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
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
How to Enroll Python for Finance: Financial Analysis for Investing course?
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