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 ( 
Various of my R scripts produce csv files as output. For instance, I run a lengthy SVM back test, the end result is a csv file containing the indicator with some additional information. The problem is that over time one loses track what exactly the file contained and what parameters were used to produce it.
2012 was a very important year for me. It was my first full year of trading only pure quantitative strategies. It was a very successful year as well, despite the fact that the S&P 500 returned 16% (including dividends) – a tough to beat benchmark. The strategy I use on the SPY, for which I
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, 
Open CPU is a great project. Few months back, I wrote a function for plotting a moving window of the market average correlation. Jeroen C.L. Ooms was nice enough to upload it to their server. Something is now changed. Quotes … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
