sector-analyst

---
name: sector-analyst
description: Use when analyzing sector rotation, cyclical vs defensive positioning, overbought or oversold conditions, or market cycle phase, using sector uptrend CSV data optionally supplemented by chart images.
category: Market & Macro
version: 1.0.0
tools: []
---

# Sector Analyst

## Overview

Analyze sector rotation and market cycle positioning: rank sectors, calculate cyclical-vs-defensive risk-regime scores, identify overbought/oversold conditions, and estimate the current market cycle phase, using sector uptrend-ratio data. Chart images can optionally supplement the data-driven analysis with industry-level detail.

## When to Use

- Sector rotation analysis (no chart images required)
- Cyclical vs defensive positioning questions
- Identifying which sectors are overbought or oversold
- Market cycle phase estimation
- Sector performance charts provided for supplementary analysis
- Sector-based scenario analysis or predictions

Example 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."

## Data Source

Sector uptrend ratios come from a public CSV dataset — no API key required. Use web_search to retrieve:
- **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

## Analysis Workflow

### Step 1: Data Collection

Retrieve the CSV data and extract: sector ranking by uptrend ratio, risk regime (cyclical vs defensive) and score, overbought/oversold sectors, and a cycle-phase estimate with confidence level. If a data freshness warning appears, note it in the analysis.

### Step 2: Market Cycle Assessment

Use the data-driven cycle-phase estimate as a starting point. Search the Knowledge Base for market cycle and sector rotation frameworks, then compare the quantitative findings against expected patterns for each phase:
- Early Cycle Recovery
- Mid Cycle Expansion
- Late Cycle
- Recession

Add qualitative interpretation informed by that knowledge base.

If chart images are provided, use them to supplement with industry-level detail: extract industry-level performance, compare 1-week vs 1-month performance for trend consistency, and note specific industries showing notable strength or weakness within sectors.

### Step 3: Current Situation Analysis

Synthesize 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, and assess confidence based on signal consistency. Use data-driven language with specific performance figures.

### Step 4: Scenario Development

Develop 2-4 potential scenarios for the next phase, based on sector rotation principles and current positioning. For each scenario, describe the market cycle transition, identify sectors likely to outperform and underperform, specify the catalysts or conditions that would confirm it, and assign a probability using the Probability Assessment Framework below. Order scenarios from most likely (highest probability) to alternative/contrarian.

### Step 5: Output Generation

Produce a markdown report with: Executive Summary (2-3 sentences), Current Situation (cycle assessment, 1-week and 1-month performance patterns, sector- and industry-level analysis), Supporting Evidence (confirming and contradictory signals), Scenario Analysis (each scenario with description, probability, outperformers/underperformers, catalysts), Recommended Positioning (strategic/medium-term and tactical/short-term), and Key Risks and Monitoring Points.

## Key Analysis Principles

1. **Objectivity First**: let the data guide conclusions, not preconceptions.
2. **Probabilistic Thinking**: express uncertainty through probability ranges.
3. **Multiple Timeframes**: compare 1-week and 1-month data for trend confirmation.
4. **Relative Performance**: focus on relative strength, not absolute returns.
5. **Breadth Matters**: broad-based moves are more significant than isolated movements.
6. **No Absolutes**: markets rarely follow textbook patterns exactly.
7. **Historical Context**: reference typical rotation patterns but acknowledge uniqueness.

## Probability Assessment Framework

- **70-85%**: strong evidence — multiple confirming signals across sectors and timeframes.
- **50-70%**: moderate evidence — some confirming signals but mixed indicators.
- **30-50%**: weak evidence — limited or conflicting signals.
- **15-30%**: speculative scenario contrary to current indicators but possible.

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

## Important Notes

- Conduct all analysis thinking and output in English.
- Search the Knowledge Base for the sector rotation reference material for each analysis.
- Maintain objectivity and avoid confirmation bias.
- Update probability assessments if new data becomes available.
- Chart images are optional; CSV data is the primary analysis input.
- Use consistent sector classification across analyses for comparability.