FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
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Abstract (arXiv)
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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References · 12
- 2023BloombergGPT: A Large Language Model for Financecited by 923
- 2023FinGPT: Open-Source Financial Large Language Modelscited by 244
- 2024TradingAgents: Multi-Agents LLM Financial Trading Frameworkcited by 182
- 2020FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Financecited by 86
- 2020Qlib: An AI-oriented Quantitative Investment Platformcited by 77
- 2023FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasetscited by 68
- 2024FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Modelscited by 39
- 2025Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategycited by 15
- 2024Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Researchcited by 14
- 2024FinRobot: AI Agent for Equity Research and Valuation with Large Language Modelscited by 10
- 2024FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMscited by 3
- 2024A Deep Reinforcement Learning Framework For Financial Portfolio Managementcited by 2
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