What Are Gamma & Gamma Exposure - GEX Full Course Ep.2

Main ideas / lessons

• Gamma Exposure (GEX) is presented as a crucial concept for traders—especially futures and CFD traders, and index traders like S&P 500 / ES and NASDAQ futures / CFDs—because it helps explain why price moves and volatility change, not just where prices are.
• The video frames market movement as influenced by options hedging flows (not only headlines/fundamentals), particularly how options market makers manage risk.
• The course goal (“Deep Gamma boot camp”) is to teach:
  • Options fundamentals (calls/puts, moneyness, expiration, key Greeks)
  • How gamma exposure affects statistical regimes in price action (e.g., volatility expansion vs. compression)
  • How to combine GEX with order flow in a later episode
• A major theme: it’s not about adding more indicators—it’s about understanding market flows and the information advantage that comes from options data.

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Concepts taught (structured)

1) Options basics recap

• Options behave like “insurance contracts”:

  • They have an expiration date that matters for valuation.
  • They have a premium based on:
    • Underlying price
    • Time to expiration (theta)
    • Implied volatility (vega)
    • Price sensitivity via delta and gamma

• Calls vs puts

  • Call option: right to buy
  • Put option: right to sell

• Moneyness (“moneyiness”)

  • Options can be in the money, at the money, or out of the money, affecting valuation and payoff behavior.

• Greeks

  • Delta: how option value changes for a $1 move in the underlying (slope of payoff/value curve)
  • Gamma: how delta changes as the underlying moves ($1 change → change in delta)
  • Theta: time decay
  • Vega: sensitivity to implied volatility changes

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2) Probability & “standard deviation” (Gaussian framing)

• The video uses a Gaussian/normal distribution to explain how markets price expected moves.

• Key teaching points:

  • Roughly 68–70% of outcomes fall within ±1 standard deviation
  • About 16% + 16% fall in the tails beyond ±1 standard deviation
  • “Tail events” are rare/extreme deviations
  • Mean reversion is referenced: moves outside the “normal range” often revert

• Implied volatility (IV) is treated as the market-implied standard deviation of expected future movement.

  • Option prices embed this expectation: the premium reflects the probability distribution of where price might go.

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3) Why buying vs selling options has different odds

• The video contrasts:
  • Buying options: benefit is potentially limited; the cost is the upfront premium
  • Selling options: may look like “infinite loss,” but odds depend on pricing and the fact that the seller collects premium first
• It explains that probability of finishing profitable depends on where breakeven sits relative to the distribution.
• Selling options can yield a higher probability of “closing profit”, even if payoff asymmetry differs.

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4) Volatility “smile” and bias

• The distribution is described as slightly “tilted.”
• This tilt is linked to bias in option pricing, connected to the volatility smile (with deeper discussion hinted for later sessions).

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5) Option structures (“you can’t just sell a call and stop”)

• Traders use combinations of strikes/legs to express views and manage risk.

• Structures discussed with examples:

  • Straddle (sell both sides at the same strike / ATM)

    • Sell a call + sell a put at the same strike
    • Profit if price stays within a range around the expected move (roughly “one standard deviation” in the explanation)
    • Probability of profit tied to implied volatility (example around ~69% when IV ≈ 10%)
    • If volatility increases, the distribution changes and outcomes worsen for the seller

  • Strangle (sell call + put at different strikes, further OTM)

    • Further OTM strikes mean lower premium, but higher probability of profit
    • Example cited: ~86%

  • Spreads

    • Example: buy a put and finance it by selling a put at a different strike
    • Presented with:
      • Defined maximum loss (example: $3,000)
      • Chance of profit given as ~53% (as stated)

• Other structures mentioned by name:

  • Iron condors
  • Butterflies
  • Put ratio back spreads
  • Broken wings

• Takeaway:

  • These structures are not purely directional bets (not just “like futures/CFDs”).
  • They can behave in an insurance-like way across different strikes and maturities.

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Market makers & how gamma exposure is defined through hedging

1) Market makers’ business model

• Market makers:
  • Provide liquidity via bid/ask and earn the spread
  • Profit primarily from the spread differential, not from forecasting direction
• If price trends strongly one way, they may accumulate unwanted risk and must hedge.

2) Options book / supply-demand differs from one “single book”

• The options market is described as having multiple books simultaneously:
  • For each expiration and strike
  • Each strike has its own supply/demand for calls and puts

3) Delta hedging as the mechanism behind GEX

• When market makers are short options (e.g., short calls / short puts), their hedging requires trading the underlying.
• Due to convexity (gamma), hedging is dynamic, not linear:
  • The video describes hedge size changing as price moves (example transitions such as buying ~50 shares per point to 60, 70, 80, 90, 100, as described).
• This dynamic adjustment links options positioning to gamma exposure.

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Short gamma vs long gamma (volatility effects)

Short gamma (often associated with selling options)

• If market makers are short gamma, they:
  • Buy when price rises and sell when price falls
  • This can exacerbate volatility (volatility expansion), potentially driving gamma squeezes
• If implied volatility rises, it increases hedging pressure
  • The speaker references additional components/Greeks-like effects (e.g., vanna, and mentions charm) as deferred for later.

Long gamma (often associated with buying options)

• If market makers are long gamma, they tend to do the opposite:
  • Buy on the fall and sell on the rise
  • This can compress volatility, reducing convexity-driven swings

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Studies / data sources referenced

1) SqueezeMetrics (2017) as an early empirical idea

• In 2017, SqueezeMetrics tested whether:
  • Negative gamma exposure days correlate with higher realized volatility
  • Positive gamma exposure days correlate with lower realized volatility
• The approach relied mostly on end-of-day open interest and assumed market makers were the counterparty.

2) Limitation caused by 0DTE growth

• After 2017, 0DTE (zero days to expiration) options grew heavily.
• Since 0DTE expires intraday, end-of-day open interest becomes less predictive of intraday hedging flows.

3) CBOE real-time data purchase (the “new certainty” claim)

• The video claims CBOE provides near-real-time transaction data (minute-by-minute) for a monthly fee.
• The speaker emphasizes this:
  • Removes “naive assumptions” (“we no longer guess”)
  • Helps distinguish whether market makers are buying or selling options in specific situations
• Deep Charts is said to have invested in this data, enabling:
  • Global gamma exposure visibility
  • A gamma exposure heat map identifying where short gamma vs long gamma exists

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How Deep Charts applies gamma exposure (use cases)

Use case 1: Heat map for volatility compression/expansion zones

• The heat map is used to infer where market makers may intervene:
  • Short-gamma zones: likely to expand/exacerbate volatility
  • Long-gamma zones: likely to compress volatility
• Claimed market behavior:
  • Price may bounce
  • Consolidate
  • Later experience “acceleration”
• Timing emphasis:
  • More intense hedging expected in the last 60–30 minutes, since theta effects accelerate near expiration (especially in 0DTE)

Use case 2: Seeing market participants’ large options trades

• Beyond gamma hedging, Deep Charts is also said to display large options trades.
• Example interpretation:
  • If a market maker is buying in one area and selling in another, it may suggest another large trader took the opposite side—potentially reflecting a directional view or a hedge-related structure.
• Framed as part of a broader theme:
  • Options flow offers extra insight beyond gamma hedging alone.

Coverage across expiries (not just 0DTE)

• The system is described as usable across:
  • 0DTE, 1DTE, 2DTE, 10DTE, 90DTE, etc.
• Longer-dated structures are suggested to matter for longer-term traders.

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Methodology / “instruction-like” elements (what traders should do)

Mostly educational rather than a procedural how-to, but it includes a workflow idea.

1. Learn options basics first

   • Calls/puts, moneyness, Greeks (delta, gamma, theta, vega)

2. Understand probability pricing

   • Treat implied volatility as expected “standard deviation”
   • Use the normal distribution framing to interpret option premiums/probabilities

3. Interpret option structures

   • Recognize that buying/selling options changes how P&L relates to the distribution
   • Understand common structures: straddle, strangle, spreads
   • Note that multi-leg structures produce varied risk/reward profiles

4. Connect positioning to hedging

   • Market makers hedge dynamically using the underlying due to gamma/convexity

5. Use gamma exposure maps with context

   • Identify likely compression vs expansion zones (short vs long gamma)
   • Consider timing—especially into the last hour for 0DTE

6. Combine with order flow (planned for next video)

   • Use order flow to contextualize levels such as “call walls” and “put walls”
   • Determine likely rejection/break conditions

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Speakers / sources featured (identified)

Speakers

• Narrator / presenter (unnamed in subtitles)
• Fabio (mentioned as co-presenter in an upcoming session)
• Rodulfo (mentioned as an engineer behind the Deep Gamma suite)
• Brian (mentioned by chat; likely present/hosted interaction but not clearly the main speaker)

Sources / studies / organizations referenced

• CBOE (Chicago Board Options Exchange) — data provider
• SqueezeMetrics (2017) — study referenced
• Deep Charts — platform hosting “Deep Gamma” and heat map features
• S&P 500 / ES / SPX / NASDAQ / SPY / ES / ETFs / ETPs — instruments referenced (not academic sources)

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