Portfolio Management using Machine Learning: Hierarchical Risk Parity – QuantInsti

Course overview

Do you want a robust technique to allocate capital to different assets in your portfolio? This is the right course for you. Learn to apply the hierarchical risk parity (HRP) approach on a group of 16 stocks and compare the performance with inverse volatility weighted portfolios (IVP), equal-weighted portfolios (EWP), and critical line algorithm (CLA) techniques. And concepts such as hierarchical clustering, dendrograms, and risk management.

Live Trading

• Allocate weights to a portfolio based on a hierarchical risk parity approach.
• Create a stock screener.
• Describe inverse volatility weighted portfolios (IVP) and critical line algorithm (CLA).
• Backtest the performance of different portfolio management techniques.
• Explain the limitations of IVPs, CLA and equal-weighted portfolios.
• Compute and plot the portfolio performance statistics such as returns, volatility, and drawdowns.
• Implement a hierarchical clustering algorithm and explain the mathematics behind the working of hierarchical clustering.
• Describe the dendrograms and interpret the linkage matrix.

Skills Required To Learn Portfolio Management using Machine Learning: Hierarchical Risk Parity

Python

• Numpy
• Pandas
• Sklearn
• Matplotlib
• Seaborn

Portfolio Management

• Inverse Volatility Portfolios
• Critical Line Algorithm
• Return/Risk Optimization
• Hierarchical Risk Parity

Maths

• Linkage Matrix
• Dendrograms
• Clustering
• Euclidean distance
• Scaling

Learning Track 7

This course is a part of the Learning Track: Portfolio Management and Position Sizing using Quantitative Methods. Enroll to the entire track to enable 10% discount.

Prerequisites

A general understanding of trading in the financial markets such as how to place orders to buy and sell is helpful. Basic knowledge of the pandas dataframe and matplotlib would be beneficial to easily work with the codes covered in this course. To learn how to use Python, check out our free course “Python for Trading: Basic”.

Syllabus

Introduction
Portfolio Basics and Stock Screening
Inverse Volatility Portfolios
Implementing Inverse Volatility Portfolios
Correlation
Markowitz Critical Line Algorithm
Implementing CLA
Hierarchical Clustering
Mathematics Behind Hierarchical Clustering
Clustering with Dendrograms
Scaling Your Data
Hierarchical Risk Parity
Live Trading on Blueshift
Live Trading Template
Capstone Project
Run Codes Locally on Your Machine
Course Summary

About Author

Quantlnsti is the world’s leading algorithmic and quantitative trading research & training institute with registered users in 190+ countries and territories. An initiative by founders of iRage, one of India’s top HFT firms, Quantlnsti has been helping its users grow in this domain throupgh its learning & financial applications based ecosystem
for 10+ years.

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