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Market Wrap

Market Universe — State of the Tape PAPER TRADING

Breadth, rotation & sentiment across 163 names · July 18, 2026
Redacted — proprietary method withheld. The space is kept so you can see where detail exists.

I track 163 liquid US names continuously, and this is my read of the whole tape as of July 18, 2026 — the backdrop I am selecting into, not a trade list. I care less about where the index closed than about how it got there: who is participating, where money is rotating, and where positioning has stretched far enough to matter. The discipline is the same every week, but the conclusions are not — the framework keeps learning, and so do I.

Macro environment

VIX
16.73
10Y−2Y
+0.41
HY OAS
2.71
Unemployment
4.2
SPY 20d
+0.30%

Here is how I read the backdrop: volatility is cheap at 16.7 — barely 1.05% a day — and cheap vol is exactly where I watch crowding hardest, the 10y−2y curve has steepened to +0.41, a quieter vote of confidence in duration, high-yield spreads at 2.71 are still contained, so credit is not yet contradicting the tape, and the S&P's trailing month is +0.3%, the trend I am actually trading against.

Volatility is the first thing I price, because it sets the size of every other bet. I scale gross exposure inversely to realised vol — target the risk, not the notional — so a calmer tape lets the book breathe and a violent one pulls it in automatically:

$$\sigma_{\text{daily}}\approx\dfrac{\mathrm{VIX}}{\sqrt{252}}\qquad w_{\text{gross}}\;\propto\;\dfrac{\sigma_{\text{target}}}{\sigma_{\text{daily}}}$$

Source: fred · as of 2026-07-17

Market breadth & participation

Across 163 names in the tracked universe, breadth measures how much of the tape is participating in the trend rather than a handful of leaders. Participation is the share of the universe trading above its own moving averages:

$$\text{Breadth}_{50} \;=\; \frac{1}{N}\sum_{x}\mathbf{1}\!\left[P_x > \mathrm{SMA}_{50}(x)\right]$$
Above SMA50
66%
Above SMA200
73%
Advancers
103
Decliners
60
Avg RSI(14)
56.7
Overbought
13
Oversold
3
Avg 20d
+3.88%

Breadth is my lie-detector for a rally: it tells me whether the index is being lifted by the many or faked by the few. As I read it, 66% of the universe is above its own 50-day line, which is broad enough that I trust the move's footing, advancers lead decliners 103:60 (an A/D ratio of 1.72), and 13 names are overbought against 3 oversold, the internal tension I weigh for mean-reversion risk.

I count participation and the advance/decline split directly, so the claim is checkable rather than asserted:

$$\mathrm{A/D}=\dfrac{\#\{r_x>0\}}{\#\{r_x<0\}}$$

My read maps to a posture; the exact breadth thresholds that set position sizing stay proprietary and withheld.

Sector rotation

Capital is leaning into Chemicals (+17.8% on the trailing month) and away from Metals & Mining (-15.0%). That 32.8-point spread is the rotation I am trading: I want the weekly book overweight where both price and participation agree, not where one is dragging the other.

Average 20-day return and SMA50 participation by sector — the raw rotation map under the read above.

SectorNamesAvg 20d% > SMA50
Chemicals9+17.77%62%
Diversified Consumer Services1+15.06%100%
Life Sciences Tools & Services2+14.91%100%
Insurance1+12.39%100%
Financials1+12.36%100%
Road & Rail4+11.91%100%
Energy10+9.86%70%
Biotechnology7+9.44%83%
Consumer products2+7.42%100%
Financial Services13+6.28%82%
Banking8+5.00%100%
Pharmaceuticals6+4.99%75%
Health Care7+4.84%83%
Textiles, Apparel & Luxury Goods2+4.31%0%
Real Estate11+4.26%70%
Logistics & Transportation2+4.01%50%
Retail10+3.53%56%
Technology5+3.32%n/a
Media10+3.01%40%
Utilities10+2.44%30%
Telecommunication4+0.73%25%
Beverages2-0.58%50%
Hotels, Restaurants & Leisure7-0.78%57%
Industrial Conglomerates2-1.13%100%
Machinery7-1.72%86%
Aerospace & Defense4-2.84%50%
Automobiles1-3.94%n/a
Communications1-4.64%n/a
Electrical Equipment3-4.70%33%
Semiconductors9-10.84%80%
Metals & Mining2-15.02%0%

Momentum leaders & laggards

The strongest names — DD, PYPL, VLO — are where momentum is already doing my work, and I respect a trend until it breaks rather than fading it on a hunch and the laggards — ORCL, INTC, ALB — I read as either falling knives or set-ups, and I refuse to confuse the two without a catalyst.

Top movers

Ticker20dRSI
DD+182.52%52.6
PYPL+34.45%n/a
VLO+29.03%56.7
MPC+27.72%58.6
PSX+23.65%72.8
ADBE+20.83%n/a
DLTR+18.30%59.5
WELL+17.87%55.4

Bottom movers

Ticker20dRSI
ORCL-31.11%n/a
INTC-28.98%65.3
ALB-27.56%29.8
MU-25.00%66.9
QCOM-24.02%56.8
NEM-15.04%37.1
FCX-14.99%56.2
ISRG-14.12%29.5

Sentiment extremes

I read sentiment as a crowding gauge, not a green light. Where the crowd is most bullish I ask what is left to buy; where it is most bearish I ask what is left to sell. The extremes below are useful precisely because they are uncomfortable — they tell me where positioning, not fundamentals, is setting the price.

$$\bar s=\dfrac{1}{N}\sum_x s_x\qquad\sigma_s=\sqrt{\dfrac{1}{N}\sum_x\left(s_x-\bar s\right)^2}$$

Most positive

TickerSentiment20d
KMI+0.98+3.13%
ESS+0.96+9.77%
D+0.91+4.44%
NTRS+0.89+5.54%
ECL+0.88+1.49%
DG+0.88+15.72%
EQR+0.86+6.78%
KNSA+0.84+16.08%

Most negative

TickerSentiment20d
TMO-0.97+15.27%
ESTA-0.39+9.99%
YUM-0.33-4.02%
TMUS-0.30+6.02%
CBC-0.25+8.50%
CE-0.25-10.83%
CHTR-0.14-0.48%
RTX-0.14+0.45%

How I read this note

This is my survey of the whole universe I track, not a trade recommendation. Breadth, the advance/decline split and the RSI extremes are standard, publicly defined measures, and I show them in full so you can check my arithmetic. What I keep back is how I combine these readings into position sizing and risk posture — that blend is the edge, and it recalibrates as the evidence does.

Indicator formula reference
RSI: $$\mathrm{RSI}_{14}=100-\frac{100}{1+RS},\quad RS=\frac{\overline{\text{gain}}_{14}}{\overline{\text{loss}}_{14}}$$
MACD: $$\mathrm{MACD}=\mathrm{EMA}_{12}-\mathrm{EMA}_{26},\quad \text{signal}=\mathrm{EMA}_9(\mathrm{MACD})$$
ATR: $$\mathrm{ATR}_{14}=\tfrac{1}{14}\sum TR,\quad TR=\max\big(H-L,\,|H-C_{-1}|,\,|L-C_{-1}|\big)$$
Bollinger: $$\mathrm{BB}=\mathrm{SMA}_{20}\pm 2\,\sigma_{20}$$
Cross-sectional z: $$z=\frac{x-\mu}{\sigma}$$
A standing note on method. I run this book in paper-trading mode, so every fill you see is simulated rather than a realised, audited track record — I would rather state that plainly than flatter the numbers. Nothing here is investment advice, an offer, or a solicitation; it is my own research, published so it can be read and argued with in the open. The blacked-out passages mark the parts of the process I keep proprietary. And because the framework recalibrates every week, where my read was wrong I expect the priors — not my ego — to be the first to say so.