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

Karl Tengelin; Alexandros Sopasakis; Karl Tengelin; Alexandros Sopasakis

doi:10.3934/NAR.2020021

National Accounting Review

2020, Volume 2, Issue 4: 354-366. doi: 10.3934/NAR.2020021

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Research article

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

Karl Tengelin ,
Alexandros Sopasakis ^,

Department of Mathematics, Lund University, Lund 22100, Sweden

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.
- tick data,
- support and resistance levels,
- clustering methods,
- Gaussian mixture model,
- K-means,
- EUR-USD exchange rates
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

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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.

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