Queue position and adverse selection

A limit price does not guarantee a fill

A passive limit order offers to buy or sell at a stated price. At an exchange using price-time priority, orders at the best price usually wait in a queue. Earlier orders are filled before later orders as incoming trades consume available quantity.

If a buy limit joins behind 5,000 contracts and only 1,000 trade at that price, the market “touched” the limit but the new order would not normally fill. Position in that queue therefore changes fill probability.

Adverse selection is the risk that a passive order fills specifically when the market is about to move against it. A bid may fill because sellers possess urgent or better information; after the fill, the midpoint falls. Earning half the spread is not attractive if the subsequent adverse move is larger.

Why passive orders face a selection problem

When the market is stable, both sides may trade and a passive order can earn spread. When new information arrives, informed or urgent traders choose the stale quote that benefits them. The market maker or passive strategy cannot observe the counterparty’s motive directly, so post-fill price behavior is used as evidence.

Filled passive orders are not a random sample of all submitted orders. The conditions that caused a fill can also cause adverse movement. Evaluating only filled orders without unfilled opportunities overstates the policy’s certainty and can understate its opportunity cost.

Queue position and adverse selection answer different questions

Queue position asks whether and when the order fills. Adverse selection asks what tends to happen after the fill. Improving one does not automatically improve the other.

An order near the front of the queue may fill more often, including during toxic price moves. An order far back may avoid those fills but miss benign trades as well. A complete passive-order policy therefore needs both a fill model and an outcome model.

A markout is one simple outcome measure: the side-adjusted change from a declared fill or quote reference to a later reference price. For a buy, a lower later midpoint is adverse; for a sell, a higher later midpoint is adverse. Five-second, thirty-second, and five-minute markouts answer different questions, so the horizon must be chosen before inspecting the results.

What data can support

Depth fidelity makes queue experimentation more meaningful; it does not make every assumption true. Cancellations, hidden orders, latency, exchange matching rules, and data loss still require explicit treatment.

A queue-aware order example

Illustrative example.

At 10:00:

• best bid is 100.00 for 500 displayed units;
• best offer is 100.02;
• the strategy submits a 100-unit buy at 100.00;
• under a price-time model, 500 units are ahead.

Next, 300 units trade at the bid. If no ahead cancellations are credited, the strategy remains behind 200 units and receives no fill. Later, 250 more units trade; 200 consume the remaining queue ahead and only 50 reach the strategy. The strategy receives a 50-unit partial fill.

A bar showing low 100.00 cannot reconstruct this. Crediting a full 100-unit fill on touch creates 50 fictitious units and ignores the chance of no fill.

If the midpoint is 99.96 thirty seconds after the fill, the signed 30-second markout for the buy is adverse. The markout horizon and reference—midpoint, microprice, or executable exit—must be declared before results are inspected.

Queue-model decisions

Cancel-replace usually loses time priority. A model that moves a limit without resetting queue position is optimistic.

Designing a fidelity ladder

Compare progressively stronger models:

1. Bar touch, labeled as a coarse optimistic sensitivity.
2. Trade-through, requiring price to trade beyond the limit.
3. Quote-aware fills at the top of book.
4. Depth queue model with explicit cancellation and latency assumptions.

Report fill rate, partial fills, time to fill, spread capture, markouts, missed opportunity, fallback cost, and sensitivity to latency and cancellation logic. Include all submitted orders, not only fills.

Depth research is most useful when the conclusion changes across models. That reveals how much of the strategy rests on execution assumptions.

Important failure modes

• Displayed quantity is treated as persistent executable liquidity.
• Cancellations ahead are credited too generously.
• Hidden and cross-venue liquidity is assumed away without sensitivity.
• Latency is ignored, preserving queue position the strategy could not obtain.
• Only filled orders enter the markout analysis.
• Bar evidence is presented as queue-accurate.
• A touch becomes a full fill regardless of quantity.

Further reading

• Glosten and Milgrom, “Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders” (1985) — Shows how asymmetric information can create an adverse-selection component of the bid–ask spread. Its stylized specialist market explains the mechanism, not modern electronic fill probability.
• Cont, Stoikov, and Talreja, “A Stochastic Model for Order Book Dynamics” (2010) — Models limit-order arrivals, cancellations, queue evolution, and conditional execution probabilities. Its tractable Markov assumptions must be calibrated and tested for the market being studied.
• Moallemi and Yuan, “A Model for Queue Position Valuation in a Limit Order Book” (2017) — Separates queue value into immediate spread-versus-adverse-selection value and the future option created by retaining priority. It is a working paper built around price-time-priority and primarily large-tick settings.
• CME Group, “Market by Order (MBO)” — Officially distinguishes order-level MBO data from aggregated market-by-price data and explains the identifiers and priority information used to determine queue position. These details are specific to CME Globex rather than universal exchange rules.