thetaOwl

QQQ

Invesco QQQ TrustClose $722.82EOD only
Max Pain
$724.00
Next expiry Jul 7, 2026
Expected Move
±$6.15
0.8% from close
Price Gap
+1.18
Distance to max pain
IV Rank
14
Low premium
P/C OI
1.41
Slightly put-heavy
Consensus
6.5/10
Bearish tilt
Published snapshot: Jul 6, 2026 close
End-of-day snapshot

This page reflects QQQ options positioning from the latest published market-close snapshot. Intraday price and contract changes are not displayed.

Published Snapshot
Jul 6, 2026 close
QQQ AI Consensus Report
Analysis based on market close June 26, 2026

Consensus-supported lens with chain history and key metrics in the rail.

Conviction
6.5

out of 10

7 not higher because theta's bullish structure undermines the bearish consensus; if theta were aligned, conviction would be 8+.

Where Perspectives Agree

Bearish bias confirmed by negative dealer gamma, spot below max pain, and aggressive put flow. All three expect further downside within 1-2 weeks.

Where They Diverge

Theta's recommended put credit spread is bullish, directly conflicting with bearish directional and flow signals. Theta's invalidation below $695 is compatible with bearish but the structure itself is not.

Top Trade
via directional

Buy 2026-07-10 $709/$699 bear put spread for $3.00 debit.

Key Risk

Break below $695 triggers dealer gamma flip, invalidating theta's range thesis and accelerating bearish momentum to $675.

How to Use These Reports
This ai consensus reflects the market close on June 26, 2026.
What the reports do

Each report translates the same market-close options snapshot into a specific lens such as directional bias, premium-selling posture, flow quality, or earnings setup.

How traders use them

Reports are most useful for narrowing the playbook, surfacing entry and risk context, and deciding which raw data page to inspect next.

What to remember

These are interpretation layers, not execution guarantees. Validate the setup against chain liquidity, expected move, and exposure before sizing risk.

If the report conviction and the raw data disagree, slow down and resolve the mismatch before sizing risk.