market-news-analyst

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

Market News Analyst

市场新闻分析师

Overview

概述

This skill enables comprehensive analysis of market-moving news events from the past 10 days, focusing on their impact on US equity markets and commodities. The skill automatically collects news from trusted sources using WebSearch and WebFetch tools, evaluates market impact magnitude, analyzes actual market reactions, and produces structured English reports ranked by market impact significance.

本技能可对过去10天内影响市场走势的新闻事件进行全面分析，重点关注其对美国股票市场和大宗商品的影响。该技能通过WebSearch和WebFetch工具自动从可信来源收集新闻，评估市场影响量级，分析实际市场反应，并生成按市场影响重要性排序的结构化英文报告。

When to Use This Skill

何时使用本技能

Use this skill when:

User requests analysis of recent major market news (past 10 days)
User wants to understand market reactions to specific events (FOMC decisions, earnings, geopolitical)
User needs comprehensive market news summary with impact assessment
User asks about correlations between news events and commodity price movements
User requests analysis of how central bank policy announcements affected markets

Example user requests:

"Analyze the major market news from the past 10 days"
"How did the latest FOMC decision impact the market?"
"What were the most important market-moving events this week?"
"Analyze recent geopolitical news and commodity price reactions"
"Review mega-cap tech earnings and their market impact"

符合以下场景时可使用本技能：

用户请求分析近期（过去10天）的重大市场新闻
用户想要了解市场对特定事件（FOMC决议、财报、地缘政治事件）的反应
用户需要带影响评估的全面市场新闻汇总
用户询问新闻事件与大宗商品价格走势之间的关联
用户请求分析央行政策公告对市场的影响

用户请求示例：

"分析过去10天的重大市场新闻"
"最新的FOMC决议对市场造成了什么影响？"
"本周最重要的市场驱动事件有哪些？"
"分析近期地缘政治新闻和大宗商品价格反应"
"梳理科技巨头财报及其对市场的影响"

Analysis Workflow

分析工作流

Follow this structured 6-step workflow when analyzing market news:

分析市场新闻时请遵循以下结构化的6步工作流：

Step 1: News Collection via WebSearch/WebFetch

步骤1：通过WebSearch/WebFetch收集新闻

Objective: Gather comprehensive news from the past 10 days covering major market-moving events.

Search Strategy:

Execute parallel WebSearch queries covering different news categories:

Monetary Policy:

Search: "FOMC meeting past 10 days", "Federal Reserve interest rate", "ECB policy decision", "Bank of Japan"
Target: Central bank decisions, forward guidance changes, inflation commentary

Inflation/Economic Data:

Search: "CPI inflation report [current month]", "jobs report NFP", "GDP data", "PPI producer prices"
Target: Major economic data releases and surprises

Mega-Cap Earnings:

Search: "Apple earnings [current quarter]", "Microsoft earnings", "NVIDIA earnings", "Amazon earnings", "Tesla earnings", "Meta earnings", "Google earnings"
Target: Results, guidance, market reactions for largest companies

Geopolitical Events:

Search: "Middle East conflict oil prices", "Ukraine war", "US China tensions", "trade war tariffs"
Target: Conflicts, sanctions, trade disputes affecting markets

Commodity Markets:

Search: "oil prices news past week", "gold prices", "OPEC meeting", "natural gas prices", "copper prices"
Target: Supply disruptions, demand shifts, price movements

Corporate News:

Search: "major M&A announcement", "bank earnings", "tech sector news", "bankruptcy", "credit rating downgrade"
Target: Large corporate events beyond mega-caps

Recommended News Sources (Priority Order):

Official sources: FederalReserve.gov, SEC.gov (EDGAR), Treasury.gov, BLS.gov
Tier 1 financial news: Bloomberg, Reuters, Wall Street Journal, Financial Times
Specialized: CNBC (real-time), MarketWatch (summaries), S&P Global Platts (commodities)

Search Execution:

Use WebSearch for broad topic searches
Use WebFetch for specific URLs from official sources or major news outlets
Collect publication dates to ensure news is within 10-day window
Capture: Event date, source, headline, key details, market context (pre-market, trading hours, after-hours)

Filtering Criteria:

Focus on Tier 1 market-moving events (see references/market_event_patterns.md)
Prioritize news with clear market impact (price moves, volume spikes)
Exclude: Stock-specific small-cap news, minor product updates, routine filings

Think in English throughout collection process. Document each significant news item with:

Date and time
Event type (monetary policy, earnings, geopolitical, etc.)
Source reliability tier
Initial market reaction (if observable)

目标： 收集过去10天内涵盖重大市场驱动事件的全面新闻。

搜索策略：

并行执行覆盖不同新闻类别的WebSearch查询：

货币政策：

搜索关键词："FOMC meeting past 10 days", "Federal Reserve interest rate", "ECB policy decision", "Bank of Japan"
目标内容：央行决议、前瞻指引调整、通胀相关评论

通胀/经济数据：

搜索关键词："CPI inflation report [current month]", "jobs report NFP", "GDP data", "PPI producer prices"
目标内容：重大经济数据发布及超预期情况

巨头企业财报：

搜索关键词："Apple earnings [current quarter]", "Microsoft earnings", "NVIDIA earnings", "Amazon earnings", "Tesla earnings", "Meta earnings", "Google earnings"
目标内容：头部企业的财报结果、前瞻指引、市场反应

地缘政治事件：

搜索关键词："Middle East conflict oil prices", "Ukraine war", "US China tensions", "trade war tariffs"
目标内容：影响市场的冲突、制裁、贸易争端

大宗商品市场：

搜索关键词："oil prices news past week", "gold prices", "OPEC meeting", "natural gas prices", "copper prices"
目标内容：供应中断、需求变化、价格走势

企业新闻：

搜索关键词："major M&A announcement", "bank earnings", "tech sector news", "bankruptcy", "credit rating downgrade"
目标内容：巨头企业之外的大型企业事件

推荐新闻来源（优先级排序）：

官方来源：FederalReserve.gov、SEC.gov (EDGAR)、Treasury.gov、BLS.gov
一线财经媒体：Bloomberg、Reuters、Wall Street Journal、Financial Times
专业媒体：CNBC（实时新闻）、MarketWatch（新闻汇总）、S&P Global Platts（大宗商品）

搜索执行：

使用WebSearch进行广泛主题搜索
使用WebFetch获取官方来源或主流媒体的特定URL内容
收集发布日期，确保新闻在10天窗口期内
抓取信息：事件日期、来源、标题、核心细节、市场背景（盘前、交易时段、盘后）

筛选标准：

重点关注一线市场驱动事件（参考references/market_event_patterns.md）
优先筛选有明确市场影响的新闻（价格波动、成交量激增）
排除内容：小盘股个股新闻、 minor产品更新、常规备案文件

收集过程全程用英语思考。记录每一条重要新闻的以下信息：

日期和时间
事件类型（货币政策、财报、地缘政治等）
来源可信度层级
初步市场反应（如果可观测）

Step 2: Load Knowledge Base References

步骤2：加载知识库参考文件

Objective: Access domain expertise to inform impact assessment.

Load relevant reference files based on collected news types:

Always Load:

```
references/market_event_patterns.md
```
- Comprehensive patterns for all major event types
```
references/trusted_news_sources.md
```
- Source credibility assessment

Conditionally Load (Based on News Collected):

If monetary policy news found:

Focus on: market_event_patterns.md → Central Bank Monetary Policy Events section
Key frameworks: Interest rate hike/cut reactions, QE/QT impacts, hawkish/dovish tone

If geopolitical events found:

Load:

references/geopolitical_commodity_correlations.md

Focus on: Energy Commodities, Precious Metals, regional frameworks matching event

If mega-cap earnings found:

Load:
```
references/corporate_news_impact.md
```
Focus on: Specific company sections, sector contagion patterns

If commodity news found:

Load:

references/geopolitical_commodity_correlations.md

Focus on: Specific commodity sections (Oil, Gold, Copper, etc.)

Knowledge Integration: Compare collected news against historical patterns to:

Predict expected market reactions
Identify anomalies (market reacted differently than historical pattern)
Assess whether reaction was typical magnitude or outsized
Determine if contagion occurred as expected

目标： 获取领域专业知识，为影响评估提供支撑。

根据收集到的新闻类型加载相关参考文件：

始终加载：

```
references/market_event_patterns.md
```
- 覆盖所有重大事件类型的完整模式库
```
references/trusted_news_sources.md
```
- 来源可信度评估标准

按需加载（基于收集到的新闻类型）：

如果收集到货币政策新闻：

重点参考：market_event_patterns.md → 央行货币政策事件板块
核心框架：加息/降息反应、QE/QT影响、鹰派/鸽派表态

如果收集到地缘政治事件：

加载：

references/geopolitical_commodity_correlations.md

重点参考：能源大宗商品、贵金属、与事件匹配的区域框架

如果收集到巨头企业财报：

加载：
```
references/corporate_news_impact.md
```
重点参考：特定企业板块、行业传导模式

如果收集到大宗商品新闻：

加载：

references/geopolitical_commodity_correlations.md

重点参考：特定大宗商品板块（石油、黄金、铜等）

知识整合： 将收集到的新闻与历史模式对比，完成以下分析：

预测预期市场反应
识别异常情况（市场反应与历史模式不同）
评估反应是符合典型量级还是超出预期
判断是否发生了预期的传导效应

Step 3: Impact Magnitude Assessment

步骤3：影响量级评估

Objective: Rank each news event by market impact significance.

Impact Assessment Framework:

For each news item, evaluate across three dimensions:

1. Asset Price Impact (Primary Factor):

Measure actual or estimated price movements:

Equity Markets:

Index-level: S&P 500, Nasdaq 100, Dow Jones
- Severe: ±2%+ in day
- Major: ±1-2%
- Moderate: ±0.5-1%
- Minor: ±0.2-0.5%
- Negligible: <0.2%
Sector-level: Specific sector ETFs
- Severe: ±5%+
- Major: ±3-5%
- Moderate: ±1-3%
- Minor: <1%
Stock-specific: Individual mega-caps
- Severe: ±10%+ (and index weight causes index move)
- Major: ±5-10%
- Moderate: ±2-5%

Commodity Markets:

Oil (WTI/Brent):
- Severe: ±5%+
- Major: ±3-5%
- Moderate: ±1-3%
Gold:
- Severe: ±3%+
- Major: ±1.5-3%
- Moderate: ±0.5-1.5%
Base Metals (Copper, etc.):
- Severe: ±4%+
- Major: ±2-4%
- Moderate: ±1-2%

Bond Markets:

10-Year Treasury Yield:
- Severe: ±20bps+ in day
- Major: ±10-20bps
- Moderate: ±5-10bps

Currency Markets:

USD Index (DXY):
- Severe: ±1.5%+
- Major: ±0.75-1.5%
- Moderate: ±0.3-0.75%

2. Breadth of Impact (Multiplier):

Assess how many markets/sectors affected:

Systemic (3x multiplier): Multiple asset classes, global markets
- Examples: FOMC surprise, banking crisis, major war outbreak
Cross-Asset (2x multiplier): Equities + commodities, or equities + bonds
- Examples: Inflation surprise, geopolitical supply shock
Sector-Wide (1.5x multiplier): Entire sector or related sectors
- Examples: Tech earnings cluster, energy policy announcement
Stock-Specific (1x multiplier): Single company (unless mega-cap with index impact)
- Examples: Individual company earnings, M&A

3. Forward-Looking Significance (Modifier):

Consider future implications:

Regime Change (+50%): Fundamental market structure shift
- Examples: Fed pivot from hiking to cutting, major geopolitical realignment
Trend Confirmation (+25%): Reinforces existing trajectory
- Examples: Consecutive strong inflation prints, sustained earnings beats
Isolated Event (0%): One-off with limited forward signal
- Examples: Single data point within range, company-specific issue
Contrary Signal (-25%): Contradicts prevailing narrative
- Examples: Good news ignored by market, bad news rallied

Impact Score Calculation:

Impact Score = (Price Impact Score × Breadth Multiplier) + Forward-Looking Modifier

Price Impact Score:
- Severe: 10 points
- Major: 7 points
- Moderate: 4 points
- Minor: 2 points
- Negligible: 1 point

Example Calculations:

FOMC 75bps Rate Hike (hawkish tone):

Price Impact: S&P 500 -2.5% (Severe = 10 points)
Breadth: Systemic (equities, bonds, USD, commodities all moved) = 3x
Forward: Trend confirmation (ongoing tightening) = +25%
Score: (10 × 3) × 1.25 = 37.5

NVIDIA Earnings Beat:

Price Impact: NVDA +15%, Nasdaq +1.5% (Severe = 10 points)
Breadth: Sector-wide (semis, tech broadly) = 1.5x
Forward: Trend confirmation (AI demand) = +25%
Score: (10 × 1.5) × 1.25 = 18.75

Geopolitical Flare-up (Middle East):

Price Impact: Oil +8%, S&P -1.2% (Severe = 10 points)
Breadth: Cross-asset (oil, equities, gold) = 2x
Forward: Isolated event (no escalation) = 0%
Score: (10 × 2) × 1.0 = 20

Single Stock Earnings (Non-Mega-Cap):

Price Impact: Stock +12%, no index impact (Major = 7 points)
Breadth: Stock-specific = 1x
Forward: Isolated = 0%
Score: (7 × 1) × 1.0 = 7

Ranking: After scoring all news items, rank from highest to lowest impact score. This determines report ordering.

目标： 按市场影响重要性对每起新闻事件排序。

影响评估框架：

对每一条新闻，从三个维度进行评估：

1. 资产价格影响（核心因素）：

衡量实际或预估的价格波动：

股票市场：

指数层面：S&P 500、Nasdaq 100、道琼斯工业平均指数
- 严重：单日涨跌幅±2%以上
- 重大：单日涨跌幅±1-2%
- 中等：单日涨跌幅±0.5-1%
- 轻微：单日涨跌幅±0.2-0.5%
- 可忽略：涨跌幅<0.2%
行业层面：特定行业ETF
- 严重：涨跌幅±5%以上
- 重大：涨跌幅±3-5%
- 中等：涨跌幅±1-3%
- 轻微：涨跌幅<1%
个股层面：巨头企业个股
- 严重：涨跌幅±10%以上（且因其指数权重带动指数波动）
- 重大：涨跌幅±5-10%
- 中等：涨跌幅±2-5%

大宗商品市场：

原油（WTI/布伦特）：
- 严重：涨跌幅±5%以上
- 重大：涨跌幅±3-5%
- 中等：涨跌幅±1-3%
黄金：
- 严重：涨跌幅±3%以上
- 重大：涨跌幅±1.5-3%
- 中等：涨跌幅±0.5-1.5%
基本金属（铜等）：
- 严重：涨跌幅±4%以上
- 重大：涨跌幅±2-4%
- 中等：涨跌幅±1-2%

债券市场：

10年期美国国债收益率：
- 严重：单日波动±20bps以上
- 重大：单日波动±10-20bps
- 中等：单日波动±5-10bps

外汇市场：

美元指数（DXY）：
- 严重：涨跌幅±1.5%以上
- 重大：涨跌幅±0.75-1.5%
- 中等：涨跌幅±0.3-0.75%

2. 影响广度（乘数）：

评估受影响的市场/行业数量：

系统性影响（3倍乘数）： 影响多个资产类别、全球市场
- 示例：FOMC意外决议、银行业危机、大规模战争爆发
跨资产影响（2倍乘数）： 影响股票+大宗商品，或股票+债券
- 示例：通胀数据超预期、地缘政治供应冲击
全行业影响（1.5倍乘数）： 影响整个行业或相关行业
- 示例：科技企业财报集中发布、能源政策公告
个股影响（1倍乘数）： 仅影响单家企业（除非是能带动指数波动的巨头企业）
- 示例：单家企业财报、并购事件

3. 前瞻意义（调整系数）：

考虑事件对未来的影响：

机制转变（+50%）： 市场基础结构发生根本性变化
- 示例：美联储从加息转向降息、重大地缘政治格局调整
趋势确认（+25%）： 强化现有走势
- 示例：通胀数据连续超预期、企业盈利持续超预期
孤立事件（0%）： 一次性事件，前瞻信号有限
- 示例：符合预期的单个数据点、企业特定问题
反向信号（-25%）： 与主流叙事矛盾
- 示例：利好消息被市场忽略、利空消息发布后市场上涨

影响得分计算：

Impact Score = (Price Impact Score × Breadth Multiplier) + Forward-Looking Modifier

Price Impact Score:
- Severe: 10 points
- Major: 7 points
- Moderate: 4 points
- Minor: 2 points
- Negligible: 1 point

计算示例：

FOMC加息75bps（鹰派表态）：

价格影响：S&P 500下跌2.5%（严重=10分）
影响广度：系统性（股票、债券、美元、大宗商品全部波动）=3倍
前瞻意义：趋势确认（持续收紧）=+25%
得分：(10 × 3) × 1.25 = 37.5

NVIDIA财报超预期：

价格影响：NVDA上涨15%，Nasdaq上涨1.5%（严重=10分）
影响广度：全行业（半导体、整个科技板块）=1.5倍
前瞻意义：趋势确认（AI需求）=+25%
得分：(10 × 1.5) × 1.25 = 18.75

中东地缘冲突升级：

价格影响：原油上涨8%，S&P 500下跌1.2%（严重=10分）
影响广度：跨资产（原油、股票、黄金）=2倍
前瞻意义：孤立事件（无升级）=0%
得分：(10 × 2) × 1.0 = 20

非巨头企业单家财报：

价格影响：个股上涨12%，无指数影响（重大=7分）
影响广度：个股=1倍
前瞻意义：孤立事件=0%
得分：(7 × 1) × 1.0 = 7

排序： 所有新闻事件得分计算完成后，按影响得分从高到低排序，决定报告的内容顺序。

Step 4: Market Reaction Analysis

步骤4：市场反应分析

Objective: Analyze how markets actually responded to each event.

For each significant news item (Impact Score >5), conduct detailed reaction analysis:

Immediate Reaction (Intraday):

Direction: Positive, negative, mixed
Magnitude: Align with price impact categories
Timing: Pre-market, during trading, after-hours
Volatility: VIX movement, bid-ask spreads

Multi-Asset Response:

Equities:

Index performance (S&P 500, Nasdaq, Dow, Russell 2000)
Sector rotation (which sectors outperformed/underperformed)
Individual stock moves (mega-caps, relevant companies)
Growth vs Value, Large vs Small Cap divergences

Fixed Income:

Treasury yields (2Y, 10Y, 30Y)
Yield curve shape (steepening, flattening, inversion)
Credit spreads (IG, HY)
TIPS breakevens (inflation expectations)

Commodities:

Energy: Oil (WTI, Brent), Natural Gas
Precious Metals: Gold, Silver
Base Metals: Copper, Aluminum (if relevant)
Agricultural: Wheat, Corn, Soybeans (if relevant)

Currencies:

USD Index (DXY)
EUR/USD, USD/JPY, GBP/USD
Emerging market currencies
Safe havens (JPY, CHF)

Derivatives:

VIX (volatility index)
Options activity (put/call ratio, unusual volume)
Futures positioning

Pattern Comparison:

Compare observed reaction against expected pattern from knowledge base:

Consistent: Reaction matched historical pattern
- Example: Fed hike → Tech stocks down, USD up (as expected)
Amplified: Reaction exceeded typical pattern
- Example: Inflation print +0.3% above consensus → Selloff 2x typical
- Investigate: Positioning, sentiment, cumulative factors
Dampened: Reaction less than historical pattern
- Example: Geopolitical event → Oil barely moved
- Investigate: Already priced in, other offsetting factors
Inverse: Reaction opposite of historical pattern
- Example: Good news ignored, bad news rallied
- Investigate: "Good news is bad news" dynamics, Fed pivot hopes

Anomaly Identification:

Flag reactions that deviate significantly from patterns:

Market shrugged off typically market-moving news
Overreaction to typically minor news
Contagion failed to spread as expected
Safe havens didn't work (correlations broke)

Sentiment Indicators:

Risk-On vs Risk-Off: Which regime dominated
Positioning: Evidence of crowded trades unwinding
Momentum: Follow-through in subsequent sessions or reversal

目标： 分析市场对每起事件的实际反应。

对每起重要新闻事件（影响得分>5），开展详细的反应分析：

即时反应（日内）：

方向：正面、负面、分化
量级：与价格影响分类对齐
时间：盘前、交易时段、盘后
波动率：VIX波动、买卖价差

跨资产反应：

股票：

指数表现（S&P 500、Nasdaq、道琼斯、罗素2000）
行业轮动（哪些行业跑赢/跑输）
个股波动（巨头企业、相关企业）
成长股 vs 价值股、大盘股 vs 小盘股分化情况

固定收益：

美国国债收益率（2年期、10年期、30年期）
收益率曲线形态（陡峭化、平坦化、倒挂）
信用利差（投资级、高收益）
TIPS盈亏平衡通胀率（通胀预期）

大宗商品：

能源：原油（WTI、布伦特）、天然气
贵金属：黄金、白银
基本金属：铜、铝（如相关）
农产品：小麦、玉米、大豆（如相关）

外汇：

美元指数（DXY）
EUR/USD、USD/JPY、GBP/USD
新兴市场货币
避险货币（日元、瑞郎）

衍生品：

VIX（波动率指数）
期权交易情况（看跌/看涨比率、异常成交量）
期货持仓情况

模式对比：

将观测到的反应与知识库中的预期模式对比：

符合预期： 反应与历史模式匹配
- 示例：美联储加息 → 科技股下跌、美元上涨（符合预期）
反应放大： 反应超出典型模式
- 示例：通胀数据比共识预期高0.3% → 抛售幅度是典型情况的2倍
- 调查方向：持仓情况、市场情绪、累积因素
反应平淡： 反应低于历史模式
- 示例：地缘政治事件发生后原油几乎没有波动
- 调查方向：已被市场定价、其他对冲因素存在
反向反应： 反应与历史模式相反
- 示例：利好消息被忽略、利空消息发布后市场上涨
- 调查方向："利好即利空"动态、美联储降息预期

异常识别：

标记明显偏离模式的反应：

市场对典型的市场驱动新闻无反应
对典型的 minor新闻过度反应
传导效应未按预期扩散
避险资产未发挥作用（相关性断裂）

情绪指标：

风险偏好 vs 风险规避：哪种机制占主导
持仓情况：拥挤交易平仓的证据
动量：后续交易日延续走势还是反转

Step 5: Correlation and Causation Assessment

步骤5：相关性与因果关系评估

Objective: Distinguish direct impacts from coincidental timing.

Multi-Event Analysis:

When multiple significant events occurred in the 10-day period, assess interactions:

Reinforcing Events:

Same directional impact
Example: Hawkish FOMC + hot CPI → Both bearish for equities, amplified move
Combined impact often non-linear (greater than sum of parts)

Offsetting Events:

Opposite directional impacts
Example: Strong earnings (positive) + geopolitical tensions (negative) → Muted net reaction
Identify which factor dominated

Sequential Events:

One event set up reaction to next
Example: First rate hike modest reaction, second rate hike severe (cumulative tightening concerns)
Path dependence matters

Coincidental Timing:

Events unrelated but occurred simultaneously
Difficult to isolate individual impacts
Note uncertainty in attribution

Geopolitical-Commodity Correlations:

For geopolitical events, specifically analyze commodity market reactions using geopolitical_commodity_correlations.md:

Energy:

Map conflict/sanction to supply disruption risk
Assess actual vs feared supply impact
Duration: Temporary spike vs sustained elevation

Precious Metals:

Safe-haven flows vs real rate drivers
Gold response to risk-off events
Central bank buying implications

Industrial Metals:

Demand destruction from economic slowdown fears
Supply chain disruptions
China factor in copper, aluminum

Agriculture:

Black Sea grain exports (Russia-Ukraine)
Weather overlays
Food security policy responses

Transmission Mechanisms:

Trace how news impacts flowed through markets:

Direct Channel:

News → Immediate asset price reaction
Example: OPEC cuts → Oil prices up immediately

Indirect Channels:

News → Economic impact → Asset prices
Example: Rate hike → Mortgage rates up → Housing slows → Homebuilder stocks down

Sentiment Channel:

News → Risk appetite shift → Broad asset reallocation
Example: Banking crisis → Flight to quality → Treasuries rally, stocks sell

Feedback Loops:

Initial reaction creates secondary effects
Example: Stock selloff → Margin calls → Forced selling → Deeper selloff

目标： 区分直接影响和时间巧合。

多事件分析：

当10天窗口期内发生多起重大事件时，评估事件之间的相互作用：

强化事件：

影响方向相同
示例：鹰派FOMC决议 + 通胀数据超预期 → 均对股市利空，放大波动
组合影响通常是非线性的（大于各部分之和）

对冲事件：

影响方向相反
示例：强劲财报（正面） + 地缘政治紧张（负面） → 净反应平淡
识别占主导的影响因素

顺序事件：

前一起事件为后一起事件的反应铺垫
示例：第一次加息反应温和，第二次加息反应剧烈（累积收紧担忧）
路径依赖会产生重要影响

时间巧合：

事件无关联但同时发生
难以隔离单个事件的影响
归因时注明不确定性

地缘政治-大宗商品相关性：

针对地缘政治事件，使用geopolitical_commodity_correlations.md专门分析大宗商品市场反应：

能源：

将冲突/制裁与供应中断风险对应
评估实际供应影响 vs 预期供应影响
持续时间：临时上涨 vs 持续高位

贵金属：

避险资金流动 vs 实际利率驱动因素
黄金对风险规避事件的反应
央行购金的影响

工业金属：

经济放缓担忧带来的需求破坏
供应链中断
中国因素对铜、铝的影响

农产品：

黑海粮食出口（俄罗斯-乌克兰）
天气因素叠加
粮食安全政策反应

传导机制：

追踪新闻影响如何在市场中传导：

直接渠道：

新闻 → 资产价格即时反应
示例：OPEC减产 → 原油价格立即上涨

间接渠道：

新闻 → 经济影响 → 资产价格
示例：加息 → 抵押贷款利率上升 → 房地产市场放缓 → 建筑商股票下跌

情绪渠道：

新闻 → 风险偏好转变 → 大类资产重新配置
示例：银行业危机 → 资金流向安全资产 → 国债上涨、股票抛售

反馈循环：

初始反应产生次生影响
示例：股票抛售 → 保证金追缴 → 强制抛售 → 更深幅度的抛售

Step 6: Report Generation

步骤6：报告生成

Objective: Create structured English Markdown report ranked by market impact.

Report Structure:

markdown

undefined

目标： 创建按市场影响排序的结构化英文Markdown报告。

报告结构：

markdown

undefined

Market News Analysis Report - [Date Range]

Executive Summary

[3-4 sentences covering:]

Period analyzed (specific dates)
Number of significant events identified
Dominant market theme/regime (risk-on/risk-off, sector rotation)
Top 1-2 highest-impact events

[3-4 sentences covering:]

Period analyzed (specific dates)
Number of significant events identified
Dominant market theme/regime (risk-on/risk-off, sector rotation)
Top 1-2 highest-impact events

Market Impact Rankings

[Table format, sorted by Impact Score descending]

Rank	Event	Date	Impact Score	Asset Classes Affected	Market Reaction
1	[Event]	[Date]	[Score]	[Equities, Commodities, etc.]	[Brief reaction]
2	...	...	...	...	...

[Table format, sorted by Impact Score descending]

Rank	Event	Date	Impact Score	Asset Classes Affected	Market Reaction
1	[Event]	[Date]	[Score]	[Equities, Commodities, etc.]	[Brief reaction]
2	...	...	...	...	...

Detailed Event Analysis

[For each event in rank order, provide comprehensive analysis]

[Rank]. [Event Name] (Impact Score: [X])

Event Date: [Date, Time] Event Type: [Monetary Policy / Earnings / Geopolitical / Economic Data / Corporate] News Source: [Source, with credibility tier]

Event Summary

[3-4 sentences describing what happened]

Key details (e.g., rate decision, earnings beat/miss magnitude, conflict developments)
Context (was this expected, surprise factor)
Forward guidance or implications stated

[3-4 sentences describing what happened]

Key details (e.g., rate decision, earnings beat/miss magnitude, conflict developments)
Context (was this expected, surprise factor)
Forward guidance or implications stated

Market Reaction

Immediate (Day-of):

Equities: S&P 500 [+/-X%], Nasdaq [+/-X%], Sector rotation [details]
Bonds: 10Y yield [change], credit spreads [movement]
Commodities: Oil [+/-X%], Gold [+/-X%], Copper [+/-X%] (if relevant)
Currencies: USD [+/-X%], [other relevant pairs]
Volatility: VIX [level/change]

Follow-Through (Subsequent Sessions):

[Direction: sustained, reversed, or consolidated]
[Additional price action details if significant]

Pattern Comparison:

Expected Reaction: [Based on historical patterns from knowledge base]
Actual vs Expected: [Consistent / Amplified / Dampened / Inverse]
Explanation of Deviation: [If applicable, why reaction differed]

Immediate (Day-of):

Equities: S&P 500 [+/-X%], Nasdaq [+/-X%], Sector rotation [details]
Bonds: 10Y yield [change], credit spreads [movement]
Commodities: Oil [+/-X%], Gold [+/-X%], Copper [+/-X%] (if relevant)
Currencies: USD [+/-X%], [other relevant pairs]
Volatility: VIX [level/change]

Follow-Through (Subsequent Sessions):

[Direction: sustained, reversed, or consolidated]
[Additional price action details if significant]

Pattern Comparison:

Expected Reaction: [Based on historical patterns from knowledge base]
Actual vs Expected: [Consistent / Amplified / Dampened / Inverse]
Explanation of Deviation: [If applicable, why reaction differed]

Impact Assessment Detail

Asset Price Impact: [Severe/Major/Moderate/Minor] - [Justification] Breadth: [Systemic/Cross-Asset/Sector/Stock-Specific] - [Affected markets] Forward Significance: [Regime Change/Trend Confirmation/Isolated/Contrary] - [Rationale]

Calculated Score: ([Price Score] × [Breadth Multiplier]) × [Forward Modifier] = [Total]

Sector-Specific Impacts

[If relevant, detail which sectors/industries were most affected]

[Sector 1]: [Impact and reason]
[Sector 2]: [Impact and reason]
[Example: Technology -3% (rate sensitivity), Energy +5% (oil price spillover)]

[If relevant, detail which sectors/industries were most affected]

[Sector 1]: [Impact and reason]
[Sector 2]: [Impact and reason]
[Example: Technology -3% (rate sensitivity), Energy +5% (oil price spillover)]

Geopolitical-Commodity Correlation Analysis

[Include this section only for geopolitical events]

[Specific commodity affected]: [Price movement]
[Historical precedent]: [Comparison to similar past events]
[Expected duration]: [Temporary shock vs sustained impact]

[Repeat detailed analysis for each ranked event]

[Include this section only for geopolitical events]

[Specific commodity affected]: [Price movement]
[Historical precedent]: [Comparison to similar past events]
[Expected duration]: [Temporary shock vs sustained impact]

[Repeat detailed analysis for each ranked event]

Thematic Synthesis

Dominant Market Narrative

[Identify overarching theme across the 10-day period]

[E.g., "Persistent inflation concerns dominated despite mixed economic data"]
[E.g., "Tech sector strength drove markets higher despite geopolitical headwinds"]

[Identify overarching theme across the 10-day period]

[E.g., "Persistent inflation concerns dominated despite mixed economic data"]
[E.g., "Tech sector strength drove markets higher despite geopolitical headwinds"]

Interconnected Events

[Analyze how events related or compounded]

[Event A] + [Event B] → [Combined impact analysis]
[Sequential causation if applicable]

[Analyze how events related or compounded]

[Event A] + [Event B] → [Combined impact analysis]
[Sequential causation if applicable]

Market Regime Assessment

Risk Appetite: [Risk-On / Risk-Off / Mixed] Evidence:

[Supporting indicators: sector performance, safe haven flows, credit spreads, VIX]

Sector Rotation Trends:

[Growth vs Value]
[Cyclicals vs Defensives]
[Outperformers and underperformers]

Risk Appetite: [Risk-On / Risk-Off / Mixed] Evidence:

[Supporting indicators: sector performance, safe haven flows, credit spreads, VIX]

Sector Rotation Trends:

[Growth vs Value]
[Cyclicals vs Defensives]
[Outperformers and underperformers]

Anomalies and Surprises

[Highlight unexpected market reactions]

[Event]: Market reacted [unexpectedly] because [explanation]
[Continue for significant anomalies]

[Highlight unexpected market reactions]

[Event]: Market reacted [unexpectedly] because [explanation]
[Continue for significant anomalies]

Commodity Market Deep Dive

[Dedicated section for commodity movements]

Energy

Crude Oil (WTI/Brent): [Price level, % change over period, key drivers]
Natural Gas: [If significant movement]
Key Events: [Specific news impacting energy: OPEC, geopolitics, inventory data]

Crude Oil (WTI/Brent): [Price level, % change over period, key drivers]
Natural Gas: [If significant movement]
Key Events: [Specific news impacting energy: OPEC, geopolitics, inventory data]

Precious Metals

Gold: [Price level, % change, safe-haven flows vs real rate dynamics]
Silver: [If significant divergence from gold]
Drivers: [Geopolitical risk premium, inflation hedging, USD strength]

Gold: [Price level, % change, safe-haven flows vs real rate dynamics]
Silver: [If significant divergence from gold]
Drivers: [Geopolitical risk premium, inflation hedging, USD strength]

Base Metals

Copper: [As economic barometer - demand signals]
Aluminum, Nickel: [If relevant supply/demand news]
China Factor: [Impact of Chinese economic data/policy]

Copper: [As economic barometer - demand signals]
Aluminum, Nickel: [If relevant supply/demand news]
China Factor: [Impact of Chinese economic data/policy]

Agricultural (If Relevant)

Grains: [Wheat, Corn, Soybeans - weather, Ukraine conflict impacts]

[For each commodity, reference geopolitical events from main analysis and draw correlations]

Grains: [Wheat, Corn, Soybeans - weather, Ukraine conflict impacts]

[For each commodity, reference geopolitical events from main analysis and draw correlations]

Forward-Looking Implications

Market Positioning Insights

[What the news suggests for current market positioning]

[Trend continuation or reversal signals]
[Overvaluation or undervaluation indications]
[Sentiment extremes (complacency or panic)]

[What the news suggests for current market positioning]

[Trend continuation or reversal signals]
[Overvaluation or undervaluation indications]
[Sentiment extremes (complacency or panic)]

Upcoming Catalysts

[Events on horizon that may be set up by recent news]

[Next FOMC meeting expectations post-recent decision]
[Upcoming earnings seasons based on guidance]
[Geopolitical developments to monitor]

[Events on horizon that may be set up by recent news]

[Next FOMC meeting expectations post-recent decision]
[Upcoming earnings seasons based on guidance]
[Geopolitical developments to monitor]

Risk Scenarios

[Based on recent news, identify key risks]

[Risk Name]: [Description, probability, potential impact]
[Risk Name]: [Description, probability, potential impact]
[Continue for 3-5 key risks]

[Based on recent news, identify key risks]

[Risk Name]: [Description, probability, potential impact]
[Risk Name]: [Description, probability, potential impact]
[Continue for 3-5 key risks]

Data Sources and Methodology

News Sources Consulted

[List primary sources used, organized by tier]

Official Sources: [e.g., FederalReserve.gov, SEC.gov]
Tier 1 Financial News: [e.g., Bloomberg, Reuters, WSJ]
Specialized: [e.g., S&P Global Platts for commodities]

[List primary sources used, organized by tier]

Official Sources: [e.g., FederalReserve.gov, SEC.gov]
Tier 1 Financial News: [e.g., Bloomberg, Reuters, WSJ]
Specialized: [e.g., S&P Global Platts for commodities]

Analysis Period

Start Date: [Specific date]
End Date: [Specific date]
Total Days: 10

Start Date: [Specific date]
End Date: [Specific date]
Total Days: 10

Market Data

Equity indices: [Data sources]
Commodity prices: [Data sources]
Economic data: [Government sources]

Equity indices: [Data sources]
Commodity prices: [Data sources]
Economic data: [Government sources]

Knowledge Base References

```
market_event_patterns.md
```
- Historical reaction patterns
```
geopolitical_commodity_correlations.md
```
- Geopolitical-commodity frameworks
```
corporate_news_impact.md
```
- Mega-cap impact analysis
```
trusted_news_sources.md
```
- Source credibility assessment

Analysis Date: [Date report generated] Language: English Analysis Thinking: English


**File Naming Convention:**
`market_news_analysis_[START_DATE]_to_[END_DATE].md`

Example: `market_news_analysis_2024-10-25_to_2024-11-03.md`

**Report Quality Standards:**
- Objective, fact-based analysis (no speculation beyond probability-weighted scenarios)
- Quantify price movements with specific percentages
- Cite sources for major claims
- Distinguish between correlation and causation
- Acknowledge uncertainty when attributing market moves to specific news
- Use proper financial terminology
- Maintain consistent English throughout

```
market_event_patterns.md
```
- Historical reaction patterns
```
geopolitical_commodity_correlations.md
```
- Geopolitical-commodity frameworks
```
corporate_news_impact.md
```
- Mega-cap impact analysis
```
trusted_news_sources.md
```
- Source credibility assessment

Analysis Date: [Date report generated] Language: English Analysis Thinking: English


**文件命名规范：**
`market_news_analysis_[START_DATE]_to_[END_DATE].md`

示例：`market_news_analysis_2024-10-25_to_2024-11-03.md`

**报告质量标准：**
- 客观、基于事实的分析（除概率加权场景外不做投机预测）
- 用具体百分比量化价格波动
- 重大结论标注来源
- 区分相关性和因果关系
- 将市场走势归因于特定新闻时注明不确定性
- 使用规范的金融术语
- 全程保持统一的英语表述

Key Analysis Principles

核心分析原则

When conducting market news analysis:

Impact Over Noise: Focus on truly market-moving news, filter out minor events
Multi-Asset Perspective: Analyze across equities, bonds, commodities, currencies to understand full impact
Pattern Recognition: Compare against historical precedents while noting unique aspects
Causation Discipline: Be rigorous about attributing market moves to specific news vs coincidental timing
Forward-Looking: Emphasize implications for future market behavior, not just backward-looking description
Objectivity: Separate market reaction (what happened) from personal market view (what should happen)
Quantification: Use specific numbers (%, bps) rather than vague terms ("significant," "large")
Source Credibility: Weight official sources and Tier 1 news over rumors and unverified reports
Breadth Analysis: Individual stock moves only significant if mega-cap or systemic signal
English Consistency: All thinking, analysis, and output in English for consistency

开展市场新闻分析时请遵循以下原则：

重影响、滤噪音： 重点关注真正驱动市场的新闻，过滤 minor事件
跨资产视角： 从股票、债券、大宗商品、外汇多个维度分析，理解完整影响
模式识别： 与历史先例对比，同时注意事件的独特性
严谨归因： 严格区分市场走势是由特定新闻驱动还是时间巧合
前瞻视角： 重点强调对未来市场行为的影响，而非仅回顾历史
客观性： 区分市场实际反应（发生了什么）和个人市场观点（应该发生什么）
量化表述： 使用具体数值（%、bps）而非模糊表述（"显著"、"大幅"）
来源可信度： 优先采信官方来源和一线财经媒体，而非传闻和未核实报道
广度分析： 个股波动仅在属于巨头企业或具有系统性信号时才纳入重点分析
英语一致性： 所有思考、分析和输出均使用英语，保持一致性

Common Pitfalls to Avoid

需避免的常见误区

Over-Attribution:

Not every market move is news-driven (technicals, flows, month-end rebalancing exist)
Acknowledge when attribution is uncertain

Recency Bias:

Latest news isn't always most important
Rank by actual impact, not chronological order

Hindsight Bias:

Distinguish "obvious in retrospect" from "surprising at the time"
Note consensus expectations vs actual outcomes

Single-Factor Analysis:

Markets respond to multiple factors simultaneously
Acknowledge interaction effects

Ignoring Magnitude:

A "hot" CPI that's 0.1% above consensus is different from 0.5% above
Quantify surprise factor

过度归因：

并非所有市场波动都由新闻驱动（技术面、资金流、月末再平衡等也会产生影响）
归因存在不确定性时明确标注

近因偏差：

最新的新闻不一定是最重要的
按实际影响排序，而非按时间顺序排序

后见之明偏差：

区分"事后看很明显"和"事发时很意外"
标注共识预期与实际结果的差异

单因素分析：

市场会同时对多个因素做出反应
明确标注因素之间的相互作用

忽略量级：

比共识预期高0.1%的通胀数据和高0.5%的通胀数据影响完全不同
量化超预期幅度

Resources

资源

references/

references/目录

market_event_patterns.md - Comprehensive knowledge base covering:

Central bank monetary policy events (FOMC, ECB, BOJ, PBOC)
Inflation data releases (CPI, PPI, PCE)
Employment data (NFP, unemployment, wages)
GDP reports
Geopolitical events (conflicts, trade wars, sanctions)
Corporate earnings (mega-cap technology, banks, energy)
Credit events and rating changes
Commodity-specific events (OPEC, weather, supply disruptions)
Recession indicators
Historical case studies (2008 crisis, COVID-19, 2022 inflation)
Pattern recognition framework and sentiment analysis

geopolitical_commodity_correlations.md - Detailed correlations covering:

Energy commodities (crude oil, natural gas, coal) and geopolitical conflicts
Precious metals (gold, silver, platinum, palladium) safe-haven dynamics
Base metals (copper, aluminum, nickel, zinc) and economic/political risks
Agricultural commodities (wheat, corn, soybeans) and weather/policy
Rare earth elements and critical minerals (China dominance, supply security)
Regional geopolitical frameworks (Middle East, Russia-Europe, Asia-Pacific, Latin America)
Correlation summary tables
Time horizon considerations

corporate_news_impact.md - Mega-cap analysis framework:

"Magnificent 7" technology stocks (NVIDIA, Apple, Microsoft, Amazon, Meta, Google, Tesla)
Financial sector mega-caps (JPMorgan, Bank of America, etc.)
Healthcare mega-caps (UnitedHealth, Pfizer, J&J, Merck)
Energy mega-caps (Exxon Mobil, Chevron)
Consumer staples mega-caps (P&G, Coca-Cola, PepsiCo)
Industrial mega-caps (Boeing, Caterpillar)
Earnings impact frameworks, product launches, M&A, regulatory issues
Sector contagion patterns
Impact magnitude framework

trusted_news_sources.md - Source credibility guide:

Tier 1 primary sources (central banks, government agencies, SEC)
Tier 2 major financial news (Bloomberg, Reuters, WSJ, FT, CNBC)
Tier 3 specialized sources (energy, tech, emerging markets, China-specific, crypto)
Tier 4 analysis and research (independent research, central bank publications, think tanks)
Search and aggregation tools
Source quality assessment criteria
Speed vs accuracy trade-offs
Recommended search strategies for 10-day analysis
Source credibility framework
Red flag sources to avoid

market_event_patterns.md - 完整知识库，覆盖：

央行货币政策事件（FOMC、ECB、BOJ、PBOC）
通胀数据发布（CPI、PPI、PCE）
就业数据（非农就业、失业率、薪资）
GDP报告
地缘政治事件（冲突、贸易战、制裁）
企业财报（科技巨头、银行、能源企业）
信用事件和评级调整
大宗商品特定事件（OPEC、天气、供应中断）
衰退指标
历史案例研究（2008年危机、COVID-19、2022年通胀）
模式识别框架和情绪分析

geopolitical_commodity_correlations.md - 详细相关性分析，覆盖：

能源大宗商品（原油、天然气、煤炭）与地缘政治冲突
贵金属（黄金、白银、铂金、钯金）避险动态
基本金属（铜、铝、镍、锌）与经济/政治风险
农产品（小麦、玉米、大豆）与天气/政策
稀土和关键矿产（中国主导地位、供应安全）
区域地缘政治框架（中东、俄欧、亚太、拉美）
相关性汇总表
时间周期考量

corporate_news_impact.md - 巨头企业分析框架：

" Magnificent 7"科技股（NVIDIA、Apple、Microsoft、Amazon、Meta、Google、Tesla）
金融行业巨头（摩根大通、美国银行等）
医疗行业巨头（联合健康、辉瑞、强生、默克）
能源行业巨头（埃克森美孚、雪佛龙）
消费品巨头（宝洁、可口可乐、百事可乐）
工业巨头（波音、卡特彼勒）
财报影响框架、产品发布、并购、监管问题
行业传导模式
影响量级框架

trusted_news_sources.md - 来源可信度指南：

一级主要来源（央行、政府机构、SEC）
二级主流财经媒体（Bloomberg、Reuters、WSJ、FT、CNBC）
三级专业来源（能源、科技、新兴市场、中国专属、加密货币）
四级分析和研究（独立研究、央行出版物、智库）
搜索和聚合工具
来源质量评估标准
速度与准确性的权衡
10天分析的推荐搜索策略
来源可信度框架
需要避免的高风险来源

Important Notes

重要提示

All analysis thinking must be conducted in English
All output Markdown files must be in English
Use WebSearch and WebFetch tools to collect news automatically
Focus on trusted news sources as defined in references
Rank events by impact score (price impact × breadth × forward significance)
Target analysis period: Past 10 days from current date
Emphasize US equity markets and commodities as primary analysis subjects
FOMC and other central bank policy decisions receive highest priority analysis
Distinguish between correlation and causation rigorously
Quantify all market reactions with specific percentages
Load appropriate reference files based on news types collected
Generate comprehensive reports ranked by market impact (highest impact first)

所有分析思考过程必须使用英语
所有输出的Markdown文件必须为英语
使用WebSearch和WebFetch工具自动收集新闻
重点关注参考文件中定义的可信新闻来源
按影响得分（价格影响 × 影响广度 × 前瞻意义）对事件排序
目标分析周期：从当前日期起过去10天
主要分析对象为美国股票市场和大宗商品
FOMC和其他央行政策决议为最高优先级分析内容
严格区分相关性和因果关系
用具体百分比量化所有市场反应
根据收集到的新闻类型加载对应的参考文件
生成按市场影响排序的完整报告（影响最高的内容排在最前）