Relevance
9/10
Directly addresses stock prediction with a regime-aware agentic system. Demonstrates 11.5% MAPE reduction and 18% Sharpe ratio improvement on held-out 2017-2025 test data. The behavioral evaluation framework diagnoses which intermediate decisions (regime detection, routing, adaptation, risk calibration) are deficient, enabling targeted correction. The closed-loop RL mechanism provides a principled way to improve trading system behavior without architectural changes. Particularly relevant for high-volatility regime management (17.3% MAPE reduction vs 7.8% in low-volatility). The methodology is application-agnostic and transferable to other agentic trading systems.
Implementation Complexity
8/10
Multi-component system requiring: (1) autoencoder regime detector with learned threshold, (2) dual node-transformer pathways, (3) SAC controller with 2D action space, (4) three LLM judge ensemble with structured prompts, (5) behavioral trace logging and JSON serialization, (6) perturbation harness for validation, (7) credit-assignment mechanism mapping dimensions to action subspaces, (8) periodic evaluation protocol with 40-day cycles and 10-epoch fine-tuning, (9) statistical validation suite (DM, GW, MCS, bootstrap). Requires access to multiple commercial LLM APIs, reinforcement learning infrastructure, and careful hyperparameter tuning (λ, θ, cycle length).
Reproducibility
3/5
Code available from corresponding author on reasonable academic request. Data partially available via Zenodo (OHLCV + VIX). Sentiment data not redistributable due to X/Twitter API restrictions. LLM judges are commercial APIs subject to provider-side updates. Full prompt template and rubric provided in Appendix A. Temperature-zero operation ensures deterministic scores. Evaluation cost ~$180. Test period (2017-2025) is held out and never used for adaptation.