Sector Rotation

Sector Rotation

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

Economic Cycle Sectors

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

Sector ETFs

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