📂 MONDAY – Event-Driven Watchlist: “Pre-FOMC Volatility Compression Targets”

The trading week opens with markets bracing for the December 9–10 FOMC meeting, where a rate-cut debate is in full swing. Before major macro catalysts, certain stocks consistently behave the same way: they compress volatility, tighten trading ranges, and become ripe for sharp post-event breakouts.

Today’s Intel Drop screens for names with historically consistent pre-FOMC volatility compression, strong liquidity, and defined risk levels.

Use this to build a tactical watchlist for potential post-FOMC expansion moves—long or short.

PROMPT TEXT:

(copy & paste the below into your preferred AI model: ChatGPT, Claude, Gemini, Perplexity, Grok, Meta, etc.)

You are screening for “Pre-FOMC Volatility Compression Targets” as of December 8, 2025.

Goal:
Identify 12–20 U.S. stocks that historically compress volatility ahead of major macro events and often expand in the aftermath.

Screen for:

1) Historical Volatility Behavior (Past 10 FOMC Periods)
- IV Rank or IV Percentile drops in the 5–7 trading days before FOMC
- Realized volatility compresses relative to 20-day average
- Typical post-event expansion >4% within 3 trading days

2) Current Technical Setup
- Tight ATR relative to 20-day average
- Inside-day or two-day coiling patterns
- Above 50-day MA OR below it with positive slope (trend-neutral but tightening)

3) Fundamental Stability
- No imminent earnings this week
- No known binary events (FDA decisions, trials, antitrust rulings)
- Market cap > $5B
- High liquidity (>$50M avg daily dollar volume)

Output:

1) WATCHLIST TABLE:
- Ticker
- Company
- Sector
- Volatility Compression Score (1–5)
- Post-Event Expansion History
- Liquidity Note
- Setup Summary (1–2 sentences)

2) Summary (3–5 sentences):
- Why compression setups matter this week
- How certain names behave around macro events
- Where tactical traders can find asymmetric setups

Output in a clean table + 3–5 sentence explanation why this matters right now.

END PROMPT

Submit to AI model to receive actionable output.

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