mlsanityData to 7 Oct 2026

TradExpert: Revolutionizing Trading with Mixture of Expert LLMs

arXiv 2411.00782 ↗First posted 16 Oct 2024cs.AI q-fin.STcited by 11

On the map: Mixture-of-experts → MoE for time series and graphs

Abstract (arXiv)

The integration of Artificial Intelligence (AI) in the financial domain has opened new avenues for quantitative trading, particularly through the use of Large Language Models (LLMs). However, the challenge of effectively synthesizing insights from diverse data sources and integrating both structured and unstructured data persists. This paper presents TradeExpert, a novel framework that employs a mix of experts (MoE) approach, using four specialized LLMs, each analyzing distinct sources of financial data, including news articles, market data, alpha factors, and fundamental data. The insights of these expert LLMs are further synthesized by a General Expert LLM to make a final prediction or decision. With specific prompts, TradeExpert can be switched between the prediction mode and the ranking mode for stock movement prediction and quantitative stock trading, respectively. In addition to existing benchmarks, we also release a large-scale financial dataset to comprehensively evaluate TradeExpert's effectiveness. Our experimental results demonstrate TradeExpert's superior performance across all trading scenarios.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in MoE for time series and graphs, one of 12 directions in Mixture-of-experts. Builds on 29 of its 42 references that are in the map. First posted on arXiv 16 Oct 2024.

Builds on

29 of 42 references.

References · 29

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17 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 6.

11 papers.

  1. 2025LLM-Powered Multi-Agent System for Automated Crypto Portfolio Managementcited by 20
  2. 2025Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?cited by 19
  3. 2025ATLAS: Adaptive Trading with LLM AgentS Through Dynamic Prompt Optimization and Multi-Agent Coordinationcited by 7
  4. 2025To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisionscited by 2
  5. 2026Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Modelscited by 2
  6. 2025Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Predictioncited by 1
  7. 2026Resisting Manipulative Bots in Meme Coin Copy Trading: A Multi-Agent Approach with Chain-of-Thought Reasoningcited by 1
  8. 2026ReguSim: Evaluating LLM Agent Rule Grounding in Financial Compliancecited by 0
  9. 2026Regime-Aware Portfolio Management via Retrieval-Augmented LLM-Guided Expert Switchingcited by 0
  10. 2026EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agentscited by 0
  11. 2026SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemescited by 0

Similar, not cited

Close in wording, no citation either way

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  2. 2025LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading
  3. 2025MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents
  4. 2024Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection
  5. 2025FinSphere, a Real-Time Stock Analysis Agent Powered by Instruction-Tuned LLMs and Domain Tools
  6. 2026Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks
  7. 2024FinTeamExperts: Role Specialized MOEs For Financial Analysis
  8. 2025Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting