用LSTM预测市场，PPO动态调仓，提升投资回报与抗风险能力。
Hybrid LSTM and PPO Networks for Dynamic Portfolio Optimization
- LSTM捕捉时间依赖，PPO在连续动作空间中自适应调仓
- 年化收益更高，最大回撤降低，交易成本后仍优于基线模型
- 适合追求智能动态调仓的量化投资者或金融AI研究者
本文提出一种融合长短期记忆网络（LSTM）与近端策略优化（PPO）的混合框架，用于动态资产组合优化。该系统利用深度循环网络捕捉时间序列依赖关系以预测市场趋势，同时通过PPO强化学习代理在连续动作空间中自适应调整持仓，实现前瞻性预判与实时响应。基于2018年1月至2024年12月期间涵盖美国和印尼股票、美国国债及主要加密货币的多资产数据集，模型在年化收益率、波动率、夏普比率和最大回撤等指标上，经交易成本调整后，均优于等权、指数型及单一模型（LSTM-only、PPO-only）基线方法。结果表明，该混合架构在非平稳市场环境下具备更强稳定性与盈利能力，展现出作为智能化动态组合优化工具的潜力。
原文摘要 · Abstract (English)
This paper introduces a hybrid framework for portfolio optimization that fuses Long Short-Term Memory (LSTM) forecasting with a Proximal Policy Optimization (PPO) reinforcement learning strategy. The proposed system leverages the predictive power of deep recurrent networks to capture temporal dependencies, while the PPO agent adaptively refines portfolio allocations in continuous action spaces, allowing the system to anticipate trends while adjusting dynamically to market shifts. Using multi-asset datasets covering U.S. and Indonesian equities, U.S. Treasuries, and major cryptocurrencies from January 2018 to December 2024, the model is evaluated against several baselines, including equal-weight, index-style, and single-model variants (LSTM-only and PPO-only). The framework's performance is benchmarked against equal-weighted, index-based, and single-model approaches (LSTM-only and PPO-only) using annualized return, volatility, Sharpe ratio, and maximum drawdown metrics, each adjusted for transaction costs. The results indicate that the hybrid architecture delivers higher returns and stronger resilience under non-stationary market regimes, suggesting its promise as a robust, AI-driven framework for dynamic portfolio optimization.
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