joshuaulrich / ttr

Technical analysis and other functions to construct technical trading rules with R

About

TTR is an R package that provides the most popular technical analysis functions for financial market data. Many of these functions are used as components of systematic trading strategies and financial charts.

TTR for enterprise

Available as part of the Tidelift Subscription.

The maintainers of TTR and thousands of other packages are working with Tidelift to deliver commercial support and maintenance for the open source dependencies you use to build your applications. Save time, reduce risk, and improve code health, while paying the maintainers of the exact dependencies you use. Learn more.

Supporting TTR development

If you are interested in supporting the ongoing development and maintenance of TTR, please consider becoming a sponsor.

Installation

The current release is available on CRAN, which you can install via:

install.packages("TTR")

To install the development version, you need to clone the repository and build from source, or run one of:

# lightweight
remotes::install_github("joshuaulrich/TTR")
# or
devtools::install_github("joshuaulrich/TTR")

You will need tools to compile C/C++ code. See the relevant appendix in the R Installation and Administration manual for your operating system:

• Windows
• MacOS (the R for Mac OS X Developer's Page might also be helpful)
• Unix-alike

Getting Started

Here are a few examples of some of the more well-known indicators:

# "TTR Composite" (simulated data)
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")])

TTR works with the chartSeries() function in quantmod. Here's an example that uses chartSeries() and adds TTR-calculated indicators and overlays to the chart.

# "TTR Composite" (simulated data)
data(ttrc)

# Use quantmod's OHLCV extractor function to help create an xts object
xttrc <- xts(OHLCV(ttrc), ttrc[["Date"]])

chartSeries(xttrc, subset = "2006-09/", theme = "white")
addBBands()
addRSI()

Have a question?

Ask your question on Stack Overflow or the R-SIG-Finance mailing list (you must subscribe to post).

Contributing

Please see the Contributing Guide.

See Also

• quantmod: quantitative financial modeling framework
• xts: eXtensible Time Series based on zoo

Author

Joshua Ulrich

ttr's Issues

KDJ indicator in TTR

Relationship between Stochastic Oscillator and KDJ index
Stochastic Oscillator / Stochastic Momentum Index

Dear Joshua,
It is quite interesting there is no KDJ indicator in TTR. Is there any possibility to add this KDJ lines to our new TTR version.

https://docs.anychart.com/Stock_Charts/Technical_Indicators/KDJ

Thanks.

stoch() NA/NaN/Inf error when no equity value change over time

Running stoch() throws an error if the equity doesn't change in price coming from the inclusion of Inf in the calculation.

equity<-getSymbols("QTWN")
stochastic<-stoch(QTWN[,2:4],nFastK = 10,nFastD = 3)
Error in runSum(x, n) : NA/NaN/Inf in foreign function call (arg 1)
traceback()
5: runSum(x, n)
4: runMean(x, n)
3: SMA(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.0123579821717995, 
   -0.474019764105834, -0.474019764105834, -0.474019764105834, -0.474019764105834, 
   -0.474019764105834, -0.474019764105834, -0.474019764105834, -0.474019764105834, 
   -0.474019764105834, -Inf, -Inf, -Inf, -Inf, -1.956, -1.956, -1.956, 
   -1.956, -1.956, -1.956, -1.956, -1.956, -1.956, -Inf, -Inf, -0.982666666666669, 
   -0.982666666666669, -0.982666666666669, -0.982666666666669, -0.982666666666669, 
   -0.982666666666669, -0.982666666666669, -0.982666666666669, -0.982666666666669, 
   -0.375290313225058, -0.375290313225058, -0.375290313225058, -0.375290313225058, 
   -0.387309488177193, -0.199608454382243, -0.158781208233093, -0.158781208233093, 
   -0.158781208233093, -0.202523184452779, -0.28688746679546, -0.28688746679546, 
   -0.28688746679546, -0.28688746679546, -0.761539102564105, -Inf, 
   -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, 
   -Inf, -Inf, -0.0582093831450938, -0.0582093831450938, -0.0582093831450938, 
   -0.0582093831450938, 0.534271952575028, 0.613656449165402, 0.613656449165402, 
   0.613656449165402, 0.629747752971875, 0.444299390774587, 0.444299390774587, 
   0.444299390774587, 0.444299390774587, -0.702666666666668, 0.254946862634259, 
   0.254946862634259, 0.254946862634259, 0.540458867161159, 0.523601483479433, 
   0.250505731625085, 0.328051247471341, 0.328051247471341, 0.554164965426406, 
   0.219145905420991, -0.500703234880453, 0.549902768834514, 0.23564916918429, 
   0.23564916918429, 0.23564916918429, 0.211976846307386, 0.205314814814815, 
   0.205314814814815, 0.205314814814815, 0.205314814814815, 0.178185678601168, 
   -13.3958125000005, -13.3958125000005, -13.3958125000005, -0.724590163934427, 
   -0.724590163934427, -0.724590163934427, -0.724590163934427, -0.691802732240438, 
   -0.413576793452097, -0.413576793452097, -0.413576793452097, -0.168031295137218, 
   -0.034605932203391, -0.00635663841807757, -0.00635663841807757, 
   -0.783191384180796, -0.825564971751417, -0.472457627118648, -0.516363636363637, 
   -0.516363636363637, -0.516363636363637, -0.516363636363637, -0.540334855403349, 
   -0.540334855403349, -1.11459968602826, -1.11459968602826, -Inf, 
   -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, 
   -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, 
   -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, 
   -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, 
   -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, 
   0.755725190839694, 0.755725190839694, 0.755725190839694, 0.774961554710605, 
   0.718631178707225, 0.718631178707225, 0.718631178707225, 0.718631178707225, 
   0.718631178707225, -0.81967213114754, -1.11652542372882, -1.11652542372882, 
   -2.60649819494588, -20.0555555555568, -Inf, -0.564468020304564, 
   -0.564468020304564, -1.27512791878172, -0.871065375302667, -0.871065375302667, 
   -0.871065375302667, -0.871065375302667, -0.871065375302667, -0.871065375302667, 
   -0.871065375302667, -0.871065375302667, -1.52436440677967, -7.37948717948744, 
   -Inf, -Inf, -Inf, -0.632762312633836, -0.632762312633836, -0.632762312633836, 
   -1.52337398373983, -1.52337398373983, -1.52337398373983, -1.52337398373983, 
   -1.52337398373983, -1.52337398373983, 0.516198044009779, 0.516198044009779, 
   0.256418398533008, 0.256418398533008, 0.256418398533008, 0.256418398533008, 
   0.256418398533008, 0.256418398533008, 0.256418398533008, -1.79013646788991, 
   -1.79013646788991, -Inf, -Inf, -Inf, -Inf, -Inf, -2.02763289473682, 
   -2.02763289473682, -2.02763289473682, -0.00317121116043121, -0.00317121116043121, 
   -0.00317121116043121, -0.00317121116043121, -0.00317121116043121, 
   -0.00317121116043121, -0.387444514901709, -0.387444514901709, 
   -0.387444514901709, -2.46202531645567, -2.46202531645567, -0.816613268608415, 
   -0.816613268608415, -0.327724919093851, -0.32793905070119, 4.63392028487137e-07, 
   5.43478259497414e-07, 5.43478259497414e-07, 0.119103335318642
   ), n = 3)
2: do.call(maType, c(list(fastK), list(n = nFastD, ...)))
1: stoch(QTWN[, 2:4], nFastK = 10, nFastD = 3)

TTR version 0.23-2

[R-Forge #2580] Have stockSymbols pull from NASDAQ FTP site

Submitted by: Joshua Ulrich
Assigned to: Joshua Ulrich
R-Forge link

Log into ftp.nasdaqtrader.com anonymously. Look in the directory SymbolDirectory. You'll notice two files: nasdaqlisted.txt and otherlisted.txt.

StockCharts link is dead in ?MACD

Dead one:
http://stockcharts.com/education/IndicatorAnalysis/indic_MACD1.html

Active one:
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_average_convergence_divergence_macd

Add Vortex Indicator

Please add the Vortex Indicator ( http://www.vortexindicator.com/VFX_VORTEX.PDF )
Thank you.

[R-Forge #2646] An improvement for the TTR::DVI function

Submitted by: ivan popivanov
Assigned to: Joshua Ulrich
R-Forge link

(A copy of my email sent to the maintainer)

Hello,

Let me first outline the 'problem' I see with the TTR::DVI indicator:

library(quantmod)
spy=getSymbols('SPY', from='1900-01-01', auto.assign=FALSE)
spy.dvi.all = DVI(Cl(spy))
spy.dvi = DVI(Cl(spy)['2011-01-01'])
spy.dvi.all['2012-01']
spy.dvi['2012-01']

The values for month of January are different. The reason is that when the rolling pctRank is performed on the 2011+ set, there are NAs in the mag and str vectors so the function ends up with different rankings.

In my opinion, we should start the computations later, when there are no NAs in the mag and str vector for the lookup period (defined by n). Please see the attachment for an illustration of the idea.

Regards,
Ivan

Followups:

Date: 2013-04-19 03:32
Sender: Joshua Ulrich
Replaced pctRank function with runPercentRank.

How to calculate week/month MACD?

I hope to calculate weekly or monthly MACD, therefore, I need a function to transfer daily data to weekly or monthly data and then to calculate MACD. However, it seem I am not familiar with ‘xts’ object. and Hope Joshua could give some help.

daily2week<-function(Object){
Object1<-split(na.omit(Object),rep(1:round(nrow(na.omit(Object))/7),each = 7))
Open=unlist(lapply(Object1,function(x) unlist(x[1,1])))
High=unlist(lapply(Object1,function(x) max(unlist(x[,2]))))
Low=unlist(lapply(Object1,function(x) max(unlist(x[,2]))))
Close=unlist(lapply(Object1,function(x) max(unlist(x[,2]))))
Volume=unlist(lapply(Object1,function(x) max(unlist(x[,2]))))
output<-data.frame(Open,High,Low,Close,Volume)
return(output)
}

Thanks.

xts/zoo are not automatically imported when loading TTR 0.23-0

I use R 3.1.2 and recently upgraded TTR from 0.22 to 0.23. However, now when I load TTR, xts/zoo are not automatically imported. Any clue? Thanks!

TTR HMA issues

Hi,

Firstly, thanks for the TTR package, it is very useful. I written a code to compute various TTRs and output the results into a nice excel file. For the SMA, EMA, WMA, the code works fine. However, when I try to use the HMA, I keep getting the following error:

Error in WMA(x, n=n/2,...):
Length of 'wts' must equal the length of 'x' or 'n'

My code is:

short = HMA(z_data,n_short)
long = HMA(z_data,n_long)

Where z_data is a price series while n_short and n_long are parameters between two values specified in the code. So to confirm, when the HMA is changed to SMA/WMA/EMA, the code works fine. I have added other inputs to the code such as: short = HMA(z_data,n_short,500) and short = HMA(z_data,n_short,"length of data"), but the error remains.

I think I am missing something pretty simple. Any ideas most welcome.

Many thanks.

[R-Forge #5935] CLV to handle Inf condition

Submitted by: Sarvesh S G
Assigned to: Joshua Ulrich
R-Forge link

add line

clv[is.infinite(clv)] <- 0 in CLV function

Followups:

Date: 2014-09-19 11:32
Sender: Joshua Ulrich
Changing to accepted. Fixed in r178. Issue arose when H == L, but H != C and L != C due to machine precision. In that case, division of a non-zero number by zero occurs, which results in +/-Inf.

Date: 2014-09-17 23:58
Sender: Joshua Ulrich
Reproducible example given by Sarvesh via email is attached in TTR_CLV_Inf.R. Issue that creates Inf values only occurs when you pass more than the 3 HLC columns to CLV. Closing/rejecting as an unnecessary change.

WMA calculation

Please see the example below. Is it a bug or something intentional?

> WMA(c(8, 8, rep(0, 10)), 3, c(6, 1, rep(0, 10)))
 [1]  NA  NA   8   8 NaN NaN NaN NaN NaN NaN NaN NaN
> WMA(c(0.8, 0.8, rep(0, 10)), 3, c(0.6, 0.1, rep(0, 10)))
 [1]   NA   NA  0.8  0.8 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0

Session info -----------------------------------------------
 setting  value                       
 version  R version 3.2.2 (2015-08-14)
 system   x86_64, linux-gnu           
 ui       RStudio (0.99.486)          
 language (EN)                        
 collate  en_US.UTF-8                 
 tz       <NA>                        
 date     2015-10-13                  

Packages --------------------------------------------------
 package    * version date       source        
 devtools     1.9.1   2015-09-11 CRAN (R 3.2.2)
 digest       0.6.8   2014-12-31 CRAN (R 3.1.2)
 lattice      0.20-33 2015-07-14 CRAN (R 3.2.1)
 memoise      0.2.1   2014-04-22 CRAN (R 3.2.0)
 rstudioapi   0.3.1   2015-04-07 CRAN (R 3.2.0)
 TTR        * 0.23-0  2015-07-10 CRAN (R 3.2.2)
 xts          0.9-7   2014-01-02 CRAN (R 3.2.0)
 zoo          1.7-12  2015-03-16 CRAN (R 3.1.3)

Column name for Parabolic SAR - unnamed

Description

Column name for Parabolic SAR is unnamed

Probably after line 84, just needs the colnames:

83 # Call C routine
84   sar <- .Call("sar", HL[,1], HL[,2], accel, initGap, PACKAGE = "TTR")
85   colnames(sar) <- "sar"
86
87   reclass( sar, HL )

***sorry, noob here. don't want to break anything

Expected behavior

Column is labelled "sar"

Minimal, reproducible example

> tail( SAR(xtsclean[[1]]))
               [,1]
2017-10-11 9.733589
2017-10-12 9.718902
2017-10-16 9.705390
2017-10-17 9.686851
2017-10-19 9.662029
2017-10-20 9.629545

Session Info

R version 3.4.0 (2017-04-21)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils    
[5] datasets  methods   base     

other attached packages:
 [1] quadprog_1.5-5  e1071_1.6-8     ranger_0.8.0   
 [4] caret_6.0-77    lattice_0.20-35 lubridate_1.6.0
 [7] plotly_4.7.1    scales_0.5.0    cowplot_0.8.0  
[10] gridExtra_2.3   RcppRoll_0.2.2  dplyr_0.7.4    
[13] ggplot2_2.2.1   quantmod_0.4-11 TTR_0.23-2     
[16] xts_0.10-0      zoo_1.8-0       pacman_0.4.6   

loaded via a namespace (and not attached):
 [1] httr_1.3.1          ddalpha_1.3.1      
 [3] tidyr_0.7.2         sfsmisc_1.1-1      
 [5] jsonlite_1.5        viridisLite_0.2.0  
 [7] splines_3.4.0       foreach_1.4.3      
 [9] prodlim_1.6.1       assertthat_0.2.0   
[11] stats4_3.4.0        DRR_0.0.2          
[13] yaml_2.1.14         robustbase_0.92-7  
[15] ipred_0.9-6         glue_1.1.1         
[17] digest_0.6.12       colorspace_1.3-2   
[19] recipes_0.1.0       htmltools_0.3.6    
[21] Matrix_1.2-11       plyr_1.8.4         
[23] timeDate_3012.100   pkgconfig_2.0.1    
[25] CVST_0.2-1          purrr_0.2.4        
[27] gower_0.1.2         lava_1.5.1         
[29] tibble_1.3.4        withr_2.0.0        
[31] nnet_7.3-12         lazyeval_0.2.0     
[33] survival_2.41-3     magrittr_1.5       
[35] nlme_3.1-131        MASS_7.3-47        
[37] dimRed_0.1.0        class_7.3-14       
[39] tools_3.4.0         data.table_1.10.4-2
[41] stringr_1.2.0       kernlab_0.9-25     
[43] munsell_0.4.3       bindrcpp_0.2       
[45] compiler_3.4.0      rlang_0.1.2        
[47] grid_3.4.0          iterators_1.0.8    
[49] htmlwidgets_0.9     gtable_0.2.0       
[51] ModelMetrics_1.1.0  codetools_0.2-15   
[53] curl_3.0            reshape2_1.4.2     
[55] R6_2.2.2            bindr_0.1          
[57] stringi_1.1.5       Rcpp_0.12.13       
[59] rpart_4.1-11        DEoptimR_1.0-8 

TTR SMA feature request parameter for NA at the end

In the documentation I did not find a parameter to move the NA to the end.
Is there one around?

> kurs <- c(1,2,3,4,5,6)
> sma3<-SMA(kurs,n=3)
> sma3
[1] NA NA  2  3  4  5
> 

Default output NA NA 2 3 4 5
With additional parameter the output changes to 2 3 4 5 NA NA

Ultimate Oscillator on monthly data error

Hi,

Using the Ultimate Oscillator function on monthly data was throwing an error

require(quantmod)
getSymbols("AAPL")
AAPLm<-to.monthly(AAPL)[,1:5]
colnames(AAPLm)<-c("Open","High","Low","Close","Volume")
res<-ultimateOscillator(HLC=AAPLm[,c('High','Low','Close')],n=c(7,14,28), wts=c(4,2,1))

Error in avgs[, 1] : incorrect number of dimensions

So I changed the sapply to lapply in the function and it worked fine. Am I doing the right thing here?

ultimateOscillatorNew <-
  function(HLC, n=c(7,14,28), wts=c(4,2,1)) {

    # Ultimate Oscillator

    if(length(n) != 3 || length(wts) != 3)
      stop("length(n) and length(wts) must both be 3")

    HLC <- try.xts(HLC, error=as.matrix)

    # avoid reclassing in ATR and runSum
    HLC.RECLASS <- attr(HLC, ".RECLASS")
    attr(HLC, ".RECLASS") <- FALSE

    # only need 'tr' and 'trueLow'
    atr <- ATR(HLC, n=1)

    buyPressure <- HLC[,3] - atr[,'trueLow']

    avgs <- lapply(n, function(i) runSum(buyPressure, n=i)/runSum(atr[,'tr'], n=i))

    # restore HLC .RECLASS attribute
    attr(HLC, ".RECLASS") <- HLC.RECLASS

    osc <- 100.0*(wts[1]*avgs[[1]] + wts[2]*avgs[[2]] + wts[3]*avgs[[3]]) / sum(wts)

    reclass(osc, HLC)
  }

Regards,
SD

TTR SMA n=1 bug

Package TTR version 0.23-1

If I do SMA with n=1 then the first number gets replaced with NA
Sample

kurs
[1] 1 2 3 4
> sma1 <- SMA(kurs,n=1)
> sma1
[1] NA  2  3  4

Expected result 1 2 3 4

> kurs <- ts(kurs)
> kurs
Time Series:
Start = 1 
End = 4 
Frequency = 1 
[1] 1 2 3 4
> sma1 <- SMA(kurs,n=1)
> sma1
Time Series:
Start = 1 
End = 4 
Frequency = 1 
[1] NA  2  3  4

stockSymbols doesn't return IPOyear Sector Industry

Hello,

I'm running the function stockSymbols(), but opposed to previous runs, today I simply don't get the data of IPOyear, Sector and Industry. It all returns NA.

Thanks!
Eran

DPO documentation is confusing

The way of calculating DPO: Detrended Price Oscillator is confusing!

For Simple Moving Average, if I have a series
v <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
ma <- TTR::SMA(v, n=5)
NA NA NA NA 3 4 5 6 7 8
It is calculated by rolling mean from 1:5 -> 5:10

However, when I do DPO, it gives
TTR::DPO(v,n=5)
NA -1 -1 -1 -1 -1 -1 NA NA NA
It is calculated by: v[1:7] - ma[4:10], shifted by (n/2+1=3)

It uses current price to substract future moving average.
The correct way is: v[4:10] - ma[1:7], using current price to substract previous moving average, and the results should be:
NA NA NA NA NA NA NA 5 5 5

A real example here using TTR::SMA and TTR::DPO:

    Date        Close   MA5      DPO5
1   2017-05-30  0.7465  NA       NA
2   2017-05-31  0.7430  NA      -0.00098
3   2017-06-01  0.7374  NA      -0.00740
4   2017-06-02  0.7443  NA      -0.00288
5   2017-06-05  0.7487  0.74398 -0.00194
6   2017-06-06  0.7506  0.74480 -0.00172
7   2017-06-07  0.7549  0.74718  0.00150
8   2017-06-08  0.7547  0.75064  0.00068
9   2017-06-09  0.7527  0.75232 -0.00204
10  2017-06-12  0.7541  0.75340 -0.00128
11  2017-06-13  0.7537  0.75402 -0.00356
12  2017-06-14  0.7585  0.75474  0.00006
13  2017-06-15  0.7579  0.75538 -0.00140
14  2017-06-16  0.7621  0.75726  0.00344
15  2017-06-19  0.7600  0.75844  0.00208
16  2017-06-20  0.7580  0.75930  0.00114
17  2017-06-21  0.7553  0.75866 -0.00126
18  2017-06-22  0.7542  0.75792  NA
19  2017-06-23  0.7568  0.75686  NA
20  2017-06-26  0.7585  0.75656  NA

This looks into future.

Errors in EMV and SNR

Hi, I found there are two errors in TTR:

1. EMV function returns two columns with names: 'emv' and 'maEMV'. But the documentation indicates the latter as 'emvMA'.

2. SNR has a bug in getting column by $:
   snr <- SNR(HLC, n=10)
Error in ATR(HLC, n, ...)$atr : $ operator is invalid for atomic vectors

I might be using the dev version:
packageVersion("TTR")
[1] ‘0.23.2’

Thanks!

WMA can't return a zoo object when nrow(wts)==n and x contains na

It seems like there is an error, or unintended behavior, in WMA in the situation where NROW(wts)==n is true.
That's because:
if( NROW(wts) == n ) {
275
276 x <- na.omit(x)

You remove all na's from the data input and then calculate ma on x.

However, when you then call reclass(ma,x) after the ma is calculated, ma and x are of different length in the situation where x contained na. I think that means reclass will never succeed in that situation. Agree? If so, is there a reason for that, or is it just an situation that hasn't come up?

Bug in runCov - n gets overwritten by Fortran routine?

Something strange seems to happen in runCov when the window is an integer and cumulative=TRUE. For example:

data <- 1:1000

window = as.integer(100)
cov <- runCov(data, data, window, cumulative = TRUE)

window2 = 100
cov2 <- runCov(data, data, window2, cumulative = TRUE)

print(window)
tail(cov)

print(window2)
tail(cov2)

Gives the following output:

> print(window)
[1] 1000
> tail(cov)
[1]       NA       NA       NA       NA       NA 83416.67
> print(window2)
[1] 100
> tail(cov2)
[1] 82585.00 82751.00 82917.17 83083.50 83250.00 83416.67
> 

It looks like window has been replaced by length(data). cov only has one entry at the end whereas cov2 has all but the first 99 observations, as you would expect.

I think the culprit might be this line where n is being updated by the Fortran routine. This updated value will be passed back to the R function and so the first 999 values are set to NA here.

Similar things happen in the runMedian and runMAD functions.

My guess is that the as.integer() call here is causing a copy of n to be made in the second case which is why the answer in the second case is different. I'm not sure what the best solution is... Perhaps to take an explicit copy of n in the Fortran routine rather than updating the variable that's passed in?

[R-Forge #2043] DPO implementation looks into the future

Submitted by: Mirko Fit
Assigned to: Joshua Ulrich
R-Forge link

The implementation of the detrended price oscillator looks into the future by shifting moving averages backwards.

Why this is problematic:

1.) The current values of DPO contain information about the future
-> I used genetic programming to find trading rules in forex markets and
found a lot of unrealisticly successful trading rules with that approach.
By now it looks like the future information within DPO made it easy
to find trading rules with high forecasting abilities.

2.) It is impossible to use it in an event based system, as the information of future MA values is not available at calculation time

From my point of view, every indicator that produces n/a values at the end of the time series, may contain future information and is therefore not practically usable.

In case of the DPO, a hotfix could be to shift the final result forward by 'shift' (option).
The most clean I can imagine is to implement it via some buffer (like shift registers in hardware). One would create a buffer of size 'shift' and store the past prices in there. When the new value of DPO is calculated, one can substract the MA value from the price of 'shift' periods ago.

Followups:

Date: 2013-02-24 23:59
Sender: Joshua Ulrich
This isn't a bug; it's how the indicator is calculated. See the references below:
http://www.fmlabs.com/reference/DPO.htm
http://www.equis.com/Customer/Resources/TAAZ/?c=3&p=48
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:detrended_price_osci

ALMA calculation does not prepend with NA's

Hi there, when calculating the ALMA moving average, the resulting vector is shorter than the original data by 'n' where n is the length of the moving average..... Checking this against other moving average types, it seems to be that the ALMA resultant vector does not pad the start of the results with an NA.

HTH

[R-Forge #2091] Correct documentation error bollingerBands

Submitted by: Evelyn Mitchell
Assigned to: Joshua Ulrich
R-Forge link

ma in docs should be mavg

--- bollingerBands.Rd.orig 2012-06-18 19:08:34.477045035 -0600
+++ bollingerBands.Rd 2012-06-18 19:10:42.277048079 -0600
@@ -31,7 +31,7 @@
A object of the same class as \code{HLC} or a matrix (if \code{try.xts}
fails) containing the columns:
\item{ dn }{ The lower Bollinger Band. }

• \item{ ma }{ The middle Moving Average (see notes). }
• \item{ mavg }{ The middle Moving Average (see notes). }
  \item{ up }{ The upper Bollinger Band. }
  \item{ pctB }{ The %B calculation. }
  }

Followups:

Date: 2013-02-25 00:06
Sender: Joshua Ulrich
Fixed as of revision 148. Thanks!

runMAD should check for NA if user specifies center argument

It's possible for a user to specify a center argument that has more NA than the number of NA in x + n - 1. This can even happen internally, for example with CCI:

R> require(TTR)
R> data(ttrc)
R> cci <- CCI(ttrc$Close, maType="DEMA")
Error in runMAD(HLC, n, center = mavg, stat = "mean") : 
  NA/NaN/Inf in foreign function call (arg 2)

This is because DEMA has more NA than n, due to double-smoothing.

R> dema <- DEMA(ttrc$Close, n=10)
R> runMAD(ttrc$Close, n=10, center=dema)
Error in runMAD(ttrc$Close, n = 10, center = dema) : 
  NA/NaN/Inf in foreign function call (arg 2)
R> sum(is.na(dema))
[1] 18

One possible solution is to set NAs to max(sum(is.na(x)), sum(is.na(center))) when center is user-specified. Note that we should also check for non-leading NA in center, as we do forx`.

Can't get GMMA working on xts

FIrst of all. Big thanks for TTR!

I've noticed a difference in GMMA from old version to new version of TTR (or other updated part of my R setup...)

This used to work:

library(quantmod)
library(TTR)
getSymbols("^GSPC")
candleChart(GSPC, theme="white" , subset="last 5 months", TA=NULL)
# Line below is not working in new versions
addTA(GMMA(Cl(GSPC)),col=c(rep(c('blue','red'),each=6)), on=1)

But doesn't work on my new setup. I get this error:

Error in array(r, dim = d, dimnames = if (!(is.null(n1 <- names(x[[1L]])) &  : 
  length of 'dimnames' [1] not equal to array extent

After digging around I've settled on this instead for my new setup. I'm switching to data.frame as this is what the ttrc object is in example in the reference manual.

library(quantmod)
library(TTR)
getSymbols("^GSPC")
candleChart(GSPC, theme="white" , subset="last 5 months", TA=NULL)
GSPC.DF <- as.data.frame(GSPC)
GSPC.DF.GMMACL <- GMMA(GSPC.DF[,"GSPC.Close"])
addTA(GSPC.DF.GMMACL,col=c(rep(c('blue','red'),each=6)), on=1)

Am I thinking wrong or is there something in column naming or such I must adhere to in new version of TTR, as I suppose TTR is still working with xts?

Old system (don't laugh please):

> sessionInfo()
R version 2.15.0 (2012-03-30)
Platform: i386-apple-darwin9.8.0/i386 (32-bit)

locale:
[1] C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] quantmod_0.3-17 TTR_0.21-1      xts_0.8-6       zoo_1.7-7
[5] Defaults_1.1-1

loaded via a namespace (and not attached):
[1] grid_2.15.0    lattice_0.20-6

New system:

> sessionInfo()
R version 3.2.3 (2015-12-10)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.5 (Yosemite)

locale:
[1] C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] quantmod_0.4-5 TTR_0.23-0     xts_0.9-7      zoo_1.7-12    

loaded via a namespace (and not attached):
 [1] coin_1.1-2        lattice_0.20-33   codetools_0.2-14  mvtnorm_1.0-3    
 [5] grid_3.2.3        stats4_3.2.3      multcomp_1.4-1    strucchange_1.5-1
 [9] party_1.0-25      sandwich_2.3-4    splines_3.2.3     TH.data_1.0-6    
[13] tools_3.2.3       survival_2.38-3   modeltools_0.2-21

Big thanks!

Add Internal Bar Strength indicator

Internal Bar Strength (IBS) is a mean-reversion indicator described in a couple posts on the QUSMA blog.

The calculation is IBS = (Close - Low) / (High - Low), which equivalent to a 1-period stochastic oscillator with no smoothing. For example:

getSymbols("SPY")
IBS <- (Cl(SPY)-Lo(SPY))/(Hi(SPY)-Lo(SPY))
ibs <- stoch(HLC(SPY), 1)
sum(ibs$fastK - IBS, na.rm=TRUE)  # 0 (or close to it)

Random Walk Index indicator

References:

• TechnicalIndicators.net
• TradingSim
• Linnsoft
• FM Labs

DVI output has incorrect colnames starting in v23-0

When the code below is run using TTR_0.22-0, the colnames are: c("dvi.mag", "dvi.str", "dvi"). When run using TTR_0.23-0, the colnames are: c("..1", "..2", "..3").

require(TTR)
data(ttrc)
d <- setNames(ttrc$Close, ttrc$Date)
colnames(dvi <- DVI(d))

It appears the cause is due to the change in #2. The affected call is:

reclass(cbind(dvi.mag, dvi.str, dvi), price)

In 0.22-0, dvi.mag, dvi.str, and dvi were all simple vectors. In 0.23-0, dvi.mag is a matrix with row names indicating datetimes, while dvi.str and dvi are both xts objects. None of these objects have column names, which is likely what causes cbind.xts/merge.xts not to deparse the object names for column names on the new object.

Thanks to @ivannp for the report, and to @jjphorn for the reminder.

has a problem about af0 = (af1 == d_xl[1]) ? d_xl[1] : (d_xl[0] + af1); in sar.c

it seems for each iteration of i, this line always uses d_xl[1] and d_xl[0], should it be d_xl[i] and d_xl[i-1]? Thank you.

SMA of 0 is negative

Hi Joshua

Thanks for the TTR package, it is quite helpful!

However, while calculating the moving average using SMA, I noticed some unexpected behavior

rm(list = ls())
options(digits = 10)

library(TTR)
library(xts)

floating0 <- 0.0000000000000000e+00
xts_floating <- xts(c(-1.218755021e-02,-4.729404862e-03, -4.114582223e-03,
                  -3.579686583e-03, -3.114327332e-03, -8.708210015e-05,
                  rep(floating0,9),4.0245789683433897e-02), 
                order.by = Sys.Date() + c(1:16))
TTR::SMA(xts_floating,5)

I am expecting the rolling average of 0s to be 0 as well, however, it returns a negative number -2.602085214e-19 instead.

                        SMA
2017-05-25               NA
2017-05-26               NA
2017-05-27               NA
2017-05-28               NA
2017-05-29 -5.545110242e-03
2017-05-30 -3.125016620e-03
2017-05-31 -2.179135648e-03
2017-06-01 -1.356219203e-03
2017-06-02 -6.402818864e-04
2017-06-03 -1.741642003e-05
2017-06-04 -2.602085214e-19
2017-06-05 -2.602085214e-19
2017-06-06 -2.602085214e-19
2017-06-07 -2.602085214e-19
2017-06-08 -2.602085214e-19
2017-06-09  8.049157937e-03

My session info

R version 3.3.3 (2017-03-06)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_Singapore.1252  LC_CTYPE=English_Singapore.1252    LC_MONETARY=English_Singapore.1252 LC_NUMERIC=C                      
[5] LC_TIME=English_Singapore.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] xts_0.9-7  zoo_1.7-14 TTR_0.23-1

loaded via a namespace (and not attached):
[1] tools_3.3.3         Rcpp_0.12.9         grid_3.3.3          roll_1.0.6          RcppParallel_4.3.20 lattice_0.20-34    

runFun error reporting

It may be helpful for debugging if the two conditions here were separated:
if( n < 1 || n > NROW(x) ) stop("Invalid 'n'")

For example:

if( n < 1 ) stop("n, Number of rows of data, 0 or negative.")
# If possible, give  the actual n.

if( n > NROW(X) ) stop("Requested n greater than the length of provided data.")
# If possible give the actual n, and the comparison NROW(x), and x.

OBV function: bug with XTS price and volume as the input

on line 10 of the function:
obv <- c(volume[1], ifelse(prChg > 0, volume, -volume)[-1])

in the case that both price and volumn argument is a numeric vector, volume[1] will return an 2d xts object but ifelse will return a numeric vector, which will return error if you combine them using c()

Issue with volatility function in TTR when pulling data from excel/bloomberg

I am having trouble pulling in data from Excel into T and calculating OHLC volatility using TTR. I am using the following code (I have also attached the code for your review).

The output generated by volatility(ohlc, calc="gk.yz") is downward sloping... almost like a decay function. It looks completely wrong when I compare it to the data pulled from Yahoo Finance. Am I doing something wrong with how I am pulling the data? Appreciate any help or advice you could offer.

Best,
Stephen

Using the following packages: quantmod, xlsx, xts, zoo.

##R Code to pull data from Bloomberg/Excel & use quantmod/TTR
x=read.xlsx("Test OHLC Data.xlsx",sheetIndex=1)
x
x[1:756,1:5]
X=x[1:756,1:5]
X
summary(X)
typeof(X)
typeof(X$Open)
is.numeric(X$Open)
is.na(X)

str(X)

xts(X[,-1],order.by=X$NA.)
NewX=xts(X[,-1],order.by=X$NA.)
chartSeries(NewX)

is.OHLC(NewX)
has.Vo(NewX)
OHLC(NewX)

Op(NewX) ##Test - brings open data
Cl(NewX) ##Test - brings close data
HiCl(NewX) ##Test - brings in % change from Hi Low

plot(HiCl(NewX),type='l')
mean(HiCl(NewX))

ohlc = OHLC(NewX)
ohlc

volatility(ohlc,calc="garman")
IntraVol1=volatility(ohlc,calc="garman")

volatility(ohlc,calc="gk.yz")
IntraVol2=volatility(ohlc,calc="gk.yz")

It seems initGap has a forward looking bias for the SAR indicator

If I am reading the code correctly, it's sd over the Hi - Lo series. That's forward looking in a way.

Extend Cl/Cl volatility estimator to use mean 0

I'm proposing an extension to the Cl/Cl volatility estimator so that there's a version that effectively uses a fixed mean of 0 rather than the sample mean. For the case of small n with a strongly trending underlying, the sample mean can be skewed resulting in an underestimation of volatility.

I have working code and can submit a pull request if you're agreeable to extending your function. One question is the best way to select this functionality via the command-line arguments. One possibility is via the "calc" argument. This method could be designated as "mean0" or "close0". Alternatively, an additional command line argument could be added to the function such as mean0 = TRUE.

Your thoughts? Thanks.

stockSymbols() not working

Test=stockSymbols()
Fetching AMEX symbols...
Error in file(file, "rt") : cannot open the connection
In addition: Warning message:
In file(file, "rt") : InternetOpenUrl failed: 'The operation timed out'

BBands(): problem with tickers containing "LOW" in their name

Dear Joshua,

While scanning some stocks I encountered a problem with the TTR::BBands function, which I use in connection with HLC(). The problem occurs with tickers containing the letter sequence LOW (such as NYSE:LOW and NYSE:PLOW). Maybe the code gets confused by the multiple occurrence of "low" in the column names while searching for the low of the price.

Here is an example:

library(quantmod)
library(TTR)
issue <- getSymbols("LOW", src="yahoo", auto.assign=FALSE)
BBands(HLC(issue))

The last command leads to the following error:

"Error in BBands(HLC(issue)) :
Price series must be either High-Low-Close, or Close/univariate."

I'm not sure if the problem arises from HLC() or from BBands(), although I suspect that the former might be the culprit. Maybe it helps to point out that, for example,

ADX(HLC(issue))

does not throw any error.
I'm using TTR version 0.23-0, quantmod version 0.4-5 and R version 3.2.2 on ubuntu 14.04.
Thanks for looking into this.

Cheers,
Riccardo

TTR::volatility seems wrong in the default case

if( isTRUE(mean0) ) {
      # This is an alternative SD calculation using an effective mean of 0
      s <- sqrt(N) * sqrt(runSum(r^2, n-1) / (n-2))
    } else {
      # This is the standard SD calculation using the sample mean
      s <- sqrt(N) * runSD(r, n-1)
    }

runSD(r, n-1) - why do we compute the SD using n-1 bars? runSD already computes the SD of the mean (the default for the "sample" parameter is TRUE). I suspect that the formula in the mean0=TRUE case has similar issue - the n-2 looks suspicious.

growth function does not work

growth() returns NA for all values because growth <- cumprod(1 + ROC(price, ...) * signals) and ROC() returns an NA for the first value in a series. cumprod cannot handle the NA and returns NAs for everything.

Example:
IBM <- getYahooData('IBM')$Open
growth(IBM, rep(1, nrow(IBM)))

getYahooData no longer works

Hi for last two days (started April 17, 2017) the TTR::getYahooData returns the following errors

> getYahooData("IBM", 19990404, 20050607)
Error in file(file, "rt") : cannot open the connection

There is absolutely no issue with my internet.

My TTR package version is 0.23-1
and R version is 2.15.2

I'd very much appreciate if someone takes a quick look and fix. This get function has been working daily for last 5 years.

Thanks

OBV calculation when there's no price change

The OBV function currently treats the no-price-change condition (Close[t] == Close[t-1]) the same as if the price change were negative. This is not the same as several references:

• MLQ4
• StockCharts
• Wikipedia

The calculations in the above references are the same as these references in ?OBV:

• FMLabs
• MetaStock

Note that the Linnsoft reference in ?OBV is consistent with the current implementation, and that the StockCharts link in ?OBV is dead.

Thanks to Anıl Çalışkan for the report.

EMA(ttrc, n) crashes randomly

I'm been investigating whether TTR::EMA works well on multidimensional (multi-column) xts objects. Eventually I called EMA on ttrc (which is a data frame, but anyway, I was just wondering what happens), and I'm consistently getting random crashes with that call. Here's my devtools::session_info():

Session info ----------------------------------------------------------------------------------------------------
 setting  value                       
 version  R version 3.2.1 (2015-06-18)
 system   x86_64, mingw32             
 ui       RStudio (0.99.441)          
 language (EN)                        
 collate  English_United States.1252  
 tz       Europe/Moscow               

Packages --------------------------------------------------------------------------------------------------------
 package    * version date       source        
 curl         0.9.2   2015-08-08 CRAN (R 3.2.1)
 devtools   * 1.8.0   2015-05-09 CRAN (R 3.2.1)
 digest       0.6.8   2014-12-31 CRAN (R 3.2.0)
 git2r        0.11.0  2015-08-12 CRAN (R 3.2.1)
 lattice      0.20-33 2015-07-14 CRAN (R 3.2.1)
 memoise      0.2.1   2014-04-22 CRAN (R 3.2.0)
 Rcpp         0.12.0  2015-07-25 CRAN (R 3.2.1)
 rstudioapi   0.3.1   2015-04-07 CRAN (R 3.2.0)
 rversions    1.0.2   2015-07-13 CRAN (R 3.2.1)
 TTR        * 0.23-0  2015-07-10 CRAN (R 3.2.1)
 xml2         0.1.1   2015-06-02 CRAN (R 3.2.1)
 xts        * 0.9-7   2014-01-02 CRAN (R 3.2.1)
 zoo        * 1.7-12  2015-03-16 CRAN (R 3.2.1)

Now, this is more or less reproducible example. I've tried ~10 times (in R, RStudio, with devel/stable version of xts), and the crash may occur on the first or the second call, but it is most likely to happen on the third.

library(TTR)
data(ttrc)
EMA(ttrc)
EMA(ttrc, 20)
EMA(ttrc, 30)

FWIW, I'm running Win 10, though sessionInfo() says it's Running under: Windows 8 x64 (build 9200).

Column name for RSI mislabelled as EMA

Description

Column name for RSI is labelled EMA

Probably just after line 131, just needs to re-label

131    rsi <- 100 * mavgUp / ( mavgUp + mavgDn )
132    colnames(rsi) <- "rsi"
133
134    reclass( rsi, price )

Expected behavior

Column is labelled "rsi"

Minimal, reproducible example

> tail( RSI( Cl( xtsclean[[1]]) ))
                EMA
2017-10-11 40.84447
2017-10-12 38.99656
2017-10-16 33.31531
2017-10-17 30.57424
2017-10-19 26.46984
2017-10-20 23.55141

Session Info

R version 3.4.0 (2017-04-21)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils    
[5] datasets  methods   base     

other attached packages:
 [1] quadprog_1.5-5  e1071_1.6-8     ranger_0.8.0   
 [4] caret_6.0-77    lattice_0.20-35 lubridate_1.6.0
 [7] plotly_4.7.1    scales_0.5.0    cowplot_0.8.0  
[10] gridExtra_2.3   RcppRoll_0.2.2  dplyr_0.7.4    
[13] ggplot2_2.2.1   quantmod_0.4-11 TTR_0.23-2     
[16] xts_0.10-0      zoo_1.8-0       pacman_0.4.6   

loaded via a namespace (and not attached):
 [1] httr_1.3.1          ddalpha_1.3.1      
 [3] tidyr_0.7.2         sfsmisc_1.1-1      
 [5] jsonlite_1.5        viridisLite_0.2.0  
 [7] splines_3.4.0       foreach_1.4.3      
 [9] prodlim_1.6.1       assertthat_0.2.0   
[11] stats4_3.4.0        DRR_0.0.2          
[13] yaml_2.1.14         robustbase_0.92-7  
[15] ipred_0.9-6         glue_1.1.1         
[17] digest_0.6.12       colorspace_1.3-2   
[19] recipes_0.1.0       htmltools_0.3.6    
[21] Matrix_1.2-11       plyr_1.8.4         
[23] timeDate_3012.100   pkgconfig_2.0.1    
[25] CVST_0.2-1          purrr_0.2.4        
[27] gower_0.1.2         lava_1.5.1         
[29] tibble_1.3.4        withr_2.0.0        
[31] nnet_7.3-12         lazyeval_0.2.0     
[33] survival_2.41-3     magrittr_1.5       
[35] nlme_3.1-131        MASS_7.3-47        
[37] dimRed_0.1.0        class_7.3-14       
[39] tools_3.4.0         data.table_1.10.4-2
[41] stringr_1.2.0       kernlab_0.9-25     
[43] munsell_0.4.3       bindrcpp_0.2       
[45] compiler_3.4.0      rlang_0.1.2        
[47] grid_3.4.0          iterators_1.0.8    
[49] htmlwidgets_0.9     gtable_0.2.0       
[51] ModelMetrics_1.1.0  codetools_0.2-15   
[53] curl_3.0            reshape2_1.4.2     
[55] R6_2.2.2            bindr_0.1          
[57] stringi_1.1.5       Rcpp_0.12.13       
[59] rpart_4.1-11        DEoptimR_1.0-8 

[R-Forge #6019] ichimoku cloud?

Submitted by: remy dodgier
Assigned to: Nobody
R-Forge link

It would be nice to have ichimoku cloud indicators in TTR.

Followups:

Date: 2015-01-25 19:17
Sender: Brian Peterson
Ilya Kipnis has implemented the ichimoku indicator, and has it on github here:

https://github.com/IlyaKipnis/IKTrading/blob/master/R/ichimoku.R

I have not tested it, but I hope it helps you out. - Brian

can't load "ttrc"

Hi, Joshua!
Please, see below:

library(TTR)
data(ttrc)

Error in force(Symbols) : object 'ttrc' not found

What's wrong?

MESA MAMA/FAMA for TTR

Is it just me or are MESA MAMA and FAMA averages not available for TTR? I'm in the process of switching a python project that uses TA-Lib to R using TTR and I can't find the MESA function.

passing parameter for ADX (smoothing) calculation

In the code it seems that n for ADX calculation is the same as n for DX calculation.

maArgs <- list(n = n, ...)

Should we separate these 2? If we change here, we should probably change in addADX as well.

package

Error in library.dynam(lib, package, package.lib) :
DLL 'TTR' not found: maybe not installed for this architecture?
Error: loading failed
Execution halted
ERROR: loading failed

• removing 'C:/Users/user/Documents/R/win-library/3.2/Accuracy'
• restoring previous 'C:/Users/user/Documents/R/win-library/3.2/Accuracy'

Exited with status 1.

Some unit tests fail when run under valgrind

The MFI unit test in TTR fails when R is run under valgrind because the result of rowMeans is slightly different when R is run under valgrind. Specifically:

> # R --vanilla
> library(TTR)
> data(ttrc)
> x <- rowMeans(ttrc[,c("High","Low","Close")])
> sprintf("%21.20f %21.20f", x[62], x[63])
[1] "3.38333333333333330373 3.38333333333333330373"
> sprintf("%21.20f %21.20f %21.20f", ttrc[62,"High"], ttrc[62,"Low"], ttrc[62,"Close"])
[1] "3.41000000000000014211 3.35999999999999987566 3.37999999999999989342"
> # R --vanilla -d "valgrind"
> library(TTR)
> data(ttrc)
> x <- rowMeans(ttrc[,c("High","Low","Close")])
> sprintf("%21.20f %21.20f", x[62], x[63])
[1] "3.38333333333333285964 3.38333333333333330373"
> sprintf("%21.20f %21.20f %21.20f", ttrc[62,"High"], ttrc[62,"Low"], ttrc[62,"Close"])
[1] "3.41000000000000014211 3.35999999999999987566 3.37999999999999989342"

This causes x[63] > x[62] to be TRUE under valgrind when it's FALSE when running R without valgrind.

Prof Ripley explains the differences as,

One possible reason is that there will be more stores to memory and so the extended precision of x86_64 CPUs will be used less. I can confirm that via the use of flag -ffloat-store in gcc/gfortran...

A couple members of the CRAN team suggest having a tolerance for the logical comparison. While that would solve this specific issue, it wouldn't prevent a similar issue from occurring via a different logical comparison in another function. That would require adding a tolerance to all logical comparisons, which would be onerous.

The MFI unit test that fails under valgrind will succeed if the call to rowMeans(HLC) is replaced by a call to apply(HLC, 1, mean). rowMeans is also used in bollingerBands and CCI.

TRIX bug

Hi Joshua, I found a bug in the TRIX function. If the "maType" is not a list variable, the "nSig" parameter will not be used when calculating the signal value from the TRIX value. Instead, the "n" parameter will be used as the "nSig" parameter.

Some of the code as bellow:
if (is.list(maType)) { signal <- do.call(maType[[4]][[1]], c(list(TRIX), maType[[4]][-1])) } else { signal <- do.call(maType, c(list(TRIX), list(n = n, ...))) }

The correct code might be:
if (is.list(maType)) { signal <- do.call(maType[[4]][[1]], c(list(TRIX), maType[[4]][-1])) } else { signal <- do.call(maType, c(list(TRIX), list(n = nSig, ...))) }