Portfolio Management using Machine Learning: Hierarchical Risk Parity

Description

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Portfolio Management using Machine Learning: Hierarchical Risk Parity

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.

• 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 COVERED

Python

• Numpy
• Pandas
• Sklearn
• Matplotlib
• Seaborn

Portfolio Managment

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

Maths

• Linkage Matrix
• Dendrogarams
• Clustering
• Euclidean distance
• Scaling

LEARNING TRACK

Machine Learning Strategy Development and Live Trading

INTERMEDIATE

• Data & Feature Engineering for Trading
• Portfolio Management using Machine Learning Hierarchial Risk Parity

ADVANCED

• Neural Networks in Trading
• Natural Language Processing in Trading
• Deep Reinforcement Learning in Trading

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

1. Introduction
2. Portfolio Basics and Stock Screening
3. Inverse Volatility Portfolios
4. Implementing Inverse Volatility Portfolios
5. Correlation
6. Markowitz Critical Line Algorithm
7. Implementing CLA
8. Hierarchical Clustering
9. Mathematics Behind Hieratchical Clustering
10. Clustering with Dendrograms
11. Scaling Your Data
12. Hierarchical Risk Parity
13. Live Trading on Blueshift
14. Live Trading Template
15. Capstone Project
16. Run Codes Locally on your Machine
17. Course Summary

ABOUT AUTHOR

QuantInsti

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

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