Blog Archives

Single Stock Plot Shiny web application

February 12, 2013
By
Single Stock Plot Shiny web application

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

Read more »

Cluster Portfolio Allocation

February 11, 2013
By
Cluster Portfolio Allocation

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

Read more »

Tracking Number of Historical Clusters in DOW 30 and S&P 500

February 4, 2013
By
Tracking Number of Historical Clusters in DOW 30 and S&P 500

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

Read more »

An Example of Seasonality Analysis

February 3, 2013
By
An Example of Seasonality Analysis

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

Read more »

Tracking Number of Historical Clusters

January 26, 2013
By
Tracking Number of Historical Clusters

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

Read more »

Weekend Reading – S&P 500 Visual History

January 19, 2013
By
Weekend Reading – S&P 500 Visual History

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

Read more »

Optimal number of clusters

January 16, 2013
By
Optimal number of clusters

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

Read more »

Examples of Current Major Market Clusters

January 11, 2013
By
Examples of Current Major Market Clusters

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 (

Read more »

More Principal Components Fun

January 6, 2013
By
More Principal Components Fun

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

Read more »

Clustering with selected Principal Components

December 28, 2012
By
Clustering with selected Principal Components

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

Read more »