This study examines the predictability of three major cryptocurrencies—bitcoin, ethereum, and litecoin—and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, 2015 to March 03, 2019, with the test sample beginning on April 13, 2018. For the test period, five out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The ensemble assuming that five models produce identical signals (Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions.

中文翻译：

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在不断变化的市场条件下通过机器学习预测和交易加密货币

本研究检验了三种主要加密货币（比特币、以太币和莱特币）的可预测性，以及根据机器学习技术（例如，线性模型、随机森林和支持向量机）设计的交易策略的盈利能力。这些模型在前所未有的动荡时期得到验证，并在熊市时期得到测试，即使市场方向在验证期和测试期之间发生变化，也可以评估预测是否正确。分类和回归方法使用 2015 年 8 月 15 日至 2019 年 3 月 3 日期间交易和网络活动的属性，测试样本从 2018 年 4 月 13 日开始。在测试期间，18 个模型中有 5 个成功率低于 50%。交易策略建立在模型组装的基础上。假设五个模型产生相同信号的集成（集成 5）实现了以太坊和莱特币的最佳性能，年化夏普比率分别为 80.17% 和 91.35%，年化回报率（按比例往返交易成本为 0.5% 后）为 9.62%和 5.73%。这些积极的结果支持这样一种说法，即机器学习提供了强大的技术来探索加密货币的可预测性和在这些市场中设计有利可图的交易策略，即使在不利的市场条件下也是如此。