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 Outlook

Overview

Generate 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-outlook

It can also run independently when upstream files are absent, but never invent missing data.

Required Inputs

Prefer the newest available files in the current project’smarkdown/directory:

1. markdown/daily-finance-YYYY-MM-DD.md
2. markdown/finance-core-analysis-YYYY-MM-DD.md
3. markdown/finance-explosive-article-YYYY-MM-DD.md

If 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 Step

Always 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 releases
• Market 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 unavailable
• News: 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.

Workflow

1. Build the Fact Base

Extract 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 relevant
• Risk appetite: VIX, put/call, market breadth, new highs/new lows, major index trend
• Capital flow: US sector ETF flow when available, A-share northbound/southbound if available, margin financing, main-board/STAR/ChiNext turnover structure
• Sector behavior: weekly relative strength vs S&P 500 and CSI 300, volume confirmation, support/resistance, breakout/failure
• Expectations and crowding: earnings revisions, revenue/EPS consensus direction, valuation percentile if available, fund positioning/crowdedness proxies
• Catalysts: earnings calendar, macro data calendar, policy events, product/order/legal/regulatory events

Tag 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 evidence

Produce an internal evidence matrix before selecting sectors:

2. Decide Macro Positioning

State 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 Industries

Cover both US equities and A-shares. Output at least:

• 2-4 bullish industries across the two markets
• 2-4 bearish industries across the two markets
• At least one bullish and one bearish view for each market unless data quality makes this unsafe; if so, explain why

For each industry, require:

• Industry-level evidence, not only a single stock move
• Market-behavior validation: price, volume, relative strength, breadth, or fund flow
• Catalyst and expectation-gap judgment
• Assumptions and what changes if assumptions fail
• Crowding/overpricing risk check

Avoid 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 Plans

For each market, provide a small number of stock calls. Prefer quality over quantity:

• US equities: 2-4 stocks total, including bullish and bearish/avoid candidates
• A-shares: 2-4 stocks total, including bullish and bearish/avoid candidates

Each stock must include:

• Name, ticker/code, market
• Direction: buy/add/hold/reduce/sell/avoid/watch
• Thesis with 2-4 evidence points
• Three-factor score: earnings expectation, valuation/sentiment, liquidity/risk premium
• Expectation-gap quadrant:
  • Good news + high expectation = risk of “priced in”
  • Good news + low expectation = bullish catalyst
  • Bad news + high expectation = bearish shock
  • Bad news + low expectation = possible exhaustion/reversal
• Concrete action plan:
  • Entry trigger or buy zone
  • Staged position sizing
  • Stop-loss or invalidation level
  • Take-profit or reduction plan
  • Hold/sell/buy decision for existing positions
  • Daily tracking indicators and action timetable

If 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 Review

Before finalizing, simulate these roles in writing or internally:

• Fundamental analyst: earnings, balance sheet, valuation, guidance
• News/policy analyst: catalysts, official data, regulatory and geopolitical events
• Technical/market analyst: trend, volume, breadth, support/resistance
• Bull researcher: best case and upside path
• Bear researcher: strongest counterargument and downside path
• Trader: translate thesis into action rules
• Risk manager: position sizing, invalidation, drawdown, correlated exposures

Use 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 stoc
k 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 Saving

Check:

• 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 Structure

Save the final Markdown to:

markdown/finance-weekly-outlook-YYYY-MM-DD.md

Use 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 Rules

• Write 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.