Mixup Barcodes for Topology-Aware Financial Decision Making
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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BibTeX
@misc{sharma2026mixup,
title = {{Mixup Barcodes for Topology-Aware Financial Decision Making}},
author = {Buddha Nath Sharma and Joe Opitz and Adam Moser and Sayam Palrecha and Sushovan Majhi and Atish Mitra},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.12396},
eprint = {2610.12396},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.12396},
}
RIS
TY - UNPB
TI - Mixup Barcodes for Topology-Aware Financial Decision Making
AU - Buddha Nath Sharma
AU - Joe Opitz
AU - Adam Moser
AU - Sayam Palrecha
AU - Sushovan Majhi
AU - Atish Mitra
PY - 2026
DA - 2026/10/08
PB - arXiv
DO - 10.48550/arxiv.2610.12396
UR - https://arxiv.org/abs/2610.12396
AB - 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.
ER -