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

• URL: http://arxiv.org/abs/2603.21330v1
• Date: Sun, 22 Mar 2026 17:13:10 GMT
• Title: FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
• Abstract summary: FinRL-X is a modular and deployment-consistent trading architecture.<n>It unifies data processing, strategy construction, backtesting, and broker execution under a weight-centric interface.
• Score: 5.839464421840208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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