I just sent quantmod_0.4-5 to CRAN, and TTR_0.23-0 has been there for a couple weeks. I’d like to thank Ivan Popivanov for many useful reports and patches to TTR. He provided patches to add HMA() (Hull MA), ALMA(), and ultimateOscillator() functions. James Toll provided a patch to the volatility() function that uses a zero mean (instead of the sample mean) in close-to-close volatility. The other big change is that moving average functions no longer return objects with column names based on the input object column names.
An updated version of TTR is now on CRAN. The biggest changes to be aware of are that all moving averages attempt to set colnames, CCI() returns an object with colnames, and the initial gap for SAR() is not hard-coded at 0.01. There are also some much-needed bug fixes - most notably to Yang Zhang volatility, MACD(), SAR(), EMA()/EVWMA(), and adjRatios(). There are some exciting new features, including a rolling single-factor model function (rollSFM(), based on a prototype from James Toll), a runPercentRank() function from Charlie Friedemann, stoch() and WPR() return 0.
An updated version of TTR is now on CRAN. It contains some much-needed bug fixes (most notably to stockSymbols()), some small changes, and a few new functions. Note that the change to wilderSum() will affect functions that use it (e.g. ADX()). Here are the full contents of the CHANGES file: TTR version 0.21-0 Changes from version 0.20-2 NEW FEATURES: Added variable moving average function, VMA(). Added Brian Peterson’s price bands function, PBands().
Michael Stokes, author of the MarketSci blog recently published a thought-provoking post about the correlation between historical and future volatility (measured as the standard deviation of daily close price percentage changes). This post is intended as an extension of his “unfinished thought”, not a critique. He suggests using his table of volatility correlations as a back-of-the-envelope approach to estimate future volatility, which led me to question the stability of the correlations in his table.
An updated version of TTR is now on CRAN. It fixes a couple bugs and includes a couple handy tweaks. Here’s the full contents of the CHANGES file: TTR version 0.20-2 Changes from version 0.20-1 NEW FEATURES: Added VWAP() and VWMA() (thanks to Brian Peterson) Added v-factor generalization to DEMA() (thanks to John Gavin) CHANGES: Updated volatility() to handle univariate case of calc='close' (thanks to Cedrick Johnson) Moved EMA(), SAR(), and wilderSum() from .
quantmod, TTR, and xts were (not so) recently featured on the Inference for R Blog. Inference for R is a Integrated Development Environment (IDE) designed specifically for R. The post gives an example of how to easily perform advanced financial stock analysis using Inference in Excel. I appreciate how they’re making R more accessible to a general audience, even though I like a command line interface and my preferred development environment is vim.
I am happy to announce a long-overdue update to the TTR package (version 0.2) is now on CRAN. This update represents a major milestone, as TTR useRs are no longer restricted to using matrix objects. TTR 0.2 uses xts internally, so all major time series classes are now supported. NEW FEATURES: Added the zig zag indicator: ZigZag() Added volatility estimators/indicators: volatility(), with the following calculations Close-to-Close Garman Klass Parkinson Rogers Satchell Added Money Flow Index: MFI() Added Donchian channel: DonchianChannel() CHANGES: