Utilizing the Quandl API to analyze data
This exercise will require you to pull some data from the Qunadl API. Qaundl is currently the most widely used aggregator of financial market data.
As a first step, you will need to register a free account on the http://www.quandl.com website.
After you register, you will be provided with a unique API key, that you should store:
API_KEY = ''
Qaundl has a large number of data sources, but, unfortunately, most of them require a Premium subscription. Still, there are also a good number of free datasets.
For this mini project, we will focus on equities data from the Frankfurt Stock Exhange (FSE), which is available for free. We'll try and analyze the stock prices of a company called Carl Zeiss Meditec, which manufactures tools for eye examinations, as well as medical lasers for laser eye surgery: https://www.zeiss.com/meditec/int/home.html. The company is listed under the stock ticker AFX_X.
You can find the detailed Quandl API instructions here: https://docs.quandl.com/docs/time-series
While there is a dedicated Python package for connecting to the Quandl API, we would prefer that you use the requests package, which can be easily downloaded using pip or conda. You can find the documentation for the package here: http://docs.python-requests.org/en/master/
Finally, apart from the requests package, you are encouraged to not use any third party Python packages, such as pandas, and instead focus on what's available in the Python Standard Library (the collections module might come in handy: https://pymotw.com/3/collections/). Also, since you won't have access to DataFrames, you are encouraged to us Python's native data structures - preferably dictionaries, though some questions can also be answered using lists. You can read more on these data structures here: https://docs.python.org/3/tutorial/datastructures.html
Keep in mind that the JSON responses you will be getting from the API map almost one-to-one to Python's dictionaries. Unfortunately, they can be very nested, so make sure you read up on indexing dictionaries in the documentation provided above.
Please note that the first 340 cells/lines were NOT related directly to the exercise. I was just having fun exploring the data.
Question (7) was not meant to be asnwered that way. I know I should have implemented my own function. I just couldn't resist the temptation of importing the statistics module. Sorry.