sector-analyst

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English

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Chinese

Sector Analyst

板块分析师Skill

Overview

概述

This skill enables comprehensive analysis of sector and industry performance charts to identify market cycle positioning and predict likely rotation scenarios. The analysis combines observed performance data with established sector rotation principles to provide objective market assessment and probabilistic scenario forecasting.

本Skill可针对板块与行业表现图表开展全面分析，识别市场周期定位并预测潜在的轮动场景。分析过程会将观测到的表现数据与成熟的板块轮动原则相结合，提供客观的市场评估及概率化的场景预测。

When to Use This Skill

适用场景

Use this skill when:

User provides sector performance charts (typically 1-week and 1-month timeframes)
User provides industry performance charts showing relative performance data
User requests analysis of current market cycle positioning
User asks for sector rotation assessment or predictions
User needs probability-weighted scenarios for market positioning

Example user requests:

"Analyze these sector performance charts and tell me where we are in the market cycle"
"Based on these performance charts, what sectors should outperform next?"
"What's the probability of a defensive rotation based on this data?"
"Review these sector and industry charts and provide scenario analysis"

在以下场景下使用本Skill：

用户提供板块表现图表（通常为1周和1个月时间周期）
用户提供展示相对表现数据的行业表现图表
用户要求分析当前市场周期定位
用户询问板块轮动评估或预测
用户需要基于市场定位的概率加权场景分析

用户请求示例：

"分析这些板块表现图表，告诉我当前处于市场周期的哪个阶段"
"基于这些表现图表，接下来哪些板块会表现优异？"
"基于这些数据，防御性轮动的概率有多大？"
"审核这些板块与行业图表并提供场景分析"

Analysis Workflow

分析流程

Follow this structured workflow when analyzing sector/industry performance charts:

分析板块/行业表现图表时，请遵循以下结构化流程：

Step 1: Data Collection and Observation

步骤1：数据收集与观测

First, carefully examine all provided chart images to extract:

Sector-level performance: Identify which sectors (Technology, Financials, Consumer Discretionary, etc.) are outperforming/underperforming
Industry-level performance: Note specific industries showing strength or weakness
Timeframe comparison: Compare 1-week vs 1-month performance to identify trend consistency or divergence
Magnitude of moves: Assess the size of relative performance differences
Breadth of movement: Determine if performance is concentrated or broad-based

Think in English while analyzing the charts. Document specific numerical performance figures for key sectors and industries.

首先，仔细检查所有提供的图表图片，提取以下信息：

板块层面表现：识别哪些板块（Technology、Financials、Consumer Discretionary等）表现优于/逊于大盘
行业层面表现：记录表现强劲或疲软的特定行业
时间维度对比：对比1周与1个月的表现，识别趋势的一致性或背离
波动幅度：评估相对表现差异的大小
影响广度：判断表现是集中于少数板块还是广泛分布

分析图表时请用英文思考，记录关键板块和行业的具体数值表现数据。

Step 2: Market Cycle Assessment

步骤2：市场周期评估

Load the sector rotation knowledge base to inform analysis:

Read
```
references/sector_rotation.md
```
to access market cycle and sector rotation frameworks
Compare observed performance patterns against expected patterns for each cycle phase:
- Early Cycle Recovery
- Mid Cycle Expansion
- Late Cycle
- Recession

Identify which cycle phase best matches current observations by:

Mapping outperforming sectors to typical cycle leaders
Mapping underperforming sectors to typical cycle laggards
Assessing consistency across multiple sectors
Evaluating alignment with defensive vs cyclical sector performance

调用板块轮动知识库辅助分析：

阅读
```
references/sector_rotation.md
```
，获取市场周期与板块轮动框架
将观测到的表现模式与每个周期阶段的预期模式进行对比：
- 早期周期复苏阶段
- 中期周期扩张阶段
- 后期周期阶段
- 衰退阶段

通过以下方式确定最匹配当前观测结果的周期阶段：

将表现优异的板块映射至典型的周期领涨板块
将表现疲软的板块映射至典型的周期滞后板块
评估多板块间的一致性
评估防御性板块与周期性板块的表现是否符合周期特征

Step 3: Current Situation Analysis

步骤3：当前态势分析

Synthesize observations into an objective assessment:

State which market cycle phase current performance most closely resembles
Highlight supporting evidence (which sectors/industries confirm this view)
Note any contradictory signals or unusual patterns
Assess confidence level based on consistency of signals

Use data-driven language and specific references to performance figures.

将观测结果整合为客观评估：

说明当前表现最接近哪个市场周期阶段
突出支持性证据（哪些板块/行业印证了该判断）
记录任何矛盾信号或异常模式
根据信号的一致性评估置信水平

使用基于数据的表述，并具体引用表现数据。

Step 4: Scenario Development

步骤4：场景开发

Based on sector rotation principles and current positioning, develop 2-4 potential scenarios for the next phase:

For each scenario:

Describe the market cycle transition
Identify which sectors would likely outperform
Identify which sectors would likely underperform
Specify the catalysts or conditions that would confirm this scenario
Assign a probability (see Probability Assessment Framework in sector_rotation.md)

Scenarios should range from most likely (highest probability) to alternative/contrarian scenarios.

基于板块轮动原则与当前定位，为下一阶段开发2-4个潜在场景：

每个场景需包含：

描述市场周期的过渡方向
识别可能表现优异的板块
识别可能表现疲软的板块
明确将印证该场景的催化剂或条件
分配概率（请参考
```
sector_rotation.md
```
中的概率评估框架）

场景应从最可能（概率最高）到备选/反向场景依次排列。

Step 5: Output Generation

步骤5：输出生成

Create a structured Markdown document with the following sections:

Required Sections:

Executive Summary: 2-3 sentence overview of key findings
Current Situation: Detailed analysis of current performance patterns and market cycle positioning
Supporting Evidence: Specific sector and industry performance data supporting the cycle assessment
Scenario Analysis: 2-4 scenarios with descriptions and probability assignments
Recommended Positioning: Strategic and tactical positioning recommendations based on scenario probabilities
Key Risks: Notable risks or contradictory signals to monitor

创建结构化Markdown文档，包含以下章节：

必填章节：

执行摘要：2-3句话概述关键发现
当前态势：详细分析当前表现模式与市场周期定位
支持证据：支持周期评估的具体板块与行业表现数据
场景分析：2-4个带描述与概率分配的场景
建议定位：基于场景概率的战略与战术定位建议
主要风险：需关注的显著风险或矛盾信号

Output Format

输出格式

Save analysis results as a Markdown file with naming convention:

sector_analysis_YYYY-MM-DD.md

Use this structure:

markdown

undefined

将分析结果保存为Markdown文件，命名规则为：

sector_analysis_YYYY-MM-DD.md

使用以下结构：

markdown

undefined

Sector Performance Analysis - [Date]

Executive Summary

[2-3 sentences summarizing key findings]

Current Situation

Market Cycle Assessment

[Which cycle phase and why]

Performance Patterns Observed

1-Week Performance

[Analysis of recent performance]

1-Month Performance

[Analysis of medium-term trends]

Sector-Level Analysis

[Detailed breakdown by sector]

Industry-Level Analysis

[Notable industry-specific observations]

Supporting Evidence

Confirming Signals

[List data points supporting cycle assessment]

[List data points supporting cycle assessment]

Contradictory Signals

[List any conflicting indicators]

[List any conflicting indicators]

Scenario Analysis

Scenario 1: [Name] (Probability: XX%)

Description: [What happens] Outperformers: [Sectors/industries] Underperformers: [Sectors/industries] Catalysts: [What would confirm this scenario]

Scenario 2: [Name] (Probability: XX%)

[Repeat structure]

[Additional scenarios as appropriate]

[Repeat structure]

[Additional scenarios as appropriate]

Recommended Positioning

Strategic Positioning (Medium-term)

[Sector allocation recommendations]

Tactical Positioning (Short-term)

[Specific adjustments or opportunities]

Key Risks and Monitoring Points

[What to watch that could invalidate the analysis]

Analysis Date: [Date] Data Period: [Timeframe of charts analyzed]

undefined

[What to watch that could invalidate the analysis]

Analysis Date: [Date] Data Period: [Timeframe of charts analyzed]

undefined

Key Analysis Principles

核心分析原则

When conducting analysis:

Objectivity First: Let the data guide conclusions, not preconceptions
Probabilistic Thinking: Express uncertainty through probability ranges
Multiple Timeframes: Compare 1-week and 1-month data for trend confirmation
Relative Performance: Focus on relative strength, not absolute returns
Breadth Matters: Broad-based moves are more significant than isolated movements
No Absolutes: Markets rarely follow textbook patterns exactly
Historical Context: Reference typical rotation patterns but acknowledge uniqueness

开展分析时需遵循以下原则：

客观优先：让数据引导结论，而非先入为主的观念
概率思维：通过概率区间表达不确定性
多时间维度：对比1周与1个月数据以确认趋势
相对表现：聚焦相对强度，而非绝对收益
广度重要：广泛的板块变动比孤立的变动更具意义
无绝对规律：市场极少完全遵循教科书式的模式
历史语境：参考典型轮动模式，但需承认市场的独特性

Probability Guidelines

概率指南

Apply these probability ranges based on evidence strength:

70-85%: Strong evidence with multiple confirming signals across sectors and timeframes
50-70%: Moderate evidence with some confirming signals but mixed indicators
30-50%: Weak evidence with limited or conflicting signals
15-30%: Speculative scenario contrary to current indicators but possible

Total probabilities across all scenarios should sum to approximately 100%.

根据证据强度应用以下概率区间：

70-85%：强证据，多板块与多时间维度存在多个印证信号
50-70%：中等证据，有部分印证信号但指标混杂
30-50%：弱证据，信号有限或存在矛盾
15-30%：投机性场景，与当前指标相反但仍有可能

所有场景的概率总和应约为100%。

Resources

资源

references/

```
sector_rotation.md
```
- Comprehensive knowledge base covering market cycle phases, typical sector performance patterns, and probability assessment frameworks

```
sector_rotation.md
```
- 涵盖市场周期阶段、典型板块表现模式及概率评估框架的综合性知识库

assets/

Sample charts demonstrating the expected input format:

```
sector_performance.jpeg
```
- Example sector-level performance chart (1-week and 1-month)
```
industory_performance_1.jpeg
```
- Example industry performance chart (outperformers)
```
industory_performance_2.jpeg
```
- Example industry performance chart (underperformers)

These samples illustrate the type of visual data this skill analyzes. User-provided charts may vary in format but should contain similar relative performance information.

展示预期输入格式的示例图表：

```
sector_performance.jpeg
```
- 板块层面表现图表示例（1周与1个月）
```
industory_performance_1.jpeg
```
- 行业表现图表示例（表现优异者）
```
industory_performance_2.jpeg
```
- 行业表现图表示例（表现疲软者）

这些示例展示了本Skill分析的可视化数据类型。用户提供的图表格式可能有所不同，但需包含类似的相对表现信息。

Important Notes

重要说明

All analysis thinking should be conducted in English
Output Markdown files must be in English
Reference the sector rotation knowledge base for each analysis
Maintain objectivity and avoid confirmation bias
Update probability assessments if new data becomes available
Charts typically show performance over 1-week and 1-month periods

所有分析思考过程需使用英文
输出的Markdown文件必须为英文
每次分析需参考板块轮动知识库
保持客观，避免确认偏差
若有新数据可用，需更新概率评估
图表通常展示1周和1个月周期的表现