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 Sharpe ratio than that of more over-fitted agents, an equal weight strategy, and the S&P DBM Index (market benchmark), offering confidence in possible deployment to a real market.

翻译：在高度动荡的加密货币市场,设计有利可图和可靠的贸易战略具有挑战性。现有的工程应用了深度强化学习方法,乐观地报告回考利润增加,而回考的利润可能因过度装配而受到影响。在本文中,我们提出一种切实可行的办法,用深度强化学习来解决加密货币交易的逆差过高问题。首先,我们将检测反差作为假设测试。然后,我们培训DRL代理,估计超配的概率,拒绝超配的代理,增加良好贸易业绩的机会。 最后,在05/01/2022至06/27/2022的测试期(在加密市场崩溃了两次的测试期间),10个误差(在10个测试期内),我们表明,不那么适合的深加固学习代理比更适合的代理高,同等重量战略,以及S&P DBM指数(市场基准),为可能部署到实际市场提供了信心。