Sector score trend last 21 days · 0–100 health score over A/B/C/D bands · the wave is the smoothed trend, the faint line + dots are the daily scores · hover a line or chip to highlight
Energy 68Information Technology 57Health Care 55Consumer Staples 47Materials 47Utilities 43Industrials 40Consumer Discretionary 40Financials 39Real Estate 39Communication Services 35
Ranker effective score ranker effective score (EMA3+median+event) — the exact per-sector scores rank_candidates used on this date
| Sector | Effective | Grade | EMA3 | Median | Same-day event | Source |
|---|---|---|---|---|---|---|
| Energy | 61.5 | B | 67.8 | 50.0 | — | json |
| Information Technology | 58.2 | B | 52.4 | 52.0 | ⬆ BREAKOUT | json |
| Health Care | 51.5 | B | 46.8 | 43.0 | ⬆ BREAKOUT | json |
| Financials | 42.9 | C | 40.6 | 47.0 | — | json |
| Consumer Staples | 42.6 | C | 46.6 | 35.0 | — | json |
| Consumer Discretionary | 40.6 | C | 41.4 | 39.0 | — | json |
| Materials | 38.8 | C | 41.9 | 33.0 | — | json |
| Industrials | 37.8 | C | 36.7 | 40.0 | — | json |
| Communication Services | 36.8 | C | 38.3 | 34.0 | — | json |
| Utilities | 35.4 | C | 39.9 | 27.0 | — | json |
| Real Estate | 34.2 | C | 35.9 | 31.0 | — | json |
Continuity analysis autocorrelation, 1-mo slope & grade persistence — median (outlier-robust) drives the grade. 1-mo slope = net fitted score change over the window (↑ rising · ↓ falling): high AutoCorr + positive slope = persistent up-trend
| Sector | AutoCorr | 1-mo slope | Median score (90% CI) | Avg | Std | Grade days (A/B/C/D) |
|---|---|---|---|---|---|---|
| Communication Services | +0.46 | -5 ↓ | 34.0 [30–38] | 34.5 | 10.5 | 0.0d 1.5d 2.5d 2.2d |
| Financials | +0.44 | -13 ↓ | 47.0 [43–51] | 45.7 | 10.3 | 0.0d 1.8d 2.8d 1.0d |
| Energy | +0.34 | +20 ↑ | 50.0 [45–55] | 52.8 | 13.5 | 1.3d 1.6d 1.7d 0.0d |
| Health Care | +0.29 | -12 ↓ | 43.0 [38–48] | 47.2 | 12.5 | 1.0d 1.6d 3.2d 1.0d |
| Information Technology | +0.13 | +3 ↑ | 52.0 [48–56] | 51.8 | 10.0 | 1.0d 3.5d 2.4d 0.0d |
| Utilities | +0.12 | +14 ↑ | 27.0 [22–32] | 30.3 | 12.2 | 0.0d 1.0d 1.6d 3.4d |
| Materials | +0.05 | +10 ↑ | 33.0 [28–38] | 37.3 | 11.8 | 0.0d 1.0d 2.0d 1.5d |
| Industrials | +0.00 | -1 → | 40.0 [36–44] | 40.3 | 9.8 | 0.0d 1.2d 1.9d 1.2d |
| Consumer Staples | -0.07 | +9 ↑ | 35.0 [31–39] | 36.7 | 10.8 | 0.0d 1.2d 1.7d 1.7d |
| Consumer Discretionary | -0.26 | +5 ↑ | 39.0 [36–42] | 39.2 | 7.8 | 0.0d 1.0d 5.2d 1.0d |
| Real Estate | -0.38 | +1 → | 31.0 [28–34] | 30.9 | 8.8 | 0.0d 1.0d 1.3d 1.4d |
Rotation events score crossing the 50-pt threshold · 21 breakouts / 9 breakdowns (most recent 25)
| Event | Date | Sector | Score |
|---|---|---|---|
| ⬆ BREAKOUT | 2026-10-09 | Health Care | 39 → 55 |
| ⬆ BREAKOUT | 2026-10-09 | Information Technology | 45 → 57 |
| ⬆ BREAKOUT | 2026-10-08 | Consumer Staples | 35 → 55 |
| ⬇ DOWN | 2026-10-07 | Energy | 72 → 49 |
| ⬇ DOWN | 2026-10-07 | Information Technology | 58 → 41 |
| ⬆ BREAKOUT | 2026-10-06 | Consumer Discretionary | 39 → 51 |
| ⬆ BREAKOUT | 2026-10-06 | Materials | 39 → 55 |
| ⬆ BREAKOUT | 2026-10-05 | Health Care | 39 → 51 |
| ⬆ BREAKOUT | 2026-10-02 | Financials | 43 → 51 |
| ⬆ BREAKOUT | 2026-10-02 | Industrials | 43 → 55 |
| ⬆ BREAKOUT | 2026-10-01 | Energy | 39 → 60 |
| ⬆ BREAKOUT | 2026-09-30 | Information Technology | 49 → 53 |
| ⬇ DOWN | 2026-09-28 | Information Technology | 52 → 37 |
| ⬇ DOWN | 2026-09-25 | Energy | 56 → 40 |
| ⬆ BREAKOUT | 2026-09-25 | Financials | 39 → 51 |
| ⬆ BREAKOUT | 2026-09-23 | Energy | 41 → 52 |
| ⬇ DOWN | 2026-09-23 | Health Care | 72 → 36 |
| ⬆ BREAKOUT | 2026-09-22 | Consumer Discretionary | 27 → 51 |
| ⬆ BREAKOUT | 2026-09-17 | Financials | 44 → 59 |
| ⬆ BREAKOUT | 2026-09-17 | Industrials | 43 → 51 |
| ⬆ BREAKOUT | 2026-09-17 | Information Technology | 44 → 63 |
| ⬆ BREAKOUT | 2026-09-17 | Materials | 31 → 51 |
| ⬇ DOWN | 2026-09-15 | Communication Services | 55 → 40 |
| ⬇ DOWN | 2026-09-15 | Financials | 55 → 44 |
| ⬇ DOWN | 2026-09-15 | Information Technology | 55 → 44 |
Full report
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1. SECTOR SCORE HEATMAP (A≥70 B≥50 C≥30 D<30)
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Each cell = sector health score. Showing last 21 trading days.
Sector 09-10 09-11 09-14 09-15 09-16 09-17 09-18 09-22 09-23 09-24 09-25 09-28 09-29 09-30 10-01 10-02 10-05 10-06 10-07 10-08 10-09
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Energy 50 51 38 69 42 53 45 41 52 56 40 40 35 39 60 64 76 72 49 76 68 ↓↓↑↓
Information Technology 37 63 55 44 44 63 52 65 53 53 52 37 49 53 68 57 66 58 41 45 57 ↓↓↑↑
Health Care 37 45 64 41 52 64 52 72 36 43 47 47 39 39 27 39 51 31 39 39 55 ↓↑→↑
Financials 48 59 55 44 44 59 47 43 31 39 51 31 31 27 43 51 39 47 31 47 39 ↑↓↑↓
Industrials 24 51 31 39 43 51 35 51 35 28 47 32 36 28 43 55 40 48 24 32 40 ↑↓↑↑
Consumer Discretionary 27 47 43 31 39 43 27 51 27 35 47 35 31 31 43 43 39 51 32 47 40 ↑↓↑↓
Consumer Staples 27 35 51 27 35 47 23 47 20 28 35 39 27 27 27 43 31 47 35 55 47 ↑↓↑↓
Communication Services 35 55 55 40 33 40 24 32 24 32 28 16 23 27 27 35 43 39 43 43 35 ↓↑→↓
Materials 25 33 20 28 31 51 23 63 32 28 44 31 27 27 23 39 39 55 24 39 47 ↑↓↑↑
Real Estate 27 35 35 27 27 43 23 35 23 27 31 23 35 23 19 39 19 43 19 39 39 ↑↓↑→
Utilities 16 27 19 23 39 47 19 27 15 15 27 15 27 19 31 43 23 47 23 43 43 ↑↓↑→
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2. CONTINUITY ANALYSIS
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Autocorrelation = corr(score[t], score[t+1]). Persistence = avg consecutive days in same grade.
Median (outlier-robust) drives the grade; 90% CI ≈ median ± 1.645·1.2533·std/√n.
Sector AutoCorr Avg Median Std 90% CI Grade A days Grade B days Grade C days Grade D days
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Communication Services +0.46 34.5 34.0 10.5 [30..38] 0.0d 1.5d 2.5d 2.2d
Financials +0.44 45.7 47.0 10.3 [43..51] 0.0d 1.8d 2.8d 1.0d
Energy +0.34 52.8 50.0 13.5 [45..55] 1.3d 1.6d 1.7d 0.0d
Health Care +0.29 47.2 43.0 12.5 [38..48] 1.0d 1.6d 3.2d 1.0d
Information Technology +0.13 51.8 52.0 10.0 [48..56] 1.0d 3.5d 2.4d 0.0d
Utilities +0.12 30.3 27.0 12.2 [22..32] 0.0d 1.0d 1.6d 3.4d
Materials +0.05 37.3 33.0 11.8 [28..38] 0.0d 1.0d 2.0d 1.5d
Industrials +0.00 40.3 40.0 9.8 [36..44] 0.0d 1.2d 1.9d 1.2d
Consumer Staples -0.07 36.7 35.0 10.8 [31..39] 0.0d 1.2d 1.7d 1.7d
Consumer Discretionary -0.26 39.2 39.0 7.8 [36..42] 0.0d 1.0d 5.2d 1.0d
Real Estate -0.38 30.9 31.0 8.8 [28..34] 0.0d 1.0d 1.3d 1.4d
Market (avg all sectors): autocorr = +0.08
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3. ROTATION EVENTS (score crosses 50-pt threshold)
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Breakout = score was <50 for ≥2 days, then jumps ≥50. Breakdown = score was ≥50 for ≥2 days, then drops <50.
Type Date Sector Prev Score New Score
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⬆ BREAKOUT 2026-10-09 Health Care 39 → 55
⬆ BREAKOUT 2026-10-09 Information Technology 45 → 57
⬆ BREAKOUT 2026-10-08 Consumer Staples 35 → 55
⬇ BREAKDOWN 2026-10-07 Energy 72 → 49
⬇ BREAKDOWN 2026-10-07 Information Technology 58 → 41
⬆ BREAKOUT 2026-10-06 Consumer Discretionary 39 → 51
⬆ BREAKOUT 2026-10-06 Materials 39 → 55
⬆ BREAKOUT 2026-10-05 Health Care 39 → 51
⬆ BREAKOUT 2026-10-02 Financials 43 → 51
⬆ BREAKOUT 2026-10-02 Industrials 43 → 55
⬆ BREAKOUT 2026-10-01 Energy 39 → 60
⬆ BREAKOUT 2026-09-30 Information Technology 49 → 53
⬇ BREAKDOWN 2026-09-28 Information Technology 52 → 37
⬇ BREAKDOWN 2026-09-25 Energy 56 → 40
⬆ BREAKOUT 2026-09-25 Financials 39 → 51
⬆ BREAKOUT 2026-09-23 Energy 41 → 52
⬇ BREAKDOWN 2026-09-23 Health Care 72 → 36
⬆ BREAKOUT 2026-09-22 Consumer Discretionary 27 → 51
⬆ BREAKOUT 2026-09-17 Financials 44 → 59
⬆ BREAKOUT 2026-09-17 Industrials 43 → 51
⬆ BREAKOUT 2026-09-17 Information Technology 44 → 63
⬆ BREAKOUT 2026-09-17 Materials 31 → 51
⬇ BREAKDOWN 2026-09-15 Communication Services 55 → 40
⬇ BREAKDOWN 2026-09-15 Financials 55 → 44
⬇ BREAKDOWN 2026-09-15 Information Technology 55 → 44
⬆ BREAKOUT 2026-09-14 Consumer Staples 35 → 51
⬇ BREAKDOWN 2026-09-14 Energy 51 → 38
⬆ BREAKOUT 2026-09-14 Health Care 45 → 64
⬆ BREAKOUT 2026-09-11 Communication Services 35 → 55
⬆ BREAKOUT 2026-09-11 Financials 48 → 59
Total events: 40 (27 breakouts, 13 breakdowns)
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4. PREDICTIVE POWER — does score[t] predict sector return[t+1]?
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Split observations into score quartiles. Compare avg next-day sector price change % per quartile.
By score grade:
Grade N obs Avg next-day chg % days positive Avg score
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A 7 -0.814% 42.9% 74.1
B 64 -0.454% 23.4% 55.8
C 147 -0.206% 40.1% 39.6
D 68 -0.122% 42.6% 24.1
Overall score→next-day-return correlation: -0.154
→ Score alone has limited next-day predictive power
Per-sector correlation (score[t] vs return[t+1]):
Sector Corr N Interpretation
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Communication Services +0.052 26 weak
Energy -0.140 26 inverse / no signal
Financials -0.160 26 inverse / no signal
Utilities -0.160 26 inverse / no signal
Information Technology -0.217 26 inverse / no signal
Health Care -0.239 26 inverse / no signal
Industrials -0.300 26 inverse / no signal
Consumer Staples -0.326 26 inverse / no signal
Materials -0.399 26 inverse / no signal
Consumer Discretionary -0.408 26 inverse / no signal
Real Estate -0.501 26 inverse / no signal
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5. LEADING DIMENSIONS — which factor best predicts next-day return?
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Correlates each dimension value on day[t] against sector avg_chg on day[t+1].
Dimension Corr vs next-day return Interpretation
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Signals (% BUY/STRONG) +0.048 ██░░░░░░░░░░░░░░░░░░ weak signal
MA Align (% 50>200) -0.027 █░░░░░░░░░░░░░░░░░░░ lagging / no edge
Breadth (% price>0) -0.088 █████░░░░░░░░░░░░░░░ lagging / no edge
SR-3 Avg -0.125 ███████░░░░░░░░░░░░░ lagging / no edge
OBV Flow (% OBV↑) -0.137 ████████░░░░░░░░░░░░ lagging / no edge
Total score -0.154 █████████░░░░░░░░░░░ lagging / no edge
Best leading indicator: Signals (% BUY/STRONG)ⓘ How to read this tab
Sector health score over the last trading days — A≥70 · B 50-69 · C 30-49 · D<30. Rising sectors lead the rotation; this contextualises which strategy picks sit in strong vs weak sectors.