Trading Analysis Using Python

Hey everyone, and welcome to this tutorial, we’re going to explain how to load data using pandas in python and how to clean the data and make it ready for our financial analysis. Although we will focus on financial data for algorithmic trading purposes, the methods described in this video are also applicable for any type of data.
To benefit most of this tutorial you need to know python basics, like understand what is a for loop or an if statement, how to create and use functions and lambda functions, if you haven’t had the chance to start with this, I have made a quick recap video describing python basics in one hour, if you are interested in this I will leave a link in the description.
For this tutorial we are using the Jupyter notebook interface because it allows us to print and display dataframes and analysis figures directly when we are writing our program, and it’s also widely used interface for data science.
Most of the data files for stocks and forex trading come in the comma separated values (CSV) format, so for example this is the bitcoin USD data file between 2017 and 2021,
the first line contains the columns names separated by commas as well, then we have the values, so I opened this file in notepad and we can see the first column is the date and time, then we have the open, high, low and closing prices and the last column is the volume.

I hope you will enjoy this one, good luck!