Python for Finance: Financial Analysis for Investing

Python for Finance: Financial Analysis for Investing

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

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.

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

Can I download Python for Finance: Financial Analysis for Investing course?

You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.
Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The Python for Finance: Financial Analysis for Investing course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.
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