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Paper ID:IJSREM61976

SIGNALIST: Stock Tracker Web Application

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM61976

Volume:

10 |

4 |

April 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM61976

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.

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Authors

RKM

Rajneesh Kumar Maurya

Information Technology

Bansal Institute of Engineering & Technology

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SS

Shivam Singh

Information Technology

Bansal Institute of Engineering & Technology

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Mentors

DJ

Dakshita Joshi

Information Technology

Bansal Institute of Engineering & Technology

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Abstract

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

Index Terms—Stock Market Analysis Real-Time Data Pro-cessing AI-Driven Insights Event-Driven Architecture FinTech Web Application Portfolio Management Sentiment Analysis.

Cite This Article

Rajneesh Kumar Maurya, Shivam Singh, Dakshita Joshi, "SIGNALIST: Stock Tracker Web Application," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-5, April 2026. https://doi.org/10.55041/IJSREM61976

Full Article/Conclusion

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.

References

[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.

Editorial & Review Transparency

Publication Date

4/30/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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