363 search results for "quantmod"

getSymbols Extra

November 25, 2013
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getSymbols Extra

The getSymbols function from the quantmod package is an easy and convenient way to bring historical stock prices into your R environment. You need to specify the list of tickers, the source of historical prices and dates. For example following commands will download historical stock prices from yahoo finance for ‘RWX’, ‘VNQ’, ‘VGSIX’ symbols: Now,

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Financial Data Accessible from R – part III

November 8, 2013
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I came across a new source of data which I think is really worth sharing: ThinkNum. It gathers around 2,000 sources of data but more importantly it allows the user to manipulate this data via functions and graphics and there is an R package available on CRAN. Interested readers can find a very good post

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Commissions

November 4, 2013
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Commissions

Today, I want to explain the commission’s functionality build in to Systematic Investor Toolbox(SIT) “share” back-test. At each re-balance time the capital is allocated given the weight such that For example, if weight is 100% (i.e. fully invested) and capital = $100 and price = $10 then The period return is equal to The total

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Weekend Reading: Market Neutral

November 1, 2013
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Weekend Reading: Market Neutral

I recently came across a very interesting idea at the The Problem with Market Neutral (and an Answer) post by Mebane Faber. Today I want to show how you can test such strategy using the Systematic Investor Toolbox: Mebane thank you very much for sharing this great observation and great strategy that works! I would

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Financial Data Accessible from R – part II

October 30, 2013
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I updated my initial post with two new sources of data and the associated R packages: Datastream and PWT. I also added the fImport package from Rmetrics. Following a reader suggestion, I made the initial table  more interactive, moved  the data description and package detail below the main table and updated them. Enjoy! Source R

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Two interesting ideas here: “trading time” price impact of a…

October 29, 2013
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Two interesting ideas here:
“trading time”
price impact of a…

Two interesting ideas here: "trading time" price impact of a trade proportional to exp( √size ) Code follows: require(quantmod) getSymbols("MER") #Merrill Lynch #Gatheral's model HiLo Op(symbol) #munging mer names(mer) = "UpDay"names(mer) = "HiLo" mer ...

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Two interesting ideas here: “trading time” price impact of a…

October 29, 2013
By
Two interesting ideas here:
“trading time”
price impact of a…

Two interesting ideas here: "trading time" price impact of a trade proportional to exp( √size ) Code follows: require(quantmod) getSymbols("MER") #Merrill Lynch #Gatheral's model HiLo Op(symbol) #munging mer names(mer) = "UpDay"names(mer) = "HiLo" mer ...

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Update for Backtesting Asset Allocation Portfolios post

October 23, 2013
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Update for Backtesting Asset Allocation Portfolios post

It was over a year since my original post, Backtesting Asset Allocation portfolios. I have expanded the functionality of the Systematic Investor Toolbox both in terms of optimization functions and helper back-test functions during this period. Today, I want to update the Backtesting Asset Allocation portfolios post and showcase new functionality. I will use the

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Knoxville R User’s Group Meeting November 1

October 22, 2013
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Knoxville R User’s Group Meeting November 1

The next meeting of the Knoxville R User’s Group will consist of four 20-minute talks followed by an open planning session. It will take place on Friday, November 1, from 2:00 p.m. to 4:00 p.m. at The University of Tennessee, … Continue reading →

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Measuring Randomness in Capital Markets

September 29, 2013
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Measuring Randomness in Capital Markets

What is Random? As previously discussed, there’s no universal measure of randomness. Randomness implies the lack of pattern and the inability to predict future outcomes. However, The lack of an obvious model doesn’t imply randomness anymore than a curve fit one implies order. So what actually constitutes randomness, how can we quantify it, and why do we care? Randomness $\neq$ Volatility, and Predictability $\neq$ Profit First...

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