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

TL;DR

FinRL-X is a modular, unified trading platform that integrates data processing, strategy development, backtesting, and execution, supporting both rule-based and AI-driven components for end-to-end quantitative trading.

Contribution

It introduces a system-level, composable trading architecture that unifies research and deployment workflows, supporting AI and rule-based strategies without changing execution semantics.

Findings

Supports both AI-driven and rule-based strategies

Provides system-level consistency between research and deployment

Enables reproducible end-to-end trading workflows

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.