{
    "abstract": "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 \"informative\" 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's potential for extracting high-quality patterns, they do not constitute an exhaustive survey of all possible asset classes.",
    "authors": [
        {
            "affiliation": "Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA.",
            "firstname": "Rishabh",
            "initials": "R",
            "lastname": "Gupta"
        },
        {
            "affiliation": "EntropyX Labs Pvt. Ltd., Ghaziabad 201010, Uttar Pradesh, India.",
            "firstname": "Shivam",
            "initials": "S",
            "lastname": "Gupta"
        },
        {
            "affiliation": "Softure Solutions Pvt. Ltd., New Delhi 110059, Delhi, India.",
            "firstname": "Jaskirat",
            "initials": "J",
            "lastname": "Singh"
        },
        {
            "affiliation": "Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA.\nDepartment of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27606, USA.",
            "firstname": "Sabre",
            "initials": "S",
            "lastname": "Kais"
        }
    ],
    "conclusions": null,
    "copyrights": null,
    "doi": "10.3390/e27040430",
    "journal": "Entropy (Basel, Switzerland)",
    "keywords": [
        "algorithmic trading",
        "entropy",
        "finance",
        "pattern identification"
    ],
    "methods": null,
    "publication_date": "2025-04-26",
    "pubmed_id": "40282665",
    "results": null,
    "title": "Entropy-Assisted Quality Pattern Identification in Finance.",
    "xml": "<Element 'PubmedArticle' at 0x788506427c40>"
}