Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Revathi, S.; Meesala, Shobha; Sudha, V.; Priyanka, R.; Manikandakumar, M.; Vigneshwaran, T. Title A name given to the resource Algorithmic Crypto Trading using EMA Strategy Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 5th International Conference on Pervasive Computing and Social Networking, ICPCSN 2025;pp.997-1002 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICPCSN65854.2025.11035368" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICPCSN65854.2025.11035368</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010204561?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010204561?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Revathi S., Cmr Institute of Technology, Bengaluru, India; Meesala S., Reva University, Bangalore, India; Sudha V., Cambridge Institute of Technology, Bangalore, India; Priyanka R., Cambridge Institute of Technology, Bangalore, India; Manikandakumar M., Christ University, Bangalore, India; Vigneshwaran T., Christ University, Bangalore, India Description An account of the resource Algorithmic trading has transformed financial markets by enabling data-driven strategies that enhance efficiency and decision-making. This paper presents a web-based crypto currency trading platform that employs the Exponential Moving Average (EMA) strategy for automated trade execution, market trend analysis, and portfolio tracking. The platform integrates key performance metrics, including win rate, average profit per trade, risk-reward ratio, and profit factor to assess trading effectiveness. Notably, EMA-based trading achieves the highest profit factor of 3.5 which outperformed deep learning and manual trading by 9.37% and 133%, respectively. Additionally, EMA exhibits a strong win rate of 60%, compared to 65% for deep learning and 40% for manual trading, while maintaining a balanced risk-reward ratio of 2.2. The system features live data visualization, customizable watchlists, and automated trading workflows, providing traders with actionable insights with minimized human error. Performance evaluation indicates that EMA offers a superior trade-off between profitability and risk management, making it a robust and adaptable solution for navigating cryptocurrency markets. This work bridges the gap between manual trading and advanced algorithmic strategies, delivering a user-friendly and efficient trading framework. 2025 IEEE. Subject The topic of the resource Algorithmic Trading; Automated Systems; Cryptocurrency; Data Visualization; Exponential Moving Average; Portfolio Analytics Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153519-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online