The volatility of various assets may impact the overall correlations between them. Therefore, to take a relevant picture of the correlations between 33 cryptocurrencies (all traded in USD on the Coinbase exchange), I thought it would be interesting to analyse their price relationship on a moderated volatility period, in the midweek, where it appears that the volumes tend to increase. Given there were lots of traded volumes last week, I decided to measure the correlations within the cryptocurrency market based on trading data for 13 Jan from 13:20 to 18:15 UTC (avoiding both the too quiet periods and the most volatile ones...).
That trading period is also very interesting due to the fact that it is the most volatile one for other markets as the American trading session and European session are overlapping.
The data may have unearthed changing tendencies among Bitcoin traders, possibly due to the increasing prevalence of institutions within the market, hence that period selection.
The timeframe picked for this experimental study was 5 min (more adapted for day trading).
To build a heatmap-style correlation matrix for all the 33 cryptos, once the trading data (from Coinbase) was exported through TradingView, I just used Excel to filter and align the data sets before to calculate the correlations with Python and finally plot the matrix with Pandas.
For more detailed guidance on how to perform this, please check my previous article here.
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