Description

Buy Portfolio Management using Machine Learning: Hierarchical Risk Parity Course at esyGB. You will have immediate access to the digital downloads in your account or your order email.

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.

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

Portfolio Management

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

Python

Numpy
Pandas
Sklearn
Matplotlib
Seaborn

Maths

Linkage Matrix
Dendrograms
Clustering
Euclidean distance
Scaling

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

Portfolio Management using Machine Learning: Hierarchical Risk Parity, what is it included (Content proof: Watch here!)

Course Introduction
Course Structure Flow Diagram
Quantra Features
Portfolio Basics and Stock Screening
Inverse Volatility Portfolios
Implementing Inverse Volatility Portfolios
Correlation
Markovitz 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
Python Installation
Course Summary

ABOUT AUTHOR

QuantInsti® Quantinsti 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 Rage, one of India’s top HET firms, Quantinsti has been helping its users grow in this domain through its learning & financial applications based ecosystem for 10+ years.

WHY QUANTRA?

screenshot 3 4 1 - eSy[GB] — Gain more in less time

screenshot 4 4 1 - eSy[GB] — Get taught by practitioners

screenshot 5 3 2 - eSy[GB] — Learn at your own pace

screenshot 6 2 1 - eSy[GB] — Get data & strategy models to practice on your own

USER TESTIMONIALS

Sean Tan

Singapore

I signed up to Quantra because when compared to other online teaching platforms, I noticed Quantra provides you with a complete package of Beginners to Advanced level courses. The content is very good and more importantly, very relevant to the real world. But you would have to explore and tweak the strategies to perform the best for you. The learning curve is steep but exciting

Alan

Hong Kong

I really liked the content of the course provided on Quantra, especially in the Machine Learning (ML) related courses. The video units make it very easy to understand complex concepts of ML. They also provide you with downloadable codes at the end of the courses which can be used by you to experiment and learn on your own. This is not very common in the online teaching industry.

Delivery Method

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Portfolio Management using Machine Learning: Hierarchical Risk Parity

Description

Portfolio Management using Machine Learning: Hierarchical Risk Parity