FinRL-X统一量化交易全流程，让研究与实盘无缝衔接。
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
- 模块化架构整合数据、策略、回测与执行，用权重接口统一流程
- 支持规则与AI组件混合使用，回测与实盘行为一致
- 适合需复现和部署的量化研究者，尤其关注端到端一致性
我们提出FinRL-X，一个以权重为中心的模块化、部署一致的量化交易架构，统一了数据处理、策略构建、回测与券商执行。现有开源平台多为回测或模型驱动，难以保证研究评估与实际部署的一致性。FinRL-X通过可组合的策略流水线，在统一协议下集成选股、资产配置、择时及组合风险控制。框架支持规则与AI组件（如强化学习分配器、基于LLM的情绪信号），且不改变下游执行语义。该系统为可复现的端到端量化研究与部署提供可扩展基础。官方实现见https://github.com/AI4Finance-Foundation/FinRL-Trading。
原文摘要 · Abstract (English)
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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