TTR Package

The TTR package (author Joshua Ulrich)  is a comprehensible collection of technical analysis indicators for R such as

• Moving average
• Oscillators
• Price channels
• Trend indicators

Users will probably be most interested in the following functions:
ADX
BBands
changes
MovingAverages
MACD
RSI
runFun
stoch
VWAP
WebData

The following sites were used to code/document this package:
http://www.fmlabs.com/reference/default.htm
http://www.equis.com/Customer/Resources/TAAZ/?p=0
http://www.linnsoft.com/tour/technicalindicators.htm
http://stockcharts.com/education/IndicatorAnalysis/
 

Examples

data(ttrc)

# Bollinger Bands
bbands <- BBands( ttrc[,c("High","Low","Close")] )

# Directional Movement Index
adx <- ADX(ttrc[,c("High","Low","Close")])

# Moving Averages
ema <- EMA(ttrc[,"Close"], n=20)
sma <- SMA(ttrc[,"Close"], n=20)

# MACD
macd <- MACD( ttrc[,"Close"] )

# RSI
rsi <- RSI(ttrc[,"Close"])

# Stochastics
stochOsc <- stoch(ttrc[,c("High","Low","Close")])

### Note: you must have a working internet connection
### for the examples below to work!

# Fetch U.S. symbols from the internet
nyseSymbols <- stockSymbols("NYSE")

# Fetch Yahoo! Finance data from the internet
ibm <- getYahooData("IBM", 19990404, 20050607)

Return series conditioned on a TTR

Description

For a series x(t) and a given TTR, computes the return series r(t) = ln( x(t+1) / x(t) ) and the conditional return series, r(t)*s(t), where s(t) is the position indicated by the TTR i.e. s(t) = 1 for long, -1 for short, 0 for neutral

Usage

cReturns(x, ttr = "macd4", params = 0, burn = 0, short = FALSE, 
condition = NULL, TC = 0.001)

Arguments

Value

Note

EXTREMELY IMPORTANT NOTE: The functions in this package evaluate past performance only. No warranty is made that the results of these tests should, or even can, be used to inform business decisions or make predictions of future events.

The author does not make any claim that any results will predict future performance. No such prediction is made, directly or implied, by the outputs of these function, and any attempt to use these function for such prediction is done solely at the risk of the end user.

Author(s)

David St John

References

William Brock, Josef Lakonishok, and Blake LeBaron. Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5):1731-1764, 1992.

Examples

## Is the mean conditional return higher than the mean unconditional return?

data(spData)
mean(diff(log(spData)))

cr <- cReturns(spData)
mean(cr[[1]])

Installation

TTR package has two dependencies -- zoo and xts

> install.packages("TTR")
also installing the dependencies 'zoo', 'xts'

trying URL 'http://cran.rstudio.com/bin/windows/contrib/3.2/zoo_1.7-12.zip'
Content type 'application/zip' length 896581 bytes (875 KB)
downloaded 875 KB

trying URL 'http://cran.rstudio.com/bin/windows/contrib/3.2/xts_0.9-7.zip'
Content type 'application/zip' length 660747 bytes (645 KB)
downloaded 645 KB

trying URL 'http://cran.rstudio.com/bin/windows/contrib/3.2/TTR_0.22-0.zip'
Content type 'application/zip' length 282375 bytes (275 KB)
downloaded 275 KB

package 'zoo' successfully unpacked and MD5 sums checked
package 'xts' successfully unpacked and MD5 sums checked
package 'TTR' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
	C:\Users\nnb\AppData\Local\Temp\RtmpMP8xNY\downloaded_packages

NEWS CONTENTS

• 20150613 : Technical Analysis with R Programming Language ( 5 comments on "Technical Analysis with R" )

Old News ;-)

[Jun 13, 2015] Technical Analysis with R Programming Language

1. To calculate Technical Analysis with R we will be using a free open-source library called "TTR" (Technical Trading Rules). This step includes instructions for installing TTR library, assuming you already have installed R on your computer. This steps only needs to be performed once per R installation on a computer.

To install the library on your computer:

1) Start R environment on your computer, then in the menu select: Packages & Data -> Package Installer

2) In Package Installer type "TTR" in the Package Search field, and click "Get List" button.

3) Select package "TTR" and click "Install Selected".

Loading Historical Data (Input)

For demo purposes we will use daily historical prices for SPY ETF from September 2013 through May 2014. Click here to download the data file. This input file for this example was generated using IB Historical Data Downloader.

2. We are going to start off by opening R shell and loading "TTR" library, which is a free R extension that contains functions for calculating some of the most common indicators.

> library("TTR")

3. The next step is to import our data file with historical prices into R environment. We will load data from sample CSV file into R environment and store it a "data frame", which an R variable type for storing data in table format in memory.

> data = read.csv(file="spy_historical_data.txt")

To display first few rows of the "data" table:

> head(data)

This by default shows first 6 rows of data along with column names (table header). To see how many rows you have in the "data" table:

> nrow(data)
[1] 187

This shows we have 187 data records in our SPY data file, for 187 trading days between Sep 3, 2013 – May 31, 2014.

We can also list table column names using "colnames" functions as follows:

> colnames(data)

Moving Averages

4. Let's now calculate 20-day Simple Moving Average (SMA) of the CLOSE price column using TTR library's R function "SMA":

> sma20 < - SMA(data[c('CLOSE')],n=20)

Now, let's see first 50 values of the "sma20" array:

> head(sma20, n=50)

Here we used function SMA from TTR library we loaded above, telling it to calculate 20-day average (value of parameter "n"), of the "CLOSE" column from data frame "data". The function returns an array of SMA values and stores it in a new variable called "sma20".

You can bring up the help with a detailed description of the function and it's parameters using ? followed by the function name, as below. It is always a good idea to read help pages for the functions you are using, since they will list all optional parameters that you can use to tweak the output. Also, many functions have variations or related functions, which could be helpful in various circumstances and will be listed on the help page.

> ?SMA

5. Calculating Exponential Moving Average is similarly easy, just use a different function, this time EMA(). Notice that we calculate EMA for 14-period length

Bollinger Bands

6. To calculate Bollinger Bands indicator we use the BBands function. There is a number of optional parameters that it takes, so we'll provide several examples.

In the example below we call BBands passing it data frame 'data' with a query that specifies that we want to use values from 'CLOSE' column, just as we've been doing above to SMA and EMA calculations above.

Second parameter 'sd' takes the number of standard deviations for upper and lower bands. Since we don't pass value for 'n' – BBands uses 20-period moving average by default.

The output contains several columns: 'dn' for "lower" band, 'mavg' for the moving average, 'up' for the "upper" band, and pctB, which quantifies a security's price relative to the upper and lower Bollinger Band, a detailed description of it can be found here.

• %B equals 1 when price is at the upper band
• %B equals 0 when price is at the lower band
• %B is above 1 when price is above the upper band
• %B is below 0 when price is below the lower band
• %B is above .50 when price is above the middle band (20-day SMA)
• %B is below .50 when price is below the middle band (20-day SMA)

> bb20 = BBands(data[c('CLOSE')], sd=2.0)

6.1 Now we'd like to create a new data frame containing all input data from the 'data' frame, plus Bollinger Bands data we just calculated.

> dataPlusBB = data.frame(data,bb20)

The data.frame() function takes any number of data frames and joins them row-wise into a new data frame, so that elements from corresponding rows are "joined" together in the result.

6.2 Bollinger Bands plot:

> plot(dataPlusBB$DATE_TIME,allData$CLOSE)
> lines(dataPlusBB$CLOSE, col = 'red')
> lines(dataPlusBB$up, col = 'purple')
> lines(dataPlusBB$dn, col = 'brown')
> lines(dataPlusBB$mavg, col = 'blue')

6.3 Alternatively, we can specify explicitly what type of moving average should be used as the basis for Bollinger Bands using function parameter 'maType', which simply take a moving average function name. Refer to ?SMA help page to see different types of moving averages supported in TTR library. For example, if you'd like to calculate an EMA Bollinger Bands, you can pass EMA to maType. Notice that in this example we are overriding default length parameter for moving average, using 14-period average this time.

> bbEMA = BBands(data[c('CLOSE')], sd=2.0, n=14, maType=EMA)

RSI – Relative Strength Indicator

7. RSI. To calculate RSI we use the RSI() function. You can use ?RSI command in R shell to get details for the function parameters. Basically, it's very similar to the functions we used above to generate moving averages. It has two required parameters: time series (such as 'CLOSE' column from our 'data' data frame, and 'n' integer value for the "length" of the RSI indicator.

> rsi14 = RSI(data[c('CLOSE')], n=14)

Here the first parameter to RSI function is: data[c('CLOSE')], which is a statement that says "take column named 'CLOSE' from the 'data' table, and return it as a list of values, and the second parameter is n=14, where the parameter name is 'n', and the value 14 indicates that we want to calculate 14-day RSI values on the close prices.

MACD

8. The MACD function takes several arguments:

1. input data series (such as 'CLOSE' price)
2. number of periods for "fast" moving average
3. number of periods for "slow" moving average
4. number of periods for the "signal" line

You can also optionally specify moving average function you want to use for MACD moving averages. See a screenshot of the help page below (you can also use ?MACD command in R shell to open the help page yourself):

Let's calculate a standard (12,26,9) MACD indicator using this function. We'll be using standard simple moving averages, so, we'll specify SMA function in 'maType' parameter:

> macd = MACD(data[c('CLOSE')], nFast=12, nSlow=26, nSig=9, maType=SMA)

Join All Data Together

9. Now, we join all of the indicators calculated above with the original input data into a single data frame:

> allData = data.frame(data,sma20,ema14,bb20,rsi14,macd)

The data.frame() function takes any number of data frames and joins them row-wise, so that elements from corresponding rows are "glued" together in the resulting data.frame 'allData'.

Write to text file

And, finally, we write contents of 'allData' data frame to a comma-separated values file. We use write.table() function, which contains a large number of optional parameters. A detailed help page is available using command "?write.table" in R shell.

> write.table(allData, file="spy_with_indicators.csv", na="", sep=",", row.names = FALSE)

When we call write.table() function we pass the following arguments:

• allData – this is simply a reference to the data frame containing data to be written to the output file.
• file = "…" – this is the path and name of the file we are creating.
• na = "" – makes sure that cells in the data frame that contain R value "NA" will contain empty values in the output file. Some cells have NA for rows where there were not enough data to generate a corresponding indicator value (for example first 19 rows for 20-day SMA).
• sep = "," – sets column separator to comma (hence comma-separated values file). To create a tab-separated file (really a preferred format for serious software systems) – use: sep = "\t".
• row.names = FALSE – it is important to set this value, otherwise first column in the output file will contain row numbers.

The resulting file is available here. Right-click and select "Save Linked File As…" Downloaded file can be opened in Excel or text editor.

10. There are more functions and features available in the "TTR" library. You can find out more by bringing up TTR's help page:

> ?TTR

CONCLUSION

R provides a convenient and versatile environment for data analysis and calculations. In addition to thousands of free open-source statistical, mathematical libraries and algorithms, R contains a great number of functions and libraries for reading and writing data to/from files, databases, URLs, Web Services, etc… That, combined with the conciseness of the language, is a powerful combination that can help traders save precious time. Traders can significantly cut down the time required to prototype and backtest trading strategies using R. There are also methods to integrate R with mainstream programming languages such as Java and C++.

Whether you are a seasoned C++ or Java programmer or an analyst – if you are serious about scientific and data-centric approach to trading – R can be an extremely powerful and useful tool in your toolbox.

----
Trading Geeks provides consulting in trading strategy and software development for independent traders and hedge funds. Please inquire for more information or a free quote for your project via Contact Us form on the right.

‹ Technical Analysis in Excel: Part II – MACD and RSI

Posted in Data Analysis, Trading Tools

5 comments on "Technical Analysis with R"

Recommended Links

Softpanorama Recommended

Top articles

Sites

Top articles

Sites

...

Reference

Softpanorama Recommended

Top articles

Sites

Top articles

Sites

• DESCRIPTION file.

Help Pages