sector-rotation

name	sector-rotation
description	Rotate between sectors based on economic and market cycles. Use when optimizing sector allocation, understanding cyclical trends, or positioning for macro shifts.
license	Apache-2.0
metadata	{"author":"ske-labs","version":"4.0"}

Sector Rotation

Rotate sector allocation based on economic cycle phase, relative strength, and macro catalysts.

Economic Cycle Sectors

Phase hypothesis	Economy	Sectors often evaluated
Early Expansion	Recovery begins	Financials, Consumer Discretionary, Information Technology
Mid Expansion	Growth accelerates	Industrials, Materials, Tech
Late Expansion	Growth peaks	Energy, Materials, Financials
Contraction	Slowdown/recession	Utilities, Healthcare, Consumer Staples

Treat this map as an economic hypothesis, not a deterministic allocation rule. Classifications, constituents, and sensitivities change.

Cyclical (economy-sensitive): Consumer Discretionary, Financials, Industrials, Materials, Energy, Information Technology Defensive (recession-resistant): Utilities, Healthcare, Consumer Staples, Real Estate

Rotation Signals

Signal	Indicates
Yield curve steepening	Early cycle -- favor cyclicals
Yield curve flattening	Late cycle -- rotate to defensives
Fed cutting rates	Early cycle starting
Fed raising rates	Late cycle
Commodities rallying	Late cycle inflation

Sector ETFs

Sector	ETF
Technology	XLK
Healthcare	XLV
Financials	XLF
Consumer Discretionary	XLY
Consumer Staples	XLP
Energy	XLE
Industrials	XLI
Utilities	XLU
Materials	XLB
Communication Services	XLC
Real Estate	XLRE

Workflow

1. Build Relative Strength Rankings

get_candles(symbol=<sector_etf>, exchange=<exchange>, interval="1D", count=260)
get_candles(symbol="SPY", exchange=<exchange>, interval="1D", count=260)

Use the current 11-sector GICS universe. Rank predeclared total-return horizons relative to the benchmark, with volatility, drawdown, liquidity, and turnover. Prevent look-ahead by using classifications and constituents known on each date; compare with equal-weight and benchmark baselines.

2. Research Macro Context

get_financial_news(topic="sector rotation economic cycle <current year>", max_results=15)
get_economics_calendar(from_date=<start>, to_date=<end>, impact="high")

Identify cycle phase, Fed policy direction, and upcoming macro catalysts that could shift rotation.

3. Detect Correlation Regime Shifts

When normally uncorrelated sectors start moving together, a macro factor is dominating individual sector dynamics:

• Tech and Utilities both rising strongly = macro factor (Fed policy, liquidity) overriding sector rotation
• All sectors falling together = risk-off regime, defensive positioning matters less
• Defensive and cyclical diverging sharply = normal rotation is active, standard cycle strategy applies

When correlation regime shifts are detected, prioritize macro analysis over traditional rotation signals.

4. Report Recommendations

Provide the as-of date, classification universe, relative-strength/volatility table, uncertain cycle assessment, turnover/tax assumptions, and proposed weights or no change. Derive weights from the portfolio mandate and concentration limits rather than always selecting three winners and losers.

Evidence and Validation

• Treat the setup as a testable hypothesis, not a prediction. Define thresholds, entry, invalidation, and exit before evaluating outcomes.
• Calibrate on the same instrument, venue, session, and timeframe. Use closed candles and a held-out or walk-forward sample; record every variant tried.
• Include spread, fees, slippage, borrow or funding, partial fills, and latency. Reject the setup when net expectancy is not positive or depends on one narrow parameter.
• Return observed inputs, missing data, cost assumptions, entry, invalidation, exit, and a valid, watch, or no-trade status.
• Research basis: Moskowitz & Grinblatt documented industry momentum; use the current GICS methodology for classification rather than assuming a fixed business-cycle map.

Key Rules

• Enforce the portfolio mandate's sector and issuer concentration limits; do not invent a universal cap.
• Rebalance on a fixed schedule and include turnover, spread, taxes, and tracking error.
• NEVER rely solely on historical cycle patterns -- confirm with current relative strength data and macro context
• Choose and validate the review frequency before testing.
• When all sectors correlate (crisis mode), standard rotation logic breaks down -- prioritize capital preservation

Related Skills

• market-correlation-trading -- Cross-asset correlations reveal regime shifts
• economic-calendar-trading -- Fed and macro data are primary rotation catalysts