Do your own research: where to find good bitcoin data?
How can someone interested in Bitcoin glean stories from data? And where can one find useful data? The first steps towards this will be explained in the article.
A motto closely associated with the crypto scene: Don’t trust, verify. Don’t just trust, verify. An attitude that is reflected in the abbreviation DYOR (Do your own research), which is also well-known in the Bitcoin ecosystem. After all, it is not simply about disclosure, but about making every BTC-ECHO reader an empowered crypto-citizen. Therefore, two of the data sources used will be presented in this article. For today, we will focus on Bitcoin.
A word up front: I use the programming language R for the data analysis. If you want to learn more about Bitcoin Machine scam this powerful statistical programming language, you can watch this video. However, all the data sources mentioned in the rest of this video are also accessible with other tools such as Python.
TradingView is well known far beyond the Bitcoin scene. The price analyses on our site are basically carried out using this powerful platform. TradingView is a fantastic platform for creating charts. What few know: A first step to juggling data can be exporting the TradingView data. Thus, TradingView has a quite powerful programming language, Pine, which we introduced in this article.
But what if you want to combine external data with the TradingView data? For this purpose, the platform has been offering the possibility of data export for a few months now. One click makes it possible to export both the Bitcoin rate and all indicators in the layout as a csv file:
CSV files are tables in which all values of a row are separated by commas. This gives you an easy-to-read format that can also be imported into Excel or similar.
The analyst-to-be can now work with this data. In R he would be able to read it in with a simple command:
data <- read.csv(„path/to/filename.csv“).
Of course, there are other possibilities. Many fans of R swear by Tidyverse, a group of libraries for R that makes working with data extremely easy. The Tidyverse is also reflected in particular in the Bitcoin Reports. However, it is beyond the scope of this article to go into more detail.
Juggling with bitcoin price data from Coingecko
One disadvantage of the above approach is that processes cannot be automated. Why work with programming languages if you still have to click three times to process the data further? Here, a more direct access to the courses would be helpful.
For this, the analyst of Bitcoin & Co. is often not simply concerned with prices. The development of supply and market capitalisation is always the focus of the analysis. It is true that TradingView can also help here and the amount of available data is growing. But analysts are dependent on additional data sources in this respect.
In many places, working with programming interfaces, so-called APIs, is a good solution. API stands for Application Programming Interface and, simply put, enables access to data from the command line or from a programme. Coingecko is an example of such a platform with an API. The good thing is: Currently, you don’t have to register for it.