Fin-Agent: A Multi-Modal Agentic AI Framework for Holistic Stock Market Analysis and Time-Series Prediction

Abstract

The contemporary financial landscape is characterized by high-velocity data streams and extreme volatility, often overwhelming retail investors who lack the computational resources of institutional hedge funds. This paper proposes \"Fin-Agent,\" an advanced Agentic AI-powered web application that synthesizes quantitative market data with qualitative sentiment analysis to provide professional-grade investment advisory. The system integrates real-time API hooks into the NSE and BSE exchanges to extract fundamental ratios and technical indicators. A Long Short-Term Memory (LSTM) neural network is deployed for predictive price modeling, while a Large Language Model (LLM)-based agent performs heuristic reasoning across financial reports and global news trends. Our methodology utilizes a weighted scoring model (40% fundamentals, 40% technicals, 20% sentiment) to ensure that the final Buy/Sell recommendation is a reasoned conclusion based on multi-source data. Experimental results demonstrate that this approach yields a 12.5% annualized return, outperforming the S&P 500.

Introduction

The text presents a real-time stock market analysis system called Fin-Agent that integrates financial data, technical indicators, fundamentals, and NLP-based sentiment analysis to improve investment decision-making. It highlights the limitations of traditional methods that rely mainly on delayed technical or fundamental analysis, and emphasizes the importance of incorporating real-time sentiment from news and social media to reduce the information gap between retail and institutional investors.

The proposed system uses low-latency data streaming (around 1.8 seconds via Polygon.io and WebSockets), sentiment scoring with VADER, and multi-source aggregation of financial signals. It shifts from conventional algorithmic trading to an “agentic advisory” model, where an AI agent evaluates combined signals such as technical trends, company fundamentals, and market sentiment using a weighted scoring system (40% fundamentals, 40% technical indicators, 20% sentiment).

The architecture is modular and includes real-time data acquisition, preprocessing (outlier removal, imputation, and text cleaning), caching using Redis for efficiency, and multi-threaded processing for handling large-scale data streams. Literature shows support for combining sentiment analysis, transformer models, and portfolio optimization techniques, which enhances prediction accuracy and decision quality.

Conclusion

This paper presented \"Fin-Agent,\" a logical framework for stock market analysis that successfully integrates multi-source data. By leveraging an Agentic AI layer, the system moves beyond the limitations of traditional algorithmic trading, offering a reasoning approach that synthesizes 40% fundamental health, 40% technical momentum, and 20% qualitative sentiment. The primary contribution is a unified ontology for stock evaluation that bridges the information gap for retail investors. Future work will focus on incorporating Environmental, Social, and Governance (ESG) metrics. Additionally, we intend to expand the framework to cryptocurrencies and global exchanges such as the Tokyo Stock Exchange. Distributed computing frameworks, such as Apache Spark, will be explored to enhance real-time processing throughput beyond 1,000 updates per second.

References

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Copyright

Copyright © 2026 P. Sowjanya, P. Y. L. Narayana, I. Umesh, K. Jeevan Rao, M. Kedhara Krishna. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.