We study machine-learning forecasts and portfolio allocation for Chinese government bonds. Penalised linear regressions and gradient-boosted trees predict next-month total returns for eight maturity buckets. Their forecasts feed mean–variance, Black–Litterman, and risk-budgeting implementations with weight, duration, and trading-cost controls; convexity, tail-risk, and attribution constraints are analysed separately as theoretical extensions. From January 2019 through December 2025, the three optimised cash portfolios earn net excess-return Sharpe ratios of 1.94–2.26 and maximum drawdowns of 0.18–0.25%. Three long-only bond-selection variants attain Sharpe ratios of 1.39–1.70. A futures-hedged arbitrage variant attains 1.08 under an 80% hedge-P&L scaling and basis-cost assumption. A stylised 10-year futures overlay reduces rather than improves cash-portfolio Sharpe ratios.

在我国当前低利率环境下，债券收益率持续下行，多元化资产配置的战略意义愈发凸显。本文基于桥水全天候理念，构建了适用于中国投资者的宏观因子增广Black-Litterman（ABL）模型，将宏观信号嵌入传统Black-Litterman（BL）模型的观点矩阵，同时整合风险平价先验基准，有效缓解了传统BL模型参数敏感性高及经济解释性不足问题。基于2012-2024年股债商汇四大类14项资产的回测表明，ABL模型实现了17.7%的年化收益和0.66的夏普率，优于传统BL模型及其他常用基准，且在波动率和最大回撤等风控维度更为稳健。ABL模型为资产配置提供了兼具理论创新与实践价值的参考方案。