A Data Sourcing Waterfall represents a prioritized sequence of data providers or methods employed to acquire market or reference data, where subsequent sources are accessed only if preceding ones fail or provide insufficient quality data. Within crypto, this system ensures that trading algorithms and risk models consistently receive reliable price feeds, order book depth, or on-chain transaction data, even amidst volatile or fragmented market conditions. Its purpose is to guarantee data availability and integrity for critical operational processes, maintaining system robustness.
Mechanism
The mechanism operates as a cascading data retrieval system. A primary data source, often a high-volume exchange or a consolidated feed, is queried first. If that source experiences latency, data corruption, or unavailability, the system automatically redirects the request to a secondary, then a tertiary provider, and so forth, following a predefined priority order. This failover process is typically automated, leveraging APIs, real-time data streaming, and validation checks at each stage to ensure data freshness and accuracy before ingestion into trading systems.
Methodology
The methodology for establishing a Data Sourcing Waterfall involves comprehensive evaluation and ranking of potential data providers based on factors like latency, reliability, data completeness, and cost. Firms develop contingency plans for each data type, mapping redundant sources to critical data requirements for RFQ, options pricing, and execution. This systematic approach reduces single points of failure, minimizes data-related trading interruptions, and strengthens decision-making accuracy by providing resilient and verified data streams, a critical architectural component in institutional crypto trading.
A fund administrator sources reliable long-dated crypto IV by architecting a hierarchical, auditable data waterfall that synthesizes exchange, vendor, and direct OTC quotes.
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