September 18, 2026

A clean backtest can hide a messy execution problem. Live markets expose the difference immediately. Arbitrage is exposed between the legs.

Good controls make hidden assumptions visible before capital discovers them. A spread can look market-neutral only after both sides are filled; the interval between fills contains directional and liquidity risk. Track filled and remaining quantity after every event. Define hedge, cancel and reroute thresholds. This is where mechanism meets friction: exposure and edge decide whether the result survives live conditions.

Put a boundary around the claim. Buying one venue first and missing the sale on another converts an apparent arbitrage into an outright position at the worst moment. The example is not a forecast or a promise; it shows which variable must be measured before the apparent opportunity can be treated as usable.

A compact operating checklist is: 1) model partial fills and venue-specific minimum sizes; 2) measure completion-time distributions; 3) stress the cost of neutralising the unmatched leg. Add hard exposure ceilings, observable rejection reasons and an event trail that lets operations reconstruct the decision without guessing. Control sequence: 1 signal, 1 order path, 1 reconciled risk state.

It is easy to confuse activity with edge and a fill with good execution. Review the median, the stressed tail and the failure path separately. A result that survives only in quiet sessions, tiny size or perfect data is a research result, not yet a production capability.

No strategy is isolated once positions compete for the same capital, hedge, venue capacity or operational attention. The objective is not to remove uncertainty. It is to size uncertainty, detect when assumptions break and preserve the ability to stop without creating a second problem.

Which assumption here is tested continuously in production, and which one is still trusted from the backtest?

#Arbitrage #HFT

The research behind this lesson

This paper explains The seminar frames electronic markets as price-time-priority queues. It separates queue value into spread capture versus adverse-selection cost and the option value of retaining a place in line.

Applied to this XCore lesson: An order lifetime therefore cannot be based on elapsed time alone: the system must reassess whether its queue position, expected spread and adverse-selection risk still justify keeping the order alive.

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Source: High-Frequency Trading and Modern Market Microstructure

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