Research article

Tick based clustering methodologies establishing support and resistance levels in the currency exchange market

  • Received: 20 August 2020 Accepted: 19 October 2020 Published: 23 October 2020
  • JEL Codes: C45, C63, C81, C83

  • We establish support and resistance levels from data in intraday currency exchange market activity based on machine learning methods. Specifically we design two semi-supervised classification neural networks. The first one is based on a variant of the K-means method while the second is based on a Gaussian mixture model with expectation maximisation. Each performs classification from tick data on very short time windows and produces the corresponding support and resistance price levels. We test the methodology on actual market data for the EUR-USD currency exchange. As a sanity check we also perform mock trades based on actual market data. We evaluate the results for statistical significance using a number of performance metrics while also comparing against traditional methods.

    Citation: Karl Tengelin, Alexandros Sopasakis. Tick based clustering methodologies establishing support and resistance levels in the currency exchange market[J]. National Accounting Review, 2020, 2(4): 354-366. doi: 10.3934/NAR.2020021

    Related Papers:

  • We establish support and resistance levels from data in intraday currency exchange market activity based on machine learning methods. Specifically we design two semi-supervised classification neural networks. The first one is based on a variant of the K-means method while the second is based on a Gaussian mixture model with expectation maximisation. Each performs classification from tick data on very short time windows and produces the corresponding support and resistance price levels. We test the methodology on actual market data for the EUR-USD currency exchange. As a sanity check we also perform mock trades based on actual market data. We evaluate the results for statistical significance using a number of performance metrics while also comparing against traditional methods.
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    © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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