Cryptocurrencies (or cryptos) captured the world's imagination during the last couple of years. From their spectacular rise in 2017, to their equally hard fall in 2018, and to their most recent revival this year. Cryptocurrencies and the blockchain technology powering them are regarded as disruptive forces, proposing an alternative decentralized monetary system. They have also gathered their fair share of criticism. Cryptocurrencies are accused of facilitating transfer of "dirty money" between criminals. They provided mind-boggling returns a couple years back and earned the reputation of being a "get rich quick" scheme.
In this article, we turn our attention to cryptocurrencies as investment vehicles sharing common characteristics with other asset classes like FX, commodities and precious metals. Cryptocurrencies, like fiat currencies, allow for easy transfer of wealth between individuals and businesses. They are similar to commodities with regards to the limited supply they offer. Cryptocurrencies have drawn comparisons to gold which is viewed as a "safe-haven" investment.
This article is divided into two parts. In this first article, I will illustrate how to source cryptocurrency historical data. For the next article, I will demonstrate how to backtest a pair-trading strategy using the cryptocurrency dataset collected here.
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- How To Scrape Cryptocurrency Data Using Python
- How To Implement A Pair-trading Strategy Using Python
You will need to install:
- Dino de Castro - Initial work - [DinodC] (https://github.com/DinodC)