This is the least complicated trend strategy in existance. You buy and hold the security as long as the security price is above a XXX-Day Simple Moving Average (SMA), and you can short it if it is below the SMA … Continue reading →

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Let’s do an easy experiment. Lets caluclate the 25-day rolling volatility of the S&P 500 from 2007 onwards. 1-Get the data: getSymbols(‘SPY’,from=’2007/01/01′) 2-Run the volatility function from the package TTR (comes along with quantmod): vol=volatility(SPY,n=25,N=252,calc=’close’) #n=25 means we want 25 … Continue reading →

PCA is a very common method for exploration and reduction of high-dimensional data. It works by making linear combinations of the variables that are orthogonal, and is thus a way to change basis to better see patterns in data. You either do spectral decomposition of the correlation matrix or singular value decomposition of the data

I got "hooked" on OOP approach of R in particular reference classes. And after my last little project on option scenario analysis I reconstructed my messy technical strategy testing code.Now to begin I would like to reason why I have done this while there exists a nice "blotter" and "quantstrat" packages.First of all "quantstrat" is faster than blotter, which...

Introduction This will serve as an introduction to natural language processing. I adapted it from slides for a recent talk at Boston Python. We will go from tokenization to feature extraction to creating a model using a machine learning algorithm. The goal is to provide a reasonable baseline on top of which more complex natural language processing can be...

I just gave a talk at Boston Python about natural language processing in general, and edX ease and discern in specific. You can find the presentation source here, and the web version of it here. There is a video of it here. Nelle Varoquaux and Micha...