Advanced Portfolio Analysis with Python

Last Update: September, 2019

1. Course Objective

Learn Advanced Portfolio Analysis main topics using Python programming language® in this practical course for expert knowledge level. 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 tradeoff (bonds, stocks).
  • Calculate portfolio performance metric (annualized Sharpe ratio).
  • Optimize global portfolios assets allocation weights (Markowitz portfolio theory).
  • Approximate global portfolios returns (optimized assets allocations) and compare them with benchmark global portfolio returns (equal weighted assets allocation).
  • Reduce global portfolio assets allocation optimization trials back-testing over-fitting or data snooping (multiple hypothesis testing adjustment, individual time series bootstrap hypothesis testing multiple comparison adjustment).
  • Estimate global portfolio optimization trials population mean statistical inference tests multiple probability values and adjust them for multiple hypothesis testing (family-wise error rate/Bonferroni procedure, false discovery rate/Benjamini-Hochberg procedure).
  • Simulate individual global portfolio optimization trial bootstrap population mean probability distribution (random fixed block re-sampling with replacement), approximate bootstrap population mean statistical inference tests percentile probability value and correct it for multiple comparison (family-wise error rate adjustment).

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 advanced quantitative finance.
  • Experienced investor who desires to research advanced optimized asset allocation strategies.
  • This course is NOT about “get rich quick” trading systems or magic formulas.