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3/7 * LE interacts with OB via in-mem priority queue. * LE adds to a liquidation-order (LEO) enough meta-info for the order book thread to evaluate if liquidation should still happen, bankruptcy price, ...
4/7 * Each LE sorts users by risk (probability of getting liquidated). * Improvement: a single shared sorted map can be generated across the threads
5/7 * Depending on the size of the position to be liquidated, LEO can be run into the book or positions can be acquired by the system and sold via more complex algos. We call it liquidation stages (LS).
6/7 * When a LEO gets processed, the OB thread evaluates (querying UD) if the user should still be liquidated and using which LS. * We tested this system with 100sK users + apocalyptic cascading liquidations and ended up always in ensuring the 0-sum game
7/7 * Complexity of this solution relies in write tests (we have more than 1000 automated simulations running) * Happy to elaborate if not really clear. * These are the types of solutions used in huge brokers with M of customers. No much way around it.
Does the probability of getting liquidated only take into account the trader's liquidation margin?
Fun fact
so a hack to get faster order processing is to have a high chance of being liquidated
Why? Not sure from where you deducted that. Users get liquidated only when they should. The system is just more scalable.
Re improvement: perhaps an rbtree ordered by an f() that provides the same ordering but price-independent (so no need to re-sort on each tick as probabilities shift), 1 for longs, 1 for shorts. There is always a "first to be liquidated" position per side independent of price.
Yup, we are using RBTrees internally. Both for our books and for liquidation priority.
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