Unused Data Cleanup

Data

Unused data cleanup within cryptocurrency, options trading, and financial derivatives represents a critical process for maintaining efficient data pipelines and accurate model inputs. It involves identifying and removing obsolete, redundant, or irrelevant datasets that accumulate from high-frequency trading, market data feeds, and historical records. Effective implementation reduces storage costs, improves query performance, and minimizes the risk of introducing bias into quantitative analyses and algorithmic trading strategies.