Correlation Analysis of Cryptocurrencies

Cryptocurrency market behavior is being written and talked about by many. From what I’ve seen thus far, the analyses are mostly speculative with biased suggestions which trends will emerge. What is missing is data and statistics to validate these claims, so I propose a data driven analysis. I do not claim to be a cryptocurrency expert and I will not offer any investment advice here – my goal of writing this is to learn by researching and publishing so that readers may also see what the data is suggesting.



Above is the log pricing chart for 10 cryptocurrencies.


And the linear view of the prices. Clearly, Bitcoin has become the dominant cryptocurrency leader, at least for now.


Correlations are very enticing for traders to use in pure arbitrage discovery. This is the beginning of a framework that can certainly be built on to create a compelling tool for traders once connected to APIs and trading programs written on top of this type of analysis.


Cryptocurrency cross-correlation over the course of 2016 shows very little correlation, with the exception of Bitcoin and Litecoin. 2017 was year that cryptocurrencies really took off in terms of public trust, hype, and also additional security protocols with each new coin.


However, moving the time scale to 2017, correlation has become much higher. Why is this happening?

One reason may be that hedge funds have recently began trading in cryptocurrency markets. These funds have vastly more capital to play with than the average trader, so if a fund is hedging their bets across multiple cryptocurrencies, and using similar trading strategies for each based on independent variables, it could make sense that this trend of increasing correlations would emerge.

Screen Shot 2017-09-03 at 7.46.56 PMOne noticeable trait of the above chart is that XRP (Ripple), is the least correlated cryptocurrency. The notable exception here is with STR (Stellar), which has a strong (0.62) correlation with XRP.

It might be that some hedge funds might be using similar trading strategies for their investments in Stellar and Ripple, due to the similarity of the blockchain services that use each token. This could explain why XRP is so much more heavily correlated with STR than with the other cryptocurrencies.

Blockchain Analysis

Up to now, I’ve only plotted prices and correlations, now let’s look at the underlying infrastructure of how these cryptocurrencies are enabled.

unconfirmed transactions
transaction time
block size



To understand just how disparate these exchanges are, I’m also including some of my research below to illustrate the matching and splicing one needs to account for in order to accurately normalize the price of a coin at any given time. Each exchange returns zero values more than once and there are also varying price differentials, shown by the Bitcoin price table below for a 5-day window across 4 exchanges.

Screen Shot 2017-09-03 at 12.03.27 PM

I think one of the reasons this is acceptable is that this is the direct result of a decentralized system. There is not one single entity controlling the price of each cryptocurrency – à la, the whole point of cryptocurrencies.

Coinbase returned quite a few, sustained zero-values.
btc2By combining four exchanges’ temporal Bitcoin price data, we see they each have information gaps.
btc3Removing all of the zero-values, gives us a true-aggregated price.
Plot 104Finally, we reach an average and true Bitcoin price.
Clearly, there is much research to be done in the way of understanding and predicting cryptocurrency prices and there is still much that we don’t know for things like lack of data and how banks will adopt cryptos into their existing workflows. Satoshi said that there isn’t a path to scalability completely without government or institutions, so the future might be a blend of both. One thing is for sure, standard currency analysis will not lend itself well to trading cryptocurrencies.

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