Enough talk. Let's get started with our application. What is a good place to start? From examining the application description mentioned earlier, it looks like we will need the following modules:

Based on these requirements, we will be using the following design:

Each term is discussed as follows:

Among all these classes, the way to manage stock information seems to be the simplest, so let's start there. What we are going to do is to create a Stock class. This class will hold information about the current stock. It will store the current price and possibly some recent price history. We can then use this class when we want to match rules later on.

To get started, create a directory called src. This directory is going to hold all our source code. In the rest of this book, we will refer to this directory as the project root. Inside the src directory, create a subdirectory called stock_alerter. This is the directory in which we are going to implement our stock alert module.

Okay, let's get started with implementing the class.

NO! Wait! Remember the TDD process that was described earlier? The first step is to write a test, before we code the implementation. By writing the test first, we now have the opportunity to think about what we want this class to do.

So what exactly do we want this class to do? Let's start with something simple:

Of course, there are many more things we will want this class to do, but we'll think about them later. Rather than coming up with a very comprehensive list of functionality, we're going to focus on tiny bits of functionality, one at a time. For now, the preceding expectation is good enough.

To convert the preceding expectation into code, create a file called stock.py in the project root, and put the following code in it:

import unittest
class StockTest(unittest.TestCase):
    def test_price_of_a_new_stock_class_should_be_None(self):
        stock = Stock("GOOG")
        self.assertIsNone(stock.price)
if __name__ == "__main__":
    unittest.main()

What does this code do?

Congratulations! You have your first failing test. Normally, a failing test would be a cause for worry, but in this case, a failing test means that we're done with the first step of the process and can move on to the next step.

Now that we've written our test, it is time to run it. To run the test, just execute the file. Assuming that the current directory is the src directory, the following is the command to execute the file:

If the python executable is not on your path, then you will have to give the full path to the executable here. In some Linux distributions, the file may be called python34 or python3.4 instead of python3.

When we run the file, the output looks like the following:

E
=====================================================================
ERROR: test_price_of_a_new_stock_class_should_be_None (__main__.StockTest)
---------------------------------------------------------------------
Traceback (most recent call last):
  File "stock_alerter\stock.py", line 6, in test_price_of_a_new_stock_class_should_be_None
    stock = Stock("GOOG")
NameError: name 'Stock' is not defined
---------------------------------------------------------------------
Ran 1 test in 0.001s

FAILED (errors=1)

As expected, the test fails, because we haven't created the Stock class yet.

Let's look at that output in a little more detail:

There are two reasons why a test might not pass: It might have failed or it might have caused an error. There is a small difference between these two. A failure indicates that we expected some outcome (usually via an assert), but got something else. For example, in our test, we are asserting that stock.price is None. Suppose stock.price has some other value apart from None, then the test will fail.

An error indicates that something unexpected happened, usually an unexpected exception was raised. In our previous example, we got an error because the Stock class has not yet been defined.

In both the cases, the test does not pass, but for different reasons, and these are reported separately as test failures and test errors.

Now that we have a failing test, let's make it pass. Add the following code to the stock.py file, after the import unittest line:

class Stock:
    def __init__(self, symbol):
        self.symbol = symbol
        self.price = None

What we have done here is to implement just enough code to pass the test. We've created the Stock class so the test shouldn't complain about it being missing, and we've initialized the price attribute to None.

What about the rest of the implementation for this class? This can wait. Our main focus right now is to pass the current expectation for this class. As we write more tests, we will end up implementing more of the class as well.

Run the file again, and this time the output should be like the following:

.
---------------------------------------------------------------------
Ran 1 test in 0.000s

OK

We've got a dot in the first line, which signifies that the test is passing. The OK message at the end tells us that all tests have passed.

The final step is to refactor the code. With so little code, there is really nothing much to clean up. So, we can skip the refactoring step and start with the next test.