Python For Finance Financial Analysis For Investing

Use Python to Find Good Investments. Learn Pandas, NumPy, Matplotlib for Financial Analysis & Automate Value Investing.

Last updated 2022-01-10 | 4.5

- How to automate financial analysis with Python using Pandas and Numpy
- Learn to find attractive companies to invest in using fundamental analysis with Pandas
- Identify when to buy and sell stocks based on technical analysis using Pandas and Numpy

What you'll learn

How to automate financial analysis with Python using Pandas and Numpy

Learn to find attractive companies to invest in using fundamental analysis with Pandas

Identify when to buy and sell stocks based on technical analysis using Pandas and Numpy

Export your financial analysis to Excel in formatted multi sheets

How to calculate a fair price (intrinsic value) of a stock with Python using Pandas

Introduction to Pandas

Numpy and Visualization of financial data

Use Monte Carlo simulation to optimize your portfolio allocation

Understand risk when buying stock shares

Learn how to evaluate an investment to lower the risk

Learn about Intrinsic value

Market value

Book value

and Shares

Master the concepts Dividend

Earnings per share (EPS)

Price/Earnings (PE) ratio

and Volume Yield

Cover a Python Crash Course with all the basic Python

How to use DataFrames for financial analysis

Use Matplotlib to visualize DataFrames with time series data

How to join

merge and concatenate DataFrame

Export data from Python to Excel in nice colorful sheets with charts

Calculate concrete intrinsic values (a fair price to buy a stock for) for 50 companies

Read and interpret Dept/Equity (DE) ratio

Current ratio

Return of Investment (ROI) and more

Use revenue

Earnings-per-share (EPS)

and Book value to determine if a company is predictable and worth investing in.

How to use Price/Earnings (PE) ratio to make calculations

How to use Pandas Datareader to read data directly form API of financial pages

To read financial statements from API's

Web scraping of pages and how to convert data to correct format and types

How to calculate rate of return (RoR)

percentage change

and to normalize stock price data

Understand and learn to calculate the CAGR (Compound Annual Growth Rate)

A deep dive case study of DOW theory

How to calculate technical indicators

Moving Average (MA)

MACD

Stochastic Oscillator

and more

Make financial calculations with NumPy

Calculate with vectors and matrices using NumPy

How to calculate the Volatility of a stock

Correlation and Linear Regression between securities between investments

How the Beta is used and how to calculate it

Deep dive into using CAPM

Optimize your portfolio of investments

Learn what Sharpe Ratio is and how to use it

How to use Monte Carlo Simulation to simulate random variables

Use Sharpe Ratio and Monte Carlo Simulation to calculate the Efficient Frontier

Advice on next books to read about investing

* Requirements

* Some knowledge of programming is recommended
* All software and data used in course is free
* Ability to install Anaconda (guide in course)

Description

How to automate financial analysis with Python using Pandas and Numpy
Learn to find attractive companies to invest in using fundamental analysis with Pandas
Identify when to buy and sell stocks based on technical analysis using Pandas and Numpy
Export your financial analysis to Excel in formatted multi sheets
How to calculate a fair price (intrinsic value) of a stock with Python using Pandas
Introduction to Pandas, Numpy and Visualization of financial data
Use Monte Carlo simulation to optimize your portfolio allocation
Understand risk when buying stock shares
Learn how to evaluate an investment to lower the risk
Learn about Intrinsic value, Market value, Book value, and Shares
Master the concepts Dividend, Earnings per share (EPS), Price/Earnings (PE) ratio, and Volume Yield
Cover a Python Crash Course with all the basic Python
How to use DataFrames for financial analysis
Use Matplotlib to visualize DataFrames with time series data
How to join, merge and concatenate DataFrame
Export data from Python to Excel in nice colorful sheets with charts
Calculate concrete intrinsic values (a fair price to buy a stock for) for 50 companies
Read and interpret Dept/Equity (DE) ratio, Current ratio, Return of Investment (ROI) and more
Use revenue, Earnings-per-share (EPS), and Book value to determine if a company is predictable and worth investing in.
How to use Price/Earnings (PE) ratio to make calculations
How to use Pandas Datareader to read data directly form API of financial pages
To read financial statements from API's
Web scraping of pages and how to convert data to correct format and types
How to calculate rate of return (RoR), percentage change, and to normalize stock price data
Understand and learn to calculate the CAGR (Compound Annual Growth Rate)
A deep dive case study of DOW theory
How to calculate technical indicators, like, Moving Average (MA), MACD, Stochastic Oscillator, and more
Make financial calculations with NumPy
Calculate with vectors and matrices using NumPy
How to calculate the Volatility of a stock
Correlation and Linear Regression between securities between investments
How the Beta is used and how to calculate it
Deep dive into using CAPM
Optimize your portfolio of investments
Learn what Sharpe Ratio is and how to use it
How to use Monte Carlo Simulation to simulate random variables
Use Sharpe Ratio and Monte Carlo Simulation to calculate the Efficient Frontier
Advice on next books to read about investing

Course content

16 sections • 185 lectures

Introduction 2 lectures

One Question Preview 02:18

What is a good investment and what is a bad investment?

Can you see that from the stock price?
You need technical indicators?
Will that help you from a market crash?

Learn about intrinsic value and how to use it.

Get the most out of this course Preview 04:11

How to get the most out of the course?

We will cover the following.

Who is the course for?
What to expect?
What resources are available?

Setup 4 lectures

Introduction Preview 00:30

Introduction to the section.

Download Anaconda (includes Python and Jupyter notebook) Preview 01:32

In this lecture we will download Anaconda.

Anaconda includes

Python
Jupyter notebook

In the resources is a link where to download the FREE version of Anaconda and how to install it on your platform.

After this lecture you should have all needed installations on your system running.

Resources and setup environment in Jupyter notebook Preview 04:24

Download the the resources, it includes.

All the source code in notebooks.
Exercises for the sections.

How to setup the environment

Two ways to ensure correct libraries are installed.
Make sure to follow this to get the same experience in the course.

Prompt rating Preview 00:34

Jupyter Notebook guide 8 lectures

Introduction Preview 00:55

Introduction to the section.

Jupyter Notebook Cheat Sheet. Preview 00:03

If you are new to Jupyter Notebook, you can download the cheat sheets.

Jupyter Notebook: The Dashboard Preview 03:23

Learn about the Dashboard in Jupyter Notebook.

You will learn how to

Create folders
Upload Notebooks
Create Notebooks
See running Notebooks.

Feel free to skip this lecture if you are familiar with Jupyter Notebook.

Jupyter Notebook: Run and restart a Notebook Preview 03:04

Learn how to Run and restart a Notebook.

You will learn how to:

How to execute a cell with code
Stop a running cell in Jupyter Notebook.
Restart and clear the output in a Notebook.

Feel free to skip this lecture if you are familiar with Jupyter Notebook.

Jupyter Notebook: Copy and reorganize code Preview 02:01

In this lecture you will learn how to:

Copy code cells
Reorganize the code cells
Delete code cells.

Feel free to skip this lecture if you are familiar with Jupyter Notebook.

Jupyter Notebook: Comment and markdown Preview 02:21

In this lecture you will learn:

How to insert comments in your code cells.
How to insert markdown cells with description

Feel free to skip this lecture if you are familiar with Jupyter Notebook.

Jupyter Notebook: Tab + Tab + Shift & Tab Preview 06:14

In this lecture you will learn:

How to autocomplete code in Jupyter Notebook.
How to list all methods available in an object.
How to get the documentation of a method.

Feel free to skip this lecture if you are familiar with Jupyter Notebook.

What did we learn? Preview 00:54

After this section you should be ready to use Jupyter Notebook.

Python Crash Course 16 lectures

Introduction Preview 01:07

In this section we will have a Python crash course.

It will cover all the basics needed to be at the level of Python needed.

This section is ideal for you:

If you are new to Python, but have experience from another programming language.
If you need a refresher on Python.

If you are new to programming - this is get you an idea about programming. But don't expect to master it after this.

Feel free to skip this section if you are familiar with Python basics.

Variables and types Preview 11:43

In this lecture we will learn about.

Variables in Python.
The types of variables.

The print statement Preview 02:53

In this lecture you will learn about:

How to use print in the most convenient way.