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Installing quantstrat from R-forge and source

Submitted by bryan on Tue, 01/10/2012 - 15:52
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R is used extensively in the financial industry; many of my recent clients have been working in or developing products for the financial sector. Some common applications are to use R to analyze market data and evaluate quantitative trading strategies. Custom solutions are almost always the best way to do this, but the quantstrat package can make it easy to quickly get a high-level understanding of a strategy's potential.

Subscriptions Feature Added

Submitted by bryan on Wed, 12/07/2011 - 13:26
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You can now subscribe to almost any content on the ProgrammingR website, including the job listings. By subscribing, you will receive email updates when new jobs are listed or when other content types you are following are posted or updated. To subscribe to any content type(s), click on "My Account" in your user menu (on the left) and then select the "Subscriptions" tab. Choose "Content types" and select the content type(s) you would like to follow. On the "Overview" tab you may set options such as how often you are notified of new content and updates.

Building Scoring and Ranking Systems in R

Submitted by bryan on Mon, 05/03/2010 - 17:17
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This guest article was written by author and consultant Tristan Yates (see his bio below). It emphasizes R's data object manipulation and scoring capabilities via a detailed financial analysis example.

Scoring and ranking systems are extremely valuable management tools. They can be used to predict the future, make decisions, and improve behavior – sometimes all of the above. Think about how the simple grade point average is used to motivate students and make admissions decisions.

R is a great tool for building scoring and ranking systems. It’s a programming language designed for analytical applications with statistical capabilities. The capability to store and manipulate data in list and table form is built right into the core language.
 

Webscraping using readLines and RCurl

Submitted by bryan on Tue, 04/14/2009 - 15:56
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There is a massive amount of data available on the web. Some of it is in the form of precompiled, downloadable datasets which are easy to access. But the majority of online data exists as web content such as blogs, news stories and cooking recipes. With precompiled files, accessing the data is fairly straightforward; just download the file, unzip if necessary, and import into R. For "wild" data however, getting the data into an analyzeable format is more difficult. Accessing online data of this sort is sometimes reffered to as "webscraping".

Helpful statistical references

Submitted by bryan on Tue, 04/07/2009 - 05:49
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In a previous article I provided a list of R programming resources. As a complement to that post, I've compiled a list of statistically oriented websites that colleagues and I have found useful below. For the most part, these sites focus on statistics and quantitative research methods rather than programming.

Positioning charts with fig and fin

Submitted by bryan on Mon, 02/09/2009 - 20:14
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R offers several ways to spatially orient multiple graphs in a single graphing space. The layout() function and mfrow/mfcol parameter settings are adequate solutions for many tasks and allow the graphing space to be broken up into tabular or matrix-based arrangements. For more fine grained manipulation, the fig and fin parameter settings are available. This article illustrates the capabilities and use of fig and fin.

First we'll create some simulation data to work with:

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