SBCA: Cross-Modal BERT-driven Actor-Critic for Multi-Asset Portfolio Optimization

Battle Count: 137

Directly addresses multi-asset portfolio optimization with practical trading constraints (transaction costs, turnover, downside risk). Uses realistic 11-year U.S. stock data, includes cost sensitivity analysis, and provides an end-to-end trainable framework. The integration of textual sentiment with price data via cross-modal fusion is highly relevant to modern quantitative trading. However, the limited asset universe (6 stocks) and daily frequency constrain immediate production deployment.

Requires integration of BERT for sentiment extraction, dual-stream feature encoding, cross-modal gated fusion with learnable parameters, Actor-Critic DRL training loop, EMA smoothing, and careful temporal alignment of news to trading days. The theoretical framework (Theorems 1-2) adds conceptual complexity. Implementation in PyTorch is feasible but requires careful handling of multi-modal data pipelines, reward shaping, and training stability. Moderate GPU requirements (RTX 3060 12G sufficient).

Fixed random seed (42), CuDNN deterministic operations enabled, detailed hyperparameters provided, time-series split described. However, the code repository URL is listed as 'XXX.git' (placeholder), and the data is on HuggingFace. The paper provides Algorithm 1 pseudocode and detailed training configuration. Reproducibility is partially hindered by the placeholder code URL.