Result: ~2000 instructions for the slow path, < 1000 for the common fast path. Under budget, no race conditions, and no capital left on the table. That’s how we do it.

Optimize for the branch predictor: Hide expensive calcs in unlikely branches guarded by a precheck. The CPU can quickly check and skip ahead Structure of Arrays: id==index (avoid hash look-ups) Tiered data structures sized for cache lines optimize for maximum cache-miss effect

The cheap path never yields a false positive, and > 95 % of orders stay in it.

THE SOLUTION Maintain a running “worst-case” tally per account. Fast O(1) update on every new/amend/cancel Cheap gate, expensive fallback If worst-case ≤ balance ⇒ accept immediately. If it breaches ⇒ trigger full margin calc (this is the slow path).

Hard budget: At 1 000 000 orders/s we get ≈ 1 000 CPU instructions per order for all margin checks. Capital-efficiency: Unlike most engines our check sees the real depth at evaluation time, so better fill prices means lower initial margin. Great for traders, but costs more CPU

Instant-liquidation guards: If the mark is far from the BBO, an order may pass a margin check yet nuke the account on execution. We scan the account’s order stack both ways to catch these edge cases.

Order-position netting: Initial margin is max (long, short). We have to pull resting orders and net them with live positions and unrealized PnL. Each step is a potential cache miss

The bottlenecks Serial dependency: Every new order can affect account margin across all instruments, so the check must be global and atomic; no sharding possible

How Nekuti checks margin inside an atomic 1 million tps matching loop: (1/9) #highperformance #systemdesign #matchingengine #lowlatency

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Lots of daylight and exercise and water during the day. Melatonin at night 😴

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Our engine now achieves 1 million orders per second on consumer hardware, with built-in margin checks, instant liquidation workflow, a full range of order types, spot, futures, inverse futures and of course perpetual swaps.

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