Data Feed Normalization is the process of transforming data from disparate sources into a consistent, standardized format and structure, eliminating discrepancies in data types, units, conventions, and terminology. Within crypto trading and analytics, this is critical for integrating market data, order book information, and on-chain metrics from various exchanges, protocols, and data providers to enable accurate comparison and algorithmic processing.
Mechanism
This mechanism typically involves a series of data pipeline operations, including schema mapping, unit conversion, timestamp synchronization, and semantic reconciliation. For instance, differing price formats, currency pairs, or liquidity representations from multiple crypto exchanges are mapped to a unified internal model, often employing data dictionaries and rule-based transformations to ensure uniformity.
Methodology
The methodology ensures data integrity and interoperability, which are essential for the accurate functioning of smart trading algorithms, risk models, and quantitative analysis platforms. By presenting a homogeneous data set, data feed normalization facilitates robust price discovery, improves the efficacy of execution algorithms, and enhances the reliability of institutional crypto investing strategies and RFQ systems.
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