A heap is the data structure you reach for when you keep asking the same question over and over: “what is the most urgent thing right now?” In a trading system that question is everywhere. Which resting order has the best price, which alert is the most severe, which K instruments moved the most today. A heap answers it in $O(\log n)$ per update instead of re-sorting everything each time, and that is exactly why it shows up in coding rounds. This lesson builds a binary heap from the array up, shows Python’s heapq, and works two of our interview questions end to end: a max-heap priority queue from scratch, and the top-K pattern that beats a full sort.
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