跳至正文
返回文库全部文档

检验加密货币深度强化学习策略的回测过拟合

文章 arXiv papers · 作者: Berend Jelmer Dirk Gort et al.
阅读原文

总结

论文将加密货币交易中深度强化学习（DRL）的回测过拟合视为假设检验问题。论文训练DRL智能体，估计每个智能体过拟合的概率，并剔除被判定为过拟合的智能体。目标是减少历史测试带来的假阳性，并提高所选策略在回测之外取得良好表现的可能性。

核心观点

  • 作者将检测回测过拟合表述为假设检验问题。
  • 作者估计已训练DRL智能体的过拟合概率，并剔除被识别为过拟合的智能体。
  • 在10种加密货币上的测试中，过拟合程度较低的智能体回报高于过拟合程度较高的智能体和文中所述基准。
  • 评估涵盖较短时期，其中包括两次加密货币市场崩盘，因此无法证明策略在其他市场条件下的表现。
  • 回测筛选为智能体选择提供依据，但不能保证其在真实市场中取得成功。

标签

加密资产机器学习统计学回测风险管理
全文
# Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting


# Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。