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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