Hello and welcome to Joe's Data Diner's first ever post!Today, I will touch on both R and Finance, but I'll try and make it accesible for those with an interest in either and not just Quants like myself!Almost everyone is now aware that asset correlati...
Installing and changing fonts in your plots comes now easy with the extrafonts-package. There is a excellent tutorial on the extrafonts github site, still I will shortly demonstrate how it worked for me. First, install the package and load it. You can now install the desired system fonts (at the moment only TrueType fonts): The 
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 
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 ( 
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
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
