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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM52272
Volume:
9 |
8 |
Aug 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
In today’s fast-evolving financial ecosystem, investors often struggle to navigate “fragmented market data” and make timely, informed decisions. To address this, we developed Investor Guidance and Predictive Analytics, a comprehensive “web-based platform” that brings together real-time market monitoring, stock fundamentals, financial analytics, news aggregation, and predictive modeling into a single, user-friendly system. The platform simplifies investment decisionmaking by providing a clear overview of major market indices, individual stock performance, and historical trends. The application ensures secure access with “hashed passwords” and session-based authentication, allowing users to explore personalized dashboards. Investors can search for stocks, view detailed company overviews, and analyze financial statements alongside historical price data. Integrated “Google News feeds” deliver the latest market updates, keeping users informed about market-moving developments, while the interface organizes all information intuitively for quick and effective analysis. At its core, the system emphasizes both “real-time tracking” and in-depth financial insights. Major indices like Nifty 50 and Sensex are updated automatically, while individual stocks display key metrics such as P/E ratio, market capitalization, revenue, profit margins, and growth indicators. Historical OHLC price data enables technical analysis, and annual and quarterly financial reports provide a comprehensive view of company performance. Built on Python with the Flask framework and a front end using HTML, CSS, and JavaScript, the platform leverages “Yahoo Finance” for data and supports future AI-driven predictions, empowering investors to make smarter, data-driven decisions.
KEYWORDS
Publication Date
8/31/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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