Tick data clustering analysis establishing support and resistance levels of the EUR-USD exchange market

Abstract: Our aim is to use clustering algorithms in order to compute support and resistance levels within an intra-day trading setting. To achieve this we use a tick data set from the EUR-USD exchange market during 2019 as a measure of market activity. Both the Gaussian Mixed Model (GMM) and an altered form of Kmeans clustering will be used as clustering methods where each method will be evaluated using a selection of common performance metrics. The computed support and resistance levels will then be put to the test by initiating mock trades during certain time windows from early 2019, which are specified by Century Analytics. Both models that were used in this thesis managed to partition the data in a way that made it possible to create support and resistance levels that are comparable to traditional methods which do not rely on market activity. Although more research needs to be made the results look promising and we can, with some confidence, say that market activity in the form of ticks can be used as an indicator for support and resistance levels within the EUR-USD exchange market. The support and resistance levels computed using GMM and Kmeans were quite similar but the GMM method performed better when examining the methods using mock trades. The GMM could predict support and resistance ”bounces” with greater statistical significance compared to the Kmeans method.