Federated Deep Learning Architecture for Technical Analysis of the Standard Souq Using Optimization Technique
Keywords:
stock market, lion swarm optimization, technical analysis, fundamental analysis, trend movement, chart patterns, volatilityAbstract
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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Copyright (c) 2023 Venkateswarlu Chandu, Elia Thagaram, Sambhana Srilakshmi , Ch. Sahyaja , P. Akthar, Gurunadham Goli , Ch V Rama Krishna Rao
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