This paper investigates whether machine learning forecasts of hourly BTC-USDT returns can be converted into economically meaningful trading performance after transaction costs. Using approximately 70,000 hourly observations from 2018-2026, XGBoost, LSTM, and iTransformer are evaluated in a 27-fold walk-forward protocol. All three models produce positive gross trading performance in selected configurations, but naive sign-based strategies fail once transaction costs of ten basis points are imposed. A cost-aware execution filter, which prevents trades only when the forecast magnitude exceeds a transaction-cost-based threshold, sharply reduces turnover and restores profitability in selected configurations. The strongest long-only XGBoost strategy produces annualised returns above 65% with a Sharpe ratio above one. Additional tests show that technical indicators improve performance in selected cases, EGARCH-derived features do not provide uniformly robust gains, and XGBoost is descriptively stronger than the neural alternatives, although bootstrap evidence does not support formal statistical dominance. Loss-function and model-selection effects are secondary and statistically fragile. The results show that the main obstacle in hourly cryptocurrency trading is not only weak predictability, but also the way forecasts are converted into trades.

翻译：本文研究了在考虑交易成本后，机器学习对每小时BTC-USDT收益率的预测能否转化为具有经济意义的交易表现。利用2018年至2026年间约70,000个每小时观测数据，我们在27次滚动向前验证协议下评估了XGBoost、LSTM和iTransformer三种模型。所有三种模型在选定的配置中均产生了正的交易毛收益，但一旦施加十个基点的交易成本，基于符号的朴素策略便无法盈利。我们提出了一种成本感知的执行过滤器，该过滤器仅在预测幅度超过基于交易成本的阈值时才阻止交易，从而在选定配置中大幅降低了周转率并恢复了盈利能力。表现最强的仅做多XGBoost策略年化收益率超过65%，夏普比率高于1。进一步测试表明，技术指标在特定情况下能提升表现，而基于EGARCH的特征未能带来稳健一致的增益；XGBoost在描述性统计上优于神经网络模型，但Bootstrap证据不支持其具有形式上的统计优势。损失函数和模型选择效应是次要的，且统计上不稳健。研究结果表明，每小时加密货币交易的主要障碍不仅在于预测能力的微弱，更在于预测结果转化为交易的方式。