Investments in the cryptocurrency market have seen an uptrend in the last few years. For many, it was after Bitcoin’s price hit the $17,000 mark that the buzz reached fever pitch. This increased profile of cryptocurrency has led to many people looking to invest in cryptocurrency projects.
Some investors put their money in cryptocurrencies as a result of the hype. However, many others such as hedge fund managers engage in careful market analysis using statistical data analysis before investing in cryptocurrency.
Cryptocurrencies experience volatility triggered by a range of things, including government regulations, social media hype and supply and demand curves. Data analysis is, therefore, crucial in understanding all the variations. So how does data science shape cryptocurrency investment?
Cryptocurrency performance analyzed using R or Python
There’s increasing the need for investment ‘advice’ that assures investors that what they are reading is not just speculation, but quantitative information that is backed up by data. Programming languages like R and Python have been used to analyze cryptocurrencies in great detail and a high degree of accuracy.
Several sites provide API tools that are essential to getting live and historical data on cryptocurrencies. Using the data from Coinmarketcap, an R language analyst can pull crypto prices from various sites and plot a correlation between them over a specified period. An investor can then be able to read an interactive chart using googleV to understand trends in the data.
Market Capitalization and Growth
Data analysis helps calculate the growth of the cryptocurrency market cap. From this report, investors can see which crypto coins are growing and/or may offer a desirable return on investment. The quantmod tool in R also has a function allowing analysts to calculate the percentage difference in market growth over a 3 month period. This allows investors to make decisions on which cryptocurrency to back.
Price trends and price predictions
To determine the market cap, analysts calculate the product of the cryptocurrency’s price and the available circulation. This information then ranks cryptocurrencies. For day trading, having access to price charts is very important – for example, an open high, low close (OHLC) chart is useful when making decisions based on price movements. Various sites will have dashboards that display this information to investors. In addition, engineers are developing predictive machine learning that will allow investors to read and predict prices.
As far back as 2014, MIT scientists were able to predict the price of Bitcoin and use that information to trade (although the growing maturity and complexity of the market, and the relative simplicity of the Bitcoin market in 2014, mean that this test was essentially conducted under lab conditions). With the vastly greater quantity of data now available in the dataset, prediction has become far harder, and is likely to be functionally impossible; but a wise person does not bet against machine learning.
Investing in cryptocurrency is not just putting money on the coin with the highest value; rather, an investor needs to have insights into the cryptocurrency’s historical charts and price movements. Data science is a vital tool for this.