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