Statistical Analysis for Portfolio Optimisation

The focus is on collaborative problem solving, this time with a little more input from the facilitator. The takeaway is how to extend the basic premise and create frontiers demonstrating the added value of multi-asset class investments, and implementing standard portfolio metrics such as beta, Sharpe/Information ratios, upside/downside capture. Efficient Frontier implementations also require a combination of more advanced charting methods and data representation.


Tech Analysts, S&T Analysts (optionally)

Learning Outcomes

Following the course, participants will be able to:
• Write functions that return portfolio statistics (vol, return, beta) in Python
• Take a principled approach to maximising portfolio efficiency
• Select example portfolios for clients depending on risk appetite
• Plot price and other information graphically
• future-proof the application by pulling in live prices from an API/webscraping
• Identify third party solutions that implement the above functionality

Course Content

• Python recap: implementing alpha, beta, Sharpe and other ratios as functions or classes
• Introduction to Efficient Frontier
• Design: What are the prerequisites of a simple Efficient Frontier generator?
• Walkthrough, with facilitator, of existing EF generator code, on a portfolio of only stocks.
• Implement enhanced version which progressively demonstrates the value added by including more asset classes: Fixed income -> commodities -> alternatives
• Making the application more powerful with e.g. live price updates from web scraping or API.


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