[2603.21330] FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading

Quantitative Finance > Trading and Market Microstructure arXiv:2603.21330 (q-fin) [Submitted on 22 Mar 2026] Title:FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading Authors:Hongyang Yang, Boyu Zhang, Yang She, Xinyu Liao, Xiaoli Zhang View a PDF of the paper titled FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading, by Hongyang Yang and 4 other authors View PDF HTML (experimental) 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 this https URL. Comments: Subjects: Trading and Market Microstructure (q-fin.TR); Machine Learning (cs.LG); Computational Finance (q-fin.CP) Cite...