From tradermonty-claude-trading-skills
Analyzes sector rotation patterns and market cycle positioning using public CSV uptrend data. Ranks sectors, scores cyclical vs defensive regimes, identifies overbought/oversold conditions, estimates cycle phases. Optional chart image support.
npx claudepluginhub joshuarweaver/cascade-business-ops --plugin tradermonty-claude-trading-skillsThis skill uses the workspace's default tool permissions.
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...
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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:
requests library (for CSV fetching)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 scripts/analyze_sector_rotation.py
# JSON output
python3 scripts/analyze_sector_rotation.py --json
# Save to file
python3 scripts/analyze_sector_rotation.py --save --output-dir reports/
Follow this structured workflow:
python3 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)