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• Portfolio Management using Machine Learning: Hierarchical Risk ParityDo 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 TRADINGAllocate 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 COVEREDPortfolio ManagementInverse Volatility PortfoliosCritical Line AlgorithmReturn/Risk OptimizationHierarchical Risk ParityPythonNumpyPandasSklearnMatplotlibSeabornMathsLinkage MatrixDendrogramsClusteringEuclidean distanceScalingPREREQUISITES 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.