  Designing profitable and reliable trading strategies is challenging in the
highly volatile cryptocurrency market. Existing works applied deep
reinforcement learning methods and optimistically reported increased profits in
backtesting, which may suffer from the false positive issue due to overfitting.
In this paper, we propose a practical approach to address backtest overfitting
for cryptocurrency trading using deep reinforcement learning. First, we
formulate the detection of backtest overfitting as a hypothesis test. Then, we
train the DRL agents, estimate the probability of overfitting, and reject the
overfitted agents, increasing the chance of good trading performance. Finally,
on 10 cryptocurrencies over a testing period from 05/01/2022 to 06/27/2022
(during which the crypto market crashed two times), we show that the less
overfitted deep reinforcement learning agents have a higher return than that of
more overfitted agents, an equal weight strategy, and the S&P DBM Index (market
benchmark), offering confidence in possible deployment to a real market.
