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

Large Language Models (LLMs) have shown strong capabilities in finance-oriented question answering, yet their evaluation on professional quantitative workflows remains fragmented and often stops at factual or document-grounded QA. We introduce QuantEval, a benchmark for evaluating whether LLMs can complete the compute--implement--validate loop required in quantitative finance. QuantEval contains 1,575 expert-validated instances across three complementary dimensions: (i) knowledge-based QA, (ii) quantitative mathematical reasoning, and (iii) quantitative strategy coding. The coding task is evaluated with a deterministic CTA-style backtesting framework that executes model-generated strategies and compares risk--return metrics against expert references, moving beyond syntax or template matching. We evaluate 13 open-source and proprietary LLMs, report prompt and reliability analyses, and observe substantial gaps relative to human experts, especially in multi-step reasoning and framework-constrained coding. We further conduct supervised fine-tuning and reinforcement learning experiments on domain-aligned data, showing that training can improve quantitative reasoning while also exposing trade-offs in factual recall. QuantEval releases the dataset, evaluation scripts, and full backtesting configuration to support reproducible research on LLMs for quantitative finance.

Although large language model agents are increasingly applied to quantitative-finance workflows, their evaluation remains fragmented across isolated tasks, while the financial relevance of benchmark tasks is often overlooked. Yet financial workflows are inherently multi-stage, spanning interdependent tasks such as forecasting, strategy construction, risk management, and trading. Existing platforms typically focus on a single task, and can therefore overstate agent competence and fail to reveal weaknesses in generalization, real-market interaction, and financially meaningful decision-making. We introduce OpenFinGym, a unified gym environment for quantitative-finance agent development that covers forecasting, market generation, real-time trading, and fraud detection under a single execution and verification interface. OpenFinGym additionally provides an automated task-construction pipeline that turns quantitative finance publications into executable task packages; a containerised runtime with a host-side verifier service that supports scalable agent rollouts and prevents runtime train-test leakage; a paper trading engine with a low-latency data-stream design; deferred-resolution support for long-horizon and event-market forecasts; and integration for SFT and RL post-training

A growing body of work explores how Large Language Models (LLMs) can be embedded in trading systems as agents that perceive market information, retrieve context, reason about decisions, emit tradable actions, and adapt under market feedback. This paper reframes LLM-based trading agents as expert-system decision pipelines and presents an audit-oriented evidence map of 77 included studies in a protocol-coded snapshot screened through 2026-03-09. A primary empirical subset (n=19) satisfies the minimum boundary of Action Output plus Closed-Loop Evaluation; the remaining 58 included studies are retained as background and design context. The central empirical finding is protocol incomparability: within the primary subset, only 2/19 studies report extractable time-consistent split protocols, 1/19 reports an explicit transaction-cost model, 1/19 documents universe or survivorship handling, 11/19 report execution timing or semantics, 15/19 are coded as R0, and no study reaches R3 reproducibility. We therefore use Architecture-Capability-Adaptation as a working analytical lens rather than a validated taxonomy, and we foreground the evidence ledger, reproducibility audit, and reporting checklist as the main contributions. The resulting survey shows that architectural experimentation is expanding rapidly, while comparable evaluation protocols, execution semantics, and reproducible artifacts remain the field's immediate bottlenecks.