Paper: arXiv 2603.21330

Authors: Hongyang Yang, Boyu Zhang, Yang She, Xinyu Liao, Xiaoli Zhang

Abstract

We present FinRL-X, a modular and deployment-consistent trading architecture that unifies data processing, strategy construction, backtesting, and broker execution under a weight-centric interface. While existing open-source platforms are often backtesting- or model-centric, they rarely provide system-level consistency between research evaluation and live deployment. FinRL-X addresses this gap through a composable strategy pipeline that integrates stock selection, portfolio allocation, timing, and portfolio-level risk overlays within a unified protocol. The framework supports both rule-based and AI-driven components, including reinforcement learning allocators and LLM-based sentiment signals, without altering downstream execution semantics. FinRL-X provides an extensible foundation for reproducible, end-to-end quantitative trading research and deployment. The official FinRL-X implementation is available at https://github.com/AI4Finance-Foundation/FinRL-Trading.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: FinRL-X introduces a novel, deployment-consistent infrastructure that bridges the gap between backtesting and live trading for AI/RL strategies. While it focuses heavily on software architecture and system engineering rather than complex mathematical proofs, its high reproducibility (backed by an open-source repository) and modular design make it a valuable framework for quantitative finance practitioners.

Research Flowchart

  flowchart TD
    A[Research Goal: Unify Quant Research & Live Deployment] --> B[Inputs: Market Data & LLM Sentiment Signals]
    B --> C[FinRL-X Modular Pipeline: Stock Selection & Timing]
    C --> D[Computational Layer: RL Allocators & Risk Overlays]
    D --> E[Weight-Centric Broker Execution Engine]
    E --> F[Outcomes: System-Level Consistency & Reproducible Live Trading]

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6 of 123 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.