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Jan 6
I once wrote a small VBA function to download multiple stock quotes from Yahoo finance, here is another Matlab file pointed out by sjev, you can download the M file @ http://quantum.meplaza.nl/get_yahoo_quote.m, it downloads quotes from yahoo and returns the data in a struct. The function supports a bunch of symbols and special tags, however, unlike the VBA function, returns the latest record only.

For instance, data = get_yahoo_quote({'MSFT','IBM','GOOG','GE'}) returns
1x4 struct array with fields:
    symbol
    desc
    lastTrade
    lastTradeTime
    dividendDate
    open
    lastClose

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Dec 31
Happy New Year. The firework movie is generated with Matlab by Joro @ http://blogs.mathworks.com/images/pick/jiro/potw_fireworks/FireworksGUI.zip.

Nice.
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Dec 22
R is powerful for statistical computing, however, it has its own shortcomings such as difficult to deal with large data, which is one of the motivations of Renjin. I haven't tested it, as described on its page:
Renjin seeks to be a pure Java implementation of the R Language for Statistical Computing.

Project Goals: Build an implementation of R that:
Runs purely in the JVM, including Google App Engine
Fully compatible with (pure) R packages written for R 2.10.x
Compiles elligible, heavily-used closures to JVM byte code
Enables R-language objects to be backed by datastores other than memory

If you happen to use Java and want to test it, download the code @ http://code.google.com/p/renjin/
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Dec 16
The Google Prediction API is a black-box system for building predictive models, it provides pattern-matching and machine learning capabilities. So Google algorithms automatically creates a model from the training models given a set of training data and makes prediction under this model given a set of explanatory variables, read http://code.google.com/apis/predict/docs/getting-started.html for an overview.

I am eager to make my hands dirty after the release of R client library for the Google Prediction API @ http://code.google.com/p/google-prediction-api-r-client/, however, as both my office's computer and my laptop are under Windows, there are few issues for the R API package:
1, the original usage example had mistake. I always had a problem when I wanted to train my local data, my.model <- PredictionApiTrain(data="MYPATH/MYFILE.csv"), it says
Error in PredictionApiTrain(data = "test.csv") : 'remote.file' should be character.
At the beginning of PredictionApiTrain() there are lines:
if (missing(remote.file) || !is.character(remote.file))
stop("'remote.file' should be character")

However, remote.file is an argument without default value, function (data, remote.file, verbose = FALSE). Now the example has been changed to my.model <- PredictionApiTrain (data="MYPATH/MYFILE.csv",remote.file="gs://MYBUCKET/MYOBJECT");
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Nov 24
Life is short, use Pythonpython

I started to play with Python two weeks ago due to the limitation of R in terms of handling large data, then a friend of mine suggested me to try Python since I had to do data massage frequently, "Python is the best choice, trust me", he said. Although I was unwilling to learn another new software, I couldn't bear with the low efficiency of R (or of my work) for large data. You may realize my learning curve as: Excellent free CSV splitter --> MySQL+RMySQL package --> Several R packages including bigmemory and ff. But to be honest, none of them satisfies me either because of the limitation of the method (slow + malfunction) or of my own computer (short of memory).

I am shocked by python's extreme power and easy-to-use design after nearly two weeks, dealing with a 10GB CSV had never become so easy. More importantly, you can access R from Python almost seamlessly with the package RPY. To get started, I would like to recommend the following readings to all Python newbies like me:
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