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The next logical step is to visualize how these pricing datasets compare. For this, we'll define a helper function to provide a single-line command to compare each column in the dataframe on a graph using Plotly. In the interest of brevity, I won't go too far into how this helper function works. Check out the documentation for Pandas and Plotly if you would like to learn more. We can see that, although the four series follow roughly the same path, there are various irregularities in each that we'll want to get rid of.
Let's remove all of the zero values from the dataframe, since we know that the price of Bitcoin has never been equal to zero in the timeframe that we are examining. We can now calculate a new column, containing the daily average Bitcoin price across all of the exchanges. Yup, looks good. We'll use this aggregate pricing series later on, in order to convert the exchange rates of other cryptocurrencies to USD. Now that we have a solid time series dataset for the price of Bitcoin, let's pull in some data on non-Bitcoin cryptocurrencies, commonly referred to as altcoins.
For retrieving data on cryptocurrencies we'll be using the Poloniex API. Most altcoins cannot be bought directly with USD; to acquire these coins individuals often buy Bitcoins and then trade the Bitcoins for altcoins on cryptocurrency exchanges. Now we have a dictionary of 9 dataframes, each containing the historical daily average exchange prices between the altcoin and Bitcoin. Now we should have a single dataframe containing daily USD prices for the ten cryptocurrencies that we're examining.
This graph gives a pretty solid "big picture" view of how the exchange rates of each currency have varied over the past few years. Note that we're using a logarithmic y-axis scale in order to compare all of currencies on the same plot. You might notice is that the cryptocurrency exchange rates, despite their wildly different values and volatility, seem to be slightly correlated.
Especially since the spike in April , even many of the smaller fluctuations appear to be occurring in sync across the entire market. We can test our correlation hypothesis using the Pandas corr method, which computes a Pearson correlation coefficient for each column in the dataframe against each other column. Computing correlations directly on a non-stationary time series such as raw pricing data can give biased correlation values.
These correlation coefficients are all over the place. Coefficients close to 1 or -1 mean that the series' are strongly correlated or inversely correlated respectively, and coefficients close to zero mean that the values tend to fluctuate independently of each other. Here, the dark red values represent strong correlations note that each currency is, obviously, strongly correlated with itself , and the dark blue values represent strong inverse correlations. What does this chart tell us? Essentially, it shows that there was very little statistically significant linkage between how the prices of different cryptocurrencies fluctuated during Now, to test our hypothesis that the cryptocurrencies have become more correlated in recent months, let's repeat the same test using only the data from These are somewhat more significant correlation coefficients.
Strong enough to use as the sole basis for an investment? Certainly not. It is notable, however, that almost all of the cryptocurrencies have become more correlated with each other across the board. The most immediate explanation that comes to mind is that hedge funds have recently begun publicly trading in crypto-currency markets 1 2.
These funds have vastly more capital to play with than the average trader, so if a fund is hedging their bets across multiple cryptocurrencies, and using similar trading strategies for each based on independent variables say, the stock market , it could make sense that this trend would emerge. For instance, one noticeable trait of the above chart is that XRP the token for Ripple , is the least correlated cryptocurrency.
The notable exception here is with STR the token for Stellar , officially known as "Lumens" , which has a stronger 0. What is interesting here is that Stellar and Ripple are both fairly similar fintech platforms aimed at reducing the friction of international money transfers between banks. It is conceivable that some big-money players and hedge funds might be using similar trading strategies for their investments in Stellar and Ripple, due to the similarity of the blockchain services that use each token.
Quick Plug - I'm a contributor to Chipper , a very early-stage startup using Stellar with the aim of disrupting micro-remittances in Africa. This explanation is, however, largely speculative. Maybe you can do better.
With the foundation we've made here, there are hundreds of different paths to take to continue searching for stories within the data. Hopefully, now you have the skills to do your own analysis and to think critically about any speculative cryptocurrency articles you might read in the near future, especially those written without any data to back up the provided predictions. Thanks for reading, and feel free to comment below with any ideas, suggestions, or criticisms regarding this tutorial.
I've got second and potentially third part in the works, which will likely be following through on some of same the ideas listed above, so stay tuned for more in the coming weeks. Step 1. In :. We'll also import Plotly and enable the offline mode. In :. In :. Step 2. In :. In :. We can inspect the first 5 rows of the dataframe using the head method. In :.
Next, we'll generate a simple chart as a quick visual verification that the data looks correct. In :. First, we will download the data from each exchange into a dictionary of dataframes. In :. In :. Now we will merge all of the dataframes together on their "Weighted Price" column.
In :. In :. In :. With the function defined, we can compare our pricing datasets like so. In :. In :. When we re-chart the dataframe, we'll see a much cleaner looking chart without the spikes. In :. In :. This new column is our Bitcoin pricing index! Let's chart that column to make sure it looks ok. In :. Coin also retails two essential tools; the filter and the tracker.
GoCharting boasts a vast range of crypto markets. All these require Pro membership on TradingView. As the name suggests, CoinTracking is a price tracker and portfolio management app retailing with a large number of advanced features. That said, we should point out that CoinTracking has quite a learning curve, so its best suited for full-time crypto traders who would like to gain a more in-depth insight into their short and long term investments.
CoinTrackinng is also available on iOS as well as Android devices. But to access the application, you will need to complete a web registration first. This website offers a clean user interface and lots of relevant cryptocurrency data that will help you make an educated decision while investing your hard-earned money in a certain currency. Launched in , this platform allows its registered users to effortlessly track price fluctuations and trading volumes of more than 2, virtual currencies.
This crypto price tracker offers basic currency data such as trading volumes, price charts, and comprehensive analyses. And the team behind this feature has also posted a detailed report on the website explaining their coin scoring formula. Just like TradingView, Koyfin also supports financial markets outside the crypto industry. The only main difference between the two price trackers is the fact Koyfin lacks most indicators found on TradingView.
Other than that, Koyfin helps you understand chart correlations between assets easily, and also allows you to view the historical price action of crypto coins as a table or a chart. As you can tell from the list above, picking the best crypto charting app is a unique process for every trader because the applications and programs offer many different features.
That said, there are several standard factors you should always consider while picking a charting app. These include:. Crypto price trackers bring a structured and calming element into the chaotic cycle of the crypto markets. So makes sure the one you pick best meets your portfolio requirements and personal preferences. CRPT is listed on. Start trading. Visit choise. What is a Cryptocurrency Price Tracker? Conclusion With cryptocurrency prices slowly recovering, those who were watching the industry from a distance without taking part are ready to jump on the bandwagon and invest their hard-earned dollars.
Top Bitcoin Crypto Price Trackers Without further ado, here are the best-rated cryptocurrency price trackers in the market today. These include: Safety and security User interface Advanced functionality Number of listings Affordability Conclusion Crypto price trackers bring a structured and calming element into the chaotic cycle of the crypto markets. Was this article helpful?
A crypto mining data center has similar properties to a typical data center. These projects typically require electrical design for power distribution, and. Data centers that support bitcoin mining are usually in the range of 1 megawatt to 5 megawatt facilities. As many bitcoin miners know firsthand. Data center cryptocurrency news articles and insights. Latest cryptocurrency news for data centers, colocation, cloud, and technology.