Investment Portfolio Analysis with Python

Last Update: March, 2018

1. Course Objective

Learn Investment Portfolio Analysis main topics using Python programming language® in this practical course for all knowledge levels. Feel free to take a look at Course Curriculum.

2. Skills Learned

At the end of this course you will know how to:

  • Compare main asset classes returns and risks tradeoffs (cash, bonds, stocks, commodities, real estate, currencies).
  • Estimate portfolio expected returns, historical volatility and market participants implied volatility.
  • Approximate portfolio expected excess returns (capital asset pricing model CAPM, Fama-French-Carhart factors model, arbitrage pricing theory APT model).
  • Calculate portfolio performance metrics (Sharpe ratio, Treynor ratio, Sortino ratio, Kelly ratio).
  • Optimize global portfolios assets allocation weights (Markowitz portfolio theory).
  • Approximate global portfolios returns (optimized assets allocations) and compare them with benchmark global portfolios returns (equal weighted assets allocation, well-known investment managers assets allocation).
  • Evaluate global portfolios performance (global risk factors model).

3. Typical Student

This course is ideal for you as:

  • Undergraduate or postgraduate who wants to learn about the subject.
  • Finance professional or academic researcher who wishes to deepen your knowledge in quantitative finance.
  • Experienced investor who desires to research optimized asset allocation strategies.
  • This course is NOT about “get rich quick” trading systems or magic formulas.