Cross-Exchange Order Dependencies

Latency Is Not the Only Link

Cross-exchange execution sounds efficient in theory. Buy on Exchange A, sell on Exchange B. Close the spread, pocket the difference. But the real structure behind this logic is brittle.

The first weak point is not latency. It’s the mismatch in execution models. Matching engines operate independently, with no awareness of external intent. One side fills. The other queues. That difference creates exposure, even in microseconds.

Systems try to bridge the gap with smart order routers or execution engines. Most use polling. Some use event subscriptions. Neither guarantees atomic execution. Between quote update and order placement, the book changes. The quote seen is not the quote executed. The result isn’t always slippage. Sometimes it’s failure to fill.

Even if both sides of a trade complete, timing alone can change the result. A fill on A at 12:01:03.574 and B at 12:01:03.978 isn’t a spread capture. It’s exposure to the entire market in between.

Execution Chains and Conditional Fragility

Strategies often assume a dependency structure: “If this executes, then trigger that.” But crypto platforms rarely support true conditional execution. Some exchanges offer reduce-only or post-only flags. Few allow contingent behavior across venues.

Execution chains become sequences, not conditionals. If step one breaks, step two still fires. That’s how a hedge turns into a directional bet. This isn’t a bug in most systems. It’s a design limitation.

Failures compound in illiquid conditions. Say a trader wants to offload $1M worth of an altcoin across three venues. One fills fast. The second delays. The third cancels due to dust rules. The position ends up partial, and rebalancing mid-stream carries fees and further latency.

Fallback logic rarely helps. Most systems can’t dynamically reassign liquidity targets without clearing previous states. That delay matters. It’s not just about price movement. It’s about losing sync between intent and outcome.

Inventory Drift and Risk Without Triggers

When strategies run across exchanges, they inherit something more dangerous than latency: drift. Not just in time, but in logic. One leg fills. The counter-order queues. Inventory moves out of sync with strategy.

To solve this, firms maintain internal position simulators. These simulate not just account balances, but expected fills. The problem: they require high-fidelity data. If depth feeds lag or show stale books, the simulation breaks. The trader thinks they’re flat. They’re not.

Synthetic orders offer partial relief. Trigger one order only if all legs are viable. But unless the platform supports atomic instructions, the trader’s system must decide. That means holding state externally and reacting to partial updates.

In this setup, every millisecond matters. But so does every assumption. Execution logic must account for time skew, fill priority, and venue throttling. Even minor differences — like how one platform handles self-trade prevention — can flip the outcome.

The Illusion of Coordination

No crypto platform truly supports global coordination. They offer order placement, cancellation, and update. The burden of logic sits outside. That means dependencies are only as reliable as the trader’s infrastructure.

Every added venue increases surface area. Each one comes with unique latency patterns, API limits, fee structures, and behavior under stress. Execution strategies that ignore these differences don’t scale. They leak. Quietly, but consistently.

Most failures in cross-exchange execution aren’t dramatic. They’re small, cumulative mismatches. One order times out. Another fills but isn't reported in time. A third gets rejected due to minimum notional value after fees. No alert sounds. But exposure builds.