机器学习预测比特币收益，扣除交易成本后仍可实现超65%年化回报。
Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting
- 用XGBoost等模型在7万小时数据上做滚动预测，优化交易触发机制。
- 成本敏感的过滤策略使策略年化收益超65%，夏普比率高于1。
- 适合关注高频加密货币交易、实盘策略设计的研究者与投资者。
本文研究机器学习对小时级比特币-泰达币收益率的预测能否在扣除交易成本后产生经济意义的交易表现。基于2018-2026年约7万小时观测数据，评估XGBoost、LSTM和iTransformer在27轮滚动向前协议下的表现。所有模型在特定配置下均产生正向总收益，但简单的信号驱动策略在引入10个基点交易成本后即失效。采用成本感知执行过滤器（仅当预测幅度超过基于成本的阈值时才交易），显著降低换手率，并在部分配置中恢复盈利能力。最强的长期仅多头XGBoost策略年化收益超65%，夏普比率高于1。额外测试表明，技术指标在特定情形下提升表现，而EGARCH特征未带来一致增益；尽管XGBoost在描述性上优于神经网络模型，但自助法检验不支持其统计显著优势。损失函数与模型选择的影响较小且统计上脆弱。结果表明，小时级加密货币交易的主要障碍不仅是预测能力弱，更在于如何将预测转化为交易。
原文摘要 · Abstract (English)
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.
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