ProStock: Professional Stock Market Navigation and Analysis Suite

This article presents ProStock: Professional Stock Market Navigation and Analysis Suite is a comprehensive platform designed for traders, investors, and financial analysts, offering advanced tools for real-time stock market analysis and portfolio management. The system leverages data from multiple financial markets, providing detailed insights through cutting-edge analytics, technical indicators, and AI-driven predictive models. With an intuitive interface, users can easily monitor stock performance, track market trends, and receive personalized alerts based on custom trading strategies, ensuring informed decision-making in fastpaced trading environments. ProStock integrates artificial intelligence and machine learning to enhance market predictions and stock valuations. The platform's algorithms analyze vast datasets, identifying patterns and anomalies to forecast stock price movements, volatility, and potential risks. Additionally, its NLP-based news sentiment analysis feature processes real-time news, social media feeds, and financial reports, helping users understand market sentiment and its potential impact on stock performance. Designed with usercentricity in mind, ProStock allows for customization, enabling users to create personalized dashboards and charts. Whether for day traders seeking minute-by-minute updates or longterm investors monitoring portfolio growth, ProStock offers flexible options for both novice and expert users. The platform also supports multi-device synchronization, making it accessible on desktop and mobile devices, ensuring seamless access to stock market data anywhere, anytime.

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Publication Details

Published
2025-03-14
DOI
https://doi.org/10.1109/icoact63339.2025.11004817
Citations
1
Primary Topic
Stock Market Forecasting Methods
Type
article
Field-Weighted Citation Impact
2.11
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ProStock: Professional Stock Market Navigation and Analysis Suite

Koushi Kumar, P. Srikrishna, S. Yazhinian, M. Prasanna et al.
1 citations
Stock Market Forecasting Methods
2.11
article

ProStock: Professional Stock Market Navigation and Analysis Suite

Koushi Kumar, P. Srikrishna, S. Yazhinian, M. Prasanna, Yeswanth Hanumanthu, U. Abhinay Srinivas
article en
1 citations

Abstract

This article presents ProStock: Professional Stock Market Navigation and Analysis Suite is a comprehensive platform designed for traders, investors, and financial analysts, offering advanced tools for real-time stock market analysis and portfolio management. The system leverages data from multiple financial markets, providing detailed insights through cutting-edge analytics, technical indicators, and AI-driven predictive models. With an intuitive interface, users can easily monitor stock performance, track market trends, and receive personalized alerts based on custom trading strategies, ensuring informed decision-making in fastpaced trading environments. ProStock integrates artificial intelligence and machine learning to enhance market predictions and stock valuations. The platform's algorithms analyze vast datasets, identifying patterns and anomalies to forecast stock price movements, volatility, and potential risks. Additionally, its NLP-based news sentiment analysis feature processes real-time news, social media feeds, and financial reports, helping users understand market sentiment and its potential impact on stock performance. Designed with usercentricity in mind, ProStock allows for customization, enabling users to create personalized dashboards and charts. Whether for day traders seeking minute-by-minute updates or longterm investors monitoring portfolio growth, ProStock offers flexible options for both novice and expert users. The platform also supports multi-device synchronization, making it accessible on desktop and mobile devices, ensuring seamless access to stock market data anywhere, anytime.

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN), Institute of Science and Technology
Openalex Percentile: Top 14%
Stock Market Forecasting Methods
2.11
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