Today, I want to demonstrate how easy it is to create a seasonality analysis study and produce a sample summary report. As an example study, I will use S&P Annual Performance After a Big January post by Avondale Asset Management. The first step is to load historical prices and find Big Januaries. All the hard 
In the prior post, Optimal number of clusters, we looked at methods of selecting number of clusters. Today, I want to continue with clustering theme and show historical Number of Clusters time series using these methods. In particular, I will look at the following methods of selecting optimal number of clusters: Minimum number of clusters 
Michael Johnston at the ETF Database shared a very interesting post with me over the holidays. The S&P 500 Visual History – is an interactive post that shows the top 10 components in the S&P 500 each year, going back to 1980. On a different note, Judson Bishop contributed a plota.recession() function to add recession 
In the last post, Examples of Current Major Market Clusters, we looked at clustering Major Markets into 4 groups based on their correlations in 2012. Today, I want to continue with clustering theme and discuss methods of selecting number of clusters. I will look at the following methods of selecting optimal number of clusters: Minimum 
I want to follow up and provide a bit more details to the excellent “A Visual of Current Major Market Clusters” post by David Varadi. Let’s first load historical for the 10 major asset classes: Gold ( GLD ) US Dollar ( UUP ) S&P500 ( SPY ) Nasdaq100 ( QQQ ) Small Cap ( 
Today, I want to continue with the Principal Components theme and show how the Principal Component Analysis can be used to build portfolios that are not correlated to the market. Most of the content for this post is based on the excellent article, “Using PCA for spread trading” by Jev Kuznetsov. Let’s start by loading 
In the Visualizing Principal Components post, I looked at the Principal Components of the companies in the Dow Jones Industrial Average index over 2012. Today, I want to show how we can use Principal Components to create Clusters (i.e. form groups of similar companies based on their distance from each other) Let’s start by loading 
Principal Component Analysis (PCA) is a procedure that converts observations into linearly uncorrelated variables called principal components (Wikipedia). The PCA is a useful descriptive tool to examine your data. Today I will show how to find and visualize Principal Components. Let’s look at the components of the Dow Jones Industrial Average index over 2012. First, 
Today I want to follow up with the XLLoop framework post. Please read the XLLoop framework post first to setup the XLLoop before trying the examples below. My first example is based on the TFX Package – to retrieve real-time FX quotes. To try this example, please first install the TFX Package. Please note that 
Today I want to highlight the XLLoop framework : Excel User-Define Functions in in any language. The XLLoop consists of two main components: An Excel addin implementation (XLL written in c++). A server and framework written in R (or/and in many other languages). The XLLoop allows you to connect Excel and R in very simple 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
