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Using real-time cluster configurations of streaming asynchronous features as online state descriptors in financial markets

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  • Dieter Hendricks

Abstract

We present a scheme for online, unsupervised state discovery and detection from streaming, multi-featured, asynchronous data in high-frequency financial markets. Online feature correlations are computed using an unbiased, lossless Fourier estimator. A high-speed maximum likelihood clustering algorithm is then used to find the feature cluster configuration which best explains the structure in the correlation matrix. We conjecture that this feature configuration is a candidate descriptor for the temporal state of the system. Using a simple cluster configuration similarity metric, we are able to enumerate the state space based on prevailing feature configurations. The proposed state representation removes the need for human-driven data pre-processing for state attribute specification, allowing a learning agent to find structure in streaming data, discern changes in the system, enumerate its perceived state space and learn suitable action-selection policies.

Suggested Citation

  • Dieter Hendricks, 2016. "Using real-time cluster configurations of streaming asynchronous features as online state descriptors in financial markets," Papers 1603.06805, arXiv.org, revised May 2017.
    • Handle: RePEc:arx:papers:1603.06805
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    References listed on IDEAS

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    1. Dieter Hendricks & Tim Gebbie & Diane Wilcox, 2015. "Detecting intraday financial market states using temporal clustering," Papers 1508.04900, arXiv.org, revised Feb 2017.
    2. Christoph Frei & Nicholas Westray, 2015. "Optimal Execution Of A Vwap Order: A Stochastic Control Approach," Mathematical Finance, Wiley Blackwell, vol. 25(3), pages 612-639, July.
    3. Maria Elvira Mancino & Paul Malliavin, 2002. "Fourier series method for measurement of multivariate volatilities," Finance and Stochastics, Springer, vol. 6(1), pages 49-61.
    4. Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.
    5. Giada, Lorenzo & Marsili, Matteo, 2002. "Algorithms of maximum likelihood data clustering with applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 315(3), pages 650-664.
    6. Dieter Hendricks & Diane Wilcox, 2014. "A reinforcement learning extension to the Almgren-Chriss model for optimal trade execution," Papers 1403.2229, arXiv.org.
    7. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
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    Cited by:

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    2. Patrick Chang & Etienne Pienaar & Tim Gebbie, 2020. "Malliavin-Mancino estimators implemented with non-uniform fast Fourier transforms," Papers 2003.02842, arXiv.org, revised Nov 2020.

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