Federated Deep Learning Architecture for Technical Analysis of the Standard Souq Using Optimization Technique

Authors

  • Venkateswarlu Chandu Assistant Professor, KL Business School,Koneru Lakshmaiah Education Foundation,Greenfields, Vaddeswaraam, AP, India,
  • Elia Thagaram Associate Professor, MBA Department,PACE Institute Of Technology & Sciences,NH-5 Near Valluramma Temple,Ongole.
  • Sambhana Srilakshmi Assistant Professor, Department of Commerce, Dr.Lankapalli Bullayya College,Visakhapatnam, India
  • Ch. Sahyaja Assistant Professor, Gitam school of business, GITAM University, Hyderabad
  • P. Akthar Senior Asst. Professor, School of Commerce & Management, Mohan Babu University
  • Gurunadham Goli Assistant professor, School of Business, SR University, Warangal, Telangana - 506371
  • Ch V Rama Krishna Rao Professor & HoD , Department of MBA, Rise Krishna Sai Prakasam Group of Institutions,Ongole, AP,

Keywords:

stock market, lion swarm optimization, technical analysis, fundamental analysis, trend movement, chart patterns, volatility

Abstract

A stock market analysis is the process of evaluating and interpreting various aspects of the financial markets, with a primary focus on stocks or equities. It serves as a vital tool for investors, traders, financial professionals, and even companies seeking to make informed decisions related to stocks. This paper presented the integration of federated deep learning and Lion Swarm Optimization as a promising approach to enhance the analysis of candlestick patterns in the stock market. The findings from this research reveal a remarkable level of accuracy in recognizing and classifying candlestick patterns, offering significant potential for advancing trading strategies. The system showcases the ability to make dynamic trading decisions that respond to ever-evolving market conditions, ultimately contributing to profitable trading strategies. Nonetheless, the study underscores the inherent complexities and uncertainties of real-world trading, emphasizing the ongoing need for model refinement and adaptability. An isolated anomaly observed in pattern classification serves as a pertinent reminder of the necessity for continued vigilance in improving the system. As the financial markets continue their evolution, this research advocates for further exploration and development in this domain. It suggests that the integration of advanced technologies, coupled with vigilant monitoring of market dynamics and ongoing model refinement, are vital steps toward realizing the full potential of such integrated systems. Ultimately, this study underscores the invaluable role of data-driven approaches in the financial sector and encourages the pursuit of innovative solutions to enhance trading strategies in dynamic and competitive markets.

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Published

30.11.2023

How to Cite

Chandu, V. ., Thagaram, E. ., Srilakshmi , S. ., Sahyaja , C. ., Akthar, P. ., Goli , G. ., & Rao, C. V. R. K. . (2023). Federated Deep Learning Architecture for Technical Analysis of the Standard Souq Using Optimization Technique. International Journal of Intelligent Systems and Applications in Engineering, 12(6s), 233–246. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/3974

Issue

Vol. 12 No. 6s (2024)

Section

Research Article

License

Copyright (c) 2023 Venkateswarlu Chandu, Elia Thagaram, Sambhana Srilakshmi , Ch. Sahyaja , P. Akthar, Gurunadham Goli , Ch V Rama Krishna Rao

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