Python for Finance: Financial Analysis for Investing
Python for Finance: Financial Analysis for Investing
MP4 | h264, 1280x720 | Lang: English | Audio: aac, 48000 Hz | 21h 14m | 18.3 GB
Use Python to Find Good Investments and when to Buy and Sell. Learn Pandas, NumPy, Matplotlib for Financial Analysis
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, 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
Requirements
Some knowledge of programming is recommended
All software and data used in course is free
Ability to install Anaconda (guide in course)
Description
Did you know that the No.1 killer of investment return is emotion?
Investors should not let fear or greed control their decisions.
How do you get your emotions out of your investment decisions?
A simple way is to perform objective financial analysis and automate it with Python!
Why?
Performing financial analysis makes your decisions objective - you are not buying companies that your analysis did not recommend.
Automating them with Python ensures that you do not compromise because you get tired of analyzing.
Finally, it ensures that you get all the calculation done correctly in the same way.
Does this sound interesting?
Do you want to learn how to use Python for financial analysis?
Find stocks to invest in and evaluate whether they are underpriced or overvalued?
Buy and sell at the right time?
This course will teach you how to use Python to automate the process of financial analysis on multiple companies simultaneously and evaluate how much they are worth (the intrinsic value).
You will get started in the financial investment world and use data science on financial data.
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Why should you enroll in this course?
Making investment decisions is like playing poker without looking at your cards if you don't know what you are doing.
You don't want to stocks in a company you did not analyze first.
This is the only course that takes you through the full process from finding attractive investments and how to time your first buy.
Similarly, you do not buy a house without looking at the condition report.
How to see if a company will grow in value, to avoid falling stock prices the day after you buy it.
This course does not assume you have a portfolio and want to optimize it - it will help you find the stocks to invest in first.
It gives you a solid foundation to invest with confidence and stop gambling.
Learn that making financial analysis on companies is not that difficult and can be automated with Python.
The market crashed in 2020 without any warning - some companies came in quickly, others did not.
Be sure to invest in companies with a solid economy and a growth market.
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How is this course structured?
This course will guide you through how to install the necessary software (Anaconda) - it's all free.
It will cover how to use Jupyter Notebook (from Anaconda package) if you are not completely familiar with it.
A crash course in Python if you need an update or come from a different programming background.
Then it starts by introducing financial concepts along with Python programming to fully understand them.
This includes understanding of stocks, volume, dividends, returns, market price, price to earnings (EPS), price to earnings (PE ratio), book value and more.
A deep introduction to Pandas, the most important library used for financial analysis with Python.
It will cover DataFrames, Series, read and write data, export to Excel, merge, join and link data and much more.
The concept of intrinsic value (a fair stock price to pay) - this is the most important concept to understand when investing.
How the risk of investment is understood and how to assess it for a company.
This is how the management of a company is assessed in an objective way.
This will include learning about debt-to-equity ratio (DE ratio), current assets, return of investment (ROI), revenue evaluation, earnings per share (EPS) evaluation, book value evaluation, free-cash-flow (FCF) evaluation and more.
This teaches you how to calculate a fair price (intrinsic value) to be paid for a company.
Matplotlib is introduced and how it can be used to visualize data for efficient data interpretation.
We visualize data and export it to color-formatted Excel sheets - all from Python.
You will learn to use free APIs to read up-to-date data on stock quotes and financial statements.
Then we dive deeper and work with historical time series data on stock prices.
This teaches you rate of return, percentage change, and normalization.
How to calculate and use the Compound Annual Growth Rate (CAGR).
There will be a case study on DOW theory.
Next, we will examine and calculate technical indicators such as moving averages (MA), MACD, stochastic oscillator and RSI, and how to use them to buy and sell.
We introduce NumPy to perform further analyzes.
This will help us calculate and understand the volatility of a stock.
Also, correlation between stocks, linear regression, beta, CAPM, and more.
How to work with a full portfolio.
This includes concepts like Sharpe ratio, Monte Carlo Simulation, Efficient Frontier and more.
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This course has
21 hours of video in 180+ lectures.
Exercises are prepared in Jupyter Notebooks.
Links to useful resources along the way.
Explains all concepts in an easy way with real examples.
Udemy has a refund guarantee with a 30 day money back guarantee that ensures if you are not satisfied, you will get your money back. Also, feel free to contact me directly if you have any questions.
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About the instructor
Rune is a Ph.D. in computer science with a background in Python programming. He has taken an MBA from Henley Business School in the UK to study business administration and economics. Rune has been teaching programming and computer science since college. He has other best-selling courses at Udemy.
Who this course is for:
Someone that wants to learn about financial analysis with Python
Anyone that wants to start data science on financial data
Programmers that want to learn about finance and investing
