  This research aims to demonstrate a dynamic cointegration-based pairs trading
strategy, including an optimal look-back window framework in the cryptocurrency
market, and evaluate its return and risk by applying three different scenarios.
We employ the Engle-Granger methodology, the Kapetanios-Snell-Shin (KSS) test,
and the Johansen test as cointegration tests in different scenarios. We
calibrate the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain
the half-life used for the asset selection phase and look-back window
estimation. By considering the main limitations in the market microstructure,
our strategy exceeds the naive buy-and-hold approach in the Bitmex exchange.
Another significant finding is that we implement a numerous collection of
cryptocurrency coins to formulate the model's spread, which improves the
risk-adjusted profitability of the pairs trading strategy. Besides, the
strategy's maximum drawdown level is reasonably low, which makes it useful to
be deployed. The results also indicate that a class of coins has better
potential arbitrage opportunities than others. This research has some
noticeable advantages, making it stand out from similar studies in the
cryptocurrency market. First is the accuracy of data in which minute-binned
data create the signals in the formation period. Besides, to backtest the
strategy during the trading period, we simulate the trading signals using best
bid/ask quotes and market trades. We exclusively take the order execution into
account when the asset size is already available at its quoted price (with one
or more period gaps after signal generation). This action makes the backtesting
much more realistic.
