Today, I want to share the Retirement : simulating wealth with random returns, inflation and withdrawals – Shiny web application (code at GitHub). This application was developed and contributed by Pierre Chretien, I only made minor updates. This is application is a great example of how easy it is to convert your R script into 
Maximum Sharpe Portfolio or Tangency Portfolio is a portfolio on the efficient frontier at the point where line drawn from the point (0, risk-free rate) is tangent to the efficient frontier. There is a great discussion about Maximum Sharpe Portfolio or Tangency Portfolio at quadprog optimization question. In general case, finding the Maximum Sharpe Portfolio 
In the Cluster Portfolio Allocation post, I have outlined the 3 steps to construct Cluster Risk Parity portfolio. At each rebalancing period: Create Clusters Allocate funds within each Cluster using Risk Parity Allocate funds across all Clusters using Risk Parity I created a helper function distribute.weights() function in strategy.r at github to automate these steps. 
Today, I want to share the Sector Rotation Back Test application (code at GitHub). This is the last application in the series of examples (I have shared 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make back-tests, and create summary reports. The motivation for this series of 
Today, I want to share the Market Filter Back Test application (code at GitHub). This is the forth application in the series of examples (I plan to share 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make back-tests, and create summary reports. The motivation for this series 
Today, I want to share the January Seasonality application (code at GitHub). This example is based on the An Example of Seasonality Analysis post. This is the third application in the series of examples (I plan to share 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make 
Today, I want to share the Multiple Stocks Plot application (code at GitHub). This is the second application in the series of examples (I plan to share 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make back-tests, and create summary reports. The motivation for this series of 
Today, I want to share the Single Stock Plot application (code at GitHub). This is the first application in the series of examples (I plan to share 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make back-tests, and create summary reports. The motivation for this series of 
Today, I want to continue with clustering theme and show how the portfolio weights are determined in the Cluster Portfolio Allocation method. One example of the Cluster Portfolio Allocation method is Cluster Risk Parity (Varadi, Kapler, 2012). The Cluster Portfolio Allocation method has 3 steps: Create Clusters Allocate funds within each Cluster Allocate funds across 
In the Tracking Number of Historical Clusters post, I looked at how 3 different methods were able to identify clusters across the 10 major asset universe. Today, I want to share the impact of clustering on the larger universe. Below I examined the historical time series of number of clusters in the DOW 30 and 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
