If you're an advanced user, and you don't want to use Anaconda, that's totally fine; I'll assume you don't need help installing the required dependencies. Feel free to skip to section 2. Once Anaconda is installed, we'll want to create a new environment to keep our dependencies organized.
This could take a few minutes to complete. Why use environments? If you plan on developing multiple Python projects on your computer, it is helpful to keep the dependencies software libraries and packages separate in order to avoid conflicts. Anaconda will create a special environment directory for the dependencies for each project to keep everything organized and separated.
Create a new Python notebook, making sure to use the Python [conda env:cryptocurrency-analysis] kernel. Once you've got a blank Jupyter notebook open, the first thing we'll do is import the required dependencies. Now that everything is set up, we're ready to start retrieving data for analysis. To assist with this data retrieval we'll define a function to download and cache datasets from Quandl.
We're using pickle to serialize and save the downloaded data as a file, which will prevent our script from re-downloading the same data each time we run the script. The function will return the data as a Pandas dataframe. If you're not familiar with dataframes, you can think of them as super-powered spreadsheets.
Let's first pull the historical Bitcoin exchange rate for the Kraken Bitcoin exchange. Here, we're using Plotly for generating our visualizations. This is a less traditional choice than some of the more established Python data visualization libraries such as Matplotlib , but I think Plotly is a great choice since it produces fully-interactive charts using D3.
These charts have attractive visual defaults, are easy to explore, and are very simple to embed in web pages. As a quick sanity check, you should compare the generated chart with publicly available graphs on Bitcoin prices such as those on Coinbase , to verify that the downloaded data is legit. You might have noticed a hitch in this dataset - there are a few notable down-spikes, particularly in late and early These spikes are specific to the Kraken dataset, and we obviously don't want them to be reflected in our overall pricing analysis.
The nature of Bitcoin exchanges is that the pricing is determined by supply and demand, hence no single exchange contains a true "master price" of Bitcoin. To solve this issue, along with that of down-spikes which are likely the result of technical outages and data set glitches we will pull data from three more major Bitcoin exchanges to calculate an aggregate Bitcoin price index.
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. Bitcoin is a cryptocurrency. This means that it is a peer-to-peer electronic cash system. Put simply, bitcoins are a completely digital form of currency. There is no bank to speak of and no physical currency such as coins, notes and cards.
It is created and stored electronically. With this cryptocurrency, the users control the payments, not the banks. This means that it is not subject to rises and falls in value linked to inflation, but it is extremely vulnerable to things like uncomplimentary press reports. It also means that transaction incur either a very small fee or no fee at all because there is no bank to pay. However, it is exceptionally good for someone wanting to send a direct payment using just an app or website.
The payment is anonymous for the large part, as no personal details are sent with the transaction. Users have their own web address to send and receive the coins to and from. Once you have an address, you are free to start trading and buying Bitcoins. Bitcoin exchange sites have rocketed in popularity in the past few years. Once people understand that using cryptocurrency is just as safe as regular transactions, they embrace it fully.
An exchange is where traders buy and sell Bitcoins using different monetary tenders, both fiat and virtual. Any Bitcoin exchange is accompanied by a cryptographic maths equation that must be solved used a supercomputer. A broker is a company that will solve the equation and complete the trade for you. Bitcoins can be used to send payments any time across the world, but the UK banking system has been reluctant to serve these companies. As a result, many brokers are within the EU. The Bitcoin Trading Brokers all have different transaction fees for completing a transaction and you can even buy Bitcoins and sell them for other cryptocurrencies with them.
Cryptocurrency evaluations are notoriously volatile. Bitcoin was created in and no-one is entirely sure who created it. After this, its value continued to grow exponentially. The unpredictability of the value is why banks are so reluctant to catch on to this seemingly burgeoning new form of currency. Much like the stock exchange can close up or down depending on what is happening in the markets, Bitcoin can increase or lose value rapidly.
There is substantial money to be made from trading, but there is huge risk as well. A Bitcoin online wallet is essential if you want to keep track of all your Bitcoins. Much like a physical wallet stuffed full of receipts, your Bitcoin online wallet keeps your public and private address in one safe place.
In order to buy Bitcoins and sell them you need two addresses, one public, one private.
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