A Cleaning Framework for Cryptocurrency Data: Toward Investable Cryptocurrency Universes

We develop a reproducible three-step protocol to clean daily cryptocurrency data from CoinMarketCap, one of the most used data providers in academic research. The procedure targets three recurring anomalies that distort market-level indicators: (1) extreme market-cap spikes, (2) one-day and multiday dips in Bitcoin dominance, and (3) abnormal trading volumes. Using more than 28,000 cryptocurrencies from 2014 to 2024, we show that the method modifies only a small subset of data while improving the reliability of key market indicators. We do not adjust prices or returns, preserving actual trading conditions. As an application, we construct dynamic investable universes using cleaned data and realistic constraints based on market capitalization and volume. This exercise shows that cleaning and filtering jointly produce more reliable universes, reducing spurious extremes and making them suitable for empirical asset pricing research and portfolio construction.