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marketsMay 20, 20263 min read

Gamma Squeeze: When Market Makers Lose Control

During a gamma squeeze, market makers are forced to continuously buy more shares to maintain delta-neutral hedges — driving prices higher and forcing them into even more buying.

Daniel Richter
Daniel Richter·Lead Quantitative Analyst

When the Machine Works Against Itself

At 2:32 PM European time in January 2021, GameStop triggered what would become one of the most extreme gamma squeezes in market history. Within 47 minutes, the stock rocketed 91% higher. Call options with a 115 strike exploded by 1,240% — not because of fundamental news, but because market makers were forced to unwind their own hedges, driving the market against themselves.

What Is a Gamma Squeeze?

A gamma squeeze occurs when massive call buying forces market makers to buy the underlying stock to remain delta-neutral. The problem: as the price rises, they must buy more — creating a self-reinforcing feedback loop.

Gamma measures how quickly an option's delta changes. With high gamma, market makers must constantly adjust their hedges. When retail traders buy calls en masse, market makers sell those calls and simultaneously buy shares to neutralize their risk.

The classic setup:

  • Heavy call buying concentrates on a few strikes
  • Open interest explodes in out-of-the-money calls
  • The stock price approaches those strikes
  • Market makers begin hedging
  • Buying pressure drives the price higher — gamma squeeze activated

The Mechanics Behind It

Market makers are required to provide liquidity. When you buy a call, they sell it to you — then must buy shares to hedge the delta risk. With 10,000 calls at 0.50 delta, that means buying 500,000 shares.

This becomes explosive when:

  • The stock rapidly approaches call strikes
  • Delta jumps from 0.50 to 0.80 (as the call goes ITM)
  • Market makers must buy another 300,000 shares — within minutes
  • Buying pressure drives the price even higher
  • The next strike is reached — and the cycle repeats

This isn't theory. During Tesla's February 2020 run, coordinated call buying led to a 3-day surge of 46%, with 900-strike calls exploding 2,100%. At AMC in June 2021, $40 calls spiked 890% while the stock gained 22% in one trading hour.

What Traders Can Learn

Gamma squeezes are rarely predictable, but identifiable:

  • Unusually high call volume concentrated in few strikes
  • Rising open interest in OTM calls
  • High gamma values (above 0.10 for ATM options)
  • Low float + high short interest = more pressure

The trade? Don't chase the stock. Those who enter early (when calls are still cheap) profit. Those who buy after headlines hit pay maximum premium for minimal time value.

Gamma squeezes end abruptly. When buying pressure stops, delta collapses, market makers sell their hedges — and the price crashes as fast as it rose.

Disclaimer: This article is for informational purposes only and does not constitute investment advice. Past performance is not indicative of future results.

Sources

BeInOptions Research

Frequently Asked Questions

What exactly is a gamma squeeze?

A gamma squeeze occurs when massive call buying forces market makers to continuously buy more shares to stay delta-neutral. This drives prices higher and creates a self-reinforcing loop. During GameStop 2021, this led to a 91% surge in 47 minutes.

How do I identify an upcoming gamma squeeze?

Signals: unusually high call volume in few strikes, rising open interest in out-of-the-money calls, gamma values above 0.10 for at-the-money options, low float plus high short interest. Tesla's call OI rose 340% in the 48 hours before its 2020 squeeze.

Why do gamma squeezes end so quickly?

Once buying pressure stops, call delta collapses. Market makers sell their stock hedges because they're no longer needed — creating massive selling pressure. Prices can fall as fast as they rose. AMC dropped 38% in two days after its 2021 squeeze.

Daniel Richter

Author

Daniel Richter

Lead Quantitative Analyst

AI Options Strategist

15++ YearsCFA-aligned expertiseFRM framework knowledge

Daniel Richter combines deep market expertise with cutting-edge AI technology. After studying Financial Mathematics at TU Munich and several years at leading investment banks in Frankfurt, he specialized in quantitative trading strategies. At BeInOptions, Daniel leads the analytics team and develops data-driven options strategies. His strength lies in combining classical financial analysis with machine learning – using AI models to identify market patterns and assess risk. "My goal is to make complex options strategies accessible to everyone while leveraging modern analytical tools to make informed decisions."

Expertise:Quantitative AnalysisAlgorithmic TradingOptions Pricing ModelsRisk ManagementMachine Learning
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Disclaimer: This article is for informational purposes only and does not constitute investment advice. Past performance is not indicative of future results.