Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

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

Tengelin, Karl and Sopasakis, Alexandros LU (2020) In National Accounting Review 2(4). p.354-366
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
National Accounting Review
volume
2
issue
4
pages
13 pages
publisher
AIMS Press
ISSN
2689-3010
DOI
10.3934/NAR.2020021
language
English
LU publication?
yes
id
696175bc-f71c-4b22-96b9-99ddbb26cc4e
date added to LUP
2021-02-02 22:31:40
date last changed
2021-05-05 15:20:39
@article{696175bc-f71c-4b22-96b9-99ddbb26cc4e,
  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.}},
  author       = {{Tengelin, Karl and Sopasakis, Alexandros}},
  issn         = {{2689-3010}},
  language     = {{eng}},
  month        = {{10}},
  number       = {{4}},
  pages        = {{354--366}},
  publisher    = {{AIMS Press}},
  series       = {{National Accounting Review}},
  title        = {{Tick based clustering methodologies establishing support and resistance levels in the currency exchange market}},
  url          = {{http://dx.doi.org/10.3934/NAR.2020021}},
  doi          = {{10.3934/NAR.2020021}},
  volume       = {{2}},
  year         = {{2020}},
}