Quote Scrubbing Latency refers to the time delay incurred during the process of filtering, validating, and preparing raw market data (quotes) for use by trading algorithms. In crypto, minimizing this latency is critical for maintaining an informational edge and ensuring that trading decisions are based on the most current and actionable prices.
Mechanism
This process involves receiving quote data from various exchanges, de-duplicating entries, filtering out stale or erroneous quotes, and normalizing data formats for internal consumption. Each step, from network transmission to data parsing and validation, adds a fractional delay. Sophisticated systems employ optimized data pipelines, high-performance computing, and efficient algorithms to perform these operations with minimal temporal overhead, often in the microsecond range.
Methodology
The strategic approach focuses on optimizing the entire data processing pipeline, from network interface card (NIC) to algorithmic input. It involves direct data feeds, hardware acceleration, and streamlined software architectures to reduce the processing burden. The methodology aims to deliver “clean” and actionable quotes to trading strategies as rapidly as possible, thereby reducing the risk of executing on outdated information and improving overall execution quality and profitability.
Firms measure and monitor quote scrubbing latency through granular timestamping and continuous profiling to optimize market data freshness and execution quality.
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