FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915881290825728 |
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| author | Yang, Hongyang Zhang, Boyu She, Yang Liao, Xinyu Zhang, Xiaoli |
| author_facet | Yang, Hongyang Zhang, Boyu She, Yang Liao, Xinyu Zhang, Xiaoli |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21330 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading Yang, Hongyang Zhang, Boyu She, Yang Liao, Xinyu Zhang, Xiaoli Trading and Market Microstructure Machine Learning Computational Finance 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. |
| title | FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading |
| topic | Trading and Market Microstructure Machine Learning Computational Finance |
| url | https://arxiv.org/abs/2603.21330 |