Bitcoin - Does It Have Legs? A Visual Analysis

Posted on Jul 24, 2016

Bitcoin – Does it Have Legs? A Visual Analysis


Bitcoin has been in the news a lot recently.  As a digital currency, it has been the subject of investigations relating to legality of goods and services for which it is used as payment.  As a technology it has been under scrutiny regarding the security of the currency, and the initial application of blockchain technology which is of interest to industry beyond just digital currencies.  Despite the excess of speculation about its usefulness as a currency, and perhaps because of the associated hype, the value of the currency has been hugely volatile.  This post will provide a basic visual analysis of the effect of the volatility of the currency on the adoption of the currency as a means of transacting for goods and services.

The Data

Most of the necessary data for this visual inspection of relationships is available via, and is provided by, a site via which one can view all of the transactions in the blockchain or central ledger in which all bitcoin transactions are recorded.  From Quandl and, I chose to look at three data sets:

Unique Addresses: An estimate of the number of unique addresses used in bitcoin transactions, daily. chose to remove activity from the top 100 most active addresses, as activity from these addresses most likely should be excluded, for reasons beyond the scope of this post.

Total Transaction Volume: An estimate of the total USD value of transactions, with the same top 100 addresses removed.  As best I can tell, this is also separate from exchange-based transactions in bitcoin (the exchange of bitcoin for other currencies).

Total Transactions: An estimate of the number of unique transactions in bitcoin on a daily basis. adjusts the total transactions for “change” that is returned to the original payer from the receiver of payment, which is present in most transactions. also provides a daily value for bitcoin vs various other currencies. However, for purposes of this study I wanted to find bar data (open, high,low,close). This can be useful in analyzing the true volatility of an instrument.  I was able to get this data from, and chose to use data from the exchange bitstamp, which has had decent trading volumes and a long-enough history from which to construct bar charts.

Analysis of Volatility

A simple visual analysis of the time series plot confirms that the currency is highly volatile.  From its humble beginnings, the value of bitcoin spiked well over $1,000, retreated back below $300 and is currently worth around $650 (at the time of this analysis).

Daily Price Chart

The candle chart for the period from the end of 2013 to early 2014 provides more evidence of the volatility of bitcoin.  The large ranges on single days and the pattern of large-move days following other large-move days highlights the volatility.

Daily Price Candle Chart

A distribution of daily returns also provides visual evidence of the volatility.  The histogram appears fairly normal, but has a standard deviation of 4.9% and has large outliers, reaching and exceeding 20% daily moves on occasion.  As a comparison, the standard deviation of returns for the USD/GBP pair is 0.56% in the same period with just a single outlier value in excess of 5% (8% around the recent "Brexit" vote).

Return Distribution

Adoption of bitcoin

Before considering the effect of volatility, one can view the adoption of bitcoin as a function of time, and see that the three metrics, unique addresses, transactions, and total transaction volume have been steadily increasing.  The three charts below of these three metrics confirm the acceptance of bitcoin as a means to pay for goods and services.

Transaction Volumes Unique Addresses Daily Transactions

Bitcoin Users Unfazed by Volatility

A visual comparison of the adoption of bitcoin and the volatility of the currency may provide insight into whether users are affected by the volatility.  A first comparison of the weighted daily price vs the number of transactions daily provides little evidence of a relationship.  The shading by date indicates that there are clusters of points separated by date.  This chart doesn’t truly reflect what we wish to see, which is the relationship between price volatility (a derivative of price) and usage, rather than the simple non-derivative price vs use.

Price vs Transactions


This second chart is a comparison of the daily returns and the value of transactions.  By splitting the daily returns into four equal-sized buckets, one is able to do a quick visual comparison of the value of exchanged bitcoins during days with large moves on the up and down side vs the value of transactions during less volatile days in the middle two buckets.  While not providing any statistical evidence, this visual allows us to see that the value of transactions seems unaffected by the daily return.  Coloring by year also suggests that there is a consistent disregard for price volatility across years.

Returns vs Transaction Volume

Finally, the chart of the daily price range vs the number of transactions provides no clear evidence of a relationship either.  With a simple coloring by year, one may be able to deduce that the price range and transaction counts are independent, and that the number of transactions may simply be a function of time.  Each year it appears there are more transactions.  This was confirmed by the earlier charts.

Price Range vs Transactions


This analysis provided no evidence that there is a relationship between the volatility of bitcoin and the adoption of bitcoin as a means to pay for goods and services.  The most likely conclusion from this analysis is that adoption of bitcoin is a function of time, and that price and volatility are completely independent variables.  Further analysis might provide statistical evidence of this, or may provide evidence of a relationship between other derivatives of price and the adoption of bitcoin.  In the absence of further analysis that may provide evidence of a relationship, it is clear that the number of users, and the number and value of transactions has been increasing steadily and will likely continue.

About Author

Ben Townson

Ben Townson graduated from the New York City Data Science Academy 12-week Data Science Bootcamp on September 23. At NYCDSA he has mastered machine learning and data analysis techniques, complementing more than ten years spent in the finance...
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