Want to do some quick, in depth technical analysis of Apple stock price using R? Theres a package for that!


The Quantmod package allows you to develop, testing, and deploy of statistically based trading models.  It provides the infrastructure for downloading/importing data from a variety of locations, analyze that data and produce charts that help determine statistical trends.  I appreciated Digital Dude calling this package to my attention  in a recent comment.  I also noticed that Revolution Analytics had highlighted the package on its finance page.  Actually, I had come across quantmod a few months ago – and it instantly got me excited about the power of R.  To give you an idea of typical usage, the following creates a stock chart of the last three months of Apple stock data.

library(‘quantmod’)


getSymbols(“AAPL”)
chartSeries(AAPL, subset=’last 3 months’)
addBBands()

The getSymbols function is used to retrieve stock data.  Data can originate in a number of locations.  In the example above, we are obtaining a single stock, Apple.  If you wanted to download several different stock quotes, you can do so in a single command.

getSymbols(c(“ORCL”,”IBM”))



You can also merge data to view comparisons.


head(as.xts(merge(ORCL,IBM)))


The chartSeries command creates the plot pictured above.  It captures a large amount of information, the date, open and close price, and volume of trading for each day.  Finally, the addBBands() call adds Bollinger Bands to the chart.  Informally, this amounts to a line indicating moving average and two lines a standard deviation above and below this moving average. For the uninitiated, technical indicators (and overlays) can be broken up into four categories – Trend, Volatility, Momentum, and Volume.  Those available in Quantmod are listed below.

Indicator	TTR Name	quantmod Name
Welles Wilder’s Directional Movement Indicator	ADX	addADX
Double Exponential Moving Average	DEMA	addDEMA
Exponential Moving Average	EMA	addEMA
Simple Moving Average	SMA	addSMA
Parabolic Stop and Reverse	SAR	addSAR
Exponential Volume Weighted Moving Average	EVWMA	addEVWMA
Moving Average Convergence Divergence	MACD	addMACD
Triple Smoothed Exponential Oscillator	TRIX	addTRIX
Weighted Moving Average	WMA	addWMA
ZLEMA	ZLEMA	addZLEMA

Indicator	TTR Name	quantmod Name
Average True Range	ATR	addATR
Bollinger Bands	BBands	addBBands
Price Envelope	N/A	addEnvelope

Indicator	TTR Name	quantmod Name
Commodity Channel Index	CCI	addCCI
Chande Momentum Oscillator	CMO	addCMO
Detrended Price Oscillator	DPO	addDPO
momentum	addMomentum
Rate of Change	ROC	addROC
Relative Strength Indicator	RSI	addRSI
Stocastic Momentum Index	SMI	addSMI
Williams %R	WPR	addWPR

Indicator	TTR Name	quantmod Name
Chaiken Money Flow	CMF	addCMF
Volume	N/A	addVo

This really just scratches the surface of what is possible with quantmod.  For instance, see this post on using quantmod with gold related data.

Later posts will include other applications – there is simply too much to cover at one time.