Deep Learning for Finance

Chapter 1. Introducing Data Science and Trading

The best way to begin learning about a complex topic is to break it down into smaller parts and understand those pieces first. Understanding deep learning for finance requires knowledge of data science and financial markets.

This chapter lays the building blocks needed to thoroughly understand data science and its uses, as well as to understand financial markets and how trading and forecasting can benefit from data science.

By the end of the chapter, you should know what data science is, what its applications are, and how you can use it in finance to extract value.

Understanding Data

It is impossible to understand the field of data science without first understanding the types and structures of data. After all, the first word for the name of this immense field is data. So, what is data? And more importantly, what can you do with it?

Data in its simplest and purest form is a collection of raw information that can be of any type (numerical, text, boolean, etc.).

The final aim of collecting data is decision-making. This is done through a complex process that ranges from the act of gathering and processing data to interpreting it and using the results to make a decision.

Let’s take an example of using data to make a decision. Suppose you have a portfolio composed of five different equally weighted dividend-paying stocks, as detailed in Table 1-1.

Table 1-1. Stocks and their dividend yields

Stock	Dividend yield
A	5.20%
B	3.99% ...