# Monthly Archives: June 2013

## MCMSki IV, Jan. 6-8, 2014, Chamonix (news #6)

June 26, 2013
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More news about MCMSki IV: First, the 9 invited and the 16 contributed sessions are about to be set into the program by the scientific committee. It should appear any time now: stay tuned. Second, after looking a wee bit around for handling the abstract, I decided to create a dedicated blog, MCMSki IV: poster

## Strategy 2: Riding the SMA Curve

June 26, 2013
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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 →

## Strategy 1 Extended (Part 2)

June 26, 2013
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We can extend our strategy and make it more profitable by incorporating short selling. Our annualized volatility will go up, but it will be interesting to see what happens to the annualized return. This is a very simple modification to … Continue reading →

## Strategy 1 Extended (Part 1)

June 26, 2013
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Like I said in my previous post, there are two ways I could think of, off the top of my head, to implement a 2-day or 5-day extension to the previous strategy. One way would be just a simple extension … Continue reading →

## Trading Strategy 1: What goes up, goes up…

June 26, 2013
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As I said earlier, my main task at my internship is to hunt for profitable strategies. As you can imagine, strategies can range from the exceedingly simple and easy to implement, to the crazily complex. Let’s start out with one … Continue reading →

## Looking out for volatility

June 26, 2013
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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 →

## Using R: Two plots of principal component analysis

June 26, 2013
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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

## Technical(and not technical) strategy testing

June 25, 2013
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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...

## Natural Language Processing Tutorial

June 25, 2013
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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 done, and...

## Natural language processing tutorial

June 25, 2013
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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...