An AI-Driven Market Analysis Platform Using Multi-Agent Architecture and Retrieval-Augmented Generation

Aarya Plakkat , Ananya H , Usha J

Multi-Agent Systems, Retrieval-Augmented Generation, Financial Market Analysis, Large Language Models, Algorithmic Trading, Market Intelligence, Decentralized AI Architecture, Portfolio Management, Financial Data Integration

This paper presents ValueCell, an open-source, AI-driven market analysis platform designed to address a wide range of financial tasks, including comprehensive market research, continuous news and event monitoring, strategy development and execution, and ongoing portfolio management. ValueCell is built on a multi-agent architecture that coordinates specialized AI agents and uses retrieval-augmented generation (RAG) to fuse LLM-based reasoning with up-to-date, domain-specific financial information.The platform integrates multiple AI agents, each tailored to a distinct financial function—for example, macroeconomic and fundamental research, quantitative signal generation, sentiment and news analysis, risk and exposure monitoring, and execution support. These agents operate over diverse data streams and analytical tasks while sharing a common communication and orchestration layer. ValueCell supports multiple large language model (LLM) providers and connects to various real-time and historical market data sources, giving users the flexibility to choose models and feeds that best fit their performance, cost, or compliance requirements.The architecture is explicitly designed around three core principles: modularity, decentralization, and user data privacy. Modularity enables users to add, remove, or modify agents and data connectors without re-engineering the entire system, making the platform suitable for rapid prototyping and long-term production use. Decentralization mitigates single points of failure and allows computational workloads to be distributed across local machines, institutional clusters, or cloud environments. A strong focus on user data privacy ensures that proprietary datasets, trading strategies, and analytical workflows can be kept on-premise or within controlled environments while still benefiting from state-of-the-art AI capabilities.We describe the system’s architecture and implementation in detail, highlighting how RAG is integrated into the agent workflows to improve the relevance, accuracy, and interpretability of outputs. We then present an experimental evaluation of key components, examining qualitative and quantitative metrics such as answer quality, responsiveness under varying loads, and robustness to market regime changes. Together, these results demonstrate that ValueCell provides a robust, extensible foundation for building AI-assisted tools for both academic research and real-world financial practice.

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"An AI-Driven Market Analysis Platform Using Multi-Agent Architecture and Retrieval-Augmented Generation", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.11, Issue 1, page no.b646-b653, January-2026, Available :https://ijsdr.org/papers/IJSDR2601180.pdf

Volume 11 Issue 1, January-2026

Pages : b646-b653

Paper Reg. ID: IJSDR_307043

Published Paper Id: IJSDR2601180

Downloads: 000214

Research Area: Science and Technology

Country: bengaluru, karnataka, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2601180

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2601180

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition