# Agentic Trading: When LLM Agents Meet Financial Markets arXiv: https://arxiv.org/abs/2605.19337 ยท HTML: https://mlsanity.com/paper/2605.19337/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. - Builds on 132 of its 310 references that are in the map. - First posted on arXiv 19 May 2026. ## Placed in - LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/) ## Builds on (most cited first, 30 of 132) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017) - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022) - [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023) - [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023) - [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023) - [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023) - [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017) - [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017) - [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023) - [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018) - [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023) - [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015) - [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023) - [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017) - [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019) - [A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration](https://mlsanity.com/paper/2310.02170/) (2023) - [PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance](https://mlsanity.com/paper/2306.05443/) (2023) - [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024) - [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023) - [QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2309.14717/) (2023) - [FinBen: A Holistic Financial Benchmark for Large Language Models](https://mlsanity.com/paper/2402.12659/) (2024) - [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024) ## Built on by (most cited first, 7 of 7) - [Look-Ahead-Freedom as Temporal Non-Interference: A Verifiable Correctness Property for Backtesting and Agentic Trading Pipelines](https://mlsanity.com/paper/2607.04958/) (2026) - [DSA: Evidence-Aware LLM-Agent Orchestration for Multi-Market Stock Research](https://mlsanity.com/paper/2608.26990/) (2026) - [Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems](https://mlsanity.com/paper/2606.08285/) (2026) - [Predictive Extrema, Unprofitable Policies: An AI-Assisted Audit of Candle-Based Binance Spot Timing Models](https://mlsanity.com/paper/2607.19453/) (2026) - [Artificial Intelligence in Equity and Crypto Markets: Progress, Profitability Evidence, and the Limits of Automated Investing](https://mlsanity.com/paper/2609.04917/) (2026) - [SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes](https://mlsanity.com/paper/2609.19705/) (2026) - [The Price of Thought: Does Test-Time Reasoning Pay in LLM Trading?](https://mlsanity.com/paper/2609.30705/) (2026) ## Abstract (arXiv) Large Language Models (LLMs) combined with autonomous agent architectures are shifting quantitative finance from isolated predictive modeling toward closed-loop trading agents. These systems perceive multimodal market signals, maintain context and memory, reason about decisions, emit executable trading actions, and adapt to non-stationary market regimes. This survey provides a systematic synthesis of agentic trading through an Architecture--Capability--Adaptation (A-C-A) conceptual framework. Architecture covers perception (textual, numerical, and multimodal), memory (working memory, vector storage, and knowledge bases), reasoning (reactive, reflective, and planning-driven), and execution (order routing and friction management). Capability examines alpha discovery, portfolio optimization, and multi-tier risk control. Adaptation covers in-context learning, supervised fine-tuning, reinforcement learning, multi-agent coordination, and architectural self-evolution. Drawing on 185 systematically selected studies, including a core empirical subset of 80 systems evaluated under closed-loop market conditions, we analyze design spaces, operational trade-offs, and emerging paradigms. We also examine evaluation practices, including backtesting split schemes, transaction-cost realism, survivorship-bias handling, and execution fidelity, and distill methodological best practices for future research. By bridging artificial intelligence, market microstructure, and quantitative finance, this survey provides a roadmap for designing, benchmarking, and deploying reliable agentic trading systems.