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FinRL-X tackles the engineering gap between quantitative trading research and live deployment by introducing a weight-centric modular architecture that unifies data ingestion, strategy composition (selection–allocation–timing–risk), backtesting, and broker execution within a single protocol. The core insight is treating portfolio weights $w_t \in \mathbb{R}^n$ as the sole interface contract, enabling composable strategies without recoding execution logic.
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.