Blog Archives

Optimal number of clusters

January 16, 2013
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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

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Examples of Current Major Market Clusters

January 11, 2013
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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 (

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More Principal Components Fun

January 6, 2013
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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

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Clustering with selected Principal Components

December 28, 2012
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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

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Visualizing Principal Components

December 22, 2012
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Visualizing Principal Components

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,

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XLLoop examples

December 10, 2012
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XLLoop examples

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

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XLLoop framework

December 7, 2012
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XLLoop framework

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

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TFX Package

December 5, 2012
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TFX Package

Today I want to highlight the TFX Package created by Garrett See. TFX is an R Interface to the TrueFX(tm) Web API for free streaming real-time and historical tick-by-tick market data for dealable interbank foreign exchange rates with millisecond detail. Garrett provided a great tutorial, examples, and shiny application of TFX at http://rpubs.com/gsee/TFX Please note

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Financial Turbulence Example

December 1, 2012
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Financial Turbulence Example

Today, I want to highlight the Financial Turbulence Index idea introduced by Mark Kritzman and Yuanzhen Li in the Skulls, Financial Turbulence, and Risk Management paper. Timely Portfolio did a great series of posts about Financial Turbulence: Part 1, Part 2, Part 3. As example, I will compute Financial Turbulence for the equal weight index

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Extending Commodity time series

November 21, 2012
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Extending Commodity time series

I want to follow up with Extending Gold time series post by showing how we can extend Commodity time series. Most Commodity ETFs began trading in 2006, please see the List of Commodity ETFs page. I will use DBC – PowerShares DB Commodity Fund, one on the most liquid Commodity ETFs as my proxy for

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