  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.
