Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM61976
Volume:
10 |
4 |
April 2026
Pages:
1-5
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.
Information Technology
Bansal Institute of Engineering & Technology
Information Technology
Bansal Institute of Engineering & Technology
Information Technology
Bansal Institute of Engineering & Technology
Abstract—Signalist is a modern web-based platform for real-time stock tracking and AI-driven market analytics. It ad-dresses the growing need for unified financial monitoring tools by integrating live market data with automated alert systems and intelligent insights. The platform delivers live candlestick and line charts, portfolio watchlists, and detailed company metrics—including Price-to-Earnings (P/E) ratio and Earnings Per Share (EPS)—sourced through the Finnhub financial API. Users may configure custom threshold alerts on price movements or volume spikes, with the system dispatching real-time email notifications upon trigger. An event-driven backend powered by Inngest manages asynchronous workflows, continuously ingesting market data, updating a MongoDB database, and orchestrating scheduled tasks such as daily market summaries and news digests without blocking the user interface. Core algorithmic components include rule-based signal generation (threshold triggers on price and volume) and machine learning-powered analysis via the Gemini API, which produces daily summaries and sentiment insights from financial news. The prototype demonstrates low-latency data updates through an optimized caching architecture and reliable alert delivery. Evaluation confirms consistent system performance across concurrent user sessions. Primary contribu-tions include a novel integration of real-time financial APIs with AI-driven summarization in a scalable, event-driven architecture, serving as a robust reference design for fintech applications.
KEYWORDS
This paper presented Signalist, a real-time, AI-enhanced, web-based stock analysis platform designed to bridge the gap between complex financial systems and everyday users. The system integrates live market data ingestion, technical indica-tor computation, threshold-based alerting, and large language model-driven market summaries within a scalable, event-driven architecture. Evaluation demonstrates low-latency API responses (avg. 147 ms), high alert delivery reliability (99.6%), and strong user task completion rates (93.4% overall). The layered modular design provides a solid foundation for future extensions, including predictive modeling and live trading integration, contributing a practical reference architecture to the growing body of intelligent FinTech systems.
[1] A. W. Lo and A. C. MacKinlay, A Non-Random Walk Down Wall Street. Princeton, NJ, USA: Princeton Univ. Press, 2002. [2] T. Hendershott, C. M. Jones, and A. J. Menkveld, “Does algorithmic trading improve liquidity?” J. Finance, vol. 66, no. 1, pp. 1–33, Feb. 2011. [3] B. M. Barber and T. Odean, “Trading is hazardous to your wealth: The common stock investment performance of individual investors,” J. Finance, vol. 55, no. 2, pp. 773–806, Apr. 2000. [4] D. Kahneman, Thinking, Fast and Slow. New York, NY, USA: Farrar, Straus and Giroux, 2011. [5] N. Barberis and R. Thaler, “A survey of behavioral finance,” in Hand-book of the Economics of Finance, G. M. Constantinides, M. Harris, and R. M. Stulz, Eds. Amsterdam, Netherlands: Elsevier, 2003, pp. 1053–1128. [6] R. J. Shiller, Irrational Exuberance, 3rd ed. Princeton, NJ, USA: Princeton Univ. Press, 2015. [7] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016. [8] P. C. Tetlock, “Giving content to investor sentiment: The role of media in the stock market,” J. Finance, vol. 62, no. 3, pp. 1139–1168, Jun. 2007. [9] E. Meijer, “Your mouse is a database,” Commun. ACM, vol. 55, no. 5, pp. 66–73, May 2012. [10] J. J. Murphy, Technical Analysis of the Financial Markets. New York, NY, USA: New York Inst. of Finance, 1999. [11] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997. [12] J. W. Wilder, New Concepts in Technical Trading Systems. Greensboro, NC, USA: Trend Research, 1978. [13] T. Loughran and B. McDonald, “When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks,” J. Finance, vol. 66, no. 1, pp. 35–65, Feb. 2011. [14] M. Fowler, Patterns of Enterprise Application Architecture. Boston, MA, USA: Addison-Wesley, 2002. [15] S. Newman, Building Microservices. Sebastopol, CA, USA: O’Reilly Media, 2015.
Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
1
Total views since publication
PDF Downloads
3
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...