## Problem You receive order book updates from two market data feeds (Feed A and Feed B) for the same instrument. Each update is `(timestamp, side, price, quantity)`. Implement a comparator that detects discrepancies: same-timestamp updates where the two feeds disagree on price levels or quantities. ```python class OrderBookComparator: def ingest_a(self, update: dict) -> None: # {"ts": int, "side": str, "price": float, "qty": float} def ingest_b(self, update: dict) -> None: def get_discrepancies( self, tolerance_ms: int = 50, qty_tolerance: float = 0.01 ) -> list[dict]: ``` ## Example ``` comp.ingest_a({"ts": 1000, "side": "bid", "price": 100.0, "qty": 10.0}) comp.ingest_b({"ts": 1000, "side": "bid", "price": 100.0, "qty": 9.5}) comp.get_discrepancies(qty_tolerance=0.1) # -> [{"ts": 1000, "side": "bid", "price": 100.0, "feed_a_qty": 10.0, "feed_b_qty": 9.5}] # qty diff = 0.5 > 0.1 tolerance ``` ## Follow-ups 1. Feed B often lags by 20-100ms — how do you match updates across feeds with timing skew? 2. What ring-buffer or sliding-window data structure best supports this real-time comparison? 3. How do you alert on systematic bias (Feed B is always lower) vs. random noise?

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Real-time Order Book Stream Comparison: Detect Discrepancies Between Two Feed Sources

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Problem You receive order book updates from two market data feeds (Feed A and Feed B) for the same instrument. Each update is (timestamp, side, price, quantity). Implement a comparator that detects discrepancies: same-timestamp updates where the two feeds disagree on price levels or quantities. Example Follow-ups Feed B often lags by 20-100ms — how do you match updates across feeds with timing skew? What ring-buffer or sliding-window data structure best supports this real-time comparison? How do…

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Real-time Order Book Stream Comparison: Detect Discrepancies Between Two Feed Sources

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