的一些想法和规划。a.自我介绍我是一个刚刚接触编程的新手,目前在学习c语言,我对编程世界充满了强烈的好奇。当然&…

2026/7/24 0:00:29 阅读更多 →

周新闻

Go语言静态资源打包方案对比与实践指南

Go语言静态资源打包方案对比与实践指南

1. 项目背景与核心需求在Go语言开发中,我们经常需要处理静态资源文件的打包问题。无论是Web应用的模板文件、前端资源,还是配置文件、证书等,都需要随程序一起分发。传统做法是将这些文件与编译后的二进制文件放在同一目录下,但这…

2026/7/24 3:59:20 阅读更多 →
Go语言实现高性能LDAP认证服务的架构与实践

Go语言实现高性能LDAP认证服务的架构与实践

1. 项目背景与核心价值LDAP(轻量级目录访问协议)作为企业级身份认证的黄金标准,已经服务了超过80%的财富500强公司。我在金融科技领域实施统一认证体系时,发现传统Java方案存在启动慢、内存占用高等痛点。而Go语言凭借其协程并发模…

2026/7/24 1:23:39 阅读更多 →
【AI面试官实战指南】:用ChatGPT模拟10类高频技术岗面试,3天提升应答精准度92%

【AI面试官实战指南】:用ChatGPT模拟10类高频技术岗面试,3天提升应答精准度92%

更多请点击: https://intelliparadigm.com 第一章:AI面试官实战指南的核心价值与适用场景 AI面试官并非替代人类HR的“黑箱工具”,而是以可解释、可审计、可迭代的方式,赋能招聘全链路的关键基础设施。其核心价值在于将主观经验沉…

2026/7/23 17:49:47 阅读更多 →

月新闻