Paper
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
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 st · Authors: Hongyang Yang, Boyu Zhang, Yang She, Xinyu Liao, Xiaoli Zhang · Published 2026-03-22 · arXiv:2603.21330
- Published
- 2026-03-22
- Authors
- 5
- arXiv
- 2603.21330
- Category
- q-fin.TR
Abstract
arXiv:2603.21330
Hongyang Yang, Boyu Zhang, Yang She, Xinyu Liao, Xiaoli Zhang
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.