**R – NYC Data Science Academy Blog**, and kindly contributed to R-bloggers)

### Introduction

The goal of this document is to provide an overview of crypto currencies, also identified simply as “coins” and derive preliminary recommendations for investors in this space. The Bitcoin white paper was written in late 2008. Today many more currencies than Bitcoin exist. Some of the questions we will answer include: what are the coins relative importance (by total value)? How have prices behaved in the last few years? How would basic investing strategies have performed during that timeframe?

### Data

The data file studied here contains OHLC (Open-High-Low-Close) data for about 1100 crypto currencies during the last 5 years. The data was scraped from https://coinmarketcap.com/ by Jvent and uploaded to Kaggle.

Source file: link

Here is a sample of the data:

Note: The ‘market’ column means market capitalization and is the sum of the values of all coins in circulation.

The file contains 1129 different coins. Here is a random sample:

### Coins creation

The following graph shows the constant rise of reported coins. We observe some volatility in the number of new coins in last few months. One factor was China’s decision to ban initial coin offerings (ICOs) on September, 4th. Nevertheless, the pace of new ICOs globally remains strong.

### Market Capitalisation Distribution

Looking at the distribution of coin market caps, we observe a large skew towards smaller valuations. In fact 230 (20.37%) of coins have no market capitalisation as of September 28th, 2017 (the market cap reported by the website is $0). 678 (60.05%) have a market cap below $1 million.

### Coins Market Cap graph

This graph shows the evolution of each coin total market cap over time (only top coins are shown).

As a few coins clearly dominate trading, we will look at their relative share of the overall crypto currency pie as measured by market cap. This graph shows a key trend in the past year: the emergence of other coins such as Ethereum and Ripple (22% and 5% of total market cap respectively) and a reduction of the Bitcoin total share from 90% to 50%.

Note: when we say ’emerge’, we are talking about market cap, not when these companies started.

### Return distribution

In the graph below, all coins prices were normalized to start trade at $1 on April 28th, 2013. Plotting prices or log prices for all coins isn’t a viable option here. The coins should be clustered so that it’s possible to read the data. This analysis is outside the scope of this document.

### Trading Strategies

In order to better understand the behavior of these coins, I implemented a basic dollar averaging trading strategy. Starting 5 years ago when data is available, everytime the daily trading volume passes a certain threshold ($100,000 to start) for a given coin, I buy $1 of it and hold it forever. The composition of the portfolio on the last day where data is available (September 28th, 2017) is shown below:

Conclusion: 50% of the portfolio is made of just 6 coins. Stratis, the biggest position, went up 200,000 times. The next step would be to check if those coins indeed could have been purchased at the initial ‘low’ prices reported on Coinmarketcap.com. Historical exchange data would be needed for this.

The value of the portfolio over time (net of the cash investment represented in red) is plotted below. Investing in about 600 coins would have resulted in a net gain of $3400. This represents a significant return, but concentrated in the last year. Very few long strategies would have failed in 2017 and this basic one is no exception.

Finally I plotted the value of the final portfolio as a function of the volume threshold at which I would buy each coin. The gains get exponentially higher as the threshold diminishes. That means that if I buy each coin “right away” -say when the daily volume is only above $1000, then some of the coins will experience a meteoric rise and the portfolio will grow more during the holding period. The same conclusion applies here: we would need to check with exchange data if these coins were truly available at such small volumes. At the very least we can conclude that this trading strategy would only be available to small players.

### Additional studies

In response to comments from the author of the data file, I created 2 more graphs. The first one looked into the alleged strong returns of Bitcoin in the first days of the month. This is not supported by the data which shows no relationship. Note that I focused on the last 12 months for this analysis as there was limited activity in the previous 4 years.

The second shows the returns of a coins index (with ‘traditional’ market cap weights) in relation to Bitcoin returns. Supposedly Bitcoin price surges were causing alternative coin holders to sell their holdings in exchange for Bitcoin. We see that in fact all crypto currencies seem to move in tandem.

### Conclusion

Although those weren’t covered here, coins carry significant risks as investments, including:

- technology risk (e.g. hackers stealing from exchanges and sapping investor’s confidence)
- technology obsolescence with new algorithms such as hashgraph
- regulatory risk
- absence of intrinsic value such as cash flow streams

Because coins are highly correlated to Bitcoin, an investor in crypto currencies couldn’t meaningfully diversify his holdings across coins. Following this basic analysis, I would recommend considering any allocation to a basket of crypto currencies to be conservatively sized.

The post Crypto Currencies as an investment vehicle appeared first on NYC Data Science Academy Blog.

**leave a comment**for the author, please follow the link and comment on their blog:

**R – NYC Data Science Academy Blog**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...