Overview of Financial Analysis with Python

Since the publication of my previous book Mastering Python for Finance, there have been significant upgrades to Python itself and many third-party libraries. Many tools and features have been deprecated in favor of new ones. This chapter walks you through how to get the latest tools available and how to prepare the environment that will be used throughout the rest of the book.

We will be using Quandl for the majority of datasets covered in this book. Quandl is a platform that serves financial, economic, and alternative data. These sources of data are contributed by various data publishers, including the United Nations, World Bank, central banks, trading exchanges, investment research firms, and even members of the Quandl community. With the Python Quandl module, you can easily download datasets and perform financial analytics to derive...

Getting Python

At the time of writing, the latest Python version is 3.7.0. You may download the latest version for Windows, macOS X, Linux/UNIX, and other operating systems from the official Python website at https://www.python.org/downloads/. Follow the installation instructions to install the base Python interpreter on your operating system.

The installation process should add Python to your environment path. To check the version of your installed Python, type the following command into the terminal if you are using macOS X/Linux, or the command prompt on Windows:

$ python --version
Python 3.7.0

For easy installation of Python libraries, consider using an all-in-one Python distribution such as Anaconda (https://www.anaconda.com/download/), Miniconda (https://conda.io/miniconda.html), or Enthought Canopy (https://www.enthought.com/product/enthought-python-distribution/). Advanced...

Introduction to Quandl

Quandl is a platform that serves financial, economic, and alternative data. These sources of data are contributed by various data publishers, including the United Nations, World Bank, central banks, trading exchanges, and investment research firms.

With the Python Quandl module, you can easily get financial datasets into Python. Quandl offers free datasets, some of which are samples. Paid access is required for access to premium data products.

Setting up Quandl for your environment

The Quandl package requires the latest versions of NumPy and pandas. Additionally, we will require matplotlib for the rest of this chapter.

To install these packages, type the following code in your terminal window:

...

Plotting a time series chart

A simple and effective technique for analyzing time series data is by visualizing it on a graph, from which we can infer certain assumptions. This section will guide you through the process of downloading a dataset of stock prices from Quandl and plotting it on a price and volume graph. We will also cover plotting candlestick charts, which will give us more information than line charts.

Retrieving datasets from Quandl

Fetching data from Quandl into Python is fairly straightforward. Suppose we are interested in ABN Amro Group from the Euronext Stock Exchange. The ticker symbol in Quandl is EURONEXT/ABN. In a Jupyter notebook cell, run the following command:

In [ ]:
    import quandl

    # Replace...

Performing financial analytics on time series data

In this section, we will visualize some statistical properties of time series data used in financial analytics.

Plotting returns

One of the classic measures of security performance is its returns over a prior period. A simple method for calculating returns in pandas is pct_change, where the percentage change from the previous row is computed for every row in the DataFrame.

In the following example, we use ABN stock data to plot a simple graph of daily percentage returns:

In [ ]:
     %matplotlib inline
     import quandl

     quandl.ApiConfig.api_key = QUANDL_API_KEY
     df = quandl.get('EURONEXT/ABN.4')
     daily_changes = df.pct_change(periods=1)
     daily_changes...

Summary

In this chapter, we set up our working environment with Python 3.7 and used the virtual environment package to manage separate package installations. The pip command is a handy Python package manager that easily downloads and installs Python modules, including Jupyter, Quandl, and pandas. Jupyter is a browser-based interactive computational environment for executing Python code and visualizing data. With a Quandl account, we can easily obtain high-quality time series datasets. These sources of data are contributed by various data publishers. Datasets directly download into a pandas DataFrame object that allows us to perform financial analytics, such as plotting daily percentage returns, histograms, Q-Q plots, correlations, simple moving averages, and exponential moving averages.