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arXiv · 2610.12396

Mixup Barcodes for Topology-Aware Financial Decision Making

Buddha Nath Sharma·Joe Opitz·Adam Moser·Sayam Palrecha·Sushovan Majhi·Atish Mitra
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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.

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Buddha Nath Sharma, Joe Opitz, Adam Moser, Sayam Palrecha, Sushovan Majhi, Atish Mitra. 2026-10-08. Mixup Barcodes for Topology-Aware Financial Decision Making. https://arxiv.org/abs/2610.12396

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