How can someone interested in Bitcoin read stories from the data? And where can you find useful data? The first steps to do this should be explained in the article.
A motto closely related to the crypto scene: Don’t trust, verify . Don’t just trust, check. An attitude that is reflected in the abbreviation DYOR ( Do your own research ) , which is also known in the Bitcoin ecosystem . After all, it’s not just about disclosure, but about turning every BTC-ECHO reader into a responsible crypto citizen. Therefore, two of the data sources used are presented in this article. For today we are focusing on Bitcoin.
One word in advance: I use the programming language R for data analysis. If you want to learn more about the powerful statistical programming language, you can learn a little more in this video . However, all of the data sources mentioned below are also accessible with other tools such as Python.
TradingView: Great source for raw data
TradingViewis known far beyond the Bitcoin scene. The course analyzes on our side are generally carried out using this powerful platform. TradingView is a fantastic platform for making charts. What few people know: A first step in juggling data can be to export the TradingView data. With Pine, TradingView has a very powerful programming language, which we presented in this article .
But what if you want to combine external data with the TradingView data? For a few months now, the platform has offered the option of data export. One click makes it possible to export both the Bitcoin rate and all indicators in the layout as a csv file:
Exporting data from TradingView made easy
CSV- Files are tables in which all values in a line are separated by commas. This gives you an easy-to-read format that can also be imported into Excel or the like, for example.
The future analyst can now work with this data. He would be able to read them into R with a simple command:
daten <- read.csv(„Pfad/zum/Dateinamen.csv“)
Of course there are other options. Many R fans swear by Tidyverse, a group of libraries for R that makes working with data extremely easy. The Tidyverse is also particularly reflected in the Bitcoin reports. However, it would now go beyond the scope to go into these in more detail.
Juggling with Bitcoin price data from Coingecko
One disadvantage of the above approach: processes cannot be automated. Why do you work with programming languages when you have to click three more times to continue processing the data? More direct access to the courses would be helpful here.
For the analyst from Bitcoin & Co., it’s often not just about prices. The development of supply and market capitalization is always the focus of the analysis. TradingView can also help here and the amount of available data is growing. But analysts are dependent on additional data sources in this regard.
Working with programming interfaces, so-called APIs, is ideal here in many places. API stands for Application Programming Interface and, in simple terms, enables access to data from the command line or from a program. Coingecko is an example of such a platform with API. The nice thing is that you don’t currently have to register for them.