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sector-analyst

Analyze sector rotation patterns and market cycle positioning from CSV uptrend data and chart images.

2|Updated Apr 6, 2026
Search Tags:#investment#data analysis#financial analysis#market cycle#sector analysis
One-click install
npx skills add https://github.com/k1064190/stock-expectation --skill sector-analyst-k1064190
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sector-analyst
Source: https://github.com/k1064190/stock-expectation/tree/main/.claude/skills/sector-analyst
Command: npx skills add https://github.com/k1064190/stock-expectation --skill sector-analyst-k1064190

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps users analyze sector rotation patterns and market cycle positioning, providing insights into market trends and sector performance.

Core Features & Use Cases

  • Sector Rotation Analysis: Analyze sector rotation patterns and market cycle positioning.
  • Data Fetching: Fetches sector uptrend data from CSV and accepts chart images for analysis.
  • Use Case: Use this skill to analyze the current market cycle phase, identify overbought/oversold sectors, and predict sector performance.

Quick Start

Use the sector-analyst skill to analyze the current market cycle phase and identify leading sectors.

Frequently Asked Questions about sector-analyst

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze sector rotation patterns for investment decisions?▼

This Skill analyzes sector rotation by fetching uptrend data from CSV files and processing chart images. It identifies the current market cycle phase and overbought or oversold sectors to support investment decision-making.

What is market cycle positioning and how does it predict sector performance?▼

Market cycle positioning identifies the current economic phase to predict sector performance. By analyzing sector rotation patterns and chart images, it highlights leading sectors and forecasts which industries will outperform next.

Do I need Python libraries like pandas and numpy for financial data analysis?▼

Yes, you need Python libraries including pandas, numpy, matplotlib, and seaborn for this financial data analysis. These dependencies are required to process CSV sector uptrend data and generate visualizations for market cycle analysis.

How do I identify overbought and oversold sectors using market cycle data?▼

You identify overbought and oversold sectors by loading market cycle data from a CSV into this Skill. It analyzes sector uptrend metrics and chart images to map performance against the current cycle, revealing extreme positioning.

Can I use chart images to analyze current market cycle phases?▼

Yes, you can use chart images to analyze market cycle phases. This Skill accepts chart images alongside CSV sector uptrend data to evaluate visual sector rotation patterns and determine current positioning within the market cycle.

What is the best way to visualize sector rotation with Python?▼

The best way to visualize sector rotation with Python is using matplotlib and seaborn to process CSV sector data. This Skill leverages these libraries to generate visualizations mapping sector performance across different market cycle phases.

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