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

Hongyang Yang, Boyu Zhang, Yang She, Xinyu Liao, Xiaoli Zhang

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.

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

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.
Paper Structure (21 sections, 2 equations, 5 figures, 4 tables, 1 algorithm)

This paper contains 21 sections, 2 equations, 5 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Framework
  4. Data Layer
  5. Strategy Layer
  6. Modular Components.
  7. Backtesting and Execution Layer
  8. Deployment-Aware Design
  9. Evaluation
  10. Experimental Setup and Metrics
  11. Baselines
  12. Portfolio Performance and Ablation Analysis
  13. Use Case Demonstrations
  14. Use Case 1: Portfolio Allocation Paradigms
  15. Use Case 2: Rolling Stock Selection
  16. ...and 6 more sections

Figures (5)

  • Figure 1: FinRL-X Framework: A layered, end-to-end trading architecture that unifies data processing, strategy construction, backtesting, and broker-integrated execution within a consistent pipeline, illustrating the workflow from data ingestion to live execution.
  • Figure 2: Ablation study of DRL-based allocation with and without timing adjustment. Incorporating the timing module improves cumulative performance and moderates drawdown relative to both the base DRL strategy and the SPY benchmark.
  • Figure 3: Backtest performance comparison across representative strategy configurations under the unified weight-centric protocol (January 7, 2018 – October 24, 2025). Results illustrate cumulative portfolio trajectories relative to benchmark references.
  • Figure 4: Paper trading performance relative to benchmark indices (October 26, 2025 -- March 12, 2026), demonstrating deployment-consistent execution under daily rebalancing.
  • Figure 5: Portfolio allocation trajectory under the unified weight-based execution framework during paper trading. The figure illustrates time-varying exposure adjustments across asset groups, demonstrating modular allocation outputs that are directly executable without architectural changes.