Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems
On the map: LLM agents and tool use → Financial trading and forecasting agents
Abstract (arXiv)
Execution realism remains weakly standardized in research on large-language-model-based trading systems, limiting comparison and reproduction across backtests, simulations, and portfolio benchmarks. This article presents a PRISMA-ScR systematic scoping review with a nested execution-reproducibility evidence audit. Eight reproducible query families returned 796 query-level records; DOI/title deduplication left 687 unique records, 101 reports were sought for retrieval, and 59 full texts were recovered through direct and fallback open-access routes. Fifty-three reports are provisionally eligible for expanded evidence charting, subject to reconciliation of two blind author-validation packets. The charting framework separately evaluates point-in-time controls, temporal splits, held-out evaluation, trading costs and turnover, execution semantics, universe construction, architecture reporting, and artifact availability. Direct-trading, portfolio or alpha-construction, and benchmark studies are retained as distinct descriptive subgroups rather than pooled as a common performance estimand. Search responses, retrieval attempts, file hashes, evidence excerpts, screening decisions, and coding worksheets are archived with the review materials. Final field-level counts are intentionally withheld until author validation is complete.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 34 of its 77 references that are in the map. First posted on arXiv 6 Jun 2026.
Builds on
34 of 77 references.
Closest ancestors · 12
Same direction, newest first
- 2026Agentic Trading: When LLM Agents Meet Financial Markets19 May
- 2026QRAFTI: An Agentic Framework for Empirical Research in Quantitative Finance20 Apr
- 2026Hubble: An LLM-Driven Agentic Framework for Safe, Diverse, and Reproducible Alpha Factor Discovery9 Mar
- 2026Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks26 Feb
- 2025AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions15 Aug
- 2025ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism1 Aug
- 2024TradingAgents: Multi-Agents LLM Financial Trading Framework28 Dec
- 2024INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent24 Dec
- 2024Large Language Model Agent in Financial Trading: A Survey26 Jul
- 2024FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making9 Jul
- 2024FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models23 May
- 2023FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design23 Nov
Other references · 22
- 2023BloombergGPT: A Large Language Model for Financecited by 923
- 2023FinanceBench: A New Benchmark for Financial Question Answeringcited by 261
- 2023FinGPT: Open-Source Financial Large Language Modelscited by 244
- 2023Large Language Models in Finance: A Surveycited by 223
- 2024FinBen: A Holistic Financial Benchmark for Large Language Modelscited by 140
- 2024A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalistcited by 137
- 2023Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Modelscited by 125
- 2024A Survey of Large Language Models in Finance (FinLLMs)cited by 77
- 2023Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investmentcited by 54
- 2024Open-FinLLMs: Open Multimodal Large Language Models for Financial Applicationscited by 36
- 2025AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decaycited by 36
- 2024Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Modelscited by 35
10 more
- 2024QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Modelcited by 30
- 2024When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environmentscited by 25
- 2025AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Marketscited by 23
- 2024FinLlama: Financial Sentiment Classification for Algorithmic Trading Applicationscited by 22
- 2024StockGPT: A GenAI Model for Stock Prediction and Tradingcited by 11
- 2026Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Financecited by 11
- 2024Sentiment trading with large language modelscited by 7
- 2026Adversarial News and Lost Profits: Manipulating Headlines in LLM-Driven Algorithmic Tradingcited by 4
- 2025Language Model Guided Reinforcement Learning in Quantitative Tradingcited by 3
- 2026PortBench: A Correlation-Aware, Full-Pipeline Benchmark for LLM-Driven Portfolio Managementcited by 3
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Close in wording, no citation either way
- 2026Toward Reliable Evaluation of LLM-Based Financial Multi-Agent Systems: Taxonomy, Coordination Primacy, and Cost Awareness
- 2026Can Agentic Trading Systems Pay for Their Own Intelligence?
- 2025When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents
- 2025Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents
- 2026The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence
- 2026QuantCode-Bench: A Benchmark for Evaluating the Ability of Large Language Models to Generate Executable Algorithmic Trading Strategies
- 2025TradeTrap: Are LLM-based Trading Agents Truly Reliable and Faithful?
- 2025StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?