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Stocker bitcoins price

Next, we will define a simple function to merge a common column of each dataframe into a new combined dataframe. Finally, we can preview last five rows the result using the tail method, to make sure it looks ok. The prices look to be as expected: they are in similar ranges, but with slight variations based on the supply and demand of each individual Bitcoin exchange.

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 generate a graph from the dataframe. 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 average daily 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 for 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 with 9 dataframes, each containing the historical daily average exchange prices between the altcoin and Bitcoin. Now we can combine this BTC-altcoin exchange rate data with our Bitcoin pricing index to directly calculate the historical USD values for each altcoin. Now we should have a single dataframe containing daily USD prices for the ten cryptocurrencies that we're examining. This graph provides a pretty solid "big picture" view of how the exchange rates for 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 the currencies on the same plot. You might notice is that the cryptocurrency exchange rates, despite their wildly different values and volatility, look 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 are not correlated, and 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 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 of increasing correlations 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 future, especially those written without any data to back up the provided predictions. Thanks for reading, and please comment below if you have any ideas, suggestions, or criticisms regarding this tutorial.

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