Analyzing Ethereum, Bitcoin, and 1200+ other Cryptocurrencies using PostgreSQL

Lately it seems like money has been growing on trees.
With trade volumes reaching billions of dollars a day and market caps hitting tens of billions of dollars, it’s no wonder that cryptocurrencies fuel the gold rush of the modern day.
We live in the age of digital currencies, with cryptocurrencies birthed within the decade. Yet already, there are more than a thousand cryptocurrencies in the market and an initial coin offering (ICO) almost daily.

As we embrace this new, proliferous market, it’s important that we try to understand what’s going on. There are many risks to observe at both the micro-level (e.g., personal investments) and macro-level (e.g., prevention of market crashes and major loss of capital). That’s where we come in.

We’re data people. Specifically, we’re the developers of TimescaleDB, a new open source time-series database built up from PostgreSQL. And we thought it would be insightful (and fun) to analyze the cryptocurrency market using PostgreSQL and TimescaleDB (plus R for data visualization).

For this analysis*, we looked at historical OHLCV price data on over 1200 cryptocurrencies (as of 6/26/2017; courtesy of CryptoCompare). While our current dataset represents only a daily record of rates, TimescaleDB scales easily to much finer-grained historical data. With the constant influx of new coins and exchanges, TimescaleDB can provide a reliable foundation for time-series data in the cryptocurrency market.