Hierarchical-Risk-Parity
Unverified Other strategy on Multi by TheRockXu. BotFinder score 18 out of 100.
This is the implementation for Hierarchical Risk Parity approach to portfolio optimization
Source: github
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Hierarchical-Risk-Parity
Introduction This is the implementation for Hierarchical Risk Parity approach to portfolio optimization as introduced by Machine Learning Asset Allocation. What is in the Jupyter Notebook? In the notebook, we will calculate optimal portfolio ['AAPL', 'PETS', 'STMP', 'VZ', 'SO', 'T', 'FXY', 'FXB', 'FXF', 'VCLT']. They are selected randomly. pricing.csv contains the price history of the tickers above from 2015 to 2020. Required Modules to run numpy-1.17.4\ pandas-0.24.0\ scipy-1.3.3 \ matplotlib-3.1.2 How Does it Work? The Problem With Markwoitz's Optimization Method The more correlated the investments, the greater the need for diversification, and yet the more likely we will receive unstable solutions. The benefits of diversification often are more than offset by estimation errors. From Geometric to Hierarchical Relationships Suppose that an investor wishes to build a diversified portfolio of securities, including hundreds of stocks, bonds and hedge fund, real estate, private placemnets and etc. Some investments seem closer substitutes for one another, and ohter investments seem compl
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