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

Karl Tengelin, Alexandros Sopasakis

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to) 354-366
Number of pages13
JournalNational Accounting Review
Volume2
Issue number4
DOIs
Publication statusPublished - 2020 Oct 23

Subject classification (UKÄ)

  • Other Computer and Information Science

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