CAST：用于股票市场回撤控制的跨资产状态空间交易系统
CAST: A Cross-Asset State-Space Trading System for Drawdown Control in Stock Markets
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中文总结 AI 辅助
提出跨资产状态空间交易系统CAST，结合跨资产协作卡尔曼滤波器与模型预测控制，利用预测不确定性控制回撤，在四个市场15年测试中实现收益-回撤帕累托最优。
中文摘要 AI 辅助
管理回撤，即投资组合价值从峰值到谷底的下降，是实际投资管理中长期生存的前提条件。然而，主流股票预测方法主要假设独立同分布（i.i.d.）条件下优化收益或夏普比率。真实市场并不遵循这一假设，从而引发灾难性的回撤。我们提出了一个跨资产状态空间交易系统（CAST），由两个组件组成：预测器，即跨资产协作卡尔曼滤波器（CoKF），在线估计每个资产的潜在状态，通过相关性耦合所有资产，并自适应地融合多个积分随机游走阶数。控制器，即模型预测控制（MPC），将预测器的预测转化为交易，使用预测不确定性作为显式风险惩罚来控制回撤。我们在四个真实股票市场上对CAST进行了评估，测试窗口为15年，结果表明它始终占据收益-回撤帕累托前沿，在保持显著低于竞争基线的最大回撤的同时，实现了强劲的风险调整后表现。跨危机时期的压力测试进一步证明了在市场冲击和分布偏移下的稳健行为。由于预测器和控制器仅通过预测价格路径交互，两者都是即插即用的，使CAST成为一个模块化、可解释的交易系统。代码可在以下https URL获取。
英文摘要
Managing drawdown, the peak-to-trough decline in an investment portfolio's value, is a precondition for long-term survival in practical investment management. However, mainstream stock forecasting methods predominantly optimize returns or Sharpe ratios under the independent and identically distributed (i.i.d.) assumption. Real markets do not follow this assumption, triggering catastrophic drawdowns. We propose a cross-asset state-space trading system (CAST), consisting of two components: The predictor, Cross-Asset Collaborative Kalman Filter (CoKF), estimates each asset's latent state online, coupling all assets through their correlations and adaptively fusing multiple integrated-random-walk orders. The controller, Model Predictive Control (MPC), converts the predictor's forecast into trades, using forecast uncertainty as an explicit risk penalty that controls drawdown. We evaluate CAST on four real-world stock markets over a 15-year test window and show that it consistently occupies the return-drawdown Pareto frontier, achieving strong risk-adjusted performance while maintaining substantially lower maximum drawdown than competitive baselines. A stress test across crisis periods further demonstrates robust behavior under market shocks and distribution shift. Because the predictor and controller interact only through the predicted price path, both are plug-and-play, making CAST a modular, interpretable trading system. The code is available at https://github.com/FanBroWell/CAST
