Rosa Del Mar

Daily Brief

Issue 71 2026-03-12

Market Microstructure Evolution: Pit To Electronic To Hft

Issue 71 Edition 2026-03-12 9 min read
General
Sources: 1 • Confidence: Medium • Updated: 2026-03-14 12:28

Key takeaways

  • In pit trading, informational edge came from reading body language, visible order flow, and instinct.
  • Sam Gaer argues crypto options can be structurally mispriced because market makers underweight crypto's fat right-tail distribution, making upside calls relatively cheap versus Black-Scholes.
  • Sam Gaer claims a major crypto deleveraging on October 10 liquidated about $20B in one day and about $40B over October, damaging market makers and order-book liquidity.
  • Sam Gaer says his late-2023 to early-2024 shift toward crypto was driven in part by observing that younger cohorts are comfortable with virtual payments.
  • In 1998 Sam Gaer built Trading Gear to serve multiple exchanges and built a penny-quote pilot system for NASDR.

Sections

Market Microstructure Evolution: Pit To Electronic To Hft

  • In pit trading, informational edge came from reading body language, visible order flow, and instinct.
  • Sam Gaer used laptop-based model pricing to quote tighter prices on complex copper structures (spreads/strips) and exploit wide inefficiencies set by humans.
  • Open-outcry pit trading was physically aggressive and left traders exhausted by the end of the day.
  • Sam Gaer built an early systematic futures approach using moving averages, volume, and open interest and traded across markets by calling Chicago.
  • As NYMEX CIO, Sam Gaer rebuilt legacy exchange technology; NYMEX migrated from open outcry to electronic trading, completed an IPO, and was later sold to CME for about $9B, with Gaer leading much of the synergy work as EVP/COO.
  • Sam Gaer frames high-frequency trading as pit market-making and arbitrage mechanics executed faster and at scale across electronic markets.

Crypto Derivatives: Convexity, Skew, And Term-Structure Anomalies

  • Sam Gaer argues crypto options can be structurally mispriced because market makers underweight crypto's fat right-tail distribution, making upside calls relatively cheap versus Black-Scholes.
  • Sam Gaer now applies derivatives and market-structure experience to crypto markets with an emphasis on volatility and options, aiming for an institutional-friendly return profile using derivatives rather than boom-bust performance.
  • Sam Gaer defines options convexity as accelerating change in option value as the underlying moves and argues out-of-the-money long gamma can pay off in crypto tail moves.
  • Sam Gaer argues that because Bitcoin has no native yield, widespread covered-call overwriting to manufacture yield has dampened upside volatility and can create opportunities to buy volatility cheaply during drawdowns.
  • Sam Gaer claims Bitcoin volatility term structure can invert such that longer-dated implied volatility (and convexity) becomes cheaper than near-term.
  • Sam Gaer states that crypto options' wide spreads and high-volatility regimes can create curve mispricings where the volatility term structure flips into backwardation.

Tradfi-To-Crypto Linkages: Basis Trades, Etf Flows, And Stress Cascades

  • Sam Gaer claims a major crypto deleveraging on October 10 liquidated about $20B in one day and about $40B over October, damaging market makers and order-book liquidity.
  • Sam Gaer states a key institutional trade going long IBIT and short CME futures weakened as the basis collapsed from about 17% to 5%, contributing to selling pressure as positions were unwound.
  • Sam Gaer says that during an unwind, IBIT saw cascading redemptions and the CME basis briefly jumped to about 9% as traders sold IBIT and bought back CME futures.
  • Sam Gaer argues the recent crypto sell-off reflects political uncertainty about the administration's ability to pass its agenda and perceived risk of future anti-crypto backlash, erasing the post-election premium.
  • Sam Gaer says IBIT options open interest exceeds Deribit's and represents net-new options activity rather than share shift from crypto-native venues.
  • Sam Gaer attributes part of a February drawdown cascade to a gamma-driven dealer hedging feedback loop and to a Hong Kong fund unwind tied to the Japanese yen carry trade.

Crypto Adoption Thesis Via Demographics And Governance Turnover

  • Sam Gaer says his late-2023 to early-2024 shift toward crypto was driven in part by observing that younger cohorts are comfortable with virtual payments.
  • Sam Gaer says he sold his high-frequency trading software to a Chicago firm as a 'burn the boats' commitment and has not looked back.
  • Sam Gaer argues future political and institutional decision-making will increasingly be led by people around his children's age, reinforcing his view that crypto will be favored.
  • Sam Gaer believed Bitcoin would benefit regardless of which candidate won the election.

Exchange Plumbing And Resilience: Clearing And Consolidation

  • In 1998 Sam Gaer built Trading Gear to serve multiple exchanges and built a penny-quote pilot system for NASDR.
  • Sam Gaer sold Trading Gear assets to NYMEX, where the system became Clearport enabling privately negotiated OTC trades to be centrally cleared via NYMEX.
  • As NYMEX CIO, Sam Gaer rebuilt legacy exchange technology; NYMEX migrated from open outcry to electronic trading, completed an IPO, and was later sold to CME for about $9B, with Gaer leading much of the synergy work as EVP/COO.

Watchlist

  • Sam Gaer says information overload is a primary operational challenge in crypto trading and that Monarch is moving from off-the-shelf AI tools toward building in-house models to process market signals.

Unknowns

  • Do cohort-level payment behaviors (virtual payments, crypto rails) actually translate into sustained on-chain settlement demand at scale?
  • What is the actual legislative status, scope, and timeline of the referenced 'Clarity Act,' and which specific institutional activities it would enable or constrain?
  • Are the reported liquidation magnitudes and dates (e.g., October 10 liquidation totals) accurate, and how did order-book depth and spreads change around the event?
  • How large was the IBIT-versus-CME basis trade in aggregate positioning, and what proportion of selling pressure can be attributed to its unwind?
  • Is IBIT options open interest actually larger than Deribit's over the relevant window, and what share of that activity is net-new versus migrated?

Investor overlay

Read-throughs

  • If upside crypto calls are structurally underpriced versus fat-tail realized outcomes, volatility surface distortions could persist and reward strategies that monetize convexity rather than spot direction. Read-through to venues and market makers: wider spreads, higher margin demands, and intermittent surface dislocations.
  • If large liquidation events materially degrade market maker balance sheets and order-book liquidity, stress could propagate through options dealer hedging and basis trades, increasing non-linear selloffs. Read-through to ETF-futures plumbing: flow-driven cascades during basis compression or redemption waves.
  • If edge in crypto is increasingly about processing information overload, firms investing in in-house models may gain execution quality advantages. Read-through: operational alpha shifts from discretionary trading to systematic signal extraction and microstructure-aware execution.

What would confirm

  • Persistent pattern of upside skew appearing cheap relative to subsequent right-tail realized moves, alongside recurring inverted term structures during stress, consistent with systematic underweighting of fat tails.
  • Documented deterioration in order-book depth and widening spreads around major liquidation days, plus measurable increases in options implied volatility and hedging-related spot moves consistent with feedback loops.
  • Evidence that internal models improve trading outcomes versus off-the-shelf tools, such as reduced slippage, better fill rates, or faster reaction to cross-venue signals during volatile periods.

What would kill

  • Empirical tests show no durable mispricing in upside calls after accounting for realized tail behavior, with skew and term structure normalizing quickly and predictably.
  • Independent data fail to corroborate claimed liquidation magnitudes and the associated liquidity damage, or show stable depth and spreads through the cited stress windows.
  • No measurable execution or risk advantage from in-house modeling efforts, with performance comparable to standard tools and no improvement during high-information-flow regimes.

Sources