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.
@article{arxiv.2603.21330,
title = {FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading},
author = {Hongyang Yang and Boyu Zhang and Yang She and Xinyu Liao and Xiaoli Zhang},
journal= {arXiv preprint arXiv:2603.21330},
year = {2026}
}