The Python For Technical Analysis Crash Course
The Python For Technical Analysis Crash Course
![The Python For Technical Analysis Crash Course]()
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.
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Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 911.48 MB | Duration: 2h 35m
An Engaging and Focused Course for Learning Financial Analysis in Python
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 911.48 MB | Duration: 2h 35m
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.
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