Clustering Cryptocurrencies
1. Introduction
Why is clustering interesting? How to value cryptocurrencies has been a major question ever since so many began finding their way to market. As Qunitero (2018) points out, “having a clear and unbiased benchmark while evaluating new decentralized projects in the crypto economy” could help to answer the question of valuation. Clustering commonly occurs around token type: thus, one routinely sees the clustering of currency tokens, platform tokens, utility tokens, brand tokens, and security tokens. Yet these are not the only clusters that may appear, the more closely one looks at the space. As clustering shows which cryptocurrencies move in tandem at the top of the market cap, it is useful to examine clustering cryptocurrencies to see what similarities in movement might tell us.
Are fundamental similarities backed by market metrics? That is the main question to be asked and an important one because clusters can be used to formulate trading strategies. However, Qunitero (2018) notes that there is more than one cluster in the cryptocurrency space—in fact, there are numerous ones. Identifying them and understanding the relationship among assets is critical to devising a successful trading strategy. Identifying clusters as part of developing a trading strategy for cryptocurrencies could help make the space far more viable for investors and speculators alike. “There do seem to exist natural clusters of coins that move in tandem,” Quintero (2018) states—which means more cryptocurrency samples need to be examined in order to clarify the seeming relationships.
2. Method and Results
Part I: Developing a Method
The problem of time series clustering can be considered as finding a function:
$$f(X_T) = y \\in [1...K]$$$$\\text{for }X_T=(x_1, ..., x_T)$$$$\\text{with }x_T \\in\\mathbb{R^d}$$
where T is timeline length and K is particular cluster. This should be conducted with representation of time series as a set of selected features vi of fixed size D independent of T.
With this representation, applying standard clustering algorithms on this feature set can be possible. The main question is what features to consider when applying the algorithm? For the purpose of this study, we identified multiple time series describing each coin and we also constructed derivative parameters to define these series.
Next, we devised a method of moving from simple to complex in terms of identifying clusters:
1. We used common, standard features for each series (parameter): Means, Medians, Standard deviations, Skewness, and Kurtosis.
2. We used tsfresh library to automate the process of...
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