{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T12:48:57Z","timestamp":1763988537649,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T00:00:00Z","timestamp":1719619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.<\/jats:p>","DOI":"10.3390\/computation12070132","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T03:38:27Z","timestamp":1719805107000},"page":"132","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Candlestick Pattern Recognition in Cryptocurrency Price Time-Series Data Using Rule-Based Data Analysis Methods"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6619-4862","authenticated-orcid":false,"given":"Illia","family":"Uzun","sequence":"first","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics, Odesa National Polytechnic University, 1, Shevchenko Av., 65044 Odesa, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4859-304X","authenticated-orcid":false,"given":"Mykhaylo","family":"Lobachev","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics, Odesa National Polytechnic University, 1, Shevchenko Av., 65044 Odesa, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5352-077X","authenticated-orcid":false,"given":"Vyacheslav","family":"Kharchenko","sequence":"additional","affiliation":[{"name":"Department of Computer Systems, Networks and Cybersecurity, National Aerospace University \u2018KhAI\u2019, 17, Vadym Manko Str., 61070 Kharkiv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5487-1862","authenticated-orcid":false,"given":"Thorsten","family":"Sch\u00f6ler","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, University of Applied Sciences, 1, An der Hochschule, 86161 Augsburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1246-4622","authenticated-orcid":false,"given":"Ivan","family":"Lobachev","sequence":"additional","affiliation":[{"name":"Amazon AWS, 440 Terry Ave N, Seattle, WA 98109, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,29]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Calcaterra, C., Kaal, W., and Rao, V. 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