Portfolio Management using Machine Learning: Hierarchical Risk Parity
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- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
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 Covered
learning track 7
This course is a part of the Learning Track: Portfolio Management and Position Sizing using Quantitative Methods
Course Fees
Full Learning Track
These courses are specially curated to help you with end-to-end learning of the subject.
Course Features
- Community
Faculty Support on Community
- Interactive Coding Exercises
Interactive Coding Practice
- Capstone Project
Capstone Project Using Real Market Data
- Trade & Learn Together
Trade and Learn Together
- Get Certified
Get Certified
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
- IntroductionThe ability of unsupervised learning to find similar patterns and group assets can be harnessed in the field of portfolio management with ease. In this section, you will acquaint yourself with the course structure, and the various teaching tools used in the course: videos, quizzes, and strategy codes. The interactive methods used help you to not only understand the concepts, but also how to implement the strategies.
- 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
Why quantra®?
- More in Less Time
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- Expert Faculty
Get taught by practitioners
- Self-paced
Learn at your own pace
- Data & Strategy Models
Get data & strategy models to practice on your own
Faqs
- When will I have access to the course content, including videos and strategies?
You will gain access to the entire course content including videos and strategies, as soon as you complete the payment and successfully enroll in the course.
- Will I get a certificate at the completion of the course?
- Are there any webinars, live or classroom sessions available in the course?
- Is there any support available after I purchase the course?
- What are the system requirements to do this course?
- What is the admission criteria?
- Is there a refund available?
- Is the course downloadable?
- Can the python strategies provided in the course be immediately used for trading?
- I want to develop my own algorithmic trading strategy. Can I use a Quantra course notebook for the same?
- If I plug in the Quantra code to my trading system, am I sure to make money?
- What does "lifetime access" mean?