{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T08:16:59Z","timestamp":1770020219374,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T00:00:00Z","timestamp":1744761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy (DOE) (Office of Basic Energy Sciences)","doi-asserted-by":"publisher","award":["DE-SC0019215"],"award-info":[{"award-number":["DE-SC0019215"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain: patterns that lead to high one-sided movements in historical data yet retain low local entropy are more \u201cinformative\u201d in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMMs), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies. This paper offers an in-depth illustration of our entropy-assisted framework through two case studies on Gold vs. USD and GBPUSD. While these examples demonstrate the method\u2019s potential for extracting high-quality patterns, they do not constitute an exhaustive survey of all possible asset classes.<\/jats:p>","DOI":"10.3390\/e27040430","type":"journal-article","created":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T04:05:31Z","timestamp":1744776331000},"page":"430","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Entropy-Assisted Quality Pattern Identification in Finance"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6450-3286","authenticated-orcid":false,"given":"Rishabh","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shivam","family":"Gupta","sequence":"additional","affiliation":[{"name":"EntropyX Labs Pvt. Ltd., Ghaziabad 201010, Uttar Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4312-6732","authenticated-orcid":false,"given":"Jaskirat","family":"Singh","sequence":"additional","affiliation":[{"name":"Softure Solutions Pvt. Ltd., New Delhi 110059, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sabre","family":"Kais","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA"},{"name":"Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27606, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"437","DOI":"10.30574\/wjaets.2024.11.2.0136","article-title":"Analyzing the impact of algorithmic trading on stock market behavior: A comprehensive review","volume":"11","author":"Oyeniyi","year":"2024","journal-title":"World J. Adv. Eng. Technol. 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