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Python for Finance: Financial Analysis for Investing

 

Python for Finance: Financial Analysis for Investing

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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

Python for Finance: Investment Fundamentals & Data Analytics


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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