Python for Finance: Financial Analysis for Investing

• 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