We propose factor models for the cross-section of daily cryptoasset returns and provide source code for data downloads, computing risk factors and backtesting them out-of-sample. In "cryptoassets" we include all cryptocurrencies and a host of various other digital assets (coins and tokens) for which exchange market data is available. Based on our empirical analysis, we identify the leading factor that appears to strongly contribute into daily cryptoasset returns. Our results suggest that cross-sectional statistical arbitrage trading may be possible for cryptoassets subject to efficient executions and shorting.

Keywords: cryptoasset, cryptocurrency, Bitcoin, factor, risk, return, regression, factor loading, residuals, regression coefficients, t-statistic, size, volume, volatility, momentum, minable, cross-section, time series, source code, backtesting, market cap, price, mean-reversion, statistical arbitrage, market

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