sector-rotation

description	Track equity sector rotation — collect sector ETF data and compute relative-strength heatmaps, sector leaders, and market cycle phase. Use when analyzing sector rotation, relative strength across sectors, or where we are in the market cycle.
name	sector-rotation
category	trading-finance
maturity	stable
tags	["relative-strength","yfinance","business-cycle","money-flow","etf"]

Sector Rotation Tracker

Quick Start

Collect + analyze (typical workflow)

cd /path/to/sector-rotation
python3 scripts/collect.py --db data/sectors.db

Then use the JSON output (or DB) to write analysis per references/analysis-prompt.md.

Query historical data

python3 scripts/query.py heatmap          # RS heatmap, all sectors × all periods
python3 scripts/query.py leaders --n 3    # top 3 sectors by 1M RS
python3 scripts/query.py cycle --days 90  # cycle phase history

Scripts

scripts/collect.py — Data Collector & Analyzer

Fetches 3 months of daily data for 17 tickers via yfinance. Computes:

• Returns and relative strength vs SPY (1W, 2W, 1M, 3M)
• Money flow proxy (OBV trend, normalized -100 to +100)
• RSI-14, 1M annualized volatility
• Market cycle phase detection (sector leadership pattern matching)
• Breadth: sectors above SPY, defensive vs cyclical flow, risk appetite
• Rotation signals: ACCELERATING, DECELERATING, RECOVERING, DETERIORATING

Flags: --db PATH, --no-db, --json Dependency: pip3 install yfinance

scripts/query.py — Database Query API

Historical queries for analysis and backtesting.

Commands:

• snapshot [--date] — full snapshot JSON
• sector XLE --days 30 [--csv] — single sector history
• rankings --period 1w|2w|1m|3m — RS rankings
• cycle --days 90 [--csv] — cycle phase history
• signals --days 30 — rotation signals log
• heatmap — RS heatmap (all sectors, all periods)
• leaders --n 3 / laggards --n 3 — top/bottom sectors
• compare XLE XLK --days 60 — head-to-head
• breadth --days 30 [--csv] — breadth/risk appetite history
• export --days 365 --csv — full export for backtests

Database

SQLite at data/sectors.db. 4 tables:

• snapshots — full JSON per run + cycle phase + risk appetite
• sector_series — flat rows per sector: close, returns, RS, flow, RSI, vol
• cycle_history — phase, confidence, component scores, breadth metrics
• rotation_signals — timestamped signal log (accelerating/decelerating/etc.)

Analysis Framework

When generating reports, read references/analysis-prompt.md for the full analysis template. Read references/cycle-model.md for the business cycle sector rotation model (Fidelity/Stovall).

The agent writes analysis covering: cycle phase, rotation scorecard, money flow themes, cross-asset confirmation, risk appetite, actionable rotation trades, and signals to watch.

Integration with Other Skills

• macro-briefing can read sector data for cross-asset confirmation
• Other skills call query.py or read data/sectors.db directly
• JSON output from collect.py provides structured data for programmatic consumption

Recommended Schedule

Weekly collection (Sunday evening or Monday pre-market) for the main rotation report. Optional mid-week check (Wednesday) to catch intra-week shifts.

References

• references/analysis-prompt.md — analysis template for report generation
• references/cycle-model.md — business cycle sector rotation model, thresholds, interpretation