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Portfolio Management using Machine Learning: Hierarchical Risk Parity



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SKU: Por6581 Category: Tag:


Portfolio Management using Machine Learning will teach you how to implement the hierarchical risk parity (HRP) strategy on a set of sixteen stocks and evaluate its performance in comparison to that of the inverse volatility weighted portfolios (IVP), equal-weighted portfolios (EWP), and critical line algorithm (CLA) techniques. And ideas like risk management, hierarchical clustering, and dendrograms are included here as well.

About Quantra/QuantInsti

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.

Portfolio Management using Machine Learning with Quantra

Are you looking for a reliable method to distribute your capital among the various assets in your portfolio? You should enroll in this particular class.

  • 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.

Refund is acceptable:

  • Firstly, item is not as explained
  • Secondly, Portfolio Management using Machine Learning do not work the way it should.
  • Thirdly, and most importantly, support extension can not be used.

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