Replication Package for "Architecture-Dependent Behavior in Deep Reinforcement Learning for Equity Statistical Arbitrage: Rolling Walk-Forward Evidence from PPO, A2C, and SAC"

This repository provides the public replication package for the study “Architecture-Dependent Behavior in Deep Reinforcement Learning for Equity Statistical Arbitrage: Rolling Walk-Forward Evidence from PPO, A2C, and SAC.” The study evaluates how different deep reinforcement learning architectures behave in an equity statistical arbitrage setting using a constituent-based U.S. large-cap equity universe, cointegration-based pair selection, engineered reinforcement-learning state variables, transaction-cost-aware rewards, rolling walk-forward validation, trade-level attribution, and VIX-conditioned market-regime analysis. The empirical framework compares three reinforcement-learning algorithms: Proximal Policy Optimization (PPO), Advantage Actor–Critic (A2C), and Soft Actor–Critic (SAC). The models are evaluated under a common statistical arbitrage environment using six rolling walk-forward test windows spanning 2020–2025. The package supports verification of the main empirical workflow, including pair-selection diagnostics, engineered feature summaries, rolling walk-forward model results, trade-level behavior summaries, pair-level reward attribution, market-regime robustness outputs, manuscript figures, manuscript tables, and source code used in the experimental pipeline. Package Contents The public replication package includes: Python scripts for data processing, pair selection, feature engineering, model training, evaluation, and robustness analysis. Cointegrated pair-selection diagnostics. Rolling walk-forward performance results. Trade-level output summaries for PPO, A2C, and SAC. Pair-level reward attribution summaries. VIX-regime robustness summaries. APA-style manuscript tables and figures. Metadata files, run-order documentation, requirements file, and checksums for verification. Bloomberg Data Restriction Notice The original empirical analysis uses market data obtained through Bloomberg. Raw Bloomberg-derived equity data are not redistributed in this public Zenodo archive. This includes, but is not limited to, raw security-level price, volume, market-capitalization, sector-classification, and full engineered datasets constructed directly from licensed Bloomberg data. The following types of files are intentionally excluded from public release: Raw Bloomberg equity data. Full row-level market datasets. Full Bloomberg-derived sector classification files. Full engineered datasets directly reconstructable from Bloomberg data. Any proprietary data that may be restricted under Bloomberg or institutional data-license agreements. Researchers who wish to fully reconstruct the raw data layer must obtain appropriate access to Bloomberg through their own institutional or commercial license. The package provides the relevant data-acquisition and processing scripts so that authorized users can regenerate the restricted files in their own environment. Reproducibility Scope This archive supports result verification and partial computational reproduction. Users can inspect the code, evaluation outputs, trade logs, summary diagnostics, figures, tables, and metadata. Full raw-data reproduction is subject to Bloomberg data-access restrictions and therefore cannot be guaranteed from the public archive alone. Because deep reinforcement learning models can be sensitive to random seeds, software versions, and computational environments, regenerated model outputs may differ slightly from the reported results unless the same configuration, dependencies, data access, and training conditions are used. Suggested Use This replication package is intended for: Verifying the empirical workflow of the study. Inspecting the rolling walk-forward evaluation design. Examining trade-level behavior of PPO, A2C, and SAC. Reproducing summary tables and figures from public outputs. Extending the framework to other equity universes, alternative pair-selection procedures, or additional reinforcement-learning algorithms. Keywords Statistical arbitrage; pair trading; deep reinforcement learning; Proximal Policy Optimization; Advantage Actor–Critic; Soft Actor–Critic; cointegration; rolling walk-forward validation; VIX regimes; market-regime analysis; Bloomberg data; replication package; financial machine learning.