In addition to making us feel better, using these results will allow us to plan our trips back in time to maximize profits. ot_stock(start_date = '', end_date = '', stats =, plot_type='pct') Maximum Daily Change = 2.08 on. For example, if we want to compare the Daily Change in price with the Adjusted Volume (number of shares) traded, we can specify those in the function call. By default, this method plots the Adjusted Closing price for the entire date range, but we can choose the range, the stats to plot, and the type of plot. This is a pretty basic plot that we could have found from a Google Search, but there is something satisfying about doing it ourselves in a few lines of Python! The plot_stockfunction has a number of optional arguments. The default value plotted is the Adjusted Closing price, which accounts for splits in the stock (when one stock is split into multiple stocks, say 2, with each new stock worth 1/2 of the original price). We can access the attribute and assign it to another variable for inspection: # Stock is an attribute of the microsoft object stock_history = microsoft.stock stock_history.head() One attribute of a Stocker object is stock data for a specific company that is attribute is associated with the object when we construct it. Methods are functions contained in the class which can act on that data. Without going into too much detail, attributes are values or data associated either with the class as a whole or with specific instances (objects) of the class. Although Microsoft might be seen as the opposite of open-source, they have recently made some changes that make me optimist they are embracing the open-source community (including Python).Ī class in Python is comprised of two main parts: attributes and methods. For this example, we will stick to Microsoft data.
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Stocker is built on the quandl WIKI database which gives us access to over 3000 US stocks with years of daily price data ( full list). Now, we have a microsoftobject with all the properties of the Stocker class. microsoft = Stocker('MSFT') MSFT Stocker Initialized. In order to make a Stocker object we need to pass in the name of a valid stock ticker ( bold indicates output).
In Python, an instance of a class is called an object, and the act of creating an object is sometimes called instantiation or construction.
We now have the Stocker class in our Python session, and we can use it to create an instance of the class. We can do this from an interactive Python session or a Jupyter Notebook started in the directory with the script. Now, let’s take a look at the analysis capabilities of Stocker!Īfter installing the required libraries, the first thing we do is import the Stocker class into our Python session. Stocker was designed to be easy to use (even for those new to Python), and I encourage anyone reading to try it out. For anyone wanting to use Stocker, the complete code can be found on GitHub along with documentation for usage. It was only when I was deep in a project faced with a problem I had not solved before that the concept finally clicked, showing once again that experience beats theoretical explanations! In addition to an exploration of Stocker, we will touch on some important topics including the basics of a Python class and additive models. I had tried several times to conquer classes, the foundation of object-oriented programming in Python, but as with most programming topics, they never quite made sense to me when I read the books.
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This article will show how to use Stocker, a Python class-based tool for stock analysis and prediction (the name was originally arbitrary, but I decided after the fact it nicely stands for “stock explorer”).
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