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.

翻译：在高波动的加密货币市场中，设计盈利且可靠的交易策略极具挑战性。现有研究应用深度强化学习方法，并在回测中乐观地报告利润提升，但这可能因过拟合而产生假阳性问题。本文提出了一种实用方法，利用深度强化学习解决加密货币交易中的回测过拟合。首先，我们将回测过拟合的检测表述为假设检验。然后，训练深度强化学习智能体，估计过拟合概率，并剔除过拟合的智能体，从而提高获得良好交易表现的可能性。最后，在2022年5月1日至2022年6月27日（期间加密货币市场经历两次崩盘）的测试期内，对10种加密货币进行的实验表明，过拟合程度较低的深度强化学习智能体相比过拟合程度较高的智能体、等权重策略以及S&P DBM指数（市场基准），能获得更高收益，这为实际市场部署提供了信心。