AlphaZeroBeta: Deep Reinforcement Learning for Market-Neutral Portfolios

Battle Count: 136

Directly addresses core quantitative trading challenges: market-neutral portfolio construction, alpha generation, beta management, transaction cost optimization, and regime adaptation. The walk-forward protocol, realistic cost modeling, and multi-market evaluation (7 indices across 3 continents) make findings highly applicable to institutional quantitative trading desks. The ablation study isolating the neutrality penalty's contribution adds practical value. The framework scales from 30 to 2200+ constituents.

High complexity due to: (1) multi-scale CNN-GRU architecture with 3 resolution channels (daily/weekly/monthly), (2) Recurrent PPO training with GAE, (3) dollar-neutral projection with L1-ball constraint, (4) composite reward with rolling volatility/correlation estimation, (5) walk-forward protocol with 22 folds × 9 seeds × 7 markets, (6) extensive feature engineering from 12+ data sources, (7) GPU infrastructure requirements (V100/A100), (8) Bloomberg data licensing. The pseudocode provides a blueprint but production implementation requires significant engineering effort.

The paper provides detailed pseudocode (Listings D.1.1-D.1.5 and D.4.1-D.4.4), complete hyperparameter tables (D4, D5), feature taxonomy (C2, C3), and architecture diagrams. However, full code cannot be shared due to Bloomberg Terminal licensing constraints. The walk-forward protocol, reward function, and environment are well-documented. Key hyperparameters (λ₁=0.5, λ₂=0.001, γ=0.99, lr=3e-4) are specified. Replication requires Bloomberg data access and GPU infrastructure (V100/A100 class).