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.
Running DEoptim in parallel has been on the development team’s wishlist for awhile. It had not been a priority though, because none of us have personally needed it. An opportunity arose when Kris Boudt approached me about collaborating to add this functionality as part of a consultancy project for a financial services firm. We were able to add and test the functionality within a week. The latest revision of DEoptim on R-Forge has the capability to evaluate the objective function on multiple cores using foreach.
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().
Dirk Eddelbuettel has recently released RQuantLib-0.3.7, which contains the necessary QuantLib builds to allow the CRAN servers to build the Windows binary. This (thankfully) makes my post on how to build RQuantLib on 32-bit Windows unnecessary for casual users, but may be useful for those who want to develop RQuantLib on Windows.
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: