{
  "abstract": "We present a topology-aware system based on mixup barcodes and persistent homology for financial decision making. The suggested approach uses topological summaries obtained from Takens delay embeddings of a univariate price series to quantify structural changes between reference and current market regimes. The 1-Wasserstein distance between persistence diagrams, a mixup barcode disruption index, and persistence entropy divergence are combined to provide a novel stress score. A Golden Cross trading method is dynamically modulated by this score, which transforms a binary buy/sell signal into a continuous position-sizing process. At embedding settings chosen by maximizing in-sample Sharpe over a 72-point grid, in-sample assessment on the S&P 500 and Bitcoin yields Sharpe ratios of 1.116 and 1.238, respectively, with much lower maximum drawdown compared with both Buy-and-Hold and the standard Golden Cross baseline. Our findings imply that topological summaries of market geometry include useful information that goes beyond traditional trend markers.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.12396",
  "author_total": 6,
  "authors": [
    {
      "name": "Buddha Nath Sharma"
    },
    {
      "name": "Joe Opitz"
    },
    {
      "name": "Adam Moser"
    },
    {
      "name": "Sayam Palrecha"
    },
    {
      "name": "Sushovan Majhi"
    },
    {
      "name": "Atish Mitra"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6dmah4hsrmybv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.12396",
  "fulltext_url": null,
  "id": "6dmah4hsrmybv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.12396",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "1703|Topological and Geometric Data Analysis"
    ]
  ],
  "openalex": "W7221125887",
  "pdf_url": "https://arxiv.org/pdf/2610.12396",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6dmah4hsrmybv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:03Z",
      "native_id": "2610.12396",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.12396"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Mixup Barcodes for Topology-Aware Financial Decision Making",
  "title_text": "Mixup Barcodes for Topology-Aware Financial Decision Making",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:03Z",
  "url": "https://research.draconitic.com/paper/6dmah4hsrmybv",
  "venue": "arXiv",
  "versions": []
}