Bitcoin - Litecoin - Ethereum 2019 Performance Analysis
The sentiment about cryptocurrency has changed drastically since BTC retraced from over 12.000 USD to now just over 8000 USD. In this article we will look at the correlation between the sentiment about cryptocurrency's and the price history of Bitcoin, Litecoin and Ethereum. Furthermore, this article will cover the statistical analysis of the price movements of all three coins. Enough talking for now, let's get started with the analysis.
In order to be able to compare any kind of data, I first need to retrieve all the data from a trusted source. For now this project I used Python to retrieve all relevant data from Binance. I will create a separate post about how this is done in the future. I started by simply getting all close prices of all three coins (BTC, LTC & ETH) and plot those into a chart with a logarithmic scale, obviously. This resulted in the image below:
BTC, LTC & ETH Prices in USDT in 2019
You probably already noticed that the Ethereum and Litecoin price lines look awfully similar. This would hint us that there is a high positive correlation coefficient between those two (based on Pearson correlation). Now we use Python to calculate the correlation coefficients of these three coins, which resulted in the following:
Correlation heatmap BTC,LTC & ETH 2019
You can interpret the above heatmap as following: ETH and LTC have a very strong positive correlation of 0.92, ETH and BTC have a strong positive correlation of 0.81 and LTC BTC have a moderately positive correlation of 0.69. The correlation coefficient of each cryptocurrency to itself is 1, this is not interesting and can be ignored. As we can see the biggest correlation is between Ethereum and Litecoin, which was expected from looking at the first chart. This was all based on USDT (Tether) pairings. So now we will have a look at the BTC trading pairs of Litecoin and Ethereum. This resulted in the following graph:
LTC-BTC & ETH-BTC Prices 2019
I proceeded by calculating the daily ROI (gain or loss) of the ETH-BTC and LTC-BTC pair and put all the data into a dataframe. Out of this dataframe I created the distribution graphs of the daily ROI:
Distribution LTC ROI 2019
Distribution ETH ROI 2019
Sentiment VS Price Action
After collecting the sentiment data from https://alternative.me, I have once again filtered the data and put it into another dataframe series. The first image shows the correlation coefficient table for the ROI's based on USDT value and the sentiment. The second image shows the correlation coefficient table for the ROI based on the BTC value of the altcoins and of course the sentiment score. In the second one BTC was excluded because BTC/BTC always is 1:1 and not needed for this analysis.
Pearson R (USDT values)
Pearson R (BTC values)
*The following second correlation tables are almost the same, only the sentiment value is now also based on % gain or loss based on the previous day*
Pearson R (USDT values)*
Pearson R (BTC values)*
We can conclude that there is almost no correlation between the sentiment and the price history in 2019, in both USDT value and BTC value. This would go well together with the theory that you should never base your trades on the market sentiment of others, for example twitter posts. Trading is a game of winning over time with good risk:reward setup, no emotion in your trading and of course the useage of stoploss.
Performance Overview 2019 (BTC, LTC & ETH)
Mean daily percentual gain in USDT
Mean daily gain in USDT
Total gain from 01-01-2019 till 17-10-2019 in USDT
This is the end of part 1 of the analysis. Part 2 will be coming soon! If you made it to the end of this article and enjoyed it, please leave a comment or a like!
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