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Just a note that Ive uploaded the initial version of vfuncs to google code. Ive released under a BSD license so you can use it in your commercial and noncommercial code easily.

Download from here [I’ll import to SVN sometime soon]. See my previous post for a description of vfuncs.

This version contains an example of a digital filter. This can be used to smooth the series data, or apply other signal processing operations. If your familiar with applying a blur filter in photoshop or gimp, using a gaussian filter kernel, this is exactly the same idea (except in one dimension).  Gaussian filter is basically just a moving average of the data.

Think of the algorithm as applying a sliding window across the data – the sliding window contains the filter weights, and at each position you apply the weighted average [dot product] of the filter weights against each data point in the window.

If the filter contains a single element of weight 1.0, then the result is just the input (the filter is just the Dirac delta function in that case). If the filter contains [0.25 0.50 0.25] its going to mix each element with its neigbours and take a weighted average, thus smoothing the data.

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It seems to me the time is ripe for Visual DSLs to appear in the financial quantitative domain – you can imagine the pure housekeeping involved in having 2500 trade strategies operating across 3500 tick feeds.. hmmm.

So you might be asking what the hell is a DSL?  A DSL or Domain Specific Language can mean many things, but usually falls into 2 classes – Visual and Textual.

Say I want to create a web app that generates some HTML page dynamically… regardless of whether I program in Ruby or Lisp I really want a nicer way to open and close HTML tags than

print('<html>\n') ... print(</html>\n')

… a textual DSL gives you a language that is a much closer match to the problem .. I want to just write something like

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