Sector Rotation Signal — Tech Lagging, Agent Says Move to Energy

# sector-rotation config
schedule: "0 7 * * 5"  # Friday 7AM UTC
sectors:
  - { name: "Technology", etf: "XLK" }
  - { name: "Energy", etf: "XLE" }
  - { name: "Healthcare", etf: "XLV" }
  - { name: "Financials", etf: "XLF" }
  - { name: "Industrials", etf: "XLI" }
  - { name: "Consumer Disc.", etf: "XLY" }
  - { name: "Consumer Staples", etf: "XLP" }
  - { name: "Utilities", etf: "XLU" }
  - { name: "Real Estate", etf: "XLRE" }
  - { name: "Materials", etf: "XLB" }
  - { name: "Communication", etf: "XLC" }

momentum_windows:
  - 5    # 1 week (trading days)
  - 21   # 1 month
  - 63   # 3 months

regime_source: macro_dashboard  # cross-reference with Monday macro data

def compute_sector_momentum(sectors, windows):
    scores = {}
    for sector in sectors:
        prices = fetch_prices(sector.etf, days=max(windows) + 10)
        momentum = {}
        for w in windows:
            ret = (prices[-1] / prices[-w] - 1) * 100
            rank = rank_among_sectors(ret, sector.name, w)
            momentum[f"{w}d"] = {"return": ret, "rank": rank}

        # Composite: equal-weight across timeframes
        avg_rank = np.mean([m["rank"] for m in momentum.values()])
        scores[sector.name] = {
            "momentum": momentum,
            "composite_rank": avg_rank,
            "signal": classify_signal(avg_rank, len(sectors))
        }
    return scores

def classify_signal(rank, total):
    if rank <= total * 0.2:
        return "STRONG_LEAD"
    elif rank <= total * 0.4:
        return "LEADING"
    elif rank >= total * 0.8:
        return "STRONG_LAG"
    elif rank >= total * 0.6:
        return "LAGGING"
    return "NEUTRAL"

REGIME_SECTOR_MAP = {
    "early_expansion":  ["Technology", "Consumer Disc.", "Industrials"],
    "mid_expansion":    ["Technology", "Industrials", "Materials"],
    "late_expansion":   ["Energy", "Financials", "Materials"],
    "contraction":      ["Healthcare", "Utilities", "Consumer Staples"],
    "recovery":         ["Financials", "Consumer Disc.", "Real Estate"]
}

def generate_rotation_signal(sector_scores, current_regime):
    favored = REGIME_SECTOR_MAP[current_regime]
    lagging = [s for s, d in sector_scores.items() if d["signal"] == "STRONG_LAG"]
    leading = [s for s, d in sector_scores.items() if d["signal"] == "STRONG_LEAD"]

    # Signal fires when: lagging sector is currently overweight AND
    # regime-favored sector is currently underweight
    recommendations = []
    for lag in lagging:
        if lag in portfolio.overweight_sectors:
            for fav in favored:
                if fav in leading or fav not in portfolio.sectors:
                    recommendations.append(Rotation(
                        reduce=lag, increase=fav,
                        reason=f"{lag} lagging 3 consecutive weeks, "
                               f"{current_regime} historically favors {fav}"
                    ))
    return recommendations

📊 SECTOR ROTATION SIGNAL — Week of March 6

Tech (XLK): -2.1% (1W) | -4.3% (1M) | +1.2% (3M) → STRONG_LAG
Energy (XLE): +3.4% (1W) | +8.7% (1M) | +14.2% (3M) → STRONG_LEAD

Regime: Late-cycle expansion (confirmed by Monday macro dashboard)
Historical precedent: Energy outperforms tech by avg 12% in
late-cycle periods (1990-2024, n=7 cycles)

Recommendation: Reduce tech allocation by 8% → add to energy
Current tech: 28% of equity sleeve → proposed 20%
Current energy: 0% → proposed 8%

Timing: momentum still accelerating on XLE, not overbought (RSI 62)