Python for Finance: Investment Fundamentals & Data Analytics

In this lesson, we will explain what you must know about programming if you are just getting started.

Python is a programming language characterized as:

• open-source
• general-purpose
• high-level

You must install Python and Jupyter on your computer. If you have them, you can still complete this lecture, because we will say a few interesting things about Jupyter.

There are various ways to install Python on your computer. But especially for new users, it is highly recommended to choose Anaconda. It will install, not only Python, but also the Jupyter Notebook App and many scientific computing and data science packages.

In this lesson, we’ll do a quick tour of the Jupyter dashboard. You’ll see how to:

• manipulate files and folders in the Jupyter dashboard
• upload and open Python files in Jupyter
• create new Python files in Jupyter

Now that we know more about the dashboard, we are ready to examine the shell and see how to code in Jupyter.  

Two distinct numeric types in Python are:

• integers
• floating points (floats)

In this lesson, we’ll learn about another type of value that can be useful when working in Python – strings. Strings are text values composed of a sequence of characters.

We’ll continue to build our Python syntax knowledge. The next topic on our agenda is arithmetic operators:

• addition (+)
• subtraction (-)
• division (/)
• multiplication (*)
• remainder (%)
• exponentiation (**)

Here, we will explore another useful operator - the double equality sign.

In this video, we will show you how to reassign variables in Python.

Learn how to use the hash sign for writing comments in Python.

In this video, we will show you a neat trick that will be extremely valuable when you become an advanced Python programmer and work with large amounts of code – using the forward slash to finish your code on a new line.

Let’s look at another important concept that will help us a great deal when working in Python - indexing. This is a technique we’ll use frequently, later in the course, especially when we focus on Python’s application in the world of finance.

The next concept for programming in Python that we will see is fundamental – it is called indentation. The way you apply it in practice is important, as this will be the only mechanism to communicate your ideas to the machine properly.

In this section, we will learn more about the operators that will help us in our work in Python. We will start with comparison operators.

Briefly, the logical operators in Python are the words “not”, “and”, and “or”. They compare a certain number of statements and return Boolean values – “True” or “False” – hence their second name, Boolean operators.

Values act as the most basic (or primitive) data elements to form not only variables, but expressions. In this video, you will learn about a prominent example of conditional statements in Python – the IF statement.

Here, we will focus on adding an ELSE statement to a conditional in Python.

You probably noticed we talked about Boolean values a few times. We would like to provide a short video that explains their application.

In this section, we’ll step it up a notch. Starting from this lesson, we’ll deal with Python’s functions - an invaluable tool for programmers.

Our next task will be to create a function with a parameter.

In this lesson, we will explore another way to organize the definition of a function.

This video provides an example of how to work with functions within functions.

Combining two of Python’s main tools:

• IF statements
• functions

We’ll learn how to work with more than one parameter in a function.

When you install Python on your computer, you are also installing some of its built-in functions, such as:

• type()
• int()
• float()
• str()
• max()
• min()
• len(), and more

In this section, we’ll cover these types of Python objects:

• lists
• tuples
• dictionaries

We will introduce you to the programming term “method” and then see how to invoke a method in Python.

In this lesson, we’ll introduce you to another important Python concept – slicing.

Let’s discover the tuple – another type of data sequence. Different from a list, the tuple is immutable.

Now that you know what lists and tuples are, you will more quickly understand what dictionaries are about. Dictionaries represent another way of storing data.

In this section, we will be dealing with iteration – a fundamental building block of all programs. It allows us to execute a certain code repeatedly.

Here, we will show you how one could use a for- loop in Python. 

The same output we obtained in the previous lesson could be achieved after using a while loop, instead of a for loop. That’s what we will do in this lecture.

In this lesson, we will show you how to create a Python list with the range() function.

Let’s see how one could apply the range() function in a for- loop in Python.

We will count the number of items whose value is less than 20 in a list. To achieve this, we will use these tools:

• Python conditional statements
• Python functions
• Python loops

Time for a more challenging topic – iterating over a dictionary.

It is time to learn about Python modules and packages. This will be necessary in the Python for Finance part of the course, too.

Let’s talk about Python’s standard library. It is a collection of modules available as soon as you install Python.

There are four ways to import a module in Python. You can decide which one you will use when working on your own.

Here, we will present the Python libraries and modules used in this course:

• numpy
• pandas
• pandas-datareader
• matplotlib
• math
• statsmodels, and more

Arrays are fundamental data structures in any programming language. We will introduce you to them in this lesson.

When working with financial data, you won’t always base your calculations on existing data. Sometimes, it will be necessary to run simulations and examine hypothetical scenarios, because historical data will be insufficient. In these situations, you will need a whole set of randomly generated numbers.

This is an important lecture, as we will explain how we have organized the Python for Finance part of the course in terms of working with financial data downloaded from Yahoo Finance.

In this video, we will take the first steps to extract data from online sources.

Here, we will add a few tools and techniques to your skillset, allowing you to analyse the data in your DataFrame.