US Equities Screener & Sector Rotation
US equities reference covering S&P 500 mega-caps, Technology, Financials, Healthcare, and Consumer sectors. Sector weights, factor returns, percentage changes, and ETF rotation signals for quantitative investors.
Monitor top daily gainers and losers, cross-sector momentum, and global index performance. See also: daily US stock signals dashboard · live market data · VPIN order flow research
US Equities & Sector Rotation — Reference Guide
How to use this screener, how to read sector data, and the quantitative frameworks behind US equity price formation.
What This Screener Covers
S&P 500 equities across Technology, Financials, Healthcare, Consumer Discretionary, Energy, and Industrials. Each row shows real-time price, daily percentage change, volume, and market cap. The sector ETF panel tracks XLK, XLF, XLV, XLE, XLY, and XLI for cross-sector rotation signals. Mega-cap stocks — Apple (AAPL), Microsoft (MSFT), NVIDIA (NVDA), Amazon (AMZN), Alphabet (GOOGL), Meta (META), Tesla (TSLA) — collectively account for over 30% of S&P 500 market capitalization and drive a disproportionate share of index-level price movement on any given day.
Understanding Sector Rotation
Sector rotation is the movement of institutional capital between market sectors in response to macroeconomic cycle changes. During early expansion, Financials (XLF) and Consumer Discretionary (XLY) typically lead. Technology (XLK) outperforms in mid-cycle phases when earnings revisions are rising. Energy (XLE) leads during inflationary regimes and commodity super-cycles. Healthcare (XLV) provides defensive positioning in late-cycle slowdowns. Tracking sector ETF relative strength over rolling 20-day and 60-day windows gives a systematic framework for identifying rotation inflection points before they become consensus trades.
How to Read Screener Data
Daily gainers and losers highlight short-term momentum and earnings-driven moves. Sector ETF relative strength identifies which parts of the market are attracting institutional flows. Green percentage changes indicate outperformance relative to the prior close; red indicates underperformance. The Volume column shows trading activity relative to each stock's normal range — above-average volume confirming a directional move is a higher-conviction signal than a low-volume drift. For rule-based signal generation across 500+ equities, see the Quantum Signals dashboard.
Market Microstructure & Order Flow
Stock price movements visible in a screener are the visible result of order flow dynamics occurring at the microsecond level. When institutional orders — large market-on-close programs, index rebalance trades, or earnings-driven algorithmic flows — reach the order book, bid-ask spreads widen and price impact increases. QuantMedia's research on VPIN order flow toxicity provides a quantitative framework for measuring the probability of informed trading. High VPIN readings indicate that a disproportionate share of volume is driven by informed traders — a warning signal of elevated adverse selection risk for passive market participants.
S&P 500 Index Concentration
The S&P 500 is a market-cap-weighted benchmark: the largest companies by market value have the most influence on index-level returns. As of 2026, the top 10 constituents account for approximately 35% of total index weight, creating meaningful concentration risk. NVIDIA's ascent to a $3+ trillion valuation reflects the AI infrastructure buildout cycle and semiconductor demand surge. Apple and Microsoft maintain dominant positions through ecosystem lock-in and cloud infrastructure. Understanding this concentration is fundamental: a 3% rally in NVIDIA alone can add 30+ basis points to the S&P 500 on a single trading day.
Risk-Adjusted Interpretation
Raw return figures must be interpreted in the context of risk. A 5% daily gain in a high-volatility small-cap may represent a lower Sharpe ratio outcome than a 1% gain in a low-volatility defensive name. QuantMedia's research on the Probabilistic Sharpe Ratio provides a statistically rigorous method for evaluating whether a strategy's return profile is likely to persist out-of-sample. For cross-sector portfolio construction, the Hierarchical Risk Parity framework offers a covariance-matrix-free approach to diversification more stable than mean-variance optimization.
Sector Rotation by Economic Cycle
| Cycle Phase | Leading Sectors | Key ETFs | Macro Signal |
|---|---|---|---|
| Early Expansion | Financials, Consumer Discretionary | XLF · XLY | Fed cutting rates, yield curve steepening |
| Mid Cycle | Technology, Industrials | XLK · XLI | Rising earnings revisions, strong PMI |
| Late Cycle | Energy, Materials | XLE · XLB | Commodity inflation, yield curve flat |
| Slowdown / Recession | Healthcare, Utilities, Staples | XLV · XLU · XLP | Inverted yield curve, rising unemployment |
S&P 500 Sector Weight Reference
| Sector | ETF | Index Weight | Key Holdings |
|---|---|---|---|
| Information Technology | XLK | ~29% | AAPL, MSFT, NVDA |
| Communication Services | XLC | ~8.5% | GOOGL, META |
| Consumer Discretionary | XLY | ~10% | AMZN, TSLA |
| Financials | XLF | ~13% | JPM, BAC, BRK-B |
| Health Care | XLV | ~11% | LLY, UNH, JNJ |
| Industrials | XLI | ~8.5% | GE, RTX, CAT |
| Energy | XLE | ~3.5% | XOM, CVX |
| Consumer Staples | XLP | ~6% | PG, KO, WMT |
| Utilities | XLU | ~2.5% | NEE, DUK |
| Real Estate | XLRE | ~2.5% | PLD, AMT |
| Materials | XLB | ~2.5% | LIN, APD |