sector-analyst by tradermonty/claude-trading-skills
npx skills add https://github.com/tradermonty/claude-trading-skills --skill sector-analyst此技能通过从 TraderMonty 的公开 CSV 数据集获取上涨趋势比率数据,实现对板块轮动和市场周期定位的全面分析。它能对板块进行排名、计算周期性 vs 防御性风险机制得分、识别超买/超卖状况,并估算当前市场周期阶段。图表图像可选择性地为数据驱动的分析提供行业层面的细节补充。
在以下情况使用此技能:
示例用户请求:
板块上涨趋势比率从 TraderMonty 的公开 GitHub 仓库获取(无需 API 密钥):
sector_summary.csv — 每个板块的上涨趋势比率、趋势、斜率和状态uptrend_ratio_timeseries.csv — 用于验证数据时效性的 max(date)# 默认:获取 CSV,打印人类可读的分析
python3 skills/sector-analyst/scripts/analyze_sector_rotation.py
# JSON 输出
python3 skills/sector-analyst/scripts/analyze_sector_rotation.py --json
# 保存到文件
python3 skills/sector-analyst/scripts/analyze_sector_rotation.py --save --output-dir reports/
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遵循此结构化工作流程:
python3 skills/sector-analyst/scripts/analyze_sector_rotation.py使用脚本的周期阶段估算作为起点:
references/sector_rotation.md 以获取市场周期和板块轮动框架如果提供了图表图像,则使用它们补充行业层面的细节:
将观察结果综合成客观评估:
使用数据驱动的语言并具体引用表现数据。
基于板块轮动原则和当前定位,为下一阶段推演 2-4 个潜在情景:
对于每个情景:
情景范围应从最可能(最高概率)到替代/逆向情景。
创建一个结构化的 Markdown 文档,包含以下部分:
必需部分:
将分析结果保存为 Markdown 文件,命名约定为:sector_analysis_YYYY-MM-DD.md
使用此结构:
# 板块表现分析 - [日期]
## 执行摘要
[2-3 句话总结关键发现]
## 当前形势
### 市场周期评估
[哪个周期阶段及原因]
### 观察到的表现模式
#### 1 周表现
[近期表现分析]
#### 1 个月表现
[中期趋势分析]
#### 板块层面分析
[按板块详细细分]
#### 行业层面分析
[值得注意的行业特定观察]
## 支持性证据
### 确认信号
- [列出支持周期评估的数据点]
### 矛盾信号
- [列出任何冲突的指标]
## 情景分析
### 情景 1:[名称] (概率:XX%)
**描述**:[会发生什么]
**跑赢者**:[板块/行业]
**跑输者**:[板块/行业]
**催化剂**:[将证实此情景的因素]
### 情景 2:[名称] (概率:XX%)
[重复结构]
[根据需要添加其他情景]
## 推荐定位
### 战略定位(中期)
[板块配置建议]
### 战术定位(短期)
[具体调整或机会]
## 关键风险与监测点
[可能使分析失效的需关注事项]
---
*分析日期:[日期]*
*数据周期:[分析的图表时间范围]*
进行分析时:
根据证据强度应用这些概率范围:
所有情景的总概率之和应约为 100%。
analyze_sector_rotation.py - 获取板块 CSV 数据并生成板块排名、风险机制评分、超买/超卖标志和周期阶段估算。无需 API 密钥。sector_rotation.md - 涵盖市场周期阶段、典型板块表现模式和概率评估框架的全面知识库展示可选基于图像分析的预期输入格式的示例图表:
sector_performance.jpeg - 示例板块层面表现图表(1 周和 1 个月)industory_performance_1.jpeg - 示例行业表现图表(跑赢者)industory_performance_2.jpeg - 示例行业表现图表(跑输者)每周安装量
158
仓库
GitHub 星标数
394
首次出现
Jan 26, 2026
安全审计
安装于
opencode149
gemini-cli148
cursor146
codex145
github-copilot142
kimi-cli139
This skill enables comprehensive analysis of sector rotation and market cycle positioning by fetching uptrend ratio data from TraderMonty's public CSV dataset. It ranks sectors, calculates cyclical vs defensive risk regime scores, identifies overbought/oversold conditions, and estimates the current market cycle phase. Chart images can optionally supplement the data-driven analysis with industry-level detail.
Use this skill when:
Example user requests:
Sector uptrend ratios are fetched from TraderMonty's public GitHub repository (no API key required):
sector_summary.csv — uptrend ratio, trend, slope, and status per sectoruptrend_ratio_timeseries.csv — max(date) used to verify data recency# Default: fetch CSV, print human-readable analysis
python3 skills/sector-analyst/scripts/analyze_sector_rotation.py
# JSON output
python3 skills/sector-analyst/scripts/analyze_sector_rotation.py --json
# Save to file
python3 skills/sector-analyst/scripts/analyze_sector_rotation.py --save --output-dir reports/
Follow this structured workflow:
python3 skills/sector-analyst/scripts/analyze_sector_rotation.pyUse the script's cycle phase estimate as a starting point:
references/sector_rotation.md to access market cycle and sector rotation frameworksIf chart images are provided, use them to supplement with industry-level detail:
Synthesize observations into an objective assessment:
Use data-driven language and specific references to performance figures.
Based on sector rotation principles and current positioning, develop 2-4 potential scenarios for the next phase:
For each scenario:
Scenarios should range from most likely (highest probability) to alternative/contrarian scenarios.
Create a structured Markdown document with the following sections:
Required Sections:
Save analysis results as a Markdown file with naming convention: sector_analysis_YYYY-MM-DD.md
Use this structure:
# 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]
### Contradictory Signals
- [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]
## 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]*
When conducting analysis:
Apply these probability ranges based on evidence strength:
Total probabilities across all scenarios should sum to approximately 100%.
analyze_sector_rotation.py - Fetches sector CSV data and produces sector rankings, risk regime scoring, overbought/oversold flags, and cycle phase estimation. No API key required.sector_rotation.md - Comprehensive knowledge base covering market cycle phases, typical sector performance patterns, and probability assessment frameworksSample charts demonstrating the expected input format for optional image-based analysis:
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)Weekly Installs
158
Repository
GitHub Stars
394
First Seen
Jan 26, 2026
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Installed on
opencode149
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kimi-cli139
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