The Python For Technical Analysis Crash Course

An Engaging and Focused Course for Learning Financial Analysis in Python

What you’ll learn
Set up a Python coding environment
Download stock, forex, futures, or crypto data directly from Python
Clean and organize your table of financial data using Python
Add new columns of data to your table
Analyze financial data with built-in Python functions and methods
Code various financial indicators, including Bollinger Bands and Fibonacci Retracement Levels
Explore the basics of financial data visualization using the Matplotlib and Plotly libraries

Requirements
The Anaconda distribution of Python. This can be downloaded for free, and I show you how to do so in one of the course’s earliest lessons
A basic understanding of data tables
Previous experience in Excel or SQL is helpful but not necessary

Description
Learn all the techniques necessary to begin undertaking financial analysis in PythonThis course teaches you everything need to know to begin using Python for financial and technical analysis. The course is designed to limit unnecessary theoretical digressions and focus on what’s useful for beginners who are eager to start applying their knowledge as soon as possible.After several years of using Excel for stock analysis, I began studying Python as a potential alternative. I found that Python offered a much faster and more flexible approach to stock analysis, but I struggled to find learning material that didn’t quickly get bogged down in excessive detail. So I essentially ended piecing together my own course by drawing on various articles, videos, and passages from books that I’d bought. The Python for Technical Analysis Crash Course is a distillation of all the months I spent gathering the information that ended up being helpful to me. It’s the kind of course I wish I’d been able to find when I started studying Python myself.The course is organized as follows:-In the first part of the course, we’ll set up your Python coding environment and go over some Python basics.-After that, we’ll see how to download and display stock data in Python, as well as how to remove and reformat data.-Once our table of stock data has been cleaned, we’ll start using that data to add new columns to the table (for example, we’ll see how to create a column containing a stock’s 20-Day Moving Average).-With our table complete, we’ll look at various methods for analyzing and visualizing the table’s data.-Finally, in the course’s Extra Credit section, I’ll show you some techniques that didn’t quite fit into the main body of the course but that still might be useful for you.

Overview
Section 1: Introduction

Lecture 1 Introduction to Course

Lecture 2 Accessing Python

Lecture 3 Python Primer

Section 2: Getting and Cleaning the Stock Data

Lecture 4 Importing Libraries

Lecture 5 Getting Stock Data, Pt. 1

Lecture 6 Getting Stock Data, Pt. 2

Lecture 7 A Closer Look at the Table

Lecture 8 Deleting and Referencing Columns

Lecture 9 Resetting the Index and Reformatting the Date Column

Lecture 10 Cleaning up the Index and Expanding the Table

Lecture 11 Rounding

Lecture 12 Recap 1

Section 3: Adding New Columns to the Table

Lecture 13 Day-to-Day Price Percentage Change

Lecture 14 20-Day Moving Average

Lecture 15 The np.where() Function

Lecture 16 Bollinger Bands, Pt. 1

Lecture 17 Bollinger Bands, Pt. 2

Lecture 18 Recap 2

Section 4: Analyzing and Visualizing the Data

Lecture 19 The describe() Method, Pt. 1

Lecture 20 The describe() Method, Pt. 2

Lecture 21 The corr() Method

Lecture 22 The groupby() Method

Lecture 23 Matplotlib, Pt. 1

Lecture 24 Matplotlib, Pt. 2

Lecture 25 Plotly

Lecture 26 Exporting the Data to Excel

Section 5: Extra Credit

Lecture 27 A Few Words on the Extra Credit Lessons

Lecture 28 Getting Data for Forex, Futures, and Crypto

Lecture 29 Getting Fundamental Data

Lecture 30 yf.download()

Lecture 31 Data Types

Lecture 32 pd.DataFrame()

Lecture 33 The assign() Method

Lecture 34 Troubleshooting

Lecture 35 Fibonacci Retracement Levels

Section 6: Farewell

Lecture 36 Farewell

Students interested in an efficient approach to learning the fundamentals of financial analysis in Python,Excel users looking for a faster and more flexible financial analysis environment,Anyone interested in the learned the basics of data analysis in Python,NOTE: This is a coding and data analytics course, not an investment course. If you’re looking for direct trading advice, this is probably not the course for you.