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In this work we present a non-parametric online market regime detectionmethod for multidimensional data structures using a path-wise two-sample testderived from a maximum mean discrepancy-based similarity metric on path spacethat uses rough path signatures as a feature map. The latter similarity metrichas been developed and applied as a discriminator in recent generative modelsfor small data environments, and has been optimised here to the setting wherethe size of new incoming data is particularly small, for faster reactivity.  On the same principles, we also present a path-wise method for regimeclustering which extends our previous work. The presented regime clusteringtechniques were designed as ex-ante market analysis tools that can identifyperiods of approximatively similar market activity, but the new results alsoapply to path-wise, high dimensional-, and to non-Markovian settings as well asto data structures that exhibit autocorrelation.  We demonstrate our clustering tools on easily verifiable synthetic datasetsof increasing complexity, and also show how the outlined regime detectiontechniques can be used as fast on-line automatic regime change detectors or asoutlier detection tools, including a fully automated pipeline. Finally, weapply the fine-tuned algorithms to real-world historical data includinghigh-dimensional baskets of equities and the recent price evolution of cryptoassets, and we show that our methodology swiftly and accurately indicatedhistorical periods of market turmoil.

Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures