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

2026-03-22

AI Summary

This work addresses the common disconnect between research and live deployment in existing open-source quantitative trading platforms, which often compromises consistency. To bridge this gap, we propose a weight-centric, modular architecture that unifies data processing, strategy construction, backtesting, and broker execution interfaces, enabling seamless transition from development to production. The framework supports interoperability between rule-based and AI-driven components—such as reinforcement learning-based asset allocators and large language model–derived sentiment signals—and employs composable strategy pipelines to integrate modules for stock selection, portfolio allocation, timing, and risk management under a consistent protocol. By providing a reproducible, extensible, and deployment-consistent infrastructure, our approach significantly enhances the efficiency and reliability of translating quantitative research into live trading systems.

Technology Category

• Knowledge Representation and Reasoning: Qualitative Reasoning
• Humans and AI: Intelligent User Interfaces
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• quantitative trading
• deployment consistency
• modular infrastructure
• AI-native framework
• system-level consistency

Innovation

• modular architecture
• deployment consistency
• weight-centric interface
• composable strategy pipeline
• AI-native trading