/finance-weekly-outlook

name: finance-weekly-outlook
description: Generate a source-backed weekly financial trading outlook from daily-finance, finance-core-analysis, and finance-explosive-article outputs plus fresh authoritative market data. Use when the user asks for future-one-week market outlook, weekly bullish/bearish sectors, stock picks, China A-share and US stock coverage, explicit buy/sell/hold trading plans, entry/exit levels, staged position sizing, sector rotation, or next-week investment opportunities; also use after the daily-finance → finance-core-analysis → finance-explosive-article pipeline when a concrete weekly action report is requested.

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's `markdown/` 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

Read `references/data-checklist.md` when planning the data pull. Read `references/tradingagents-method.md` when 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:

| Market | Constraint | Indicator | Latest value/status | Source | Decision impact | |---|---|---|---|---|---| | US | Rates/liquidity/risk | e.g. 10Y, DXY, VIX | value + date | source | supports/weakens/neutral | | China A | Liquidity/policy/breadth | e.g. DR007, turnover, margin | value + date | source | supports/weakens/neutral |

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 i