Sector Analyst

Sector Analyst

Overview

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.

When to Use This Skill

Use this skill when:

• User requests sector rotation analysis (no chart images required)
• User asks about cyclical vs defensive positioning
• User wants to know which sectors are overbought or oversold
• User requests market cycle phase estimation
• User provides sector performance charts for supplementary analysis
• User asks for sector-based scenario analysis or predictions

Example user requests:

• "Run a sector rotation analysis"
• "Which sectors are leading — cyclical or defensive?"
• "Are any sectors overbought right now?"
• "What phase of the market cycle are we in?"
• "Analyze these sector performance charts and tell me where we are in the market cycle"

Prerequisites

• Python 3.9+; no third-party libraries required (CSV fetched via stdlib urllib)
• No API keys required — data is fetched from a public GitHub repository
• Optional: Sector performance chart images for supplementary analysis

Data Source

Sector uptrend ratios are fetched from TraderMonty's public GitHub repository (no API key required):

• Sector Summary: sector_summary.csv — uptrend ratio, trend, slope, and status per sector
• Freshness Check: uptrend_ratio_timeseries.csv — max(date) used to verify data recency

Running the Script

# 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/

Analysis Workflow

Follow this structured workflow:

Step 1: CSV Data Collection

1. Run the analysis script: python3 scripts/analyze_sector_rotation.py
2. Extract from the output:
   • Sector ranking by uptrend ratio
   • Risk regime (cyclical vs defensive) and score
   • Overbought/oversold sectors
   • Cycle phase estimate and confidence level
3. If a data freshness warning appears, note it in the analysis

Step 2: Market Cycle Assessment

Use the script's cycle phase estimate as a starting point:

• Read references/sector_rotation.md to access market cycle and sector rotation frameworks
• Compare the script's quantitative findings against expected patterns for each cycle phase:
  • Early Cycle Recovery
  • Mid Cycle Expansion
  • Late Cycle
  • Recession
• Add qualitative interpretation informed by the knowledge base

If chart images are provided, use them to supplement with industry-level detail:

• Extract industry-level performance data from chart images
• Compare 1-week vs 1-month performance for trend consistency
• Note specific industries showing strength or weakness within sectors

Step 3: Current Situation Analysis

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.

Step 4: Scenario Development

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.